U.S. patent application number 17/473447 was filed with the patent office on 2021-12-30 for user training by intelligent digital assistant.
The applicant listed for this patent is Apple Inc.. Invention is credited to Thomas R. GRUBER, Donald W. PITSCHEL.
Application Number | 20210407318 17/473447 |
Document ID | / |
Family ID | 1000005839822 |
Filed Date | 2021-12-30 |
United States Patent
Application |
20210407318 |
Kind Code |
A1 |
PITSCHEL; Donald W. ; et
al. |
December 30, 2021 |
USER TRAINING BY INTELLIGENT DIGITAL ASSISTANT
Abstract
The method includes receiving, from a user, a first speech input
spoken in a first language; inferring a user intent based on at
least the first speech input in the first language; based on the
inferred user intent, generating one or more alternative
expressions of the first speech input in the first language; and
providing feedback to the user introducing the alternative
expressions as a more preferred input to express the inferred user
intent than the first speech input provided by the user.
Inventors: |
PITSCHEL; Donald W.; (San
Francisco, CA) ; GRUBER; Thomas R.; (Seattle,
WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Apple Inc. |
Cupertino |
CA |
US |
|
|
Family ID: |
1000005839822 |
Appl. No.: |
17/473447 |
Filed: |
September 13, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14213852 |
Mar 14, 2014 |
11151899 |
|
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17473447 |
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61800846 |
Mar 15, 2013 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G10L 2015/225 20130101;
G09B 19/06 20130101; G06F 40/58 20200101 |
International
Class: |
G09B 19/06 20060101
G09B019/06; G06F 40/58 20060101 G06F040/58 |
Claims
1. An electronic device, comprising: one or more processors; a
memory; and one or more programs, wherein the one or more programs
are stored in the memory and configured to be executed by the one
or more processors, the one or more programs including instructions
which when executed, cause the electronic device to: during a first
interaction with a user: receive a first speech input from the user
while the user is located in a first geographic area; determine a
first user intent based on the first speech input; provide a first
paraphrase of the first speech input based on the first user
intent; and execute a respective task flow to accomplish the first
user intent; during a second interaction with the user: receive a
second speech input from the user while the user is located in a
second geographic, the second speech input being substantially
identical to the first speech input; determine a second user intent
based on the second speech input, the second user intent being
identical to the first user intent; determine that a location
change from the first geographic area to the second geographic area
is associated with a change in language or locale-specific
vocabulary for at least one word or expression in the second speech
input; in response to said determination, provide a second
paraphrase based on the second user intent, wherein the second
paraphrase is different from the first paraphrase based on the
change in language or vocabulary; and execute the respective task
flow to accomplish the second user intent.
2. The electronic device of claim 1, wherein the first geographic
area and the second geographic area are both associated with a
primary language of the user.
3. The electronic device of claim 1, wherein the change in
locale-specific vocabulary includes use of a respective local slang
in the second geographic area for the at least one word or
expression in the second speech input, and wherein the second
paraphrase utilizes the respective local slang.
4. The electronic device of claim 3, wherein the one or more
programs cause the electronic device to: receive user input to
start a learning session regarding the respective local slang; and
in response to receiving the user input, provide an explanation of
the usage of the respective local slang.
5. The method of claim 1, wherein the change in language includes
use of a respective local accent in the second geographic area for
the at least one word or expression in the second speech input, and
wherein the second paraphrase utilizes the respective local
accent.
6. The method of claim 5, further comprising: receiving user input
to start a learning session regarding the respective local accent
provided in the second paraphrase; and in response to receiving the
user input, providing one or more additional examples of the usage
of the respective local accent in the second geographic area.
7. A computer-implemented method, comprising: at an electronic
device with one or more processors and memory: during a first
interaction with a user: receiving a first speech input from the
user while the user is located in a first geographic area;
determining a first user intent based on the first speech input;
providing a first paraphrase of the first speech input based on the
first user intent; and executing a respective task flow to
accomplish the first user intent; during a second interaction with
the user: receiving a second speech input from the user while the
user is located in a second geographic, the second speech input
being substantially identical to the first speech input;
determining a second user intent based on the second speech input,
the second user intent being identical to the first user intent;
determining that a location change from the first geographic area
to the second geographic area is associated with a change in
language or locale-specific vocabulary for at least one word or
expression in the second speech input; in response to said
determination, providing a second paraphrase based on the second
user intent, wherein the second paraphrase is different from the
first paraphrase based on the change in language or vocabulary; and
executing the respective task flow to accomplish the second user
intent.
8. The method of claim 7, wherein the first geographic area and the
second geographic area are both associated with a primary language
of the user.
9. The method of claim 7, wherein the change in locale-specific
vocabulary includes use of a respective local slang in the second
geographic area for the at least one word or expression in the
second speech input, and wherein the second paraphrase utilizes the
respective local slang.
10. The method of claim 9, further comprising: receiving user input
to start a learning session regarding the respective local slang;
and in response to receiving the user input, providing an
explanation of the usage of the respective local slang.
11. The method of claim 7, wherein the change in language includes
use of a respective local accent in the second geographic area for
the at least one word or expression in the second speech input, and
wherein the second paraphrase utilizes the respective local
accent.
12. The method of claim 11, further comprising: receiving user
input to start a learning session regarding the respective local
accent provided in the second paraphrase; and in response to
receiving the user input, providing one or more additional examples
of the usage of the respective local accent in the second
geographic area.
13. A non-transitory computer-readable storage medium storing one
or more programs, the one or more programs comprising instructions,
which when executed by one or more processors of a first electronic
device, cause the first electronic device to: during a first
interaction with a user: receive a first speech input from the user
while the user is located in a first geographic area; determine a
first user intent based on the first speech input; provide a first
paraphrase of the first speech input based on the first user
intent; and execute a respective task flow to accomplish the first
user intent; during a second interaction with the user: receive a
second speech input from the user while the user is located in a
second geographic, the second speech input being substantially
identical to the first speech input; determine a second user intent
based on the second speech input, the second user intent being
identical to the first user intent; determine that a location
change from the first geographic area to the second geographic area
is associated with a change in language or locale-specific
vocabulary for at least one word or expression in the second speech
input; in response to said determination, provide a second
paraphrase based on the second user intent, wherein the second
paraphrase is different from the first paraphrase based on the
change in language or vocabulary; and execute the respective task
flow to accomplish the second user intent.
14. The computer readable medium of claim 13, wherein the first
geographic area and the second geographic area are both associated
with a primary language of the user.
15. The computer readable medium of claim 13, wherein the change in
locale-specific vocabulary includes use of a respective local slang
in the second geographic area for the at least one word or
expression in the second speech input, and wherein the second
paraphrase utilizes the respective local slang.
16. The computer readable medium of claim 15, wherein the one or
more programs cause the electronic device to: receive user input to
start a learning session regarding the respective local slang; and
in response to receiving the user input, provide an explanation of
the usage of the respective local slang.
17. The computer readable medium of claim 13, wherein the change in
language includes use of a respective local accent in the second
geographic area for the at least one word or expression in the
second speech input, and wherein the second paraphrase utilizes the
respective local accent.
18. The computer readable medium of claim 17, further comprising:
receiving user input to start a learning session regarding the
respective local accent provided in the second paraphrase; and in
response to receiving the user input, providing one or more
additional examples of the usage of the respective local accent in
the second geographic area.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This Application is a continuation of U.S. Nonprovisional
application Ser. No. 14/213,852, filed on Mar. 14, 2014, entitled
"USER TRAINING BY INTELLIGENT DIGITAL ASSISTANT", which claims the
benefit of U.S. Provisional Application No. 61/800,846, filed on
Mar. 15, 2013, entitled "USER TRAINING BY INTELLIGENT DIGITAL
ASSISTANT", all of which are hereby incorporated by reference in
their entity for all purposes.
TECHNICAL FIELD
[0002] The disclosed embodiments relate generally to digital
assistants, and more specifically to digital assistants that
intelligently provide training opportunities and assistance to
users.
BACKGROUND
[0003] Just like human personal assistants, digital assistants or
virtual assistants can perform requested tasks and provide
requested advice, information, or services. An assistant's ability
to fulfill a user's request is dependent on the assistant's correct
comprehension of the request or instruction. Recent advances in
natural language processing have enabled users to interact with
digital assistants using natural language, in spoken or textual
forms, rather than employing a conventional user interface (e.g.,
menus or programmed commands). Such digital assistants can
interpret the user's input to infer the user's intent; translate
the inferred intent into actionable tasks and parameters; execute
operations or deploy services to perform the tasks; and produce
outputs that are intelligible to the user. Ideally, the outputs
produced by a digital assistant should fulfill the user's intent
expressed during the natural language interaction between the user
and the digital assistant. A digital assistant can perform searches
in a selection domain (e.g., a restaurant domain, etc.) and present
qualifying selection items (e.g., restaurants) in response to a
search request received from a user.
[0004] The ability of a digital assistant system to produce
satisfactory responses to user requests depends on the natural
language processing, knowledge base, and artificial intelligence
implemented by the system. Conventional digital assistants respond
to user commands of a single language, and provide responses in the
same language. These digital assistants are not adequate when a
user visits a region where a different language is used. In
addition, although some conventional digital assistants respond to
training or customization by a user, conventional digital
assistants have not been useful in providing training to a user by
leveraging its natural language and intent processing
capabilities.
SUMMARY
[0005] The embodiments disclosed herein provide methods, systems, a
computer readable storage medium and user interfaces for a digital
assistant to intelligently and proactively provide training
opportunities and assistance to a user by leveraging its natural
language processing and intent processing capabilities,
particularly in foreign language training and assistance, and/or in
introducing locale-specific accents/slangs to the user. An
intelligent digital assistant with multi-lingual capabilities can
be more effective in foreign language training because it does not
simply perform a direct literal translation of the user's input;
instead, the training samples and foreign language assistance are
provided based on user intent inferred based on the user's input.
Intent inference may be contextual, and can utilize relevant
information about the user's current needs from many different
sources. Many limitations of direct translation (e.g., mistakes due
to awkward and incorrect sentence structure, grammar, and usage in
the input) may be avoided. In addition, digital assistants are
aware of the current context associated with the user, and may
provide more appropriate foreign language training exercises to the
user for the current context and provide motivation and real life
practice opportunities for the user's learning.
[0006] Accordingly, some embodiments provide a method for operating
a digital assistant, the method including, at a device including
one or more processors and memory storing one or more programs:
receiving, from a user, a first speech input spoken in a first
language; inferring a user intent based on at least the first
speech input in the first language; based on the inferred user
intent, generating one or more alternative expressions of the first
speech input in the first language; and providing feedback to the
user introducing the alternative expressions as a more preferred
input to express the inferred user intent than the first speech
input provided by the user.
[0007] In some embodiments, providing the feedback further
includes: providing the feedback in a second language different
from the first language, wherein the second language is a primary
language associated with the user, and the first language is a
secondary language associated with the user.
[0008] In some embodiments, the one or more alternative expressions
of the first speech input includes at least a respective
alternative expression that corrects a pronunciation of at least
one word in the first speech input.
[0009] In some embodiments, the one or more alternative expressions
of the first speech input includes at least a respective
alternative expression that corrects a grammatical usage of at
least one word in the first speech input.
[0010] In some embodiments, the one or more alternative expressions
of the first speech input includes at least a respective
alternative expression that replaces at least one word or phrase in
the first speech input with another word or phrase.
[0011] In some embodiments, the method further includes: providing
at least a command mode and a foreign language training mode,
wherein the digital assistant executes a task flow to fulfill the
inferred user intent in the command mode, and wherein the digital
assistant generates the one or more alternative expressions and
provides the feedback to the user in the foreign Language training
mode.
[0012] In some embodiments, the method further includes:
concurrently providing both the command mode and the foreign
language training mode, wherein the digital assistant executes the
task flow to fulfill the inferred user intent, in addition to
generating the one or more alternative expressions and providing
the feedback to the user.
[0013] In some embodiments, the method further includes: receiving
user selection of the foreign language training mode; and enabling
the foreign language training mode in response to the user
selection of the foreign language training mode.
[0014] In some embodiments, the method further includes:
automatically, without user intervention, enabling the foreign
language training mode based on a current location of the user,
wherein a primary language associated with the current location of
the user is the first language.
[0015] In some embodiments, inferring the user intent based on the
first speech input in the first language further includes:
identifying a customized speech-to-text model of the first language
for the user, wherein the customized speech-to-text model has been
established based on training samples provided by native speakers
of a second language (of which language the user is also a native
speaker); processing the first speech input to generate a text
string using the customized speech-to-text model; and using the
text string as input for an intent inference model of the digital
assistant.
[0016] In some embodiments, generating the one or more alternative
expressions of the first speech input in the first language further
includes: identifying a second speech input previously provided by
a native speaker of the first language, wherein the second speech
input had been associated with a respective user intent that is
identical to the inferred user intent of the first speech input,
and wherein a task flow executed for the respective user intent had
been satisfactory to said native speaker; and utilizing the second
speech input as one of the alternative expressions of the first
speech input. The speech input previously provided by the native
speakers are good source of example expressions showing customary
usage of language and vocabulary in a particular region.
[0017] In some embodiments, providing the feedback to the user
introducing the alternative expressions as a more preferred input
to express the inferred user intent further includes: providing, in
a second language, an explanation of a difference between a first
alternative expression and the first speech input, wherein the
second language is a primary language associated with the user, and
the first language is a secondary language associated with the
user.
[0018] In some embodiments, the method further includes: receiving
a second speech input in the first language from the user, the
second speech input utilizing at least one of the alternative
expressions; determining whether the second speech input is a
satisfactory vocal utterance of the at least one alternative
expression; and upon determining that the second speech input is a
satisfactory vocal utterance of the at least one alternative
expression, executing a task flow to fulfill the inferred user
intent.
[0019] In some embodiments, the method further includes: providing,
in a second language, a paraphrase of the first speech input based
on the inferred user intent to confirm the correctness of the
inferred user intent, wherein the digital assistant generates the
alternative expressions and provides the feedback after receiving
user confirmation that the inferred user intent is the correct user
intent.
[0020] In some embodiments, inferring the user intent based on at
least the first speech input in the first language further includes
inferring the user intent further based on a current context
associated with the user.
[0021] In some embodiments, the current context associated with the
user includes at least a current location of the user.
[0022] In some embodiments, the current context associated with the
user includes at least a current time at which the first speech
input was received.
[0023] In some embodiments, the current context associated with the
user includes at least a type of place that is located at the
user's current location.
[0024] In some embodiments, the current context associated with the
user includes at least a correlation between a schedule item of the
user and the current location.
[0025] In some embodiments, the current context associated with the
user includes at least a correlation between a schedule item of the
user and the current time.
[0026] In some embodiments, the current context associated with the
user includes at least a current transportation mode of the
user.
[0027] In some embodiments, the current context associated with the
user includes at least a correlation between a directions request
entered by the user and the user's current location.
[0028] In some embodiments, the method further includes storing the
one or more alternative expressions for future review by the
user.
[0029] In some embodiments, the method further implements features
of any combination of the methods described above and in the
remainder of this specification.
[0030] Accordingly, some embodiments provide a method for operating
a digital assistant, the method including, at a device including
one or more processors and memory storing one or more programs:
receiving, from a user, a first speech input spoken in a first
language; inferring a user intent based on at least the first
speech input; based on the inferred user intent, generating one or
more alternative expressions of the first speech input in a second
language; and providing feedback to the user introducing the
alternative expressions as a means to accomplish the inferred user
intent when the user speaks at least one of the one or more
alternative expressions to another user who understands the second
language. In these embodiments, the digital assistant does not
provide information or perform the task requested by the user,
instead, the digital assistant teaches what the user needs to say
to another person to obtain the information and/or to get the task
accomplished. This is useful when the digital assistant can only
correctly infer the user's intent, but does not have sufficient
capabilities to accomplish the task for the user. Instead, the
digital assistant teaches the user the correct foreign language
expressions, such that the user can solicit and employ the help of
another person who does not speak the native language of the
user.
[0031] In some embodiments, the first language is a primary
language associated with the user, and the second language is a
primary language associated with a geographic area in which the
user is currently located.
[0032] In some embodiments, the first language is a primary
language associated with the user, and the second language is a
secondary language associated with the user.
[0033] In some embodiments, the second language is different from
the first language and at least one of the alternative expressions
is not a translation of the first speech input from the first
language to the second language.
[0034] In some embodiments, the digital assistant generates the
alternative expressions and provides the feedback in a foreign
language assistance mode in response to user selection of the
foreign language assistance mode.
[0035] In some embodiments, the digital assistant initiates a
foreign language assistance mode in response to detecting that the
user's current location is outside of a geographic area for which
the first language is a primary language, and wherein the digital
assistant generates the alternative expressions and provides the
feedback in the foreign language assistance mode.
[0036] In some embodiments, the digital assistant initiates a
foreign language assistance mode in response to detecting that the
user's current location is outside of a geographic area for which
the first language is a primary language, and that the digital
assistant is not able to fulfill the inferred user intent.
[0037] In some embodiments, the method further includes: in the
feedback provided to the user, presenting, in the first language, a
name of the second language as a respective language of the one or
more alternative expressions.
[0038] In some embodiments, the method further includes: providing
a practice session for the user to vocally practice at least one of
the one or more alternative expressions; and during the practice
session: receiving a second speech input from the user speaking at
least one of the one or more alternative expressions; determining
whether the second speech input is a satisfactory vocal utterance
of the at least one alternative expressions; and upon determining
that the second speech input is a satisfactory vocal utterance of
the at least one alternative expressions, providing an output to
the user indicating that the second speech input is
satisfactory.
[0039] In some embodiments, the method further includes: during the
practice session: providing, to the user, a sample vocal utterance
for at least one of the one or more alternative expressions.
[0040] In some embodiments, the method further includes during the
practice session: receiving a third speech input from the user
speaking at least one of the one or more alternative expressions;
detecting an error in the third speech input based on a difference
between the third speech input and a standard vocal utterance of
the at least one alternative expressions; and providing a sample
vocal utterance to the user one or more times, the sample vocal
utterance tailored for correcting the error in the third speech
input.
[0041] In some embodiments, the first language is a first dialect
of a respective language associated with the user, and the second
language is a second dialect of the respective language, and
wherein the second dialect is different from the first dialect and
is associated with a respective geographic area in which the user
is currently located.
[0042] In some embodiments, the one or more alternative expressions
of the first speech input includes at least a respective
alternative expression that changes a pronunciation of at least one
word in the first speech input.
[0043] In some embodiments, the one or more alternative expressions
of the first speech input includes at least a respective
alternative expression that changes a grammatical usage of at least
one word in the first speech input.
[0044] In some embodiments, the one or more alternative expressions
of the first speech input includes at least a respective
alternative expression that replaces at least one word in the first
speech input.
[0045] In some embodiments, the respective alternative expression
that replaces at least one word or expression in the first speech
input is a local slang for the at least one word or expression in
the geographic area in which the user is currently located.
[0046] In some embodiments, the digital assistant generates the
alternative expressions and provides the feedback in a foreign
language assistance mode, and the method further includes: while in
the foreign language assistance mode: receiving input from the user
for entering a live session for the user to utilize at least one of
the alternative expressions to accomplish the inferred user intent;
and providing the live session for the user; and during the live
session: listening for the user speaking the at least one of the
alternative expression to a second user; listening for a verbal
response from the second user; based on the verbal response
received from the second user, determining that additional foreign
language assistance is needed by the user; and providing one or
more speech outputs in the second language to assist the user in
accomplishing the inferred user intent.
[0047] In some embodiments, the method further includes: providing,
to the user, a textual transcript of a verbal exchange between the
digital assistant and the second user in a user interface displayed
on the device.
[0048] In some embodiments, the method further includes: providing,
to the user, a translation of the textual transcript from the
second language to the first language in the user interface
displayed on the device.
[0049] In some embodiments, the method further includes storing a
transcript of a user session conducted in the foreign language
assistance mode for future review by the user.
[0050] In some embodiments, the digital assistant generates a
different set of alternative expressions for the inferred user
intent depending on a respective current context associated with
the user.
[0051] In some embodiments, the current context associated with the
user includes a current location of the user.
[0052] In some embodiments, the current context associated with the
user includes a current time at which the first speech input was
received.
[0053] In some embodiments, the current context associated with the
user includes a type of place that is located at the user's current
location.
[0054] In some embodiments, the current context associated with the
user includes a correlation between a schedule item of the user and
the current location.
[0055] In some embodiments, the current context associated with the
user includes a correlation between a schedule item of the user and
the current time.
[0056] In some embodiments, the current context associated with the
user includes a current transportation mode of the user.
[0057] In some embodiments, the current context associated with the
user includes a correlation between a directions request entered by
the user and the user's current location.
[0058] In some embodiments, the method further implements features
of any combination of the methods described above and in the
remainder of this specification.
[0059] Accordingly, some embodiments provide a method for operating
a digital assistant, the method including, at a device including
one or more processors and memory storing one or more programs: (1)
during a first interaction with a user: receiving a first speech
input from the user while the user is located in a first geographic
area; inferring a first user intent based on the first speech
input; providing a first paraphrase of the first speech input based
on the inferred first user intent; and optionally executing a
respective task flow to accomplish the inferred first user intent;
(2) during a second interaction with the user: receiving a second
speech input from the user while the user is located in a second
geographic, the second speech input being substantially identical
to the first speech input; inferring a second user intent based on
the second speech input, the inferred second user intent being
identical to the inferred first user intent; determining that a
location change from the first geographic area to the second
geographic area is associated with a change in language or
locale-specific vocabulary for at least one word or expression in
the second speech input; in response to said determination,
providing a second paraphrase based on the second inferred user
intent, wherein the second paraphrase is different from the first
paraphrase based on the change in language or vocabulary; and
optionally executing the respective task flow to accomplish the
inferred second user intent.
[0060] In some embodiments, the first geographic area and the
second geographic area are both associated with a primary language
of the user.
[0061] In some embodiments, the change in locale-specific
vocabulary includes use of a respective local slang in the second
geographic area for the at least one word or expression in the
second speech input, and wherein the second paraphrase utilizes the
respective local slang.
[0062] In some embodiments, the method further includes: receiving
user input to start a learning session regarding the respective
local slang provided in the second paraphrase; and in response to
receiving the user input, providing an explanation of the usage of
the respective local slang in the second geographic area.
[0063] In some embodiments, the change in language includes use of
a respective local accent in the second geographic area for the at
least one word or expression in the second speech input, and
wherein the second paraphrase utilizes the respective local
accent.
[0064] In some embodiments, the method further includes: receiving
user input to start a learning session regarding the respective
local accent provided in the second paraphrase; and in response to
receiving the user input, providing one or more additional examples
of the usage of the respective local accent in the second
geographic area.
[0065] In some embodiments, the method further implements features
of any combination of the methods described above and in the
remainder of this specification.
[0066] Accordingly, some embodiments provide a method for operating
a digital assistant, the method including, at a device including
one or more processors and memory storing one or more programs:
evaluating a present context associated with a user; identifying a
respective foreign language training scenario associate with the
present context; and providing a foreign language training session
for the user, the foreign language training session containing one
or more foreign language exercises tailored for the current
context. In some embodiments, the digital assistant keeps track of
the present context associated with the user based on various
sensors (e.g., GPS, temperature sensors, light sensors,
accelerometers, compass, etc.) and events occurring on the device
(e.g., phone calls, email communications, notifications generated,
alerts by schedule items, searches performed, and directions
request fulfilled, etc.).
[0067] In some embodiments, the method further includes:
automatically, without user intervention, selecting a respective
language for the one or more foreign language exercises based on a
primary language associated with a geographic area in which the
user is currently located; and generating the one or more foreign
language exercises in the automatically selected language.
[0068] In some embodiments, the method further includes: receiving
user input selecting a respective language for the one or more
foreign language exercises; and generating the one or more foreign
language exercises in the user-selected language.
[0069] In some embodiments, the present context associated with the
user includes the user's presence inside a store located in a
geographic area in which a respective foreign language is a primary
language, and the one or more foreign language exercises include at
least vocabulary or dialogue in the respective foreign language
that is associated with shopping in the store.
[0070] In some embodiments, the present context associated with the
user includes the user's presence in proximity to a terminal of
public transportation located in a geographic area in which a
respective foreign language is a primary language, and the one or
more foreign language exercises include at least vocabulary or
dialogue in the respective foreign language that is associated with
use of the public transportation.
[0071] In some embodiments, the present context associated with the
user includes the user's presence inside a dining facility located
in a geographic area in which a respective foreign language is a
primary language, and the one or more foreign language exercises
include at least vocabulary or dialogue in the respective foreign
language that is associated with dining at the dining facility.
[0072] In some embodiments, the present context associated with the
user includes the user's presence inside a lodging facility located
in a geographic area in which a respective foreign language is a
primary language, and the one or more foreign language exercises
include at least vocabulary or dialogue in the respective foreign
language that is associated with lodging at the lodging
facility.
[0073] In some embodiments, the present context associated with the
user includes the user's presence inside a public transport vehicle
moving toward a destination for which the user has recently
requested directions and the destination is located in a geographic
area in which a respective foreign language is a primary language,
and wherein the one or more foreign language exercises include at
least vocabulary or dialogue in the respective foreign language
that is associated with visiting to said destination.
[0074] In some embodiments, the present context associated with the
user includes the user's presence inside a healthcare facility, and
wherein the one or more foreign language exercises include at least
vocabulary or dialogue in the respective foreign language that is
associated with obtaining healthcare services at the healthcare
facility.
[0075] In some embodiments, the present context associated with the
user includes the user's presence inside a business premise
offering beverage services, and wherein the one or more foreign
language exercises include at least vocabulary or dialogue in the
respective foreign language that is associated with ordering
beverages at the business premise.
[0076] In some embodiments, the method further includes presenting
images associated with vocabulary used in the foreign language
exercises.
[0077] In some embodiments, the method further implements features
of any combination of the methods described above and in the
remainder of this specification.
[0078] The above embodiments, and other embodiments described in
this specification may help realize one or more of the following
advantages.
[0079] In some embodiments, the digital assistant provides
alternative expressions for a user input based on the user intent
inferred from the user input. The alternative expressions may be
more grammatically correct, have better pronunciation, and/or are
more customary to the geographic region in which the user is
currently located. This is helpful for non-native speakers visiting
a foreign country (e.g., overseas students, business travelers,
tourists, etc.), to learn and remember the local language and usage
in context.
[0080] In addition, in some embodiments, the alternative
expressions are not necessarily a direct translation of the user
input, but are based on the user intent inferred from the user
input. Alternative expressions generated based on intent is more
tolerant of informalities, errors, and missing information in the
user input than results produced by direct translation. Thus, many
limitations of direct translation may be avoided.
[0081] In addition, in some embodiments, intent inference is
contextual, and can leverage many sources of information that the
digital assistant has about the user. Thus, more appropriate
expressions to accomplish the true intent of the user can be
provided by the digital assistant.
[0082] In some embodiments, the better expressions may be
introduced to the user in the user's native language, such that the
user can better understand the subtle differences between the
alternative expressions provided by the digital assistant and
his/her direct input.
[0083] In some embodiments, the digital assistant teaches foreign
language alternative expressions to a user after the user has
expressed his/her intent in his/her native language. This is useful
when the user wishes to speak to a real person about his/her needs
in the foreign language (e.g., when the real person only
understands the foreign language), and wants the digital assistant
to teach him/her the correct expressions in that foreign language.
Since the capabilities of a digital assistant are sometimes limited
and some assistance is better provided by a real person. The
ability to let the user to express his/her needs in his/her native
language to the digital assistant, and learn the correct foreign
language expressions needed to be said to a real person, can
greatly expand the helpfulness of the digital assistant in many
scenarios (e.g., when the user is traveling abroad).
[0084] In some embodiments, after teaching the foreign language
expressions to the user, the digital assistant can continue to
listen to the conversation between the user and the third-party
native speaker, and provides additional language assistance when
needed.
[0085] In some embodiments, the digital assistant service provider
already supports inputs and responses in multiple languages, and
implementing foreign language or mixed language assistance to a
user can effectively leverage the existing natural language
processing and intent processing capabilities of the digital
assistant.
[0086] In some embodiments, the digital assistant teaches the user
about a locale-specific accent or slang when the user is found at
that location. For example, if the user provides an input in one
location, and the input is completely congruent with the local
language usage, the digital assistant performs the task without
providing any language training. However, when the user provides
the same input at a different location, and the user input differs
from the local language usage in one or more aspects, the digital
assistant optionally provides some "in-context language training"
about those differences, in addition to performing the task. This
is helpful for both native speakers of a language and non-native
speakers for a language, since there are many regional variations
(e.g., expressions, word usage, accents, slangs, etc.) even for the
same language.
[0087] In some embodiments, in order to be less intrusive in the
training process, the digital assistant provides the differences
between the user's input and the locale-specific expressions in a
paraphrase of the user input, such that the user can hear the
differences, and learn the new language information without
expending too much time and efforts. In some embodiments,
additional information about those locale-specific language
differences may be provided to the user upon user request.
[0088] In some embodiments, the digital assistant keeps track of
the current context associated with the user, and provides foreign
language training opportunities to the user based on the user's
current context. For example, training exercises related to
shopping can be provided to the user, when the digital assistant
detects that the user is inside a store. More specific training
exercises and vocabulary may be provided to the user depending on
the type of store that the user is in. Sometimes, in addition to
the type of places that the user is current visiting, previous
directions requests, the user's calendar items, and searches
performed by the user can also be used to determine the current
context, which is then used to generate relevant foreign language
training exercises for the user. These contextually relevant
foreign language exercises can help motivate the user's learning,
and the surrounding environment also provides additional
opportunities and visual cues to help the user practice and
memorize the content of the foreign language exercises.
[0089] The details of one or more embodiments of the subject matter
described in this specification are set forth in the accompanying
drawings and the description below. Other features, aspects, and
advantages of the subject matter will become apparent from the
description, the drawings, and the claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0090] FIG. 1 is a block diagram illustrating an environment in
which a digital assistant operates in accordance with some
embodiments.
[0091] FIG. 2 is a block diagram illustrating a digital assistant
client system in accordance with some embodiments.
[0092] FIG. 3A is a block diagram illustrating a digital assistant
system or a server portion thereof in accordance with some
embodiments.
[0093] FIG. 3B is a block diagram illustrating functions of the
digital assistant shown in FIG. 3A in accordance with some
embodiments.
[0094] FIG. 3C is a diagram of a portion of an ontology in
accordance with some embodiments.
[0095] FIGS. 4A-4E are a flow chart of an exemplary process for
intelligently providing language training to a user in accordance
with some embodiments.
[0096] FIGS. 5A-5F are a flow chart of an exemplary process for
intelligently providing language assistance to a user in accordance
with some embodiments.
[0097] FIGS. 6A-6B are a flow chart of an exemplary process for
intelligently providing locale-specific language training to a user
in accordance with some embodiments.
[0098] FIGS. 7A-7C are a flow chart of an exemplary process for
intelligently providing context-based language training in
accordance with some embodiments.
[0099] Like reference numerals refer to corresponding parts
throughout the drawings.
DESCRIPTION OF EMBODIMENTS
[0100] FIG. 1 is a block diagram of an operating environment 100 of
a digital assistant according to some embodiments. The terms
"digital assistant," "virtual assistant," "intelligent automated
assistant," or "automatic digital assistant," refer to any
information processing system that interprets natural language
input in spoken and/or textual form to infer user intent, and
performs actions based on the inferred user intent. For example, to
act on an inferred user intent, the system can perform one or more
of the following: identifying a task flow with steps and parameters
designed to accomplish the inferred user intent, inputting specific
requirements from the inferred user intent into the task flow;
executing the task flow by invoking programs, methods, services,
APIs, or the like; and generating output responses to the user in
an audible (e.g. speech) and/or visual form.
[0101] Specifically, a digital assistant is capable of accepting a
user request at least partially in the form of a natural language
command, request, statement, narrative, and/or inquiry. Typically,
the user request seeks either an informational answer or
performance of a task by the digital assistant. A satisfactory
response to the user request is either provision of the requested
informational answer, performance of the requested task, or a
combination of the two. For example, a user may ask the digital
assistant a question, such as "Where am I right now?" Based on the
user's current location, the digital assistant may answer, "You are
in Central Park." The user may also request the performance of a
task, for example, "Please remind me to call mom at 4 pm today." In
response, the digital assistant may acknowledge the request and
then creates an appropriate reminder item in the user's electronic
schedule. During performance of a requested task, the digital
assistant sometimes interacts with the user in a continuous
dialogue involving multiple exchanges of information over an
extended period of time. There are numerous other ways of
interacting with a digital assistant to request information or
performance of various tasks. In addition to providing verbal
responses and taking programmed actions, the digital assistant also
provides responses in other visual or audio forms, e.g., as text,
alerts, music, videos, animations, etc. In some embodiments, the
digital assistant accepts input in more than one language, and
provides responses in the language of the input, the user's primary
language, a user's selected language, and/or a mixture of multiple
languages.
[0102] An example of a digital assistant is described in
Applicant's U.S. Utility application Ser. No. 12/987,982 for
"Intelligent Automated Assistant," filed Jan. 10, 2011, the entire
disclosure of which is incorporated herein by reference.
[0103] As shown in FIG. 1, in some embodiments, a digital assistant
is implemented according to a client-server model. The digital
assistant includes a client-side portion 102a, 102b (hereafter "DA
client 102") executed on a user device 104a, 104b, and a
server-side portion 106 (hereafter "DA server 106") executed on a
server system 108. The DA client 102 communicates with the DA
server 106 through one or more networks 110. The DA client 102
provides client-side functionalities such as user-facing input and
output processing and communications with the DA-server 106. The DA
server 106 provides server-side functionalities for any number of
DA-clients 102 each residing on a respective user device 104.
[0104] In some embodiments, the DA server 106 includes a
client-facing I/O interface 112, one or more processing modules
114, data and models 116, and an I/O interface to external services
118. The client-facing I/O interface facilitates the client-facing
input and output processing for the digital assistant server 106.
The one or more processing modules 114 utilize the data and models
116 to determine the user's intent based on natural language input
and perform task execution based on inferred user intent. In some
embodiments, the DA-server 106 communicates with external services
120 through the network(s) 110 for task completion or information
acquisition. The I/O interface to external services 118 facilitates
such communications.
[0105] Examples of the user device 104 include, but are not limited
to, a handheld computer, a personal digital assistant (PDA), a
tablet computer, a laptop computer, a desktop computer, a cellular
telephone, a smart phone, an enhanced general packet radio service
(EGPRS) mobile phone, a media player, a navigation device, a game
console, a television, a remote control, or a combination of any
two or more of these data processing devices or other data
processing devices. More details on the user device 104 are
provided in reference to an exemplary user device 104 shown in FIG.
2.
[0106] Examples of the communication network(s) 110 include local
area networks ("LAN") and wide area networks ("WAN"), e.g., the
Internet. The communication network(s) 110 are, optionally,
implemented using any known network protocol, including various
wired or wireless protocols, such as e.g., Ethernet, Universal
Serial Bus (USB), FIREWIRE, Global System for Mobile Communications
(GSM), Enhanced Data GSM Environment (EDGE), code division multiple
access (CDMA), time division multiple access (TDMA), Bluetooth,
Wi-Fi, voice over Internet Protocol (VoIP), Wi-MAX, or any other
suitable communication protocol.
[0107] The server system 108 is implemented on one or more
standalone data processing apparatus or a distributed network of
computers. In some embodiments, the server system 108 also employs
various virtual devices and/or services of third party service
providers (e.g., third-party cloud service providers) to provide
the underlying computing resources and/or infrastructure resources
of the server system 108.
[0108] Although the digital assistant shown in FIG. 1 includes both
a client-side portion (e.g., the DA-client 102) and a server-side
portion (e.g., the DA-server 106), in some embodiments, the
functions of a digital assistant is implemented as a standalone
application installed on a user device. In addition, the divisions
of functionalities between the client and server portions of the
digital assistant can vary in different embodiments. For example,
in some embodiments, the DA client is a thin-client that provides
only user-facing input and output processing functions, and
delegates all other functionalities of the digital assistant to a
backend server.
[0109] FIG. 2 is a block diagram of a user-device 104 in accordance
with some embodiments. The user device 104 includes a memory
interface 202, one or more processors 204, and a peripherals
interface 206. The various components in the user device 104 are
coupled by one or more communication buses or signal lines. The
user device 104 includes various sensors, subsystems, and
peripheral devices that are coupled to the peripherals interface
206. The sensors, subsystems, and peripheral devices gather
information and/or facilitate various functionalities of the user
device 104.
[0110] For example, a motion sensor 210, a light sensor 212, and a
proximity sensor 214 are coupled to the peripherals interface 206
to facilitate orientation, light, and proximity sensing functions.
One or more other sensors 216, such as a positioning system (e.g.,
GPS receiver), a temperature sensor, a biometric sensor, a gyro, a
compass, an accelerometer, and the like, are also connected to the
peripherals interface 206, to facilitate related
functionalities.
[0111] In some embodiments, a camera subsystem 220 and an optical
sensor 222 are utilized to facilitate camera functions, such as
taking photographs and recording video clips. Communication
functions are facilitated through one or more wired and/or wireless
communication subsystems 224, which can include various
communication ports, radio frequency receivers and transmitters,
and/or optical (e.g., infrared) receivers and transmitters. An
audio subsystem 226 is coupled to speakers 228 and a microphone 230
to facilitate voice-enabled functions, such as voice recognition,
voice replication, digital recording, and telephony functions.
[0112] In some embodiments, an I/O subsystem 240 is also coupled to
the peripheral interface 206. The I/O subsystem 240 includes a
touch screen controller 242 and/or other input controller(s) 244.
The touch-screen controller 242 is coupled to a touch screen 246.
The touch screen 246 and the touch screen controller 242 can, for
example, detect contact and movement or break thereof using any of
a plurality of touch sensitivity technologies, such as capacitive,
resistive, infrared, surface acoustic wave technologies, proximity
sensor arrays, and the like. The other input controller(s) 244 can
be coupled to other input/control devices 248, such as one or more
buttons, rocker switches, thumb-wheel, infrared port, USB port,
and/or a pointer device such as a stylus.
[0113] In some embodiments, the memory interface 202 is coupled to
memory 250. The memory 250 can include high-speed random access
memory and/or non-volatile memory, such as one or more magnetic
disk storage devices, one or more optical storage devices, and/or
flash memory (e.g., NAND, NOR).
[0114] In some embodiments, the memory 250 stores an operating
system 252, a communication module 254, a graphical user interface
module 256, a sensor processing module 258, a phone module 260, and
applications 262. The operating system 252 includes instructions
for handling basic system services and for performing hardware
dependent tasks. The communication module 254 facilitates
communicating with one or more additional devices, one or more
computers and/or one or more services. The graphical user interface
module 256 facilitates graphic user interface processing. The
sensor processing module 258 facilitates sensor-related processing
and functions. The phone module 260 facilitates phone-related
processes and functions. The application module 262 facilitates
various functionalities of user applications, such as
electronic-messaging, web browsing, media processing, Navigation,
imaging and/or other processes and functions.
[0115] As described in this specification, the memory 250 also
stores client-side digital assistant instructions (e.g., in a
digital assistant client module 264) and various user data 266
(e.g., user-specific vocabulary data, preference data, and/or other
data such as the user's electronic address book, to-do lists,
shopping lists, etc.) to provide the client-side functionalities of
the digital assistant.
[0116] In various embodiments, the digital assistant client module
264 is capable of accepting voice input (e.g., speech input), text
input, touch input, and/or gestural input through various user
interfaces (e.g., the I/O subsystem 244) of the user device 104.
The digital assistant client module 264 is also capable of
providing output in audio (e.g., speech output), visual, and/or
tactile forms. For example, output can be provided as voice, sound,
alerts, text messages, menus, graphics, videos, animations,
vibrations, and/or combinations of two or more of the above. During
operation, the digital assistant client module 264 communicates
with the digital assistant server using the communication
subsystems 224.
[0117] In some embodiments, the digital assistant client module 264
utilizes the various sensors, subsystems peripheral devices to
gather additional information from the surrounding environment of
the user device 104 to establish a context associated with a user,
the current user interaction, and/or the current user input. In
some embodiments, the digital assistant client module 264 provides
the context information or a subset thereof with the user input to
the digital assistant server to help infer the user's intent. In
some embodiments, the digital assistant also uses the context
information to determine how to prepare and delivery outputs to the
user.
[0118] In some embodiments, the context information that
accompanies the user input includes sensor information, e.g.,
lighting, ambient noise, ambient temperature, images or videos of
the surrounding environment, etc. in some embodiments, the context
information also includes the physical state of the device, e.g.,
device orientation, device location, device temperature, power
level, speed, acceleration, motion patterns, cellular signals
strength, etc. In some embodiments, information related to the
software state of the user device 106, e.g., running processes,
installed programs, past and present network activities, background
services, error logs, resources usage, etc., of the user device 104
are provided to the digital assistant server as context information
associated with a user input.
[0119] In some embodiments, the DA client module 264 selectively
provides information (e.g., user data 266) stored on the user
device 104 in response to requests from the digital assistant
server. In some embodiments, the digital assistant client module
264 also elicits additional input from the user via a natural
language dialogue or other user interfaces upon request by the
digital assistant server 106. The digital assistant client module
264 passes the additional input to the digital assistant server 106
to help the digital assistant server 106 in intent inference and/or
fulfillment of the user's intent expressed in the user request.
[0120] In various embodiments, the memory 250 includes additional
instructions or fewer instructions. Furthermore, various functions
of the user device 104 may be implemented in hardware and/or in
firmware, including in one or more signal processing and/or
application specific integrated circuits.
[0121] FIG. 3A is a block diagram of an example digital assistant
system 300 in accordance with some embodiments. In some
embodiments, the digital assistant system 300 is implemented on a
standalone computer system. In some embodiments, the digital
assistant system 300 is distributed across multiple computers. In
some embodiments, some of the modules and functions of the digital
assistant are divided into a server portion and a client portion,
where the client portion resides on a user device (e.g., the user
device 104) and communicates with the server portion (e.g., the
server system 108) through one or more networks, e.g., as shown in
FIG. 1. In some embodiments, the digital assistant system 300 is an
embodiment of the server system 108 (and/or the digital assistant
server 106) shown in FIG. 1. It should be noted that the digital
assistant system 300 is only one example of a digital assistant
system, and that the digital assistant system 300 may have more or
fewer components than shown, may combine two or more components, or
may have a different configuration or arrangement of the
components. The various components shown in FIG. 3A may be
implemented in hardware, software instructions for execution by one
or more processors, firmware, including one or more signal
processing and/or application specific integrated circuits, or a
combination of thereof.
[0122] The digital assistant system 300 includes memory 302, one or
more processors 304, an input/output (I/O) interface 306, and a
network communications interface 308. These components communicate
with one another over one or more communication buses or signal
Lines 310.
[0123] In some embodiments, the memory 302 includes a
non-transitory computer readable medium, such as high-speed random
access memory and/or a non-volatile computer readable storage
medium (e.g., one or more magnetic disk storage devices, flash
memory devices, or other non-volatile solid-state memory
devices).
[0124] In some embodiments, the I/O interface 306 couples
input/output devices 316 of the digital assistant system 300, such
as displays, a keyboards, touch screens, and microphones, to the
user interface module 322. The I/O interface 306, in conjunction
with the user interface module 322, receive user inputs (e.g.,
voice input, keyboard inputs, touch inputs, etc.) and process them
accordingly. In some embodiments, e.g., when the digital assistant
is implemented on a standalone user device, the digital assistant
system 300 includes any of the components and I/O and communication
interfaces described with respect to the user device 104 in FIG. 2.
In some embodiments, the digital assistant system 300 represents
the server portion of a digital assistant implementation, and
interacts with the user through a client-side portion residing on a
user device (e.g., the user device 104 shown in FIG. 2).
[0125] In some embodiments, the network communications interface
308 includes wired communication port(s) 312 and/or wireless
transmission and reception circuitry 314. The wired communication
port(s) receive and send communication signals via one or more
wired interfaces, e.g., Ethernet, Universal Serial Bus (USB),
FIREWIRE, etc. The wireless circuitry 314 receives and sends RF
signals and/or optical signals from/to communications networks and
other communications devices. The wireless communications,
optionally, use any of a plurality of communications standards,
protocols and technologies, such as GSM, EDGE, CDMA, TDMA,
Bluetooth, Wi-Fi, VoIP, Wi-MAX, or any other suitable communication
protocol. The network communications interface 308 enables
communication between the digital assistant system 300 with
networks, such as the Internet, an intranet and/or a wireless
network, such as a cellular telephone network, a wireless local
area network (LAN) and/or a metropolitan area network (MAN), and
other devices.
[0126] In some embodiments, memory 302, or the computer readable
storage media of memory 302, stores programs, modules,
instructions, and data structures including all or a subset of: an
operating system 318, a communications module 320, a user interface
module 322, one or more applications 324, and a digital assistant
module 326. The one or more processors 304 execute these programs,
modules, and instructions, and reads/writes from/to the data
structures.
[0127] The operating system 318 (e.g., Darwin, RTXC, LINUX, UNIX,
OS X, WINDOWS, or an embedded operating system such as VxWorks)
includes various software components and/or drivers for controlling
and managing general system tasks (e.g., memory management, storage
device control, power management, etc.) and facilitates
communications between various hardware, firmware, and software
components.
[0128] The communications module 320 facilitates communications
between the digital assistant system 300 with other devices over
the network communications interface 308. For example, the
communication module 320, optionally, communicates with the
communication interface 254 of the device 104 shown in FIG. 2. The
communications module 320 also includes various components for
handling data received by the wireless circuitry 314 and/or wired
communications port 312.
[0129] The user interface module 322 receives commands and/or
inputs from a user via the I/O interface 306 (e.g., from a
keyboard, touch screen, pointing device, controller, and/or
microphone), and generates user interface objects on a display. The
user interface module 322 also prepares and delivers outputs (e.g.,
speech, sound, animation, text, icons, vibrations, haptic feedback,
and light, etc.) to the user via the I/O interface 306 (e.g.,
through displays, audio channels, speakers, and touch-pads,
etc.).
[0130] The applications 324 include programs and/or modules that
are configured to be executed by the one or more processors 304.
For example, if the digital assistant system is implemented on a
standalone user device, the applications 324, optionally, include
user applications, such as games, a calendar application, a
navigation application, or an email application. If the digital
assistant system 300 is implemented on a server farm, the
applications 324, optionally, include resource management
applications, diagnostic applications, or scheduling applications,
for example.
[0131] The memory 302 also stores the digital assistant module (or
the server portion of a digital assistant) 326. In some
embodiments, the digital assistant module 326 includes the
following sub-modules, or a subset or superset thereof: an
input/output processing module 328, a speech-to-text (STT)
processing module 330, a natural language processing module 332, a
dialogue flow processing module 334, a task flow processing module
336, a service processing module 338, and a user training module
340. Each of these modules has access to one or more of the
following data and models of the digital assistant 326, or a subset
or superset thereof: ontology 360, vocabulary index 344, user data
348, task flow models 354, service models 356, and user training
data 358.
[0132] In some embodiments, using the processing modules, data, and
models implemented in the digital assistant module 326, the digital
assistant performs at least some of the following: identifying a
user's intent expressed in a natural language input received from
the user; actively eliciting and obtaining information needed to
fully infer the user's intent (e.g., by disambiguating words,
names, intentions, etc.); determining the task flow for fulfilling
the inferred intent; and executing the task flow to fulfill the
inferred intent. In this specifications, more details regarding the
user training module 340 and its use of the user training data 358
are provided later in FIGS. 4A-7C and accompanying
descriptions.
[0133] In some embodiments, as shown in FIG. 3B, the I/O processing
module 328 interacts with the user through the I/O devices 316 in
FIG. 3A or with a user device (e.g., a user device 104 in FIG. 1)
through the network communications interface 308 in FIG. 3A to
obtain user input (e.g., a speech input) and to provide responses
(e.g., as speech outputs) to the user input. The I/O processing
module 328, optionally, obtains context information associated with
the user input from the user device, along with or shortly after
the receipt of the user input. The context information includes
user-specific data, vocabulary, and/or preferences relevant to the
user input. In some embodiments, the context information also
includes software and hardware states of the device (e.g., the user
device 104 in FIG. 1) at the time the user request is received,
and/or information related to the surrounding environment of the
user at the time that the user request was received. In some
embodiments, the I/O processing module 328 also sends follow-up
questions to, and receives answers from, the user regarding the
user request. When a user request is received by the I/O processing
module 328 and the user request contains a Speech input, the I/O
processing module 328 forwards the speech input to the
speech-to-text (STT) processing module 330 for speech-to-text
conversions.
[0134] The speech-to-text processing module 330 receives Speech
input (e.g., a user utterance captured in a voice recording)
through the I/O processing module 328. In some embodiments, the
speech-to-text processing module 330 uses various acoustic and
language models to recognize the speech input as a sequence of
phonemes, and ultimately, a sequence of words or tokens written in
one or more languages. The speech-to-text processing module 330 can
be implemented using any suitable speech recognition techniques,
acoustic models, and language models, such as Hidden Markov Models,
Dynamic Time Warping (DTW)-based speech recognition, and other
statistical and/or analytical techniques. In some embodiments, the
speech-to-text processing can be performed at least partially by a
third party service or on the user's device. In some embodiments,
the speech-to-text processing module 330 handles input in multiple
languages and have locale-specific acoustic models and language
models tailored for each language and locale-specific inputs. In
some embodiments, the speech-to-text processing module 330 includes
multiple acoustic and language models for each single language to
accommodate regional and other variations. Once the speech-to-text
processing module 330 obtains the result of the speech-to-text
processing, e.g., a sequence of words or tokens, it passes the
result to the natural language processing module 332 for intent
inference.
[0135] More details on the speech-to-text processing are described
in U.S. Utility application Ser. No. 13/236,942 for "Consolidating
Speech Recognition Results," filed on Sep. 20, 2011, the entire
disclosure of which is incorporated herein by reference.
[0136] The natural language processing module 332 ("natural
language processor") of the digital assistant takes the sequence of
words or tokens ("token sequence") generated by the speech-to-text
processing module 330, and attempts to associate the token sequence
with one or more "actionable intents" recognized by the digital
assistant. An "actionable intent" represents a task that can be
performed by the digital assistant, and has an associated task flow
implemented in the task flow models 354. The associated task flow
is a series of programmed actions and steps that the digital
assistant takes in order to perform the task. The scope of a
digital assistant's capabilities is dependent on the number and
variety of task flows that have been implemented and stored in the
task flow models 354, or in other words, on the number and variety
of "actionable intents" that the digital assistant recognizes. The
effectiveness of the digital assistant, however, is also dependent
on the assistant's ability to infer the correct "actionable
intent(s)" from the user request expressed in natural language.
[0137] In some embodiments, the natural language processing module
332 handles input in multiple languages and have locale-specific
vocabulary and language usage models tailored for each language and
locale-specific inputs. In some embodiments, the natural language
processing module 332 includes multiple locale-specific vocabulary
and language usage models for each single language to accommodate
regional and other variations.
[0138] In some embodiments, in addition to the sequence of words or
tokens obtained from the speech-to-text processing module 330, the
natural language processor 332 also receives context information
associated with the user request, e.g., from the I/O processing
module 328. The natural language processor 332, optionally, uses
the context information to clarify, supplement, and/or further
define the information contained in the token sequence received
from the speech-to-text processing module 330. The context
information includes, for example, user preferences, hardware
and/or software states of the user device, sensor information
collected before, during, or shortly after the user request, prior
interactions (e.g., dialogue) between the digital assistant and the
user, and the like.
[0139] In some embodiments, the natural language processing is
based on ontology 360. The ontology 360 is a hierarchical structure
containing many nodes, each node representing either an "actionable
intent" or a "property" relevant to one or more of the "actionable
intents" or other "properties". As noted above, an "actionable
intent" represents a task that the digital assistant is capable of
performing, i.e., it is "actionable" or can be acted on. A
"property" represents a parameter associated with an actionable
intent, a domain concept or entity, or a sub-aspect of another
property. A linkage between an actionable intent node and a
property node in the ontology 360 defines how a parameter
represented by the property node pertains to the task represented
by the actionable intent node.
[0140] In some embodiments, the ontology 360 is made up of
actionable intent nodes and property nodes. Within the ontology
360, each actionable intent node is linked to one or more property
nodes either directly or through one or more intermediate property
nodes. Similarly, each property node is linked to one or more
actionable intent nodes either directly or through one or more
intermediate property nodes. For example, as shown in FIG. 3C, the
ontology 360 may include a "restaurant reservation" node (i.e., an
actionable intent node). Property node "restaurant," (a domain
entity represented by a property node) and property nodes
"date/time" (for the reservation) and "party size" are each
directly linked to the actionable intent node (i.e., the
"restaurant reservation" node). In addition, property nodes
"cuisine," "price range," "phone number," and "location" are
sub-nodes of the property node "restaurant," and are each linked to
the "restaurant reservation" node (i.e., the actionable intent
node) through the intermediate property node "restaurant." For
another example, as shown in FIG. 3C, the ontology 360 may also
include a "set reminder" node (i.e., another actionable intent
node). Property nodes "date/time" (for the setting the reminder)
and "subject" (for the reminder) are each linked to the "set
reminder" node. Since the property "date/time" is relevant to both
the task of making a restaurant reservation and the task of setting
a reminder, the property node "date/time" is linked to both the
"restaurant reservation" node and the "set reminder" node in the
ontology 360.
[0141] An actionable intent node, along with its linked concept
nodes, may be described as a "domain." In the present discussion,
each domain is associated with a respective actionable intent, and
refers to the group of nodes (and the relationships therebetween)
associated with the particular actionable intent. For example, the
ontology 360 shown in FIG. 3C includes an example of a restaurant
reservation domain 362 and an example of a reminder domain 364
within the ontology 360. The restaurant reservation domain includes
the actionable intent node "restaurant reservation," property nodes
"restaurant," "date/time," and "party size," and sub-property nodes
"cuisine," "price range," "phone number," and "location." The
reminder domain 364 includes the actionable intent node "set
reminder," and property nodes "subject" and "date/time." In some
embodiments, the ontology 360 is made up of many domains. Each
domain may share one or more property nodes with one or more other
domains. For example, the "date/time" property node may be
associated with many different domains (e.g., a scheduling domain,
a travel reservation domain, a movie ticket domain, etc.), in
addition to the restaurant reservation domain 362 and the reminder
domain 364.
[0142] While FIG. 3C illustrates two example domains within the
ontology 360, other domains (or actionable intents) include, for
example, "initiate a phone call," "find directions," "schedule a
meeting," "send a message," and "provide an answer to a question,"
and so on. A "send a message" domain is associated with a "send a
message" actionable intent node, and optionally further includes
property nodes such as "recipient(s)", "message type", and "message
body." The property node "recipient" is optionally further defined,
for example, by the sub-property nodes such as "recipient name" and
"message address."
[0143] In some embodiments, the ontology 360 includes all the
domains (and hence actionable intents) that the digital assistant
is capable of understanding and acting upon. In some embodiments,
the ontology 360 is optionally modified, such as by adding or
removing entire domains or nodes, or by modifying relationships
between the nodes within the ontology 360.
[0144] In some embodiments, nodes associated with multiple related
actionable intents are optionally clustered under a "super domain"
in the ontology 360. For example, a "travel" super-domain
optionally includes a cluster of property nodes and actionable
intent nodes related to travels. The actionable intent nodes
related to travels optionally includes "airline reservation,"
"hotel reservation," "car rental," "get directions," "find points
of interest," and so on. The actionable intent nodes under the same
super domain (e.g., the "travels" super domain) sometimes have many
property nodes in common. For example, the actionable intent nodes
for "airline reservation," "hotel reservation," "car rental," "get
directions," "find points of interest" sometimes share one or more
of the property nodes "start location," "destination," "departure
date/time," "arrival date/time," and "party size."
[0145] In some embodiments, each node in the ontology 360 is
associated with a set of words and/or phrases that are relevant to
the property or actionable intent represented by the node. The
respective set of words and/or phrases associated with each node is
the so-called "vocabulary" associated with the node. The respective
set of words and/or phrases associated with each node can be stored
in the vocabulary index 344 in association with the property or
actionable intent represented by the node. For example, returning
to FIG. 3B, the vocabulary associated with the node for the
property of "restaurant" optionally includes words such as "food,"
"drinks," "cuisine," "hungry," "eat," "pizza," "fast food," "meal,"
and so on. For another example, the vocabulary associated with the
node for the actionable intent of "initiate a phone call"
optionally includes words and phrases such as "call," "phone,"
"dial," "ring," "call this number," "make a call to," and so on.
The vocabulary index 344, optionally, includes words and phrases in
different languages.
[0146] The natural language processor 332 receives the token
sequence (e.g., a text string) from the speech-to-text processing
module 330, and determines what nodes are implicated by the words
in the token sequence. In some embodiments, if a word or phrase in
the token sequence is found to be associated with one or more nodes
in the ontology 360 (via the vocabulary index 344), the word or
phrase will "trigger" or "activate" those nodes. Based on the
quantity and/or relative importance of the activated nodes, the
natural language processor 332 will select one of the actionable
intents as the task that the user intended the digital assistant to
perform. In some embodiments, the domain that has the most
"triggered" nodes is selected. In some embodiments, the domain
having the highest confidence value (e.g., based on the relative
importance of its various triggered nodes) is selected. In some
embodiments, the domain is selected based on a combination of the
number and the importance of the triggered nodes. In some
embodiments, additional factors are considered in selecting the
node as well, such as whether the digital assistant has previously
correctly interpreted a similar request from a user.
[0147] In some embodiments, the digital assistant also stores names
of specific entities in the vocabulary index 344, so that when one
of these names is detected in the user request, the natural
language processor 332 will be able to recognize that the name
refers to a specific instance of a property or sub-property in the
ontology. In some embodiments, the names of specific entities are
names of businesses, restaurants, people, movies, and the like. In
some embodiments, the digital assistant searches and identifies
specific entity names from other data sources, such as the user's
address book, a movies database, a musicians database, and/or a
restaurant database. In some embodiments, when the natural language
processor 332 identifies that a word in the token sequence is a
name of a specific entity (such as a name in the user's address
book), that word is given additional significance in selecting the
actionable intent within the ontology for the user request.
[0148] For example, when the words "Mr. Santo" are recognized from
the user request, and the last name "Santo" is found in the
vocabulary index 344 as one of the contacts in the user's contact
list, then it is likely that the user request corresponds to a
"send a message" or "initiate a phone call" domain. For another
example, when the words "ABC Cafe" are found in the user request,
and the term "ABC Cafe" is found in the vocabulary index 344 as the
name of a particular restaurant in the user's city, then it is
likely that the user request corresponds to a "restaurant
reservation" domain.
[0149] User data 348 includes user-specific information, such as
user-specific vocabulary, user preferences, user address, user's
default and secondary languages, user's contact list, and other
short-term or long-term information for each user. In some
embodiments, the natural language processor 332 uses the
user-specific information to supplement the information contained
in the user input to further define the user intent. For example,
for a user request "invite my friends to my birthday party," the
natural language processor 332 is able to access user data 348 to
determine who the "friends" are and when and where the "birthday
party" would be held, rather than requiring the user to provide
such information explicitly in his/her request.
[0150] Other details of searching an ontology based on a token
string is described in U.S. Utility application Ser. No. 12/341,743
for "Method and Apparatus for Searching Using An Active Ontology,"
filed Dec. 22, 2008, the entire disclosure of which is incorporated
herein by reference.
[0151] In some embodiments, once the natural language processor 332
identifies an actionable intent (or domain) based on the user
request, the natural language processor 332 generates a structured
query to represent the identified actionable intent. In some
embodiments, the structured query includes parameters for one or
more nodes within the domain for the actionable intent, and at
least some of the parameters are populated with the specific
information and requirements specified in the user request. For
example, the user may say "Make me a dinner reservation at a sushi
place at seven o'clock." In this case, the natural language
processor 332 may be able to correctly identify the actionable
intent to be "restaurant reservation" based on the user input.
According to the ontology, a structured query for a "restaurant
reservation" domain optionally includes parameters such as
{Cuisine}, {Time}, {Date}, {Party Size}, and the like. In some
embodiments, based on the information contained in the user's
utterance, the natural language processor 332 generates a partial
structured query for the restaurant reservation domain, where the
partial structured query includes the parameters {Cuisine="Sushi"}
and {Time="7 pm"}. However, in this example, the user's utterance
contains insufficient information to complete the structured query
associated with the domain. Therefore, other necessary parameters
such as {Party Size} and {Date} are not specified in the structured
query based on the information currently available. In some
embodiments, the natural language processor 332 populates some
parameters of the structured query with received context
information. For example, in some embodiments, if the user
requested a sushi restaurant "near me," the natural language
processor 332 populates a {location} parameter in the structured
query with GPS coordinates from the user device 104.
[0152] In some embodiments, the natural language processor 332
passes the structured query (including any completed parameters) to
the task flow processing module 336 ("task flow processor"). The
task flow processor 336 is configured to receive the structured
query from the natural language processor 332, complete the
structured query, if necessary, and perform the actions required to
"complete" the user's ultimate request. In some embodiments, the
various procedures necessary to complete these tasks are provided
in task flow models 354. In some embodiments, the task flow models
include procedures for obtaining additional information from the
user, and task flows for performing actions associated with the
actionable intent.
[0153] As described above, in order to complete a structured query,
the task flow processor 336 sometimes needs to initiate additional
dialogue with the user in order to obtain additional information,
and/or disambiguate potentially ambiguous utterances. When such
interactions are necessary, the task flow processor 336 invokes the
dialogue processing module 334 ("dialogue processor 334") to engage
in a dialogue with the user. In some embodiments, the dialogue
processor 334 determines how (and/or when) to ask the user for the
additional information, and receives and processes the user
responses. The questions are provided to and answers are received
from the users through the I/O processing module 328. In some
embodiments, the dialogue processor 334 presents dialogue output to
the user via audio and/or visual output, and receives input from
the user via spoken or physical (e.g., clicking) responses.
Continuing with the example above, when the task flow processor 336
invokes the dialogue flow processor 334 to determine the "party
size" and "date" information for the structured query associated
with the domain "restaurant reservation," the dialogue flow
processor 335 generates questions such as "For how many people?"
and "On which day?" to pass to the user. Once answers are received
from the user, the dialogue flow processor 334 can then populate
the structured query with the missing information, or pass the
information to the task flow processor 336 to complete the missing
information from the structured query.
[0154] In some cases, the task flow processor 336 receives a
structured query that has one or more ambiguous properties, e.g.,
when a structured query for the "send a message" domain indicates
that the intended recipient is "Bob," and the user happens to have
multiple contacts named "Bob." In such cases, the task flow
processor 336, optionally, requests that the dialogue processor 334
disambiguate this property of the structured query. In turn, the
dialogue processor 334, optionally, asks the user "Which Bob?", and
displays (or reads) a list of contacts named "Bob" from which the
user may choose.
[0155] Once the task flow processor 336 has completed the
structured query for an actionable intent, the task flow processor
336 proceeds to perform the ultimate task associated with the
actionable intent. Accordingly, the task flow processor 336
executes the steps and instructions in the task flow model
according to the specific parameters contained in the structured
query. For example, the task flow model for the actionable intent
of "restaurant reservation", optionally, includes steps and
instructions for contacting a restaurant and actually requesting a
reservation for a particular party size at a particular time. For
example, using a structured query such as: {restaurant reservation,
restaurant=ABC Cafe, date=3/12/2012, time=7 pm, party size=5}, the
task flow processor 336, optionally, performs the steps of: (1)
logging onto a server of the ABC Cafe or a restaurant reservation
system such as OPENTABLE.RTM., (2) entering the date, time, and
party size information in a form on the website, (3) submitting the
form, and (4) making a calendar entry for the reservation in the
user's calendar.
[0156] In some embodiments, the task flow processor 336 employs the
assistance of a service processing module 338 ("service processor")
to complete a task requested in the user input or to provide an
informational answer requested in the user input. For example, the
service processor 338 can act on behalf of the task flow processor
336 to make a phone call, set a calendar entry, invoke a map
search, invoke or interact with other user applications installed
on the user device, and invoke or interact with third party
services (e.g. a restaurant reservation portal, a social networking
website, a banking portal, etc.). In some embodiments, the
protocols and application programming interfaces (API) required by
each service can be specified by a respective service model among
the services models 356. The service processor 338 accesses the
appropriate service model for a service and generates requests for
the service in accordance with the protocols and APIs required by
the service according to the service model.
[0157] For example, if a restaurant has enabled an online
reservation service, the restaurant can submit a service model
specifying the necessary parameters for making a reservation and
the APIs for communicating the values of the necessary parameter to
the online reservation service. When requested by the task flow
processor 336, the service processor 338 can establish a network
connection with the online reservation service using the web
address stored in the service model, and send the necessary
parameters of the reservation (e.g., time, date, party size) to the
online reservation interface in a format according to the API of
the online reservation service.
[0158] in some embodiments, the natural language processor 332,
dialogue processor 334, and task flow processor 336 are used
collectively and iteratively to infer and define the user's intent,
obtain information to further clarify and refine the user intent,
and finally generate a response (i.e., an output to the user, or
the completion of a task) to fulfill the user's intent.
[0159] In some embodiments, after all of the tasks needed to
fulfill the user's request have been performed, the digital
assistant 326 formulates a confirmation response, and sends the
response back to the user through the I/O processing module 328. If
the user request seeks an informational answer, the confirmation
response presents the requested information to the user. In some
embodiments, the digital assistant also requests the user to
indicate whether the user is satisfied with the response produced
by the digital assistant 326.
[0160] More details on the digital assistant can be found in the
U.S. Utility application Ser. No. 12/987,982, entitled "Intelligent
Automated Assistant", filed Jan. 10, 2011, U.S. Provisional
Application No. 61/493,201, entitled "Generating and Processing
Data Items That Represent Tasks to Perform", filed Jun. 3, 2011,
the entire disclosures of which are incorporated herein by
reference.
[0161] As described in this specification, in some embodiments, a
digital assistant provides training, in particular, locale-specific
language training and/or foreign language training and assistance
to the user. The exemplary processes provided below may be
implemented by the user training module 340, using the information
stored in the user training data 358. In some embodiments, the user
training data 358 includes suitable alternative expressions and
vocabulary in various languages indexed by user intent, and
templates for additional foreign language exercises.
[0162] FIGS. 4A-4E illustrate an exemplary process 400 for
providing alternative expressions for a direct user input to the
user in accordance with some embodiments. In some embodiments, the
process 400 is performed by the user training module 340 of the
digital assistant 326 based on user training data 358, e.g., shown
in FIGS. 3A and 3B.
[0163] In the process 400, the digital assistant receives (402),
from a user, a first speech input spoken in a first language. The
digital assistant infers (404) a user intent based on at least the
first speech input in the first language. Based on the inferred
user intent, the digital assistant generates (406) one or more
alternative expressions of the first speech input in the first
language. The digital assistant provides (408) feedback to the user
introducing the alternative expressions as a more preferred input
to express the inferred user intent than the first speech input
provided by the user.
[0164] In an example scenario, the user provides a speech input in
English to the digital assistant "Where can I buy a torch?" The
user, being a non-native English speaker, may not be aware that the
term "torch" has a different meaning in the United States than in
other English-speaking countries. The digital assistant receiving
the speech input is aware of the different meanings for the term
"torch" in different locales (e.g., in England, "torch" refers to a
type of illumination device relying on dry batteries (i.e.,
"flashlight" in the United States), but in the United States,
"torch" refers to a type of illumination devices relying on burning
an organic fuel). The digital assistant infers an intent based on
the user's input, and determines that the user is more likely to be
asking about the type of illumination devices using dry batteries.
Based on the inferred intent and the user's current location (e.g.,
in the United States), the digital assistant generates at least one
alternative expression for the term "torch," such as "flashlight"
and provides that alternative expression to the user. For example,
in some embodiments, the digital assistant displays in a user
interface names and directions to stores (e.g., hardware stores)
that sell flashlights, and in addition, the digital assistant also
teaches the user that the term "flashlight" is more customarily
used in the United States than the term "torch." In some
embodiments, instead of teaching the user about the term "torch"
directly, the digital assistant optionally provides a paraphrase of
the user's speech input, where the paraphrase introduces the term
"flashlight" to the user. For example, the paraphrase provided by
the digital assistant can be in the form of a confirmation request
"Did you mean you want to buy a "flashlight" which uses dry
batteries instead of a burning fuel?" Alternatively, the digital
assistant optionally says "Searching for stores nearby that sell
flashlights . . . ."
[0165] In another example, when a non-native English speaker
provides a speech input in English, but speaks one or more words
with a heavy accent. The digital assistant can infer the user's
intent based on other content in the speech input and the current
context. Based on the inferred user intent, the digital assistant
can generate alternative expressions that correct the accent of the
particular words. For example, many proper nouns (e.g., names of
international stores and businesses) are used worldwide, and the
pronunciations for those proper nouns are localized in different
countries and regions. When a non-native speaker speaks those
proper nouns in a request to the digital assistant, they frequently
use the pronunciations in their native languages, even though the
rest of the request is spoken with a proper English accent. Based
on the inferred user intent, the digital assistant can determine
what those words are, and provide alternative expressions that have
the correct pronunciation in an American or British accent. For
example, when the user says "I want to fine a MaiDanglao to get a
burger." Although the pronunciation of the user is imperfect, the
digital assistant infers that the user wishes to find a McDonald's
to get a burger, and presents a speech output saying "OK,
McDonald's is what you want to find. I will show you the
directions." The digital assistant optionally places vocal emphasis
on the terms "McDonald's" and "find" to indicate to the user the
proper pronunciation of these words. In some embodiments, spelling
of the words "McDonald's" and "find" is shown to the user in a user
interface as well.
[0166] In some embodiments, when providing the feedback to the
user, the digital assistant provides (410) the feedback in a second
language different from the first language, where the second
language is a primary language associated with the user, and the
first language is a secondary language associated with the user.
For example, if the user is not a native English speaker, and
provides a speech input in English, the digital assistant
optionally provides the feedback in the native language of the
speaker (e.g., Chinese). In other words, part of the response
provided by the digital assistant is in the user's native language,
while the alternative expressions are in English. Continuing with
the earlier example, the digital assistant may provide a speech
output "______ McDonald's ______." The English translation of the
speech output is "I think you want to find McDonald's. I found two
nearby."
[0167] In some embodiments, the one or more alternative expressions
of the first speech input includes (412) at least a respective
alternative expression that corrects a pronunciation of at least
one word in the first speech input.
[0168] In some embodiments, the one or more alternative expressions
of the first speech input includes (414) at least a respective
alternative expression that corrects a grammatical usage of at
least one word in the first speech input.
[0169] In some embodiments, the one or more alternative expressions
of the first speech input includes (416) at least a respective
alternative expression that replaces at least one word or phrase in
the first speech input with another word or phrase.
[0170] In some embodiments, the digital assistant provide (418) at
least a command mode and a foreign language training mode, where
the digital assistant (1) executes a task flow to fulfill the
inferred user intent in the command mode, and (2) generates the one
or more alternative expressions and provides the feedback to the
user in the foreign language training mode.
[0171] In some embodiments, the digital assistant concurrently
provides (420) both the command mode and the foreign language
training mode (e.g., in a hybrid mode), where the digital assistant
executes the task flow to fulfill the inferred user intent, in
addition to generating the one or more alternative expressions and
providing the feedback to the user.
[0172] In some embodiments, the digital assistant receives (422)
user selection of the foreign language training mode; and enables
(424) the foreign language training mode in response to the user
selection of the foreign language training mode.
[0173] In some embodiments, the digital assistant automatically,
without user intervention, enables (426) the foreign language
training mode based on a current location of the user, where a
primary language associated with the current location of the user
is the first language.
[0174] In some embodiments, to infer (428) the user intent based on
the first speech input in the first language, the digital assistant
identifies (430) a customized speech-to-text model of the first
language for the user, where the customized speech-to-text model
has been established based on training samples provided by native
speakers of a second language of which the user is also a native
speaker. The digital assistant then process (432) the first speech
input to generate a text string using the customized speech-to-text
model. In some embodiments, the digital assistant uses (434) the
text string as input for an intent inference model of the digital
assistant.
[0175] In some embodiments, to generate (436) the one or more
alternative expressions of the first speech input in the first
language, the digital assistant identifies (438) a second speech
input previously provided by a native speaker of the first
language, where the second speech input had been associated with a
respective user intent that is identical to the inferred user
intent of the first speech input, and where a task flow executed
for the respective user intent had been satisfactory to said native
speaker. The digital assistant then utilizes (440) the second
speech input as one of the alternative expressions of the first
speech input.
[0176] In some embodiments, to provide (442) the feedback to the
user introducing the alternative expressions as a more preferred
input to express the inferred user intent, the digital assistant
provides (444), in a second language, an explanation of a
difference between a first alternative expression and the first
speech input, where the second language is a primary language
associated with the user, and the first language is a secondary
language associated with the user.
[0177] In some embodiments, the digital assistant receives (446) a
second speech input in the first language from the user, the second
speech input utilizing at least one of the alternative expressions
provided to the user. In some embodiments, the digital assistant
determines (448) whether the second speech input is a satisfactory
vocal utterance of the at least one alternative expression. In some
embodiments, upon determining that the second speech input is a
satisfactory vocal utterance of the at least one alternative
expression, the digital assistant executes (450) a task flow to
fulfill the inferred user intent.
[0178] In some embodiments, the digital assistant provides (452),
in a second language, a paraphrase of the first speech input based
on the inferred user intent to confirm the correctness of the
inferred user intent, where the digital assistant generates the
alternative expressions and provides the feedback after receiving
user confirmation that the inferred user intent is the correct user
intent.
[0179] In some embodiments, inferring the user intent based on at
least the first speech input in the first language further includes
(454) inferring the user intent further based on a current context
associated with the user.
[0180] In some embodiments, the current context associated with the
user includes (456) at least a current location of the user.
[0181] In some embodiments, the current context associated with the
user includes (458) at least a current time at which the first
speech input was received.
[0182] In some embodiments, the current context associated with the
user includes (460) at least a type of place that is located at the
user's current location.
[0183] In some embodiments, the current context associated with the
user includes (462) at least a correlation between a schedule item
of the user and the current location.
[0184] In some embodiments, the current context associated with the
user includes (464) at least a correlation between a schedule item
of the user and the current time.
[0185] In some embodiments, the current context associated with the
user includes (466) at least a current transportation mode of the
user.
[0186] In some embodiments, the current context associated with the
user includes (468) at least a correlation between a directions
request entered by the user and the user's current location.
[0187] In some embodiments, the digital assistant stores (470) the
one or more alternative expressions for future review by the
user.
[0188] In some embodiments, the process 400 includes any
combination of the features described above and in the remainder of
this specification.
[0189] FIGS. 5A-5F illustrate an exemplary process 500 for
providing foreign language assistance for a user based on a direct
user input expressing the user's intent and needs, in accordance
with some embodiments. In some embodiments, the process 500 is
performed by the user training module 340 of the digital assistant
326 based on user training data 358, e.g., shown in FIGS. 3A and
3B.
[0190] In the process 500, in some embodiments, the digital
assistant receives (502), from a user, a first speech input spoken
in a first language. The digital assistant infers (504) a user
intent based on at least the first speech input. Based on the
inferred user intent, the digital assistant generates (506) one or
more alternative expressions of the first speech input in a second
language. The digital assistant then provides (508) feedback to the
user introducing the alternative expressions as a means to
accomplish the inferred user intent when the user speaks at least
one of the one or more alternative expressions to another user who
understands the second language.
[0191] In an example scenario, the user is in a foreign country
(e.g., China) and does not speak the native language (e.g.,
Chinese) of the region (e.g., southern China). The user can employ
the assistance of his/her digital assistant, but sometimes, the
capabilities of the digital assistant are not adequate in the
current situation. For example, if the user is visiting a client
"Mr. Santo," and the user is already in Mr. Santo's office
building. He cannot ask the digital assistant for information
regarding Mr. Santo's whereabouts in the office building. Instead,
the user needs to speak to a real person in the foreign language
(e.g., Mandarin) that is understood in this region. In this case,
the user asks the digital assistant for foreign language
assistance. For example, the user may enable a foreign language
assistance mode, and ask the digital assistant with a speech input
in his native language (e.g., English), "I need to find Mr. Santo."
The digital assistant can correctly infer the user's intent based
on the user's speech input. Instead of providing directions to the
user, the digital assistant provides expressions in Chinese that
would be useful for the user to enlist help of a Chinese person.
For example, the digital assistant optionally provides sample
speech outputs saying "______, ______ Santo ______? (meaning
"Hello, which floor is Mr. Santo located?"), and/or "______, ______
Santo ______? (meaning "Sir, is Mr. Santo in?"), and/or "______,
______ Santo ______, ______? (meaning "Miss, I am looking for Mr.
Santo. Is he here today?"). In some embodiments, the digital
assistant provides each of these alternative expressions in
Chinese, and plays back sample recordings of these alternative
Chinese expressions. In some embodiments, the digital assistant
also helps the user to practice the pronunciation of these
expressions in Chinese, e.g., by providing phonetic spellings of
these sentences."
[0192] In another example, if the user (e.g., an English speaker)
is driving in a foreign country (e.g., Taiwan), and gets a flat
tire. The user may ask the digital assistant to find tire shops or
towing companies on a map, but may not be able to get the services
needed without speaking the local language. In some embodiments,
the user enables the foreign language assistance mode, and provides
a speech input explaining his/her needs. For example, the user may
say to the digital assistant in English "I have a flat tire and
need to call a tow truck." The digital assistant processes the
speech input, and determines that the user needs to speak to a
person at a towing service. Based on the user intent, the digital
assistant generates a number of expressions in the local language
(e.g., Mandarin), and provides the expressions to the user. For
example, in an output interface of the digital assistant, the
digital assistant optionally provides the following expressions:
"______, ______, ______ A ______ 10 ______ (meaning "Hi, I need
towing service. I am located at the intersection of A street and
No. 10 road), and/or "______, ______, ______ (meaning "Hi, my tire
blew. I need a tow truck or a mechanic to come."). In some
embodiments, the digital assistant teaches the user how to say
these expressions in the foreign language properly, and let the
user practice a few times before letting the user call the local
roadside assistance services. In some embodiments, as shown in this
example, the digital assistant optionally includes additional
information the assistant has about the user (e.g., the user's
current location) in the expressions, even though the user
him/herself may not possess this information or have included this
information in his/her speech input to the digital assistant.
[0193] In some embodiments, the first language is (510) a primary
language associated with the user, and the second language is a
primary language associated with a geographic area in which the
user is currently located.
[0194] In some embodiments, the first language is (512) a primary
language associated with the user, and the second language is a
secondary language associated with the user.
[0195] In some embodiments, the second language is (514) different
from the first language and at least one of the alternative
expressions is not a translation of the first speech input from the
first language to the second language.
[0196] In some embodiments, the digital assistant generates (516)
the alternative expressions and provides the feedback in a foreign
language assistance mode in response to user selection of the
foreign language assistance mode.
[0197] In some embodiments, the digital assistant initiates (518) a
foreign language assistance mode in response to detecting that the
user's current location is outside of a geographic area for which
the first language is a primary language, and wherein the digital
assistant generates the alternative expressions and provides the
feedback in the foreign language assistance mode.
[0198] In some embodiments, the digital assistant initiates (520) a
foreign language assistance mode in response to detecting that the
user's current location is outside of a geographic area for which
the first language is a primary language, and that the digital
assistant is not able to fulfill the inferred user intent.
[0199] In some embodiments, in the feedback provided to the user,
the digital assistant presents (522), in the first language, a name
of the second language as a respective language of the one or more
alternative expressions.
[0200] In some embodiments, the digital assistant provides (524) a
practice session for the user to vocally practice at least one of
the one or more alternative expressions. During the practice
session (526): the digital assistant receives (528) a second speech
input from the user speaking at least one of the one or more
alternative expressions; determines (530) whether the second speech
input is a satisfactory vocal utterance of the at least one
alternative expressions; and upon determining that the second
speech input is a satisfactory vocal utterance of the at least one
alternative expressions, provides (532) an output to the user
indicating that the second speech input is satisfactory.
[0201] In some embodiments, during the practice session, the
digital assistant provides (534), to the user, a sample vocal
utterance for at least one of the one or more alternative
expressions.
[0202] In some embodiments, during the practice session, the
digital assistant receives (536) a third speech input from the user
speaking at least one of the one or more alternative expressions.
In some embodiments, the digital assistant detects (538) an error
in the third speech input based on a difference between the third
speech input and a standard vocal utterance of the at least one
alternative expressions. In some embodiments, the digital assistant
provides (540) a sample vocal utterance to the user one or more
times, the sample vocal utterance tailored for correcting the error
in the third speech input
[0203] In some embodiments, the first language is (542) a first
dialect of a respective language associated with the user, and the
second language is a second dialect of the respective language, and
where the second dialect is different from the first dialect and is
associated with a respective geographic area in which the user is
currently located.
[0204] In some embodiments, the one or more alternative expressions
of the first speech input includes (544) at least a respective
alternative expression that changes a pronunciation of at least one
word in the first speech input.
[0205] In some embodiments, the one or more alternative expressions
of the first speech input includes (546) at least a respective
alternative expression that changes a grammatical usage of at least
one word in the first speech input.
[0206] In some embodiments, the one or more alternative expressions
of the first speech input includes (548) at least a respective
alternative expression that replaces at least one word in the first
speech input.
[0207] In some embodiments, the respective alternative expression
that replaces at least one word or expression in the first speech
input is (550) a local slang for the at least one word or
expression in the geographic area in which the user is currently
located.
[0208] In some embodiments, the digital assistant generates (552)
the alternative expressions and provides the feedback in a foreign
language assistance mode. In some embodiments, while in the foreign
language assistance mode (554), the digital assistant receives
(556) input from the user for entering a live session for the user
to utilize at least one of the alternative expressions to
accomplish the inferred user intent. In some embodiments, the
digital assistant provides (558) the live session for the user. In
some embodiments, during the live session (560), the digital
assistant listens (562) for the user speaking the at least one of
the alternative expression to a second user. The digital assistant
also listens (564) for a verbal response from the second user.
Based on the verbal response received from the second user, the
digital assistant determines (566) that additional foreign language
assistance is needed by the user; and provides (568) one or more
speech outputs in the second language to assist the user in
accomplishing the inferred user intent.
[0209] In some embodiments, the digital assistant provides (570),
to the user, a textual transcript of a verbal exchange between the
digital assistant and the second user in a user interface displayed
on the device.
[0210] In some embodiments, the digital assistant provides (572),
to the user, a translation of the textual transcript from the
second language to the first language in the user interface
displayed on the device.
[0211] In some embodiments, the digital assistant stores (574) a
transcript of a user session conducted in the foreign language
assistance mode for future review by the user.
[0212] In some embodiments, the digital assistant generates (576) a
different set of alternative expressions for the inferred user
intent depending on a respective current context associated with
the user.
[0213] In some embodiments, the current context associated with the
user includes (578) a current location of the user.
[0214] In some embodiments, the current context associated with the
user includes (580) a current time at which the first speech input
was received.
[0215] In some embodiments, the current context associated with the
user includes (582) a type of place that is located at the user's
current location. Example types of places include places to shop
(e.g., shopping mall, grocery stores, clothing outlets, shoe
stores, electronic stores, supermarkets, etc.), places to get
drinks (e.g., bars, pubs, etc.), places to get coffee or other
beverages (e.g. coffee shops, juice bar, ice cream shops, tea
houses, etc.), places to eat (e.g., fining dining restaurants, fast
food restaurants, cafe, cafeteria, etc.) places to send mail (e.g.,
postal offices, mail boxes, commercial shipping services, etc.),
places to get healthcare services (e.g., hospitals and clinics,
emergency services, etc.), places to get banking services (e.g.,
banks, check cashing services, etc.), places to take public
transportation (e.g., train stations, bus stops, airports, etc.),
places to see movies (e.g., theatres, movie theatres, video stores,
video rental stores, etc.), tourist sites, places to get police
assistance (e.g., police station, through police dispatchers),
etc.
[0216] In some embodiments, the current context associated with the
user includes (584) a correlation between a schedule item of the
user and the current location.
[0217] In some embodiments, the current context associated with the
user includes (586) a correlation between a schedule item of the
user and the current time.
[0218] In some embodiments, the current context associated with the
user includes (588) a current transportation mode of the user.
[0219] In some embodiments, the current context associated with the
user includes (590) a correlation between a directions request
entered by the user and the user's current location.
[0220] In some embodiments, the process 500 further implements any
combination of the features described above and in the remainder of
this specification.
[0221] FIGS. 6A-6B illustrate an exemplary process 600 for
providing locale-specific language information in response changes
in the user's current location, in accordance with some
embodiments. In some embodiments, the process 600 is performed by
the user training module 340 of the digital assistant 326 based on
user training data 358, e.g., shown in FIGS. 3A and 3B.
[0222] In the process 600, in some embodiments, during a first
interaction with a user (602): the digital assistant receives (604)
a first speech input from the user while the user is located in a
first geographic area. The digital assistant infers (606) a first
user intent based on the first speech input. The digital assistant
provides (608) a first paraphrase of the first speech input based
on the inferred first user intent. The digital assistant then
executes (610) a respective task flow to accomplish the inferred
first user intent. During a second interaction with the same user
(612): the digital assistant receives (614) a second speech input
from the user while the user is located in a second geographic, the
second speech input being substantially identical to the first
speech input. The digital assistant infers (616) a second user
intent based on the second speech input, the inferred second user
intent being identical to the inferred first user intent. The
digital assistant determines (618) that a location change from the
first geographic area to the second geographic area is associated
with a change in language or locale-specific vocabulary for at
least one word or expression in the second speech input. In
response to said determination, the digital assistant provides
(620) a second paraphrase based on the second inferred user intent,
where the second paraphrase is different from the first paraphrase
based on the change in language or vocabulary. In some embodiments,
the digital assistant executes (622) the respective task flow to
accomplish the inferred second user intent.
[0223] In an example scenario, during one user session, if the user
says "I want to buy some pencils and erasers" while the user is in
the United States, the digital assistant will infer that the user
needs to find a stationery shop, and provides search results and
directions to one or more stationery shops nearby. In some
embodiments, to confirm the user intent, the digital assistant
optionally provides a paraphrase of the user input, e.g., "Search
for stores that sell erasers and pencils . . . . Here are a few
stationery stores I found . . . ." When the user travels to a
different location (e.g., England) where the language usage and
vocabulary are somewhat different from the United States, the
digital assistant optionally provides opportunities for the user to
learn about the local language usage and vocabulary. For example,
in another user session occurring while the user is in England, if
the user says "I want to buy some erasers and pencils," the digital
assistant will infer the same user intent as before (e.g., the user
needs to find a stationery shop). In addition to providing search
results and directions to one or more stationery shops nearby, the
digital assistant optionally provides a different paraphrase of the
user input. In this paraphrase, the digital assistant can teach the
user about the language difference in the U.S. and England for the
term "eraser." For example, the digital assistant optionally
provides a paraphrase that says "Search for stores that sell
rubbers and pencils . . . . Here are a few stationery stores I
found . . . " or "Erasers are called `rubbers` in England. Here are
a few stationery shops that sell rubbers and pencils." By including
the change in language or locale-specific vocabulary in the
paraphrase, the digital assistant can provide some information to
the user in context, without making the interaction with the user
too cumbersome. Other similar examples include "gas" and "petrol,"
"apartment" and "flat," "can" and "tin," "closet" and "wardrobe,"
"elevator" and "lift," etc.
[0224] In some embodiments, the first geographic area and the
second geographic area are (624) both associated with a primary
language of the user.
[0225] In some embodiments, the change in locale-specific
vocabulary includes (626) use of a respective local slang in the
second geographic area for the at least one word or expression in
the second speech input, and wherein the second paraphrase utilizes
the respective local slang.
[0226] In some embodiments, the digital assistant receives (628)
user input to start a learning session regarding the respective
local slang provided in the second paraphrase. In response to
receiving the user input, the digital assistant provides (630) an
explanation of the usage of the respective local slang in the
second geographic area.
[0227] In some embodiments, the change in language includes (632)
use of a respective local accent in the second geographic area for
the at least one word or expression in the second speech input, and
wherein the second paraphrase utilizes the respective local
accent.
[0228] In some embodiments, the digital assistant receives (634)
user input to start a learning session regarding the respective
local accent provided in the second paraphrase. In response to
receiving the user input, the digital assistant provides (636) one
or more additional examples of the usage of the respective local
accent in the second geographic area.
[0229] In some embodiments, the digital assistant further
implements any combination of the features described above and in
the remainder of this specification.
[0230] FIGS. 7A-7C illustrate an exemplary process 700 for
providing context-based foreign language training exercises based
on the current context associated with the user, in accordance with
some embodiments. In some embodiments, the process 700 is performed
by the user training module 340 of the digital assistant 326 based
on user training data 358, e.g., shown in FIGS. 3A and 3B.
[0231] In the process 700, in some embodiments, the digital
assistant evaluates (702) a present context associated with a user.
The digital assistant identifies (704) a respective foreign
language training scenario associate with the present context. The
digital assistant then provides (706) a foreign language training
session for the user, the foreign language training session
containing one or more foreign language exercises tailored for the
current context.
[0232] In an example scenario, when the user is in a foreign
country, the digital assistant keeps track of the user's current
location. When the user is located inside a grocery store in the
foreign country, the digital assistant optionally generates foreign
language exercises that are suitable for the current context. For
example, the foreign language exercises may include vocabulary
about food products (e.g., words for names of food products,
categories of food products, description of food products,
nutrition information of food products, prices, discounts, etc.).
In addition, the foreign language exercises may also include
dialogues and/or phrases related to interactions that commonly
occur in a grocery store. For example, the digital assistant
optionally generates foreign language exercises related to asking
for help to locate a particular food item, asking for different
alternatives for a food product, asking about origins or sources of
a particular type of food product, asking about discounts for a
particular product, and errors in prices for particular products
and/or the total bill, etc.
[0233] In some embodiments, the digital assistant can carry out a
foreign language dialogue with the user to practice various aspects
of shopping in the grocery store. Dynamically generating foreign
language exercises based on the current context is helpful for the
user to learn a foreign language more quickly. The user may better
remember words in the foreign language, when he or she can see in
person the particular items that those words describe.
Comprehension and memorization of the foreign language dialogues
and vocabulary can also be enhanced when the user see the same
words in context (e.g., food labels in the grocery store), and hear
the dialogue spoken by native speakers in real life (e.g., other
customers asking about prices and discounts in the grocery
store).
[0234] In some embodiments, the digital assistant automatically,
without user intervention, selects (708) a respective language for
the one or more foreign language exercises based on a primary
language associated with a geographic area in which the user is
currently located. Then, the digital assistant generates (710) the
one or more foreign language exercises in the automatically
selected language.
[0235] In some embodiments, the digital assistant receives (712)
user input selecting a respective language for the one or more
foreign language exercises, and generates (714) the one or more
foreign language exercises in the user-selected language.
[0236] In some embodiments, the present context associated with the
user includes (716) the user's presence inside a store located in a
geographic area in which a respective foreign language is a primary
language, and the one or more foreign language exercises include at
least vocabulary or dialogue in the respective foreign language
that is associated with shopping in the store.
[0237] In some embodiments, the present context associated with the
user includes (718) the user's presence in proximity to a terminal
of public transportation located in a geographic area in which a
respective foreign language is a primary language, and the one or
more foreign language exercises include at least vocabulary or
dialogue in the respective foreign language that is associated with
use of the public transportation.
[0238] In some embodiments, the present context associated with the
user includes (720) the user's presence inside a dining facility
located in a geographic area in which a respective foreign language
is a primary language, and the one or more foreign language
exercises include at least vocabulary or dialogue in the respective
foreign language that is associated with dining at the dining
facility.
[0239] In some embodiments, the present context associated with the
user includes (722) the user's presence inside a lodging facility
located in a geographic area in which a respective foreign language
is a primary language, and the one or more foreign language
exercises include at least vocabulary or dialogue in the respective
foreign language that is associated with lodging at the lodging
facility.
[0240] In some embodiments, the present context associated with the
user includes (724) the user's presence inside a public transport
vehicle moving toward a destination for which the user has recently
requested directions and the destination is located in a geographic
area in which a respective foreign language is a primary language,
and wherein the one or more foreign language exercises include at
least vocabulary or dialogue in the respective foreign language
that is associated with visiting to said destination.
[0241] In some embodiments, the present context associated with the
user includes (726) the user's presence inside a healthcare
facility, and wherein the one or more foreign language exercises
include at least vocabulary or dialogue in the respective foreign
language that is associated with obtaining healthcare services at
the healthcare facility.
[0242] In some embodiments, the present context associated with the
user includes (728) the user's presence inside a business premise
offering beverage services, and wherein the one or more foreign
language exercises include at least vocabulary or dialogue in the
respective foreign language that is associated with ordering
beverages at the business premise.
[0243] In some embodiments, the digital assistant presents (730)
images associated with vocabulary used in the foreign language
exercises.
[0244] In some embodiments, the digital assistant further
implements any combination of the features described above.
[0245] The foregoing description, for purpose of explanation, has
been described with reference to specific embodiments. However, the
illustrative discussions above are not intended to be exhaustive or
to limit the invention to the precise forms disclosed. Many
modifications and variations are possible in view of the above
teachings. The embodiments were chosen and described in order to
best explain the principles of the invention and its practical
applications, to thereby enable others skilled in the art to best
utilize the invention and various embodiments with various
modifications as are suited to the particular use contemplated.
* * * * *