CTNF 18/903,848 CTNF 102051 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION. —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claim 5 – 7 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 5 recites the limitation "The contextual store". There is insufficient antecedent basis for this limitation in the claim. Claim 1 includes “a context store”, but does not recite “a contextual store”. For examination purposes the examiner has interpreted the “contextual store” in claim 5 to be the “content store” recited earlier in claim 1. Claim 6 and 7 depend upon claim 5 and therefore incorporate the same indefiniteness discussed above and are rejected under the same grounds. Claim 20 recites “The computer program product of claim 10”. However, claim 10 is directed to a computer hardware system, whereas claim 20 is directed to a computer program product. Therefore, it is unclear what subject matter claim 20 is intended to further limit. For purposes of expedited prosecution, claim 20 has been interpreted as depending on claim 19. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1 – 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sohum (US 20250363542 A1), in view of Kuyda (US 10990619 B1) . Regarding claim 1, Sohum discloses: A computer-implemented method by a computer hardware system including a hybrid router/manager (A model integration AI agent/personalized AI model 302 (also referred to as primary agent 302 or model integration AI agent 302) may serve as the central decision-maker in this framework [0058]) , a generative artificial intelligence (AI) conversational agent (These agents can be specialized AI/ML models, data processing units, or more complex entities like Coordinators and Recommenders [0059]). Sohum fails to disclose a rule-based conversational AI agent. While Sohum discloses multiple AI conversational agents, they fail to specifically disclose a rule-based AI conversational agent. However, Kuyda teaches a rule-based AI conversational agent (a chatbot maintains a conversation with a user by providing a plurality of separate conversational tiers that allow the chatbot to interact with the user, each of the conversational tiers having a set of production rules that are independent of production rules for other ones of the tiers, the production rules indicating a flow of conversation between the user and the chatbot [col. 2, lines 30-37 of Kuyda]).--- Sohum and Kuyda are both considered to be analogous to the claimed invention because they are in the same field of utilizing AI models. 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 Sohum’s disclosure of multiple conversational AI agents to incorporate the teachings of Kuyda and provide a rule-based conversational AI agent as one of the possible AI/ML agents to be selected by the primary AI agent. Kuyda teaches that such rule-based conversational tiers contribute to increased intelligence and conversational capabilities (it is desirable to create a new generation of user friendly chatbots with high retention rates and increased intelligence and conversational capabilities [col. 2, lines 24-26 of Kuyda]). It would have predictably expanded the range of conversational mechanisms by making a rule-based AI agent made available to the primary AI agent, therefore improving the system’s ability to provide responses. The combination further discloses: a content store (The storage unit 204 may store, but is not limited to, agent data marketplace data, model data, coordinator data, any other data, and combinations thereof [Sohum 0051 of Sohum]) comprising: receiving, from a client device associated with a user (receive an input from an external system or a user [0079 of Sohum]), a present query (takes user's initial query [0073 of Sohum]); selecting, by the hybrid router/manager (At block 406, the primary AI agent 302 may identify [0080 of Sohum]) and based upon a query analysis of the present query (At block 404, the primary AI agent 302 may determine a set of requirements based on the input [0079 of Sohum]) between the generative AI conversational agent and the rule-based AI conversational agent (identify one or more candidate AI agents 304 from the plurality of AI agents [0080 of Sohum] now including Kuyda’s rule-based AI conversational agent referenced above); routing, (identify one or more candidate AI agents 304 from the plurality of AI agents [0080 of Sohum]) by the hybrid router/manager (At block 408, the primary AI agent 302 may deploy the one or more candidate AI agents 304 [0081 of Sohum]) and based upon the query analysis (At block 404, the primary AI agent 302 may determine a set of requirements based on the input [0079 of Sohum]), the present query to a selected (the system 102, and the user device 106 connected to the database 104 [0039 of Sohum]) one of the generative AI conversational agent and the rule-based AI conversational agent (including the AI/ML agents 304-1 and coordinators 304-N [0058 of Sohum] now including Kuyda’s rule-based AI conversational agent referenced above); generating, by the selected one of the generative AI conversational agent and the rule-based AI conversational agent, (With the models selected and potentially customized, they are deployed within the software system [0085 of Sohum] potentially including Kuyda’s rule-based AI conversational agent referenced above) a response to the present query (At block 414, the primary AI agent 302 may obtain at least one recommendation from the optimal AI agent [0083 of Sohum]); forwarding the response to the client device associated with the user (providing, by the primary AI agent, at least one recommendation to the external system or the user in response to the input [0083 of Sohum]); generating a feedback (The user's interaction (clicking on certain items, dismissing others) provides feedback [0070 of Sohum]) analysis associated with the response (evaluate performance of each candidate AI agent 304 based on feedback obtained from monitoring of analysis of the one or more candidate AI agents 304 [0081 of Sohum]); updating one of the generative AI conversational agent and the rule-based AI conversational agent (requests for updates or modifications to the AI and ML models to ensure they align with the changing requirements of the system [0087 of Sohum] now including Kuyda’s rule-based AI conversational agent referenced above) based upon the feedback analysis (based on feedback received from the AI/ML agents 304-1 and the evolving needs of the system [0087 of Sohum] ; Regarding claim 2, in addition to the elements stated above regarding claim 1, Sohum in the combination further discloses: the generative AI conversational agent (These agents can be specialized AI/ML models, data processing units, or more complex entities like Coordinators and Recommenders [0059]) was selected to generate the response (With the models selected and potentially customized, they are deployed within the software system [0085]); the updating (requests for updates or modifications to the AI and ML models to ensure they align with the changing requirements of the system [0087]) includes updating a ruleset within the rule-based AI conversational agent based upon the response (requests for updates or modifications to the AI and ML models to ensure they align with the changing requirements of the system [0087]); Regarding claim 3, in addition to the elements stated above regarding claim 1, Sohum in the combination further discloses: the updating (requests for updates or modifications to the AI and ML models to ensure they align with the changing requirements of the system [0087]) includes configuring the hybrid router/manager (may communicate with the model integration AI agent 302, requesting modifications [0062]) to forward queries similar to the present query to the rule-based AI conversational agent (modifications to the models to ensure they remain aligned with the evolving needs of the system [0062]); Regarding claim 4, in addition to the elements stated above regarding claim 1, Sohum in the combination further discloses: the updating includes updating training data (fine-tuning parameters, training data, or model architecture to ensure they align with the system's unique context and objectives [0084]) for the generative AI conversational agent (the AI agent 304-1 customizes these models [0084]); Regarding claim 5, in addition to the elements stated above regarding claim 1, Sohum in the combination further discloses: the contextual store (“Family Coordinator” agent (a specialized 304-N type) [0068]; further, as noted above the storage unit 204 may store, but is not limited to, agent data marketplace data, model data, coordinator data, any other data [0051]) includes context information for the present query (takes user's initial query (“gift for teenage son”) and the rich contextual data from the Family Coordinator. [0073]); Regarding claim 6, in addition to the elements stated above regarding claim 1, Sohum in the combination further discloses: the context information for the present query ((takes user's initial query (“gift for teenage son”) and the rich contextual data from the Family Coordinator. [0073]) includes information regarding a least one prior query and at least one prior response (recommendations based on criteria derived from the Family Coordinator (e.g., son's past wish lists, items already owned) [0070]) in a conversation to which the query is associated (accesses information like “the son's public wish lists on e-commerce sites”, “mentions of desired items in family chat logs (e.g., “I wish I had those new headphones”) [0071]); Regarding claim 7, in addition to the elements stated above regarding claim 1, Sohum in the combination further discloses: the conversation includes a first reply from the generative AI conversational agent and a second reply from the rule-based AI conversational agent (if multiple recommendation agents are available for a task, agent 302 can deploy them in parallel or sequentially [0065]); Regarding claim 8, in addition to the elements stated above regarding claim 1, Sohum in the combination further discloses: the feedback analysis (feedback loop used by the model integration AI agent [0105]) is based upon explicit feedback provided by the user (combining explicit user ratings, implicit signals (e.g., task completion time, hesitation scores, biometric responses if available from user device 106), goal attainment success, resource consumption [0105] ) ; Regarding claim 9, in addition to the elements stated above regarding claim 1, Sohum in the combination further discloses: the feedback analysis (feedback loop used by the model integration AI agent [0105]) is based upon inferred feedback based upon an analysis of an interaction of the user with the computer hardware system (implicit signals (e.g., task completion time, hesitation scores, biometric responses if available from user device 106), goal attainment success, resource consumption by secondary agents, and qualitative feedback processed via NLP [0105]); Claim 10 contains similar limitations to claim 1 and therefore is rejected for the same reasons. Additionally, Sohum further discloses: a hardware processor (The system 102 includes one or more hardware processor(s) [0037[) configured to initiate the following executable operations (executing machine-readable program instructions [0037]); Claim 11 contains similar limitations to claim 2 and therefore is rejected for the same reasons. Claim 12 contains similar limitations to claim 3 and therefore is rejected for the same reasons. Claim 13 contains similar limitations to claim 4 and therefore is rejected for the same reasons. Claim 14 contains similar limitations to claim 5 and therefore is rejected for the same reasons. Claim 15 contains similar limitations to claim 6 and therefore is rejected for the same reasons. Claim 16 contains similar limitations to claim 7 and therefore is rejected for the same reasons. Claim 17 contains similar limitations to claim 8 and therefore is rejected for the same reasons. Claim 18 contains similar limitations to claim 9 and therefore is rejected for the same reasons. Claim 19 contains similar limitations to claim 1 and therefore is rejected for the same reasons. Additionally, Sohum further discloses: A computer readable storage medium (These methods, functions, and other processes may be embodied as machine-readable instructions stored on a computer-readable medium [0110]) having stored therein program code (The “software” may comprise one or more objects, agents, threads, lines of code, subroutines, separate software applications, two or more lines of code, or other suitable software structures operating in one or more software applications or on one or more processors [0037]); The program code, which when executed by a computer hardware system (The hardware platform may be a computer system such as the system 102 that may be used with the embodiments described herein [0110]); Claim 20 contains similar limitations to claim 2 and claim 3 and therefore is rejected for the same reasons. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kyle Espinas whose telephone number is (571) 270-0596. The examiner can normally be reached Monday Friday, 8 a.m. 5 p.m. ET.. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. 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. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Kylenino Espinas/ Patent Examiner Art Unit 2655 6/1/2026 /ANDREW C FLANDERS/Supervisory Patent Examiner, Art Unit 2655 Application/Control Number: 18/903,848 Page 2 Art Unit: 2655 Application/Control Number: 18/903,848 Page 3 Art Unit: 2655 Application/Control Number: 18/903,848 Page 4 Art Unit: 2655 Application/Control Number: 18/903,848 Page 5 Art Unit: 2655 Application/Control Number: 18/903,848 Page 6 Art Unit: 2655 Application/Control Number: 18/903,848 Page 7 Art Unit: 2655 Application/Control Number: 18/903,848 Page 8 Art Unit: 2655 Application/Control Number: 18/903,848 Page 9 Art Unit: 2655 Application/Control Number: 18/903,848 Page 10 Art Unit: 2655 Application/Control Number: 18/903,848 Page 11 Art Unit: 2655 Application/Control Number: 18/903,848 Page 12 Art Unit: 2655
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