DETAILED ACTION
This action is in response to the original filing of 6-15-2022. Claims 1-20 are pending and have been considered below:
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.
Claim(s) 1-4, 8-11 and 15-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Buhmann et al. (“Buhmann” 11416732 B2) in view of Motwani et al. (“Motwani” 20240119287 A1) and Mulligan et al. (“Mulligan” 20240059312 A1).
Claim 1: Buhmann discloses a system comprising:
a hardware processor and a memory storing a software code; the hardware processor configured to execute the software code to (Column 11, Lines 21-45; processor): receive interaction data, the interaction data identifying an action and a plurality of personality profiles corresponding respectively to a plurality of participant cohorts in the action (Column 2, Lines 16-25, Column 4, Lines 40-52 (interaction by multiple characters) and Column 4, Lines 53-65 (personality profile));
Buhmann may not explicitly disclose
generate, using the interaction data, an interaction graph of behaviors of the plurality of participant cohorts in the action; simulate, using a behavior model, participation of each of the plurality of participant cohorts in the action to provide a predicted interaction graph for the plurality of participant cohorts;
compare the predicted interaction graph and the generated interaction graph to identify a similarity score for the predicted interaction graph relative to the generated interaction graph;
Motwani is provided because it discloses a graph functionality, and further provides a graph comparison functionality of features (abstract, Paragraphs 99-100). Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device and provide graph comparison determination of Buchmann. One would have been motivated to provide the functionality in order to better evaluate similarities for training purposes.
Additionally, Mulligan is provided because it discloses a functionality to compare actions and future behaviors between simulated agents (interactions)(Paragraph 10).
Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device and provide comparison of agent behaviors in the system of Buchmann. One would have been motivated to provide the functionality in order to ensure more accurate prediction of behaviors for consistent actions (Mulligan: Paragraph 12).
Buhmann also may not explicitly disclose when the similarity score satisfies a similarity criterion, train, using the model, an artificial intelligence (AI) character for interactions; and when the similarity score fails to satisfy the similarity criterion, modify the model based on one or more differences between the predicted interaction graph and the generated interaction graph.
Motwani is provided because it discloses a graph functionality, which uses a model generator, a threshold (score)is used to determine if a model is used or updated based on differences (Paragraphs 38 and 43). Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device and provide a model generator evaluation for the character determination of Buchmann. One would have been motivated to provide the functionality in order to optimize the agents for an accurate interaction.
Claim 2: Buhmann, Motwani and Mulligan disclose a system of claim 1, wherein the hardware processor is further configured to execute the software code to: when the similarity score fails to satisfy the similarity criterion, repeat the simulating and the comparing until another similarity score for another predicted interaction graph satisfies the similarity criterion; and train, using a modified behavior model providing the another predicted interaction graph, the AI character for the interactions(Motwani: Paragraphs 38 and 43; below threshold retrain).
Claim 3: Buhmann, Motwani and Mulligan disclose a system of claim 1, wherein the action comprises at least one of a same speech directed at each of the plurality of participant cohorts or a same event engaged in by each of the plurality of participant cohorts, and wherein the predicted interaction graph includes a plurality of edges each identifying an intent of one or more of the plurality of participant cohorts (Buhmann: Column 2, Lines 16-25, Column 4, Lines 40-52 (interaction by multiple characters) and Column 4, Lines 53-65 (personality profile)).
Claim 4: Buhmann, Motwani and Mulligan disclose a system of claim 3, wherein the plurality of edges each further identifies at least one of an utterance, a facial expression, or a gesture attributed to one or more of the plurality of participant cohorts (Buhmann: Column 12, Lines 15-27; facial expression).
Claim 8 and 15 are similar in scope to claim 1 and therefore rejected under the same rationale.
Non-transitory medium (Buhmann: Column 2, Lines 16-25)
Claim 9 and 16 are similar in scope to claim 2 and therefore rejected under the same rationale.
Claim 10 and 17 are similar in scope to claim 3 and therefore rejected under the same rationale.
Claim 11 and 18 are similar in scope to claim 4 and therefore rejected under the same rationale.
Claim(s) 5 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Buhmann et al. (“Buhmann” 11416732 B2), Motwani et al. (“Motwani” 20240119287 A1) and Mulligan et al. (“Mulligan” 20240059312 A1) in further view of Ajgaonkar et al. (“Ajgaonkar” 20250094723 A1).
Claim 5: Buhmann, Motwani and Mulligan disclose a system of claim 1, however may not explicitly disclose wherein the behavior model comprises at least one of a multimodal foundation model or a large-language model.
Ajgaonkar is provided because it discloses a generative artificial intelligence and further provides simulation functions utilizing large language models (Paragraphs 56-57 ). Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device and provide a large language model for the character interaction of Buchmann. One would have been motivated to provide the functionality as a way to expand interaction capability in more dynamic manner by utilizing generative functions.
Claim 12 are similar in scope to claim 5 and therefore rejected under the same rationale.
Claim(s) 6-7, 13-14 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Buhmann et al. (“Buhmann” 11416732 B2), Motwani et al. (“Motwani” 20240119287 A1) and Mulligan et al. (“Mulligan” 20240059312 A1) in further view of Bastide et al. (“Bastide” 20220166730 A1).
Claim 6: Buhmann, Motwani and Mulligan disclose a system of claim 1, further comprising a plurality of trained machine learning (ML) models, wherein the hardware processor is further configured to execute the software code to: receive qualitative feedback describing a plurality of interaction experiences by a plurality of human users with the trained AI character; segment the qualitative feedback, using a first ML model of the plurality of trained ML models, into a plurality of segments each corresponding respectively to a single interaction specific topic; analyze, using the qualitative feedback and at least one of the first ML model or a second ML model of the plurality of trained ML models, a respective sentiment of each of one or more of the plurality of human users with respect to each interaction specific topic; obtain interaction quality evaluation data generated by a control system for the AI character; identify, using the interaction quality evaluation data and based on the analyzing, tuning data for improving an interaction performance by the AI character; and tune, based on the tuning data, one of the behavior model or the modified behavior model.
(Buhmann: Column 12, Lines 27-37; user feedback)
Bastide is further provided because it discloses a graph modeling functionality (Paragraph 6), and additionally provides qualitative assessment (feedback) regarding words/topics (Paragraph 49) and also discloses adjustment/tuning capabilities based on the feedback (Paragraph 54). Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device and incorporate additional feedback with the user feedback of Buchmann. One would have been motivated to provide the functionality in order to update responses for improved interactions.
Claim 7: Buhmann, Motwani, Mulligan and Bastide disclose a system of claim 6, wherein the hardware processor is further configured to execute the software code to: receive quantitative feedback data rating the plurality of interaction experiences by the plurality of human users with the trained AI character; wherein identifying the tuning data further uses the quantitative feedback data (Buhmann: Column 12, Lines 27-37; user feedback and Bastide: Paragraph 49; quantitative feedback).
Claim 13 and 19 are similar in scope to claim 6 and therefore rejected under the same rationale.
Claim 14 and 20 are similar in scope to claim 7 and therefore rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
20230351217 A1 ABSTRACT
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHERROD KEATON whose telephone number is 571-270-1697. The examiner can normally be reached 9:30am to 5:00pm.
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/SHERROD L KEATON/ Primary Examiner, Art Unit 2148
6-7-2026
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