CTNF 18/789,704 CTNF 84686 DETAILED ACTION 12-151 AIA 26-51 12-51 Status of Claims This is a non-final action in reply to the application filed on July 31, 2024. Claims 1-20 are currently pending and have been examined. 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 Claims 1-20 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. As per claim 1 recites “trigger modelling of a questionnaire based on the set of relevant elementary vectors, the questionnaire comprising questions being hierarchically-linked questions in relation to the insight associated with the aspect, […] wherein each of the subsequent questions is increasingly proximate to the insight associated with the aspect as compared to the immediately preceding question, and wherein the subsequent questions are determined until a response to at least one question, from amongst the subsequent questions, provides a requisite insight associated with the aspect” Examiner is not clear, if the questions are already related to the insight associated with the aspect, why determine until it provides a requisite associated with the aspect? What are the metes and bound of requisite insight, how much requisite insight is the questions associated with the aspect? What are the metes and bound of increasingly proximate to the insight? The same rationales applies to claims 11 and 17. Appropriate correction is required. Claim Rejections- 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Per MPEP 2106.03 Eligibility Step 1: The Four Categories of Statutory Subject Matter [R-07.2022]. Step 1 is directed to determining whether or not the claims fall within a statutory class. Herein, claims 1-10 falls within statutory class of a machine, claims 11-16 falls within statutory class of a process and claims 17-20 falls within statutory class of an article of manufacturing. Hence, the claims qualify as potentially eligible subject matter under 35 U.S.C §101. With Step 1 being directed to a statutory category, per MPEP 2106.04 Eligibility Step 2A: Whether a Claim is Directed to a Judicial Exception [R-07.2022]. Step 2 is the two-part analysis from Alice Corp. (also called the Mayo test). The 2019 PEG makes two changes in Step 2A: It sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. The two-prong inquiry is as follows: Prong One: evaluate whether the claim recites a judicial exception. If claim recites an exception, then Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception . The claim(s) recite(s) the following abstract idea indicated by non-boldface font and additional limitations indicated by boldface font: Claim 1 : a processor to: receive an activation signal comprising a query corresponding to an offering, the query comprising textual content relevant to the offering; encode the textual content into a set of query vectors, the set of query vectors being representative of the textual content; compute, from a vector database having a set of elementary vectors derived based on descriptive information associated with the offering, a relevance metric for each elementary vector in the set of elementary vectors, the relevance metric being computed based on a semantic relationship between each query vector in the set of query vectors and each elementary vector in the set of elementary vectors, wherein the descriptive information comprises information usable for deriving an insight associated with an aspect related to the offering; identify a set of relevant elementary vectors, from amongst the set of elementary vectors, based on the relevance metric computed for each elementary vector in the set of elementary vectors, the set of relevant elementary vectors being representative of selective descriptive information, from amongst the descriptive information, pertinent to the query; trigger modelling of a questionnaire based on the set of relevant elementary vectors, the questionnaire comprising questions being hierarchically-linked questions in relation to the insight associated with the aspect, the modelling comprising: determining an opening question, in relation to the insight associated with the aspect, based on the set of relevant elementary vectors; determining a plurality of subsequent questions, each being determined based on a probable response to an immediately preceding question thereto, the probable response being derived from the selective descriptive information encoded as the set of relevant elementary vectors, wherein each of the subsequent questions is increasingly proximate to the insight associated with the aspect as compared to the immediately preceding question, and wherein the subsequent questions are determined until a response to at least one question, from amongst the subsequent questions, provides a requisite insight associated with the aspect; and generate a questionnaire delivery signal to cause rendering of the modelled questionnaire Claim 11 : receiving a query corresponding to an offering, the query comprising textual content relevant to the offering; encoding the textual content into a set of query vectors, the set of query vectors being representative of the textual content; computing, from a set of elementary vectors derived based on descriptive information associated with the offering, a relevance metric for each elementary vector in the set of elementary vectors, the relevance metric being computed based on a semantic similarity between each query vector in the set of query vectors and each elementary vector in the set of elementary vectors, wherein the descriptive information comprises information usable for deriving an insight associated with an aspect related to the offering; selecting a set of relevant elementary vectors, from amongst the set of elementary vectors, based on the relevance metric computed for each elementary vector in the set of elementary vectors, the set of relevant elementary vectors being representative of selective descriptive information, from amongst the descriptive information, pertinent to the query; triggering modelling of a questionnaire based on the set of relevant elementary vectors, the questionnaire comprising questions being hierarchically-linked questions in relation to the insight associated with the aspect, the modelling comprising: determining an opening question, in relation to the insight associated with the aspect, based on the set of relevant elementary vectors; determining a plurality of subsequent questions, each being determined based on a response, from amongst two responses associated with an immediately preceding question, derived for the immediately preceding question, the response being derived based on the selective descriptive information encoded as the set of relevant elementary vectors, wherein the subsequent questions are determined until a response to at least one question, from amongst the subsequent questions, provides a required insight associated with the aspect; and generating a questionnaire delivery signal to cause rendering of the modelled questionnaire. Claim 17 : receive a query corresponding to a set of offerings, the query comprising textual content relevant to the set of offerings; encode the textual content into a set of query vectors, the set of query vectors numerically representing the textual content; compute, from a vector database having a set of elementary vectors derived based on descriptive information associated with the set of offerings, a relevance metric for each elementary vector in the set of elementary vectors, the relevance metric being computed based on a semantic relationship between each query vector in the set of query vectors and each elementary vector in the set of elementary vectors, wherein the descriptive information comprises information usable for deriving an insight associated with an aspect related to the set of offerings; identify a set of relevant elementary vectors, from amongst the set of elementary vectors, based on the relevance metric computed for each elementary vector in the set of elementary vectors, the set of relevant elementary vectors being representative of selective descriptive information, from amongst the descriptive information, pertinent to the query; trigger modelling of a questionnaire based on the set of relevant elementary vectors, the questionnaire comprising questions being hierarchically-linked questions in relation to the insight associated with the aspect, the modelling comprising: determining an opening question, in relation to the insight associated with the aspect, based on the set of relevant elementary vectors; determining a plurality of subsequent questions, each being determined based on a probable response to an immediately preceding question thereto, the probable response being derived from the selective descriptive information, wherein each of the subsequent questions is increasingly proximate to the insight associated with the aspect as compared to the immediately preceding question, and wherein the subsequent questions are determined until a response to at least one question, from amongst the subsequent questions, provides a requisite insight associated with the aspect; and generate a questionnaire delivery signal to cause rendering of the modelled questionnaire. Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within Mental Processes, concepts performed in the human mind including observations, evaluation, judgement and opinion and Certain Methods of Organizing Human Activity such as commercial or legal interactions including advertising, marketing or sales activities or behaviors, business relations. Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The processor is recited at a high level of generality, i.e., as a generic computing and processing system. This processor is no more than mere instructions to apply the exception using a generic computing devices each comprising at least a processor, memory and display device. Further, processor configured to cause receiving/determining/transmitting data is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, MPEP 2106.05 Eligibility Step 2B: Whether a Claim Amounts to Significantly More [R-07.2022] is directed to Step 2B. Therein, per Step 2B the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of a processor. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, executing all the steps/functions by a user/service subsystem is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic processor type structure at paragraphs 0036: “the system 102 may include a processor 104 […] The processor 104 may be implemented as a dedicated processor, a shared processor, or a plurality of individual processors, some of which may be shared. Examples of the processor 104 may include, but are not limited to, microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, Artificial Intelligence (AI) based processors, processing circuitries including one or more modules or engines, and/or any other devices that may manipulate signals and data based on computer-readable instructions.” See also figures 1A-1C, 2 and 3. Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp . that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp ., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)); ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp ., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc ., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos , 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook . The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine for performing the present claims); and receiving or transmitting data (e.g., the present claims). The dependent claims 2-10, 12-16 and 18-20 do not cure the above stated deficiencies, and in particular, the dependent claims further narrow the abstract idea without reciting additional elements that integrate the exception into a practical application of the exception or providing significantly more than the abstract idea. Claim 2 further limit the abstract idea that a vector database communicably coupled with the processor, the vector database having stored therein the set of elementary vectors derived based on the descriptive information associated with the offering (a more detailed abstract idea remains an abstract idea). Claim 3 further limit the abstract idea by rendering of the modelled questionnaire having the hierarchically-linked questions, wherein each of the subsequent questions is linked with the preceding question, the subsequent questions having increased proximity towards the insight as compared to the preceding question thereto (a more detailed abstract idea remains an abstract idea). Claim 4 further limit the abstract idea to receive, from an actionable component, a questionnaire modification signal indicating a request to modify at least one of the questions of the modelled questionnaire (a more detailed abstract idea remains an abstract idea). Claim 5 further limit the abstract idea by rendering of a feedback option to receive at least one of a positive feedback and a negative feedback for each question of the modelled questionnaire, the positive feedback indicating acceptance of the question and the negative feedback indicating rejection of the question (a more detailed abstract idea remains an abstract idea). Claim 6 further limit the abstract idea to render an updated questionnaire in response to receiving the negative feedback for the question (a more detailed abstract idea remains an abstract idea). Claim 7 further limit the abstract idea that to tune subsequent determinations of at least one of an opening question and subsequent questions based on at least one of the positive feedback and the negative feedback (a more detailed abstract idea remains an abstract idea). Claim 8 further limit the abstract idea that to trigger an advanced learning model for modelling the questionnaire, wherein the advanced learning model is one of a supervised large language model and an unsupervised large language model (a more detailed abstract idea remains an abstract idea). Claim 9 further limit the abstract idea that the modelled questionnaire further comprises the query, wherein the opening question is hierarchically linked with the query, the opening question being increasingly proximate, compared to the query, towards the insight associated with the aspect (a more detailed abstract idea remains an abstract idea). Claim 10 further limit the abstract idea that to compare the relevance metric, computed for each elementary vector in the set of elementary vectors, with a threshold relevance metric; and identify, based on the comparison, the set of relevant elementary vectors from amongst the set of elementary vectors (a more detailed abstract idea remains an abstract idea). Claim 12 further limit the abstract idea that the two responses comprise a first response and a second response, wherein, upon derivation of the first response as the response for the immediately preceding question, a question with increased proximity to the insight associated with the aspect is determined, and wherein, upon derivation of the second response as the response for the immediately preceding question, another question with reduced proximity to the insight is determined, the other question being distinct from the question with increased proximity (a more detailed abstract idea remains an abstract idea). Claim 13 further limit the abstract idea that triggering modelling of the questionnaire comprises triggering of an advanced learning model (a more detailed abstract idea remains an abstract idea). Claim 14 further limit the abstract idea by configuring the advanced learning model based on at least one of historically modelled questionnaires, historically derived responses for each question of the historically modelled questionnaires, aspects derived from each of the historically modelled questionnaires, and descriptive information associated with the offering (a more detailed abstract idea remains an abstract idea). Claim 15 further limit the abstract idea that the aspect is related to recall of the offering (a more detailed abstract idea remains an abstract idea). Claim 16 further limit the abstract idea by rendering, in response to generation of the questionnaire delivery signal, the modelled questionnaire having the hierarchically-linked questions, wherein each of the subsequent questions is linked with the preceding question (a more detailed abstract idea remains an abstract idea). Claim 18 further limit the abstract idea that the set of offerings comprises at least one of a batch of products and one or more service offerings (a more detailed abstract idea remains an abstract idea). Claim 19 further limit the abstract idea to render, in response to generation of the questionnaire delivery signal, the modelled questionnaire having the hierarchically-linked questions, wherein each of the subsequent questions is linked with the preceding question (a more detailed abstract idea remains an abstract idea). And claim 20 further limit the abstract idea to render a feedback option to receive at least one of a positive feedback and a negative feedback for each question of the modelled questionnaire, the positive feedback indicating acceptance of the question and the negative feedback indicating rejection of the question; render an updated questionnaire based on the response received on the feedback option; and tune subsequent determinations of at least one of an opening question and subsequent questions based on the positive feedback and the negative feedback (a more detailed abstract idea remains an abstract idea).The identified recitation of the dependents claims falls within the Mental Processes, concepts performed in the human mind including observations, evaluation, judgement and opinion and Certain Methods of Organizing Human Activity such as commercial or legal interactions including advertising, marketing or sales activities or behaviors, business relations. Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101. Thus, viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 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 1-4, 8-14 and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Russell Kislal et al., (US 2024/0012842 A1) hereinafter “Kislal” in both view of Aaron Hersh Goldstein (US 2025/0232258 A1) hereinafter “Goldstein” and Mark deGroat, Generating Quizzes with RAG and LLMs on Databricks, https://www.rearc.io/blog/quizzes-with-rag-and-llms-on-databricks, published on June 6, 2024, hereinafter “deGroat” . Claim 1: Kislal as shown discloses a system, the system comprising: a processor to: (¶ 0166: “include one or more microprocessors, microcontrollers, and/or digital signal processors that provide processing functionality, as well as other computation and control functionality.”); receive an activation signal comprising a query corresponding to an offering, the query comprising textual content relevant to the offering (¶ 0004: “using a set of questions (a “query set”) (that preferably encompasses questions pertaining to a large number of concepts or subject matter areas)”); encode the textual content into a set of query vectors, the set of query vectors being representative of the textual content (¶ 0072: “the Q-A system 120 is configured to transform the query data of the query set (i.e., the multiple questions covering a range of subject matter areas, concepts, and topics) into transformed query data compatible with the transformed source content (e.g., compatible with one or more of the transformed content records in the DOM repository). For example, if the pre-processed document 112 includes data representation compatible with BERT-based transformation data, the query set 104 is similarly processed to transform it to a BERT-based representation (e.g., to produce parameterized/vector representations of the questions comprising the query set).”); compute, from a vector database having a set of elementary vectors derived based on descriptive information associated with the offering, a relevance metric for each elementary vector in the set of elementary vectors, the relevance metric being computed based on a semantic relationship between each query vector in the set of query vectors and each elementary vector in the set of elementary vectors, wherein the descriptive information comprises information usable for deriving an insight associated with an aspect related to the offering; identify a set of relevant elementary vectors, from amongst the set of elementary vectors, based on the relevance metric computed for each elementary vector in the set of elementary vectors, the set of relevant elementary vectors being representative of selective descriptive information, from amongst the descriptive information, pertinent to the query; (¶ 0006: “The question-and-answer system will return answer data to the submitted questions which will indicate (expressly or inferentially) the relevance of the content to the documents to the questions being asked. For example, if a returned answer for a particular pre-determined question (from the pre-determined library of questions) is associated with a low matching (relevance) score, that score indicates that a document(s) to which the pre-determined question was applied includes content likely unrelated to the particular question submitted. It can consequently be inferred that the document being processed has low relevance to concepts or subject matter associated with the particular question. On the other hand, a returned answer with a high match (relevance) score or with a high level of detail can indicate that the content is relevant to the question asked and consequently the subject matter or concepts of the document's content can be classified/determined.” See also ¶ 0046: “Such relevance scores may be computed, for example, based on distance measurements between semantic content of an answer and a corresponding question, based on output of a trained machine learning engine to assess relevance, etc. For example, one type of scoring process may be based a Transform-Based-Distance (TBD) between a question and an answer, or the posterior probability equivalent of the TBD. A particular question and answer pair with a high relevance score may be indicative that a particular document from which the question-answer pair was generated is related to a concept or topic associated with the particular question.” ¶ 0007: “the framework described herein allow users to steer the result of automatic processing of a set of documents towards key insights of general interest and/or the user's personal interests. In the proposed solutions, an a priori a set of important questions is constructed to target the content for which associated output data is generated (according to particular downstream processes that may be invoked based, for example, on an initial classification of the content of the documents analyzed). The questions in the prior set of question may be personalized to the particular interests of the user.” And ¶ 0050, 0117, 0133-0135); trigger modelling of a questionnaire based on the set of relevant elementary vectors, the questionnaire comprising questions being hierarchically-linked questions in relation to the insight associated with the aspect (¶ 0070: “the framework described herein may generate additional questions as part of a question-augmentation procedure. […] may have access to ontologies and synonym datasets to construct new questions that have a semantically similar meaning to questions that were deemed to be relevant to the concepts/topics of the document, or that are determined to be appropriate follow-up questions given the structured information discovered for the document 102 or 112 and/or contextual information associated with the user(s) and entity on whose behalf the initial, exploratory, question set was submitted.”); the modelling comprising: determining an opening question, in relation to the insight associated with the aspect, based on the set of relevant elementary vectors (¶ 0069: “Based on the answers that were deemed to be more relevant to questions in the query set, supplemental queries/questions can be determined that can be used to perform subsequent question-and-answer searches on the document 102 or 112 ”, see also ¶ 0091-0095 which describe opening questions); determining a plurality of subsequent questions, each being determined based on a probable response to an immediately preceding question thereto, the probable response being derived from the selective descriptive information encoded as the set of relevant elementary vectors, wherein each of the subsequent questions is increasingly proximate to the insight associated with the aspect as compared to the immediately preceding question, and wherein the subsequent questions are determined until a response to at least one question, from amongst the subsequent questions, provides a requisite insight associated with the aspect; and (¶ 0070: “FIG. 1 may have access to ontologies and synonym datasets to construct new questions that have a semantically similar meaning to questions that were deemed to be relevant to the concepts/topics of the document, or that are determined to be appropriate follow-up questions given the structured information discovered for the document 102 or 112 and/or contextual information associated with the user(s) and entity on whose behalf the initial, exploratory, question set was submitted. The determination of supplemental questions can be performed, for example, through rule-based processes, or through machine learning processes (e.g., to generate labels representative of supplemental or follow-up questions to previously-asked questions). The new question(s) are processes by the Q-A system 120 to produce additional answer data, and the process may repeat again (e.g., the generation of new question may continue for a fixed number of iterations, or until certain conditions, such as reaching a level of answer responsiveness associated with some specified confidence level, is met).” See also ¶ 0135); Kislal as explained above “generate additional questions as part of a question-augmentation procedure”. Kislal is silent with regard to the following limitations. However, Goldstein in an analogous art of questions generation management for the purpose of providing the following limitations as shown does: the questionnaire comprising questions being hierarchically-linked questions (¶ 0130: “the plurality of questions may be stored in a hierarchical format. For example, the plurality of questions may include branching sets of questions that can include branches for sub-questions or questions that provide clarification or expansion on earlier questions. The top level questions may serve or operate as gating questions that determine whether the remaining sub-questions within that branch are relevant to the project being initiated.”); Both Kislal and Goldstein teach questions generations. Kislal teaches in the Abstract: “obtaining a query set (e.g., a universal set of questions), performing a question-and-answer (Q-A) search on one or more documents using the query set to produce answer data responsive to one or more questions included in the query set.” Goldstein teaches in ¶ 0006 “an iterative posed question and classification approach to assist a user in defining requirements for successful project and program delivery.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Goldstein would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Goldstein to the teaching of Kislal would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as the questionnaire comprising questions being hierarchically-linked questions into similar systems. Further, as noted by Goldstein “The user interface can be integrated with an LLM that can be used to simulate interactions and to generate questions to prompt the user to submit project initiation data efficiently and optionally to create one or more project initiation documents.” (Goldstein, ¶ 0077). Kislal as explained above “generate additional questions as part of a question-augmentation procedure”. Kislal in view of Goldstein is silent with regard to the following limitations. However, deGroat in an analogous art of questions generation management for the purpose of providing the following limitations as shown does: generate a questionnaire delivery signal to cause rendering of the modelled questionnaire (pages 1-17 deGroat describe a modelled questionnaire, Introduction “we'll use LangChain to define a set of "agents" that perform the steps of the quiz generation process. Finally, we'll explore how output parsers and retry logic can validate the quality and improve the reliability of the generated quizzes.”); Both Kislal and deGroat teach questions generations. Kislal teaches in the Abstract: “obtaining a query set (e.g., a universal set of questions), performing a question-and-answer (Q-A) search on one or more documents using the query set to produce answer data responsive to one or more questions included in the query set.” deGroat teaches in page 1 “Learn how to build an end-to-end pipeline for automatically generating quiz questions from a corpus of technical documentation using large language models and retrieval augmented generation on the Databricks Lakehouse Platform.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of deGroat would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of deGroat to the teaching of Kislal in view of Goldstein would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as to generate a questionnaire delivery signal to cause rendering of the modelled questionnaire into similar systems. Further, as noted by deGroat “other key components of a production RAG system include human feedback loops to continually refine the prompts and fine-tune the models, as well as monitoring systems to evaluate the quality of the generated outputs over time.” (deGroat, Conclusion, page 16). Claims 11 and 17: The limitations of claims 11 and 17 (¶ 0033) encompass substantially the same scope as claim 1. Accordingly, those similar limitations are rejected in substantially the same manner as claim 1, as described above. The following limitations differs from claim 1: Claim 11: Kislal as shown discloses a method, the system comprising: determining an opening question, in relation to the insight associated with the aspect, based on the set of relevant elementary vectors (¶ 0069: “Based on the answers that were deemed to be more relevant to questions in the query set, supplemental queries/questions can be determined that can be used to perform subsequent question-and-answer searches on the document 102 or 112 ”, see also ¶ 0091-0095 which describe opening questions); determining a plurality of subsequent questions, each being determined based on a response, from amongst two responses associated with an immediately preceding question, derived for the immediately preceding question, the response being derived based on the selective descriptive information encoded as the set of relevant elementary vectors, wherein the subsequent questions are determined until a response to at least one question, from amongst the subsequent questions, provides a required insight associated with the aspect (¶ 0070: “FIG. 1 may have access to ontologies and synonym datasets to construct new questions that have a semantically similar meaning to questions that were deemed to be relevant to the concepts/topics of the document, or that are determined to be appropriate follow-up questions given the structured information discovered for the document 102 or 112 and/or contextual information associated with the user(s) and entity on whose behalf the initial, exploratory, question set was submitted. The determination of supplemental questions can be performed, for example, through rule-based processes, or through machine learning processes (e.g., to generate labels representative of supplemental or follow-up questions to previously-asked questions). The new question(s) are processes by the Q-A system 120 to produce additional answer data, and the process may repeat again (e.g., the generation of new question may continue for a fixed number of iterations, or until certain conditions, such as reaching a level of answer responsiveness associated with some specified confidence level, is met).” See also ¶ 0031: “The interactive data may include disambiguation data provided in response to prompt data generated by a Q-A system to select answers from multiple matches in the answer data related to one or more similar concepts.” And ¶ 0135); Claim 2: Kislal as shown discloses the following limitations: the system further comprising the vector database communicably coupled with the processor, the vector database having stored therein the set of elementary vectors derived based on the descriptive information associated with the offering (¶ 0028: “applying to the one or more segmented documents one or more vector-transforms to transform the one or more segmented documents into vector answers in respective one or more vector spaces” see also ¶ 0048: “document segmentation and vectorization (parametrization) operations, and storage operations to store ingested documents in a repository of the system 100 .” And ¶ 0066: “one DOM record may be a collection of items that includes an original portion of a source document, metadata for that source document portion, contextual information associated with that source document portion, and/or vectors (also referred to as embeddings) resulting from one or more transformations applied to segments of the source content.”); Claims 3, 16 and 19: Kislal as explained above “generate additional questions as part of a question-augmentation procedure”. Kislal is silent with regard to the following limitations. However, Goldstein in an analogous art of questions generation management for the purpose of providing the following limitations as shown does: wherein the processor is to cause rendering of the modelled questionnaire having the hierarchically-linked questions, wherein each of the subsequent questions is linked with the preceding question, the subsequent questions having increased proximity towards the insight as compared to the preceding question thereto (¶ 0130: “the plurality of questions may be stored in a hierarchical format. For example, the plurality of questions may include branching sets of questions that can include branches for sub-questions or questions that provide clarification or expansion on earlier questions. The top level questions may serve or operate as gating questions that determine whether the remaining sub-questions within that branch are relevant to the project being initiated.” See also ¶ 0030 and 0226); Both Kislal and Goldstein teach questions generations. Kislal teaches in the Abstract: “obtaining a query set (e.g., a universal set of questions), performing a question-and-answer (Q-A) search on one or more documents using the query set to produce answer data responsive to one or more questions included in the query set.” Goldstein teaches in ¶ 0006 “an iterative posed question and classification approach to assist a user in defining requirements for successful project and program delivery.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Goldstein would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Goldstein to the teaching of Kislal would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as the modelled questionnaire having the hierarchically-linked questions, wherein each of the subsequent questions is linked with the preceding question, the subsequent questions having increased proximity towards the insight as compared to the preceding question thereto into similar systems. Further, as noted by Goldstein “The user interface can be integrated with an LLM that can be used to simulate interactions and to generate questions to prompt the user to submit project initiation data efficiently and optionally to create one or more project initiation documents.” (Goldstein, ¶ 0077). Claims 16 and 19: The limitations of claims 16 and 19 encompass substantially the same scope as claim 3. Accordingly, those similar limitations are rejected in substantially the same manner as claim 3, as described above. Claim 4: Kislal as shown discloses the following limitations: wherein the processor is to receive, from an actionable component, a questionnaire modification signal indicating a request to modify at least one of the questions of the modelled questionnaire (¶ 0137: “Follow-up questions can be generated by paraphrasing the query submitted, e.g., transforming and/or normalizing the submitting query to modify the question submitted using, for example, a trained learning engine.” See also ¶ 0068: “The query set 104 can be intermittently adjusted (at regular or irregular intervals) to update the query set according to changing natures of various popular concepts)”); Claim 8: Kislal as shown discloses the following limitations: wherein the processor is to trigger an advanced learning model for modelling the questionnaire, wherein the advanced learning model is one of a supervised large language model and an unsupervised large language model (¶ 0051: “In the BERT-based approach, a network may first be trained on a masked language model task in which a word is omitted from the input, and predicted by the network by an output layer that provides a probability distribution over words of the vocabulary. Having trained the network on the masked language model task, the output layer is removed, and in the case of the question answering task, a layer is added to yield the start, end, and confidence outputs, and the network is further trained (e.g., fine-tuned, transfer learning) on supervised training data for the target domain (e.g., using Stanford Question Answering Dataset, or SQuAD).”); Claim 13: The limitations of claim 13 encompasses substantially the same scope as claim 8. Accordingly, those similar limitations are rejected in substantially the same manner as claim 8, as described above. Claim 9: Kislal as explained above “generate additional questions as part of a question-augmentation procedure”. Kislal is silent with regard to the following limitations. However, Goldstein in an analogous art of questions generation management for the purpose of providing the following limitations as shown does: wherein the modelled questionnaire further comprises the query, wherein the opening question is hierarchically linked with the query, the opening question being increasingly proximate, compared to the query, towards the insight associated with the aspect (¶ 0130: “the plurality of questions may be stored in a hierarchical format. For example, the plurality of questions may include branching sets of questions that can include branches for sub-questions or questions that provide clarification or expansion on earlier questions. The top level questions may serve or operate as gating questions that determine whether the remaining sub-questions within that branch are relevant to the project being initiated.” See also ¶ 0030 and 0226); Both Kislal and Goldstein teach questions generations. Kislal teaches in the Abstract: “obtaining a query set (e.g., a universal set of questions), performing a question-and-answer (Q-A) search on one or more documents using the query set to produce answer data responsive to one or more questions included in the query set.” Goldstein teaches in ¶ 0006 “an iterative posed question and classification approach to assist a user in defining requirements for successful project and program delivery.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Goldstein would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Goldstein to the teaching of Kislal would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein the modelled questionnaire further comprises the query, wherein the opening question is hierarchically linked with the query, the opening question being increasingly proximate, compared to the query, towards the insight associated with the aspect into similar systems. Further, as noted by Goldstein “The user interface can be integrated with an LLM that can be used to simulate interactions and to generate questions to prompt the user to submit project initiation data efficiently and optionally to create one or more project initiation documents.” (Goldstein, ¶ 0077). Claim 10: Kislal as shown discloses the following limitations: wherein the processor is to: compare the relevance metric, computed for each elementary vector in the set of elementary vectors, with a threshold relevance metric; and identify, based on the comparison, the set of relevant elementary vectors from amongst the set of elementary vectors (¶ 0072: “the matching operations may be based on some closeness or similarity criterion corresponding to some computed distance metric between, for example, a computed vector derived from transformation applied to the query data, and vector records comprising the document 112 . In some embodiments, matching/relevance scores are derived to represent the relevance of each of the questions in the query set to the content of the document 112 (and thus to the content of the original source document 102 ).” See also ¶ 0134); Claim 12: Kislal as shown discloses the following limitations: wherein the two responses comprise a first response and a second response (¶ 0031: “The interactive data may include disambiguation data provided in response to prompt data generated by a Q-A system to select answers from multiple matches in the answer data related to one or more similar concepts.”); Kislal as explained above “generate additional questions as part of a question-augmentation procedure”. Kislal is silent with regard to the following limitations. However, Goldstein in an analogous art of questions generation management for the purpose of providing the following limitations as shown does: wherein, upon derivation of the first response as the response for the immediately preceding question, a question with increased proximity to the insight associated with the aspect is determined, and wherein, upon derivation of the second response as the response for the immediately preceding question, another question with reduced proximity to the insight is determined, the other question being distinct from the question with increased proximity (¶ 0130: “the plurality of questions may be stored in a hierarchical format. For example, the plurality of questions may include branching sets of questions that can include branches for sub-questions or questions that provide clarification or expansion on earlier questions. The top level questions may serve or operate as gating questions that determine whether the remaining sub-questions within that branch are relevant to the project being initiated.” See also ¶ 0061: “The module may further include scoring logic to rank or filter the generated candidates based on fluency, completeness, or relevance to the source material. In some cases, semantic similarity metrics or cosine distance calculations may be used to eliminate redundant or low-utility questions.” And ¶ 0177: “the query embedding may be compared against stored embeddings of previously generated questions in the content repository using similarity search algorithms such as cosine similarity or approximate nearest neighbor (ANN) search. The system may retrieve one or more top-ranked question-answer pairs that are semantically aligned with the query. Ranking heuristics may further refine the result set using relevance scores, topic filters, content type, or metadata constraints.”); Both Kislal and Goldstein teach questions generations. Kislal teaches in the Abstract: “obtaining a query set (e.g., a universal set of questions), performing a question-and-answer (Q-A) search on one or more documents using the query set to produce answer data responsive to one or more questions included in the query set.” Goldstein teaches in ¶ 0006 “an iterative posed question and classification approach to assist a user in defining requirements for successful project and program delivery.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Goldstein would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Goldstein to the teaching of Kislal would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein, upon derivation of the first response as the response for the immediately preceding question, a question with increased proximity to the insight associated with the aspect is determined, and wherein, upon derivation of the second response as the response for the immediately preceding question, another question with reduced proximity to the insight is determined, the other question being distinct from the question with increased proximity into similar systems. Further, as noted by Goldstein “The user interface can be integrated with an LLM that can be used to simulate interactions and to generate questions to prompt the user to submit project initiation data efficiently and optionally to create one or more project initiation documents.” (Goldstein, ¶ 0077). Claim 14: Kislal as shown discloses the following limitations: the method further comprising configuring the advanced learning model based on at least one of historically modelled questionnaires, historically derived responses for each question of the historically modelled questionnaires, aspects derived from each of the historically modelled questionnaires, and descriptive information associated with the offering (¶ 0136: “The query cache 335 stores, among other things, answers/contents corresponding to frequently asked questions. Such answers/contents may include content previously retrieved from the DOM documents (and/or from their corresponding raw source content) in response to previously submitted queries.)” and ¶ 0137: “The query processing module may also include a question generation engine that can determine (e.g., based on a trained learning engine and/or using a repository of question data) follow-up or related questions to one or more questions submitted through the query data.”); Claim 18: Kislal as shown discloses the following limitations: wherein the set of offerings comprises at least one of a batch of products and one or more service offerings (¶ 0157: “the query set may be a universal set of multiple questions relating to a plurality of different content subject matter areas. That is, the query set may include a wide range of questions covering multiple topics, concepts, and subject matter areas that include financial matters, legal matters, sports, domestic and international affairs, and so on.” See also ¶ 0043); 07-21-aia AIA Claim s 5, 6, 7 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Russell Kislal et al., (US 2024/0012842 A1) hereinafter “Kislal”, Aaron Hersh Goldstein (US 2025/0232258 A1) hereinafter “Goldstein” and Mark deGroat, Generating Quizzes with RAG and LLMs on Databricks, https://www.rearc.io/blog/quizzes-with-rag-and-llms-on-databricks, published on June 6, 2024, hereinafter “deGroat” as applied to claim 1, further in view of Croskey et al., (US 2026/0030274 A1) hereinafter “Croskey” . Claim 5: Kislal as explained above “generate additional questions as part of a question-augmentation procedure”. deGroat teaches in the Conclusion, page 16 “other key components of a production RAG system include human feedback loops to continually refine the prompts and fine-tune the models, as well as monitoring systems to evaluate the quality of the generated outputs over time.” Kislal in view of Goldstein and deGroat is silent with regard to the following limitations. However, Croskey in an analogous art of questions generation management for the purpose of providing the following limitations as shown does: wherein the processor is to cause rendering of a feedback option to receive at least one of a positive feedback and a negative feedback for each question of the modelled questionnaire, the positive feedback indicating acceptance of the question and the negative feedback indicating rejection of the question (¶ 0080: “Review engine 140 may also include a user interface component that enables human reviewers to inspect generated content, approve or reject question-answer pairs, suggest edits, and provide feedback signals for retraining. This component may display content in context with original source material, highlight discrepancies or ambiguities, and capture reviewer actions and justifications.”); Both Kislal and Croskey teach questions generations. Kislal teaches in the Abstract: “obtaining a query set (e.g., a universal set of questions), performing a question-and-answer (Q-A) search on one or more documents using the query set to produce answer data responsive to one or more questions included in the query set.” Croskey teaches in the Abstract “ Each question-answer pair is reviewed using automated or human-in-the-loop mechanisms for accuracy and alignment.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Croskey would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Croskey to the teaching of Kislal in view of Goldstein and deGroat would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein the processor is to cause rendering of a feedback option to receive at least one of a positive feedback and a negative feedback for each question of the modelled questionnaire, the positive feedback indicating acceptance of the question and the negative feedback indicating rejection of the question into similar systems. Further, as noted by Croskey “ The purpose of review engine 140 is to ensure that generated outputs meet predefined standards of accuracy, clarity, consistency, and stylistic alignment before being committed to downstream systems or presented to end users.” (Croskey ¶ 0076). Claim 6: Kislal teaches in ¶ 0068: “The query set 104 can be intermittently adjusted (at regular or irregular intervals) to update the query set according to changing natures of various popular concepts.” Goldstein teaches in ¶ 0011: “ updating, at the processor, the ML-assisted project initiation user interface with the sub-grouping of questions.” Kislal in view of Goldstein and deGroat is silent with regard to the following limitations. However, Croskey in an analogous art of questions generation management for the purpose of providing the following limitations as shown does: wherein the processor is to render an updated questionnaire in response to receiving the negative feedback for the question (¶ 0080: “Review engine 140 may also include a user interface component that enables human reviewers to inspect generated content, approve or reject question-answer pairs, suggest edits, and provide feedback signals for retraining. This component may display content in context with original source material, highlight discrepancies or ambiguities, and capture reviewer actions and justifications.” And ¶ 0081: “ Approved content may be forwarded to content repository 145 for indexing and deployment. Flagged or ambiguous content may be recycled through upstream modules with updated parameters, routed to a secondary review agent, or subjected to additional training or reinforcement procedures.”); Both Kislal and Croskey teach questions generations. Kislal teaches in the Abstract: “obtaining a query set (e.g., a universal set of questions), performing a question-and-answer (Q-A) search on one or more documents using the query set to produce answer data responsive to one or more questions included in the query set.” Croskey teaches in the Abstract “ Each question-answer pair is reviewed using automated or human-in-the-loop mechanisms for accuracy and alignment.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Croskey would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Croskey to the teaching of Kislal in view of Goldstein and deGroat would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein the processor is to render an updated questionnaire in response to receiving the negative feedback for the question into similar systems. Further, as noted by Croskey “ The purpose of review engine 140 is to ensure that generated outputs meet predefined standards of accuracy, clarity, consistency, and stylistic alignment before being committed to downstream systems or presented to end users.” (Croskey ¶ 0076). Claim 7: Kislal teaches in ¶ 0051: “the network is further trained (e.g., fine-tuned, transfer learning) on supervised training data for the target domain (e.g., using Stanford Question Answering Dataset, or SQuAD).” Kislal in view of Goldstein and deGroat is silent with regard to the following limitations. However, Croskey in an analogous art of questions generation management for the purpose of providing the following limitations as shown does: wherein the processor is to tune subsequent determinations of at least one of an opening question and subsequent questions based on at least one of the positive feedback and the negative feedback (¶ 0059: “the module may utilize one or more generative models, such as encoder-decoder transformers, trained or fine-tuned to produce interrogative sentences from source content. These models may include, for example, T5, FLAN-T5, BART, GPT variants, or custom transformer-based architectures optimized for enterprise domains.” And ¶ 0162: “The interface may allow reviewers to approve, reject, edit, or comment on the content. Reviewer decisions may be logged along with timestamps, rationale annotations, and reviewer identity for traceability. In some embodiments, reviewer actions may be used to generate fine-tuning signals or reinforcement feedback for model refinement in future iterations.”); Both Kislal and Croskey teach questions generations. Kislal teaches in the Abstract: “obtaining a query set (e.g., a universal set of questions), performing a question-and-answer (Q-A) search on one or more documents using the query set to produce answer data responsive to one or more questions included in the query set.” Croskey teaches in the Abstract “ Each question-answer pair is reviewed using automated or human-in-the-loop mechanisms for accuracy and alignment.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Croskey would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Croskey to the teaching of Kislal in view of Goldstein and deGroat would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein the processor is to tune subsequent determinations of at least one of an opening question and subsequent questions based on at least one of the positive feedback and the negative feedback into similar systems. Further, as noted by Croskey “ The purpose of review engine 140 is to ensure that generated outputs meet predefined standards of accuracy, clarity, consistency, and stylistic alignment before being committed to downstream systems or presented to end users.” (Croskey ¶ 0076). Claim 20: The limitations of claim 20 encompasses substantially the same scope as claims 5, 6 and 7. Accordingly, those similar limitations are rejected in substantially the same manner as claims 5, 6 and 7, as described above . 07-21-aia AIA Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Russell Kislal et al., (US 2024/0012842 A1) hereinafter “Kislal”, Aaron Hersh Goldstein (US 2025/0232258 A1) hereinafter “Goldstein” and Mark deGroat, Generating Quizzes with RAG and LLMs on Databricks, https://www.rearc.io/blog/quizzes-with-rag-and-llms-on-databricks, published on June 6, 2024, hereinafter “deGroat” as applied to claim 11, further in view of Pedersen et al., (US 2025/0217209 A1) hereinafter “Pedersen” . Claim 15 Kislal teaches in ¶ 0157: “the query set may be a universal set of multiple questions relating to a plurality of different content subject matter areas. That is, the query set may include a wide range of questions covering multiple topics, concepts, and subject matter areas that include financial matters, legal matters, sports, domestic and international affairs, and so on.” Kislal in view of Goldstein and deGroat is silent with regard to the following limitations. However, Pedersen in an analogous art of Q&A management for the purpose of providing the following limitations as shown does: wherein the aspect is related to recall of the offering (¶ 0142: “Preprocessing operations 104 , performed by machine-learned preprocessing system 106 , can include determining the relevance of the query to various types of session data 102 . This could include data related to the user's past orders, user account information, or other relevant contextual information, such as support bulletins related to supply chain delays for the product, a manufacturer's recall notices, etc.”); Both Kislal and Pedersen teach questions/answers generations. Kislal teaches in the Abstract: “obtaining a query set (e.g., a universal set of questions), performing a question-and-answer (Q-A) search on one or more documents using the query set to produce answer data responsive to one or more questions included in the query set.” Pedersen teaches in ¶ 0062: “ These inputs can include requests for tasks to be performed, such as answering a question.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Pedersen would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Pedersen to the teaching of Kislal in view of Goldstein and deGroat would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein the aspect is related to recall of the offering into similar systems. Further, as noted by Pedersen “Preprocessing operations 104 can include embedding the user's text query into a latent space for comparison with embedded session data objects.” (Pedersen ¶ 0142). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to NADJA CHONG whose telephone number is (571)270-3939 . The examiner can normally be reached on Monday-Friday 8:00 am - 2:00 pm 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, RUTAO WU can be reached on 571.272.6045 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /NADJA N CHONG CRUZ/ Primary Examiner, Art Unit 3623 Application/Control Number: 18/789,704 Page 2 Art Unit: 3623 Application/Control Number: 18/789,704 Page 3 Art Unit: 3623 Application/Control Number: 18/789,704 Page 4 Art Unit: 3623 Application/Control Number: 18/789,704 Page 5 Art Unit: 3623 Application/Control Number: 18/789,704 Page 6 Art Unit: 3623 Application/Control Number: 18/789,704 Page 7 Art Unit: 3623 Application/Control Number: 18/789,704 Page 8 Art Unit: 3623 Application/Control Number: 18/789,704 Page 9 Art Unit: 3623 Application/Control Number: 18/789,704 Page 10 Art Unit: 3623 Application/Control Number: 18/789,704 Page 11 Art Unit: 3623 Application/Control Number: 18/789,704 Page 12 Art Unit: 3623 Application/Control Number: 18/789,704 Page 13 Art Unit: 3623 Application/Control Number: 18/789,704 Page 14 Art Unit: 3623 Application/Control Number: 18/789,704 Page 15 Art Unit: 3623 Application/Control Number: 18/789,704 Page 16 Art Unit: 3623 Application/Control Number: 18/789,704 Page 17 Art Unit: 3623 Application/Control Number: 18/789,704 Page 18 Art Unit: 3623 Application/Control Number: 18/789,704 Page 19 Art Unit: 3623 Application/Control Number: 18/789,704 Page 20 Art Unit: 3623 Application/Control Number: 18/789,704 Page 21 Art Unit: 3623 Application/Control Number: 18/789,704 Page 22 Art Unit: 3623 Application/Control Number: 18/789,704 Page 23 Art Unit: 3623 Application/Control Number: 18/789,704 Page 24 Art Unit: 3623 Application/Control Number: 18/789,704 Page 25 Art Unit: 3623 Application/Control Number: 18/789,704 Page 26 Art Unit: 3623 Application/Control Number: 18/789,704 Page 27 Art Unit: 3623 Application/Control Number: 18/789,704 Page 28 Art Unit: 3623 Application/Control Number: 18/789,704 Page 29 Art Unit: 3623 Application/Control Number: 18/789,704 Page 30 Art Unit: 3623 Application/Control Number: 18/789,704 Page 31 Art Unit: 3623 Application/Control Number: 18/789,704 Page 32 Art Unit: 3623 Application/Control Number: 18/789,704 Page 33 Art Unit: 3623
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