U.S. patent application number 13/339086 was filed with the patent office on 2012-04-26 for system and method for recognizing proper names in dialog systems.
This patent application is currently assigned to Robert Bosch GmbH. Invention is credited to Zhe Feng, Zhongnan Shen, Fuliang Weng.
Application Number | 20120101823 13/339086 |
Document ID | / |
Family ID | 41557545 |
Filed Date | 2012-04-26 |
United States Patent
Application |
20120101823 |
Kind Code |
A1 |
Weng; Fuliang ; et
al. |
April 26, 2012 |
SYSTEM AND METHOD FOR RECOGNIZING PROPER NAMES IN DIALOG
SYSTEMS
Abstract
Embodiments of a dialog system that utilizes contextual
information to perform recognition of proper names are described.
Unlike present name recognition methods on large name lists that
generally focus strictly on the static aspect of the names,
embodiments of the present system take into account of the
temporal, recency and context effect when names are used, and
formulates new questions to further constrain the search space or
grammar for recognition of the past and current utterances.
Inventors: |
Weng; Fuliang; (Mountain
View, CA) ; Shen; Zhongnan; (Shanghai, CN) ;
Feng; Zhe; (Shanghai, CN) |
Assignee: |
Robert Bosch GmbH
Stuttgart
DE
|
Family ID: |
41557545 |
Appl. No.: |
13/339086 |
Filed: |
December 28, 2011 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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12274267 |
Nov 19, 2008 |
8108214 |
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13339086 |
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Current U.S.
Class: |
704/254 ;
704/E15.005 |
Current CPC
Class: |
G10L 15/22 20130101 |
Class at
Publication: |
704/254 ;
704/E15.005 |
International
Class: |
G10L 15/04 20060101
G10L015/04 |
Claims
1. A computer-implemented method in a dialog system, comprising:
receiving a user utterance including a proper name; recognizing the
proper name in the user utterance; determining a first confidence
score for the recognized proper name; generating a first machine
response the user utterance including a confirmation question
related to the proper name, wherein the first machine response does
not repeat the proper name, if the first confidence score is below
a defined threshold value; receiving a user response to the
confirmation question; and modifying the first confidence score to
generate a second confidence score based on the user response.
2. The method of claim 1 wherein the proper name comprises any part
of speech describing a person, place or thing.
3. The method of claim 1 wherein the user utterance comprises a
query input to a speech recognition stage of the dialog system, the
method further comprising: parsing the proper name from the user
utterance; generating one or more hypotheses to identify a
plurality of candidate names, each candidate name potentially
corresponding to the uttered proper name; and selecting the best
proper name from the plurality of candidate names.
4. The method of claim 2 wherein the best candidate name is the
name with the first confidence score that is closest to the defined
threshold.
5. The method of claim 1 further comprising: defining one or more
characteristics associated with the uttered proper name; and
formulating the confirmation question by incorporating at least one
characteristic of the one or more characteristics in the first
machine response.
6. The method of claim 5 wherein the confirmation question is
formulated based on an n-best list.
7. The method of claim 5 further comprising generating a second
machine response the user utterance including an additional
confirmation question related to the proper name, wherein the
second machine response does not repeat the proper name, if the
second confidence score is below the defined threshold value.
8. The method of claim 7 further comprising formulating the
additional confirmation question by incorporating at least one
additional characteristic of the one or more characteristics from
the first machine response in the second machine response.
9. The method of claim 1 further comprising continuing with a
normal dialog response process if the first confidence score is
above the defined threshold value.
10. The method of claim 1 further comprising continuing with a
normal dialog response process if the second confidence score is
above the defined threshold value.
11. A dialog system, comprising: a speech recognition unit
receiving a user utterance including a proper name; a recognizer
unit recognizing the proper name in the user utterance; a scoring
unit determining a first confidence score for the recognized proper
name; and a question formulation unit generating a first machine
response the user utterance including a confirmation question
related to the proper name, wherein the first machine response does
not repeat the proper name, if the first confidence score is below
a defined threshold value, wherein the speech recognizer unit
receives a user response to the confirmation question, and the
scoring unit modifies the first confidence score to generate a
second confidence score based on the user response.
12. The system of claim 11 wherein the proper name comprises any
part of speech describing a person, place or thing.
13. The system of claim 11 further comprising: a parser parsing the
proper name from the user utterance; and a decision making unit
generating one or more hypotheses to identify a plurality of
candidate names, each candidate name potentially corresponding to
the uttered proper name, and selecting the best proper name from
the plurality of candidate names.
14. The system of claim 12 wherein the best candidate name is the
name with the first confidence score that is closest to the defined
threshold.
15. The system of claim 11 wherein the question formulation unit
defines one or more characteristics associated with the uttered
proper name; and formulates the confirmation question by
incorporating at least one characteristic of the one or more
characteristics in the first machine response.
16. The system of claim 15 wherein the confirmation question is
formulated based on an n-best list.
17. The system of claim 15 wherein the system generates a second
machine response the user utterance including an additional
confirmation question related to the proper name, wherein the
second machine response does not repeat the proper name, if the
second confidence score is below the defined threshold value.
18. The system of claim 17 wherein the question formulation unit
further formulates the additional confirmation question by
incorporating at least one additional characteristic of the one or
more characteristics from the first machine response in the second
machine response.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS/INCORPORATION BY
REFERENCE
[0001] This application is a continuation of U.S. patent
application Ser. No. 12/274,267 entitled "SYSTEM AND METHOD FOR
RECOGNIZING PROPER NAMES IN DIALOG SYSTEMS", filed Nov. 19, 2008.
The complete subject matter of this patent application is hereby
incorporated herein by reference, in its entirety.
FIELD
[0002] Embodiments of the invention relate generally to dialog
systems, and more specifically to recognizing proper names in
dialog systems.
BACKGROUND
[0003] Spoken language is the most natural and convenient
communication tool for people. With data storage capacities
increasing rapidly, people tend to store greater amounts of
information in databases. Accessing this data with spoken language
interfaces offers people convenience and efficiency, but only if
the spoken language interface is reliable. This is especially
important for applications in eye-busy and hand-busy situations,
such as driving a car. Man-machine interfaces that utilize spoken
commands and voice recognition are generally based on dialog
systems. A dialog system is a computer system that is designed to
converse with a human using a coherent structure and text, speech,
graphics, or other modes of communication on both the input and
output channel. Dialog systems that employ speech are referred to
as spoken dialog systems and generally represent the most natural
type of machine-man interface. With the ever-greater reliance on
electronic devices, spoken dialog systems are increasingly being
implemented in many different machines.
[0004] In many spoken language interface applications, proper
names, such as names of people, locations, companies, places, and
similar things are very widely used. In fact, it is often the case
that the number of proper names used in these applications is
significantly large, and may involve foreign names, such as street
names in a navigation domain or restaurant names in a restaurant
selection domain. When used in high-stress environments, such as
driving a car, flying a helicopter, or operating machinery, people
tend to use short-hand terms, such as partial proper names and
their slight variations. The present problems of proper name
recognition in conventional spoken language interface applications
include inadequate speech recognition accuracy in the speech
recognizer component for these names, and inadequate recognition
accuracy of these names with regard to the presence of these names
in the system database.
[0005] Present name recognition methods on large name lists
generally focus strictly on the static aspect of the names. Such
systems do not utilize certain contextual elements that can
significantly aid in the recognition process for proper names. Such
contextual elements can include the temporal, recency, and context
effect when names are used.
[0006] Present recognition systems may also be configured to
confirm proper names by means of direct confirmation. In this
method, the system responds to a question by rephrasing the user's
utterance and directly mentioning the name or names, as they were
understood by the system. One type of direct confirmation system
explicitly asks the user whether he or she mentioned a specific
name or names. For example, if the user is making an airplane
reservation, he might say "I want to fly from Boston to New York".
The system may then respond by saying: "You said Boston to New
York, is that correct?" The user must then answer that this was
correct or incorrect and provide any correction necessary. In order
to make the system seem more conversational, the confirmation may
be restated in a less direct manner. For example, if the user says
"I want to fly from Boston to New York" the system my respond by
saying "OK, when would you like to fly from Boston to New York?"
This type of confirmation, called implicit confirmation, relies on
the fact that if the system incorrectly understood and wrongly
stated one or more of the names, the user would provide a
correction; but if the system correctly repeated the names, the
user would not say anything about the names. By including the
proper names in the response, the system has directly confirmed the
names as understood by the system. Direct confirmation systems are
generally cumbersome in that they involve restatement of the proper
names uttered by the user and are thus overly repetitive, adding
time and possibly frustration to the user experience. These systems
are also disadvantageous in that they may tend to repeat or
propagate errors that are made during the speech recognition
process.
[0007] What is needed, therefore, is a dialog system that utilizes
contextual information and tries to address the issues in the
proper name recognition task for spoken language interface
applications, namely improving the speech recognition accuracy for
these names, and the recognition accuracy of these names.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] Embodiments of the present invention are illustrated by way
of example and not limitation in the figures of the accompanying
drawings, in which like references indicate similar elements and in
which:
[0009] FIG. 1 is a block diagram of a spoken dialog system that
incorporates an improved proper name recognition unit, according to
an embodiment.
[0010] FIG. 2 is a block diagram that illustrates the components
for generating an indirect confirmation statement, under an
embodiment.
[0011] FIG. 3 is a flowchart illustrating a method of generating an
indirect confirmation statement, under an embodiment.
[0012] FIG. 4 is a block diagram of the functional components of
the dialog strategy component, under an embodiment.
DETAILED DESCRIPTION
[0013] Embodiments of a dialog system that utilizes contextual
information to perform recognition of proper names are described.
Unlike present name recognition methods on large name lists that
generally focus strictly on the static aspect of the names,
embodiments of the present system take into account of the
temporal, recency and context effect when names are used, and
formulates new questions to further constrain the search space or
grammar for recognition of the past and current utterances.
[0014] In the following description, numerous specific details are
introduced to provide a thorough understanding of, and enabling
description for, embodiments of the dialog system response
generation system and method. One skilled in the relevant art,
however, will recognize that these embodiments can be practiced
without one or more of the specific details, or with other
components, systems, etc. In other instances, well-known structures
or operations are not shown, or are not described in detail, to
avoid obscuring aspects of the disclosed embodiments.
[0015] During typical dialog interaction sessions, the confidence
level for proper name recognition is usually not very high, at
least for certain names. To improve proper name recognition,
systems have been developed to use certain contextual information,
such as using knowledge of a specific domain or a user model.
Embodiments of the proper name recognition system build and utilize
the contextual information through the formulation of indirect
confirmations that may be provided in the form of questions derived
from user input in previous dialog turns.
[0016] FIG. 1 is a block diagram of a spoken dialog system that
incorporates a proper name recognition unit that utilizes
contextual information, according to an embodiment. For purposes of
the present description, any of the processes executed on a
processing device may also be referred to as modules or components,
and may be standalone programs executed locally on a respective
device computer, or they can be portions of a distributed client
application run on one or more devices. The core components of
system 100 include a spoken language understanding (SLU) module 104
with multiple understanding strategies for imperfect input, an
information-state-update or other kind of dialog manager (DM) 106
that handles multiple dialog threads and mixed initiatives, a
knowledge manager (KM) 110 that controls access to ontology-based
domain knowledge, and a content optimizer 112 that connects the
dialog manager and the knowledge manager for resolving ambiguities
from the users' requests, regulating the amount of information to
be presented to the user, as well as providing recommendations to
users. In one embodiment, spoken user input 101 produces acoustic
waves that are received by a speech recognition unit 102. The
speech recognition unit 102 can include components to provide
functions, such as dynamic grammars and class-based n-grams. In the
case where the user input 101 is text-based rather than
voice-based, the speech recognition unit 102 is bypassed, and
simulated user input is provided directly to the spoken language
understanding unit 104. A response generator 108 provides the
output of the system 100. The response generator 108 generates
audio and/or text output based on the user input. Such output can
be an answer to a query, a request for clarification or further
information, reiteration of the user input, or any other
appropriate response. The response generator 108 utilizes domain
information when generating responses. Thus, different wordings of
saying the same thing to the user will often yield very different
results.
[0017] System 100 illustrated in FIG. 1 includes a large data store
118 that stores a large number of names. Throughout the
description, the term "name" is used to denote any type of entity
label, such as the name of a person, place or thing, or any other
descriptor or label for an object or entity. In general, depending
upon the domain or dialog system application, the number of names
in data store 118 could be very large, e.g., on the order of tens
to hundreds of thousands of names. To improve the recognition
accuracy of names in the user utterance, the large name list can be
pared down into a smaller list of names with weight values attached
based on the context of the names used in the input speech of
recent conversations. Names outside of the smaller list are
assigned a weight value of zero.
[0018] Data store 118 can hold names organized into one or more
databases. One database can be a static database that contains all
possible names, commonly used names (such as common trademarks or
references), or names frequently used by the user (such as derived
from a user profile or model). In a static database, the weight
values are precomputed before a conversation is started, and is
typically based on frequency of usage. A second database may be a
dynamic database that constantly takes the names in the context of
the utterance (such as names just mentioned) from the DM unit 106.
A name list can be built that contains full and partial names that
are appended with proper weighting values depending on the context
in which the names are used and other characteristics of the names.
For example, a high weight is given to names that have been
mentioned recently, a lower weight is given to common names, and a
lowest weight is given to names that have not been used. In
general, each name in the name list or lists are assigned weights
depending upon the databases from which they were derived. In
general, names from the dynamic database are weighted higher than
names from the static database. Weights can be assigned based on
any appropriate scale, such as 0 to 100%, or any similar scale, and
are used to help the recognition system improve the recognition
accuracy.
[0019] The embodiment of system 100 also includes a dialog strategy
component 114. The dialog strategy component is invoked when the
dialog manager 106 detects that a name is recognized with a
relatively low degree of confidence. For names that the dialog
manager detects a high enough level of recognition, dialogs are
processed through the standard response process defined by the
system.
[0020] The dialog strategy component 114 implements a name
recognition system that includes an indirect confirmation method.
Unlike direct confirmation in which the names uttered by the user
are directly restated by the system (e.g., "You said Boston to New
York, correct?"), an indirect confirmation system generates new
questions for the user that are based on the names, but do not
restate the names. This type of system reduces the repetitiveness
of direct confirmation, is more conversational, and adds
potentially relevant data to the user model. For example, if the
user says "I want to fly from Boston to New York" the system my
respond by saying "OK, when would you like to leave Massachusetts?"
This type of indirect confirmation requires the formulation of a
related question based on the properly recognized proper names in
the user utterance. If the system had misunderstood "Boston" for
"Austin," for example, the indirect confirmation may have been
stated as "OK, when would you like to leave Texas?" In this case,
the user would need to correct the system by restating the question
or clarifying the stated names. By using a different name and not
trying to repeat the name uttered by the user, the indirect
confirmation system eliminates the potential problem associated
with direct confirmation systems of the user not recognizing that
the repeated name was incorrect. That is, if the system stated
"Austin" instead of "Boston", the user may hear "Boston" instead of
"Austin", as he originally anticipated and not realize that the
system made a mistake. By formulating a different statement, the
system more fully engages the user and provides a different basis
of understanding and clarification.
[0021] The related question can be formulated based on different
types of information available to the system as well, such as user
location, device type, and any other objective information
available to the system. For example, if the user is in a car
driving through Northern California, and requests that the system
find a restaurant in Mountain View, the system may confuse this
place name with Monterey. In this case, the system could state back
to the user: "As you drive through Silicon Valley . . . " This
indirect confirmation generated by the system utilizes the fact
that the location of the user was placed in the vicinity of Silicon
Valley rather than the Monterey peninsula and that the user was in
an automobile at the time of the request. If the system's
understanding was correct, the user could continue the dialog with
the system, otherwise he or she could provide correction
information. Additional indirect confirmation questions or
statements can be provided based on the user response to the system
output. The system confidence levels for the speech recognition
stage to generate responses until a sufficient level of recognition
accuracy is attained.
[0022] FIG. 2 is a block diagram that illustrates the components
for generating an indirect confirmation statement, under an
embodiment. As shown in system 200, the dialog strategy component
takes data from both the user input 202 and objective data sources
204 to generate the indirect confirmation statement or question
210. The objective data 204 could be provided from various sources,
such as user profile databases, location sensor, device
descriptors, and so on.
[0023] In one embodiment, dialog strategy component 114 keeps track
of the user utterances, semantic content and data obtained from the
user utterances in the past to recognize the current utterance
during the interaction. Confidence levels are utilized to measure
the accuracy of the recognition. One or more threshold confidence
levels may be defined to implement the process. Specifically, if
the confidence score of the current recognized utterance is high,
the recognized utterance, semantic content and data retrieved from
the utterance are used for continuing the interaction with the
user. If the confidence score of the recognized utterance or the
semantic content is below a certain defined threshold, a related
indirect confirmation question or statement is generated and is
provided to the user by the system as part of the dialog
process.
[0024] FIG. 3 is a flowchart illustrating a method of generating an
indirect confirmation statement, under an embodiment. In block 302,
the speech recognizer component receives the user utterance, and
the system parses the proper name or names in the utterance. The
system attempts to recognize the proper name and determines an
initial confidence score for this recognition. A threshold
confidence level is set. In one embodiment, the threshold
confidence level is set empirically based on the speech recognizer.
The confidence level can be provided by the recognizer 102 unit
automatically (such as in the case of a commercially available
unit), or it can be defined by a system administrator or designer.
Confidence levels are typically specified in a percentage range of
0 to 100%, and a typical threshold value may be around 75-85%. In
this case, if a recognizer returns a hypothesis that has a
confidence level greater than the threshold, the system will accept
the system response as an accurately recognized name. Any value
less than the threshold will result in the hypothesis being
rejected. Different recognizers may have different threshold levels
depending on application requirements and system constraints.
[0025] The speech recognizer unit 102 may generate one or more
hypotheses of a recognized name. For example, for the flight
booking question above, the speech recognizer may produce the
following three recognition hypotheses: Boston, Austin, and
Houston. Of these three, or any number of hypotheses, one might be
selected as better than the others based on the confidence score,
or other data. For example, the system may know that the user is on
the east coast of the United States at the time of the utterance.
In this case, Boston is a better choice than either Austin or
Houston, even if one of those city names has a higher confidence
score. In block 305, the system selects the best hypothesis out of
the number available. This choice can be made on the basis of
confidence score and/or any external information available to the
system, and can be dictated by system and/or user defined
rules.
[0026] The confidence score of the selected hypothesis is then
compared to the defined confidence threshold, block 306. If the
confidence score of the recognized utterance or the semantic
content is low, a related question, which is formulated based on
contextual information, is prompted to the user by the system,
block 308. The user response to this related question is then
received and processed, block 310. This response is then used to
constrain the re-recognition or re-scoring of the previous
unconfident user utterances and information obtained in the past
interaction, block 312. This process repeats from block 306 in
which the threshold comparison is performed, until a sufficiently
high confident result or a high confidence of combined results from
the user is obtained. Once the recognized result and information
obtained from the answer utterance has a high enough confidence
level, that is, one that is greater than the defined threshold, the
proper name is accepted as recognized, and the dialog system
continues with a normal system response.
[0027] As shown in block 308 of FIG. 3, a related question is
formed if the confidence level of the selected hypothesis is below
the defined confidence threshold. The related question can be
formulated in different ways. In one embodiment, the question may
be formulated based on the n-best list or lattice produced by the
system for the current user utterance, knowledge base or relations
in a data base for the applications. The n-best list is generated
from the speech recognizer which takes the input acoustic signal to
produce one or more hypotheses of recognition, and a lattice is a
compressed representation of the n-best list. When the user answers
to the question, the recognized result can be used to constrain the
re-recognition or re-scoring of the previous user utterance if it
has a high confidence. During the re-recognition, the name
candidates are refined based on information collected from user's
answer. If more than one hypothesis was available for selection,
the iterative process of posing related indirect confirmation
questions and refining the confidence scoring will help the system
select among the different possible hypotheses. For example, if the
hypotheses comprise the following: Boston, Austin, and Houston, a
positive user response to the related question "So, you plan to fly
out of Massachusetts" will result in the system selecting Boston as
the recognized name. If, however, the user responds by saying "No,
I plan to fly out of Texas", the system must then ask another
follow up question, because although Boston has been eliminated,
either
[0028] Austin or Houston are still possible candidates. In this
case, the system may follow up with another question, such as "So,
you will be flying out of the state capital . . . " The user
response to this additional related question will then allow the
system to select between the two remaining choices.
[0029] In one embodiment, a highly confident answer can also be
used to re-score the previous recognized result and the data
retrieved by the user utterance. For instance, if there is an
overlap between the user utterances or the data obtained from these
user utterances, the confidence for the overlap part is combined by
a predefined model or function, e.g., a certain weighted
aggregating function. Multiple steps can be performed until a
highly confident result or a high confidence of combined results
from the user is obtained. In this case, overlaps may comprise
repeated words between the system response and user utterances.
[0030] FIG. 4 is a block diagram of the functional components of
the dialog strategy component, under an embodiment. As shown in
FIG. 4, the dialog strategy component includes a question
formulation module 404 that formulates the related questions, a
decision making component 406, and a re-scoring/re-recognition
component, 408. In general, the related questions, impacts the
language model portion of the speech recognizer. The language
module constrains the search. The change of the model will produce
different results for following questions. This introduces a degree
of dynamic adaptiveness to the system.
[0031] The dialog strategy component uses contextual information
that is incorporated in constraining and refining name candidates
for speech recognition. Anchoring on the confident portions of the
utterance with clarification dialogs can make use of the semantic
relation internal in the data to narrow down the types of names for
recognition.
[0032] Aspects of the name recognition process described herein may
be implemented as functionality programmed into any of a variety of
circuitry, including programmable logic devices ("PLDs"), such as
field programmable gate arrays ("FPGAs"), programmable array logic
("PAL") devices, electrically programmable logic and memory devices
and standard cell-based devices, as well as application specific
integrated circuits. Some other possibilities for implementing
aspects include: microcontrollers with memory (such as EEPROM),
embedded microprocessors, firmware, software, etc. Furthermore,
aspects of the content serving method may be embodied in
microprocessors having software-based circuit emulation, discrete
logic (sequential and combinatorial), custom devices, fuzzy
(neural) logic, quantum devices, and hybrids of any of the above
device types. The underlying device technologies may be provided in
a variety of component types, e.g., metal-oxide semiconductor
field-effect transistor ("MOSFET") technologies like complementary
metal-oxide semiconductor ("CMOS"), bipolar technologies like
emitter-coupled logic ("ECL"), polymer technologies (e.g.,
silicon-conjugated polymer and metal-conjugated polymer-metal
structures), mixed analog and digital, and so on.
[0033] It should also be noted that the various functions disclosed
herein may be described using any number of combinations of
hardware, firmware, and/or as data and/or instructions embodied in
various machine-readable or computer-readable media, in terms of
their behavioral, register transfer, logic component, and/or other
characteristics. Computer-readable media in which such formatted
data and/or instructions may be embodied include, but are not
limited to, non-volatile storage media in various forms (e.g.,
optical, magnetic or semiconductor storage media) and carrier waves
that may be used to transfer such formatted data and/or
instructions through wireless, optical, or wired signaling media or
any combination thereof. Examples of transfers of such formatted
data and/or instructions by carrier waves include, but are not
limited to, transfers (uploads, downloads, e-mail, etc.) over the
Internet and/or other computer networks via one or more data
transfer protocols (e.g., HTTP, FTP, SMTP, and so on).
[0034] Unless the context clearly requires otherwise, throughout
the description and the claims, the words "comprise," "comprising,"
and the like are to be construed in an inclusive sense as opposed
to an exclusive or exhaustive sense; that is to say, in a sense of
"including, but not limited to." Words using the singular or plural
number also include the plural or singular number respectively.
Additionally, the words "herein," "hereunder," "above," "below,"
and words of similar import refer to this application as a whole
and not to any particular portions of this application. When the
word "or" is used in reference to a list of two or more items, that
word covers all of the following interpretations of the word: any
of the items in the list, all of the items in the list and any
combination of the items in the list.
[0035] The above description of illustrated embodiments of the
response generation process is not intended to be exhaustive or to
limit the embodiments to the precise form or instructions
disclosed. While specific embodiments of, and examples for,
processes in computing devices are described herein for
illustrative purposes, various equivalent modifications are
possible within the scope of the disclosed methods and structures,
as those skilled in the relevant art will recognize. The elements
and acts of the various embodiments described above can be combined
to provide further embodiments. These and other changes can be made
to the response generation process in light of the above detailed
description.
[0036] In general, in the following claims, the terms used should
not be construed to limit the disclosed method to the specific
embodiments disclosed in the specification and the claims, but
should be construed to include all operations or processes that
operate under the claims. Accordingly, the disclosed structures and
methods are not limited by the disclosure, but instead the scope of
the recited method is to be determined entirely by the claims.
[0037] While certain aspects of the disclosed system and method are
presented below in certain claim forms, the inventors contemplate
the various aspects of the methodology in any number of claim
forms. For example, while only one aspect may be recited as
embodied in machine-readable medium, other aspects may likewise be
embodied in machine-readable medium. Accordingly, the inventors
reserve the right to add additional claims after filing the
application to pursue such additional claim forms for other
aspects.
* * * * *