DETAILED ACTION
Introduction
Applicant's submission filed on 11/04/2024 has been entered. Claims 1-18 are pending in the application and have been examined.
Notice of Pre-AIA or 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 .
Information Disclosure Statement
The information disclosure statement filed 11/04/2024 fails to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed. It has been placed in the application file, but the information referred to therein has not been considered.
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 (i.e., changing from AIA to pre-AIA ) 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, 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 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Subramaniam et. al. PgPub. US 2020/0342032 in view of Renard US PgPub. 2018/0173999.
Regarding claim 1, Subramaniam teaches a system comprising: at least one processor; memory storing instructions that, when executed, cause the at least one processor to be operable to: receive, from a developer, developer input to train a voice bot (see Subramaniam, [0060] the intents of the chatbot are indicated, an owner of a restaurant (e.g., a pizza shop) may use DABP 102 to create and deploy a digital assistant that enables customers of the restaurant to order food (e.g., order pizza); Subramaniam, [0086] ); obtain, based on the developer input, a plurality of training instances ( see Subramaniam, [0045-0046] training corpus from customer for training a chatbot ); train the voice bot based on the plurality of training instances to generate a plurality of corresponding behaviors for the voice bot (see Subramaniam, [0048, 0059, 0088] conversation history uses present and past actions, context, and intents to train the bot system ); subsequent to training the voice bot: cause the voice bot to be deployed for conducting conversations with users and on behalf of an entity associated with the developer (see Subramaniam, [0061] once deployed the users perform tasks on behalf of the entity associated with the developer ); subsequent to causing the voice bot to be deployed for conducting conversations with the users and on behalf of the entity associated with the developer: identify a given error during a given conversation, of the conversations, with a given user, of the users(see Subramanian, [0094, 0113, 0118] bot identifies error events); obtain, based on the given error, a plurality of additional training instances (see Subramanian, [0179-0180] discusses the updating of the training corpus based on notifications and user inputs ); further train the voice bot based on the plurality of additional training instances to add a new behavior, to the plurality of corresponding behaviors, for the voice bot, or to modify an existing behavior, of the plurality of corresponding behaviors, of the voice bot( see Subramaniam, [0048, 0109, 0123-0125] discusses the errors present, Subramanian [0189-0190] the bot system is retrained using user input ); and subsequent to further training the voice bot: cause the voice bot to be re-deployed for conducting the conversations with the users and on behalf of the entity associated with the developer (see Subramaniam, [0099] discussing deploying the skill bot based on the skill developed).
Subramaniam teaches subsequent to further training the voice bot: cause the voice bot to be re-deployed for conducting the conversations with the users and on behalf of the entity associated with the developer, however, Renard is used to further teach train the voice bot based on the plurality of training instances to generate a plurality of corresponding behaviors for the voice bot (see Renard, [0069] At block 402, the training initiation engine 324 creates a natural training session); subsequent to training the voice bot: cause the voice bot to be deployed for conducting conversations with users and on behalf of an entity associated with the developer (see Renard, [0069] At block 408, the AI/agent/bot is put into service); subsequent to further training the voice bot: cause the voice bot to be re-deployed for conducting the conversations with the users and on behalf of the entity associated with the developer (see Renard, [0069] At block 410, additional training is provided via the additional training engine 332. In one embodiment, blocks 408 and 410 may be repeated).
Subramaniam and Renard are considered to be analogous to the claimed invention because they relate to bot systems. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Subramaniam on error reporting analysis of bot systems with the natural training teachings of Renard to allow a non-machine learning expert to train an AI, particularly in an ad hoc manner as needed ( see Renard, [0002]).
Regarding claim 2, Subramaniam in view of Renard teaches the system of claim 1. Subramaniam further teaches generate a notification that includes an indication of the given error from the given conversation of the given user, and/or an indication of an action that, when performed, corrects the given error (see Subramaniam, [0051, 0094, 0113, 0118] developer-based bot system identifies errors in processing; [0140] context usage and error handling by the bot; [0109 0123-0125] in order to correct bot behavior in addition to machine learning, developer receives a report which shows what changed/errors are present ).
Regarding claim 3, Subramaniam in view of Renard teaches the system of claim 2. Subramaniam further teaches wherein the indication of the action that, when performed, corrects the given error includes one or more of: an indication to re-label one or more of the plurality of training instances that were utilized to train the voice bot to generate one or more of the plurality of additional training instances (see Subramanian, [0124] discusses use corrected the classification results to retrain the classification model( relabeling & correcting the error)); an indication to add one or more of the plurality of additional training instances to generate one or more of the plurality of additional training instances (see Subramaniam, Subramaniam, [0048, 0109, 0123-0125] discusses the errors present, The bot improvement report may provide information regarding the intent classification results, which may be used by the administrator or developer of the bot system to correct some classification results and use the corrected classification results to retrain the classification models. the insights reports may present one or more potential intents based upon likelihoods such that the user may select an intent from the one or more potential intents so that the utterance may be added to a training dataset used for training the classification model for identifying the intent from a utterance; Subramanian [0189-0190] the bot system is retrained using user input).
Regarding claim 4, Subramaniam in view of Renard teaches the system of claim 3. Subramaniam further teaches wherein the action is automatically performed in response to identifying the given error during the given conversation with the given user (see Subramaniam, [0048, 0051, 0124] discusses the analytic system which is used to retrain the bot system (automatically performed) to improve or correct the bot system).
Regarding claim 5, Subramaniam in view of Renard teaches the system of claim 3. Subramaniam further teaches wherein the action is performed in response to receiving developer input, that is from the developer and that is responsive to the notification, associated with the action (see Subramaniam, [0183, 0189-0190] discusses the bot system retrained based on the user input based on the analytic notifications).
Regarding claim 6, Subramaniam in view of Renard teaches the system of claim 1. Subramaniam further teaches wherein the developer is a third-party developer that is associated with a third-party entity, and wherein the entity is a third-party entity (see Subramaniam, [0060] user 104 represents a particular enterprise can use DABP 102 to create and deploy a digital assistant 106 for users of the particular enterprise ( and an example or owner of pizza shop ( third-party entity) ).
Regarding claim 7, is directed to a method corresponding to the system claim presented in claim 1 and is rejected under the same grounds stated above regarding claim 1.
Regarding claim 8, is directed to a method corresponding to the system claim presented in claim 2 and is rejected under the same grounds stated above regarding claim 2.
Regarding claim 9, is directed to a method corresponding to the system claim presented in claim 3 and is rejected under the same grounds stated above regarding claim 3.
Regarding claim 10, is directed to a method corresponding to the system claim presented in claim 4 and is rejected under the same grounds stated above regarding claim 4.
Regarding claim 11, is directed to a method corresponding to the system claim presented in claim 5 and is rejected under the same grounds stated above regarding claim 5.
Regarding claim 12, is directed to a method corresponding to the system claim presented in claim 6 and is rejected under the same grounds stated above regarding claim 6.
Regarding claim 13, is directed to a non-transitory computer-readable storage medium claim corresponding to the system claim presented in claim 1 and is rejected under the same grounds stated above regarding claim 1.
Regarding claim 14, is directed to a non-transitory computer-readable storage medium claim corresponding to the system claim presented in claim 2 and is rejected under the same grounds stated above regarding claim 2.
Regarding claim 15, is directed to a non-transitory computer-readable storage medium claim corresponding to the system claim presented in claim 3 and is rejected under the same grounds stated above regarding claim 3.
Regarding claim 16, is directed to a non-transitory computer-readable storage medium claim corresponding to the system claim presented in claim 4 and is rejected under the same grounds stated above regarding claim 4.
Regarding claim 17, is directed to a non-transitory computer-readable storage medium claim corresponding to the system claim presented in claim 5 and is rejected under the same grounds stated above regarding claim 5.
Regarding claim 18, is directed to a non-transitory computer-readable storage medium claim corresponding to the system claim presented in claim 6 and is rejected under the same grounds stated above regarding claim 6.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hosabettu et. al. US PgPub. 2017/0372227 teaches method for dynamically training bots in response to change in process environment (Hosabettu, Fig. 4).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NANDINI SUBRAMANI whose telephone number is (571)272-3916. The examiner can normally be reached Monday - Friday 12:00pm - 5:00 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bhavesh M Mehta can be reached at (571)272-7453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NANDINI SUBRAMANI/Examiner, Art Unit 2656
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