DETAILED ACTION
Notice of AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
The non-statutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A non-statutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on non-statutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a non-statutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based e-Terminal Disclaimer may be filled out completely online using web-screens. An e-Terminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about e-Terminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 2, 3, 4, 7, 8, 9, 10, 11, 14, 15, 17 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1, 8, 4, 6, 7, 10, 16, 12, 14, 15, 18, 20 of U.S. Patent No. 12,284,148. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1, 2, 3, 4, 7, 8, 9, 10, 11, 14, 15, 17 of the instant application similar in scope and content of the patented claims 1, 8, 4, 6, 7, 10, 16, 12, 14, 15, 18, 20 of the patent issued to the same Applicant.
It is clear that all the elements of the application claims 1, 2, 3, 4, 7, 8, 9, 10, 11, 14, 15, 17 are to be found in patented claims 1, 8, 4, 6, 7, 10, 16, 12, 14, 15, 18, 20 (as the application claims 1-6, 8-13 and 15-20 fully encompasses patented claims 1, 8, 4, 6, 7, 10, 16, 12, 14, 15, 18, 20). The difference between the application claims and the patent claims lies in the fact that the patented claims includes many more elements and are thus much more specific. Thus the invention of claims 1, 8, 4, 6, 7, 10, 16, 12, 14, 15, 18, 20 of the patent is in effect a “species” of the “generic” invention of the application claims 1, 2, 3, 4, 7, 8, 9, 10, 11, 14, 15, 17. It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993). Since application claims 1, 2, 3, 4, 7, 8, 9, 10, 11, 14, 15, 17 are anticipated by claims 1, 8, 4, 6, 7, 10, 16, 12, 14, 15, 18, 20 of the patent, it is not patentably distinct from of the patented claims.
Application No: 19/085,840
Patent No: 12,284,148
1.A system for suggesting a topic in a messaging system, the system comprising:
a processor;
and a memory device that stores program code structured to cause the processor to:
generate a vector representation for each unhandled query in a set of unhandled queries, each unhandled query comprising an interaction between a user and a virtual agent in which the virtual agent did not select a correspondence topic;
cluster the vector representations for the set of unhandled queries to generate a plurality of clusters of vector representations, each cluster corresponding to a group of unhandled queries;
and provide a suggested topic corresponding to one of the clusters to an authoring tool that comprises an interactive element to add the suggested topic to a set of topics implemented in the virtual agent, wherein a selection of the interaction element configures the virtual agent to access the set of topics that includes the suggested topic in a future conversation involving the virtual agent.
1.A system for suggesting a topic in a messaging system, the system comprising:
a processor;
and a memory device that stores program code structured to cause the processor to:
receive a set of queries from a chat transcript history that comprises at least a partial transcript of one or more conversations involving a bot, wherein the set of queries includes a set of unhandled queries, and each unhandled query comprises a query for which the bot did not identify a corresponding topic;
generate a vector representation for each unhandled query in the set of unhandled queries;
cluster the vector representations for the set of unhandled queries to generate a plurality of clusters of vector representations, each cluster corresponding to a group of unhandled queries; for each cluster, generate a corresponding suggested topic for implementation in the bot;
provide each suggested topic to an authoring tool, wherein the authoring tool comprises an interactive element to enable an author to select one of the suggested topics for implementation in the bot;
implement the one of the suggested topics in the bot in response to a selection of the interactive element;
and in a subsequent conversation involving the bot, select a particular topic of conversation from a list of topics that includes the one of the suggested topics.
2. The system of claim 1, wherein the virtual agent utilizes natural language processing to simulate human interaction with a user.
8. The system of claim 1, wherein the bot comprises a chat bot that simulates human conversation with a user.
3. The system of claim 1, wherein the program code is structured to cause the processor to cluster the vector representations by generating groupings based on an unsupervised clustering algorithm.
4. The system of claim 1, wherein the program code is structured to cause the processor to cluster the vector representations for the set of unhandled queries by applying an unsupervised clustering algorithm to the vector representations for the set of unhandled queries,
the unsupervised clustering algorithm grouping unhandled queries together based on a semantic similarity of the unhandled queries.
4. The system of claim 1, wherein the program code is further structured to provide, to the authoring tool, a suggested trigger phrase that corresponds to the suggested topic, wherein the virtual agent is configured to select the selected topic in response to receiving a query that is semantically similar with the suggested trigger phrase.
6. The system of claim 1, wherein the program code is structured to cause the processor to generate the corresponding suggested topic for implementation in the bot for each cluster by generating a suggested trigger phrase for each suggested topic, the suggested trigger phrase based at least on a portion of the group of unhandled queries corresponding to the cluster for the suggested topic.
5. The system of claim 4, wherein the suggested trigger phrase is based on an unhandled query that is at a center of a cluster corresponding to the suggested topic.
6. The system of claim 1, wherein the program code is further structured to provide, to the authoring tool, a plurality of suggested trigger phrases that correspond to the suggested topic.
7. The system of claim 1, wherein the program code is further structured to provide, to the authoring tool, a score for the suggested topic, the score indicative of a number of sessions in which an unhandled query corresponding to the suggested topic was observed in a conversation involving the virtual agent.
7. The system of claim 1, wherein the program code is further structured to cause the processor to provide each suggested topic to the authoring tool by: assigning a score to each topic based at least on a number of sessions in which an unhandled query corresponding to the suggested topic was observed in a conversation involving the bot;
and providing each suggested topic to the authoring tool as part of a list that ranks each suggested topic by its corresponding score.
8. A method for suggesting a topic in a messaging system, the method comprising:
generating a vector representation for each unhandled query in a set of unhandled queries, each unhandled query comprising an interaction between a user and a virtual agent in which the virtual agent did not select a correspondence topic;
clustering the vector representations for the set of unhandled queries to generate a plurality of clusters of vector representations, each cluster corresponding to a group of unhandled queries;
and providing a suggested topic corresponding to one of the clusters to an authoring tool that comprises an interactive element to add the suggested topic to a set of topics implemented in the virtual agent,
wherein a selection of the interaction element configures the virtual agent to access the set of topics that includes the suggested topic in a future conversation involving the virtual agent.
10. A method for suggesting a topic in a messaging system, the method comprising: receiving a set of queries from a chat transcript history that comprises at least a partial transcript of one or more conversations involving a bot, wherein the set of queries includes a set of unhandled queries, and each unhandled query comprises a query for which the bot did not identify a corresponding topic;
generating a vector representation for each unhandled query in the set of unhandled queries;
clustering the vector representations for the set of unhandled queries to generate a plurality of clusters of vector representations, each cluster corresponding to a group of unhandled queries; for each cluster, generating a corresponding suggested topic for implementation in the bot; providing each suggested topic to an authoring tool, wherein the authoring tool comprises an interactive element to enable an author to select one of the suggested topics for implementation in the bot;
implementing the one of the suggested topics in the bot in response to a selection of the interactive element; and in a subsequent conversation involving the bot, selecting a particular topic of conversation from a list of topics that includes the one of the suggested topics.
9. The method of claim 8, wherein the virtual agent utilizes natural language processing to simulate human interaction with a user.
16. The method of claim 10, wherein the bot comprises a chat bot simulates human conversation with a user.
10. The method of claim 8, wherein the clustering the vector representations comprises:
generating groupings based on an unsupervised clustering algorithm.
12. The method of claim 10, wherein the clustering the vector representations for the set of unhandled queries comprises applying an unsupervised clustering algorithm to the vector representations for the set of unhandled queries, the unsupervised clustering algorithm grouping unhandled queries together based on a semantic similarity of the unhandled queries.
11. The method of claim 8, further comprising: providing, to the authoring tool, a suggested trigger phrase that corresponds to the suggested topic, wherein the virtual agent is configured to select the selected topic in response to receiving a query that is semantically similar with the suggested trigger phrase.
14. The method of claim 10, wherein the generating the corresponding suggested topic for implementation in the bot for each cluster comprises: generating a suggested trigger phrase for each suggested topic, the suggested trigger phrase based at least on a portion of the group of unhandled queries corresponding to the cluster for the suggested topic.
12. The method of claim 11, wherein the suggested trigger phrase is based on an unhandled query that is at a center of a cluster corresponding to the suggested topic.
13. The method of claim 8, further comprising: providing, to the authoring tool, a plurality of suggested trigger phrases that correspond to the suggested topic.
14. The method of claim 8, further comprising:
providing, to the authoring tool, a score for the suggested topic, the score indicative of a number of sessions in which an unhandled query corresponding to the suggested topic was observed in a conversation involving the virtual agent.
15. The method of claim 10, wherein the providing each suggested topic to the authoring tool comprises: assigning a score to each topic based at least on a number of sessions in which an unhandled query corresponding to the suggested topic was observed in a conversation involving the bot;
and providing each suggested topic to the authoring tool as part of a list that ranks each suggested topic by its corresponding score.
15. A computer-readable storage medium having program instructions recorded thereon that, when executed by at least one processor of a computing device, perform a method, the method comprising:
generating a vector representation for each unhandled query in a set of unhandled queries, each unhandled query comprising an interaction between a user and a virtual agent in which the virtual agent did not select a correspondence topic;
clustering the vector representations for the set of unhandled queries to generate a plurality of clusters of vector representations, each cluster corresponding to a group of unhandled queries;
and providing a suggested topic corresponding to one of the clusters to an authoring tool that comprises an interactive element to add the suggested topic to a set of topics implemented in the virtual agent, wherein a selection of the interaction element configures the virtual agent to access the set of topics that includes the suggested topic in a future conversation involving the virtual agent.
18. A computer-readable storage medium having program instructions recorded thereon that, when executed by at least one processor of a computing device, perform a method, the method comprising: receiving a set of queries from a chat transcript history that comprises at least a partial transcript of one or more conversations involving a bot, wherein the set of queries includes a set of unhandled queries, and each unhandled query comprises a query for which the bot did not identify a corresponding topic;
generating a vector representation for each unhandled query in the set of unhandled queries;
clustering the vector representations for the set of unhandled queries to generate a plurality of clusters of vector representations, each cluster corresponding to a group of unhandled queries; for each cluster, generating a corresponding suggested topic for implementation in the bot; providing each suggested topic to an authoring tool, wherein the authoring tool comprises an interactive element to enable an author to select one of the suggested topics for implementation in the bot;
implementing the one of the suggested topics in the bot in response to a selection of the interactive element; and in a subsequent conversation involving the bot, selecting a particular topic of conversation from a list of topics that includes the one of the suggested topics.
16. The computer-readable storage medium of claim 15, wherein the clustering the vector representations comprises:
generating groupings based on an unsupervised clustering algorithm.
17. The computer-readable storage medium of claim 15, wherein the method further comprises:
providing, to the authoring tool, a suggested trigger phrase that corresponds to the suggested topic, wherein the virtual agent is configured to select the selected topic in response to receiving a query that is semantically similar with the suggested trigger phrase.
20. The computer-readable storage medium of claim 18, wherein the generating the corresponding suggested topic for implementation in the bot for each cluster comprises: generating a suggested trigger phrase for each suggested topic, the suggested trigger phrase based at least on a portion of the group of unhandled queries corresponding to the cluster for the suggested topic.
18. The computer-readable storage medium of claim 17, wherein the suggested trigger phrase is based on an unhandled query that is at a center of a cluster corresponding to the suggested topic.
19. The computer-readable storage medium of claim 15, wherein the method further comprises:
providing, to the authoring tool, a plurality of suggested trigger phrases that correspond to the suggested topic.
20. The computer-readable storage medium of claim 15, wherein the method further comprises:
providing, to the authoring tool, a score for the suggested topic, the score indicative of a number of sessions in which an unhandled query corresponding to the suggested topic was observed in a conversation involving the virtual agent.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 4, 6- 9, 11, 13-15, 17, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Aharoni et al. ( US 20220255885 A1), hereinafter referenced as Aharoni, in view of Subramaniam et al. (US 11,651,033 B2), hereinafter referenced as Subramaniam.
Regarding Claim 1, Aharoni teaches a system for suggesting a topic in a messaging system, the system comprising:
a processor ( Aharoni: Para.[0134], Fig. 7, processor 714);
and a memory device that stores program code structured to cause the processor to ( Aharoni: Para.[0138], Fig. 7, Memory 725 used in the storage subsystem 724 can include a number of memories including a main random-access memory (RAM) 730 for storage of instructions and data during program execution and a read only memory (ROM) 732 in which fixed instructions are stored and executed by processor 714 alone or in combination with other processors):
generate a vector representation for each unhandled query in a set of unhandled queries, each unhandled query comprising an interaction between a user and a virtual agent in which the virtual agent did not select a correspondence topic ( Aharoni: Para.[0005], [0045], Fig. 2, One or more embeddings associated with the corresponding conversation for which the given behavioral error ( unhandled queries) is identified can be generated by the embedding engine and can be obtained from the voice bot activity database);
cluster the vector representations for the set of unhandled queries to generate a plurality of clusters of vector representations, each cluster corresponding to a group of unhandled queries ( Aharoni: Para.[0095]-[0100], Figs. 3A-3D, the voice bot development platform can process the behavioral errors embedding in different categories ( cluster), such as behavioral error related to a missing feature error and/or a mislabeled feature error, can be classified into category 1. Behavioral error caused by a sparsity of training instances can be classified into category 2. Behavioral error caused by RPC errors (remote procedure call) can be classified into category 3);
and provide a suggested topic corresponding to one of the clusters to an authoring tool [that comprises an interactive element] to add the suggested topic to a set of topics implemented in the virtual agent ( Aharoni: Para.[0111]-[0113], Fig. 4, at block 458, the system determines based on the given behavioral error of the trained voice bot, an action that is directed to correcting the given behavioral error of the trained voice bot based on the one or more disparate categories of behavioral errors into which the given behavioral error of the trained voice bot is classified. For example, in implementations where the given behavioral error is classified into a category associated with a missing feature error, the action directed to correcting the given behavioral error may be a labeling action associated with labeling missing features ( suggested topic) of one or more existing training instances. At block 460, the system generates a notification to be presented to a third-party developer. At block 462, the system causes the notification to be presented to the third-party developer. The notification can be presented to the user via a user interface associated with the voice development platform, and can be presented to the user visually and/or audibly via a client device of the third-party developer ( authoring tool)),
wherein [a selection of the interaction element configures the virtual agent to access the set of topics] that includes the suggested topic in a future conversation involving the virtual agent ( Aharoni: Para.[0114],[0115], Fig. 4, at block 464, the system determines whether to update the voice bot based on an updated corpus of training instances that includes at least one or more modified training instances and/or one or more additional training instances. At block 466, the system causes the updated voice bot to be deployed for conducting conversations on behalf of the third-party).
Aharoni, while teaching the system of claim 1, fails to explicitly teach the claimed, and provide a suggested topic corresponding to one of the clusters to an authoring tool that comprises an interactive element to add the suggested topic to a set of topics implemented in the virtual agent, wherein a selection of the interaction element configures the virtual agent to access the set of topics that includes the suggested topic in a future conversation involving the virtual agent.
However, Subramaniam does teach the claimed, and provide a suggested topic corresponding to one of the clusters to an authoring tool that comprises an interactive element to add the suggested topic to a set of topics implemented in the virtual agent, wherein a selection of the interaction element configures the virtual agent to access the set of topics that includes the suggested topic in a future conversation involving the virtual agent ( Subramaniam: Column 28, lines 47-57, column 33, lines 1-4, Figs. 6A, 6L illustrates a graphical user interface screen 600 displaying summarized information of conversations associated with a digital assistant. These dashboard insights reports can be used to offer developer-oriented analytics to pinpoint issues with skills so a user can address them. The Retrainer lets a user incorporate user input into their training corpus to improve a skill or chatbot. Column 38, lines 24-67, Fig. 8, bot system may be retrained to more accurately determining the user intents by using one or more user-selectable items which may include a user-editable item, such as an utterance and/or an intent associated with the utterance. An administrator or developer of the bot system may add, remove, or edit the utterance and/or add, remove, or edit the intent for the utterance. Column 36, lines 61-67, column 37, lines 1-7, Fig. 7I, by using the closest prediction chart and retrainer ( interactive element), if there's a number of unresolved user utterance, then the user might consider adding an intent ( or even creating a standalone skill) to handle unresolved issue),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Subramaniam’s teaching of an analytic system integrated with a bot system for monitoring, analyzing, visualizing, diagnosing, and improving the performance of the bot system, into the system and method of updating a trained voice bot in response to identifying occurrence(s) of behavioral error(s) of the trained voice bot while the conversations are being conducted on behalf of the third-party , taught by Aharoni, because, this would improve the performance of the chatbot and user experience with the chatbot by monitoring, debugging, and retraining (Subramaniam, Column 8, lines 33-56).
Claim 8 is a method claim performing the steps in system claim 1 above and as such, claim 8 is similar in scope and content to claim 1 and therefore, claim 8 is rejected under similar rationale as presented against claim 1 above.
Claim 15 is a computer-readable storage medium claim having program instructions recorded thereon that, when executed by at least one processor of a computing device ( Aharoni: Para.[0138], Fig. 7, Memory 725 used in the storage subsystem 724 can include a number of memories including a main random-access memory (RAM) 730 for storage of instructions and data during program execution and a read only memory (ROM) 732 in which fixed instructions are stored and executed by processor 714 alone or in combination with other processors), performing the steps in system claim 1 above and as such, claim 15 is similar in scope and content to claim 1 and therefore, claim 15 is rejected under similar rationale as presented against claim 1 above.
Regarding Claim 2, Aharoni in view of Subramaniam teach the system of claim 1. Subramaniam further teaches, wherein the virtual agent utilizes natural language processing to simulate human interaction with a user ( Subramaniam: Column 14, lines 4-21, Fig. 1, a machine learning based NLP engine may learn to understand and categorize the natural language conversations from the end users and to extract necessary information from the conversations to be able to take precise actions, such as performing a transaction or looking up data from a backend system of record. The NLU processing or portions is performed by digital assistant 106).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Subramaniam’s teaching of an analytic system integrated with a bot system for monitoring, analyzing, visualizing, diagnosing, and improving the performance of the bot system, into the system and method of updating a trained voice bot in response to identifying occurrence(s) of behavioral error(s) of the trained voice bot while the conversations are being conducted on behalf of the third-party , taught by Aharoni, because, this would improve the performance of the chatbot and user experience with the chatbot by monitoring, debugging, and retraining (Subramaniam, Column 8, lines 33-56).
Claim 9 is a method claim performing the steps in system claim 2 above and as such, claim 9 is similar in scope and content to claim 2 and therefore, claim 9 is rejected under similar rationale as presented against claim 2 above.
Regarding Claim 4, Aharoni in view of Subramaniam teach the system of claim 1. Subramaniam further teaches, wherein the program code is further structured to provide, to the authoring tool, a suggested trigger phrase that corresponds to the suggested topic, wherein the virtual agent is configured to select the selected topic in response to receiving a query that is semantically similar with the suggested trigger phrase ( Subramaniam: Column 17, lines 18- 31, for a banking skill, an Account Type entity may be defined by the skill bot designer that enables various banking transactions by checking the user input for keywords ( trigger phrase) like checking, savings, and credit cards, etc. A skill bot is configured to receive user input, parse or otherwise process the received input, and identify or select an intent that is relevant to the received user input).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Subramaniam’s teaching of an analytic system integrated with a bot system for monitoring, analyzing, visualizing, diagnosing, and improving the performance of the bot system, into the system and method of updating a trained voice bot in response to identifying occurrence(s) of behavioral error(s) of the trained voice bot while the conversations are being conducted on behalf of the third-party , taught by Aharoni, because, this would improve the performance of the chatbot and user experience with the chatbot by monitoring, debugging, and retraining (Subramaniam, Column 8, lines 33-56).
Claim 11 is a method claim performing the steps in system claim 4 above and as such, claim 11 is similar in scope and content to claim 4 and therefore, claim 11 is rejected under similar rationale as presented against claim 4 above.
Claim 17 is a computer-readable storage medium claim performing the steps in system claim 4 above and as such, claim 17 is similar in scope and content to claim 4 and therefore, claim 17 is rejected under similar rationale as presented against claim 4 above.
Regarding Claim 6, Aharoni in view of Subramaniam teach the system of claim 1. Subramaniam further teaches, wherein the program code is further structured to provide, to the authoring tool, a plurality of suggested trigger phrases that correspond to the suggested topic ( Subramaniam: Column 17, lines 18- 31, the skill bot designer can define keywords like checking, savings, and credit cards ( trigger phrase) for banking transactions ( suggested topic)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Subramaniam’s teaching of an analytic system integrated with a bot system for monitoring, analyzing, visualizing, diagnosing, and improving the performance of the bot system, into the system and method of updating a trained voice bot in response to identifying occurrence(s) of behavioral error(s) of the trained voice bot while the conversations are being conducted on behalf of the third-party , taught by Aharoni, because, this would improve the performance of the chatbot and user experience with the chatbot by monitoring, debugging, and retraining (Subramaniam, Column 8, lines 33-56).
Claim 13 is a method claim performing the steps in system claim 6 above and as such, claim 13 is similar in scope and content to claim 6 and therefore, claim 13 is rejected under similar rationale as presented against claim 6 above.
Claim 19 is a computer-readable storage medium claim performing the steps in system claim 6 above and as such, claim 19 is similar in scope and content to claim 6 and therefore, claim 19 is rejected under similar rationale as presented against claim 6 above.
Regarding Claim 7, Aharoni in view of Subramaniam teach the system of claim 1. Subramaniam further teaches, wherein the program code is further structured to provide, to the authoring tool, a score for the suggested topic, the score indicative of a number of sessions in which an unhandled query corresponding to the suggested topic was observed in a conversation involving the virtual agent ( Subramaniam: Column 36, lines 53-67, Figs. 7H, 7I illustrates a graphical user interface where unresolved intent count and unresolved utterances with scores are shown. There are a couple of utterances that catch the attention of the user because they can help the user's skill fulfill its primary goal even if the customer input contains typos, slang, or unconventional shorthand: “get flowerss” (68%) and “i wud like to order flwrs.” (64%). User can add these utterance as training data ( suggested topic)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Subramaniam’s teaching of an analytic system integrated with a bot system for monitoring, analyzing, visualizing, diagnosing, and improving the performance of the bot system, into the system and method of updating a trained voice bot in response to identifying occurrence(s) of behavioral error(s) of the trained voice bot while the conversations are being conducted on behalf of the third-party , taught by Aharoni, because, this would improve the performance of the chatbot and user experience with the chatbot by monitoring, debugging, and retraining (Subramaniam, Column 8, lines 33-56).
Claim 14 is a method claim performing the steps in system claim 7 above and as such, claim 14 is similar in scope and content to claim 7 and therefore, claim 14 is rejected under similar rationale as presented against claim 7 above.
Claim 20 is a computer-readable storage medium claim performing the steps in system claim 7 above and as such, claim 20 is similar in scope and content to claim 7 and therefore, claim 20 is rejected under similar rationale as presented against claim 7 above.
Claims 3, 10 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Aharoni et al. ( US 20220255885 A1), hereinafter referenced as Aharoni, in view of Subramaniam et al. (US 11,651,033 B2), hereinafter referenced as Subramaniam, further in view of Madhusudhan et al. (US 20220238103 A1), hereinafter referenced as Madhusudhan.
Regarding Claim 3, Aharoni in view of Subramaniam teach the system of claim 1. Aharoni in view of Subramaniam fail to explicitly teach the claimed, wherein the program code is structured to cause the processor to cluster the vector representations by generating groupings based on an unsupervised clustering algorithm.
However, Madhusudhan does teach the claimed, wherein the program code is structured to cause the processor to cluster the vector representations by generating groupings based on an unsupervised clustering algorithm ( Madhusudhan: Para.[0096], Fig. 15, the learned multimodal distribution model 178 and the learned unimodal distribution model 180 can provide word distributions (e.g., defined vector spaces of word vectors) that are generated using unsupervised learning or other general clustering algorithms).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Madhusudhan’s teaching of a domain-aware vector encoding (DAVE) system for a natural language understanding (NLU) framework , into the system and method, taught by Aharoni in view of Subramaniam, because, this would improve the ability of virtual agents to apply NLU techniques to properly derive meaning from complex natural language utterances and enhances the performance of the NLU system within the specific domain of the client, improves the quality of predictions of the NLU system for various tasks, such as intent recognition, entity recognition, and so forth. (Madhusudhan, Para.[0006]-[0010]).
Claim 10 is a method claim performing the steps in system claim 3 above and as such, claim 10 is similar in scope and content to claim 3 and therefore, claim 10 is rejected under similar rationale as presented against claim 3 above.
Claim 16 is a computer-readable storage medium claim performing the steps in system claim 3 above and as such, claim 16 is similar in scope and content to claim 3 and therefore, claim 16 is rejected under similar rationale as presented against claim 3 above.
Allowable Subject Matter
Claims 5, 12 and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant's disclosure.
Beaver et al. (US 20200387673 A1) teaches a scalable system provides automated conversation review that can identify potential miscommunications. The system may provide suggested actions to fix errors in intelligent virtual assistant (IVA) understanding, may prioritize areas of language model repair, and may automate the review of conversations. By the use of an automated system for conversation review, problematic interactions can be surfaced without exposing the entire set of conversation logs to human reviewers, thereby minimizing privacy invasion. A scalable system processes conversations and autonomously marks the interactions where the IVA is misunderstanding the user.
Yang et al. (US 20180143968 A1) teaches a conversation analysis method which includes: receiving, by a processor, a conversation data including a plurality of sentences sorted by time; performing, by the processor, distributional clustering of context vectors to a plurality of words shown in the sentences to obtain a word order between the words; analyzing, by the processor, the words shown in the sentence to obtain a basic conversation matrix according to the word order; performing, by the processor, a fuzzy matching to the basic conversation matrix to obtain a conversation matrix based on the basic conversation matrix; detecting, by the processor, a topic trend according to the conversation matrix to determine the topic of the conversation data; and outputting, by the processor, the conversation matrix and the topic trend corresponding to the conversation data to a database.
Vaughn et al. (US 20200382450 A1) teaches a computer-implemented method, where a chat data set is received, which chat data set includes information indicative of a plurality of natural language chat transcripts of chats that occurred between a virtual agent and a human. Machine logic analyzes the chat data set to identify an error that occurred in the operation of the virtual agent. The machine logic updates a chat model based on the chat data set.
Munavalli et al. (US 20220108080 A1 ) teaches techniques related to using reinforcement learning to generate a dialogue policy. A computer system may perform an iterative training operation to train a deep Q-learning network (DQN) based on conversation logs from prior conversations. In various embodiments, the DQN may include an input layer to receive an input value indicative of a current state of a given conversation, one or more hidden layers, and an output layer that includes a set of nodes corresponding to available responses. During the iterative training operation, the disclosed techniques may analyze utterances from a conversation log and, based on the utterances, use the DQN to determine appropriate responses. Reward values may be determined based on the selected responses and, based on the reward values, the DQN may be updated. Once generated, the dialogue policy may be used by a chatbot system to guide conversations with users.
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/NADIRA SULTANA/Examiner, Art Unit 2653
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