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 .
DETAILED ACTION
Double Patenting
The nonstatutory 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 nonstatutory obviousness-type 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); and 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 a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1, 11, and 20, with claims 7 and 17, and any dependent claims thereof, are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1, 19, and 20 any dependent claims thereof of U.S. Patent No. 11902222. Although the conflicting claims are not identical, they are not patentably distinct from each other because said claims of the instant application includes all of the features of said claims of U.S. Patent No. 11902222. It would have been obvious to one of ordinary skill in the art to omit the step of utilizing ML layers, otherwise amounting to substantially similar scope, In re Karlson 136 USPQ 184 (1963): "Omission of an element and its function is an obvious expedient if the remaining elements perform the same functions as before"
Present invention Conflicting claims
1. A method implemented by one or more processors, the method comprising:identifying a trained voice bot;obtaining voice bot activity for the trained voice bot, the voice bot activity including a plurality of previously conducted conversations, each of the previously conducted conversations being conducted between the trained voice bot and a corresponding human, and each of the previously conducted conversations including at least a corresponding conversation and a corresponding prior context for one or more portions of the corresponding conversation;identifying, based on the plurality of previously conducted conversations, a given behavioral error of the trained voice bot; andsubsequent to identifying the given behavioral error of the trained voice bot:processing one or more of the plurality of previously conducted conversations to generate one or more additional training instances and in furtherance of correcting the given behavioral error;causing, based on one or more of the additional training instances, the trained voice bot to be updated to generate an updated voice bot; andcausing the updated voice bot to be deployed, in lieu of the trained voice bot, in conducting a plurality of subsequent conversations.
7. The method of claim 1, wherein identifying the given behavioral error of the trained voice bot comprises:processing, using a plurality of machine learning (ML) layers of a ML model, one or more of the portions of a given corresponding conversation, of the plurality of previously conducted conversations, and a given corresponding prior context of the given corresponding conversation for one or more of the portions of the given corresponding conversation to identify the given behavioral error; andprocessing, using the plurality of ML layers of the ML model or an additional plurality of ML layers of the ML model or an additional ML model, the given behavioral error to classify the given behavioral error into one or more disparate categories of behavioral errors.
1. (Currently Amended) A method implemented by one or more processors, the method comprising:identifying, via a bot development system, a trained voice bot that is associated with a third-party and a corpus of training instances utilized to train the trained voice bot; obtaining, via the voice bot development system, voice bot activity for the trained voice bot, wherein the voice bot activity includes a plurality of previously conducted conversations between the trained voice bot, on behalf of the third-party, and a corresponding human, and wherein each of the previously conducted conversations include at least a corresponding conversation and a corresponding prior context for one or more portions of the corresponding conversation; identifying, via the voice bot development system, and based on processing the plurality of previously conducted conversations, a given behavioral error of the trained voice bot, wherein identifying the given behavioral error of the trained voice bot comprises:processing, using a plurality of machine learning (ML) layers of a ML model, one or more of the portions of a given corresponding conversation, of the plurality of previously conducted conversations, and a given corresponding prior context of the given corresponding conversation for one or more of the portions of the given corresponding conversation to identify the given behavioral error; and processing, using the plurality of ML layers of the ML model or an additional plurality of ML layers of the ML model or an additional ML model, the given behavioral error to classify the given behavioral error into one or more disparate categories of behavioral errors; determining, via the voice bot development system, and 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; and causing a notification to be presented to a third-party developer, via a user interface of the bot development system, based on the action that is directed to correcting the given behavioral error of the trained voice bot, wherein the third-party developer is associated with the trained voice bot.
Claims 1, 11, and 20, with claims 6 and 16, and any dependent claims thereof, are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1, 11, and 20 with any dependent claims thereof of U.S. Patent No. 12255856. Although the conflicting claims are not identical, they are not patentably distinct from each other because said claims of the instant application includes all of the features of said claims of U.S. Patent No. 12255856. It would have been obvious to one of ordinary skill in the art to omit the step of utilizing operations for a 3rd party, amounting to substantially similar context, In re Karlson 136 USPQ 184 (1963): "Omission of an element and its function is an obvious expedient if the remaining elements perform the same functions as before"
Present invention Conflicting claims
1. A method implemented by one or more processors, the method comprising:identifying a trained voice bot;obtaining voice bot activity for the trained voice bot, the voice bot activity including a plurality of previously conducted conversations, each of the previously conducted conversations being conducted between the trained voice bot and a corresponding human, and each of the previously conducted conversations including at least a corresponding conversation and a corresponding prior context for one or more portions of the corresponding conversation;identifying, based on the plurality of previously conducted conversations, a given behavioral error of the trained voice bot; andsubsequent to identifying the given behavioral error of the trained voice bot:processing one or more of the plurality of previously conducted conversations to generate one or more additional training instances and in furtherance of correcting the given behavioral error;causing, based on one or more of the additional training instances, the trained voice bot to be updated to generate an updated voice bot; andcausing the updated voice bot to be deployed, in lieu of the trained voice bot, in conducting a plurality of subsequent conversations.
6. The method of claim 1, wherein the trained voice bot is deployed on behalf of a third- party entity, and wherein each of the previously conducted conversations are conducted by the trained voice bot and on behalf of the third-party.
1. A method implemented by one or more processors, the method comprising:identifying, via a bot development system, a trained voice bot that is associated with a third-party and a corpus of training instances utilized to train the trained voice bot; obtaining, via the voice bot development system, voice bot activity for the trained voice bot, wherein the voice bot activity includes a plurality of previously conducted conversations between the trained voice bot, on behalf of the third-party, and a corresponding human, and wherein each of the previously conducted conversations include at least a corresponding conversation and a corresponding prior context for one or more portions of the corresponding conversation; determining, via the voice bot development system, and based the plurality of previously conducted conversations, the trained voice bot does not include a desired behavior; generating, via the voice bot development platform, a notification that indicates one or more additional training instances need to be added to the corpus of training instances to generate an updated corpus of training instances, the one or more additional training instances being associated with the desired behavior; causing the notification to be presented to a third-party developer associated with the third-party via a user interface of the bot development system; and subsequent to obtaining the one or more additional training instances: causing the trained voice to be updated based on the updated corpus of training instances to generate an updated voice bot; and causing the updated voice bot to replace the trained voice bot for conducting additional conversations on behalf of the third-party.
Allowable Subject Matter
Claims 1-20 are allowed.
The following is an examiner’s statement of reasons for allowance:
After a full review of the complex claims as a whole, the examiner believes that the prior art taken alone or in combination fails to teach the claims as a whole such as identifying a trained voice bot; obtaining voice bot activity for the trained voice bot, the voice bot activity including a plurality of previously conducted conversations, each of the previously conducted conversations being conducted between the trained voice bot and a corresponding human, and each of the previously conducted conversations including at least a corresponding conversation and a corresponding prior context for one or more portions of the corresponding conversation; identifying, based on the plurality of previously conducted conversations, a given behavioral error of the trained voice bot; and subsequent to identifying the given behavioral error of the trained voice bot: processing one or more of the plurality of previously conducted conversations to generate one or more additional training instances and in furtherance of correcting the given behavioral error; causing, based on one or more of the additional training instances, the trained voice bot to be updated to generate an updated voice bot; and causing the updated voice bot to be deployed, in lieu of the trained voice bot, in conducting a plurality of subsequent conversations..
The above claims are deemed allowable given the complex nature of the claims as a whole as precisely limited. The closest prior art teaches manual replacement of bot outputs by a user (or developer per se), wherein the user can edit or swap out the bot outputs during training phases thus updating the model or analogously a corpus. Further closest prior art teaches replacement of a bot in general by a user such as during real time if a bot has extensive failures and a human must be involved, for instance during dialogs in the realm of customer service. Additional prior art teaches classification of the input itself based on context such as “driving directions”, “music”, “text messaging”, etc. where disambiguation is involved which updates learning models so that in the future when the system encounters the correction it does not need to disambiguate. Additionally, manual correction is taught in the instance a computer misses an error. Such errors and disambiguation are related to misrecognition or no recognition of speech. User history influences the processing of speech. Additional prior art teaches learning the manner in which a user speaks, the accent, and classification of emotion or sentiment. Additionally, prior art also teaches errors in the scope of infinite loops, timeouts, or system processes unrelated to disambiguation. The prior art under BRI at best teaches context/intent extraction with adaptive learning and user-based manual replacement of bot outputs, for training disambiguation and reduction of system bottlenecks/errors to handle slang or custom inputs with the identification of emotion or sentiment. When reasonably considered the prior art fails short of teaching the claims as a whole utilizing complexities of multi-layer model learning with multiple category classification of behavioral bot errors with notification to a 3rd party user, and further thereof since classification of speech or system errors is not classification of a bot behavioral error such as self-correction, as precisely limited in the claims as a whole. Although disambiguation is learned, it requires user intervention, however there is no classification per se of the correction itself. In another permutation of the prior art, concepts such as identifying a mismatch or wrong intent are taught, as well as a generic summary of the errors. The claims are specific as a whole inclusive of the identifying, determining, and causing limitations, wherein error categories must be classified, followed by user notification for correction of the trained bot. The prior art would require for instance a mismatch with types 1 through n and also wrong-text with types 1 through m, and similarly a non-generic notification such as a binary display if an error exists and the number thereof. At best disambiguation is performed per context and without a model there is no context and therefore no classification, i.e. the system is not actively classifying and bot error using layers to subsequently correct and notify as claimed. In a loosely applied piecewise context on the fringe of BRI, the claims appear to fall short of the aforementioned limitations and would not be reasonable to bridge a gap thereof using replacement in conjunction with manual + adaptive disambiguation in speech with emotion classification. Therefore, the prior art fails to teach or suggest the complex claims as a whole as precisely limited and tied together.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL COLUCCI whose telephone number is (571)270-1847. The examiner can normally be reached on M-F 9 AM - 7 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Flanders can be reached at (571)272-7516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL COLUCCI/Primary Examiner, Art Unit 2655 (571)-270-1847
Examiner FAX: (571)-270-2847
Michael.Colucci@uspto.gov
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