DETAILED ACTION
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 .
Response to Amendment
Applicants’ amendment to claims filed on 06/02/26 has been entered. Claims 1, 10, 16 have been amended. No claims have been canceled. No new claims have been added. Claims 1-20 are still pending in this application, with claims 1, 10, 16 being independent.
Applicants’ amendment to specification filed on 06/02/26 has been entered. Specification paragraphs [0004], [0069], [0070], and [0087] are amended.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2023/0115098 to Miller et al. (“Miller”) in view of U.S. Patent Application Publication No. 2021/0326742 to Rosset et al. (“Rosset”) in further view of U.S. Patent Application Publication No. 2021/0097110 to Asthana et al. (“Asthana”).
As to claims 1, 10 and 16, Miller discloses a method, a system and a non-transitory computer-readable medium, the method comprising: receiving an initial query about a virtual communication session from a user [Fig. 4: 406, paragraphs 0018, 0023, 0026, 0030, 0076]; accessing virtual communication data associated with the virtual communication session [Fig. 4: 406, paragraphs 0018, 0023, 0026, 0030, 0076]; executing a first pre-trained generative artificial intelligence (AI) model to generate an initial response to the initial query based on the virtual communication data [paragraph 0076: returning the results of the user’s query]; generating a first set of follow-up queries based on the initial response using a second pre-trained generative AI model [paragraphs 0006, 0073-74, 0076-77]; receiving a selection of a first follow-up query out of the first set of follow-up queries [paragraphs 0073, 0077]; and providing a first response to the first follow-up query using the first pre-trained generative AI model [paragraph 0077 : “the selected query may be executed against live transcript 414, and a query results may be returned”].
Miller does not expressly disclose executing a second pre-trained generative AI model to generate a first set of follow-up queries and providing a first response to the first follow-up query using the first pre-trained generative AI model. Even though, it is extremely obvious and well known in the art when generating the follow-up query the original query of the user is also taken into account and also the query responses are generated using a first pre-trained generative AI model. Miller also does not expressly disclose obtaining user feedback data associated with the first set of follow-up queries based on the selection of the first follow-up query: and fine-tuning the second pre-trained generative AI model based on the user feedback data to obtain a second fine-tuned generative AI model.
In the same or similar field of invention, Rosset discloses features of executing a second pre-trained generative AI model to generate a first set of follow-up queries based, providing a first response to the first follow-up query using the first pre-trained generative AI model [Rosset paragraphs 0005-0006, 0052-0053] and obtaining user feedback data associated with the first set of follow-up queries based on the selection of the first follow-up query [Rosset Fig. 11: 1110, 1112—1116, paragraphs 0107-0112] and fine-tuning the second pre-trained generative AI model based on the user feedback data to obtain a second fine-tuned generative AI model [Rosset Fig. 11, paragraphs 0107-0112, 0115-0118].
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Miller to have above features as taught by Rosset. The suggestion/motivation would have been to provide a computer-implemented technique for assisting a user in interacting with a query-processing system. The technique uses a suggestion-generating system to provide one or more suggestions to a user in response to at least a last-submitted query provided by the user [Rosset paragraph 0003].
Miller and Rosset do not expressly disclose generative AI model to generate a first set of follow-up queries based on the initial response.
In the same or similar field of invention, Asthana discloses features of generating a first set of follow-up queries based on the initial response [Asthana paragraphs 0008, 0019: “The chatbot responses lead to more specific user questions and chatbot answers that ultimately progress to user fulfillment”, Fig. 6: chatbot 300 suggested questions 328, also see paragraph 0140].
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Miller and Rosset to have above features as taught by Asthana. The suggestion/motivation would have been to provide a chatbot application for transactions with users, where the application responds to questions from the users, which are typically general questions at first. The chatbot responses lead to more specific user questions and chatbot answers that ultimately progress to user fulfillment [Asthana paragraph 0019].
As to claim 2, Miller discloses wherein the virtual communication session is an online chat session, and wherein the virtual communication data comprises multiple chat messages in the online chat session [paragraphs 0030-31, 0044-45, 0055, 0057].
As to claim 3, Miller discloses wherein the virtual communication session is a virtual conference, and wherein the virtual communication data comprises a transcript for the virtual conference [paragraphs 0005-0006, 0076-0076].
As to claim 4, Miller discloses wherein the virtual communication session is an email thread, and wherein the virtual communication data comprises a sequence of emails [paragraphs 0084].
As to claims 5, 11 and 17, Miller discloses prior to receiving an initial query about the virtual communication session from a user, training a first generative AI model to obtain the first pre-trained generative AI model using a set of question-answer pairs as a first set of training output and a set of communication data as a first set of training input [Fig. 3, paragraphs 0065, 0070]; and training a second generative AI model to obtain the second pre-trained generative AI model using a sequence of questions as a second set of training output and the set of communication data as a second set of training input [Fig. 3, paragraphs 0065, 0070, 0073].
As to claims 6, 12 and 18, Miller discloses providing the initial response to the user; and providing the initial response to the second pre-trained generative AI model [paragraphs 0065, 0070].
As to claims 7, 13 and 19, Miller discloses receiving user feedback about the first set of follow-up queries; retraining the second pre-trained generative AI model based on the user feedback to obtain a second retrained generative AI model; and regenerating the first set of follow-up queries using the second retrained generative Al model [paragraph 0065, 0073].
As to claims 8, 14 and 20, Miller discloses receiving a follow-up query created by the user; and generating an answer to the follow-up query using the first pre-trained generative Al model [paragraphs 0076-0077, Fig. 4].
As to claims 9 and 15, Miller discloses generating a second set of follow-up queries based on the first response using the second pre-trained generative AI model; and receiving a selection of a second follow-up query out of the second set of follow-up queries; and providing a second response to the second follow-up query using the first pre-trained generative AI model [paragraphs 0076-77, Fig. 4]. Further, Rosset discloses features of generating a first set of follow-up queries based on the initial response using a second pre-trained generative AI model and providing a first response to the first follow-up query using the first pre-trained generative AI model [Rosset paragraphs 0005-0006, 0052-0053]. In addition, the same motivation is used as the rejection of claims 1 and 10.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
U.S. Patent Application Publication No. 20200007474 to Zhang et al. (Figs. 4-6 and corresponding paragraphs).
U.S. Patent Application Publication No. 20240290331 to Liu et al. (paragraphs 0022, 0037, also see Figs. 1-7, 13, 19 and corresponding paragraphs).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANTIM G SHAH whose telephone number is (571)270-5214. The examiner can normally be reached Mon-Fri 7:30am-4pm.
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/ANTIM G SHAH/Primary Examiner, Art Unit 2693
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