U.S. patent application number 13/609145 was filed with the patent office on 2013-12-05 for methods and systems for speech adaptation data.
This patent application is currently assigned to Elwha LLC, a limited liability company of the State of Delaware. The applicant listed for this patent is Royce A. Levien, Richard T. Lord, Robert W. Lord, Mark A. Malamud. Invention is credited to Royce A. Levien, Richard T. Lord, Robert W. Lord, Mark A. Malamud.
Application Number | 20130325453 13/609145 |
Document ID | / |
Family ID | 49671314 |
Filed Date | 2013-12-05 |
United States Patent
Application |
20130325453 |
Kind Code |
A1 |
Levien; Royce A. ; et
al. |
December 5, 2013 |
METHODS AND SYSTEMS FOR SPEECH ADAPTATION DATA
Abstract
Computationally implemented methods and systems include
receiving speech data correlated to one or more words spoken by a
particular party, receiving adaptation data that is at least partly
based on at least one speech interaction of a particular party that
is discrete from the received speech data, wherein at least a
portion of the adaptation data has been stored on a particular
device associated with the particular party, obtaining target data
regarding a target configured to process at least a portion of the
received speech data, and determining whether to apply the
adaptation data for processing at least a portion of the received
speech data, at least partly based on the acquired target data. In
addition to the foregoing, other aspects are described in the
claims, drawings, and text.
Inventors: |
Levien; Royce A.;
(Lexington, MA) ; Lord; Richard T.; (Tacoma,
WA) ; Lord; Robert W.; (Seattle, WA) ;
Malamud; Mark A.; (Seattle, WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Levien; Royce A.
Lord; Richard T.
Lord; Robert W.
Malamud; Mark A. |
Lexington
Tacoma
Seattle
Seattle |
MA
WA
WA
WA |
US
US
US
US |
|
|
Assignee: |
Elwha LLC, a limited liability
company of the State of Delaware
|
Family ID: |
49671314 |
Appl. No.: |
13/609145 |
Filed: |
September 10, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13609143 |
Sep 10, 2012 |
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13609145 |
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13485733 |
May 31, 2012 |
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13609143 |
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13485738 |
May 31, 2012 |
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13485733 |
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13538855 |
Jun 29, 2012 |
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13485738 |
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13538866 |
Jun 29, 2012 |
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13538855 |
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13564647 |
Aug 1, 2012 |
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13538866 |
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13564649 |
Aug 1, 2012 |
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13564647 |
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13564650 |
Aug 1, 2012 |
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13564649 |
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13564651 |
Aug 1, 2012 |
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13564650 |
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13609139 |
Sep 10, 2012 |
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13564651 |
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13609142 |
Sep 10, 2012 |
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13609139 |
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Current U.S.
Class: |
704/201 |
Current CPC
Class: |
G10L 21/00 20130101;
G10L 15/063 20130101; G10L 15/065 20130101; G10L 15/06 20130101;
G10L 15/07 20130101; G10L 15/30 20130101 |
Class at
Publication: |
704/201 |
International
Class: |
G10L 21/00 20060101
G10L021/00 |
Claims
1-329. (canceled)
330. A device, comprising: a speech data correlated to one or more
particular party spoken words receiving module; an adaptation data
at least partly based on discrete speech interaction of particular
party separate from detected speech data, and has been stored on a
particular party-associated particular device receiving module
configured to receive adaptation data that is at least partly based
on at least one speech interaction of a particular party that is
discrete from the received speech data, wherein at least a portion
of the adaptation data has been stored on a particular device
associated with the particular party; a target data regarding a
target configured to process at least a portion of the received
speech data obtaining module configured to obtain target data
regarding a target configured to process at least a portion of the
received speech data; an application of adaptation data for
processing at least a portion of the received speech data
determining module; and an adaptation result data based on at least
one aspect of the received speech data transmitting module.
331. (canceled)
332. (canceled)
333. (canceled)
334. (canceled)
335. (canceled)
336. (canceled)
337. (canceled)
338. The device of claim 330, wherein said speech data correlated
to one or more particular party spoken words receiving module
comprises: a speech data comprising a representation of particular
party spoken word receiving module.
339. (canceled)
340. (canceled)
341. (canceled)
342. The device of claim 338, wherein said speech data comprising a
representation of particular party spoken word receiving module
comprises: a speech data corresponding to partially processed
particular party spoken word receiving module configured to receive
speech data corresponding to received speech spoken by the
particular party that has been at least partially processed.
343. (canceled)
344. (canceled)
345. (canceled)
346. (canceled)
347. (canceled)
348. (canceled)
349. (canceled)
350. (canceled)
351. (canceled)
352. (canceled)
353. (canceled)
354. The device of claim 330, wherein said speech data correlated
to one or more particular party spoken words receiving module
comprises: a speech data correlated to one or more particular party
spoken words receiving from further device module.
355. The device of claim 354, wherein said speech data correlated
to one or more particular party spoken words receiving from further
device module comprises: an audio data derived from one or more
particular party spoken words receiving from further device
module.
356. (canceled)
357. (canceled)
358. The device of claim 355, wherein said audio data derived from
one or more particular party spoken words receiving from further
device module comprises: an audio data derived from one or more
particular party words detected by particular device receiving from
further device module.
359. (canceled)
360. (canceled)
361. (canceled)
362. (canceled)
363. (canceled)
364. (canceled)
365. (canceled)
366. (canceled)
367. (canceled)
368. (canceled)
369. (canceled)
370. (canceled)
371. (canceled)
372. (canceled)
373. (canceled)
374. (canceled)
375. (canceled)
376. (canceled)
377. (canceled)
378. The device of claim 330, wherein said adaptation data at least
partly based on discrete speech interaction of particular party
separate from detected speech data, and has been stored on a
particular party-associated particular device receiving module
comprises: an adaptation data at least partly based on discrete
speech interaction of particular party separate from detected
speech data, and has been stored on a particular party-associated
particular device acquiring from a further device module.
379. The device of claim 378, wherein said adaptation data at least
partly based on discrete speech interaction of particular party
separate from detected speech data, and has been stored on a
particular party-associated particular device acquiring from a
further device module comprises: an adaptation data originating at
further device and at least partly based on discrete speech
interaction of particular party separate from detected speech data,
and has been stored on a particular party-associated particular
device acquiring from a further device module.
380. The device of claim 378, wherein said adaptation data at least
partly based on discrete speech interaction of particular party
separate from detected speech data, and has been stored on a
particular party-associated particular device acquiring from a
further device module comprises: an adaptation data at least partly
based on discrete speech interaction of particular party separate
from detected speech data, and has been stored on a particular
party-associated particular device acquiring from a further device
related to the particular device module.
381. The device of claim 380, wherein said adaptation data at least
partly based on discrete speech interaction of particular party
separate from detected speech data, and has been stored on a
particular party-associated particular device acquiring from a
further device related to the particular device module comprises:
an adaptation data at least partly based on discrete speech
interaction of particular party separate from detected speech data,
and has been stored on a particular party-associated particular
device acquiring from a further device associated with the
particular party module.
382. (canceled)
383. (canceled)
384. The device of claim 378, wherein said adaptation data at least
partly based on discrete speech interaction of particular party
separate from detected speech data, and has been stored on a
particular party-associated particular device acquiring from a
further device module comprises: an adaptation data at least partly
based on discrete speech interaction of particular party separate
from detected speech data, and has been stored on a particular
party-associated particular device acquiring from a further device
that received the adaptation data from the particular device
module.
385. (canceled)
386. (canceled)
387. (canceled)
388. (canceled)
389. (canceled)
390. (canceled)
391. (canceled)
392. (canceled)
393. (canceled)
394. (canceled)
395. (canceled)
396. (canceled)
397. (canceled)
398. (canceled)
399. (canceled)
400. The device of claim 330, wherein said adaptation data at least
partly based on discrete speech interaction of particular party
separate from detected speech data, and has been stored on a
particular party-associated particular device receiving module
comprises: an adaptation data at least partly based on discrete
speech interaction of particular party separate from detected
speech data and using same utterance as speech that is part of
speech data, and has been stored on a particular party-associated
particular device receiving module.
401. (canceled)
402. (canceled)
403. (canceled)
404. (canceled)
405. The device of claim 330, wherein said adaptation data at least
partly based on discrete speech interaction of particular party
separate from detected speech data, and has been stored on a
particular party-associated particular device receiving module
comprises: an adaptation data comprising instructions for modifying
one or more portions of a speech recognition component of a target
device that are at least partly based on one or more particular
party speech interactions, and has been stored on a particular
party-associated particular device receiving module.
406. The device of claim 330, wherein said adaptation data at least
partly based on discrete speech interaction of particular party
separate from detected speech data, and has been stored on a
particular party-associated particular device receiving module
comprises: an adaptation data comprising a location of instructions
for modifying one or more portions of a speech recognition
component of a target device that are at least partly based on one
or more particular party speech interactions, and has been stored
on a particular party-associated particular device receiving
module.
407. (canceled)
408. (canceled)
409. (canceled)
410. (canceled)
411. The device of claim 330, wherein said adaptation data at least
partly based on discrete speech interaction of particular party
separate from detected speech data, and has been stored on a
particular party-associated particular device receiving module
comprises: an adaptation data at least partly based on discrete
speech interaction of particular party separate from detected
speech data, and at least a portion of which originated at a
particular party-associated particular device receiving module.
412. The device of claim 330, wherein said adaptation data at least
partly based on discrete speech interaction of particular party
separate from detected speech data, and has been stored on a
particular party-associated particular device receiving module
comprises: an adaptation data at least partly based on discrete
speech interaction of particular party separate from detected
speech data, and at least a portion of which was transmitted to a
remote location from a particular party-associated particular
device receiving from remote location module.
413. The device of claim 330, wherein said adaptation data at least
partly based on discrete speech interaction of particular party
separate from detected speech data, and has been stored on a
particular party-associated particular device receiving module
comprises: an adaptation data at least partly based on discrete
speech interaction of particular party separate from detected
speech data receiving module; and a further data adding to
adaptation data module configured to add further data to the
received adaptation data.
414. (canceled)
415. (canceled)
416. (canceled)
417. (canceled)
418. (canceled)
419. (canceled)
420. The device of claim 330, wherein said target data regarding a
target configured to process at least a portion of the received
speech data obtaining module comprises: a data indicating that the
adaptation data is configured to be applied to the target device to
assist in processing at least a portion of the speech data
receiving from a speech processing component module.
421. (canceled)
422. (canceled)
423. (canceled)
424. (canceled)
425. The device of claim 330, wherein said target data regarding a
target configured to process at least a portion of the received
speech data obtaining module comprises: a target data regarding a
target configured to process at least a portion of the received
speech data generating module.
426. The device of claim 330, wherein said target data regarding a
target configured to process at least a portion of the received
speech data obtaining module comprises: a speech data configurable
to be processed by a speech recognition component to which the
adaptation data has been applied determining module; and a target
data regarding intended target device generating based on
determination module.
427. The device of claim 426, wherein said speech data configurable
to be processed by a speech recognition component to which the
adaptation data has been applied determining module comprises: an
at least a portion of speech data analyzing module; and a target
data regarding intended target device determining at least partly
based on the analyzing at least a portion of speech data
module.
428. The device of claim 427, wherein said at least a portion of
speech data analyzing module comprises: an at least a portion of
speech data analyzing using an adaptation data-applied speech
recognition component module.
429. The device of claim 428, wherein said at least a portion of
speech data analyzing using an adaptation data-applied speech
recognition component module comprises: an at least a portion of
speech data analyzing using an adaptation data-applied speech
recognition component of target device module.
430. The device of claim 428, wherein said at least a portion of
speech data analyzing using an adaptation data-applied speech
recognition component module comprises: an at least a portion of
speech data analyzing using an adaptation data-applied speech
recognition component of further device module.
431. (canceled)
432. (canceled)
433. The device of claim 330, wherein said target data regarding a
target configured to process at least a portion of the received
speech data obtaining module comprises: a target device
configurable to process received speech data determining module;
and a target data regarding target device generating based on
determination regarding the target device module.
434. The device of claim 433, wherein said target device
configurable to process received speech data determining module
comprises: an at least a portion of the speech data analyzing
module; and a target device configurable to process speech data
determining at least partly based on result of analyzing at least a
portion of the speech data module.
435. The device of claim 433, wherein said target device
configurable to process received speech data determining module
comprises: a header data indicating a type of intended target
device that is configured to process received speech data
extracting from received speech data header module; and a type of
target device is same type as type of intended target device
determining module.
436. The device of claim 435, wherein said header data indicating a
type of intended target device that is configured to process
received speech data extracting from received speech data header
module comprises: a header data indicating a manufacturer of
intended target device that is configured to process received
speech data extracting from received speech data header module.
437. The device of claim 435, wherein said header data indicating a
type of intended target device that is configured to process
received speech data extracting from received speech data header
module comprises: a header data indicating a type of input accepted
by one or more intended target devices configured to process
received speech data extracting from received speech data header
module.
438. The device of claim 437, wherein said header data indicating a
type of input accepted by one or more intended target devices
configured to process received speech data extracting from received
speech data header module comprises: a header data indicating a
data format accepted by one or more intended target devices
configured to process received speech data extracting from received
speech data header module.
439. The device of claim 437, wherein said header data indicating a
type of input accepted by one or more intended target devices
configured to process received speech data extracting from received
speech data header module comprises: a header data indicating one
or more word categories accepted by one or more intended target
devices configured to process received speech data extracting from
received speech data header module.
440. (canceled)
441. (canceled)
442. The device of claim 330, wherein said target data regarding a
target configured to process at least a portion of the received
speech data obtaining module comprises: a target data regarding a
target device configured to process at least a portion of speech
data receiving module.
443. (canceled)
444. The device of claim 442, wherein said target data regarding a
target device configured to process at least a portion of speech
data receiving module comprises: a target data regarding a target
device configured to process at least a portion of speech data
receiving from a further device module.
445. (canceled)
446. The device of claim 444, wherein said target data regarding a
target device configured to process at least a portion of speech
data receiving from a further device module comprises: a target
data regarding a target device configured to process at least a
portion of speech data receiving from a further device configured
to apply at least a portion of the adaptation data module.
447. The device of claim 444, wherein said target data regarding a
target device configured to process at least a portion of speech
data receiving from a further device module comprises: a target
data regarding a target device configured to process at least a
portion of speech data receiving from a further device configured
to process the speech data less efficiently than the target device
module.
448. The device of claim 444, wherein said target data regarding a
target device configured to process at least a portion of speech
data receiving from a further device module comprises: a target
data regarding a target device configured to process at least a
portion of speech data receiving from a further device for which
the speech data is unintended module.
449. The device of claim 444, wherein said target data regarding a
target device configured to process at least a portion of speech
data receiving from a further device module comprises: a target
data regarding a target device configured to process at least a
portion of speech data and target data indicating the speech data
was determined to be intended for the target device receiving from
a further device module.
450. The device of claim 330, wherein said target data regarding a
target configured to process at least a portion of the received
speech data obtaining module comprises: a data identifying the
target device receiving module.
451. (canceled)
452. (canceled)
453. The device of claim 330, wherein said target data regarding a
target configured to process at least a portion of the received
speech data obtaining module comprises: an address of the target
device receiving module.
454. (canceled)
455. The device of claim 330, wherein said target data regarding a
target configured to process at least a portion of the received
speech data obtaining module comprises: a target data regarding an
intended application module configured to process at least a
portion of the received speech data obtaining module.
456. (canceled)
457. (canceled)
458. (canceled)
459. (canceled)
460. (canceled)
461. (canceled)
462. The device of claim 330, wherein said application of
adaptation data for processing at least a portion of the received
speech data determining module comprises: an application of
adaptation data for processing at least a portion of the received
speech data determining based on acquired target data comprising an
indication of intended device module.
463. The device of claim 330, wherein said application of
adaptation data for processing at least a portion of the received
speech data determining module comprises: an application of
adaptation data for processing at least a portion of the received
speech data determining based on acquired target data comprising an
indication that speech data has arrived at intended device
module.
464. (canceled)
465. (canceled)
466. (canceled)
467. The device of claim 330, wherein said application of
adaptation data for processing at least a portion of the received
speech data determining module comprises: an application of
adaptation data for processing at least a portion of the received
speech data determining when acquired target data indicates
capability of adaptation data application module.
468. (canceled)
469. The device of claim 330, wherein said application of
adaptation data for processing at least a portion of the received
speech data determining module comprises: an application of
adaptation data for processing at least a portion of the received
speech data determining against based on acquired target data
indicating presence of one or more other devices configured to
efficiently apply adaptation data module.
470. (canceled)
471. The device of claim 330, wherein said application of
adaptation data for processing at least a portion of the received
speech data determining module comprises: an application of
adaptation data for processing at least a portion of the received
speech data determining based on one or more characteristics of one
or more applications and target data indicating a presence of the
one or more applications module.
472. The device of claim 330, wherein said application of
adaptation data for processing at least a portion of the received
speech data determining module comprises: an application of
adaptation data for processing at least a portion of the received
speech data determining against based acquired target data
comprising one or more characteristics of one or more applications
module.
473. (canceled)
474. The device of claim 472, wherein said application of
adaptation data for processing at least a portion of the received
speech data determining against based acquired target data
comprising one or more characteristics of one or more applications
module comprises: an application of adaptation data for processing
at least a portion of the received speech data determining against
based acquired target data comprising a developer of one or more
applications module.
475. (canceled)
476. The device of claim 330, wherein said application of
adaptation data for processing at least a portion of the received
speech data determining module comprises: an application of
adaptation data for processing at least a portion of the received
speech data determining based on one or more user-controlled
preference flags module.
477. The device of claim 330, wherein said application of
adaptation data for processing at least a portion of the received
speech data determining module comprises: an application of
adaptation data for processing at least a portion of the received
speech data determining based on operating system decision
module.
478. (canceled)
479. (canceled)
480. The device of claim 330, wherein said adaptation result data
based on at least one aspect of the received speech data
transmitting module comprises: an adaptation result data based on
processing received speech data transmitting module.
481. The device of claim 480, wherein said adaptation result data
based on processing received speech data transmitting module
comprises: an adaptation result data indicating at least a portion
of received speech data has been processed transmitting module.
482. The device of claim 330, wherein said adaptation result data
based on at least one aspect of the received speech data
transmitting module comprises: an adaptation result data indicating
that at least a portion of the received speech data is intended for
an other device transmitting module.
483. The device of claim 482, wherein said adaptation result data
indicating that at least a portion of the received speech data is
intended for an other device transmitting module comprises: an
adaptation result data indicating that at least a portion of the
received speech data is intended for an other device transmitting
to the other device module.
484. The device of claim 482, wherein said adaptation result data
indicating that at least a portion of the received speech data is
intended for an other device transmitting module comprises: an
adaptation result data comprising the adaptation data and
indicating that at least a portion of the received speech data is
intended for an other device transmitting module.
485. (canceled)
486. The device of claim 330, wherein said adaptation result data
based on at least one aspect of the received speech data
transmitting module comprises: an adaptation result data based on a
measure of success of at least one portion of a speech-facilitated
transaction corresponding to the received speech data transmitting
module.
487. (canceled)
488. (canceled)
489. (canceled)
490. (canceled)
491. The device of claim 330, wherein said adaptation result data
based on at least one aspect of the received speech data
transmitting module comprises: an adaptation result data comprising
a list of at least one word that was a portion of the received
speech data and that was improperly interpreted during speech data
processing transmitting module.
492. (canceled)
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application is related to and claims the benefit
of the earliest available effective filing date(s) from the
following listed application(s) (the "Related Applications") (e.g.,
claims earliest available priority dates for other than provisional
patent applications or claims benefits under 35 USC .sctn.119(e)
for provisional patent applications, for any and all parent,
grandparent, great-grandparent, etc. applications of the Related
Application(s)). All subject matter of the Related Applications and
of any and all parent, grandparent, great-grandparent, etc.
applications of the Related Applications, including any priority
claims, is incorporated herein by reference to the extent such
subject matter is not inconsistent herewith.
RELATED APPLICATIONS
[0002] For purposes of the USPTO extra-statutory requirements, the
present application constitutes a continuation-in-part of U.S.
patent application Ser. No. 13/485,733, entitled SPEECH RECOGNITION
ADAPTATION SYSTEMS BASED ON ADAPTATION DATA, naming Royce A.
Levien, Richard T. Lord, Robert W. Lord, Mark A. Malamud, and John
D. Rinaldo, Jr. as inventors, filed 31 May 2012, which is currently
co-pending or is an application of which a currently co-pending
application is entitled to the benefit of the filing date.
[0003] For purposes of the USPTO extra-statutory requirements, the
present application constitutes a continuation-in-part of U.S.
patent application Ser. No. 13/485,738, entitled SPEECH RECOGNITION
ADAPTATION SYSTEMS BASED ON ADAPTATION DATA, naming Royce A.
Levien, Richard T. Lord, Robert W. Lord, Mark A. Malamud, and John
D. Rinaldo, Jr. as inventors, filed 31 May 2012, which is currently
co-pending or is an application of which a currently co-pending
application is entitled to the benefit of the filing date.
[0004] For purposes of the USPTO extra-statutory requirements, the
present application constitutes a continuation-in-part of U.S.
patent application Ser. No. 13/538,855, entitled SPEECH RECOGNITION
ADAPTATION SYSTEMS BASED ON ADAPTATION DATA, naming Royce A.
Levien, Richard T. Lord, Robert W. Lord, Mark A. Malamud, and John
D. Rinaldo, Jr. as inventors, filed 29 Jun. 2012, which is
currently co-pending or is an application of which a currently
co-pending application is entitled to the benefit of the filing
date.
[0005] For purposes of the USPTO extra-statutory requirements, the
present application constitutes a continuation-in-part of U.S.
patent application Ser. No. 13/538,866, entitled SPEECH RECOGNITION
ADAPTATION SYSTEMS BASED ON ADAPTATION DATA, naming Royce A.
Levien, Richard T. Lord, Robert W. Lord, Mark A. Malamud, and John
D. Rinaldo, Jr. as inventors, filed 29 Jun. 2012, which is
currently co-pending or is an application of which a currently
co-pending application is entitled to the benefit of the filing
date.
[0006] For purposes of the USPTO extra-statutory requirements, the
present application constitutes a continuation-in-part of U.S.
patent application Ser. No. 13/564,647, entitled SPEECH RECOGNITION
ADAPTATION SYSTEMS BASED ON ADAPTATION DATA, naming Royce A.
Levien, Richard T. Lord, Robert W. Lord, Mark A. Malamud, and John
D. Rinaldo, Jr. as inventors, filed 1 Aug. 2012, which is currently
co-pending or is an application of which a currently co-pending
application is entitled to the benefit of the filing date.
[0007] For purposes of the USPTO extra-statutory requirements, the
present application constitutes a continuation-in-part of U.S.
patent application Ser. No. 13/564,649, entitled SPEECH RECOGNITION
ADAPTATION SYSTEMS BASED ON ADAPTATION DATA, naming Royce A.
Levien, Richard T. Lord, Robert W. Lord, Mark A. Malamud, and John
D. Rinaldo, Jr. as inventors, filed 1 Aug. 2012, which is currently
co-pending or is an application of which a currently co-pending
application is entitled to the benefit of the filing date.
[0008] For purposes of the USPTO extra-statutory requirements, the
present application constitutes a continuation-in-part of U.S.
patent application Ser. No. 13/564,650, entitled SPEECH RECOGNITION
ADAPTATION SYSTEMS BASED ON ADAPTATION DATA, naming Royce A.
Levien, Richard T. Lord, Robert W. Lord, Mark A. Malamud, and John
D. Rinaldo, Jr. as inventors, filed 1 Aug. 2012, which is currently
co-pending or is an application of which a currently co-pending
application is entitled to the benefit of the filing date.
[0009] For purposes of the USPTO extra-statutory requirements, the
present application constitutes a continuation-in-part of U.S.
patent application Ser. No. 13/564,651, entitled SPEECH RECOGNITION
ADAPTATION SYSTEMS BASED ON ADAPTATION DATA, naming Royce A.
Levien, Richard T. Lord, Robert W. Lord, Mark A. Malamud, and John
D. Rinaldo, Jr. as inventors, filed 1 Aug. 2012, which is currently
co-pending or is an application of which a currently co-pending
application is entitled to the benefit of the filing date.
[0010] For purposes of the USPTO extra-statutory requirements, the
present application constitutes a continuation-in-part of U.S.
patent application Ser. No. To Be Assigned, entitled METHODS AND
SYSTEMS FOR SPEECH ADAPTATION DATA, naming Royce A. Levien, Richard
T. Lord, Robert W. Lord, Mark A. Malamud, and John D. Rinaldo, Jr.
as inventors, filed 10 Sep. 2012, which is currently co-pending or
is an application of which a currently co-pending application is
entitled to the benefit of the filing date.
[0011] For purposes of the USPTO extra-statutory requirements, the
present application constitutes a continuation-in-part of U.S.
patent application Ser. No. To Be Assigned, entitled METHODS AND
SYSTEMS FOR SPEECH ADAPTATION DATA, naming Royce A. Levien, Richard
T. Lord, Robert W. Lord, Mark A. Malamud, and John D. Rinaldo, Jr.
as inventors, filed 10 Sep. 2012, which is currently co-pending or
is an application of which a currently co-pending application is
entitled to the benefit of the filing date.
[0012] The United States Patent Office (USPTO) has published a
notice to the effect that the USPTO's computer programs require
that patent applicants reference both a serial number and indicate
whether an application is a continuation, continuation-in-part, or
divisional of a parent application. Stephen G. Kunin, Benefit of
Prior-Filed Application, USPTO Official Gazette Mar. 18, 2003. The
present Applicant Entity (hereinafter "Applicant") has provided
above a specific reference to the application(s) from which
priority is being claimed as recited by statute. Applicant
understands that the statute is unambiguous in its specific
reference language and does not require either a serial number or
any characterization, such as "continuation" or
"continuation-in-part," for claiming priority to U.S. patent
applications. Notwithstanding the foregoing, Applicant understands
that the USPTO's computer programs have certain data entry
requirements, and hence Applicant has provided designation(s) of a
relationship between the present application and its parent
application(s) as set forth above, but expressly points out that
such designation(s) are not to be construed in any way as any type
of commentary and/or admission as to whether or not the present
application contains any new matter in addition to the matter of
its parent application(s).
BACKGROUND
[0013] This application is related to adaptation data related to
speech processing.
SUMMARY
[0014] In one or more various aspects, a method includes but is not
limited to receiving speech data correlated to one or more words
spoken by a particular party, receiving adaptation data that is at
least partly based on at least one speech interaction of a
particular party that is discrete from the received speech data,
wherein at least a portion of the adaptation data has been stored
on a particular device associated with the particular party,
obtaining target data regarding a target configured to process at
least a portion of the received speech data, determining whether to
apply the adaptation data for processing at least a portion of the
received speech data, at least partly based on the acquired target
data, and means for transmitting adaptation result data that is
based on at least one aspect of the received speech data. In
addition to the foregoing, other method aspects are described in
the claims, drawings, and text forming a part of the disclosure set
forth herein.
[0015] In one or more various aspects, one or more related systems
may be implemented in machines, compositions of matter, or
manufactures of systems, limited to patentable subject matter under
35 U.S.C. 101. The one or more related systems may include, but are
not limited to, circuitry and/or programming for effecting the
herein-referenced method aspects. The circuitry and/or programming
may be virtually any combination of hardware, software, and/or
firmware configured to effect the herein-referenced method aspects
depending upon the design choices of the system designer, and
limited to patentable subject matter under 35 USC 101.
[0016] In one or more various aspects, a system includes, but is
not limited to, means for receiving speech data correlated to one
or more words spoken by a particular party, means for receiving
adaptation data that is at least partly based on at least one
speech interaction of a particular party that is discrete from the
received speech data, wherein at least a portion of the adaptation
data has been stored on a particular device associated with the
particular party, means for obtaining target data regarding a
target configured to process at least a portion of the received
speech data, means for determining whether to apply the adaptation
data for processing at least a portion of the received speech data,
at least partly based on the acquired target data, and means for
transmitting adaptation result data that is based on at least one
aspect of the received speech data. In addition to the foregoing,
other system aspects are described in the claims, drawings, and
text forming a part of the disclosure set forth herein.
[0017] In one or more various aspects, a system includes, but is
not limited to, circuitry for receiving speech data correlated to
one or more words spoken by a particular party, circuitry for
receiving adaptation data that is at least partly based on at least
one speech interaction of a particular party that is discrete from
the received speech data, wherein at least a portion of the
adaptation data has been stored on a particular device associated
with the particular party, circuitry for obtaining target data
regarding a target configured to process at least a portion of the
received speech data, circuitry for determining whether to apply
the adaptation data for processing at least a portion of the
received speech data, at least partly based on the acquired target
data, and circuitry for transmitting adaptation result data that is
based on at least one aspect of the received speech data. In
addition to the foregoing, other system aspects are described in
the claims, drawings, and text forming a part of the disclosure set
forth herein.
[0018] In one or more various aspects, a computer program product,
comprising a signal bearing medium, bearing one or more
instructions including, but not limited to, one or more
instructions for receiving speech data correlated to one or more
words spoken by a particular party, one or more instructions for
receiving adaptation data that is at least partly based on at least
one speech interaction of a particular party that is discrete from
the received speech data, wherein at least a portion of the
adaptation data has been stored on a particular device associated
with the particular party, one or more instructions for obtaining
target data regarding a target configured to process at least a
portion of the received speech data, one or more instructions for
determining whether to apply the adaptation data for processing at
least a portion of the received speech data, at least partly based
on the acquired target data, and one or more instructions for
transmitting adaptation result data that is based on at least one
aspect of the received speech data. In addition to the foregoing,
other computer program product aspects are described in the claims,
drawings, and text forming a part of the disclosure set forth
herein.
[0019] In one or more various aspects, a device is defined by a
computational language, such that the device comprises one or more
interchained physical machines ordered for receiving speech data
correlated to one or more words spoken by a particular party, one
or more interchained physical machines ordered for receiving
adaptation data that is at least partly based on at least one
speech interaction of a particular party that is discrete from the
received speech data, wherein at least a portion of the adaptation
data has been stored on a particular device associated with the
particular party, one or more interchained physical machines
ordered for obtaining target data regarding a target configured to
process at least a portion of the received speech data, one or more
interchained physical machines ordered for determining whether to
apply the adaptation data for processing at least a portion of the
received speech data, at least partly based on the acquired target
data, and one or more interchained physical machines ordered for
transmitting adaptation result data that is based on at least one
aspect of the received speech data.
[0020] In addition to the foregoing, various other method and/or
system and/or program product aspects are set forth and described
in the teachings such as text (e.g., claims and/or detailed
description) and/or drawings of the present disclosure.
[0021] The foregoing is a summary and thus may contain
simplifications, generalizations, inclusions, and/or omissions of
detail; consequently, those skilled in the art will appreciate that
the summary is illustrative only and is NOT intended to be in any
way limiting. Other aspects, features, and advantages of the
devices and/or processes and/or other subject matter described
herein will become apparent by reference to the detailed
description, the corresponding drawings, and/or in the teachings
set forth herein.
BRIEF DESCRIPTION OF THE FIGURES
[0022] For a more complete understanding of embodiments, reference
now is made to the following descriptions taken in connection with
the accompanying drawings. The use of the same symbols in different
drawings typically indicates similar or identical items, unless
context dictates otherwise. The illustrative embodiments described
in the detailed description, drawings, and claims are not meant to
be limiting. Other embodiments may be utilized, and other changes
may be made, without departing from the spirit or scope of the
subject matter presented here.
[0023] FIG. 1A shows a high-level block diagram of an exemplary
environment 100, according to an embodiment.
[0024] FIG. 1B shows a high-level block diagram of a device 130
operating in an exemplary embodiment 100, according to an
embodiment.
[0025] FIG. 1C shows a high-level diagram of an exemplary
environment 100', which is an example of an exemplary embodiment
100 having a device 130, according to an embodiment.
[0026] FIG. 1D shows a high-level diagram of an exemplary
environment 100'', which is an example of an exemplary embodiment
100 having a device 130 according to an embodiment.
[0027] FIG. 1E shows a high-level diagram of an exemplary
environment 100''', which is an example of an exemplary embodiment
100 having a device 130, according to an embodiment.
[0028] FIG. 2, including FIGS. 2A-2D, shows a particular
perspective of the speech data correlated to one or more particular
party spoken words receiving module 152 of the device 130 of
environment 100 of FIG. 1B.
[0029] FIG. 3, including FIGS. 3A-3K, shows adaptation data at
least partly based on discrete speech interaction of particular
party separate from detected speech data, and has been stored on a
particular party-associated particular device receiving module 154
of the device 130 of environment 100 of FIG. 1B.
[0030] FIG. 4, including FIGS. 4A-4F, shows target data regarding a
target configured to process at least a portion of the received
speech data obtaining module 156 of the device 130 of environment
100 of FIG. 1B.
[0031] FIG. 5, including FIGS. 5A-5C, shows application of
adaptation data for processing at least a portion of the received
speech data determining module 158 of the device 130 of environment
100 of FIG. 1B.
[0032] FIG. 6, including FIGS. 6A-6B, shows adaptation result data
based on at least one aspect of the received speech data
transmitting module 160 of the device 130 of environment 100 of
FIG. 1B.
[0033] FIG. 7 is a high-level logic flow chart of a process, e.g.,
operational flow 700, according to an embodiment.
[0034] FIG. 8A is a high-level logic flowchart of a process
depicting alternate implementations of a receiving speech data
operation 702 of FIG. 7, according to one or more embodiments.
[0035] FIG. 8B is a high-level logic flowchart of a process
depicting alternate implementations of a receiving speech data
operation 702 of FIG. 7, according to one or more embodiments.
[0036] FIG. 8C is a high-level logic flowchart of a process
depicting alternate implementations of a receiving speech data
operation 702 of FIG. 7, according to one or more embodiments.
[0037] FIG. 8D is a high-level logic flowchart of a process
depicting alternate implementations of a receiving speech data
operation 702 of FIG. 7, according to one or more embodiments.
[0038] FIG. 9A is a high-level logic flowchart of a process
depicting alternate implementations of a receiving adaptation data
operation 704 of FIG. 7, according to one or more embodiments.
[0039] FIG. 9B is a high-level logic flowchart of a process
depicting alternate implementations of a receiving adaptation data
operation 704 of FIG. 7, according to one or more embodiments.
[0040] FIG. 9C is a high-level logic flowchart of a process
depicting alternate implementations of a receiving adaptation data
operation 704 of FIG. 7, according to one or more embodiments.
[0041] FIG. 9D is a high-level logic flowchart of a process
depicting alternate implementations of a receiving adaptation data
operation 704 of FIG. 7, according to one or more embodiments.
[0042] FIG. 9E is a high-level logic flowchart of a process
depicting alternate implementations of a receiving adaptation data
operation 704 of FIG. 7, according to one or more embodiments.
[0043] FIG. 9F is a high-level logic flowchart of a process
depicting alternate implementations of a receiving adaptation data
operation 704 of FIG. 7, according to one or more embodiments.
[0044] FIG. 9G is a high-level logic flowchart of a process
depicting alternate implementations of a receiving adaptation data
operation 704 of FIG. 7, according to one or more embodiments.
[0045] FIG. 9H is a high-level logic flowchart of a process
depicting alternate implementations of a receiving adaptation data
operation 704 of FIG. 7, according to one or more embodiments.
[0046] FIG. 9I is a high-level logic flowchart of a process
depicting alternate implementations of a receiving adaptation data
operation 704 of FIG. 7, according to one or more embodiments.
[0047] FIG. 9J is a high-level logic flowchart of a process
depicting alternate implementations of a receiving adaptation data
operation 704 of FIG. 7, according to one or more embodiments.
[0048] FIG. 9K is a high-level logic flowchart of a process
depicting alternate implementations of a receiving adaptation data
operation 704 of FIG. 7, according to one or more embodiments.
[0049] FIG. 9L is a high-level logic flowchart of a process
depicting alternate implementations of a receiving adaptation data
operation 704 of FIG. 7, according to one or more embodiments.
[0050] FIG. 9M is a high-level logic flowchart of a process
depicting alternate implementations of a receiving adaptation data
operation 704 of FIG. 7, according to one or more embodiments.
[0051] FIG. 9N is a high-level logic flowchart of a process
depicting alternate implementations of a receiving adaptation data
operation 704 of FIG. 7, according to one or more embodiments.
[0052] FIG. 9P is a high-level logic flowchart of a process
depicting alternate implementations of a receiving adaptation data
operation 704 of FIG. 7, according to one or more embodiments.
[0053] FIG. 9Q is a high-level logic flowchart of a process
depicting alternate implementations of a receiving adaptation data
operation 704 of FIG. 7, according to one or more embodiments.
[0054] FIG. 10A is a high-level logic flowchart of a process
depicting alternate implementations of an obtaining target data
operation 706 of FIG. 7, according to one or more embodiments.
[0055] FIG. 10B is a high-level logic flowchart of a process
depicting alternate implementations of an obtaining target data
operation 706 of FIG. 7, according to one or more embodiments.
[0056] FIG. 10C is a high-level logic flowchart of a process
depicting alternate implementations of an obtaining target data
operation 706 of FIG. 7, according to one or more embodiments.
[0057] FIG. 10D is a high-level logic flowchart of a process
depicting alternate implementations of an obtaining target data
operation 706 of FIG. 7, according to one or more embodiments.
[0058] FIG. 10E is a high-level logic flowchart of a process
depicting alternate implementations of an obtaining target data
operation 706 of FIG. 7, according to one or more embodiments.
[0059] FIG. 10F is a high-level logic flowchart of a process
depicting alternate implementations of an obtaining target data
operation 706 of FIG. 7, according to one or more embodiments.
[0060] FIG. 10G is a high-level logic flowchart of a process
depicting alternate implementations of an obtaining target data
operation 706 of FIG. 7, according to one or more embodiments.
[0061] FIG. 11A is a high-level logic flowchart of a process
depicting alternate implementations of a determining whether to
apply the adaptation data operation 708 of FIG. 7, according to one
or more embodiments.
[0062] FIG. 11B is a high-level logic flowchart of a process
depicting alternate implementations of a determining whether to
apply the adaptation data operation 708 of FIG. 7, according to one
or more embodiments.
[0063] FIG. 11C is a high-level logic flowchart of a process
depicting alternate implementations of a determining whether to
apply the adaptation data operation 708 of FIG. 7, according to one
or more embodiments.
[0064] FIG. 12A is a high-level logic flowchart of a process
depicting alternate implementations of a transmitting adaptation
result data operation 710 of FIG. 7, according to one or more
embodiments.
[0065] FIG. 12B is a high-level logic flowchart of a process
depicting alternate implementations of a transmitting adaptation
result data operation 710 of FIG. 7, according to one or more
embodiments.
DETAILED DESCRIPTION
[0066] In the following detailed description, reference is made to
the accompanying drawings, which form a part hereof. In the
drawings, similar symbols typically identify similar or identical
components or items, unless context dictates otherwise. The
illustrative embodiments described in the detailed description,
drawings, and claims are not meant to be limiting. Other
embodiments may be utilized, and other changes may be made, without
departing from the spirit or scope of the subject matter presented
here.
[0067] In accordance with various embodiments, computationally
implemented methods, systems, circuitry, articles of manufacture,
and computer program products are designed to, among other things,
provide an interface for receiving speech data correlated to one or
more words spoken by a particular party, receiving adaptation data
that is at least partly based on at least one speech interaction of
a particular party that is discrete from the received speech data,
wherein at least a portion of the adaptation data has been stored
on a particular device associated with the particular party,
obtaining target data regarding a target configured to process at
least a portion of the received speech data, determining whether to
apply the adaptation data for processing at least a portion of the
received speech data, at least partly based on the acquired target
data, and transmitting adaptation result data that is based on at
least one aspect of the received speech data.
[0068] The present application uses formal outline headings for
clarity of presentation. However, it is to be understood that the
outline headings are for presentation purposes, and that different
types of subject matter may be discussed throughout the application
(e.g., device(s)/structure(s) may be described under
process(es)/operations heading(s) and/or process(es)/operations may
be discussed under structure(s)/process(es) headings; and/or
descriptions of single topics may span two or more topic headings).
Hence, the use of the formal outline headings is not intended to be
in any way limiting.
[0069] Throughout this application, examples and lists are given,
with parentheses, the abbreviation "e.g.," or both. Unless
explicitly otherwise stated, these examples and lists are merely
exemplary and are non-exhaustive. In most cases, it would be
prohibitive to list every example and every combination. Thus,
smaller, illustrative lists and examples are used, with focus on
imparting understanding of the claim terms rather than limiting the
scope of such terms.
[0070] With respect to the use of substantially any plural and/or
singular terms herein, those having skill in the art can translate
from the plural to the singular and/or from the singular to the
plural as is appropriate to the context and/or application. The
various singular/plural permutations are not expressly set forth
herein for sake of clarity.
[0071] One skilled in the art will recognize that the herein
described components (e.g., operations), devices, objects, and the
discussion accompanying them are used as examples for the sake of
conceptual clarity and that various configuration modifications are
contemplated. Consequently, as used herein, the specific exemplars
set forth and the accompanying discussion are intended to be
representative of their more general classes. In general, use of
any specific exemplar is intended to be representative of its
class, and the non-inclusion of specific components (e.g.,
operations), devices, and objects should not be taken limiting.
[0072] Although user 105 is shown/described herein as a single
illustrated figure, those skilled in the art will appreciate that
user 105 may be representative of a human user, a robotic user
(e.g., computational entity), and/or substantially any combination
thereof (e.g., a user may be assisted by one or more robotic
agents) unless context dictates otherwise. Those skilled in the art
will appreciate that, in general, the same may be said of "sender"
and/or other entity-oriented terms as such terms are used herein
unless context dictates otherwise.
[0073] Those having skill in the art will recognize that the state
of the art has progressed to the point where there is little
distinction left between hardware, software, and/or firmware
implementations of aspects of systems; the use of hardware,
software, and/or firmware is generally (but not always, in that in
certain contexts the choice between hardware and software can
become significant) a design choice representing cost vs.
efficiency tradeoffs. Those having skill in the art will appreciate
that there are various vehicles by which processes and/or systems
and/or other technologies described herein can be effected (e.g.,
hardware, software, and/or firmware), and that the preferred
vehicle will vary with the context in which the processes and/or
systems and/or other technologies are deployed. For example, if an
implementer determines that speed and accuracy are paramount, the
implementer may opt for a mainly hardware and/or firmware vehicle;
alternatively, if flexibility is paramount, the implementer may opt
for a mainly software implementation; or, yet again alternatively,
the implementer may opt for some combination of hardware, software,
and/or firmware in one or more machines, compositions of matter,
and articles of manufacture, limited to patentable subject matter
under 35 USC 101. Hence, there are several possible vehicles by
which the processes and/or devices and/or other technologies
described herein may be effected, none of which is inherently
superior to the other in that any vehicle to be utilized is a
choice dependent upon the context in which the vehicle will be
deployed and the specific concerns (e.g., speed, flexibility, or
predictability) of the implementer, any of which may vary. Those
skilled in the art will recognize that optical aspects of
implementations will typically employ optically-oriented hardware,
software, and or firmware.
[0074] In some implementations described herein, logic and similar
implementations may include software or other control structures.
Electronic circuitry, for example, may have one or more paths of
electrical current constructed and arranged to implement various
functions as described herein. In some implementations, one or more
media may be configured to bear a device-detectable implementation
when such media hold or transmit device detectable instructions
operable to perform as described herein. In some variants, for
example, implementations may include an update or modification of
existing software or firmware, or of gate arrays or programmable
hardware, such as by performing a reception of or a transmission of
one or more instructions in relation to one or more operations
described herein. Alternatively or additionally, in some variants,
an implementation may include special-purpose hardware, software,
firmware components, and/or general-purpose components executing or
otherwise invoking special-purpose components. Specifications or
other implementations may be transmitted by one or more instances
of tangible transmission media as described herein, optionally by
packet transmission or otherwise by passing through distributed
media at various times.
[0075] Alternatively or additionally, implementations may include
executing a special-purpose instruction sequence or invoking
circuitry for enabling, triggering, coordinating, requesting, or
otherwise causing one or more occurrences of virtually any
functional operations described herein. In some variants,
operational or other logical descriptions herein may be expressed
as source code and compiled or otherwise invoked as an executable
instruction sequence. In some contexts, for example,
implementations may be provided, in whole or in part, by source
code, such as C++, or other code sequences. In other
implementations, source or other code implementation, using
commercially available and/or techniques in the art, may be
compiled//implemented/translated/converted into a high-level
descriptor language (e.g., initially implementing described
technologies in C or C++ programming language and thereafter
converting the programming language implementation into a
logic-synthesizable language implementation, a hardware description
language implementation, a hardware design simulation
implementation, and/or other such similar mode(s) of expression).
For example, some or all of a logical expression (e.g., computer
programming language implementation) may be manifested as a
Verilog-type hardware description (e.g., via Hardware Description
Language (HDL) and/or Very High Speed Integrated Circuit Hardware
Descriptor Language (VHDL)) or other circuitry model which may then
be used to create a physical implementation having hardware (e.g.,
an Application Specific Integrated Circuit). Those skilled in the
art will recognize how to obtain, configure, and optimize suitable
transmission or computational elements, material supplies,
actuators, or other structures in light of these teachings.
[0076] The claims, description, and drawings of this application
may describe one or more of the instant technologies in
operational/functional language, for example as a set of operations
to be performed by a computer. Such operational/functional
description in most instances would be understood by one skilled
the art as specifically-configured hardware (e.g., because a
general purpose computer in effect becomes a special purpose
computer once it is programmed to perform particular functions
pursuant to instructions from program software).
[0077] Importantly, although the operational/functional
descriptions described herein are understandable by the human mind,
they are not abstract ideas of the operations/functions divorced
from computational implementation of those operations/functions.
Rather, the operations/functions represent a specification for the
massively complex computational machines or other means. As
discussed in detail below, the operational/functional language must
be read in its proper technological context, i.e., as concrete
specifications for physical implementations.
[0078] The logical operations/functions described herein are a
distillation of machine specifications or other physical mechanisms
specified by the operations/functions such that the otherwise
inscrutable machine specifications may be comprehensible to the
human mind. The distillation also allows one of skill in the art to
adapt the operational/functional description of the technology
across many different specific vendors' hardware configurations or
platforms, without being limited to specific vendors' hardware
configurations or platforms.
[0079] Some of the present technical description (e.g., detailed
description, drawings, claims, etc.) may be set forth in terms of
logical operations/functions. As described in more detail in the
following paragraphs, these logical operations/functions are not
representations of abstract ideas, but rather representative of
static or sequenced specifications of various hardware elements.
Differently stated, unless context dictates otherwise, the logical
operations/functions will be understood by those of skill in the
art to be representative of static or sequenced specifications of
various hardware elements. This is true because tools available to
one of skill in the art to implement technical disclosures set
forth in operational/functional formats--tools in the form of a
high-level programming language (e.g., C, java, visual basic),
etc.), or tools in the form of Very high speed Hardware Description
Language ("VHDL," which is a language that uses text to describe
logic circuits)--are generators of static or sequenced
specifications of various hardware configurations. This fact is
sometimes obscured by the broad term "software," but, as shown by
the following explanation, those skilled in the art understand that
what is termed "software" is shorthand for a massively complex
interchaining/specification of ordered-matter elements. The term
"ordered-matter elements" may refer to physical components of
computation, such as assemblies of electronic logic gates,
molecular computing logic constituents, quantum computing
mechanisms, etc.
[0080] For example, a high-level programming language is a
programming language with strong abstraction, e.g., multiple levels
of abstraction, from the details of the sequential organizations,
states, inputs, outputs, etc., of the machines that a high-level
programming language actually specifies. See, e.g., Wikipedia,
High-level programming language,
http://en.wikipedia.org/wiki/High-level_programming_language (as of
Jun. 5, 2012, 21:00 GMT). In order to facilitate human
comprehension, in many instances, high-level programming languages
resemble or even share symbols with natural languages. See, e.g.,
Wikipedia, Natural language,
http://en.wikipedia.org/wiki/Natural_language (as of Jun. 5, 2012,
21:00 GMT).
[0081] It has been argued that because high-level programming
languages use strong abstraction (e.g., that they may resemble or
share symbols with natural languages), they are therefore a "purely
mental construct." (e.g., that "software" --a computer program or
computer programming--is somehow an ineffable mental construct,
because at a high level of abstraction, it can be conceived and
understood in the human mind). This argument has been used to
characterize technical description in the form of
functions/operations as somehow "abstract ideas." In fact, in
technological arts (e.g., the information and communication
technologies) this is not true.
[0082] The fact that high-level programming languages use strong
abstraction to facilitate human understanding should not be taken
as an indication that what is expressed is an abstract idea. In
fact, those skilled in the art understand that just the opposite is
true. If a high-level programming language is the tool used to
implement a technical disclosure in the form of
functions/operations, those skilled in the art will recognize that,
far from being abstract, imprecise, "fuzzy," or "mental" in any
significant semantic sense, such a tool is instead a near
incomprehensibly precise sequential specification of specific
computational machines--the parts of which are built up by
activating/selecting such parts from typically more general
computational machines over time (e.g., clocked time). This fact is
sometimes obscured by the superficial similarities between
high-level programming languages and natural languages. These
superficial similarities also may cause a glossing over of the fact
that high-level programming language implementations ultimately
perform valuable work by creating/controlling many different
computational machines.
[0083] The many different computational machines that a high-level
programming language specifies are almost unimaginably complex. At
base, the hardware used in the computational machines typically
consists of some type of ordered matter (e.g., traditional
electronic devices (e.g., transistors), deoxyribonucleic acid
(DNA), quantum devices, mechanical switches, optics, fluidics,
pneumatics, optical devices (e.g., optical interference devices),
molecules, etc.) that are arranged to form logic gates. Logic gates
are typically physical devices that may be electrically,
mechanically, chemically, or otherwise driven to change physical
state in order to create a physical reality of Boolean logic.
[0084] Logic gates may be arranged to form logic circuits, which
are typically physical devices that may be electrically,
mechanically, chemically, or otherwise driven to create a physical
reality of certain logical functions. Types of logic circuits
include such devices as multiplexers, registers, arithmetic logic
units (ALUs), computer memory, etc., each type of which may be
combined to form yet other types of physical devices, such as a
central processing unit (CPU)--the best known of which is the
microprocessor. A modern microprocessor will often contain more
than one hundred million logic gates in its many logic circuits
(and often more than a billion transistors). See, e.g., Wikipedia,
Logic gates, http://en.wikipedia.org/wiki/Logic_gates (as of Jun.
5, 2012, 21:03 GMT).
[0085] The logic circuits forming the microprocessor are arranged
to provide a microarchitecture that will carry out the instructions
defined by that microprocessor's defined Instruction Set
Architecture. The Instruction Set Architecture is the part of the
microprocessor architecture related to programming, including the
native data types, instructions, registers, addressing modes,
memory architecture, interrupt and exception handling, and external
Input/Output. See, e.g., Wikipedia, Computer architecture,
http://en.wikipedia.org/wiki/Computer_architecture (as of Jun. 5,
2012, 21:03 GMT).
[0086] The Instruction Set Architecture includes a specification of
the machine language that can be used by programmers to use/control
the microprocessor. Since the machine language instructions are
such that they may be executed directly by the microprocessor,
typically they consist of strings of binary digits, or bits. For
example, a typical machine language instruction might be many bits
long (e.g., 32, 64, or 128 bit strings are currently common). A
typical machine language instruction might take the form
"11110000101011110000111100111111" (a 32 bit instruction).
[0087] It is significant here that, although the machine language
instructions are written as sequences of binary digits, in
actuality those binary digits specify physical reality. For
example, if certain semiconductors are used to make the operations
of Boolean logic a physical reality, the apparently mathematical
bits "1" and "0" in a machine language instruction actually
constitute shorthand that specifies the application of specific
voltages to specific wires. For example, in some semiconductor
technologies, the binary number "1" (e.g., logical "1") in a
machine language instruction specifies around +5 volts applied to a
specific "wire" (e.g., metallic traces on a printed circuit board)
and the binary number "0" (e.g., logical "0") in a machine language
instruction specifies around -5 volts applied to a specific "wire."
In addition to specifying voltages of the machines' configuration,
such machine language instructions also select out and activate
specific groupings of logic gates from the millions of logic gates
of the more general machine. Thus, far from abstract mathematical
expressions, machine language instruction programs, even though
written as a string of zeros and ones, specify many, many
constructed physical machines or physical machine states.
[0088] Machine language is typically incomprehensible by most
humans (e.g., the above example was just ONE instruction, and some
personal computers execute more than two billion instructions every
second). See, e.g., Wikipedia, Instructions per second,
http://en.wikipedia.org/wiki/Instructions_per_second (as of Jun. 5,
2012, 21:04 GMT). Thus, programs written in machine language--which
may be tens of millions of machine language instructions long--are
incomprehensible. In view of this, early assembly languages were
developed that used mnemonic codes to refer to machine language
instructions, rather than using the machine language instructions'
numeric values directly (e.g., for performing a multiplication
operation, programmers coded the abbreviation "mult," which
represents the binary number "011000" in MIPS machine code). While
assembly languages were initially a great aid to humans controlling
the microprocessors to perform work, in time the complexity of the
work that needed to be done by the humans outstripped the ability
of humans to control the microprocessors using merely assembly
languages.
[0089] At this point, it was noted that the same tasks needed to be
done over and over, and the machine language necessary to do those
repetitive tasks was the same. In view of this, compilers were
created. A compiler is a device that takes a statement that is more
comprehensible to a human than either machine or assembly language,
such as "add 2+2 and output the result," and translates that human
understandable statement into a complicated, tedious, and immense
machine language code (e.g., millions of 32, 64, or 128 bit length
strings). Compilers thus translate high-level programming language
into machine language.
[0090] This compiled machine language, as described above, is then
used as the technical specification which sequentially constructs
and causes the interoperation of many different computational
machines such that humanly useful, tangible, and concrete work is
done. For example, as indicated above, such machine language--the
compiled version of the higher-level language--functions as a
technical specification which selects out hardware logic gates,
specifies voltage levels, voltage transition timings, etc., such
that the humanly useful work is accomplished by the hardware.
[0091] Thus, a functional/operational technical description, when
viewed by one of skill in the art, is far from an abstract idea.
Rather, such a functional/operational technical description, when
understood through the tools available in the art such as those
just described, is instead understood to be a humanly
understandable representation of a hardware specification, the
complexity and specificity of which far exceeds the comprehension
of most any one human. With this in mind, those skilled in the art
will understand that any such operational/functional technical
descriptions--in view of the disclosures herein and the knowledge
of those skilled in the art--may be understood as operations made
into physical reality by (a) one or more interchained physical
machines, (b) interchained logic gates configured to create one or
more physical machine(s) representative of sequential/combinatorial
logic(s), (c) interchained ordered matter making up logic gates
(e.g., interchained electronic devices (e.g., transistors), DNA,
quantum devices, mechanical switches, optics, fluidics, pneumatics,
molecules, etc.) that create physical reality representative of
logic(s), or (d) virtually any combination of the foregoing.
Indeed, any physical object which has a stable, measurable, and
changeable state may be used to construct a machine based on the
above technical description. Charles Babbage, for example,
constructed the first computer out of wood and powered by cranking
a handle.
[0092] Thus, far from being understood as an abstract idea, those
skilled in the art will recognize a functional/operational
technical description as a humanly-understandable representation of
one or more almost unimaginably complex and time sequenced hardware
instantiations. The fact that functional/operational technical
descriptions might lend themselves readily to high-level computing
languages (or high-level block diagrams for that matter) that share
some words, structures, phrases, etc. with natural language simply
cannot be taken as an indication that such functional/operational
technical descriptions are abstract ideas, or mere expressions of
abstract ideas. In fact, as outlined herein, in the technological
arts this is simply not true. When viewed through the tools
available to those of skill in the art, such functional/operational
technical descriptions are seen as specifying hardware
configurations of almost unimaginable complexity.
[0093] As outlined above, the reason for the use of
functional/operational technical descriptions is at least twofold.
First, the use of functional/operational technical descriptions
allows near-infinitely complex machines and machine operations
arising from interchained hardware elements to be described in a
manner that the human mind can process (e.g., by mimicking natural
language and logical narrative flow). Second, the use of
functional/operational technical descriptions assists the person of
skill in the art in understanding the described subject matter by
providing a description that is more or less independent of any
specific vendor's piece(s) of hardware.
[0094] The use of functional/operational technical descriptions
assists the person of skill in the art in understanding the
described subject matter since, as is evident from the above
discussion, one could easily, although not quickly, transcribe the
technical descriptions set forth in this document as trillions of
ones and zeroes, billions of single lines of assembly-level machine
code, millions of logic gates, thousands of gate arrays, or any
number of intermediate levels of abstractions. However, if any such
low-level technical descriptions were to replace the present
technical description, a person of skill in the art could encounter
undue difficulty in implementing the disclosure, because such a
low-level technical description would likely add complexity without
a corresponding benefit (e.g., by describing the subject matter
utilizing the conventions of one or more vendor-specific pieces of
hardware). Thus, the use of functional/operational technical
descriptions assists those of skill in the art by separating the
technical descriptions from the conventions of any vendor-specific
piece of hardware.
[0095] In view of the foregoing, the logical operations/functions
set forth in the present technical description are representative
of static or sequenced specifications of various ordered-matter
elements, in order that such specifications may be comprehensible
to the human mind and adaptable to create many various hardware
configurations. The logical operations/functions disclosed herein
should be treated as such, and should not be disparagingly
characterized as abstract ideas merely because the specifications
they represent are presented in a manner that one of skill in the
art can readily understand and apply in a manner independent of a
specific vendor's hardware implementation.
[0096] Those skilled in the art will recognize that it is common
within the art to implement devices and/or processes and/or
systems, and thereafter use engineering and/or other practices to
integrate such implemented devices and/or processes and/or systems
into more comprehensive devices and/or processes and/or systems.
That is, at least a portion of the devices and/or processes and/or
systems described herein can be integrated into other devices
and/or processes and/or systems via a reasonable amount of
experimentation. Those having skill in the art will recognize that
examples of such other devices and/or processes and/or systems
might include--as appropriate to context and application--all or
part of devices and/or processes and/or systems of (a) an air
conveyance (e.g., an airplane, rocket, helicopter, etc.), (b) a
ground conveyance (e.g., a car, truck, locomotive, tank, armored
personnel carrier, etc.), (c) a building (e.g., a home, warehouse,
office, etc.), (d) an appliance (e.g., a refrigerator, a washing
machine, a dryer, etc.), (e) a communications system (e.g., a
networked system, a telephone system, a Voice over IP system,
etc.), (f) a business entity (e.g., an Internet Service Provider
(ISP) entity such as Comcast Cable, Qwest, Southwestern Bell,
etc.), or (g) a wired/wireless services entity (e.g., Sprint,
Cingular, Nextel, etc.), etc.
[0097] In certain cases, use of a system or method may occur in a
territory even if components are located outside the territory. For
example, in a distributed computing context, use of a distributed
computing system may occur in a territory even though parts of the
system may be located outside of the territory (e.g., relay,
server, processor, signal-bearing medium, transmitting computer,
receiving computer, etc. located outside the territory).
[0098] A sale of a system or method may likewise occur in a
territory even if components of the system or method are located
and/or used outside the territory. Further, implementation of at
least part of a system for performing a method in one territory
does not preclude use of the system in another territory
[0099] One skilled in the art will recognize that the herein
described components (e.g., operations), devices, objects, and the
discussion accompanying them are used as examples for the sake of
conceptual clarity and that various configuration modifications are
contemplated. Consequently, as used herein, the specific exemplars
set forth and the accompanying discussion are intended to be
representative of their more general classes. In general, use of
any specific exemplar is intended to be representative of its
class, and the non-inclusion of specific components (e.g.,
operations), devices, and objects should not be taken limiting.
[0100] The herein described subject matter sometimes illustrates
different components contained within, or connected with, different
other components. It is to be understood that such depicted
architectures are merely exemplary, and that in fact many other
architectures may be implemented which achieve the same
functionality. In a conceptual sense, any arrangement of components
to achieve the same functionality is effectively "associated" such
that the desired functionality is achieved. Hence, any two
components herein combined to achieve a particular functionality
can be seen as "associated with" each other such that the desired
functionality is achieved, irrespective of architectures or
intermedial components. Likewise, any two components so associated
can also be viewed as being "operably connected", or "operably
coupled," to each other to achieve the desired functionality, and
any two components capable of being so associated can also be
viewed as being "operably couplable," to each other to achieve the
desired functionality. Specific examples of operably couplable
include but are not limited to physically mateable and/or
physically interacting components, and/or wirelessly interactable,
and/or wirelessly interacting components, and/or logically
interacting, and/or logically interactable components.
[0101] In some instances, one or more components may be referred to
herein as "configured to," "configured by," "configurable to,"
"operable/operative to," "adapted/adaptable," "able to,"
"conformable/conformed to," etc. Those skilled in the art will
recognize that such terms (e.g. "configured to") generally
encompass active-state components and/or inactive-state components
and/or standby-state components, unless context requires
otherwise.
[0102] In a general sense, those skilled in the art will recognize
that the various embodiments described herein can be implemented,
individually and/or collectively, by various types of
electro-mechanical systems having a wide range of electrical
components such as hardware, software, firmware, and/or virtually
any combination thereof, limited to patentable subject matter under
35 U.S.C. 101; and a wide range of components that may impart
mechanical force or motion such as rigid bodies, spring or
torsional bodies, hydraulics, electro-magnetically actuated
devices, and/or virtually any combination thereof. Consequently, as
used herein "electro-mechanical system" includes, but is not
limited to, electrical circuitry operably coupled with a transducer
(e.g., an actuator, a motor, a piezoelectric crystal, a Micro
Electro Mechanical System (MEMS), etc.), electrical circuitry
having at least one discrete electrical circuit, electrical
circuitry having at least one integrated circuit, electrical
circuitry having at least one application specific integrated
circuit, electrical circuitry forming a general purpose computing
device configured by a computer program (e.g., a general purpose
computer configured by a computer program which at least partially
carries out processes and/or devices described herein, or a
microprocessor configured by a computer program which at least
partially carries out processes and/or devices described herein),
electrical circuitry forming a memory device (e.g., forms of memory
(e.g., random access, flash, read only, etc.)), electrical
circuitry forming a communications device (e.g., a modem,
communications switch, optical-electrical equipment, etc.), and/or
any non-electrical analog thereto, such as optical or other analogs
(e.g., graphene based circuitry). Those skilled in the art will
also appreciate that examples of electro-mechanical systems include
but are not limited to a variety of consumer electronics systems,
medical devices, as well as other systems such as motorized
transport systems, factory automation systems, security systems,
and/or communication/computing systems. Those skilled in the art
will recognize that electro-mechanical as used herein is not
necessarily limited to a system that has both electrical and
mechanical actuation except as context may dictate otherwise.
[0103] In a general sense, those skilled in the art will recognize
that the various aspects described herein which can be implemented,
individually and/or collectively, by a wide range of hardware,
software, firmware, and/or any combination thereof can be viewed as
being composed of various types of "electrical circuitry."
Consequently, as used herein "electrical circuitry" includes, but
is not limited to, electrical circuitry having at least one
discrete electrical circuit, electrical circuitry having at least
one integrated circuit, electrical circuitry having at least one
application specific integrated circuit, electrical circuitry
forming a general purpose computing device configured by a computer
program (e.g., a general purpose computer configured by a computer
program which at least partially carries out processes and/or
devices described herein, or a microprocessor configured by a
computer program which at least partially carries out processes
and/or devices described herein), electrical circuitry forming a
memory device (e.g., forms of memory (e.g., random access, flash,
read only, etc.)), and/or electrical circuitry forming a
communications device (e.g., a modem, communications switch,
optical-electrical equipment, etc.). Those having skill in the art
will recognize that the subject matter described herein may be
implemented in an analog or digital fashion or some combination
thereof.
[0104] Those skilled in the art will recognize that at least a
portion of the devices and/or processes described herein can be
integrated into a data processing system. Those having skill in the
art will recognize that a data processing system generally includes
one or more of a system unit housing, a video display device,
memory such as volatile or non-volatile memory, processors such as
microprocessors or digital signal processors, computational
entities such as operating systems, drivers, graphical user
interfaces, and applications programs, one or more interaction
devices (e.g., a touch pad, a touch screen, an antenna, etc.),
and/or control systems including feedback loops and control motors
(e.g., feedback for sensing position and/or velocity; control
motors for moving and/or adjusting components and/or quantities). A
data processing system may be implemented utilizing suitable
commercially available components, such as those typically found in
data computing/communication and/or network computing/communication
systems.
[0105] For the purposes of this application, "cloud" computing may
be understood as described in the cloud computing literature. For
example, cloud computing may be methods and/or systems for the
delivery of computational capacity and/or storage capacity as a
service. The "cloud" may refer to one or more hardware and/or
software components that deliver or assist in the delivery of
computational and/or storage capacity, including, but not limited
to, one or more of a client, an application, a platform, an
infrastructure, and/or a server The cloud may refer to any of the
hardware and/or software associated with a client, an application,
a platform, an infrastructure, and/or a server. For example, cloud
and cloud computing may refer to one or more of a computer, a
processor, a storage medium, a router, a switch, a modem, a virtual
machine (e.g., a virtual server), a data center, an operating
system, a middleware, a firmware, a hardware back-end, a software
back-end, and/or a software application. A cloud may refer to a
private cloud, a public cloud, a hybrid cloud, and/or a community
cloud. A cloud may be a shared pool of configurable computing
resources, which may be public, private, semi-private,
distributable, scaleable, flexible, temporary, virtual, and/or
physical. A cloud or cloud service may be delivered over one or
more types of network, e.g., a mobile communication network, and
the Internet.
[0106] As used in this application, a cloud or a cloud service may
include one or more of infrastructure-as-a-service ("IaaS"),
platform-as-a-service ("PaaS"), software-as-a-service ("SaaS"),
and/or desktop-as-a-service ("DaaS"). As a non-exclusive example,
IaaS may include, e.g., one or more virtual server instantiations
that may start, stop, access, and/or configure virtual servers
and/or storage centers (e.g., providing one or more processors,
storage space, and/or network resources on-demand, e.g., EMC and
Rackspace). PaaS may include, e.g., one or more software and/or
development tools hosted on an infrastructure (e.g., a computing
platform and/or a solution stack from which the client can create
software interfaces and applications, e.g., Microsoft Azure). SaaS
may include, e.g., software hosted by a service provider and
accessible over a network (e.g., the software for the application
and/or the data associated with that software application may be
kept on the network, e.g., Google Apps, SalesForce). DaaS may
include, e.g., providing desktop, applications, data, and/or
services for the user over a network (e.g., providing a
multi-application framework, the applications in the framework, the
data associated with the applications, and/or services related to
the applications and/or the data over the network, e.g., Citrix).
The foregoing is intended to be exemplary of the types of systems
and/or methods referred to in this application as "cloud" or "cloud
computing" and should not be considered complete or exhaustive.
[0107] The proliferation of automation in many transactions is
apparent. For example, Automated Teller Machines ("ATMs") dispense
money and receive deposits. Airline ticket counter machines check
passengers in, dispense tickets, and allow passengers to change or
upgrade flights. Train and subway ticket counter machines allow
passengers to purchase a ticket to a particular destination without
invoking a human interaction at all. Many groceries and pharmacies
have self-service checkout machines which allow a consumer to pay
for goods purchased by interacting only with a machine. Large
companies now staff telephone answering systems with machines that
interact with customers, and invoke a human in the transaction only
if there is a problem with the machine-facilitated transaction.
[0108] Nevertheless, as such automation increases, convenience and
accessibility may decrease. Self-checkout machines at grocery
stores may be difficult to operate. ATMs and ticket counter
machines may be mostly inaccessible to disabled persons or persons
requiring special access. Where before, the interaction with a
human would allow disabled persons to complete transactions with
relative ease, if a disabled person is unable to push the buttons
on an ATM, there is little the machine can do to facilitate the
transaction to completion. While some of these public terminals
allow speech operations, they are configured to the most generic
forms of speech, which may be less useful in recognizing particular
speakers, thereby leading to frustration for users attempting to
speak to the machine. This problem may be especially challenging
for the disabled, who already may face significant challenges in
completing transactions with automated machines.
[0109] In addition, smartphones and tablet devices also now are
configured to receive speech commands. Speech and voice controlled
automobile systems now appear regularly in motor vehicles, even in
economical, mass-produced vehicles. Home entertainment devices,
e.g., disc players, televisions, radios, stereos, and the like, may
respond to speech commands. Additionally, home security systems may
respond to speech commands. In an office setting, a worker's
computer may respond to speech from that worker, allowing faster,
more efficient work flows. Such systems and machines may be trained
to operate with particular users, either through explicit training
or through repeated interactions. Nevertheless, when that system is
upgraded or replaced, e.g., a new television is purchased, that
training may be lost with the device. Thus, in some embodiments
described herein, adaptation data for speech recognition systems
may be separated from the device which recognizes the speech, and
may be more closely associated with a user, e.g., through a device
carried by the user, or through a network location associated with
the user.
[0110] Further, in some environments, there may be more than one
device that transmits and receives data within a range of
interacting with a user. For example, merely sitting on a couch
watching television may involve five or more devices, e.g., a
television, a cable box, an audio/visual receiver, a remote
control, and a smartphone device. Some of these devices may
transmit or receive speech data. Some of these devices may
transmit, receive, or store adaptation data, as will be described
in more detail herein. Thus, in some embodiments, which will be
described in more detail herein, there may be methods, systems, and
devices for determining which devices in a system should perform
actions that allow a user to efficiently interact with an intended
device through that user's speech.
[0111] Referring now to FIG. 1, e.g., FIG. 1A, FIG. 1A illustrates
an example environment 100 in which the methods, systems,
circuitry, articles of manufacture, and computer program products
and architecture, in accordance with various embodiments, may be
implemented by one or more of personal device 20A, personal device
20B, intermediate device 40, target device 30A, and target device
30B. In some embodiments, e.g., as shown in FIG. 1B, device 39,
which in some embodiments, may be an example of one of target
device 30A, target device 30B, and intermediate device 40. The
device 130, in various embodiments, may be endowed with logic that
is designed for receiving speech data correlated to one or more
words spoken by a particular party, logic that is designed for
receiving adaptation data that is at least partly based on at least
one speech interaction of a particular party that is discrete from
the received speech data, wherein at least a portion of the
adaptation data has been stored on a particular device associated
with the particular party, logic that is designed for obtaining
target data regarding a target configured to process at least a
portion of the received speech data, determining whether to apply
the adaptation data for processing at least a portion of the
received speech data, at least partly based on the acquired target
data, and transmitting adaptation result data that is based on at
least one aspect of the received speech data.
[0112] Referring again to the exemplary embodiment in FIG. 1A, a
user 105 may engage in a speech facilitated transaction with one or
more of a terminal device 30A and a terminal device 30B. In some
embodiments, the speech-facilitated transaction may be directed to
one of terminal device 30A or terminal device 30B. In some
embodiments, the user may not specifically direct her speech toward
terminal device 30A or terminal device 30B, but rather to both of
them, with indifference toward which device carries out the
speech-facilitated transaction. In some embodiments, one of the
terminal device 30A and terminal device 30B negotiate between
themselves to determine which device will carry out the
speech-facilitated transaction. In some embodiments, one or more of
the personal device 20A, the personal device 20B, and the
intermediate device 40 may determine which of the terminal device
30A and terminal device 30B carries out the speech-facilitated
transaction. In some embodiments, one or more of personal device
20A, personal device 20B, and intermediate device 40 may detect one
or more of terminal device 30A and terminal device 30B, establish a
connection, or negotiate with one or more of terminal devices 30A
and 30B. In some embodiments, one or more of terminal device 30A,
terminal device 30B, and intermediate device 40 may detect one or
more of personal device 20A and personal device 20B, establish a
connection, or negotiate with one or more of the detected
devices.
[0113] The dashed-line arrows shown in environment 100 of FIG. 1A
are not labeled, but are intended to show the flow of data from one
device to the other. Some data connections are omitted for
simplicity of drawing, e.g., although there is no arrow, personal
device 20A may communicate directly with terminal device 30A and
terminal device 30B. The flow of data may include one or more
adaptation data, speech data in any format, including raw speech
from the user, adaptation result data, intended target data, target
data, and the like. The dotted line arrows show an association
between the user 105 and one or more of personal device 20A,
personal device 20B, and intermediate device 40.
[0114] Although it is not shown in FIG. 1A, any or all of personal
devices 20A, 20B, and 40 may communicate with any or all of
terminal device 30A and terminal device 30B, either directly, or
indirectly. In some embodiments, these devices communicate with
each other via a server 110, which may be local or remote to any of
the devices 20A, 20B, 30A, 30B, and 40. In some embodiments, these
devices communicate with each other via one or more communication
networks 140, which may be local or remote to any of the devices
20A, 20B, 30A, 30B, and 40. Although server 110 and communication
network 40 are pictured in each of the embodiments in FIGS. 1A and
1C-1E, server 110 and communication network 140 are not required,
and are shown merely for purposes of illustration.
[0115] Referring again to FIG. 1A, FIG. 1A shows personal device
20A, personal device 20B, intermediate device 40, terminal device
30A, terminal device 30B, and server 110. The number of devices is
shown merely for illustrative purposes. In some embodiments,
however, there may be a different number of personal devices,
intermediate devices, terminal devices, servers, and communication
networks. In some embodiments, one or more of the personal devices,
intermediate devices, terminal devices, servers, and communication
networks may be omitted entirely.
[0116] Referring again to FIG. 1A, personal device 20A and 20B are
shown as associated with user 105. This association may be
attenuated, e.g., they may merely be in the same physical
proximity. In other embodiments, the association may be one of
ownership, mutual contract, information storing, previous usage, or
other factors. The examples described further herein will provide a
non-exhaustive list of examples of relationships between user 105
and a personal device, e.g., personal device 20A or personal device
20B (hereinafter collectively referred to as "personal device
20*"). In some embodiments, personal device 20* may be any size and
have any specification. Personal device 20* may be a custom device
of any shape or size, configured to transmit, receive, and store
data. Personal device 20* may include, but is not limited to, a
smartphone device, a tablet device, a personal computer device, a
laptop device, a keychain device, a key, a personal digital
assistant device, a modified memory stick, a universal remote
control, or any other piece of electronics. In addition, personal
device 20 may be a modified object that is worn, e.g., eyeglasses,
a wallet, a credit card, a watch, a chain, or an article of
clothing. Anything that is configured to store, transmit, and
receive data may be a personal device 20*, and personal device 20*
is not limited in size to devices that are capable of being carried
by a user. Additionally, personal device 20* may not be in direct
proximity to the user, e.g., personal device 20 may be a computer
sitting on a desk in a user's home or office.
[0117] Although terminal devices 30A and 30B are described as
"terminal device," this is merely for simplicity of illustration.
Device 130, e.g., of which terminal devices 30A and 30B may be
examples, may be any device that is configured to receive speech.
For example, terminal device 130 may be a terminal, a computer, a
navigation system, a phone, a piece of home electronics (e.g., a
DVD player, Blu-Ray player, media player, game system, television,
receiver, alarm clock, and the like). Device 130 may, in some
embodiments, be a home security system, a safe lock, a door lock, a
kitchen appliance configured to receive speech, and the like. In
some embodiments, device 130 may be a motorized vehicle, e.g., a
car, boat, airplane, motorcycle, golf cart, wheelchair, and the
like. In some embodiments, device 130 may be a piece of portable
electronics, e.g., a laptop computer, a netbook computer, a tablet
device, a smartphone, a cellular phone, a radio, a portable
navigation system, or any other piece of electronics capable of
receiving speech. Device 130 may be a part of an enterprise
solution, e.g., a common workstation in an office, a copier, a
scanner, a personal workstation in a cubicle, an office directory,
an interactive screen, and a telephone. These examples and lists
are not meant to be exhaustive, but merely to illustrate a few
examples of the terminal device. Some of these examples are shown
in more detail with respect to FIGS. 1C, 1D, and 1E.
[0118] In some embodiments, a terminal device, e.g., device 130
receives adaptation data from a personal device, in a process that
will be described in more detail herein. In some embodiments, the
adaptation data is transmitted over one or more communication
network(s) 140. In various embodiments, the communication network
140 may include one or more of a local area network (LAN), a wide
area network (WAN), a metropolitan area network (MAN), a wireless
local area network (WLAN), a personal area network (PAN), a
Worldwide Interoperability for Microwave Access (WiMAX), public
switched telephone network (PTSN), a general packet radio service
(GPRS) network, a cellular network, and so forth. The communication
networks 140 may be wired, wireless, or a combination of wired and
wireless networks. It is noted that "communication network" here
refers to one or more communication networks, which may or may not
interact with each other.
[0119] In some embodiments, the adaptation data does not come
directly from the personal device, e.g., personal device 20A. In
some embodiments, the personal device merely facilitates
communication of the adaptation data, e.g., by providing one or
more of an address, credentials, instructions, authorization, and
recommendations. For example, in some embodiments, the personal
device provides a location at server 10 at which adaptation data
may be received. In some embodiments, the personal device retrieves
adaptation data from server 10 upon a request from the device 130,
and then relays or facilitates in the relaying of the adaptation
data to the device 130.
[0120] In some embodiments, a personal device broadcasts the
adaptation data regardless of whether a terminal device is
listening, e.g., at predetermined, regular, or otherwise-defined
intervals. In other embodiments, a personal device listens for a
request from a terminal device, and transmits or broadcasts
adaptation data in response to that request. In some embodiments,
user 105 determines when a personal device, e.g., personal device
20A, broadcasts adaptation data. In still other embodiments, a
third party (not shown) triggers the transmission of adaptation
data to the device 130, in which the transmission is facilitated by
the personal device.
[0121] FIG. 1B shows a more detailed description of a device 130 in
an exemplary embodiment 100. Device 130 may be an example of
terminal device 30A or 30B of FIG. 1A, intermediate device 40 of
FIG. 1A, device 31 of FIG. 1C, operating system application 91 of
FIG. 1C, first application 91, second application 92, speech
processing application 83, or enterprise client 82 of FIG. 1C, any
of devices 51, 52, 53, and 54 of FIG. 1D, motor vehicle control
system 41 of FIG. 1E, GPS navigation device 41 of FIG. 1E, and the
like. The foregoing is not intended to be exhaustive of the
possible devices that correspond to device 130 of FIG. 1B, but are
merely exemplary of the types of devices that may have a structure
as outlined in FIG. 1B.
[0122] Referring again to FIG. 1B, in various embodiments, the
device 130 may comprise, among other elements, a processor 132, a
memory 134, a user interface 135, a speech detection interface 138,
and a data transmission interface 137. Each of these elements may
be absent in various embodiments of device 130, e.g., some devices
130 may not have a speech detection interface 138, or a memory 134,
or a user interface 135.
[0123] Processor 132 may include one or more microprocessors,
Central Processing Units ("CPU"), a Graphics Processing Units
("GPU"), Physics Processing Units, Digital Signal Processors,
Network Processors, Floating Point Processors, and the like. In
some embodiments, processor 132 may be a server. In some
embodiments, processor 132 may be a distributed-core processor.
Although processor 132 is as a single processor that is part of a
single device 130, processor 132 may be multiple processors
distributed over one or many computing devices 130, which may or
may not be configured to operate together. Processor 132 is
illustrated as being configured to execute computer readable
instructions in order to execute one or more operations described
above, and as illustrated in FIGS. 7, 8A-8D, 9A-9Q, 10A-9G,
11A-11C, and 12A-12B. In some embodiments, processor 132 is
designed to be configured to operate as processing module 150,
which may include one or more of speech data correlated to one or
more particular party spoken words receiving module 152, adaptation
data at least partly based on discrete speech interaction of
particular party separate from detected speech data, and has been
stored on a particular party-associated particular device receiving
module 154, target data regarding a target configured to process at
least a portion of the received speech data obtaining module 156,
application of adaptation data for processing at least a portion of
the received speech data determining module 158, and adaptation
result data based on at least one aspect of the received speech
data transmitting module 160.
[0124] Referring again to FIG. 1B, as set forth above, device 130
may include a memory 134. In some embodiments, memory 134 may
comprise of one or more of one or more mass storage devices,
read-only memory (ROM), programmable read-only memory (PROM),
erasable programmable read-only memory (EPROM), cache memory such
as random access memory (RAM), flash memory, synchronous random
access memory (SRAM), dynamic random access memory (DRAM), and/or
other types of memory devices. In some embodiments, memory 134 may
be located at a single network site. In some embodiments, memory
134 may be located at multiple network sites, including sites that
are distant from each other.
[0125] Referring again to FIG. 1B, as set forth above, device 130
may include a user interface 135. The user interface may be
implemented in hardware or software, or both, and may include
various input and output devices to allow an operator of device 130
to interact with the device 130. For example, user interface 135
may include, but is not limited to, an audio display, e.g., a
speaker 108, a video display, e.g., a screen 102, a microphone, a
camera, a keyboard, e.g., keyboard 103, a trackball, e.g.,
trackball 104, a mouse, e.g., mouse 105, one or more soft keys,
e.g., hard/soft keys 106, a touch input, e.g., touchscreen 107,
e.g., which may also be a video display screen, a joystick, a game
controller, a touchpad, a handset, or any other device that allows
interaction between a device and a user.
[0126] Referring again to FIG. 1B, as set forth above, device 130
may include a speech detection interface 138. Speech detection
interface 138 may be configured to receive and/or process speech as
input, or to observe and/or record speech of a speech-facilitated
transaction Although not present in some embodiments, in some
embodiments, a speech detection interface 138 may include a speech
indicator receiver 112, which may be a sensor of any type, or a
communication port that receives a signal, or a sensor that detects
a button press, or any other module that can detect a change of
state of any kind in the environment 100, whether internal or
external to the device. The speech detection interface 138 may, in
some embodiments, include a microphone 110, which may or may not
communicate with speech indicator receiver 112. In some
embodiments, microphone 110 may detect speech, either selectively
or always-on, and may be controlled by one or more of speech
indicator receiver 112 and processor 132.
[0127] Referring again to FIG. 1B, as set forth above, device 130
may include a data transmission interface 137. Data transmission
interface 137 may, in some embodiments, handle the transmission and
reception of data by the device. For example, in some embodiments,
data transmission interface 137 may include an adaptation data
transmitter/receiver 114, which handles the reception and
transmission of adaptation data over any type of network or
internal form of communication, e.g., internal bus, and the like.
Data transmission interface 137 may, in some embodiments, include
speech data transmitter/receiver 116, which may handle the
reception and transmission of speech data, including raw speech,
over any form of moving data.
[0128] Referring again to FIG. 1B, as set forth above, device 130
may have one or more sensors 182. These sensors include, but are
not limited to, a Global Positioning System (GPS) sensor, a still
camera, a video camera, an altimeter, an air quality sensor, a
barometer, an accelerometer, a charge-coupled device, a radio, a
thermometer, a pedometer, a heart monitor, a moisture sensor, a
humidity sensor, a microphone, a seismometer, and a magnetic field
sensor. Sensors 182 may interface with sensor interface 180.
Although FIG. 1B illustrates sensors 182 as part of device 130, in
some embodiments, sensors 182 may be separated from device 130, and
communicate via one or more communication networks, e.g.,
communication networks 140.
[0129] Referring now to FIG. 1C, FIG. 1C shows an example
embodiment of an exemplary environment 100', which is a
non-limiting example of an environment 100. As shown in FIG. 1C,
environment 100' may include a user (not shown), which user may
have one or more of a first personal device 21A and a second
personal device 21B. First personal device 21A may be, for example,
a USB drive, and second personal device 21B may be, for example, a
cellular telephone device, although both personal device 21A and
personal device 21B may be any form of personal device 120 as
previously described. One or more of first personal device 21A and
second personal device 21B may interact with device 31, which may
be any type of computing device, e.g., laptop computer, desktop
computer, server, netbook, tablet device, smartphone, and the like.
Device 31 may have an operating system 81 loaded thereon. Operating
system 81 may include, but is not limited to, Microsoft Windows,
Google Android, Apple iOS, Apple Mountain Lion, UNIX, Linux, Chrome
OS, Symbian, and the like.
[0130] In addition, in some embodiments, device 31 may include an
enterprise client 82 onboard. For example, some systems, e.g., in
an office environment, may have a client software, e.g., Citrix, or
the like, loaded on their systems to integrate the user experience
for their workers. In some embodiments, this module may play a role
in determining the role of the interpretation of speech data (e.g.,
speech data 101) and the application of adaptation data. In some
embodiments, device 31 also may include one or more of first
application 91 and second application 92. First and second
application 91 and 92 may be any type of application, e.g., game,
spreadsheet, word processor, web browser, chat client, picture
viewer, picture manipulator, webcam application, and the like. In
some embodiments, these modules may play a role in determining the
role of the interpretation of speech data and the application of
adaptation data. For example, the complexity of the application may
play a role in determining how much of the speech processing occurs
at the application level. In some embodiments, device 31 may
communicate with one or more communication networks 140 and one or
more servers 110.
[0131] Referring now to FIG. 1D, FIG. 1D shows an example
embodiment of an exemplary environment 100'', which is a
non-limiting example of an environment 100. As shown in FIG. 1D,
environment 100'' may include a user 105, which user may have one
or more of a personal device 22A and a personal device 22B.
Personal device 22A may be, for example, a universal remote
control, and personal device 22B may be, for example, a cellular
telephone device, although both personal device 22A and personal
device 22B may be any form of personal device 120 as previously
described. In some embodiments, one or both of personal device 22A,
personal device 22B, and computing device 54 may transmit, store,
and/or receive adaptation data. In some embodiments, one of
personal device 22A, personal device 22B, and computing device 54
may determine to which of the devices shown in FIG. 1D the user 105
is directing her speech. In other embodiments, one or more of
receiver device 51, media player device 52, and television device
53 may transmit one or more of speech data and adaptation data back
and forth, and one or more of receiver device 51, media player
device 52, and television device 53 may determine which device
should apply the adaptation data, and which device should process
the speech data, out of devices 22A, 22B, 51, 52, 53, and 54.
[0132] Referring now to FIG. 1E, FIG. 1E shows an example
embodiment of an exemplary environment 100''', which is a
non-limiting example of an environment 100. As shown in FIG. 1E,
environment 100''' may include a user (not shown) driving an
automobile (interior only shown), wherein the automobile is
equipped with a motor vehicle control system 42, which may control
the non-driving features of the automobile, e.g., music, climate,
temperature, fuel management, seat position, media playing, lights,
and the like. The automobile also may have a smart key device 26,
which, in some embodiments, may store, receive, and/or transmit
adaptation data, either wirelessly or through the system of the
automobile. In some embodiments, environment 100' may also include
a GPS navigation device 41, which may be an example of intermediate
device 40, which also may be a personal device 120. In some
embodiments, GPS navigation device 41 may serve as a terminal
device, receiving speech data and adaptation data in order to
process a user's request. In other embodiments, GPS navigation
device 41 may serve as a personal device, storing adaptation data
derived from navigation commands of the user, and transmitting the
adaptation data to a target device, e.g., motor vehicle control
system 42, when needed. Intermediate devices 40, e.g., as shown in
FIG. 1A, and GPS navigation device 41, which may be an example of
intermediate device 40, may be a personal device for a first
transaction and a terminal in a second transaction. In some
embodiments, GPS navigation device 41 may change its role based on
an analysis of data received by GPS navigation device 41.
[0133] Referring again to FIG. 1E, in some embodiments, GPS
navigation device 41, motor vehicle control system 42, smart key
device 26, and the user's personal device (not shown) may
communicate with one or more communication networks 140 and one or
more servers 110. As in all shown exemplary embodiments, however,
these elements are optional and some embodiments may exclude
them.
[0134] Referring now to FIG. 2, FIG. 2 illustrates an exemplary
implementation of the speech data correlated to one or more
particular party spoken words receiving module 152. As illustrated
in FIG. 2, the speech data correlated to one or more particular
party spoken words receiving module 152 may include one or more
sub-logic modules in various alternative implementations and
embodiments. For example, as shown in FIG. 2, e.g., FIG. 2A, in
some embodiments, module 152 may include one or more words spoken
by the particular party receiving module 202. In some embodiments,
module 202 may include one or more of one or more words spoken by
the particular party receiving using a microphone module 204, one
or more words spoken by the particular party receiving in response
to target device query module 206, and one or more words spoken by
the particular party and directed to the target device receiving
module 212. In some embodiments, module 206 may include one or more
of one or more words spoken by the particular party receiving in
response to displaying an on-screen question of the target device
module 208 and one or more words spoken by the particular party
receiving in response to a question read aloud by a target device
generated voice module 210. In some embodiments, module 212 may
include one or more words configured to be interpreted by the
target device and spoken by the particular party in a manner
directed to the target device receiving module 214.
[0135] Referring again to FIG. 2, e.g., FIG. 2B, in some
embodiments, module 152 may include speech data comprising a
representation of particular party spoken word receiving module
216. In some embodiments, module 216 may include one or more of
speech data comprising a recording of particular party spoken word
receiving module 218, speech data comprising a retransmission of
particular party spoken word receiving module 220, speech data
comprising a numeric representation of particular party spoken word
receiving module 222, and speech data corresponding to partially
processed particular party spoken word receiving module 224. In
some embodiments, module 224 may include one or more of speech data
corresponding to partially processed particular party spoken speech
with non-lexical vocable removed receiving module 226 and speech
data corresponding to partially anonymized particular party spoken
word receiving module 228.
[0136] Referring again to FIG. 2, e.g., FIG. 2C, in some
embodiments, module 152 may include module 216, which may include
module 224, as described above. In some embodiments, module 224 may
include one or more of speech data corresponding to analyzed and
unaltered particular party spoken word receiving module 230 (e.g.,
which, in some embodiments, may include speech data corresponding
to analyzed particular party spoken word to determine a target
device to which the particular party spoken word is directed
receiving module 232) and speech data correspond to particular
party spoken word with header added receiving module 234 (e.g.,
which, in some embodiments, may include speech data correspond to
particular party spoken word with header identifying receiving
device added receiving module 236). In some embodiments, module 152
may include one or more of signal indicating that a device has
obtained speech data receiving module 238 (e.g., which, in some
embodiments, may include signal indicating that particular device
has obtained speech data receiving module 242) and speech data
receiving from indicated device module 240.
[0137] Referring again to FIG. 2, e.g., FIG. 2D, in some
embodiments, module 152 may include one or more of speech data
comprising previously recorded one or more particular party spoken
words and a timestamp of the time of recording of the one or more
particular party spoken words 244, speech data comprising a
compressed version of data correlated to one or more particular
party spoken words receiving module 246, speech data comprising
audio data corresponding to one or more particular party spoken
words receiving module 248, and speech data correlated to one or
more particular party spoken words receiving from further device
module 250. In some embodiments, module 250 may include audio data
derived from one or more particular party spoken words receiving
from further device module 252. In some embodiments, module 252 may
include one or more of audio data derived from one or more
particular party spoken words detected by further device receiving
from further device module 254, audio data derived from one or more
particular party spoken words recorded by further device receiving
from further device module 256, and audio data derived from one or
more particular party words detected by particular device receiving
from further device module 258.
[0138] FIG. 3 illustrates an exemplary implementation of adaptation
data at least partly based on discrete speech interaction of
particular party separate from detected speech data, and has been
stored on a particular party-associated particular device receiving
module 154. As illustrated in FIG. 3, the adaptation data at least
partly based on discrete speech interaction of particular party
separate from detected speech data, and has been stored on a
particular party-associated particular device receiving module 154
may include one or more sub-logic modules in various alternative
implementations and embodiments. For example, as shown in FIG. 3
(e.g., FIG. 3A), in some embodiments, module 154 may include
adaptation data comprising one or more words and corresponding
pronunciations of the one or more words at least partly based on
discrete speech interaction of particular party separate from
detected speech data, and has been stored on a particular
party-associated particular device receiving module 302. In some
embodiments, module 302 may include adaptation data comprising one
or more words and corresponding pronunciations of the one or more
words at least partly based on at least one previous training by
the particular party separate from detected speech data, and has
been stored on a particular party-associated particular device
receiving module 304. In some embodiments, module 304 may include
adaptation data comprising one or more words and corresponding
pronunciations of the one or more words at least partly based on at
least one previous training by the particular party separate from
detected speech data corresponding to an order placed by the
particular party at an automated drive-thru terminal that accepts
speech input, and has been stored on a particular party-associated
particular device receiving module 306. In some embodiments, module
306 may include adaptation data comprising one or more words and
corresponding pronunciations of the one or more words at least
partly based on at least one previous training by the particular
party in response to cellular telephone device prompting separate
from detected speech data corresponding to an order placed by the
particular party at an automated drive-thru terminal that accepts
speech input, and has been stored on a particular party-associated
particular device receiving module 308. In some embodiments, module
308 may include adaptation data comprising one or more words and
corresponding pronunciations of the one or more words at least
partly based on at least one previous training by the particular
party in response to cellular telephone device prompting separate
from detected speech data corresponding to an order placed by the
particular party at an automated drive-thru terminal that accepts
speech input, and has been stored on a particular device linked to
the particular party through a contract with a telecommunications
provider receiving module 310.
[0139] Referring again to FIG. 3, e.g., FIG. 3B, in some
embodiments, module 154 may include one or more of adaptation data
at least partly based on discrete speech interaction of particular
party at different time and location to speech interaction
generating detected speech data, and has been stored on a
particular party-associated particular device receiving module 312
and adaptation data at least partly based on discrete speech
interaction of particular party occurring prior to speech
interaction generating detected speech data, and has been stored on
a particular party-associated particular device receiving module
314. In some embodiments, module 314 may include one or more of
adaptation data at least partly based on discrete speech
interaction of particular party occurring prior to speech
interaction generating detected speech data, and has been stored on
a particular party-associated particular device receiving from the
particular device module 316 (e.g., which, in some embodiments, may
include adaptation data at least partly based on discrete speech
interaction of particular party occurring prior to speech
interaction generating detected speech data, and has been stored on
a particular party-associated particular device receiving directly
from the particular device memory module 318) and adaptation data
at least partly based on discrete speech interaction of particular
party occurring prior to speech interaction generating detected
speech data, and has been stored on a particular party-associated
particular device receiving from a communication network provider
module 320 (e.g., which, in some embodiments, may include
adaptation data at least partly based on discrete speech
interaction of particular party occurring prior to speech
interaction generating detected speech data, and has been stored on
a particular party-associated particular device and transmitted
over the communication network receiving from a communication
network provider module 322).
[0140] Referring again to FIG. 3, e.g., FIG. 3C, in some
embodiments, module 154 may include module 314, as previously
described. In some embodiments, module 314 may include one or more
of adaptation data at least partly based on discrete speech
interaction of particular party occurring prior to speech
interaction generating detected speech data, and has been stored on
a particular party-associated particular device receiving from a
device connected to a same network as a target device to which the
detected speech data is directed module 324 and adaptation data at
least partly based on discrete speech interaction of particular
party occurring prior to speech interaction generating detected
speech data, and has been stored on a particular party-associated
particular device receiving in response to reception of speech data
module 326. In some embodiments, module 154 may include adaptation
data at least partly based on discrete speech interaction of
particular party occurring prior to speech interaction generating
detected speech data, and has been stored on a particular
party-associated particular device acquiring in response to
condition module 328. In some embodiments, module 328 may include
adaptation data at least partly based on discrete speech
interaction of particular party occurring prior to speech
interaction generating detected speech data, and has been stored on
a particular party-associated particular device acquiring in
response to the particular party interacting with a target device
module 330. In some embodiments, module 330 may include one or more
of adaptation data at least partly based on discrete speech
interaction of particular party occurring prior to speech
interaction generating detected speech data, and has been stored on
a particular party-associated particular device acquiring in
response to the particular party inserting a key into a motor
vehicle interacting with a target device module 332 and adaptation
data at least partly based on discrete speech interaction of
particular party occurring prior to speech interaction generating
detected speech data, and has been stored on a particular
party-associated particular device acquiring in response to the
particular party executing a program on a computing device module
334.
[0141] Referring again to FIG. 3, e.g., FIG. 3D, in some
embodiments, module 154 may include module 328, as previously
described. In some embodiments, module 328 may include one or more
of adaptation data at least partly based on discrete speech
interaction of particular party occurring prior to speech
interaction generating detected speech data, and has been stored on
a particular party-associated particular device receiving in
response to detection of the particular party at a particular
location module 336 and adaptation data at least partly based on
discrete speech interaction of particular party occurring prior to
speech interaction generating detected speech data, and has been
stored on a particular party-associated particular device receiving
in response to detection of the particular party within a
particular proximity of a target device module 338.
[0142] Referring again to FIG. 3, e.g., FIG. 3E, in some
embodiments, module 154 may include adaptation data at least partly
based on discrete speech interaction of particular party separate
from detected speech data, and has been stored on a particular
party-associated particular device acquiring from a further device
module 340. In some embodiments, module 340 may include one or more
of adaptation data originating at further device and at least
partly based on discrete speech interaction of particular party
separate from detected speech data, and has been stored on a
particular party-associated particular device acquiring from a
further device module 342, adaptation data at least partly based on
discrete speech interaction of particular party separate from
detected speech data, and has been stored on a particular
party-associated particular device acquiring from a further device
related to the particular device module 344, and adaptation data at
least partly based on discrete speech interaction of particular
party separate from detected speech data, and has been stored on a
particular party-associated particular device acquiring from a
further device that received the adaptation data from the
particular device module 352. In some embodiments, module 344 may
include one or more of adaptation data at least partly based on
discrete speech interaction of particular party separate from
detected speech data, and has been stored on a particular
party-associated particular device acquiring from a further device
associated with the particular party module 346, adaptation data at
least partly based on discrete speech interaction of particular
party separate from detected speech data, and has been stored on a
particular party-associated particular device acquiring from a
further device in communication with the particular device module
348, and adaptation data at least partly based on discrete speech
interaction of particular party separate from detected speech data,
and has been stored on a particular party-associated particular
device acquiring from a further device at least partially
controlled by the particular device module 350.
[0143] Referring again to FIG. 3, e.g., FIG. 3F, in some
embodiments, module 154 may include module 340, as previously
described. In some embodiments, module 340 may include adaptation
data comprising instructions for modifying a pronunciation
dictionary, said adaptation data at least partly based on discrete
speech interaction of particular party separate from detected
speech data, and has been stored on a particular party-associated
particular device acquiring from a further device module 354. In
some embodiments, module 354 may include adaptation data comprising
a first instruction for modifying a pronunciation dictionary based
on a first particular party interaction and a second instruction
for modifying a pronunciation dictionary based on a second
particular party interaction, and has been stored on a particular
party-associated particular device acquiring from a further device
module 356. In some embodiments, module 356 may include adaptation
data comprising a first instruction for modifying a pronunciation
dictionary based on a first particular party interaction and a
second instruction for modifying a pronunciation dictionary based
on a second particular party interaction, said first instruction
has been stored on a particular party-associated particular device
acquiring from a further device module 358.
[0144] Referring again to FIG. 3, e.g., FIG. 3G, in some
embodiments, module 154 may include one or more of adaptation data
at least partly based on discrete speech interaction of particular
party separate from detected speech data, and has been stored on a
particular party-associated particular device generating module
360, adaptation data at least partly based on discrete speech
interaction of particular party separate from detected speech data,
and has been stored on a particular party-associated particular
device retrieving module 362, and adaptation data at least partly
based on discrete speech interaction of particular party with
particular type of device separate from detected speech data, and
has been stored on a particular party-associated particular device
receiving module 364. In some embodiments, module 364 may include
one or more of adaptation data at least partly based on discrete
speech interaction of particular party with device of same type as
target device configured to receive speech data, said discrete
interaction separate from detected speech data, and has been stored
on a particular party-associated particular device receiving module
366 and adaptation data at least partly based on discrete speech
interaction of particular party with device having particular
characteristic separate from detected speech data, and has been
stored on a particular party-associated particular device receiving
module 368. In some embodiments, module 368 may include one or more
of adaptation data at least partly based on discrete speech
interaction of particular party with device communicating on a same
communication network as target device and separate from detected
speech data, and has been stored on a particular party-associated
particular device receiving module 370 and adaptation data at least
partly based on discrete speech interaction of particular party
with device configured to carry out a same function as the target
device and separate from detected speech data, and has been stored
on a particular party-associated particular device receiving module
372.
[0145] Referring again to FIG. 3, e.g., FIG. 3H, in some
embodiments, module 154 may include module 364, and module 364 may
include module 368, as previously described. In some embodiments,
module 368 may include adaptation data at least partly based on
discrete speech interaction of particular party with device
configured to accept a same type of input as the target device and
separate from detected speech data, and has been stored on a
particular party-associated particular device receiving module. In
some embodiments, module 154 may include adaptation data at least
partly based on discrete speech interaction of particular party
with particular device separate from detected speech data, and has
been stored on a particular party-associated particular device
receiving module 376. In some embodiments, module 376 may include
adaptation data at least partly based on discrete speech
interaction of particular party with cellular telephone device
separate from detected speech data, and has been stored on a
particular party-associated cellular telephone device receiving
module 378. In some embodiments, module 378 may include one or more
of adaptation data at least partly based on particular party
telephone conversation carried out using cellular telephone device
separate from detected speech data, and has been stored on a
particular party-associated cellular telephone receiving module 380
and adaptation data at least partly based on particular party
speech command given to cellular telephone device separate from
detected speech data, and has been stored on a particular
party-associated cellular telephone receiving module 382.
[0146] Referring again to FIG. 3, e.g., FIG. 3I, in some
embodiments, module 154 may include one or more of adaptation data
at least partly based on discrete speech interaction of particular
party separate from detected speech data and using same utterance
as speech that is part of speech data, and has been stored on a
particular party-associated particular device receiving module 384,
adaptation data at least partly based on discrete speech
interaction of particular party and using same utterance as speech
that is part of speech data at a different time than speech that is
part of the speech data receiving module 386, adaptation data
comprising a phoneme dictionary based on one or more particular
party pronunciations, such that at least one entry has been stored
on a particular party-associated particular device receiving module
388, adaptation data comprising a sentence diagramming path
selection algorithm based on one or more particular party discrete
speech interactions, and has been stored on a particular
party-associated particular device receiving module 390, adaptation
data at least partly based on discrete speech interaction of
particular party separate from detected speech data, and at least
partly collected by a particular party-associated particular device
receiving module 392, and adaptation data comprising instructions
for modifying one or more portions of a speech recognition
component of a target device that are at least partly based on one
or more particular party speech interactions, and has been stored
on a particular party-associated particular device receiving module
394.
[0147] Referring again to FIG. 3, e.g., FIG. 3J, in some
embodiments, module 154 may include one or more of adaptation data
comprising a location of instructions for modifying one or more
portions of a speech recognition component of a target device that
are at least partly based on one or more particular party speech
interactions, and has been stored on a particular party-associated
particular device receiving module 396, adaptation data at least
partly based on discrete speech interaction of particular party
separate from detected speech data, and transmitted from a
particular party-associated particular device receiving module 398,
adaptation data at least partly based on discrete speech
interaction of particular party separate from detected speech data,
and stored on a particular party-associated particular device
receiving module 301, adaptation data at least partly based on
discrete speech interaction of particular party separate from
detected speech data, and is temporarily stored on the
particular-party associated particular device until remote server
deposit receiving module 303, and adaptation data at least partly
based on discrete speech interaction of particular party separate
from detected speech data, and was transmitted from a first device
to a second device using the particular party-associated particular
device as a channel configured to facilitate the transaction
receiving module 305.
[0148] Referring again to FIG. 3, e.g., FIG. 3K, in some
embodiments, module 154 may include one or more of adaptation data
at least partly based on discrete speech interaction of particular
party separate from detected speech data, and at least a portion of
which originated at a particular party-associated particular device
receiving module 307, adaptation data at least partly based on
discrete speech interaction of particular party separate from
detected speech data, and at least a portion of which was
transmitted to a remote location from a particular party-associated
particular device receiving from remote location module 309,
adaptation data at least partly based on discrete speech
interaction of particular party separate from detected speech data
receiving module 311, and further data adding to adaptation data
module 313. In some embodiments, module 313 may include one or more
of additional adaptation data adding to adaptation data module 315,
header data identifying receiving entity adding to adaptation data
module 317, and header data identifying transmitting entity adding
to adaptation data module 319.
[0149] Referring now to FIG. 4, FIG. 4 illustrates an exemplary
implementation of the target data regarding a target configured to
process at least a portion of the received speech data obtaining
module 156. As illustrated in FIG. 4, the target data regarding a
target configured to process at least a portion of the received
speech data obtaining module 156 may include one or more sub-logic
modules in various alternative implementations and embodiments. For
example, as shown in FIG. 4, e.g., FIG. 4A, in some embodiments,
module 156 may include one or more of data indicating the target
device is configured to process at least a portion of received
speech data receiving module 402, data indicating that the
adaptation data is configured to be applied to the target device to
assist in processing at least a portion of the speech data
receiving from a speech processing component module 408, and data
indicating that the adaptation data has been applied to the target
device to assist in processing at least a portion of the speech
data receiving from a speech processing component module 410. In
some embodiments, module 402 may include data indicating the target
device is configured to process at least a portion of received
speech data receiving from speech processing component at central
processing component module 404. In some embodiments, module 404
may include one or more of data indicating the target device is
configured to process at least a portion of received speech data
receiving from speech processing component at central processing
component of which the speech processing component is a
subcomponent module 406. In some embodiments, module 410 may
include data indicating that the adaptation data has been applied
to an automated teller machine device to assist in processing at
least a portion of the speech data receiving from a speech
processing component module 412. In some embodiments, module 412
may include data indicating that the adaptation data has been
applied to an automated teller machine device to assist in
processing at least a portion of the data corresponding to a spoken
request by the particular party receiving from a speech processing
component module 414. In some embodiments, module 414 may include
data indicating that the list of the way that the particular party
pronounces numbers zero through nine has been applied to an
automated teller machine device to assist in processing at least a
portion of the data corresponding to a spoken request by the
particular party receiving from a speech processing component
module 416.
[0150] Referring again to FIG. 4, e.g., FIG. 4B, in some
embodiments, module 156 may include one or more of target data
regarding a target configured to process at least a portion of the
received speech data generating module 418, speech data
configurable to be processed by a speech recognition component to
which the adaptation data has been applied determining module 420,
and target data regarding intended target device generating based
on determination module 422. In some embodiments, module 420 may
include one or more of at least a portion of speech data analyzing
module 424 and target data regarding intended target device
determining at least partly based on the analyzing at least a
portion of speech data module 426. In some embodiments, module 424
may include at least a portion of speech data analyzing using an
adaptation data-applied speech recognition component module 428. In
some embodiments, module 428 may include one or more of at least a
portion of speech data analyzing using an adaptation data-applied
speech recognition component of target device module 430 and at
least a portion of speech data analyzing using an adaptation
data-applied speech recognition component of further device module
432. In some embodiments, module 422 may include one or more of
Boolean that resolves to true when the analysis of the speech data
portion indicates the received speech data is configured to be
successfully processed module 434 and numeric indicator indicating
that at least a portion of the received speech data is configured
to be successfully processed generating based on analysis of at
least a portion of speech data module 436.
[0151] Referring again to FIG. 4, e.g., FIG. 4C, in some
embodiments, module 156 may include one or more of target device
configurable to process received speech data determining module 438
and target data regarding target device generating based on
determination regarding the target device module 400. In some
embodiments, module 438 may include one or more of at least a
portion of the speech data analyzing module 442, target device
configurable to process speech data determining at least partly
based on result of analyzing at least a portion of the speech data
module 444, header data indicating a type of intended target device
that is configured to process received speech data extracting from
received speech data header module 446, and type of target device
is same type as type of intended target device determining module
448. In some embodiments, module 446 may include one or more of
header data indicating a manufacturer of intended target device
that is configured to process received speech data extracting from
received speech data header module 450 and header data indicating a
type of input accepted by one or more intended target devices
configured to process received speech data extracting from received
speech data header module 452. In some embodiments, module 452 may
include one or more of header data indicating a data format
accepted by one or more intended target devices configured to
process received speech data extracting from received speech data
header module 454 and header data indicating one or more word
categories accepted by one or more intended target devices
configured to process received speech data extracting from received
speech data header module 456.
[0152] Referring again to FIG. 4, e.g., FIG. 4D, in some
embodiments, module 156 may include one or more of module 438 and
module 440, as previously described. In some embodiments, module
438 may include received speech data into target device
recognizable data converting module 458 and received speech data
into one or more commands or command modifiers configured to be
recognized by a target device control component converting module
460. In some embodiments, module 156 may include target data
regarding a target device configured to process at least a portion
of speech data receiving module 462. In some embodiments, module
462 may include one or more of target data regarding a target
device configured to process at least a portion of speech data
receiving from the particular device module 464 and target data
regarding a target device configured to process at least a portion
of speech data receiving from a further device module 466. In some
embodiments, module 466 may include one or more of target data
regarding a target device configured to process at least a portion
of speech data receiving from a further device configured to
process at least a portion of the speech data module 468 and target
data regarding a target device configured to process at least a
portion of speech data receiving from a further device configured
to apply at least a portion of the adaptation data module 470.
[0153] Referring again to FIG. 4, e.g., FIG. 4E, in some
embodiments, module 156 may include module 462, and module 462 may
include module 466, as previously described. In some embodiments,
module 466 may include one or more of target data regarding a
target device configured to process at least a portion of speech
data receiving from a further device configured to process the
speech data less efficiently than the target device module 472,
target data regarding a target device configured to process at
least a portion of speech data receiving from a further device for
which the speech data is unintended module 474, and target data
regarding a target device configured to process at least a portion
of speech data and target data indicating the speech data was
determined to be intended for the target device receiving from a
further device module 476. In some embodiments, module 156 may
include one or more of data identifying the target device receiving
module 494, address of the target device receiving module 498, and
location of the target device receiving module 499. In some
embodiments, module 494 may include one or more of name of the
target device receiving module 496 and device identifier of the
target device receiving module
[0154] Referring again to FIG. 4, e.g., FIG. 4F, in some
embodiments, module 156 may include one or more of target data
regarding an intended application module configured to process at
least a portion of the received speech data obtaining module 478
and target data regarding a first application module configured to
process at least a portion of the received speech data and a second
application module configured to process at least a portion of the
received speech data obtaining module 484. In some embodiments,
module 478 may include one or more of target data regarding an
intended application module configured to process, facilitated by
the adaptation data, at least a portion of the received speech data
obtaining module 480 and target data regarding a speech data
processing capability of an intended application module configured
to process, facilitated by the adaptation data, at least a portion
of the received speech data obtaining module 482. In some
embodiments, module 484 may include one or more of target data
regarding a word processing application module configured to
process at least a portion of the received speech data and a speech
recognition application module configured to process at least a
portion of the received speech data obtaining module 486, target
data regarding a word processing application module configured to
process at least a portion of the received speech data and an
operating system application module configured to process at least
a portion of the received speech data obtaining module 488, and
target data regarding a word processing application module
configured to process at least a portion of the received speech
data and a spreadsheet processing application module configured to
process at least a portion of the received speech data obtaining
module 490.
[0155] Referring now to FIG. 5, FIG. 5 illustrates an exemplary
implementation of the application of adaptation data for processing
at least a portion of the received speech data determining module
158. As illustrated in FIG. 5, the application of adaptation data
for processing at least a portion of the received speech data
determining module 158 may include one or more sub-logic modules in
various alternative implementations and embodiments. For example,
as shown in FIG. 5, e.g., FIG. 5A, in some embodiments, module 158
may include one or more of application of adaptation data for
processing at least a portion of the received speech data
determining based on acquired target data comprising an indication
of intended device module 502, application of adaptation data for
processing at least a portion of the received speech data
determining based on acquired target data comprising an indication
that speech data has arrived at intended device module 504,
application of adaptation data for processing at least a portion of
the received speech data determining based on acquired target data
comprising an indication that speech data has not arrived at
intended device module 506 (e.g., which, in some embodiments, may
include application of adaptation data for processing at least a
portion of the received speech data choosing against based on
acquired target data comprising an indication that speech data has
not arrived at intended device module 508), application of
adaptation data for processing at least a portion of the received
speech data determining based on acquired target data comprising an
indication that speech data has arrived at other device than an
intended device module 510, and application of adaptation data for
processing at least a portion of the received speech data
determining when acquired target data indicates capability of
adaptation data application module 512.
[0156] Referring again to FIG. 5, e.g., FIG. 5B, in some
embodiments, module 158 may include one or more of application of
adaptation data for processing at least a portion of the received
speech data determining based on acquired target data indicating
presence of one or more other devices configured to apply
adaptation data module 514, application of adaptation data for
processing at least a portion of the received speech data
determining against based on acquired target data indicating
presence of one or more other devices configured to efficiently
apply adaptation data module 516, application of adaptation data
for processing at least a portion of the received speech data
determining based on acquired target data indicating presence of
one or more other applications module 518, and application of
adaptation data for processing at least a portion of the received
speech data determining based on one or more characteristics of one
or more applications and target data indicating a presence of the
one or more applications module 520.
[0157] Referring again to FIG. 5, e.g., FIG. 5C, in some
embodiments, module 158 may include one or more of application of
adaptation data for processing at least a portion of the received
speech data determining against based acquired target data
comprising one or more characteristics of one or more applications
module 522, application of adaptation data for processing at least
a portion of the received speech data determining based on one or
more application preference flags module 528, application of
adaptation data for processing at least a portion of the received
speech data determining based on one or more user-controlled
preference flags module 530, and application of adaptation data for
processing at least a portion of the received speech data
determining based on operating system decision module 532. In some
embodiments, module 522 may include one or more of application of
adaptation data for processing at least a portion of the received
speech data determining against based acquired target data
comprising a presence of one or more applications and one or more
characteristics of the one or more applications module 524 and
application of adaptation data for processing at least a portion of
the received speech data determining against based acquired target
data comprising a developer of one or more applications module
526.
[0158] Referring now to FIG. 6, FIG. 6 illustrates an exemplary
implementation of the adaptation result data based on at least one
aspect of the received speech data transmitting module 160. As
illustrated in FIG. 6, the adaptation result data based on at least
one aspect of the received speech data transmitting module 160 may
include one or more sub-logic modules in various alternative
implementations and embodiments. For example, as shown in FIG. 6,
e.g., FIG. 6A, in some embodiments, module 160 may include one or
more of adaptation result data based on applying the adaptation
data transmitting module 602, adaptation result data based on
processing received speech data transmitting module 606, adaptation
result data indicating that at least a portion of the received
speech data is intended for an other device transmitting module
652, and adaptation result data indicating completed determination
regarding intended target of received speech data transmitting
module 658. In some embodiments, module 602 may include adaptation
result data based on applying the adaptation data to a speech
recognition component of a target device transmitting module 604.
In some embodiments, module 606 may include adaptation result data
indicating at least a portion of received speech data has been
processed transmitting module 650. In some embodiments, module 652
may include one or more of adaptation result data indicating that
at least a portion of the received speech data is intended for an
other device transmitting to the other device module 654 and
adaptation result data comprising the adaptation data and
indicating that at least a portion of the received speech data is
intended for an other device transmitting module 656.
[0159] Referring again to FIG. 6, e.g., FIG. 6B, in some
embodiments, module 160 may include one or more of adaptation
result data based on a measure of success of at least one portion
of a speech-facilitated transaction corresponding to the received
speech data transmitting module 608, adaptation result data
comprising a list of at least one word that was a portion of the
received speech data and that was improperly interpreted during
speech data processing transmitting module 618, and adaptation
result data comprising at least one phoneme appearing in at least
one improperly interpreted word transmitting module 620. In some
embodiments, module 608 may include adaptation result data
comprising a representation of success of at least one portion of a
speech-facilitated transaction corresponding to the received speech
data transmitting module 610. In some embodiments, module 610 may
include one or more of adaptation result data comprising a numeric
representation of success provided by the particular party of at
least one portion of a speech-facilitated transaction corresponding
to the received speech data transmitting module 612 and adaptation
result data comprising a numeric representation of success of at
least one portion of a speech-facilitated transaction corresponding
to the received speech data transmitting module 614. In some
embodiments, module 614 may include adaptation result data
comprising confidence rate of correct interpretation of at least
one portion of the speech-facilitated transaction corresponding to
the received speech data transmitting module 616.
[0160] Following are a series of flowcharts depicting
implementations. For ease of understanding, the flowcharts are
organized such that the initial flowcharts present implementations
via an example implementation and thereafter the following
flowcharts present alternate implementations and/or expansions of
the initial flowchart(s) as either sub-component operations or
additional component operations building on one or more
earlier-presented flowcharts. Those having skill in the art will
appreciate that the style of presentation utilized herein (e.g.,
beginning with a presentation of a flowchart(s) presenting an
example implementation and thereafter providing additions to and/or
further details in subsequent flowcharts) generally allows for a
rapid and easy understanding of the various process
implementations. In addition, those skilled in the art will further
appreciate that the style of presentation used herein also lends
itself well to modular and/or object-oriented program design
paradigms.
[0161] Further, in FIGS. 7-12 and in the figures to follow
thereafter, various operations may be depicted in a
box-within-a-box manner. Such depictions may indicate that an
operation in an internal box may comprise an optional example
embodiment of the operational step illustrated in one or more
external boxes. However, it should be understood that internal box
operations may be viewed as independent operations separate from
any associated external boxes and may be performed in any sequence
with respect to all other illustrated operations, or may be
performed concurrently. Still further, these operations illustrated
in FIG. 7 as well as the other operations to be described herein
may be performed by at least one of a machine, an article of
manufacture, or a composition of matter.
[0162] Referring again to FIG. 7, FIG. 7 shows operation 700, which
may include operation 702 depicting receiving speech data
correlated to one or more words spoken by a particular party. For
example, FIG. 1, e.g., FIG. 1E, shows speech data correlated to one
or more particular party spoken words receiving module 152
receiving (e.g., either by receiving data, or by a sensor providing
notification, e.g., a microphone of a device) speech data (e.g.,
compressed audio data) correlated to one or more words (e.g.,
speech of a user placing an order for hot wings and fries at an
automated drive-thru window that accepts speech input) spoken by a
particular party (e.g., a user of the automated drive-thru
window).
[0163] Referring again to FIG. 7, operation 700 may include
operation 704 depicting receiving adaptation data that is at least
partly based on at least one speech interaction of a particular
party that is discrete from the received speech data, wherein at
least a portion of the adaptation data has been stored on a
particular device associated with the particular party. For
example, FIG. 1, e.g., FIG. 1E, shows adaptation data at least
partly based on discrete speech interaction of particular party
separate from detected speech data, and has been stored on a
particular party-associated particular device receiving module 154
receiving adaptation data (e.g., a set of proper noun
pronunciations, e.g., food items, e.g., "Chunky's Best Wings," or
"Big Mac") that is at least partly based on at least one speech
interaction (e.g., a previous fast food order at a similar
automated drive-thru window at a Big Boy restaurant) of a
particular party (e.g., the user, sitting in her car, ordering a
meal) that is discrete from the detected speech data (e.g., the
speech data of the user placing the order for hot wings and fries
at the automated drive-thru window), wherein at least a portion of
the adaptation data (e.g., a set of proper noun pronunciations,
e.g., food items, e.g., "Chunky's Best Wings," or "Big Mac") has
been stored (e.g., at one point was stored, if only temporarily) on
a particular device (e.g., a user's cellular phone, on removable
memory) associated with the particular party (e.g., in this
instance it may merely be carried by the user and in range of the
automated drive thru window, or it may broadcast a signal
indicating that the device is associated with the party that is
speaking when it detects that the user is speaking).
[0164] Referring again to FIG. 7, operation 700 may include
operation 706 depicting obtaining target data regarding a target
configured to process at least a portion of the received speech
data. For example, FIG. 1, e.g., FIG. 1E, shows target data
regarding a target configured to process at least a portion of the
received speech data acquiring module 156 obtaining (e.g.,
generating, receiving from an internal component, receiving from an
external device, or the like) target data (e.g., a listing of, and,
in some embodiments, more information about a target device, e.g.,
a status of the automated drive-thru window, e.g., "ready to
receive an order") regarding a target (e.g., a device for which the
speech is intended, e.g., the automated drive thru-window, or in
other embodiments, a list of devices that can process a portion of
the speech data) configured to process at least a portion of the
received speech data (e.g., capable of performing one or more
operations on the speech data that was received, e.g., speech of a
user placing an order for hot wings and fries at an automated
drive-thru window that accepts speech input)
[0165] Referring again to FIG. 7, operation 700 may include
operation 708 depicting determining whether to apply the adaptation
data for processing at least a portion of the received speech data,
at least partly based on the acquired target data. For example,
FIG. 1, e.g., FIG. 1E, shows application of adaptation data for
processing at least a portion of the received speech data
determining module 158 determining whether to apply (e.g., load the
proper noun pronunciations into the processing unit so that the
proper noun pronunciations can be used in interpreting the speech)
the adaptation data (e.g., a set of proper noun pronunciations,
e.g., food items, e.g., "Chunky's Best Wings," or "Big Mac") for
processing at least a portion of the received speech data (e.g.,
the user's order), at least partly based on the acquired target
data (e.g., the data that indicates the automated drive thru is
ready to receive and process speech).
[0166] Referring again to FIG. 7, operation 700 may include
operation 710 depicting transmitting adaptation result data that is
based on at least one aspect of the received speech data. For
example, FIG. 1, e.g., FIG. 1E, shows adaptation result data based
on at least one aspect of the received speech data transmitting
module 160 transmitting adaptation result data (e.g., a signal
indicating that the speech was received, or, in some embodiments, a
signal indicating that the speech was received and processed, or,
in some embodiments, a signal indicating that the speech was
received, but not processed, or, in some embodiments, a signal
indicating that the speech was received and determined to be
intended for the device that received it, or, in some embodiments,
a signal indicating that the speech was received and determined to
be intended for a different device than the device that received
the speech data) that is based on at least one aspect of the
received speech data (e.g., either a characteristic of the speech
data itself, or a result of attempting to process the speech data,
or other data that will be discussed in more detail herein, and
which was elaborated upon in one or more of the previous
applications incorporated by reference, supra).
[0167] FIGS. 8A-8E depict various implementations of operation 702,
according to embodiments. Referring now to FIG. 8A, operation 704
may include operation 802 depicting receiving speech that is spoken
by the particular party. For example, FIG. 2, e.g., FIG. 2A, shows
words spoken by the particular party receiving module 202 receiving
speech (e.g., the user says "please withdraw two hundred dollars
from checking account number 8675309") that is spoken by the
particular party (e.g., the user).
[0168] Referring again to FIG. 8A, operation 802 may include
operation 804 depicting receiving, using a microphone, speech that
is spoken by the particular party. For example, FIG. 2, e.g., FIG.
2A, shows words spoken by the particular party receiving using a
microphone module 204 receiving, using a microphone (e.g., a
microphone built in to an airline ticket dispensing terminal
device), speech that is spoken by the particular party (e.g.,
"please show me if there are any earlier flights to Washington,
D.C.").
[0169] Referring again to FIG. 8A, operation 802 may include
operation 806 depicting receiving speech that is spoken by the
particular party in response to a query from a target device. For
example, FIG. 2, e.g., FIG. 2A, shows words spoken by the
particular party receiving in response to target device query
module 206 receiving speech (e.g., receiving a user command given
to a video game, e.g., a first person shooter, being played on a
video game system) that is spoken by the particular party (e.g.,
the player of the video game) in response to a query (e.g., "please
give a command to your teammate player" from a target device (e.g.,
from the video game system, or from the game itself).
[0170] Referring again to FIG. 8A, operation 806 may include
operation 808 depicting receiving speech that is spoken by the
particular party in response to a displaying of a question on a
screen of the target device. For example, FIG. 2, e.g., FIG. 2A,
shows words spoken by the particular party receiving in response to
displaying an on-screen question of the target device module 208
receiving speech that is spoken by the particular party (e.g., a
user orders a large pizza with ham, pepperoni, and sausage) in
response to a displaying of a question (e.g., "please state your
order") on a screen of the target device (e.g., an automated
order-placing terminal device inside a restaurant).
[0171] Referring again to FIG. 8A, operation 806 may include
operation 810 depicting receiving speech that is spoken by the
particular party in response to a question spoken by a
computer-generated voice generated by the target device. For
example, FIG. 2, e.g., FIG. 2A, shows words spoken by the
particular party receiving in response to a question read aloud by
a target device generated voice module 210 receiving speech that is
spoken by the particular party (e.g., the user speaks the command
"show me directions to 410 4th Street") in response to a question
spoken by a computer generated voice (e.g., "please say the place
you would like to travel to") generated by the target device (e.g.,
a personal navigation system, e.g., personal navigation system
41).
[0172] Referring again to FIG. 8A, operation 802 may include
operation 812 depicting receiving speech that is spoken by the
particular party and directed to a target device. For example, FIG.
2, e.g., FIG. 2A, shows words spoken by the particular party and
directed to the target device receiving module 212 receiving speech
that is spoken by the particular party (e.g., the user gives a
command "make twenty-five copies at fifty percent contrast") and
directed (e.g., the particular party is speaking to) to a target
device (e.g., a speech-enabled copying machine device).
[0173] Referring again to FIG. 8A, operation 812 may include
operation 814 depicting receiving speech that is spoken by the
particular party, said speech configured to be interpreted by the
target device. For example, FIG. 2, e.g., FIG. 2A, shows words
configured to be interpreted by the target device and spoken by the
particular party in a manner directed to the target device
receiving module 214 receiving speech that is spoken by the
particular party (e.g., the user gives a command, e.g., "play my
playlist number six"), said speech configured to be interpreted
(e.g., processed into a command that can be executed) by the target
device (e.g., a speech-enabled media player hooked into a home
theater system).
[0174] Referring now to FIG. 8B, operation 702 may include
operation 816 depicting receiving speech data comprising a
representation of speech that is spoken by the particular party.
For example, FIG. 2, e.g., FIG. 2B, shows speech data comprising a
representation of particular party spoken word receiving module 216
receiving speech data comprising a representation of speech (e.g.,
a Waveform Audio File ("WAV") file) that is spoken by the
particular party (e.g., the user gives a command "activate the home
security perimeter system").
[0175] Referring again to FIG. 8B, operation 816 may include
operation 818 depicting receiving speech data comprising a
recording of speech that is spoken by the particular party. For
example, FIG. 2, e.g., FIG. 2B, shows speech data comprising a
recording of particular party spoken word receiving module 218
receiving speech data comprising a recording of speech that is
spoken by the particular party (e.g., the user gives a command
"compile the program `never_rush`).
[0176] Referring again to FIG. 8B, operation 816 may include
operation 820 depicting receiving speech data comprising a
retransmission of speech that is spoken by the particular party.
For example, FIG. 2, e.g., FIG. 2B, shows speech data comprising a
speech data comprising a retransmission of particular party spoken
word receiving module 220 receiving speech data (e.g., a user
ordering a bag of chili cheese fries) comprising a retransmission
(e.g., the device that received the speech, either as speech
directly from the user or as speech data from a different device,
has retransmitted the received speech, whether modified or not)
that is spoken by the particular party (e.g., a user ordering chili
cheese fries).
[0177] Referring again to FIG. 8B, operation 816 may include
operation 822 depicting receiving speech data comprising a numeric
representation of speech that is spoken by the particular party.
For example, FIG. 2, e.g., FIG. 2B, shows speech data comprising a
numeric representation of particular party spoken word receiving
module 222 receiving speech data comprising a numeric
representation of speech (e.g., speech compressed into a numeric
string representing one or more components of speech) that is
spoken by the particular party (e.g., a user says to a microwave
oven "operate at eighty percent power for ninety seconds").
[0178] Referring again to FIG. 8B, operation 816 may include
operation 824 depicting receiving speech data corresponding to
received speech spoken by the particular party that has been at
least partially processed. For example, FIG. 2, e.g., FIG. 2B,
shows speech data corresponding to partially processed particular
party spoken word receiving module 224 receiving speech data (e.g.,
compressed data representing speech) corresponding to received
speech spoken by the particular party (e.g., "increase the volume"
spoken by a user to into a remote control device, but directed at
the television) that has been at least partially processed (e.g.,
the speech data may be compressed, or in other embodiments, a
header may be added, or in other embodiments, one or more of the
words may be interpreted, or in other embodiments, noise or
non-word utterances may be filtered, flagged, or removed).
[0179] Referring again to FIG. 8B, operation 824 may include
operation 826 depicting receiving speech data corresponding to
received speech spoken by the particular party that has had one or
more non-lexical vocables removed from the speech data. For
example, FIG. 2, e.g., FIG. 2B, shows speech data corresponding to
partially processed particular party spoken speech with non-lexical
vocable removed receiving module 226 receiving speech data (e.g.,
received speech processed and compressed into MPEG-2 Audio Layer
III ("MP3") format corresponding to received speech (e.g., the user
speaks, "la la, I would like a western bacon cheeseburger and large
fries") that has had one or more non-lexical vocables (e.g., "la
la") removed from the speech data.
[0180] Referring again to FIG. 8B, operation 824 may include
operation 828 depicting receiving speech data corresponding to
received speech spoken by the particular party that has been at
least partially anonymized. For example, FIG. 2, e.g., FIG. 2B,
shows speech data corresponding to partially anonymized particular
party spoken word receiving module 228 receiving speech data (e.g.,
a representation of speech that is operable on by one or more
processors) corresponding to received speech (e.g., "I would like
to purchase a train ticket to Colorado") spoken by the particular
party (e.g., a user speaking to an automated train ticket
transaction terminal device) that has been at least partially
anonymized (e.g., particular features that the speaker has, e.g.,
accent, etc., have been filtered out of the speech data).
[0181] Referring now to FIG. 8C, operation 824 may include
operation 830 depicting receiving speech data corresponding to
received speech spoken by the particular party that has been at
least partially analyzed without altering the speech data. For
example, FIG. 2, e.g., FIG. 2C, shows speech data corresponding to
analyzed and unaltered particular party spoken word receiving
module 230 receiving speech data corresponding to received speech
spoken by the particular party (e.g., representation of the user
speaking the words "tune to channel four two seven") that has been
at least partially analyzed (e.g., the received speech data
contains a flag indicating that a device previously has determined
that this speech is intended for the television, which was
determined by partially analyzing the speech to determine that the
word "channel" and a number were present).
[0182] Referring again to FIG. 8C, operation 830 may include
operation 832 depicting receiving speech data corresponding to
received speech spoken by the particular party that has been at
least partially analyzed to determine a target device to which the
speech spoken by the particular party is directed. For example,
FIG. 2, e.g., FIG. 2C, shows speech data corresponding to analyzed
particular party spoken word to determine a target device to which
the particular party spoken word is directed receiving module 232
receiving speech data corresponding to received speech spoken by
the particular party (e.g., a data representation of the user
speaking "reduce the ambient temperature by five degrees" that has
been at least partially analyzed to determine a target device to
which the speech spoken by the particular party is directed (e.g.,
in a system with a GPS navigation device, e.g., GPS navigation
device 41, and a motor vehicle control system, e.g., motor vehicle
control system 42, the received speech data has been partially
analyzed, e.g., by the GPS navigation device, that determined that
the speech data regarding temperature control is directed to a
motor vehicle control system, e.g., motor vehicle control system
42, because a GPS navigation device cannot change the ambient
temperature).
[0183] Referring again to FIG. 8C, operation 824 may include
operation 834 depicting receiving speech data corresponding to
received speech spoken by the particular party to which header data
has been added. For example, FIG. 2, e.g., FIG. 2C, shows speech
data correspond to particular party spoken word with header added
receiving module 234 receiving speech data corresponding to
received speech spoken by the particular party (e.g., data
corresponding to the speech "record Friends on channel 429 at 8:30
pm on Saturday") to which header data (e.g., data indicating that
the user had the "TV command" button on the remote control
depressed when she spoke the command to record Friends) has been
added (e.g., the remote control may have added that data, or in
another embodiment, the remote control may have transferred the
data that the "TV command" button was depressed to an intermediate
device, e.g., an audio/visual receiver, which speech data and adds
the header, and the header-added speech data is received).
[0184] Referring again to FIG. 8C, operation 834 may include
operation 836 depicting receiving speech data corresponding to
received speech spoken by the particular party to which header data
identifying a device that received the speech spoken by the
particular party has been added. For example, FIG. 2, e.g., FIG.
2C, shows speech data correspond to particular party spoken word
with header identifying spoken word receiving device added
receiving module 236 receiving speech data corresponding to
received speech spoken by the particular party (e.g., data
corresponding to a driver speaking the words "display a map of the
twenty-fifth floor" spoken toward an automated help/information
terminal) to which header data identifying a device that received
the speech spoken by the particular party (e.g., a cellular
telephone device that picked up the words spoken by the particular
party) has been added (e.g., the identifying information may be
general, e.g., "a smart phone," or "an Apple-branded smartphone) or
may be specific (e.g., Apple iPhone 5 with identification number
#B352062").
[0185] Referring again to FIG. 8C, operation 702 may include
operation 838 depicting receiving a signal indicating that a device
has obtained speech data. For example, FIG. 2, e.g., FIG. 2C, shows
signal indicating that a device has obtained speech data receiving
module 238 receiving a signal (e.g., data indicating that speech
data has been obtained) indicating that a device (e.g., a
particular device, e.g., a cellular telephone device) has obtained
speech data (e.g., data corresponding to a user speaking "show me a
map to the nearest bathroom" that is directed toward an automated
help/information terminal).
[0186] Referring again to FIG. 8C, operation 702 may include
operation 840 depicting receiving the speech data from the
indicated device. For example, FIG. 2, e.g., FIG. 2C, shows speech
data receiving from indicated device module 240 receiving the
speech data (e.g., the data corresponding to the user saying "show
me a map to the nearest bathroom" from the indicated device (e.g.,
receiving the speech data from the cellular telephone device).
[0187] Referring again to FIG. 8C, operation 838 may include
operation 842 depicting receiving a signal indicating that the
particular device has obtained speech data. For example, FIG. 2,
e.g., FIG. 2C, shows signal indicating that particular device has
obtained speech data receiving module 242 receiving a signal
indicating that the particular device (e.g., a universal remote
control) has obtained speech data (e.g., data corresponding to the
user speaking the words "switch input from cable box to Blu-ray
player").
[0188] Referring now to FIG. 8D, operation 702 may include
operation 844 depicting receiving speech data comprising previously
recorded one or more words spoken by the particular party, and a
timestamp corresponding to a time at which the one or more words
spoken by the particular party were recorded. For example, FIG. 2,
e.g., FIG. 2D, shows speech data comprising previously recorded one
or more particular party spoken words and a timestamp of the time
of recording of the one or more particular party spoken words 244
receiving speech data comprising previously recorded one or more
words spoken by the particular party (e.g., "make fifty-five copies
using 84-brightness paper"), and a timestamp corresponding to a
time at which the one or more words spoken by the particular party
were recorded (e.g., 4:02:02 pm, Sep. 3, 2012).
[0189] Referring again to FIG. 8D, operation 702 may include
operation 846 depicting receiving speech data that comprises a
compressed version of data correlated to one or more words spoken
by the particular party. For example, FIG. 2, e.g., FIG. 2D, shows
speech data comprising a compressed version of data correlated to
one or more particular party spoken words receiving module 246
receiving speech data that comprises a compressed version of data
(e.g., compressed using a Lempel-Ziv compression method) correlated
to one or more words spoken by the particular party (e.g., "I would
like to buy two Nats tickets").
[0190] Referring again to FIG. 8D, operation 702 may include
operation 848 depicting receiving audio data corresponding to one
or more words spoken by the particular party. For example, FIG. 2,
e.g., FIG. 2D, shows speech data comprising audio data
corresponding to one or more particular party spoken words
receiving module 248 receiving audio data corresponding to one or
more words spoken by the particular party (e.g., speaking the words
"I would like a large popcorn, two sodas, and a box of M&Ms" to
an automated movie concession dispensing stand).
[0191] Referring again to FIG. 8D, operation 702 may include
operation 850 depicting receiving, from a further device, speech
data correlated to one or more words spoken by a particular party.
For example, FIG. 2, e.g., FIG. 2D, shows speech data correlated to
one or more particular party spoken words receiving from further
device module 250 receiving, from a further device (e.g., from a
universal remote control, e.g., personal device 22A), speech data
correlated to one or more words spoken by a particular party (e.g.,
"play the DVD in slot twenty-five").
[0192] Referring again to FIG. 8D, operation 850 may include
operation 852 depicting receiving, from the further device, audio
data derived from one or more words spoken by the particular party.
For example, FIG. 2, e.g., FIG. 2D, shows audio data derived from
one or more particular party spoken words receiving from further
device module 252 receiving, from the further device (e.g., a
cellular telephone device), audio data derived from (e.g.,
processed from the actual speech of) one or more words spoken by
the particular party (e.g., a user ordering a pizza at an automated
order-taking device).
[0193] Referring again to FIG. 8D, operation 852 may include
operation 854 depicting receiving, from the further device, audio
data derived from one or more words spoken by the particular party
and detected by the further device. For example, FIG. 2, e.g., FIG.
2D, shows audio data derived from one or more particular party
spoken words detected by further device receiving from further
device module 254 receiving, from the further device (e.g., a
speech receiving module of a home security system installed in each
room), audio data derived from one or more words spoken by the
particular party (e.g., "unlock the safe in the closet") and
detected by the further device (e.g., a speech receiving module of
a home security system).
[0194] Referring again to FIG. 8D, operation 852 may include
operation 856 depicting receiving, from the further device, audio
data derived from one or more words spoken by the particular party
and recorded by the further device. For example, FIG. 2, e.g., FIG.
2D shows audio data derived from one or more particular party
spoken words recorded by further device receiving from further
device module 256 receiving, from the further device (e.g., a
personal conversation monitoring device), audio data derived from
one or more words spoken by the particular party (e.g., speaking
the words "twelve blue pens and three legal pads" to an automated
office supply dispenser).
[0195] Referring again to FIG. 8D, operation 852 may include
operation 858 depicting receiving, from the further device, audio
data derived by the further device from one or more words spoken by
the particular party and detected by the particular device. For
example, FIG. 2, e.g., FIG. 2D, shows audio data derived from one
or more particular party words detected by particular device
receiving from further device module 258 receiving, from the
further device (e.g., a GPS navigation device, e.g., GPS navigation
device 41), audio data derived by the further device from one or
more words spoken by the particular party (e.g., "give me
directions to the nearest Five Guys burger shack") and detected by
the particular device (e.g., a cellular telephone device).
[0196] FIGS. 9A-9Q depict various implementations of operation 704,
according to embodiments. Referring now to FIG. 9A, operation 704
may include operation 902 depicting receiving data comprising one
or more words and corresponding pronunciations of the one or more
words that is at least partly based on at least one speech
interaction of the particular party, said at least one speech
interaction of the particular party discrete from the detected
speech data, wherein at least a portion of the adaptation data has
been stored on the particular device associated with the particular
party. For example, FIG. 3, e.g., FIG. 3A, shows adaptation data
comprising one or more words and corresponding pronunciations of
the one or more words at least partly based on discrete speech
interaction of particular party separate from detected speech data,
and has been stored on a particular party-associated particular
device acquiring module 302 acquiring data comprising one or more
words (e.g., "pepperoni," "cheese," and "anchovies") and
corresponding pronunciations of the one or more words that is at
least partly based on one speech interaction of the particular
party (e.g., using a cellular telephone device to order a pizza),
said at least one speech interaction of the particular party
discrete from the detected speech data (e.g., the user is placing
an order at an automated drive-thru window), wherein at least a
portion of the adaptation data has been stored on the particular
device (e.g., the cellular telephone used to order the pizza)
associated with the particular party (e.g., owned by the user).
[0197] Referring again to FIG. 9A, operation 902 may include
operation 904 depicting receiving data comprising one or more words
and corresponding pronunciations of the one or more words that is
at least partly based on at least one previous training by the
particular party providing the pronunciations of the one or more
words in response to prompting, that is discrete from the detected
speech data, wherein at least a portion of the adaptation data has
been stored on the particular device associated with the particular
party. For example, FIG. 3, e.g., FIG. 3A, shows adaptation data
comprising one or more words and corresponding pronunciations of
the one or more words at least partly based on at least one
previous training by the particular party separate from detected
speech data, and has been stored on a particular party-associated
particular device acquiring module 304 acquiring data comprising
one or more words (e.g., "Boston," "Austin," and "flossed") and
corresponding pronunciations of the one or more words that is at
least partly based on at least one previous training by the
particular party providing the pronunciations of the one or more
words in response to prompting (e.g., displaying on a computer
screen), that is discrete from the detected speech data (e.g., data
used in a transaction of buying a train ticket from a
speech-enabled automated ticket dispenser), wherein at least a
portion of the adaptation data has been stored on the particular
device (e.g., a USB device that can also transmit and receive, that
was previously inserted into the computer during or after the
user's training, and is now carried by the user) associated with
the particular party (e.g., the USB device is a necklace,
wristband, watch, or pair of eyeglasses that the user is
wearing).
[0198] Referring again to FIG. 9A, operation 904 may include
operation 906 depicting receiving adaptation data comprising one or
more words and corresponding pronunciations of the one or more
words that is at least partly based on at least one previous
training by the particular party repeating the pronunciations of
the one or more words in response to prompting by the particular
device, that is discrete from the detected speech data
corresponding to an order placed by the particular party at an
automated drive-thru terminal that accepts speech input, wherein at
least a portion of the adaptation data has been stored on the
particular device associated with the particular party. For
example, FIG. 3, e.g., FIG. 3, shows adaptation data comprising one
or more words and corresponding pronunciations of the one or more
words at least partly based on at least one previous training by
the particular party separate from detected speech data
corresponding to an order placed by the particular party at an
automated drive-thru terminal that accepts speech input, and has
been stored on a particular party-associated particular device
acquiring module 306 acquiring adaptation data comprising one or
more words (e.g., "national," "first," "bank," "money," and
"personal identification number") and corresponding pronunciations
of the one or more words that is at least partly based on at least
one previous training by the particular party repeating the
pronunciations of the one or more words in response to prompting by
the particular device (e.g., a custom headset that the user wears
and which provides audio prompting to the user through the earphone
portion of the headset), that is discrete from the detected speech
data corresponding to an order placed by the particular party at an
automated drive-thru terminal that accepts speech input, wherein at
least a portion of the adaptation data has been stored on the
particular device (e.g., the training data was briefly stored at
the headset and then transferred to a location within a cloud
network) associated with the particular party (e.g., used by the
user at one point previously).
[0199] Referring again to FIG. 9A, operation 906 may include
operation 908 depicting receiving adaptation data comprising one or
more words and corresponding pronunciations of the one or more
words that is at least partly based on at least one previous
training by the particular party repeating the pronunciations of
the one or more words in response to prompting by a cellular
telephone device with a screen and a memory, that is discrete from
the detected speech data corresponding to an order for food placed
by the particular party at an automated drive-thru terminal that
accepts speech input, wherein at least a portion of the adaptation
data has been stored on the cellular telephone device associated
with the particular party. For example, FIG. 3, e.g., FIG. 3A,
shows adaptation data comprising one or more words and
corresponding pronunciations of the one or more words at least
partly based on at least one previous training by the particular
party in response to cellular telephone device prompting separate
from detected speech data corresponding to an order placed by the
particular party at an automated drive-thru terminal that accepts
speech input, and has been stored on a particular party-associated
particular device acquiring module 308 acquiring adaptation data
comprising one or more words (e.g., "cheeseburger," "small,"
"medium," and "large") and corresponding pronunciations of the one
or more words that is at least partly based on at least one
previous training by the particular party repeating the
pronunciations of the one or more words in response to prompting by
a cellular telephone device with a screen (e.g., user interface
135) and a memory (e.g., memory 134), that is discrete from the
detected speech data corresponding to an order for food placed by
the particular party at an automated drive-thru terminal that
accepts speech input, wherein at least a portion of the adaptation
data has been stored on the cellular telephone device associated
with the particular party.
[0200] Referring now to FIG. 9B, operation 908 (e.g., operations
904, 906, and 908 have been abbreviated for clarity, but are the
same as in FIG. 9A) may include operation 910 depicting receiving
adaptation data comprising one or more words and corresponding
pronunciations of the one or more words that is at least partly
based on at least one previous training by the particular party
repeating the pronunciations of the one or more words in response
to prompting by a cellular telephone device with a screen and a
memory, that is discrete from the detected speech data
corresponding to an order for food placed by the particular party
at an automated drive-thru terminal that accepts speech input,
wherein at least a portion of the adaptation data has been stored
on the cellular telephone device that is linked to the particular
party through a contract with a telecommunications provider. For
example, FIG. 3, e.g., FIG. 3A, shows adaptation data comprising
one or more words and corresponding pronunciations of the one or
more words at least partly based on at least one previous training
by the particular party in response to cellular telephone device
prompting separate from detected speech data corresponding to an
order placed by the particular party at an automated drive-thru
terminal that accepts speech input, and has been stored on a
particular device linked to the particular party through a contract
with a telecommunications provider acquiring module 310 comprising
one or more words and corresponding pronunciations (e.g., "money,"
"yes," "no," and "please repeat that") of the one or more words
that is at least partly based on at least one previous training by
the particular party repeating the pronunciations of the one or
more words in response to prompting by a cellular telephone device
with a screen (e.g., user interface 135) and a memory (e.g., memory
134),
[0201] Referring now to FIG. 9C, operation 704 may include
operation 912 depicting receiving adaptation data that is at least
partly based on at least one speech interaction of the particular
party that occurred that occurred at a different time and a
different location than a speech interaction prior to a speech
interaction that generated the detected speech data, wherein at
least a portion of the adaptation data has been stored on the
particular device associated with the particular party. For
example, FIG. 3, e.g., FIG. 3B, shows adaptation data at least
partly based on discrete speech interaction of particular party
prior to speech interaction generating detected speech data, and
has been stored on a particular party-associated particular device
receiving module 312 acquiring adaptation data (e.g., a noise level
dependent filtration algorithm) that is at least partly based on at
least one speech interaction (e.g., giving speech commands to an
automated teller machine device at a Jun. 20, 2011 baseball game in
Washington, D.C.) of the particular party that occurred at a
different time (e.g., Jun. 20, 2011) and a different location
(e.g., Washington, D.C.) than a speech interaction prior to a
speech interaction that generated the speech adaptation data (e.g.,
using an automated teller machine at a KISS concert in
Philadelphia, Pa., on Nov. 4, 2011), wherein at least a portion of
the adaptation data has been stored on a particular device
associated with the particular party (e.g., the adaptation data,
which usually resides in cloud storage, was transmitted to the
user's cellular telephone device, then transmitted to the automated
teller machine device).
[0202] Referring again to FIG. 9C, operation 704 may include
operation 914 depicting acquiring at least a portion of adaptation
data that is at least partly based on at least one speech
interaction of the particular party that occurred prior to a speech
interaction that generated the detected speech data, wherein at
least a portion of the adaptation data has been stored on the
particular device associated with the particular party. For
example, FIG. 3, e.g., FIG. 3B, shows adaptation data at least
partly based on discrete speech interaction of particular party
occurring prior to speech interaction generating detected speech
data, and has been stored on a particular party-associated
particular device acquiring module 314 acquiring at least a portion
of adaptation data (e.g., an emotion-based pronunciation adjustment
algorithm) that is at least partly based on at least one speech
interaction of the particular party (e.g., programming a
speech-operated microwave oven) that occurred prior to a speech
interaction that generated the detected speech data (e.g.,
programming a PVR to record the "30 Rock" television show), wherein
at least a portion of the adaptation data has been stored on a
particular device (e.g., in a hard drive on a home computer that is
networked to other devices in the house) associated with the
particular party (e.g., the home computer is configured to manage
the adaptation data for the particular party and to transmit it to
personal devices and/or to target devices).
[0203] Referring again to FIG. 9C, operation 914 may include
operation 916 depicting receiving, from the particular device,
adaptation data that is at least partly based on at least one
speech interaction of the particular party that occurred prior to a
speech interaction that generated the detected speech data, wherein
at least a portion of the adaptation data has been stored on the
particular device associated with the particular party. For
example, FIG. 3, e.g., FIG. 3B, shows adaptation data at least
partly based on discrete speech interaction of particular party
occurring prior to speech interaction generating detected speech
data, and has been stored on a particular party-associated
particular device receiving from the particular device module 316
receiving (e.g., a cellular telephone device, e.g., an iPhone,
carried by a user, receives), from the particular device (e.g., a
programmable universal remote control) adaptation data (e.g., a
syllable pronunciation database) that is at least partly based on
at least one speech interaction of the particular party (e.g.,
using speech to enter in "ESPN" and "Comedy Central" as favorite
networks into the cable box) that occurred prior to a speech
interaction that generated the detected speech data (e.g., the user
using speech to command a television to move to a particular
channel, e.g., ESPN-2), wherein at least a portion of the
adaptation data has been stored on a particular device associated
with the particular party (e.g., at least a portion of the syllable
pronunciation database) has been stored on the particular device
associated with the particular party (e.g., the universal remote
control, which has been programmed by the user, and that is
configured to store at least a portion of adaptation data).
[0204] Referring again to FIG. 9C, operation 916 may include
operation 918 depicting receiving, from a memory of the particular
device, adaptation data that is at least partly based on at least
one speech interaction of the particular party that occurred prior
to a speech interaction that generated the detected speech data,
wherein the adaptation data has been stored on the particular
device associated with the particular party. For example, FIG. 3,
e.g., FIG. 3B, shows adaptation data at least partly based on
discrete speech interaction of particular party occurring prior to
speech interaction generating detected speech data, and has been
stored on a particular party-associated particular device receiving
directly from the particular device memory module 318 receiving
(e.g., a CPU of a tablet device, e.g., an Asus A500 internally
receiving from a bus connected to the processor), from a memory of
the particular device (e.g., which may be removable memory, e.g.,
an SD or Micro SD card) or fixed memory (e.g., internal device
RAM), adaptation data (e.g., an accent-based pronunciation
modification algorithm) that is at least partly based on at least
one speech interaction of the particular party (e.g., the user,
when driving his Honda Civic motor vehicle commanding that the
windows be lowered) that occurred prior to a speech interaction
that generated the detected speech data (e.g., after the user
trades in a Honda Civic motor vehicle for an Acura TL motor
vehicle, the user commands the Acura TL to lower the windows),
wherein the adaptation data has been stored on a particular device
(e.g., the tablet device, e.g., the Asus A500) associated with the
particular party (e.g., is known by the vehicle as associated with
a particular party).
[0205] Referring now to FIG. 9D, operation 914 may include
operation 920 depicting receiving, from a communication network
provider, adaptation data that is at least partly based on at least
one speech interaction of the particular party that occurred prior
to a speech interaction that generated the detected speech data,
wherein at least a portion of the adaptation data has been stored
on the particular device associated with the particular party. For
example, FIG. 3, e.g., FIG. 3B, shows adaptation data at least
partly based on discrete speech interaction of particular party
occurring prior to speech interaction generating detected speech
data, and has been stored on a particular party-associated
particular device receiving from a communication network provider
module 320 receiving (e.g., a cellular telephone device), from a
communication provider (e.g., a provider for the cellular telephone
device, e.g., AT&T), adaptation data (e.g., instructions for
replacing a word frequency table with a modified word frequency
table that reflects the particular party's word usage) that is at
least partly based on at least one speech interaction of the
particular party (e.g., a command given to the cellular phone
device of "update calendar to add Mrs. Jones's birthday party on
July 19th at 6 pm") that occurred prior to a speech interaction
that generated the detected speech data (e.g., a command given to
an automated ticket dispensing machine), wherein at least a portion
of the adaptation data has been stored on a particular device
(e.g., data storing the word frequency of the interactions with the
cellular phone device (e.g., one usage each of the words
"calendar," "July," "birthday," party," "nineteenth" and "6 pm") is
stored on the cellular telephone device before sending to the
communication network provider for aggregation into the modified
word frequency table and/or conversion into instructions for
replacing the word frequency table with the modified word frequency
table).
[0206] Referring again to FIG. 9D, operation 920 may include
operation 922 depicting receiving, from a communication network
provider, adaptation data that is at least partly based on at least
one speech interaction of the particular party that occurred prior
to a speech interaction that generated the detected speech data,
wherein at least a portion of the adaptation data has been stored
on the particular device associated with the particular party and
previously transmitted to the communication network provider. For
example, FIG. 3, e.g., FIG. 3B, shows adaptation data at least
partly based on discrete speech interaction of particular party
occurring prior to speech interaction generating detected speech
data, and has been stored on a particular party-associated
particular device and transmitted over the communication network
receiving from a communication network provider module 322
receiving, from a communication network provider (e.g., AT&T),
adaptation data (e.g., a phoneme pronunciation database) that is at
least partly based on at least one speech interaction of the
particular party (e.g., placing a food order at an automated
walk-thru window (e.g., similar to a drive-thru window, except you
walk or conveyor belt ride through)) that occurred prior to a
speech interaction that generated the detected speech data (e.g.,
withdrawing money from a speech-enabled automated teller machine
device), wherein at least a portion of the adaptation data (e.g.,
the phoneme pronunciation database) has been stored on the
particular device associated with the particular party and
previously transmitted to the communication network provider.
[0207] Referring again to FIG. 9D, operation 914 may include
operation 924 depicting receiving adaptation data, from a device
connected to a same network as a target device to which the
detected speech data is directed, said adaptation data at least
partly based on at least one speech interaction of the particular
party that occurred prior to a speech interaction that generated
the detected speech data, wherein at least a portion of the
adaptation data has been stored on the particular device associated
with the particular party. For example, FIG. 3, e.g., FIG. 3C,
shows adaptation data at least partly based on discrete speech
interaction of particular party occurring prior to speech
interaction generating detected speech data, and has been stored on
a particular party-associated particular device receiving from a
device connected to a same network as a target device to which the
detected speech data is directed module 324 receiving adaptation
data (e.g., a stochastic state transition network), from a device
connected to a same network (e.g., a tablet device connected to a
home network via a router) as a target device (e.g., a safe in a
home that responds to speech commands and is connected to the home
network) to which the detected speech data is directed (e.g., it is
determined, e.g., by the tablet, that the detected speech is
intended for the tablet device), said adaptation data at least
partly based on at least one speech interaction of the particular
party (e.g., the user's previous interaction with other portions of
the home security system, and the user's previous interactions with
a speech- and network-enabled coffee maker) that occurred prior to
a speech interaction that generated the detected speech data (e.g.,
the user programming the safe with the code phrase that will unlock
one section of the safe), wherein at least a portion of the
adaptation data has been stored on the particular device (e.g., the
tablet device) associated with the particular party (e.g., owned by
the user).
[0208] Referring again to FIG. 9D, operation 914 may include
operation 926 depicting retrieving adaptation data in response to
reception of the speech data, said adaptation data at least partly
based on at least one speech interaction of the particular party
that occurred prior to a speech interaction that generated the
detected speech data, wherein at least a portion of the adaptation
data has been stored on the particular device associated with the
particular party. For example, FIG. 3, e.g., FIG. 3C, shows
adaptation data at least partly based on discrete speech
interaction of particular party occurring prior to speech
interaction generating detected speech data, and has been stored on
a particular party-associated particular device receiving in
response to reception of speech data module 326 retrieving
adaptation data (e.g., an office assistant device carried by
employees (e.g., that might double as a security badge/access card
for certain areas) receives adaptation data when it receives the
speech data from the user) in response to reception of the speech
data (e.g., in response to the user speaking a command to a piece
of office equipment, e.g., a copier, a vending machine, or an
automated security checkpoint), said adaptation data (e.g., a
speech disfluency detection algorithm) at least partly based on at
least one speech interaction of the particular party (e.g.,
training of the particular party's speech that happened at the
beginning of her employment, e.g., at new employee orientation)
that occurred prior to a speech interaction that generated the
detected speech data (e.g., speaking a particular code phrase to an
additional security lock to access a limited-access portion of a
company, e.g., a document retention room where confidential,
protected, or limited access, e.g., medical, records are kept),
wherein at least a portion of the adaptation data (e.g., a speech
disfluency detection algorithm) has been stored on the particular
device (e.g., the office assistant device) associated with the
particular party.
[0209] Referring now to FIG. 9E, operation 704 may include
operation 928 depicting receiving adaptation data in response to a
detection of a particular condition, said adaptation data at least
partly based on at least one speech interaction of the particular
party that occurred prior to a speech interaction that generated
the detected speech data, wherein at least a portion of the
adaptation data has been stored on the particular device associated
with the particular party. For example, FIG. 3, e.g., FIG. 3C,
shows adaptation data at least partly based on discrete speech
interaction of particular party occurring prior to speech
interaction generating detected speech data, and has been stored on
a particular party-associated particular device receiving in
response to condition module 320 acquiring adaptation data (e.g.,
retrieving, from a cloud storage service, a context-based repaired
utterance processing matrix) in response to a detection of a
particular condition (e.g., in response to detecting a broadcasting
signal being sent from a target device indicating that the target
device (e.g., an automated fast food drive-thru window) is
configured to receive adaptation data and use the adaptation data
in speech processing), said adaptation data at least partly based
on at least one speech interaction of the particular party that
occurred prior to a speech interaction that generated the detected
speech data (e.g., the particular party ordering a #6 combo meal at
a popular fast food restaurant), wherein at least a portion of the
particular data has been stored on the particular device associated
with the particular party (e.g., at times when the particular party
requests the adaptation data from the cloud storage service, it is
temporarily stored on the particular device before being passed
along to the target device).
[0210] Referring again to FIG. 9E, operation 928 may include
operation 930 depicting receiving adaptation data in response to
the particular party interacting with a target device to which the
speech data is directed, said adaptation data at least partly based
on at least one speech interaction of the particular party that
occurred prior to a speech interaction that generated the detected
speech data, wherein at least a portion of the adaptation data has
been stored on the particular device associated with the particular
party. For example, FIG. 3, e.g., FIG. 3C, shows adaptation data at
least partly based on discrete speech interaction of particular
party occurring prior to speech interaction generating detected
speech data, and has been stored on a particular party-associated
particular device receiving in response to the particular party
interacting with a target device module 330 acquiring adaptation
data (e.g., a non-lexical vocable removal algorithm) in response to
the particular party interacting (e.g., pushing a button on) with a
target device (e.g., a speech-enabled automated teller machine
device) to which the speech data is directed (e.g., the user is
speaking to the speech-enabled automated teller machine device),
said adaptation data at least partly based on at least one speech
interaction of the particular party (e.g., one or more previous
interactions with other automated teller machine devices) that
occurred prior to a speech interaction that generated the detected
speech data (e.g., the user commanding the automated teller machine
device to dispense two hundred dollars in cash from the user's
savings account), wherein at least a portion of the adaptation data
has been stored on the particular device (e.g., transmit, store,
and receive-enabled eyeglasses) associated with the particular
party (e.g., being worn by the user).
[0211] Referring again to FIG. 9E, operation 930 may include
operation 932 depicting receiving adaptation data in response to
the particular party inserting a key into a motor vehicle to which
the speech data is directed, said adaptation data at least partly
based on at least one speech interaction of the particular party
that occurred prior to a speech interaction that generated the
detected speech data, wherein at least a portion of the adaptation
data has been stored on the particular device associated with the
particular party. For example, FIG. 3, e.g., FIG. 3C, shows
adaptation data at least partly based on discrete speech
interaction of particular party occurring prior to speech
interaction generating detected speech data, and has been stored on
a particular party-associated particular device receiving in
response to the particular party inserting a key into a motor
vehicle interacting with a target device module 332 acquiring
adaptation data (e.g., a set of proper noun pronunciations, e.g.,
names of hamburger joints) in response to the particular party
inserting a key into a motor vehicle to which the speech data is
directed (e.g., the speech data is a command "give me directions to
Beastly Burger hamburger joint"), wherein at least a portion of the
adaptation data has been stored on the particular device (e.g., the
particular device could be the key itself, if the key is configured
to store, transmit, and receive data, or the particular device
could be the user's smartphone, e.g., the particular device does
not necessarily need to be the device (e.g., the key) that triggers
the acquisition of adaptation data).
[0212] Referring again to FIG. 9E, operation 930 may include
operation 934 depicting receiving adaptation data in response to
the particular party executing a program on a computing device to
which the speech data is directed, said adaptation data at least
partly based on at least one speech interaction of the particular
party that occurred prior to a speech interaction that generated
the detected speech data, wherein at least a portion of the
adaptation data has been stored on the particular device associated
with the particular party. For example, FIG. 3, e.g., FIG. 3D,
shows adaptation data at least partly based on discrete speech
interaction of particular party occurring prior to speech
interaction generating detected speech data, and has been stored on
a particular party-associated particular device receiving in
response to the particular party executing a program on a computing
device module 334 acquiring adaptation data (e.g., a part-of-speech
labeling algorithm) in response to the particular party executing a
program on a computing device (e.g., a word processing program) to
which speech data is directed (e.g., that is configured to receive
dictation of documents), said adaptation data at least partly based
on at least one speech interaction of the particular party (e.g.,
previous dictations of documents into a different word processing
program on a different computer) that occurred prior to a speech
interaction that generated the detected speech data (e.g., the
speech data that will be generated by the user's dictation),
wherein at least a portion of the adaptation data has been stored
on the particular device (e.g., a USB key that is owned by the user
and that stores her adaptation data along with other information)
associated with the particular party (e.g., owned by the user).
[0213] Referring now to FIG. 9F, operation 928 may include
operation 936 depicting receiving adaptation data in response to a
detection of the particular party at a particular location, said
adaptation data at least partly based on at least one speech
interaction of the particular party that occurred prior to a speech
interaction that generated the detected speech data, wherein at
least a portion of the adaptation data has been stored on the
particular device associated with the particular party. For
example, FIG. 3, e.g., FIG. 3D shows adaptation data at least
partly based on discrete speech interaction of particular party
occurring prior to speech interaction generating detected speech
data, and has been stored on a particular party-associated
particular device receiving in response to detection of the
particular party at a particular location module 336 acquiring
adaptation data in response to a detection of the particular party
at a particular location (e.g., within two feet of a target device,
e.g., an automated airline ticket dispensing counter), said
adaptation data at least partly based on at least one speech
interaction of the particular party that occurred prior to a speech
interaction that generated the detected speech data (e.g., speaking
the name of the destination of the user's airline ticket), wherein
at least a portion of the adaptation data (e.g., a French language
substitution algorithm) has been stored on the particular device
(e.g., a smartphone with GPS sensors) associated with the
particular party (e.g., carried by the user).
[0214] Referring again to FIG. 9F, operation 928 may include
operation 938 depicting receiving adaptation data in response to a
detection of the particular party within a particular proximity of
a target device, said adaptation data at least partly based on at
least one speech interaction of the particular party that occurred
prior to a speech interaction that generated the detected speech
data, wherein at least a portion of the adaptation data has been
stored on the particular device associated with the particular
party. For example, FIG. 3, e.g., FIG. 3D, shows adaptation data at
least partly based on discrete speech interaction of particular
party occurring prior to speech interaction generating detected
speech data, and has been stored on a particular party-associated
particular device receiving in response to detection of the
particular party within a particular proximity of a target device
module 338 acquiring adaptation data (e.g., an utterance ignoring
algorithm) in response to a detection of the particular party
(e.g., the user) within a particular proximity of a target device
(e.g., the particular device acquires the adaptation data from a
cloud storage service when it receives a signal from the target
device that the target device (e.g., an automated drink dispensing
device) detected the particular party was within screen-viewing
distance of the automated drive-thru window), said adaptation data
at least partly based on at least one speech interaction of the
particular party (e.g., the particular party dictating a memorandum
to speech-enabled word processing that is stored on a cloud) that
occurred prior to a speech interaction that generated the detected
speech data (e.g., ordering a cherry-and-chocolate twisted lime
soda drink), wherein at least a portion of the adaptation data has
been stored on the particular device (e.g., a "smart wallet" that,
in addition to holding cash and credit cards, also can store,
transmit, and receive adaptation data, and that acquires the
adaptation data when it learns that a particular party is within
proximity to a particular type of target device) associated with
the particular party (e.g., carried by the particular party and
configured to store, at least temporarily, the particular party's
adaptation data).
[0215] Referring now to FIG. 9G, operation 704 may include
operation 940 depicting receiving adaptation data from a further
device, said adaptation data at least partly based on at least one
speech interaction of the particular party that is discrete from
the detected speech data, wherein at least a portion of the
adaptation data has been stored on the particular device associated
with the particular party. For example, FIG. 3, e.g., FIG. 3E,
shows adaptation data at least partly based on discrete speech
interaction of particular party separate from detected speech data,
and has been stored on a particular party-associated particular
device acquiring from a further device module 340 acquiring
adaptation data, from a further device (e.g., from a cellular
telephone device), said adaptation data at least partly based on at
least one speech interaction of the particular party (e.g.,
previous commands given to a navigation device requesting
directions) that is discrete from the detected speech data (e.g.,
requesting directions to Big Boy Pizza), wherein at least a portion
of the adaptation data has been stored on the particular device
(e.g., a smart key inserted into a vehicle that can store,
transmit, and receive adaptation data) associated with the
particular party (e.g., the driver of a car that has both onboard
navigation and a personal GPS navigation system removably mounted
to the windshield).
[0216] Referring again to FIG. 9G, operation 940 may include
operation 942 depicting receiving adaptation data from a further
device, said adaptation data originating at the further device and
at least partly based on least one speech interaction of the
particular party that is discrete from the detected speech data,
wherein at least a portion of the adaptation data has been stored
on the particular device associated with the particular party. For
example, FIG. 3, e.g., FIG. 3E, shows adaptation data originating
at further device and at least partly based on discrete speech
interaction of particular party separate from detected speech data,
and has been stored on a particular party-associated particular
device acquiring from a further device module 342 acquiring
adaptation data from a further device (e.g., an office personal
device, which may be owned by the company that the user works for,
and stores at least a portion, or a version of the adaptation
data), said adaptation data originating at the further device
(e.g., the adaptation data is stored on the further device once and
then transmitted from there; e.g., the further device does not
receive the adaptation data from another source on demand) and at
least partly based on at least one speech interaction of the
particular party that is discrete from the detected speech data
(e.g., operating a piece of machinery used in that field that
responds to speech commands), wherein at least a portion of the
adaptation data has been stored on the particular device associated
with the particular party (e.g., the adaptation data is transferred
from a further device to a particular device (e.g., the user's
cellular telephone, which may perform additional modifications, or
may transmit it as is to the target device, e.g., the piece of
machinery).
[0217] Referring again to FIG. 9G, operation 940 may include
operation 944 depicting receiving adaptation data from a further
device related to the particular device, said adaptation data
originating at the further device and at least partly based on
least one speech interaction of the particular party that is
discrete from the detected speech data, wherein at least a portion
of the adaptation data has been stored on the particular device
associated with the particular party. For example, FIG. 3, e.g.,
FIG. 3E, shows adaptation data at least partly based on discrete
speech interaction of particular party separate from detected
speech data, and has been stored on a particular party-associated
particular device acquiring from a further device related to the
particular device module 944 acquiring adaptation data from a
further device (e.g., a desktop computer that stores adaptation
data for a user, e.g., or for the user's entire family) related to
(e.g., both the particular device and the further device have a
login saved for the user) the particular device (e.g., a cellular
telephone device), said adaptation data originating at the further
device (e.g., the adaptation data is stored at the further device
and transmitted to the particular device over a network, e.g., a
Wi-Fi network) and at least partly based on at least one speech
interaction of the particular party that is discrete from the
detected speech data (e.g., speech-programming a convection oven,
wherein the convection oven isn't connected by Wi-Fi but does have
a Bluetooth connection and the cellular telephone device, as the
particular device, acquires the adaptation data from the desktop
computer via Wi-Fi, and relays the adaptation data to the
convection oven via Bluetooth), wherein at least a portion of the
adaptation data has been stored on the particular device (e.g., the
adaptation data is stored on the cellular telephone device, at
least temporarily, as it is received over Wi-Fi and transmitted
over Bluetooth) associated with the particular party.
[0218] Referring again to FIG. 9G, operation 944 may include
operation 946 depicting receiving adaptation data from a further
device associated with the particular party, said adaptation data
originating at the further device and at least partly based on
least one speech interaction of the particular party that is
discrete from the detected speech data, wherein at least a portion
of the adaptation data has been stored on the particular device
associated with the particular party. For example, FIG. 3, e.g.,
FIG. 3E, shows adaptation data at least partly based on discrete
speech interaction of particular party separate from detected
speech data, and has been stored on a particular party-associated
particular device acquiring from a further device associated with
the particular party module 946 acquiring adaptation data from a
further device associated with the particular party (e.g., a
customized gaming controller that the user, e.g., the player,
brings to use in various guest video game systems as well as her
own), said adaptation data originating at the further device (e.g.,
the adaptation data is stored on the further device and derived
from interactions of the player with the game system using speech)
and at least partly based on at least one speech interaction of the
particular party (e.g., giving voice commands in a first-person
shooter game) that is discrete from the detected speech data (e.g.,
giving voice commands in an online soccer game), wherein at least a
portion of the adaptation data has been stored on the particular
device (e.g., a headset used by the player that pulls adaptation
data from the particular party, and either passes the adaptation
data to the target device, modifies the adaptation data, or
performs some amount of processing on the speech data received
through the microphone of the headset) associated with the
particular party.
[0219] Referring again to FIG. 9G, operation 944 may include
operation 948 depicting receiving adaptation data from a further
device in communication with the particular device, said adaptation
data originating at the further device and at least partly based on
least one speech interaction of the particular party that is
discrete from the detected speech data, wherein at least a portion
of the adaptation data has been stored on the particular device
associated with the particular party. For example, FIG. 3, e.g.,
FIG. 3E, shows adaptation data at least partly based on discrete
speech interaction of particular party separate from detected
speech data, and has been stored on a particular party-associated
particular device acquiring from a further device in communication
with the particular device module 348 acquiring adaptation data
from a further device (e.g., a tablet device, e.g., an iPad) in
communication with (e.g., operating on a same network, whether
through 3G or Wi-Fi communication) the particular device (e.g., a
cellular device, e.g., an iPhone), said adaptation data originating
at the further device (e.g., the adaptation data is stored and
maintained on the iPad) and at least partly based on at least one
speech interaction of the particular party (e.g., conversations
that occurred more than two days ago) that is discrete from the
detected speech data (e.g., speech from the user buying a train
ticket from an automated train ticket dispensing device), wherein
at least a portion of the adaptation data has been stored on the
particular device (e.g., the iPhone receives the adaptation data
from the iPad, and determines if any speech interactions have
occurred in the last two days that would result in changing the
adaptation data, and, if so, modifies the adaptation data, before
sending the adaptation data to the target device, e.g., the
automated train ticket dispensing device) associated with the
particular party (e.g., the user).
[0220] Referring now to FIG. 9H, operation 944 may include
operation 950 depicting receiving adaptation data from a further
device that is at least partially controlled by the particular
device, said adaptation data originating at the further device and
at least partly based on least one speech interaction of the
particular party that is discrete from the detected speech data,
wherein at least a portion of the adaptation data has been stored
on the particular device associated with the particular party. For
example, FIG. 3, e.g., FIG. 3E, shows adaptation data at least
partly based on discrete speech interaction of particular party
separate from detected speech data, and has been stored on a
particular party-associated particular device acquiring from a
further device at least partially controlled by the particular
device module 350 acquiring adaptation data from a further device
(e.g., a laptop computer plugged into a network) that is at least
partially controlled (e.g., has been set up so that portable
devices can access its files and execute limited commands on it) by
the particular device (e.g., a tablet device, e.g., an Apple iPad),
said adaptation data originating at the further device and at least
partly based on at least one speech interaction of the particular
party (e.g., the user programming a convection oven) that is
discrete from the detected speech data (e.g., the user programming
a microwave oven), wherein at least a portion of the adaptation
data (e.g., an utterance ignoring algorithm) has been stored on the
particular device (e.g., the Apple iPad) associated with the
particular party (e.g., carried by the particular party).
[0221] Referring again to FIG. 9H, operation 940 may include
operation 952 depicting receiving adaptation data from a further
device, said adaptation data received by the further device from
the particular device, and said adaptation data at least partly
based on least one speech interaction of the particular party that
is discrete from the detected speech data, wherein at least a
portion of the adaptation data has been stored on the particular
device associated with the particular party. For example, FIG. 3,
e.g., FIG. 3E, shows adaptation data at least partly based on
discrete speech interaction of particular party separate from
detected speech data, and has been stored on a particular
party-associated particular device acquiring from a further device
that received the adaptation data from the particular device module
352 acquiring adaptation data (e.g., an uncommon word pronunciation
guide), said adaptation data received by the further device (e.g.,
a portable personal navigation system device) from the particular
device (e.g., a user's cellular telephone), and said adaptation
data at least partly based on at least one speech interaction of
the particular party (e.g., the user giving commands into his
cellular telephone to add contact information) that is discrete
from the detected speech data (e.g., a request to lower the windows
of the motor vehicle), wherein at least a portion of the adaptation
data (e.g., at least one word of the uncommon word pronunciation
guide) has been stored on the particular device associated with the
particular party (e.g., the user).
[0222] Referring now to FIG. 9I, operation 940 may include
operation 954 depicting receiving adaptation data, from a further
device, said adaptation data comprising instructions for modifying
a pronunciation dictionary, and said adaptation data at least
partly based on at least one speech interaction of the particular
party that is discrete from the detected speech data, wherein at
least a portion of the adaptation data has been stored on the
particular device associated with the particular party. For
example, FIG. 3, e.g., FIG. 3F shows adaptation data comprising
instructions for modifying a pronunciation dictionary, said
adaptation data at least partly based on discrete speech
interaction of particular party separate from detected speech data,
and has been stored on a particular party-associated particular
device acquiring from a further device module 354 acquiring
adaptation data, from a further device (e.g., a personal navigation
system device), said adaptation data comprising instructions for
modifying a pronunciation dictionary, and said adaptation data at
least partly based on at least one speech interaction of the
particular party (e.g., requesting directions to the nearest
emergency room) that is discrete from the detected speech data
(e.g., requesting instructions to the nearest pizza parlor),
wherein at least a portion of the adaptation data has been stored
on the particular device (e.g., a cellular telephone with GPS
positioning enabled) associated with the particular party.
[0223] Referring again to FIG. 9I, operation 954 may include
operation 956 depicting receiving adaptation data, from a further
device, said adaptation data comprising a first instruction for
modifying the pronunciation dictionary based on a first speech
interaction of the particular party and a second instruction for
modifying the pronunciation dictionary based on a second speech
interaction of the particular party, and said adaptation data is at
least partly based on at least one speech interaction of the
particular party that is discrete from the detected speech data,
wherein at least a portion of the adaptation data has been stored
on the particular device associated with the particular party. For
example, FIG. 3, e.g., FIG. 3F, shows adaptation data comprising a
first instruction for modifying a pronunciation dictionary based on
a first particular party interaction and a second instruction for
modifying a pronunciation dictionary based on a second particular
party interaction, and has been stored on a particular
party-associated particular device acquiring from a further device
module 356 acquiring adaptation data, from a further device (e.g.,
a tablet device, e.g., a Samsung Galaxy Tab), said adaptation data
comprising a first instruction for modifying the pronunciation
dictionary (e.g., "modify a pronunciation of the word `twenty`")
based on a first speech interaction of the particular party (e.g.,
the user withdrawing two hundred dollars and requesting twenty
dollar bills from an automated teller machine device that accepts
speech input) and a second instruction for modifying the
pronunciation dictionary (e.g., "modify a pronunciation of the word
`hamburger`") based on a second speech interaction of the
particular party (e.g., the user placing a lunch order for a
hamburger and french fries with an automated drive thru window),
and said adaptation data is at least partly based on at least one
speech interaction of the particular party (e.g., the user
withdrawing two hundred dollars and requesting twenty dollar bills
from an automated teller machine device that accepts speech input
and/or the user placing a lunch order for a hamburger and French
fries) that is discrete from the detected speech data (e.g., giving
a speech command to an automated ticket taking device), wherein at
least a portion of the adaptation data has been stored on the
particular device (e.g., a cellular telephone device that
originally transmitted the adaptation data to the tablet)
associated with the particular party (e.g., owned by the user).
[0224] Referring again to FIG. 9I, operation 956 may include
operation 958 depicting receiving adaptation data, from a further
device, said adaptation data comprising a first instruction for
modifying the pronunciation dictionary based on a first speech
interaction of the particular party and a second instruction for
modifying the pronunciation dictionary based on a second speech
interaction of the particular party, and said adaptation data is at
least partly based on at least one speech interaction of the
particular party that is discrete from the detected speech data,
wherein the first instruction for modifying the pronunciation data
has been stored on the particular device associated with the
particular party. For example, FIG. 3, e.g., FIG. 3F, shows
adaptation data comprising a first instruction for modifying a
pronunciation dictionary based on a first particular party
interaction and a second instruction for modifying a pronunciation
dictionary based on a second particular party interaction, said
first instruction has been stored on a particular party-associated
particular device acquiring from a further device module 358
acquiring adaptation data, from a further device (e.g., a tablet
device, e.g., a Samsung Galaxy Tab), said adaptation data
comprising a first instruction for modifying the pronunciation
dictionary (e.g., "modify a pronunciation of the word `twenty`")
based on a first speech interaction of the particular party (e.g.,
the user withdrawing two hundred dollars and requesting twenty
dollar bills from an automated teller machine device that accepts
speech input) and a second instruction for modifying the
pronunciation dictionary (e.g., "modify a pronunciation of the word
`hamburger`.epsilon.) based on a second speech interaction of the
particular party (e.g., the user placing a lunch order for a
hamburger and french fries with an automated drive thru window),
and said adaptation data is at least partly based on at least one
speech interaction of the particular party (e.g., the user
withdrawing two hundred dollars and requesting twenty dollar bills
from an automated teller machine device that accepts speech input
and/or the user placing a lunch order for a hamburger and French
fries) that is discrete from the detected speech data (e.g., giving
a speech command to an automated ticket taking device), wherein the
first instruction for modifying the pronunciation data has been
stored on the particular device (e.g., a cellular telephone device
that originally transmitted at least that portion of the adaptation
data to the tablet) associated with the particular party (e.g.,
associated to the user with a service contract through a
communication network provider).
[0225] Referring now to FIG. 9J, operation 704 may include
operation 960 depicting generating adaptation data that is at least
partly based on at least one speech interaction of the particular
party that is discrete from the detected speech data, wherein at
least a portion of the adaptation data has been stored on the
particular device associated with the particular party. For
example, FIG. 3, e.g., FIG. 3G, shows adaptation data at least
partly based on discrete speech interaction of particular party
separate from detected speech data, and has been stored on a
particular party-associated particular device generating module 360
generating (e.g., creating, modifying, adapting, calculating,
developing, evolving, or constructing) adaptation data (e.g., a
latent dialogue act matrix)
[0226] Referring again to FIG. 9J, operation 704 may include
operation 962 depicting retrieving adaptation data that is at least
partly based on at least one speech interaction of the particular
party that is discrete from the detected speech data, wherein at
least a portion of the adaptation data has been stored on the
particular device associated with the particular party. For
example, FIG. 3, e.g., FIG. 3G, shows adaptation data at least
partly based on discrete speech interaction of particular party
separate from detected speech data, and has been stored on a
particular party-associated particular device retrieving module 362
retrieving (e.g., requesting and receiving, obtaining, gathering,
getting, fetching, and/or procuring) adaptation data (e.g., speech
disfluency detection algorithm) that is at least partly based on at
least one speech interaction (e.g., dictating a memorandum using
Dragon speech software with a headset) of the particular party that
is discrete from the detected speech data (e.g., ordering an ice
cream cone with chocolate sprinkles from an automated ice cream
dispenser), wherein at least a portion of the adaptation data has
been stored on the particular device (e.g., a modified USB key that
stores adaptation data, that was plugged into the computer when the
memorandum was dictated, thereby retrieving the data) and, at the
time of the speech interaction with the automated ice cream
dispenser, is communicating with the automated ice cream dispenser,
either by being directly plugged into the automated ice cream
dispenser, or by being plugged into a tablet device carried by the
user, where the tablet device retrieves the adaptation data and
transmits it to the automated ice cream dispenser).
[0227] Referring again to FIG. 9J, operation 704 may include
operation 964 depicting receiving adaptation data that is at least
partly based on at least one speech interaction of the particular
party with a particular type of device, said at least one speech
interaction discrete from the detected speech data, wherein at
least a portion of the adaptation data has been stored on the
particular device associated with the particular party. For
example, FIG. 3, e.g., FIG. 3G, shows adaptation data at least
partly based on discrete speech interaction of particular party
with particular type of device separate from detected speech data,
and has been stored on a particular party-associated particular
device acquiring module 364 acquiring (e.g., retrieving from
memory) adaptation data (e.g., a word and/or syllable dependency
parser) that is at least partly based on at least one speech
interaction of the particular party with a particular type of
device (e.g., a Sony-branded home entertainment product, e.g., a
television, Blu-Ray player, home theater system, etc.), said at
least one speech interaction discrete from the detected speech data
(e.g., an interaction with a brand new Sony-manufactured
television), wherein at least a portion of the adaptation data
(e.g., the word and/or syllable dependency parser) has been stored
on the particular device (e.g., a cellular telephone device with an
app designed by Sony configured to filter adaptation data)
associated with the particular party (e.g., owned by the particular
party).
[0228] Referring again to FIG. 9J, operation 964 may include
operation 966 depicting receiving adaptation data that is at least
partly based on at least one speech interaction of the particular
party with the particular type of device that is a same type of
device as a target device configured to receive the speech data,
said at least one speech interaction discrete from the detected
speech data, wherein at least a portion of the adaptation data has
been stored on the particular device associated with the particular
party. For example, FIG. 3, e.g., FIG. 3G, shows adaptation data at
least partly based on discrete speech interaction of particular
party with device of same type as target device configured to
receive speech data, said discrete interaction separate from
detected speech data, and has been stored on a particular
party-associated particular device acquiring module 366 acquiring
adaptation data (e.g., a syllable pronunciation database) that is
at least partly based on at least one speech interaction of the
particular party (e.g., ordering a particular type and flavor of
soda from an automated drink dispensing machine, e.g., "cherry diet
Coke with a twist of vanilla") with the particular type of device
(e.g., automated food dispensing machines) that is a same type of
device as a target device (e.g., an automated ice cream dispenser)
configured to receive the speech data (e.g., the particular party
ordering a "double scoop of vanilla with nuts, chocolate sprinkles,
and chocolate syrup"), said at least one speech interaction
discrete from the detected speech data, wherein at least a portion
of the adaptation data (e.g., the syllable pronunciation database)
has been stored on the particular device (e.g., a "food preference
smartcard" that can store, receive, and transmit data, and that a
child can carry with him or her, and that also may be configured to
prevent the child from ordering food that he or she is allergic to)
associated with the particular party (e.g., carried by the user,
e.g., the particular party).
[0229] Referring again to FIG. 9J, operation 964 may include
operation 968 depicting receiving adaptation data that is at least
partly based on at least one speech interaction of the particular
party with a device that has at least one characteristic in common
with a target device that is configured to receive the speech data,
said at least one speech interaction is discrete from the detected
speech data, wherein at least a portion of the adaptation data has
been stored on the particular device associated with the particular
party. For example, FIG. 3, e.g., FIG. 3G, shows adaptation data at
least partly based on discrete speech interaction of particular
party with device having particular characteristic separate from
detected speech data, and has been stored on a particular
party-associated particular device acquiring module 368 acquiring
adaptation data (e.g., a syllable pronunciation database) that is
at least partly based on at least one speech interaction of the
particular party (e.g., inputting a playlist via speech) with a
device (e.g., a media player) that has at least one characteristic
in common (e.g., an ability to play music files) with a target
device that is configured to receive the speech data (e.g., a
speech-enabled clock radio that plays music files), said at least
one speech interaction is discrete from the detected speech data,
wherein at least a portion of the adaptation data (e.g., the
syllable pronunciation database) has been stored on the particular
device (e.g., the user's cellular telephone device) associated with
the particular party.
[0230] Referring now to FIG. 9K, operation 968 depicting operation
970 depicting receiving adaptation data that is at least partly
based on at least one speech interaction of the particular party
with a device that communicates on a same type of communication
network as the target device that is configured to receive the
speech data, said at least one speech interaction is discrete from
the detected speech data, wherein at least a portion of the
adaptation data has been stored on the particular device associated
with the particular party. For example, FIG. 3, e.g., FIG. 3G,
shows adaptation data at least partly based on discrete speech
interaction of particular party with device communicating on a same
communication network as target device and separate from detected
speech data, and has been stored on a particular party-associated
particular device acquiring module 370 acquiring adaptation data
(e.g., a context-based repaired utterance processing matrix) that
is at least partly based on at least one speech interaction of the
particular party (e.g., a speech interaction with the user
commanding an office photocopier) with a device (e.g., the office
photocopier) that communicates on a same type of communication
network (e.g., local area network, as opposed to 4G LTE, or
Bluetooth) as the target device that is configured to receive the
speech data (e.g., an office computer), said at least one speech
interaction is discrete from the detected speech data (e.g.,
dictating a memorandum to the office computer), wherein at least a
portion of the adaptation data has been stored on the particular
device (e.g., an office-issued device that can transmit, store, and
receive adaptation data, e.g., an advanced keycard) associated with
the particular party.
[0231] Referring again to FIG. 9K, operation 968 may include
operation 972 depicting receiving adaptation data that is at least
partly based on at least one speech interaction of the particular
party with a device that is configured to carry out a similar
function as the target device that is configured to receive the
speech data, said at least one speech interaction is discrete from
the detected speech data, wherein at least a portion of the
adaptation data has been stored on the particular device associated
with the particular party. For example, FIG. 3, e.g., FIG. 3G,
shows adaptation data at least partly based on discrete speech
interaction of particular party with device configured to carry out
a same function as the target device and separate from detected
speech data, and has been stored on a particular party-associated
particular device acquiring module 372 acquiring adaptation data
(e.g., a regional dialect application algorithm) that is at least
partly based on at least one speech interaction of the particular
party with a device (e.g., a portable navigation system) that is
configured to carry out a similar function as the target device
(e.g., an onboard navigation system in a motor vehicle) that is
configured to receive the speech data (e.g., requesting directions
on how to get home from the present location), said at least one
speech interaction is discrete from the detected speech data (e.g.,
because the interactions are with two similar, but different
devices), wherein at least a portion of the adaptation data has
been stored on the particular device (e.g., a cellular telephone
device).
[0232] Referring now to FIG. 9L, operation 968 may include
operation 974 depicting receiving adaptation data that is at least
partly based on at least one speech interaction of the particular
party with a type of device that accepts a same type of input as
the target device that is configured to receive the speech data,
said at least one speech interaction is discrete from the detected
speech data, wherein at least a portion of the adaptation data has
been stored on the particular device associated with the particular
party. For example, FIG. 3, e.g., FIG. 3H, shows adaptation data at
least partly based on discrete speech interaction of particular
party with device configured to accept a same type of input as the
target device and separate from detected speech data, and has been
stored on a particular party-associated particular device acquiring
module 374 acquiring adaptation data that is at least partly based
on at least one speech interaction of the particular party (e.g.,
ordering food at an automated drive-thru window) with a type of
device (e.g., an automated ordering window) that accepts a same
type of input (e.g., food orders) as the target device (e.g., an
automated terminal inside a restaurant that gives out more detail
about a menu option in response to a speech prompt) that is
configured to receive the speech data (e.g., a request to know more
about the Kobe beef entree), said at least one speech interaction
is discrete from the detected speech data, wherein at least a
portion of the adaptation data has been stored on the particular
device (e.g., a user's tablet device) associated with the
particular party (e.g., owned by the user).
[0233] Referring now to FIG. 9M, operation 704 may include
operation 976 depicting receiving adaptation data that is at least
partly based on at least one speech interaction of the particular
party with the particular device, said at least one speech
interaction is discrete from the detected speech data, wherein at
least a portion of the adaptation data has been stored on the
particular device associated with the particular party. For
example, FIG. 3, e.g., FIG. 3H, shows adaptation data at least
partly based on discrete speech interaction of particular party
with particular device separate from detected speech data, and has
been stored on a particular party-associated particular device
acquiring module 376 acquiring adaptation data (e.g., a list of the
way that the particular party pronounces ten words) that is at
least partly based on at least one speech interaction of the
particular party (e.g., the user giving commands to play a
particular game to a headset that also can transmit and receive
adaptation data to and from a video game system) with the
particular device (e.g., the headset), said at least one speech
interaction is discrete from the detected speech data (e.g., giving
an automated command to the video game system in a first person
shooter, e.g., "arm the machine gun"), wherein at least a portion
of the adaptation data has been stored on the particular device
(e.g., the headset) associated with the particular party (e.g., has
been set up for use with the user).
[0234] Referring again to FIG. 9M, operation 976 may include
operation 978 depicting receiving adaptation data that is at least
partly based on at least one speech interaction of the particular
party with a cellular telephone device, said at least one speech
interaction is discrete from the detected speech data, wherein at
least a portion of the adaptation data has been stored on the
cellular telephone device associated with the particular party. For
example, FIG. 3, e.g., FIG. 3H, shows adaptation data at least
partly based on discrete speech interaction of particular party
with cellular telephone device separate from detected speech data,
and has been stored on a particular party-associated cellular
telephone device acquiring module 378 acquiring adaptation data
(e.g., instructions for replacing a word frequency table with a
modified word frequency table that reflects the particular party's
word usage) that is at least partly based on at least one speech
interaction of the particular party (e.g., the user) with a
cellular telephone device (e.g., playing a word-fill-in based game
using speech, which game is designed to also generate training
data), said at least one speech interaction is discrete from the
detected speech data (e.g., interacting with an automated
drive-thru window), wherein at least a portion of the adaptation
data has been stored on the cellular telephone device associated
with the particular party.
[0235] Referring again to FIG. 9M, operation 978 may include
operation 980 depicting receiving adaptation data that is at least
partly based on at least one telephone conversation carried out
using the cellular telephone device, said at least one telephone
conversation is different than speech that is part of the detected
speech data, wherein at least a portion of the adaptation data has
been stored on the cellular telephone device associated with the
particular party. For example, FIG. 3, e.g., FIG. 3H, shows
adaptation data at least partly based on particular party telephone
conversation carried out using cellular telephone device separate
from detected speech data, and has been stored on a particular
party-associated cellular telephone acquiring module 380 acquiring
adaptation data (e.g., a phrase completion algorithm) that is at
least partly based on at least one telephone conversation carried
out using the cellular telephone device, said at least one
telephone conversation is different than speech that is part of the
detected speech data (e.g., dictating a memorandum to a
speech-enabled computer that also is configured to communicate with
the cellular telephone device), wherein at least a portion of the
adaptation data has been stored on the cellular telephone device
associated with the particular party (e.g., the particular party
has a service contract with a communication network provider that
sold the cellular telephone device to the user at a discount based
on the service contract).
[0236] Referring again to FIG. 9M, operation 980 may include
operation 982 depicting receiving adaptation data that is at least
partly based on at least one speech instruction given to the
cellular telephone device by the particular party, said at least
one speech instruction different from the detected speech data,
wherein at least a portion of the adaptation data has been stored
on the particular device associated with the particular party. For
example, FIG. 3, e.g., FIG. 3H, shows adaptation data at least
partly based on particular party speech command given to cellular
telephone device separate from detected speech data, and has been
stored on a particular party-associated cellular telephone
acquiring module 382 acquiring adaptation data (e.g., a basic
pronunciation adjustment algorithm) that is at least partly based
on at least one speech instruction given to the cellular telephone
device by the particular party (e.g., dictating a text message to
be sent to Jenny and Rob), said at least one speech instruction
different from the detected speech data, wherein at least a portion
of the adaptation data has been stored on the particular device
associated with the particular party.
[0237] Referring now to FIG. 9N, operation 704 may include
operation 984 depicting receiving adaptation data that is at least
partly based on at least one speech interaction of the particular
party that used one or more same utterances as speech used in the
detected speech data, said one or more same utterances spoken to a
different device than a target device to which the detected speech
data is directed. For example, FIG. 3, e.g., FIG. 3I shows
adaptation data at least partly based on discrete speech
interaction of particular party separate from detected speech data
and using same utterance as speech that is part of speech data, and
has been stored on a particular party-associated particular device
acquiring module 384 acquiring adaptation data (e.g., an
emotion-based pronunciation adjustment algorithm) that is at least
partly based on at least one speech interaction of the particular
party (e.g., using voice commands to operate a motor vehicle
control system) that used one or more same utterances (e.g., spoke
one or more of the same words, e.g., "music," "play," "MP3," and
"CD Number Four") spoken to a different device (e.g., the motor
vehicle control system) than a target device to which the detected
speech data is directed (e.g., a home media player).
[0238] Referring again to FIG. 9N, operation 704 may include
operation 986 depicting receiving adaptation data that is at least
partly based on at least one speech interaction of the particular
party that used one or more same utterances, said one or more same
utterances spoken at a different time than speech used in the
detected speech data. For example, FIG. 3, e.g., FIG. 3I, shows
adaptation data at least partly based on discrete speech
interaction of particular party and using same utterance as speech
that is part of speech data at a different time than speech that is
part of the speech data acquiring module 386 acquiring adaptation
data (e.g., a sentence diagramming path selection algorithm) that
is at least partly based on at least one speech interaction of the
particular party (e.g., a player of a speech-controlled video game
system playing a soccer game) that used one or more same utterances
(e.g., "kick," "run," jump," "control player two"), said one or
more same utterances spoken at a different time (e.g., while
playing a different game) than speech used in the detected speech
data (e.g., the player playing a new soccer game at a different
time).
[0239] Referring again to FIG. 9N, operation 704 may include
operation 988 depicting acquiring a phoneme database based on one
or more pronunciations by the particular party that are discrete
from the detected speech data, wherein at least one entry of the
phoneme database has been stored on a particular device associated
with the particular party. For example, FIG. 3, e.g., FIG. 3I,
shows adaptation data comprising a phoneme dictionary based on one
or more particular party pronunciations, such that at least one
entry has been stored on a particular party-associated particular
device acquiring module 388 acquiring a phoneme database based on
one or more pronunciations by the particular party (e.g.,
pronunciations given while a driver is giving commands to a motor
vehicle control system to raise the volume on the stereo, open the
sunroof, lower the windows, brighten the interior lights, and stop
using the overdrive mode, because the driver is going to start
driving fast while listening to loud music) that are discrete from
the detected speech data (e.g., the driver, having wrecked his
vehicle, now is using the onboard automated help system to call for
help and describe his situation), wherein at least one entry of the
phoneme database has been stored on a particular device (e.g., a
smart key that is used to activate the car and store the phoneme
database for that particular driver, so that a different driver
would use a different key and the vehicle would have a different
phoneme database for the different driver) associated with the
particular party (e.g., it stores adaptation data that is based at
least in part on speech from the driver).
[0240] Referring again to FIG. 9N, operation 704 may include
operation 990 depicting acquiring a sentence diagramming path
selection algorithm based on at least one speech interaction of the
particular party that is discrete from the detected speech data,
wherein at least a portion of the adaptation data has been stored
on a particular device associated with the particular party. For
example, FIG. 3, e.g., FIG. 3I, shows adaptation data comprising a
sentence diagramming path selection algorithm based on one or more
particular party pronunciations, and has been stored on a
particular party-associated particular device acquiring module 390
acquiring a sentence diagramming path selection algorithm based on
at least one speech interaction of the particular party (e.g.,
programming, using speech commands, favorite channels on an old
television made by a particular manufacturer, e.g., Samsung) that
is discrete from the detected speech data (e.g., programming, using
speech commands, favorite channels on a new flat screen plasma
television made by a different manufacturer, e.g., Panasonic),
wherein at least a portion of the adaptation data has been stored
on a particular device (e.g., a universal remote control, e.g.,
manufactured by a still different manufacturer from either the old
television or the new television, e.g., Logitech) associated with
the particular party (e.g., the owner of the universal remote
control).
[0241] Referring again to FIG. 9N, operation 704 may include
operation 992 depicting receiving adaptation data that is at least
partly based on at least one speech interaction of the particular
party that is discrete from the detected speech data, wherein at
least a portion of the adaptation data was collected by the
particular device associated with the particular party. For
example, FIG. 3, e.g., FIG. 3I, shows adaptation data at least
partly based on discrete speech interaction of particular party
separate from detected speech data, and at least partly collected
by a particular party-associated particular device acquiring module
392 acquiring adaptation data that is at least partly based on at
least one speech interaction of the particular party (e.g., speech
interactions with speech-controlled kitchen devices) that is
discrete from the detected speech data (e.g., controlling a
speech-commanded clock radio in the bedroom), wherein at least a
portion of the adaptation data was collected by the particular
device (e.g., a desktop computer that is networked to each of the
speech-controlled kitchen devices and the speech-controlled clock
radio) associated with the particular party (e.g., the user has a
login on the desktop computer).
[0242] Referring again to FIG. 9N, operation 704 may include
operation 994 depicting acquiring one or more instructions for
modifying one or more portions of a speech recognition component of
a target device, said instructions at least partly based on at
least one speech interaction of the particular party that is
discrete from the detected speech data, wherein at least a portion
of the adaptation data has been stored on a particular device
associated with the particular party. For example, FIG. 3, e.g.,
FIG. 3I, shows adaptation data comprising instructions for
modifying one or more portions of a speech recognition component of
a target device that are at least partly based on one or more
particular party speech interactions, and has been stored on a
particular party-associated particular device acquiring module 394
acquiring one or more instructions (e.g., modifying one or more
parameters of one or more algorithms) for modifying one or more
portions of a speech recognition component (e.g., a set of logic
gates configured to execute one or more of the algorithms for
processing speech) of a target device (e.g., an automated teller
machine device), said instructions at least partly based on at
least one speech interaction of the particular party that is
discrete from the detected speech data (e.g., based on previous
speech interactions with automated teller machine devices), wherein
at least a portion of the adaptation data has been stored on a
particular device associated with the particular party (e.g., a
cellular telephone device owned by the user).
[0243] Referring now to FIG. 9P (there is no FIG. 9O to avoid
confusing the figure with a nonexistent FIG. "ninety," e.g., "90"),
operation 704 may include operation 996 depicting acquiring a
location of one or more instructions for modifying one or more
portions of a speech recognition component of a target device, said
instructions at least partly based on at least one speech
interaction of the particular party that is discrete from the
detected speech data, wherein at least a portion of the adaptation
data has been stored on a particular device associated with the
particular party. For example, FIG. 3, e.g., FIG. 3J, shows
adaptation data comprising a location of instructions for modifying
one or more portions of a speech recognition component of a target
device that are at least partly based on one or more particular
party speech interactions, and has been stored on a particular
party-associated particular device acquiring module 396 acquiring a
location (e.g., a location in memory, or a location of a server) of
one or more instructions for modifying one or more portions of a
speech recognition component (e.g., an order in which speech
algorithms are applied) of a target device (e.g., a computer with
speech recognition software and word processing software loaded
onto it), said instructions at least partly based on at least one
speech interaction of the particular party that is discrete from
the detected speech data (e.g., based on at least one previous
dictation of one or more documents), wherein at least a portion of
the adaptation data (e.g., the location of one or more instructions
for modifying one or more portions of a speech recognition
component of a target device) has been stored on a particular
device (e.g., a headset worn by the user) associated with the
particular party (e.g., set up and associated with the user).
[0244] Referring again to FIG. 9P, operation 704 may include
operation 998 depicting receiving adaptation data that is at least
partly based on at least one speech interaction of the particular
party that is discrete from the detected speech data, wherein at
least a portion of the adaptation data is transmitted from the
particular device associated with the particular party. For
example, FIG. 3, e.g., FIG. 3J, shows adaptation data at least
partly based on discrete speech interaction of particular party
separate from detected speech data, and transmitted from a
particular party-associated particular device acquiring module 398
acquiring adaptation data (e.g., an ungrammatical utterance
deletion algorithm) that is at least partly based on at least one
speech interaction of the particular party (e.g., a history of the
user's musical selections for automated, speech-controlled
jukeboxes) that is discrete from the detected speech data (e.g.,
selecting a new song at the speech-commanded jukebox), wherein at
least a portion of the adaptation data is transmitted from the
particular device (e.g., a near-field communications device held by
the user that stores adaptation data) associated with the
particular party).
[0245] Referring again to FIG. 9P, operation 704 may include
operation 901 depicting receiving adaptation data that is at least
partly based on at least one speech interaction of the particular
party that is discrete from the detected speech data, wherein at
least a portion of the adaptation data is stored on the particular
device associated with the particular party. For example, FIG. 3,
e.g., FIG. 3J, shows adaptation data at least partly based on
discrete speech interaction of particular party separate from
detected speech data, and stored on a particular party-associated
particular device acquiring module 301 acquiring adaptation data
(e.g., a set of proper noun pronunciations, e.g., city names) that
is at least partly based on at least one speech interaction of the
particular party (e.g., the particular party dictating directions
into a word processor), wherein at least a portion of the
adaptation data is stored on the particular device (e.g., a USB
stick, e.g., the first personal device 20A) associated with the
particular party (e.g., the user).
[0246] Referring again to FIG. 9P, operation 704 may include
operation 903 depicting receiving adaptation data that is at least
partly based on at least one speech interaction of the particular
party that is discrete from the detected speech data, wherein at
least a portion of the adaptation data is temporarily stored on the
particular device associated with the particular party until it is
deposited at a remote server. For example, FIG. 3, e.g., FIG. 3J,
shows adaptation data at least partly based on discrete speech
interaction of particular party separate from detected speech data,
and is temporarily stored on the particular-party associated
particular device until remote server deposit acquiring module 303
acquiring (e.g., receiving from a remote server, e.g., Amazon cloud
services) adaptation data (e.g., a set of proper noun
pronunciations, e.g., city names) that is at least partly based on
at least one speech interaction of the particular party (e.g.,
previous interactions with automated ticket dispensing devices
using speech) that is discrete from the detected speech data (e.g.,
speech data that comes from a speech interaction with an automated
train ticket dispensing device located at Union Station in
Washington, D.C.), wherein at least a portion of the adaptation
data is temporarily stored on the particular device (e.g., in one
or more of the previous interactions with automated ticket
dispensing devices, the particular party's pronunciation of a city
is stored on the cellular telephone device associated with the
particular party) until it is deposited at a remote server (e.g.,
the Amazon cloud services from where it was retrieved along with
the rest of the adaptation data).
[0247] Referring again to FIG. 9P, operation 704 may include
operation 905 depicting receiving adaptation data that is at least
partly based on at least one speech interaction of the particular
party that is discrete from the detected speech data, wherein at
least a portion of the adaptation data was transmitted from a first
device to a second device using the particular device associated
with the particular party as a conduit configured to facilitate the
transmission. For example, FIG. 3, e.g., FIG. 3J, shows adaptation
data at least partly based on discrete speech interaction of
particular party separate from detected speech data, and was
transmitted from a first device to a second device using the
particular party-associated particular device as a channel
configured to facilitate the transaction acquiring module 305
acquiring adaptation data (e.g., a partial pattern tree model) that
is at least partly based on at least one speech interaction of the
particular party (e.g., the user giving speech commands to request
a re-route to a GPS navigation device) that is discrete from the
detected speech data (e.g., the user giving a command to the GPS
navigation device to find a cheese shop), wherein at least a
portion of the adaptation data was transmitted from a first device
(e.g., a GPS navigation device, e.g., GPS navigation device 41,
that may be good at re-routing traffic but has no information on
cheese shops) to a second device (e.g., an onboard motor vehicle
control system, e.g., motor vehicle control system 42, which may be
bad at re-routing traffic but has an extensive cheese shop
database) using the particular device (e.g., a smart key device,
e.g., smart key 26, or a cellular telephone device) associated with
the particular party as a conduit (e.g., the smart key device 26
communicates with the GPS navigation device 41 and the motor
vehicle control system 42) configured to facilitate (e.g., take one
or more steps that aid or assist in) the transmission of the
adaptation data.
[0248] Referring now to FIG. 9Q, operation 704 may include
operation 907 depicting receiving adaptation data that is at least
partly based on at least one speech interaction of the particular
party that is discrete from the detected speech data, wherein at
least a portion of the adaptation data originated at the particular
device associated with the particular party. For example, FIG. 3,
e.g., FIG. 3K, shows adaptation data at least partly based on
discrete speech interaction of particular party separate from
detected speech data, and at least a portion of which originated at
a particular party-associated particular device acquiring module
307 acquiring adaptation data (e.g., a discourse marker detecting
module) that is at least partly based on at least one speech
interaction of the particular party that is discrete from the
detected speech data, wherein at least a portion of the adaptation
data originated at the particular device (e.g., a universal remote
control, e.g., personal device 22A).
[0249] Referring again to FIG. 9Q, operation 704 may include
operation 909 depicting receiving adaptation data from a remote
location, said adaptation data at least partly based on at least
one speech interaction of the particular party that is discrete
from the detected speech data, wherein at least a portion of the
adaptation data was transmitted to the remote location from the
particular device associated with the particular party. For
example, FIG. 3, e.g., FIG. 3K, shows adaptation data at least
partly based on discrete speech interaction of particular party
separate from detected speech data, and at least a portion of which
was transmitted to a remote location from a particular
party-associated particular device receiving from remote location
module 309 acquiring adaptation data (e.g., an accent-based
pronunciation modification algorithm) from a remote location (e.g.,
a remote server, e.g., server 110), said adaptation data at least
partly based on at least one speech interaction of the particular
party that is discrete from the detected speech data (e.g.,
previous commands given to a headset during an augmented reality
gaming session where the headset is worn outside), wherein at least
a portion of the adaptation data was transmitted to the remote
location (e.g., the adaptation data collected from the speech
interactions with the headset does not stay on the headset, but is
transmitted to a remote location) from the particular device (e.g.,
an augmented reality headset) associated with the particular party
(e.g., being worn by the user).
[0250] Referring again to FIG. 9Q, operation 704 may include
operation 911 depicting receiving adaptation data that is at least
partly based on at least one speech interaction of the particular
party that is discrete from the detected speech data. For example,
FIG. 3, e.g., FIG. 3K, shows adaptation data at least partly based
on discrete speech interaction of particular party separate from
detected speech data receiving module 311 receiving adaptation data
(e.g., a list of the way that the particular party pronounces ten
words) that is at least partly based on at least one speech
interaction of the particular party that is discrete from the
detected speech data (e.g., ordering a triple bacon cheeseburger
from the automated drive-thru window).
[0251] Referring again to FIG. 9Q, operation 704 may include
operation 913 depicting adding further data to the received
adaptation data. For example, FIG. 3, e.g., FIG. 3K, shows further
data adding to adaptation data module 313 adding further data
(e.g., adding one or more additional words to the list of the way
that the particular party pronounces ten words, e.g., the word
"bacon,").
[0252] Referring again to FIG. 9Q, operation 913 may include
operation 915 depicting adding additional adaptation data to the
received adaptation data. For example, FIG. 3, e.g., FIG. 3K, shows
additional adaptation data adding to adaptation data module 315
adding additional adaptation data (e.g., another algorithm, e.g.,
adding an accent-based pronunciation modification algorithm to be
executed serially with or parallel to the existing acquired
adaptation data) to the received adaptation data (e.g., a phrase
completion algorithm).
[0253] Referring again to FIG. 9Q, operation 913 may include
operation 917 depicting adding header data identifying an entity
that received the adaptation data. For example, FIG. 3, e.g., FIG.
3K, shows header data identifying receiving entity adding to
adaptation data module 317 adding header data identifying an entity
(e.g., either specific identification, like a MAC address or IP
address, specific type identification, such as "I am a cellular
telephone device," e.g., personal device 22B, or general identity
information, e.g., "I am not the ultimate destination of this
adaptation data" that received this information) that received the
adaptation data (e.g., an emotion-based pronunciation adjustment
algorithm).
[0254] Referring again to FIG. 9Q, operation 913 may include
operation 919 depicting adding header data identifying an entity
that transmitted the adaptation data. For example, FIG. 3, e.g.,
FIG. 3K, shows header data identifying transmitting entity adding
to adaptation data module 319 adding header data identifying an
entity (e.g., specific or general, similarly to as described above,
e.g., "received from a universal remote control," or, e.g.,
personal device 22A) that transmitted the adaptation data (e.g., a
partial pattern tree model).
[0255] FIGS. 10A-10G depict various implementations of operation
706, according to embodiments. Referring now to FIG. 10A, in some
embodiments, operation 706 may include operation 1002 depicting
receiving an indication from a speech processing component that the
target device is configured to process at least a portion of the
received speech data. For example, FIG. 4, e.g., FIG. 4A, shows
data indicating the target device is configured to process at least
a portion of received speech data receiving module 402 receiving an
indication (e.g., a signal, or having an internal flag set, or
receiving status information) from a speech processing component
(e.g., a component that applies a path selection algorithm to the
received speech data) that the target device (e.g., the automated
teller machine device) is configured to process at least a portion
of the received speech data (e.g., "withdraw two hundred dollars
from the checking account"). It is noted that, in some embodiments,
the indication from the speech processing component is entirely
internal to a device, e.g., the speech processing component is
integral to the component receiving the indication. In some
embodiments, the speech processing component is located on the same
hardware (e.g., chip, memory, etc.) as the component that receives
the indication.
[0256] Referring again to FIG. 10A, in some embodiments, operation
1002 may include operation 1004 depicting receiving an indication
at a central processing component of the target device that a
speech processing component of the target device is configured to
process at least a portion of the received speech data. For
example, FIG. 4, e.g., FIG. 4A, shows data indicating the target
device is configured to process at least a portion of received
speech data receiving from speech processing component at central
processing component module 404 receiving an indication at a
central processing component of the target device (e.g. a computer
that receives speech input and has a complex word processing
application running) that a speech processing component (e.g., word
processing application) of the target device (e.g., the computer)
is configured to process at least a portion of the received speech
data (e.g., a dictation of a letter to the editor).
[0257] Referring again to FIG. 10A, operation 1004 may include
operation 1006 depicting receiving an indication at a central
processing component of the target device that the speech
processing component of the target device, which is a subcomponent
of the central processing component of the target device, is
configured to process at least a portion of the received speech
data. For example, FIG. 4, e.g., FIG. 4A, shows data indicating the
target device is configured to process at least a portion of
received speech data receiving from speech processing component at
central processing component of which the speech processing
component is a subcomponent module 406 receiving an indication at a
central processing unit (e.g., a processor of a tablet device,
e.g., an Apple iPad) of the target device (e.g., the tablet device,
e.g., the Apple iPad) that a speech processing component (e.g., a
speech processing application, e.g., an application configured to
apply an algorithm to convert received speech into one or more
words) of the target device (e.g., the tablet device, e.g., the
Apple iPad), which is a subcomponent of the central processing
component of the target device (e.g., the speech processing
application does not have a dedicated separate processor, but may
have dedicated hardware that is part of the main central processing
unit, or may have non-dedicated hardware that is part of the main
central processing unit), is configured to process at least a
portion of the received speech data (e.g., a request to load the
web browser and browse to espn.com).
[0258] Referring again to FIG. 10A, operation 706 may include
operation 1008 depicting receiving an indication from a speech
processing component that the adaptation data is configured to be
applied to the target device to assist in processing at least a
portion of the received speech data. For example, FIG. 4, e.g.,
FIG. 4A, shows data indicating that the adaptation data is
configured to be applied to the target device to assist in
processing at least a portion of the speech data receiving from a
speech processing component module 408 receiving an indication from
a speech processing component (e.g., hardware configured to receive
the speech data and filter out certain types of noise in the speech
data) that the adaptation data (e.g., a low level noise filtration
algorithm) is configured to be applied to the target device (e.g.,
an automated ticket dispensing machine) to assist in processing at
least a portion of the received speech data (e.g., here, the speech
processing component only removes low-level noise, and further
processing is handled by a different component, but the adaptation
data is a low level noise filtration algorithm, so the adaptation
data is applied to the speech processing component to assist in
this portion of the processing of the received speech data, e.g., a
request for four tickets to the new Matt and Kim show).
[0259] Referring now to FIG. 10B, operation 706 may include
operation 1012 depicting receiving an indication from the speech
processing component that the adaptation data has been applied to
the target device to assist in processing at least a portion of the
received speech. For example, FIG. 4, e.g., FIG. 4A, shows data
indicating that the adaptation data has been applied to the target
device to assist in processing at least a portion of the speech
data receiving from a speech processing component module 410
receiving an indication from the speech processing component (e.g.,
the portion of the motor vehicle control system that processes the
speech, which may be in a different physical location than the
portion that executes the one or more commands derived from the
interpreted speech) that the adaptation data (e.g., an utterance
ignoring algorithm) has been applied to the target device (e.g.,
the motor vehicle control system) to assist in processing at least
a portion of the received speech (e.g., a command to lower the
windows and open the sunroof).
[0260] Referring again to FIG. 10B, operation 1010 may include
operation 1012 depicting receiving an indication from the speech
processing component that the adaptation data is configured to be
applied to an automated teller machine device to assist in
processing at least a portion of the received speech data. For
example, FIG. 4, e.g., FIG. 4A, shows data indicating that the
adaptation data has been applied to an automated teller machine
device to assist in processing at least a portion of the speech
data receiving from a speech processing component module 412
receiving an indication from the speech processing component (e.g.,
a chip configured to interpret the speech data into recognizable
commands) that the adaptation data (e.g., a syllable pronunciation
database) is configured to be applied to an automated teller
machine device to assist in processing at least a portion of the
received speech data (e.g., a selection of one of three accounts in
which to deposit the check that was inserted into the slot).
[0261] Referring again to FIG. 10B, operation 1012 may include
operation 1014 depicting receiving the indication from the speech
processing component of the automated teller machine device that
the adaptation data is configured to be applied to the automated
teller machine device to assist in processing at least a portion of
data corresponding to a spoken request by the particular party to
withdraw two hundred dollars from a bank account. For example, FIG.
4, e.g., FIG. 4A, shows data indicating that the adaptation data
has been applied to an automated teller machine device to assist in
processing at least a portion of the data corresponding to a spoken
request by the particular party receiving from a speech processing
component module 414 receiving the indication from the speech
processing component of the automated teller machine device that
the adaptation data (e.g., an uncommon word pronunciation guide) is
configured to be applied to the automated teller machine device to
assist in processing at least a portion of data corresponding to a
spoken request by the particular party to withdraw two hundred
dollars from a bank account.
[0262] Referring again to FIG. 10B, operation 1014 may include
operation 1016 depicting receiving the indication from the speech
processing component of the automated teller machine device that
the automated teller machine device is configured to apply a list
of the way that the particular party pronounces numbers zero
through nine to assist in processing at least a portion of data
corresponding to a spoken request by the particular party to
withdraw two hundred dollars from the bank account. For example,
FIG. 4, e.g., FIG. 4A, shows data indicating that the list of the
way that the particular party pronounces numbers zero through nine
has been applied to an automated teller machine device to assist in
processing at least a portion of the data corresponding to a spoken
request by the particular party receiving from a speech processing
component module 1016 receiving the indication from the speech
processing component of the automated teller machine device that
the automated teller machine device is configured to apply a list
of the way that the particular party pronounces numbers zero
through nine to assist in processing at least a portion of data
corresponding to a spoken request by the particular party to
withdraw two hundred dollars from the bank account.
[0263] Referring again to FIG. 10B, operation 706 may include
operation 1018 depicting generating target data regarding a target
device configured to process at least a portion of the received
speech data. For example, FIG. 4, e.g., FIG. 4B, shows target data
regarding a target configured to process at least a portion of the
received speech data generating module 418 generating target data
(e.g., information about a device that can process the received
speech data, e.g., a name of the device, a type of the device, a
location of the device, a characteristic of the device, and the
like) regarding a target device (e.g., a video game system)
configured to process at least a portion of the received speech
data (e.g., a command to load a particular game, e.g., Halo).
[0264] Referring again to FIG. 10B, operation 706 may include
operation 1020 depicting determining whether the speech data is
configured to be processed by a speech recognition component to
which the adaptation data has been applied. For example, FIG. 4,
e.g., FIG. 4B, shows speech data configurable to be processed by a
speech recognition component to which the adaptation data has been
applied determining module 420 determining whether the speech data
(e.g., MP3-formatted speech data corresponding to a request to play
the Guns `n` Roses CD) is configured to be processed by a speech
recognition component (e.g., in some embodiments, whether the
intended device can interpret MP3-formatted speech data) to which
the adaptation data has been applied (e.g., an algorithm that
deletes repeated words has been applied to the MP3 to remove
particular portions of the file).
[0265] Referring now to FIG. 10C, operation 706 may include
operation 1022 depicting generating target data regarding the
intended target device based on the determination. For example,
FIG. 4, e.g., FIG. 4B, shows target data regarding intended target
device generating based on determination module 422 generating
target data (e.g., data indicating how much speech processing
should be performed at the location, and if the speech data and/or
the adaptation data should be transmitted to another device, and if
so, one or more pieces of information (e.g., name, location,
permissions, communication network protocol) regarding the one or
more devices to which the speech data and/or the adaptation data
should be transmitted.
[0266] Referring again to FIG. 10C, operation 1020 may include
operation 1024 depicting analyzing at least a portion of the speech
data. For example, FIG. 4, e.g., FIG. 4B, shows at least a portion
of speech data analyzing module 424 analyzing (e.g., reading
headier information) at least a portion (e.g., the header portion,
in some embodiments the body data also may be read and/or analyzed,
but such reading and/or analyzing is not required) of the speech
data (e.g., data corresponding to the user giving a speech command
to turn on the headlights).
[0267] Referring again to FIG. 10C, operation 1020 may include
operation 1026 depicting determining target data regarding the
intended target device at least partly based on a result of
analyzing the at least a portion of the received speech data. For
example, FIG. 4, e.g., FIG. 4B, shows target data regarding
intended target device determining at least partly based on the
analyzing at least a portion of speech data module 426 determining
(e.g., inferring based on one or more pieces of data) target data
(e.g., data indicating that a motor vehicle control system is a
system for which the user intended her speech to operate and/or
command) regarding the intended target device (e.g., a motor
vehicle control system) at least partly based on a result of
analyzing the at least a portion of the received speech data (e.g.,
reading the header information).
[0268] Referring again to FIG. 10C, operation 1024 may include
operation 1028 depicting analyzing at least a portion of the speech
data using a speech recognition component to which the adaptation
data has been applied. For example, FIG. 4, e.g., FIG. 4B, shows at
least a portion of speech data analyzing using an adaptation
data-applied speech recognition component module 428 analyzing at
least a portion of the speech data (e.g., analyzing a portion of
the data corresponding to the user speaking the words "turn on
headlights") using a speech recognition component (e.g., a
component of a device that is configured to interpret speech data
corresponding to the user speaking the words "play Norah Jones
track four" into a command "play," an artist, "Norah Jones," and a
track number "four," using pattern recognition and/or other known
techniques for interpreting speech) to which the adaptation data
(e.g., a pronunciation dictionary) has been applied (e.g., the
pronunciation dictionary replaces the generic pronunciation of the
proper noun "Norah Jones," as well as other artists, e.g., "Red Hot
Chili Peppers," "U2," and "The Beatles," and replaces it with the
user's pronunciation of those artists, to allow for more accurate
recognition).
[0269] Referring again to FIG. 10C, operation 1028 may include
operation 1030 depicting analyzing at least a portion of the speech
data using a speech recognition component of the target device to
which the adaptation data has been applied. For example, FIG. 4,
e.g., FIG. 4B, shows at least a portion of speech data analyzing
using an adaptation data-applied speech recognition component of
target device module 430 analyzing at least a portion of the speech
data (e.g., "open the web page for ESPN.com") using a speech
recognition component (e.g., speech recognition processing module
on a Dell computer) of the target device (e.g., the Dell computer)
to which the adaptation data (e.g., the sentence diagramming path
selection algorithm) has been applied. In this example, analyzing
the speech data reveals that this speech data is intended for the
web browser, as opposed to a word processing document or a game
program.
[0270] Referring again to FIG. 10C, operation 1028 may include
operation 1032 depicting analyzing at least a portion of the speech
data using a speech recognition component of a further device to
which the adaptation data has been applied. For example, FIG. 4,
e.g., FIG. 4B, shows at least a portion of speech data analyzing
using an adaptation data-applied speech recognition component of
further device module 432 analyzing at least a portion of the
speech data (e.g., data corresponding to the user speaking "give me
access to the accounts payable directory, password GHBQ1535#")
using a speech recognition component of a further device (e.g., a
device on an enterprise network that handles the processing of
speech) to which the adaptation data (e.g., that personal user's
uncommon word pronunciation guide, which may include a specific
pronunciation guide for the user's password) has been applied
(e.g., the further device receives the adaptation data over the
network (said adaptation data having been stored on a USB drive
that the computer user inserted prior to making the speech
command), and applies the uncommon word pronunciation guide to
assist in recognition of the user's commands, including the
password for a particular directory.
[0271] Referring again to FIG. 10C, operation 1022 may include
operation 1034 depicting generating a boolean that resolves to true
when the analysis of the at least a portion of the received speech
data indicates that the received speech data is configured to be
successfully processed. For example, FIG. 4, e.g., FIG. 4B, shows
Boolean that resolves to true when the analysis of the speech data
portion indicates the received speech data is configured to be
successfully processed module 434 generating a Boolean that
resolves to true when the analysis of the at least a portion of the
received speech data indicates that the received speech data is
configured to be successfully processed (e.g., the received speech
data is formatted, converted, compressed, encrypted, encoded, or
any combination thereof in such a way that the device generating
the Boolean can process the received speech data).
[0272] Referring again to FIG. 10C, operation 1022 may include
operation 1036 depicting generating a numeric indicator indicating,
based on the analysis of the at least a portion of the received
speech data, that at least a portion of the received speech data is
configured to be successfully processed. For example, FIG. 4, e.g.,
FIG. 4B, shows numeric indicator indicating that at least a portion
of the received speech data is configured to be successfully
processed generating based on analysis of at least a portion of
speech data module 436 generating a numeric indicator (e.g., a
number from 0 to 9) indicating, based on the analysis of the at
least a portion of the received speech data, that at least a
portion of the received speech data is configured to be
successfully processed (e.g., the received speech data is
formatted, converted, compressed, encrypted, encoded, or any
combination thereof in such a way that the device generating the
numeric indicator cannot process the received speech data if the
numeric indicator is zero, and the numbers from one to nine
indicate how difficult it will be for the device to process the
received speech data (e.g., if a specific codec must be retrieved
then the number may be a six, and if it is a
computing-resources-on-demand type system, e.g., some form of cloud
or distributed computing system, then the number may be from three
to five depending on how many resources must be obtained, and/or
the availability of those resources).
[0273] Referring now to FIG. 10D, operation 706 may include
operation 1038 depicting determining whether a target device is
configured to process the received speech data. For example, FIG.
4, e.g., FIG. 4C, shows target device configurable to process
received speech data determining module 438 determining whether a
target device (e.g., an audio/visual receiver) is configured to
process (e.g., whether the received speech data is intended for the
audio/visual receiver, or for another component of a home system)
the received speech data.
[0274] Referring again to FIG. 10D, operation 706 may include
operation 1040 depicting generating target data regarding the
target device based on the determination regarding the target
device. For example, FIG. 4, e.g., FIG. 4C, shows target data
regarding target device generating based on determination regarding
the target device module 440 generating target data generating
target data (e.g., a type of device that can process the received
speech data, e.g., a cable box with digital video recording ("DVR")
capabilities) regarding the target device (e.g., a DVR cable box)
based on the determination regarding the target device (e.g., the
analysis shows that in order to execute the command, a cable box
and DVR capabilities are necessary, and a determination is made
that the target device is not a cable box, or does not have DVR
capabilities, and thus those features are made part of the
generated target data).
[0275] Referring again to FIG. 10D, operation 1038 may include
operation 1042 depicting analyzing at least a portion of the speech
data. For example, FIG. 4, e.g., FIG. 4C, shows at least a portion
of the speech data analyzing module 442 analyzing at least a
portion of the speech data (e.g., a placed order of a Deluxe
burger, garlic fries, and S'mores Shake from Good Stuff
eatery).
[0276] Referring again to FIG. 10D, operation 1038 may include
operation 1044 depicting determining that the target device is
configured to process the speech data at least partly based on the
analysis of the speech data. For example, FIG. 4, e.g., FIG. 4C,
shows target device configurable to process speech data determining
at least partly based on result of analyzing at least a portion of
the speech data module 444 determining that the target device
(e.g., an automated order-placing terminal) is configured to
process the speech data (e.g., the automated order-placing terminal
is the device to which the user's speech was directed, and not to
the user's Apple iPhone cellular telephone device, which the user
is speaking a message into that will be converted into a text
message) at least partly based on the analysis of the speech data
(e.g., based on the words containing "garlic fries" and "deluxe
burger," it is determined that this is an order for food from Good
Stuff eatery).
[0277] Referring again to FIG. 10D, operation 1038 may include
operation 1046 depicting extracting header data from a header of
the received speech data indicating a type of the intended target
device that is configured to process the received speech data. For
example, FIG. 4, e.g., FIG. 4C, shows header data indicating a type
of intended target device that is configured to process received
speech data extracting from received speech data header module 446
extracting header data (e.g., data indicating that the target is a
home electronics device) from a header of the received speech data
(e.g., data corresponding to a user saying "raise the volume five
units" and including a header file that identifies the type of
device this speech is intended for as a home electronics device)
indicating a type of the intended target device (e.g., a home
electronics device) that is configured to process the received
speech data (e.g., "raise the volume five units")
[0278] Referring again to FIG. 10D, operation 1038 may include
operation 1048 depicting determining that a type of the target
device is a same type as the type of the intended target device.
For example, FIG. 4, e.g., FIG. 4C, shows type of target device is
same type as type of intended target device determining module 448
determining that a type of the target device (e.g., the target
device, e.g., a Blu-ray player, is a home electronics device) is a
same type as the type of the intended target device (e.g., the
intended target device is a Sony Blu-Ray player).
[0279] Referring again to FIG. 10D, operation 1046 may include
operation 1050 depicting extracting header data from a header of
the received speech data indicating a manufacturer of one or more
intended target devices that are configured to process the received
speech data. For example, FIG. 4, e.g., FIG. 4C, shows header data
indicating a manufacturer of intended target device that is
configured to process received speech data extracting from received
speech data header module 448 extracting header data from a header
(e.g., an encoding that indicates that this received speech data is
for devices manufactured by a particular electronics manufacturer,
e.g., Samsung, because the speech data is encoded using a
proprietary encoding by Samsung, and the header data identifies
this) of the received speech data (e.g., "play chapter four of the
Blu-Ray") indicating a manufacturer of the intended target device
(e.g., a Samsung electronics device) that is configured to process
the received speech data (e.g., at least decode the data into a
coded format that is not proprietary, and, in some embodiments,
also perform interpreting of the speech data, or further
determination regarding whether the speech data has reached the
device that is its intended target).
[0280] Referring again to FIG. 10D, operation 1046 may include
operation 1052 depicting extracting header data from a header of
the received speech data indicating a type of input accepted by one
or more intended target devices configured to process the received
speech data. For example, FIG. 4, e.g., FIG. 4C, shows header data
indicating a type of input accepted by one or more intended target
devices configured to process received speech data extracting from
received speech data header module 450 extracting header data
(e.g., data indicating which type of user authorization must have
been completed) from a header of the received speech data (e.g.,
data corresponding to a user speaking a command to "withdraw two
hundred dollars from a checking account") indicating a type of
input accepted by one or more intended target devices (e.g., input
from that particular user that has established an authorization
with a particular machine (e.g., by inserting his card, and thus
the header may be an encrypted version of the card number) accepted
by one or more intended target devices (e.g., of a long line of
automated teller machine devices in a row, the intended target
device is the one with which the user has established a connection,
e.g., by inserting his card) configured to process the received
speech data (e.g., withdraw two hundred dollars from a checking
account).
[0281] Referring again to FIG. 10D, operation 1052 may include
operation 1054 depicting extracting header data from a header of
the received speech data indicating a data format accepted by one
or more intended target devices configured to process the received
speech data. For example, FIG. 4, e.g., FIG. 4C, shows header data
indicating a data format accepted by one or more intended target
devices configured to process received speech data extracting from
received speech data header module 452 extracting header data
(e.g., the following speech data requires an Advanced Audio Coding
("AAC") decoder) from a header of the received speech data (e.g.,
data corresponding to the user giving a command to present
directions to the nearest Big Boy restaurant) indicating a data
format (AAC) accepted by one or more intended target devices (e.g.,
personal navigation systems, onboard vehicle navigation systems,
and the like) configured to process the received speech data (e.g.,
data corresponding to the user giving a command to present
directions to the nearest Big Boy restaurant).
[0282] Referring again to FIG. 10D, operation 1052 may include
operation 1056 depicting extracting header data from a header of
the received speech data indicating one or more word categories
accepted by one or more intended target devices configured to
process the received speech data. For example, FIG. 4, e.g., FIG.
4C, shows header data indicating one or more word categories
accepted by one or more intended target devices configured to
process received speech data extracting from received speech data
header module 456 extracting header data from a header of the
received speech data indicating one or more word categories (e.g.,
"words that control home theater components," "words that control
temperature," or, "words that control navigation," or specific sets
of words, e.g., "volume," "digital video disc," "channel," etc.)
accepted by one or more intended target devices (e.g., home theater
systems) configured to process the received speech data (e.g.,
"lower the volume five units").
[0283] Referring now to FIG. 10E, operation 1038 may include
operation 1058 converting the received speech data into data that
is recognizable by a target device. For example, FIG. 4, e.g., FIG.
4D, shows received speech data into target device recognizable data
converting module 458 converting the received speech data (e.g.,
dictation of a memorandum with a lot of background noise, e.g.,
machinery, children yelling, in the background) into data that is
recognizable by a target device (e.g., by applying one or more
filters to remove the non-speech data).
[0284] Referring again to FIG. 10E, operation 1058 may include
operation 1060 depicting converting the received speech data into
one or more commands or command modifiers configured to be
recognized by a control component of the target device. For
example, FIG. 4, e.g., FIG. 4D, shows received speech data into one
or more commands or command modifiers configured to be recognized
by a target device control component converting module 460
converting the received speech data (e.g., the received speech at a
microphone, e.g., the user's speech, e.g., the words "withdraw two
hundred dollars from checking account") into one or more commands
(e.g., "withdraw" "200" "checking account," or in some embodiments,
"withdraw" "200" "account number 6204620") configured to be
recognized by a control component (e.g., a component configured to
carry out the "deposit" "withdraw" and "display" commands of the
target device (e.g., an automated teller machine device).
[0285] Referring again to FIG. 10E, operation 706 may include
operation 1062 depicting receiving target data regarding a target
device configured to process at least a portion of the received
speech data. For example, FIG. 4, e.g., FIG. 4D, shows target data
regarding a target device configured to process at least a portion
of speech data receiving module 462 receiving target data (e.g.,
data identifying a target device) regarding a target device (e.g.,
a video game system) configured to process at least a portion of
the received speech data (e.g., a command to kick the soccer ball
given to a headset to command a player in a sports soccer
game).
[0286] Referring again to FIG. 10E, operation 1062 may include
operation 1064 depicting receiving target data regarding a target
device configured to process at least a portion of the received
speech data from the particular device. For example, FIG. 4, e.g.,
FIG. 4D, shows target data regarding a target device configured to
process at least a portion of speech data receiving from the
particular device module 464 receiving target data regarding a
target device (e.g., data that identifies the video game system as
the target of the speech) configured to process at least a portion
of the received speech data (e.g., a command to switch control to a
different player in the soccer game) from the particular device
(e.g., from the headset that the video game player is wearing).
[0287] Referring again to FIG. 10E, operation 1062 may include
operation 1066 depicting receiving target data regarding a target
device configured to process at least a portion of the received
speech data from a further device that is different than the
particular device. For example, FIG. 4, e.g., FIG. 4D, shows target
data regarding a target device configured to process at least a
portion of speech data receiving from a further device module 466
receiving target data regarding a target device (e.g., data
indicating which component of a home theater system is the target
device, e.g., the television) configured to process at least a
portion of the received speech data (e.g., data corresponding to
the user giving the command ("increase brightness 75% and set the
contrast to twenty-four") from a further device (e.g., a computer,
e.g., computing device 54 of FIG. 1D, that communicates with
devices in home theater system, e.g., receiver device 51, media
player device 52, and television device 53 of FIG. 1D) that is
different than the particular device (e.g., the universal remote
control, e.g., personal device 22A of FIG. 1D).
[0288] Referring again to FIG. 10E, operation 1066 may include
operation 1068 depicting receiving target data regarding a target
device configured to process at least a portion of the received
speech data from the further device, said further device configured
to process at least a portion of the received speech data. For
example, FIG. 4, e.g., FIG. 4D, shows target data regarding a
target device configured to process at least a portion of speech
data receiving from a further device configured to process at least
a portion of the speech data module 468 receiving target data
regarding a target device (e.g., "this data is intended for the
television component of the home theater system) configured to
process at least a portion of the received speech data (e.g., data
corresponding to the user speaking the command "change the input to
VIDEO-2") from the further device (e.g., a universal remote
control), said further device configured to process at least a
portion of the received speech data (e.g., the universal remote
control receives the speech from the user, converts it to a data
stream, and adds the target data that says "this data is intended
for the television component" based on the universal remote
control's detection that the user had pressed down the "Television"
button on the personal device prior to speaking the command).
[0289] Referring again to FIG. 10E, operation 1066 may include
operation 1070 depicting receiving target data regarding a target
device configured to process at least a portion of the received
speech data from the further device, said further device configured
to apply at least a portion of the acquired adaptation data. For
example, FIG. 4, e.g., FIG. 4D, shows target data regarding a
target device configured to process at least a portion of speech
data receiving from a further device configured to apply at least a
portion of the adaptation data module 470 receiving target data
regarding a target device (e.g., data indicating an address of a
computer, e.g., a desktop computing system on a desk in the
bedroom, and not a networked laptop sitting on a couch in the
living room) configured to process at least a portion of the
received speech data (e.g., "open the web browser") from the
further device (e.g., a cellular telephone device carried by the
user), said further device configured to apply at least a portion
of the acquired adaptation data (e.g., the cellular telephone
device retrieves the adaptation data from memory and applies the
adaptation data to its speech recognition component, in case the
cellular telephone device is requested by the computer to perform
some or all of the speech data processing).
[0290] Referring now to FIG. 10F, operation 1066 may include
operation 1072 depicting receiving target data from the further
device, said target data regarding a target device configured to
process at least a portion of the speech data, said further device
configured to process at least a portion of the speech data less
efficiently than the target device. For example, FIG. 4, e.g., FIG.
4E, shows target data regarding a target device configured to
process at least a portion of speech data receiving from a further
device configured to process the speech data less efficiently than
the target device module 472 receiving target data (e.g., a type of
device that the speech data is directed toward, e.g., an automated
drive through window) from the further device (e.g., a user's
cellular telephone device), said target data regarding a target
device (e.g., an automated drive through window) configured to
process at least a portion of the speech data (e.g., a user's order
of a large pizza with pepperoni and sausage), said further device
configured to process at least a portion of the speech data less
efficiently than the target device (e.g., the cellular telephone
device could process the speech data, but without knowing a
vocabulary of the menu of the pizza place, cannot do so as
efficiently as the target device, e.g., the automated drive thru
window at the pizza place).
[0291] Referring again to FIG. 10F, operation 1066 may include
operation 1074 depicting receiving target data from a further
device that is different than the particular device, wherein said
speech data is unintended for the further device. For example, FIG.
4, e.g., FIG. 4E, shows target data regarding a target device
configured to process at least a portion of speech data receiving
from a further device for which the speech data is unintended
module 474 receiving target data (e.g., data indicating that the
user is intending his speech to be directed to a media player) from
a further device (e.g., an audio visual receiver) that is different
from the particular device (e.g., a universal remote control),
wherein said speech data (e.g., "play my song playlist number
three") is unintended for the further device (e.g., the audio
visual receiver does not play media).
[0292] Referring again to FIG. 10F, operation 1066 may include
operation 1076 depicting receiving target data from a further
device, said target data indicating that the speech data was
determined to be intended for the target device. For example, FIG.
4, e.g., FIG. 4E, shows target data regarding a target device
configured to process at least a portion of speech data and target
data indicating the speech data was determined to be intended for
the target device receiving from a further device module 476
receiving target data (e.g., data stating "this data is intended
for a motor vehicle control system") from a further device (e.g., a
personal GPS navigation system, e.g., personal GPS navigation
system 41), said target data indicating that the speech data was
determined (e.g., by the personal GPS navigation system) to be
intended for the target device (e.g., the GPS navigation system
tried to process the speech data, but could not, and then
determined, based on the failure to recognize, that the speech data
was intended for the motor vehicle control system).
[0293] Referring again to FIG. 10F, operation 706 may include
operation 1094 depicting receiving data identifying the target
device configured to process at least a portion of the received
speech data. For example, FIG. 4, e.g., FIG. 4E, shows data
identifying the target device receiving module 494 receiving data
identifying the target device (e.g., a name of the device, whether
general or specific, e.g., "video game system number 532162462" or
"Billy's netbook") configured to process at least a portion of the
received speech data (e.g., a command to "load the game Call of
Duty").
[0294] Referring again to FIG. 10F, operation 1094 may include
operation 1096 depicting receiving a name of the target device
configured to process at least a portion of the received speech
data. For example, FIG. 4, e.g., FIG. 4E, shows name of the target
device (e.g., a name on a network, e.g., "computer NA00326W")
configured to process at least a portion of the received speech
data (e.g., a memorandum outlining new human resources policy).
[0295] Referring again to FIG. 10F, operation 1094 may include
operation 1097 depicting receiving a device identifier of the
target device configured to process at least a portion of the
received speech data. For example, FIG. 4, e.g., FIG. 4E, shows
device identifier of the target device receiving module 497
receiving a device identifier (e.g., a MAC address of a network
card of the device) configured to process at least a portion of the
received speech data (e.g., a command to open up the address book
program).
[0296] Referring again to FIG. 10F, operation 706 may include
operation 1098 depicting receiving an address of the target device
configured to process at least a portion of the received speech
data. For example, FIG. 4, e.g., FIG. 4E, shows address of the
target device receiving module 498 receiving an address of the
target device (e.g., an IP address, or a network address)
configured to process at least a portion of the received speech
data (e.g., a request to use Skype to dial a particular
number).
[0297] Referring again to FIG. 10F, operation 706 may include
operation 1099 depicting receiving a location of the target device
configured to process at least a portion of the received speech
data. For example, FIG. 4, e.g., FIG. 4E, shows location of the
target device receiving module 499 receiving a location of the
target device (e.g., it could be a relative location to the
particular party, e.g., "directly in front of the particular
party," e.g., within a bank of automated teller machines, or an
absolute location, e.g., "the automated ticket dispensing device
located on the third floor of Union Station, at location 38.89774
degrees N and 77.00643 degrees W) configured to process at least a
portion of the received speech data.
[0298] Referring now to FIG. 10G, operation 706 may include
operation 1078 depicting acquiring target data regarding an
intended application module configured to process at least a
portion of the received speech data. For example, FIG. 4, e.g.,
FIG. 4E, shows target data regarding an intended application module
configured to process at least a portion of the received speech
data obtaining module 478 acquiring target data (e.g., data
identifying a word processing application) regarding an intended
application module (e.g., a word processor, e.g., Microsoft Word)
configured to process at least a portion of the received speech
data (e.g., a dictation of a memorandum).
[0299] Referring again to FIG. 10G, operation 1078 may include
operation 1080 depicting acquiring target data regarding an
intended application module configured to process at least a
portion of the received speech data, wherein at least a portion of
said processing is configured to be facilitated by the adaptation
data. For example, FIG. 4, e.g., FIG. 4E, shows target data
regarding an intended application module configured to process,
facilitated by the adaptation data, at least a portion of the
received speech data obtaining module 480 acquiring target data
(e.g., data identifying the intended target) regarding an intended
application module (e.g., an operating system shell) configured to
process at least a portion of the received speech data (e.g.,
increase screen brightness by 30%), wherein at least a portion of
said processing is facilitated by the adaptation data (e.g., the
adaptation data contains a pronunciation of the numbers zero to
ninety-nine, and facilitates the operating system identifying that
the user said "thirty percent," as opposed to "thirteen percent" or
some other incorrect interpretation).
[0300] Referring again to FIG. 10G, operation 1080 may include
operation 1082 depicting acquiring target data regarding a speech
data processing capability of an intended application module
configured to process at least a portion of the received speech
data. For example, FIG. 4, e.g., FIG. 4F, shows target data
regarding a speech data processing capability of an intended
application module configured to process, facilitated by the
adaptation data, at least a portion of the received speech data
obtaining module 482 acquiring target data (e.g., data showing a
capability, e.g., `completely incapable`) regarding a speech data
processing capability (e.g., no capability) of an intended
application module (e.g., a basic web browser) configured to
process at least a portion of the received speech data (e.g., "open
espn.com").
[0301] Referring again to FIG. 10G, operation 706 may include
operation 1084 depicting acquiring target data regarding a first
application module configured to process at least a portion of the
received speech data, and a second application module configured to
process at least a portion of the received speech data. For
example, FIG. 4, e.g., FIG. 4F, shows target data regarding a first
application module configured to process at least a portion of the
received speech data and a second application module configured to
process at least a portion of the received speech data obtaining
module 484 acquiring target data (e.g., a list of the application
modules running on a computer) regarding a first application module
(e.g., a web browser) configured to process at least a portion of
the received speech data (e.g., data corresponding to the user's
request to look up Aunt Sally's address), and a second application
module (e.g., an address book application) configured to process at
least a portion of the received speech data (e.g., the data
corresponding to the user's request to look up Aunt Sally's
address).
[0302] Referring again to FIG. 10G, operation 1084 may include
operation 1086 depicting acquiring target data regarding a word
processing application module configured to process at least a
portion of the received speech data, and a speech recognition
application module configured to process at least a portion of the
received speech data. For example, FIG. 4, e.g., FIG. 4F, shows
target data regarding a word processing application module
configured to process at least a portion of the received speech
data and a speech recognition application module configured to
process at least a portion of the received speech data obtaining
module 486 acquiring target data (e.g., a list of the application
modules on the computer that are configured to process speech)
regarding a word processing application module configured to
process at least a portion of the received speech data (e.g., a
dictation of a letter to the user's sister), and a speech
recognition application module configured to process at least a
portion of the received speech data (e.g., the dictation of a
letter to the user's sister).
[0303] Referring again to FIG. 10G, operation 1084 may include
operation 1088 depicting acquiring target data regarding a word
processing application module configured to process at least a
portion of the received speech data, and an operating system module
configured to process at least a portion of the received speech
data. For example, FIG. 4, e.g., FIG. 4F, shows target data
regarding a word processing application module configured to
process at least a portion of the received speech data and an
operating system application module configured to process at least
a portion of the received speech data obtaining module 488
acquiring target data (e.g., data listing one or more applications'
capabilities to process speech data) regarding a word processing
application module (e.g., Notepad) configured to process at least a
portion of the received speech data, and an operating system module
(e.g., Chrome OS) configured to process at least a portion of the
received speech data.
[0304] Referring again to FIG. 10G, operation 1084 may include
operation 1090 depicting acquiring target data regarding a word
processing application module configured to process at least a
portion of the received speech data, and a spreadsheet processing
application module configured to process at least a portion of the
received speech data. For example, FIG. 4, e.g., FIG. 4F, shows
target data regarding a word processing application module
configured to process at least a portion of the received speech
data and a spreadsheet processing application module configured to
process at least a portion of the received speech data obtaining
module 490 acquiring target data (e.g., a list of open
applications) regarding a word processing application module
configured to process at least a portion of the received speech
data, and a spreadsheet processing application module configured to
process at least a portion of the received speech data.
[0305] FIGS. 11A-11C depict various implementations of operation
708, according to embodiments. Referring now to FIG. 11A, in some
embodiments, operation 708 may include operation 1102 depicting
determining to apply the adaptation data for processing at least a
portion of the received speech data when the acquired target data
indicates that the received speech data was not intended for a
further device. For example, FIG. 5, e.g., FIG. 5A, shows
application of adaptation data for processing at least a portion of
the received speech data determining based on acquired target data
comprising an indication of intended device module 502 determining
to apply the adaptation data (e.g., instructions for replacing a
word frequency table with a modified word frequency table that
reflects the particular party's word usage) for processing at least
a portion of the received speech data (e.g., a request to withdraw
money from a checking account) when the acquired target data (e.g.,
data identifying the automated teller machine device that the user
swiped her card into) indicates that the received speech data was
not intended for a further device (e.g., a different automated
teller machine device in a same cluster of automated teller machine
devices).
[0306] Referring again to FIG. 11A, operation 708 may include
operation 1104 depicting determining to apply the adaptation data
for processing at least a portion of the received speech data when
the acquired target data indicates that the received speech data
has arrived at its intended target device. For example, FIG. 5,
e.g., FIG. 5A, shows application of adaptation data for processing
at least a portion of the received speech data determining based on
acquired target data comprising an indication that speech data has
arrived at intended device module 504 determining to apply the
adaptation data (e.g., stochastic state transition network) for
processing at least a portion of the received speech data (e.g., a
request to receive dictation of a memorandum) when the acquired
target data indicates that the received speech data has arrived at
its intended target device (e.g., a computer running Microsoft
Word).
[0307] Referring again to FIG. 11A, operation 708 may include
operation 1106 depicting determining against application of the
adaptation data for processing at least a portion of the received
speech data when the acquired target data indicates that the
received speech data has not arrived at its intended target device.
For example, FIG. 5, e.g., FIG. 5A, shows application of adaptation
data for processing at least a portion of the received speech data
determining based on acquired target data comprising an indication
that speech data has not arrived at intended device module 506
determining against application of the adaptation data (e.g., an
uncommon word pronunciation guide) for processing at least a
portion of the received speech data (e.g., a command to lower the
rear windows and open the sunroof) when the acquired target data
(e.g., data indicating that the speech data is intended for a motor
vehicle control system) indicates that the received speech data has
not arrived at its intended target device (e.g., it has arrived at
a personal GPS navigation system, which is not a device it was
intended for).
[0308] Referring again to FIG. 11A, operation 1106 may include
operation 1108 depicting choosing against application of the
adaptation data for processing at least a portion of the received
speech data when the acquired target data indicates that the
received speech data has not arrived at its intended target device.
For example, FIG. 5, e.g., FIG. 5A shows application of adaptation
data for processing at least a portion of the received speech data
choosing against based on acquired target data comprising an
indication that speech data has not arrived at intended device
module 508 choosing against application of the adaptation data
(e.g., an utterance ignoring algorithm) for processing at least a
portion of the received speech data (e.g., a request to raise the
volume by fifteen units) when the acquired target data (e.g., data
indicating that the speech data is intended for a television)
indicates that the received speech data has not arrived at its
intended target device (e.g., it has arrived at an audio/visual
receiver, which can raise its volume by fifteen units, but the
target data indicates that the speech data is intended for a
television).
[0309] Referring again to FIG. 11A, operation 708 may include
operation 1110 depicting determining against application of the
adaptation data for processing at least a portion of the received
speech data when the acquired target data indicates that the
received speech data has arrived at a device other than its
intended target device. For example, FIG. 5, e.g., FIG. 5A, shows
application of adaptation data for processing at least a portion of
the received speech data determining based on acquired target data
comprising an indication that speech data has arrived at other
device than an intended device module 510 determining against
application of the adaptation data (e.g., an uncommon word
pronunciation guide) for processing at least a portion of the
received speech data (e.g., a request for directions home) when the
acquired target data indicates that the received speech data has
arrived at a device (e.g., a cellular telephone device with
navigational features) other than its intended target device (e.g.,
a personal GPS navigation system).
[0310] Referring again to FIG. 11A, operation 708 may include
operation 1112 depicting determining to apply the adaptation data
for processing at least a portion of the received speech data when
the acquired target data indicates a capability of applying the
adaptation data. For example, FIG. 5, e.g., FIG. 5A, shows
application of adaptation data for processing at least a portion of
the received speech data determining when acquired target data
indicates capability of adaptation data application module 512
determining to apply the adaptation data (e.g., a noise level
dependent filtration algorithm) for processing at least a portion
of the received speech data (e.g., a request to withdraw two
hundred dollars from a checking account) when the acquired target
data (e.g., data internal to the device that indicates that
adaptation data can be applied) indicates a capability of applying
the adaptation data (e.g., has components that allow application of
the noise level dependent filtration algorithm).
[0311] Referring now to FIG. 11B, operation 708 may include
operation 1114 depicting determining against application of the
adaptation data for processing at least a portion of the received
speech data when the acquired target data indicates that there are
one or more other devices present that are configured to apply the
adaptation data for processing at least a portion of the received
speech data. For example, FIG. 5, e.g., FIG. 5B, shows application
of adaptation data for processing at least a portion of the
received speech data determining based on acquired target data
indicating presence of one or more other devices configured to
apply adaptation data module 514 determining against application of
the adaptation data (e.g., a speech disfluency detection algorithm)
for processing at least a portion of the received speech data
(e.g., a spoken request to power off) when the acquired target data
(e.g., a list of devices in a home theater system that are
currently powered on) indicates that there are one or more other
devices present (e.g., in a home theater system, there may be
several pieces of equipment that can be powered off) that are
configured to apply the adaptation data (e.g., a speech disfluency
detection algorithm) for processing at least a portion of the
received speech data (e.g., a spoken request to power off).
[0312] Referring again to FIG. 11B, operation 708 may include
operation 1116 depicting determining against application of the
adaptation data for processing at least a portion of the received
speech data when the acquired target data indicates that there are
one or more other devices present that are configured to
efficiently apply the adaptation data for processing at least a
portion of the received speech data. For example, FIG. 5, e.g.,
FIG. 5B, shows application of adaptation data for processing at
least a portion of the received speech data determining against
based acquired target data indicating presence of one or more other
devices configured to efficiently apply adaptation data module 516
determining against application of the adaptation data (e.g., an
accent-based pronunciation modification algorithm) for processing
at least a portion of the received speech data (e.g., a request to
show directions to the nearest cheese shop) when the acquired
target data (e.g., a list including a motor vehicle control system
and a GPS navigation system that can communicate with each other)
indicates that there are one or more other devices present (e.g., a
GPS navigation system) that are configured to efficiently apply the
adaptation data (e.g., the GPS navigation system may have more
processing power and thus may be able to apply the algorithm
efficiently) for processing at least a portion of the received
speech data (e.g., a request to show directions to the nearest
cheese shop).
[0313] Referring again to FIG. 11B, operation 708 may include
operation 1118 depicting determining whether to apply the
adaptation data for processing at least a portion of the received
speech data when the acquired target data indicates a presence of
one or more applications configured to process the received speech
data. For example, FIG. 5, e.g., FIG. 5B, shows application of
adaptation data for processing at least a portion of the received
speech data determining based on acquired target data indicating
presence of one or more other applications module 518 determining
whether to apply the adaptation data (e.g., a phoneme pronunciation
database) for processing at least a portion of the received speech
data (e.g., the user speaking a list of numbers) when the acquired
target data indicates a presence of one or more applications of the
target device (e.g., a word processing application and a
spreadsheet application of a computer).
[0314] Referring again to FIG. 11B, operation 708 may include
operation 1120 depicting determining whether to apply the
adaptation data for processing at least a portion of the received
speech data based on one or more characteristics of one or more
applications of the target device, wherein the acquired target data
includes data regarding a presence of the one or more applications.
For example, FIG. 5, e.g., FIG. 5B, shows application of adaptation
data for processing at least a portion of the received speech data
determining based on one or more characteristics of one or more
applications and target data indicating a presence of the one or
more applications module 520 determining whether to apply the
adaptation data (e.g., a part-of-speech labeling algorithm) for
processing at least a portion of the received speech data (e.g., a
dictation of a memorandum) based on one or more characteristics
(e.g., an ability to successfully process numbers recited in
speech) of one or more applications of the target device (e.g., a
computer having two different spreadsheet processing applications),
wherein the acquired target data includes data regarding a presence
of the one or more applications (e.g., the acquired target data
lists the available applications, and their efficiency rate at
processing numbers as speech).
[0315] Referring now to FIG. 11C, operation 708 may include
operation 1122 depicting determining whether to apply the
adaptation data for processing at least a portion of the received
speech data based on acquired target data comprising one or more
characteristics of one or more applications of the target device.
For example, FIG. 5, e.g., FIG. 5C, shows application of adaptation
data for processing at least a portion of the received speech data
determining against based acquired target data comprising one or
more characteristics of one or more applications module 522
determining whether to apply the adaptation data (e.g., a regional
dialect application algorithm) for processing at least a portion of
the received speech data (e.g., a request to play a particular
video from the user's on-demand video library) based on acquired
target data comprising one or more characteristics (e.g., processor
power available at the time of interpreting the speech) of one or
more applications (e.g., a video-on-demand equipped cable box that
is running a menuing application and an on-demand application) of
the target device (e.g., a video cable box).
[0316] Referring again to FIG. 11C, operation 1122 may include
operation 1124 depicting determining whether to apply the
adaptation data for processing at least a portion of the received
speech data based on acquired target data comprising a detection of
one or more applications and corresponding one or more
characteristics of the one or more applications. For example, FIG.
5, e.g., FIG. 5C, shows application of adaptation data for
processing at least a portion of the received speech data
determining against based acquired target data comprising a
presence of one or more applications and one or more
characteristics of one or more applications module 524 determining
whether to apply the adaptation data (e.g., a syllable
pronunciation database) for processing at least a portion of the
received speech data (e.g., speaking commands to fill in fields on
a web page) based on acquired target data comprising a detection of
one or more applications (e.g., whether a web browser is open and
information about the open web browser) and corresponding one or
more characteristics (e.g., whether the web browser can process the
speech data, e.g., and how much pre-processing, if any, should be
performed) of the one or more applications (e.g., a web
browser).
[0317] Referring again to FIG. 11C, operation 1122 may include
operation 1126 depicting determining whether to apply the
adaptation data for processing at least a portion of the received
speech data at least partly based on acquired target data
comprising a developer of one or more applications of the target
device. For example, FIG. 5, e.g., FIG. 5C, shows application of
adaptation data for processing at least a portion of the received
speech data determining against based acquired target data
comprising a developer of one or more applications module 526
determining whether to apply the adaptation data (e.g., a
context-based repaired utterance processing matrix) for processing
at least a portion of the received speech data at least partly
based on acquired target data comprising a developer (e.g., for
Microsoft-developed applications, the adaptation data may not be
applied, but for Corel-developed applications, the adaptation data
may be applied) of one or more applications (e.g., a word
processing application) of the target device (e.g., a laptop
computer).
[0318] Referring again to FIG. 11C, operation 706 may include
operation 1128 depicting determining whether to apply the
adaptation data for processing at least a portion of the received
speech data at least partly based on a preference flag set on the
one or more applications. For example, FIG. 5, e.g., FIG. 5C, shows
application of adaptation data for processing at least a portion of
the received speech data determining based on one or more
application preference flags module 528 determining whether to
apply the adaptation data for processing at least a portion of the
received speech data (e.g., the user is reading numbers off of a
list to be entered into a spreadsheet) at least partly based on a
preference flag set on the one or more applications (e.g., the
spreadsheet application has an internal flag that lets the
application decide whether to use the adaptation data). In some
embodiments, the decision is based on current conditions within the
device, e.g., available processing power, etc. In some embodiments,
the decision is based on the application estimating the success of
previously applying the adaptation data. In some embodiments, the
decision is based on a user selection.
[0319] Referring again to FIG. 11C, operation 706 may include
operation 1130 depicting determining whether to apply the
adaptation data for processing at least a portion of the received
speech data at least partly based on a user-controlled preference
flag set on the one or more applications. For example, FIG. 5,
e.g., FIG. 5C, shows application of adaptation data for processing
at least a portion of the received speech data determining based on
one or more user-controlled preference flags module 530 determining
whether to apply the adaptation data (e.g., a partial pattern tree
model) for processing at least a portion of the received speech
data (e.g., a dictation of a memorandum) at least partly based on a
user-controlled preference flag set on the one or more applications
(e.g., the word processing application has a user preference
setting for allowing the user to select whether she wants the word
processing application to process the adaptation data).
[0320] Referring again to FIG. 11C, operation 706 may include
operation 1132 depicting determining whether to apply the
adaptation data based on a decision by an operating system of a
device configured to process at least a portion of the received
speech data, when the acquired target data indicates that there are
one or more applications present configured to process the received
speech data. For example, FIG. 5, e.g., FIG. 5C, shows application
of adaptation data for processing at least a portion of the
received speech data determining based on operating system decision
module 532 determining whether to apply the adaptation data (e.g.,
a part-of-speech labeling algorithm) based on a decision by an
operating system of a device (e.g., a Windows operating system,
e.g., Windows 7, loaded on a Dell desktop computer) configured to
process at least a portion of the received speech data (e.g., a
verbal listing of contact information to be saved in the computer),
wherein the acquired target data (e.g., a list of currently running
applications) indicates that there are one or more applications
present (e.g., a word processing application, a calendar
application, a contact management application) configured to
process the received speech data (e.g., one or more of the word
processing application, calendar application, and contact
management application can process the received speech device, but
the contact management application cannot use the adaptation data,
and so the operating system determines whether to apply the
adaptation data based on the existence of an application that
cannot use the adaptation data). In some embodiments, the operating
system also may decide for which application the speech data is
intended. In other embodiments, the operating system may determine
for which application the speech data is intended based on other
information, e.g., a window that was active when the user spoke the
words).
[0321] FIGS. 12A-12C depict various implementations of operation
710, according to embodiments. Referring now to FIG. 12A, in some
embodiments, operation 710 may include operation 1202 depicting
transmitting adaptation result data that is based on applying the
adaptation data. For example, FIG. 6, e.g., FIG. 6A, shows
adaptation result data based on applying the adaptation data
transmitting module 602 transmitting adaptation result data that is
based on applying the adaptation data (e.g., transmitting a "1" if
the adaptation result data was successfully applied, and a "0"
otherwise, or in other embodiments, transmitting a number
indicating a percentage of the adaptation data (e.g., a list of the
way that the particular party pronounces ten words) that was
applied).
[0322] Referring again to FIG. 12A, operation 1202 may include
operation 1204 depicting transmitting adaptation result data that
is based on applying the adaptation data to a speech recognition
component of a target device. For example, FIG. 6, e.g., FIG. 6A,
shows adaptation result data based on applying the adaptation data
to a speech recognition component of a target device transmitting
module 604 transmitting adaptation result data that is based on
applying the adaptation data (e.g., an emotion-based pronunciation
adjustment algorithm) to a speech recognition component of a target
device (e.g., a speech-enabled video game system).
[0323] Referring again to FIG. 12A, operation 710 may include
operation 1206 transmitting adaptation result data that is based on
processing the received speech data. For example, FIG. 6, e.g.,
FIG. 6A, shows adaptation result data based on processing received
speech data transmitting module 606 transmitting adaptation result
data (e.g., transmitting a signal, e.g., internally to a portion of
the device that processed the speech data, or externally to a
different device) indicating that at least a portion of the
received speech data has been processed (e.g., in some embodiments,
the processing of the speech data may include determining that the
speech data is intended for a different device, and in some
embodiments, also may include sending a signal indicating that the
speech data is intended for a different device) that is based on
processing the received speech data.
[0324] Referring again to FIG. 12A, operation 1206 may include
operation 1250 depicting transmitting adaptation result data
indicating that at least a portion of the received speech data has
been processed. For example, FIG. 6, e.g., FIG. 6A, shows
adaptation result data indicating at least a portion of received
speech data has been processed transmitting module 650 transmitting
adaptation result data indicating that at least a portion of the
received speech data (e.g., a request to withdraw two hundred
dollars from a speech-enabled automated teller machine device).
[0325] Referring again to FIG. 12A, operation 710 may include
operation 1252 depicting transmitting adaptation result data
indicating that at least a portion of the received speech data is
intended for an other device. For example, FIG. 6, e.g., FIG. 6A,
shows adaptation result data indicating that at least a portion of
the received speech data is intended for an other device
transmitting module 652 transmitting adaptation result data
indicating that at least a portion of the received speech data
(e.g., "dial the number 252-256-6356" is intended for an other
device (e.g., an automated wall dialer in a house or office
setting).
[0326] Referring again to FIG. 12A, operation 1252 may include
operation 1254 depicting transmitting adaptation result data
indicating that at least a portion of the received speech data is
intended for an other device to the other device. For example, FIG.
6, e.g., FIG. 6A, shows adaptation result data indicating that at
least a portion of the received speech data is intended for an
other device transmitting to the other device module 654
transmitting adaptation result data (e.g., that is based on at
least one aspect of the received speech data, e.g., that the
received speech data is not intended for the device that received
it, whether or not the device made that determination or another
device made that determination, e.g., and encoded it into a header
of the speech data, or transmitted it separately) indicating that
at least a portion of the received speech data (e.g., the data that
says "turn the air conditioner down by two degrees") is intended
for an other device (e.g., a climate control system of a house) to
the other device (e.g., a home climate control system).
[0327] Referring again to FIG. 12A, operation 1252 may include
operation 1256 depicting transmitting adaptation result data that
comprises the adaptation data and an indicator that at least a
portion of the received speech data is intended for the other
device. For example, FIG. 6, e.g., FIG. 6A, shows adaptation result
data comprising the adaptation data and indicating that at least a
portion of the received speech data is intended for an other device
transmitting module 656 transmitting adaptation result data,
comprising the adaptation data (e.g., a path selection algorithm),
and an indicator (e.g., data) that at least a portion of the
received speech data (e.g., data that says "turn the air
conditioner down by two degrees") is intended for the other device
(e.g., a motor vehicle control system).
[0328] Referring again to FIG. 12A, operation 710 may include
operation 1258 depicting transmitting adaptation result data
indicating that a determination was made regarding an intended
target of the received speech data. For example, FIG. 6, e.g., FIG.
6A, shows adaptation result data indicating completed determination
regarding intended target of received speech data transmitting
module 658 transmitting adaptation result data indicating that a
determination was made (e.g., either determining the target of the
received speech data, or determining merely that some other device
is the target of the received speech data) regarding an intended
target (e.g., an office copier) of the received speech data (e.g.,
data corresponding to a speech instruction of "make twenty-five
color copies with sixty percent less yellow in them").
[0329] Referring now to FIG. 12B, operation 710 may include
operation 1208 depicting transmitting adaptation result data that
is based on a measure of success of at least one portion of a
speech-facilitated transaction corresponding to the received speech
data. For example, FIG. 6, e.g., FIG. 6B, shows adaptation result
data based on a measure of success of at least one portion of a
speech-facilitated transaction corresponding to the received speech
data transmitting module 608 transmitting adaptation result data
that is based on a measure of success (e.g., an observer not
directly related to the transaction, e.g., a monitoring network
computer, or a cellular telephone device, or a device specifically
installed to monitor quality of speech-facilitated transactions,
e.g., which, in some embodiments, may be integral with a terminal
designed to receive the speech-facilitated transactions) of at
least one portion of a speech-facilitated transaction (e.g., giving
commands to a laptop computer to open various programs, e.g., a web
browser, a word processor, and the like) corresponding to the
received speech data.
[0330] Referring again to FIG. 12B, operation 1208 may include
operation 1210 depicting transmitting adaptation result data that
comprises a representation of success of at least one portion of a
speech-facilitated transaction corresponding to the received speech
data. For example, FIG. 6, e.g., FIG. 6B, shows adaptation result
data comprising a representation of success of at least one portion
of a speech-facilitated transaction corresponding to the received
speech data transmitting module 610 transmitting adaptation result
data that comprises a representation of success (e.g., a
representation in the form of answers to open-ended survey
questions presented to the particular party at the end of a
speech-facilitated transaction) of at least one portion of a
speech-facilitated transaction (e.g., an interaction with an
automated banking center in which the particular party speaks the
words "obtain a home equity loan," and the various steps are
carried out through speech of the user) corresponding to the
received speech data (e.g., data corresponding to at least one of
the words spoken by the user during the speech-facilitated
transaction).
[0331] Referring again to FIG. 12B, operation 1210 may include
operation 1212 depicting transmitting adaptation result data that
comprises a numeric representation of success of at least one
portion of a speech-facilitated transaction corresponding to the
received speech data, said numeric representation of success
provided by the particular party. For example, FIG. 6, e.g., FIG.
6B, shows adaptation result data comprising a numeric
representation of success provided by the particular party of at
least one portion of a speech-facilitated transaction corresponding
to the received speech data transmitting module 612 transmitting
adaptation result data that comprises a numeric representation of
success (e.g., 42 out of 100) of at least one portion of a
speech-facilitated transaction (e.g., a user trying to get walking
directions from an automated "help terminal" located on a New York
City street corner") corresponding to the received speech data,
said numeric representation of success provided by the particular
party (e.g., the particular party is asked through a feedback
mechanism, e.g., a survey, to rate ten different portions of their
transaction on a scale of 0-10, and the scores are aggregated to
arrive at a score of 42).
[0332] Referring again to FIG. 12B, operation 1210 may include
operation 1214 depicting transmitting adaptation result data that
comprises a numeric representation of success of at least one
portion of a speech-facilitated transaction corresponding to the
received speech data. For example, FIG. 6, e.g., FIG. 6A, shows
adaptation result data comprising a numeric representation of
success of at least one portion of a speech-facilitated transaction
corresponding to the received speech data transmitting module 614
transmitting adaptation result data that comprises a numeric
representation (e.g., 8 out of 10) of success of at least one
portion of a speech-facilitated transaction corresponding to the
received speech data (e.g., a user speaking a command to withdraw
two hundred dollars from an automated teller machine device).
[0333] Referring again to FIG. 12A, operation 1214 may include
operation 1216 depicting transmitting a confidence rate of correct
interpretation of at least one portion of the speech-facilitated
transaction corresponding to the received speech data. For example,
FIG. 6, e.g., FIG. 6A shows adaptation result data comprising
confidence rate of correct interpretation of at least one portion
of the speech-facilitated transaction corresponding to the received
speech data transmitting module 616 transmitting a confidence rate
of correct interpretation (e.g., fifty percent) of at least one
portion of the speech-facilitated transaction (e.g., a user
ordering a bacon cheeseburger, a large French fries, a coke, and a
shake) corresponding to the received speech data (e.g., data
corresponding to a computer-readable representation of the user's
speech).
[0334] Referring now to FIG. 12B, operation 710 may include
operation 1218 depicting transmitting adaptation result data
comprising a list of at least one word that was a portion of the
received speech data and that was improperly interpreted during
processing of the received speech data. For example, FIG. 6, e.g.,
FIG. 6B, shows adaptation result data comprising a list of at least
one word that was a portion of the received speech data and that
was improperly interpreted during speech data processing
transmitting module 618 transmitting adaptation result data
comprising a list of at least one word that was a portion of the
received speech data (e.g., "forty") and that was improperly
interpreted (e.g., interpreted as "four tie" instead of "forty")
during processing of the received speech data (e.g., "show me
destinations within forty miles" spoken to an automated train
ticket dispensing device).
[0335] Referring again to FIG. 12B, operation 710 may include
operation 1220 depicting transmitting adaptation result data
comprising at least one phoneme appearing in at least one word that
was improperly interpreted when speech is received from the
particular party. For example, FIG. 6, e.g., FIG. 6B, shows
adaptation result data comprising at least one phoneme appearing in
at least one improperly interpreted word transmitting module 620
transmitting adaptation result data comprising at least one phoneme
appearing in at least one word (e.g., "Cincinnati") that was
improperly interpreted when speech is received from the particular
party (e.g., "I would like to order three orders of five-way
Cincinnati chili").
[0336] All of the above U.S. patents, U.S. patent application
publications, U.S. patent applications, foreign patents, foreign
patent applications and non-patent publications referred to in this
specification and/or listed in any Application Data Sheet, are
incorporated herein by reference, to the extent not inconsistent
herewith.
[0337] While particular aspects of the present subject matter
described herein have been shown and described, it will be apparent
to those skilled in the art that, based upon the teachings herein,
changes and modifications may be made without departing from the
subject matter described herein and its broader aspects and,
therefore, the appended claims are to encompass within their scope
all such changes and modifications as are within the true spirit
and scope of the subject matter described herein. It will be
understood by those within the art that, in general, terms used
herein, and especially in the appended claims (e.g., bodies of the
appended claims) are generally intended as "open" terms (e.g., the
term "including" should be interpreted as "including but not
limited to," the term "having" should be interpreted as "having at
least," the term "includes" should be interpreted as "includes but
is not limited to," etc.).
[0338] It will be further understood by those within the art that
if a specific number of an introduced claim recitation is intended,
such an intent will be explicitly recited in the claim, and in the
absence of such recitation no such intent is present. For example,
as an aid to understanding, the following appended claims may
contain usage of the introductory phrases "at least one" and "one
or more" to introduce claim recitations. However, the use of such
phrases should not be construed to imply that the introduction of a
claim recitation by the indefinite articles "a" or "an" limits any
particular claim containing such introduced claim recitation to
claims containing only one such recitation, even when the same
claim includes the introductory phrases "one or more" or "at least
one" and indefinite articles such as "a" or "an" (e.g., "a" and/or
"an" should typically be interpreted to mean "at least one" or "one
or more"); the same holds true for the use of definite articles
used to introduce claim recitations. In addition, even if a
specific number of an introduced claim recitation is explicitly
recited, those skilled in the art will recognize that such
recitation should typically be interpreted to mean at least the
recited number (e.g., the bare recitation of "two recitations,"
without other modifiers, typically means at least two recitations,
or two or more recitations).
[0339] Furthermore, in those instances where a convention analogous
to "at least one of A, B, and C, etc." is used, in general such a
construction is intended in the sense one having skill in the art
would understand the convention (e.g., "a system having at least
one of A, B, and C" would include but not be limited to systems
that have A alone, B alone, C alone, A and B together, A and C
together, B and C together, and/or A, B, and C together, etc.). In
those instances where a convention analogous to "at least one of A,
B, or C, etc." is used, in general such a construction is intended
in the sense one having skill in the art would understand the
convention (e.g., "a system having at least one of A, B, or C"
would include but not be limited to systems that have A alone, B
alone, C alone, A and B together, A and C together, B and C
together, and/or A, B, and C together, etc.). It will be further
understood by those within the art that typically a disjunctive
word and/or phrase presenting two or more alternative terms,
whether in the description, claims, or drawings, should be
understood to contemplate the possibilities of including one of the
terms, either of the terms, or both terms unless context dictates
otherwise. For example, the phrase "A or B" will be typically
understood to include the possibilities of "A" or "B" or "A and
B."
[0340] With respect to the appended claims, those skilled in the
art will appreciate that recited operations therein may generally
be performed in any order. Also, although various operational flows
are presented in a sequence(s), it should be understood that the
various operations may be performed in other orders than those
which are illustrated, or may be performed concurrently. Examples
of such alternate orderings may include overlapping, interleaved,
interrupted, reordered, incremental, preparatory, supplemental,
simultaneous, reverse, or other variant orderings, unless context
dictates otherwise. Furthermore, terms like "responsive to,"
"related to," or other past-tense adjectives are generally not
intended to exclude such variants, unless context dictates
otherwise.
[0341] This application may make reference to one or more
trademarks, e.g., a word, letter, symbol, or device adopted by one
manufacturer or merchant and used to identify and/or distinguish
his or her product from those of others. Trademark names used
herein are set forth in such language that makes clear their
identity, that distinguishes them from common descriptive nouns,
that have fixed and definite meanings, or, in many if not all
cases, are accompanied by other specific identification using terms
not covered by trademark. In addition, trademark names used herein
have meanings that are well-known and defined in the literature, or
do not refer to products or compounds for which knowledge of one or
more trade secrets is required in order to divine their meaning.
All trademarks referenced in this application are the property of
their respective owners, and the appearance of one or more
trademarks in this application does not diminish or otherwise
adversely affect the validity of the one or more trademarks. All
trademarks, registered or unregistered, that appear in this
application are assumed to include a proper trademark symbol, e.g.,
the circle R or bracketed capitalization (e.g., [trademark name]),
even when such trademark symbol does not explicitly appear next to
the trademark. To the extent a trademark is used in a descriptive
manner to refer to a product or process, that trademark should be
interpreted to represent the corresponding product or process as of
the date of the filing of this patent application.
[0342] Those skilled in the art will appreciate that the foregoing
specific exemplary processes and/or devices and/or technologies are
representative of more general processes and/or devices and/or
technologies taught elsewhere herein, such as in the claims filed
herewith and/or elsewhere in the present application.
* * * * *
References