CTNF 18/369,065 CTNF 83126 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. Continued Examination Under 37 CFR 1.114 07-42-04 A request for continued examination under, including the fee set forth in 37 CFR1.17(e), was filed in this application after final rejection. Since this application is eligiblefor continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e)has been timely paid, the finality of the previous Office action has been withdrawnpursuant to 37 CFR 1.114. Applicant's submission filed on 06/08/2026 has been entered. Status of the Claims Claims 1-11, 13, and 15-22 are pending. Response to Applicant’s Arguments In response to “ Bacarella's relied-upon disclosures describe system-level operations involving data selection, data replacement, knowledge graph updating, and model augmentation or training. Even if those operations may influence what data is ultimately presented to or learned by a model, they do not disclose a verbal instruction, distinct from the triples or their representation, that is expressed in a natural language format in the modified user input and interpreted by the large language model during response generation. In Bacarella, the alleged "instruction" is inferred from the fact that data has been replaced or that model state has been updated. The amended claims recite something different: directive content included in the modified user input that tells the large language model how to treat the added triples or their representation ”. In view of such amendment to claims 1 and 18, rejections under Bhatia and Bacarella are withdrawn. Upon further search and consideration, please see details of a new combination of references set forth below. In response to “ Claim 20 recites both types of verbal instruction as in claims 1 and 16 ”. In view of such amendment to claim 20, rejection under Bhatia and Bacarella has been withdrawn. Upon further search and consideration, claim 20 is allowed over the prior arts. See details below. Claim Rejections - 35 USC § 103 07-20-aia AIA The following is a quotation of the appropriate paragraphs of 35 U.S.C. 103 that form the basis for the rejections under this section made 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 Claims 1- 11, 13, 16-20 and 22 are re jected under 35 USC 103(a) as being unpatentable over Bh atia et al. (US 11997056 B2) in view of Ge et al. (US 2020/0380991 A1) . Re garding Claim 1, Bhatia discloses a computing system ( Fig. 6, Col 3, Rows 20-22, environment 100 of Fig. 1 operates on one or more computing devices such as computing device 600 per Col 9, Rows 55-58 ) comprising: at least one memory ( Col 3, Rows 41-43 and Col 10, Rows 33-39, memory / computer readable media ); one or more hardware processing units coupled to the at least one memory ( Col 3, Rows 41-43 and Col 10, Rows 15-16, processors 614 have memory per Col 10, Row 25 ); and one or more computer readable storage media storing computer-executable instructions that, when executed, cause the computing system ( Col 3, Rows 41-43, functions carried out by a processor executing instructions stored in memory ) to perform operations comprising: receiving, from a user, user input ( Col 3, Row 63 – Col 4, Row 15, receive natural language question from a user through audio and textual inputs ) comprising a plurality of tokens ( Col 4, Rows 54-56, provide natural language input 101 to locate and classify named entity comprising one or more words or tokens ); analyzing at least a portion of the plurality of tokens ( Col 4, Rows 54-67, perform information extraction on natural language input 101 to locate and classify named entities mentioned in unstructured text into pre-defined entity classes ); based on the analyzing, determining one or more entities of a semantic framework represented in the at least a portion of the plurality of tokens ( Col 5, Rows 6-16, identify one or more entities within the natural language input 101 to search and retrieve information from knowledge base 120 for each entity; per Col 5, Rows 26—27, the knowledge base 120 being a knowledge graph ); determining one or more triples of the semantic framework for at least a portion of the one or more entities or for associated entities ( Col 5, Rows 40-41, retrieve triples related to the identified entities ); adding at least a portion of the one or more triples, or a representation thereof, to the user input to provide modified user input ( Col 6, Rows 1-5, concatenate triple head entity, the relationship, and the tail to form a natural language phrase; Col 6, Rows 15-29, select the top-k triples that constitute contextual knowledge to be fed as input along with input text X to a NLP model 112 ); submitting the modified user input to a large language model ( Col 6, Rows 15-29, select the top-k triples that constitute contextual knowledge to be fed as input along with input text X to the NLP model 112; per Col 4, Rows 32-33, the NLP model 112 being a BERT, RoBERTa, T5, and GPT ); processing the modified user input using the large language model to provide a response ( Col 2, Rows 61-64, NLP model processes both the natural language input and the triples to generate a response; e.g., Col 4, Rows 3-5, search applications receive a natural language query for input and provide one or more results that are responsive to the query ); and returning the response in response to the receiving the user input ( Col 4, Rows 3-5, search applications receive a natural language query for input and provide one or more results that are responsive to the query; e.g., Col 4, Rows 9-12 ). Bhatia does not disclose adding to the user input to provide modified user input further includes a verbal instruction, distinct from the one or more triples or the representation thereof and expressed in a natural language format in the modified user input, directing a large language model to only consider the at least a portion of the one or more triples, or a representation thereof, as a sole factual basis for generating a response to the user input. Ge discloses querying AI generative language model in a computing system ( Fig. 1A ), the AI generative language model including a knowledge base containing entity properties and relationships between entities ( ¶18 ): receiving user input comprising a plurality of tokens and analyzing at least a portion of the plurality of tokens ( ¶20, Fig. 1A, using a predefined language model to detect speech acts for user utterance ); determining one or more entities of a semantic framework represented in the plurality of tokens ( ¶29, determine user utterance referring to one or more entities by searching in a knowledge base of entities ); determining one or more triples of the semantic framework for the one or more entities ( ¶63, using the predefined language model to recognize a suggested entity in an unresolved user utterance via coreference; e.g., Fig. 2G, ¶64, “How about Jeff Bezos”; ¶¶65-66, looking up Jeff Bezos in a knowledge base to extract triple comprising Jeff Bezos, FoundCompany or Networth, and Founder ); adding to the user input to provide modified user input ( Fig. 2G, user input being “How about Jeff Bezos” ) includes a verbal instruction, distinct from the one or more triples or the representation thereof and expressed in a natural language format in the modified user input, directing a large language model to only consider the at least a portion of the one or more triples, or a representation thereof, as a sole factual basis for generating a response to the user input ( ¶23 and Fig. 2G, “How about Jeff Bezos” is not a fully-defined query by itself and therefore rewrite the query in an unambiguous way (i.e., “factual basis”) that incorporates relevant information from conversation history; ¶67 and Fig. 2G, based on conversation history of user utterance 208G “what is Bill Gate’s net worth?”, rewrite or add to “How about Jeff Bezos?” into modified user input “What is Jeff Bezos’s net worth?” by replacing entity “Bill Gates” with “Jeff Bezos”; i.e., the rewrite directs the AI generative language model to consider “Jeff Bezos” and corresponding “net worth” property of “Jeff Bezos” as the sole factual basis to generate response ). It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to add to the user input to provide modified user input to further include a verbal instruction, distinct from the one or more triples or the representation thereof and expressed in a natural language format in the modified user input, directing the AI generative language model to only consider the at least a portion of the one or more triples, or a representation thereof, as a sole factual basis for generating a response to the user input in order to rewrite or modify unresolved user utterance that replaces ambiguous entity with disambiguating entity ( Ge, Abstract ). Regarding Claim 2, Bhatia discloses wherein the analyzing the at least a portion of the plurality of tokens comprises providing the at least a plurality of tokens to a named entity recognition service ( Col 4, Rows 54-61, provide natural language input 101 to named-entity recognition system 114, which locates and classifies named entities mentioned in the unstructured text into pre-defined entity classes ). Regarding Claims 3 and 19, Bhatia discloses wherein adding at least a portion of the one or more triples, or a representation thereof, to the user input to provide modified using input comprises: submitting triples of the at least a portion of the plurality of triples to a verbalization function to provide the representation, the representation being verbalized triples ( Col 6, Rows 1-5, triple verbalizer 140 verbalizes the knowledge base triples by concatenating the head entity, the relationship, and the tail to form a natural language phrase ). Regarding Claim 4, Bhatia discloses wherein the semantic framework comprises a knowledge graph ( Col 5, Rows 26-30, knowledge base 120 takes the form of a knowledge graph ). Regarding Claim 5, Bhatia discloses identifying one or more associated entities for a subset of the one or more entities by traversing the semantic framework through one or more levels of indirection from each respective entity within the set of associated entities ( Col 4, Rows 9-15, for “I like whales” that is not a question or query (i.e., indirect input or natural language input without explicit direction or instruction), use the knowledge base to retrieve knowledge about whales to formulate the response “did you know that blue whales are the largest whales?”; per Col 5, Rows 10-16, retrieve information from knowledge base for each entity identified by searching (i.e., traversing) the knowledge base for an identified entity; e.g., “I like whales”, retrieve information about “blue whales” with relationship labeled “largest label” ). Regarding Claim 6, Bhatia discloses wherein the identifying is carried out up to a specified level of indirection ( Col 4, Rows 9-15, for “I like whales”, generating the response “did you know that blue whales are the largest whales?” requires searching for at least “blue whales” even though “I like whales” did not direct or instruct conversational system to search / query “blue whales” ). Regarding Claim 7, Bhatia discloses wherein the identifying is carried out until a threshold number of entities has been identified ( Col 6, Rows 23-29 in view of Col 2, Rows 61-64 and Col 5, Rows 14-18 and Rows 30-32, information retrieved for identified entity comprises knowledge base triple where two nodes of each triple correspond to entities; select top-k triples where k is a threshold means selecting top k entities corresponding to nodes of knowledge base triples ). Regarding Claim 8, Bhatia discloses wherein the triples are in the form of (subject, object, predicate) ( Col 5, Rows 19-20, a knowledge base triple comprises a subject, a predicate, and an object ), and the identifying one or more associated entities is carried out for relationships where a respective entity of the one or more entities serves as a subject and for relationships where a respective entity of the one or more entities serves as an object ( Col 5, Rows 14-22, retrieve triples from the knowledge base for an identified entity where the identified entity is either the subject or the object of a triple retrieved from the knowledge base ). Regarding Claim 9, Bhatia discloses wherein the modified input is not provided to the user ( Col 6, Rows 25-28, select top-k triples 150 to be fed as input along with x to the NLP model 112; in view of Col 2, Rows 61-64, provide a threshold amount of triples as input to the NLP model along with a natural language input ). Regarding Claim 10, Bhatia discloses wherein the user input prior to modification is not provided to the large language model without the content of the modification ( Col 2, Rows 61-64, provide a threshold amount of triples as input to NLP model along with a natural language input so that the NLP model processes both the natural language input and the triples to generate a response ). Regarding Claim 11, Bhatia discloses adding a length constraint to the modified user input ( Col 2, Rows 61-64, provide a threshold amount (e.g., top five) of triples along with a natural language input; see also Col 6, Rows 29-31 ). Regarding Claim 13, Bhatia discloses adding a contextual instruction to the modified user input ( Col 6, Rows 25-28, select the top-k triples 150 that constitute the contextual knowledge to be fed as input along with input text x to the NLP model 112 ). Regarding Claim 16, Bhatia discloses wherein the based on the analyzing, determining one or more entities of a semantic framework represented in the at least a portion of the plurality of tokens comprises analyzing multiple discrete semantic frameworks ( Col 8, Rows 52-55, retrieve information related to an entity in the plurality of entities from a knowledge base; per Col 2, Rows 39-41, analyzing knowledge bases storing explicit relationships between entities to determine the knowledge base to retrieve information in the form of a triple ). Regarding Claim 17, Bhatia discloses wherein the based on the analyzing, determining one or more entities of a semantic framework represented in the at least a portion of the plurality of tokens comprises sending an analysis request to be executed on a semantic framework located on a remote computing system ( Col 3, Rows 18-23, server side device in environment 100 implementing operations of embedder 112, NLP model 112, NER model 114, knowledge base 120, triple verbalizer 140, and similarity ranker 142 ). Regarding Claim 18, Bhatia discloses a computing system ( Fig. 6, Col 3, Rows 20-22, environment 100 of Fig. 1 operates on one or more computing devices such as computing device 600 per Col 9, Rows 55-58 ) comprising: at least one memory ( Col 3, Rows 41-43 and Col 10, Rows 33-39, memory / computer readable media ); one or more hardware processing units coupled to the at least one memory ( Col 3, Rows 41-43 and Col 10, Rows 15-16, processors 614 have memory per Col 10, Row 25 ); and one or more computer readable storage media storing computer-executable instructions that, when executed, cause the computing system ( Col 3, Rows 41-43, functions carried out by a processor executing instructions stored in memory ) to perform operations comprising: receiving, from a user, user input ( Col 3, Row 63 – Col 4, Row 15, receive natural language question from a user through audio and textual inputs ) comprising a plurality of tokens ( Col 4, Rows 54-56, provide natural language input 101 to locate and classify named entity comprising one or more words or tokens ); analyzing at least a portion of the plurality of tokens ( Col 4, Rows 54-67, perform information extraction on natural language input 101 to locate and classify named entities mentioned in unstructured text into pre-defined entity classes ); based on the analyzing, determining one or more entities of a semantic framework represented in the at least a portion of the plurality of tokens ( Col 5, Rows 6-16, identify one or more entities within the natural language input 101 to search and retrieve information from knowledge base 120 for each entity; per Col 5, Rows 26—27, the knowledge base 120 being a knowledge graph ); determining one or more triples of the semantic framework for at least a portion of the one or more entities or for associated entities ( Col 5, Rows 40-41, retrieve triples related to the identified entities ); adding at least a portion of the one or more triples, or a representation thereof, to the user input to provide modified user input ( Col 6, Rows 1-5, concatenate triple head entity, the relationship, and the tail to form a natural language phrase; Col 6, Rows 15-29, select the top-k triples that constitute contextual knowledge to be fed as input along with input text X to a NLP model 112 ); submitting the modified user input to a large language model ( Col 6, Rows 15-29, select the top-k triples that constitute contextual knowledge to be fed as input along with input text X to the NLP model 112; per Col 4, Rows 32-33, the NLP model 112 being a BERT, RoBERTa, T5, and GPT ); processing the modified user input using the large language model to provide a response ( Col 2, Rows 61-64, NLP model processes both the natural language input and the triples to generate a response; e.g., Col 4, Rows 3-5, search applications receive a natural language query for input and provide one or more results that are responsive to the query ); and returning the response in response to the receiving the user input ( Col 4, Rows 3-5, search applications receive a natural language query for input and provide one or more results that are responsive to the query; e.g., Col 4, Rows 9-12 ). Bhatia does not disclose adding to the user input to provide modified user input further includes a verbal instruction, distinct from the one or more triples or the representation thereof and expressed in a natural language format in the modified user input, directing a large language model to treat the added triples, or the representation thereof, as system provided new information introduced by the computing system. Ge discloses querying AI generative language model in a computing system ( Fig. 1A ), the AI generative language model including a knowledge base containing entity properties and relationships between entities ( ¶18 ): receiving user input comprising a plurality of tokens and analyzing at least a portion of the plurality of tokens ( ¶20, Fig. 1A, using a predefined language model to detect speech acts for user utterance ); determining one or more entities of a semantic framework represented in the plurality of tokens ( ¶29, determine user utterance referring to one or more entities by searching in a knowledge base of entities ); determining one or more triples of the semantic framework for the one or more entities ( ¶63, using the predefined language model to recognize a suggested entity in an unresolved user utterance via coreference; e.g., Fig. 2G, ¶64, “How about Jeff Bezos”; ¶¶65-66, looking up Jeff Bezos in a knowledge base to extract triple comprising Jeff Bezos, FoundCompany or Networth, and Founder ); adding to the user input to provide modified user input ( Fig. 2G, user input being “How about Jeff Bezos” ) includes a verbal instruction, distinct from the one or more triples or the representation thereof and expressed in a natural language format in the modified user input, directing the AI generative language model to treat the added triples, or the representation thereof, as system provided new information introduced by the computing system ( ¶23 and Fig. 2G, “How about Jeff Bezos” is not a fully-defined query by itself and therefore rewrite the query in an unambiguous way that incorporates relevant information (i.e., “system provided new information introduced by the computing system”) from conversation history; ¶67, based on conversation history “What is Bill Gates’ net worth?”, rewrite or add information to “How about Jeff Bezos?” into modified user input “What is Jeff Bezos’s net worth?” ). It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to add to the user input to provide modified user input to further include a verbal instruction, distinct from the one or more triples or the representation thereof and expressed in a natural language format in the modified user input, directing the AI generative language model to treat the added triples, or the representation thereof, as system provided new information introduced by the computing system in order to rewrite or modify unresolved user utterance that replaces ambiguous entity with disambiguating entity ( Ge, Abstract ). Regarding Claim 22, Bhatia as modified by Ge discloses wherein the modified user input further comprises an instruction directing the large language model to treat the added triples, or the representation thereof, as system provided new information introduced by the computing system ( ¶23 and Fig. 2G, “How about Jeff Bezos” is not a fully-defined query by itself and therefore rewrite the query in an unambiguous way that incorporates relevant information (i.e., “system provided new information introduced by the computing system”) from conversation history; ¶67, based on conversation history “What is Bill Gates’ net worth?”, rewrite or add information to “How about Jeff Bezos?” into modified user input “What is Jeff Bezos’s net worth?” ) . 07-21 AIA Claim 15 is rejected under 35 USC 103(a) as being unpatentable over Bhatia et al. (US 11997056 B2) in view of Ge et al. (US 2020/0380991 A1) as applied to claim 1, in further view of Raniere (US 2015/0081663 A1) . Regarding Claim 15, Bhatia discloses wherein the one or more entities correspond to a first set of one or more entities ( Col 4, Rows 9-15, for “I like whales”, search for entity such as “whales” in the knowledge base ), the operations further comprising: identifying a second set of one or more entities in the response ( Col 4, Rows 9-15, for “I like whales”, generating the response “did you know that blue whales are the largest whales?” means the conversational system searched the knowledge base for “blue whales” ); linking one or more entities of the second set of one or more entities to supplemental content ( Col 4, Rows 9-15, generating the response “did you know that blue whales are the largest whales?” based on entity “blue whales” ), wherein the displaying the response in response to the user input comprises displaying the response ( Col 3, Rows 25-29, information such as response “did you know that blue whales are the largest whales” are displayed on a suer device ). Bhatia does not disclose wherein the displaying the response in response to the user input comprises displaying the response with one or more links to supplemental content. Raniere discloses a computing system analyzing user input to determine one or more entities corresponding to a first set of one or more entities ( ¶43, based on user message that he is planning on taking a vacation to a beach, processor 103 recognizes “planning”, “vacation”, and “beach” as keywords; per ¶5, parsing user input into at least one keyword to search an information repository for information related to the keyword ), identifying a second set of one or more entities in response ( ¶43, suggest other content such as rental cars, hotels, surfing lessons, boat rentals; per ¶51, add or identify related keywords to the list of keywords parsed from received user input ), linking one or more entities of the second set of one or more entities to supplemental content and displaying a response to the user input comprises displaying the response with one or more links to supplemental content ( ¶43, suggest beach vacation destination and in addition other content such as rental cars, hotels, surfing lessons, boat rentals etc.; in view of ¶62, the suggestion includes presenting / suggesting the user to check out corresponding links by displaying information retrieved relating to a search of the information repository per ¶5 ), and receiving user input selecting a linked entity and displaying the supplemental content for the linked entity ( ¶27, user may manually select the information being collected pertaining to active search of user inputs; per ¶51, the step of identifying related keywords bolsters the amount of overall content researched by the computing system and ultimately presented to the user; i.e., upon user manually selecting suggested links to additional keywords “rental cars”, “hotels”, “surfing lessons”, and “boat rentals”, the system present / display the overall content corresponding to “rental cars”, “hotels”, “surfing lessons”, and “boat rentals” ). It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to display the response with one or more links to supplemental content for user to select a linked entity and to display supplemental content for the linked entity in order to bolster the amount of overall content that may be researched by the computing system based on related keywords / entities and ultimately presented to the user ( Raniere, ¶51 ) . 07-21 AIA Claim 21 is rejected under 35 USC 103(a) as being unpatentable over Bhatia et al. (US 11997056 B2) in view of Ge et al. (US 2020/0380991 A1) as applied to claim 1, in further view of Padmanabhan et al. (US 2025/0005299 A1) . Regarding Claim 21, Bhatia does not disclose wherein the modified user input further comprises an instruction restricting the large language model from generating content that violates a domain specific policy associated with the semantic framework. Padmanabhan discloses a database system with a generative language model ( ¶19 and Fig. 1 ) receiving user input to determine instructions and policies to include in a prompt to the generative language model ( ¶20 ) comprising an instruction restricting the large language model from generating content that violates a domain specific policy associated with a semantic framework ( ¶48, determine one or more policies for the prompt template based on user input, client machine tenant, or database system service provider; ¶49, potential policies include restrictions on the type of output generated by the generative language model, restrictions on access to or use of potentially sensitive information, restrictions on the use of intellectual property and the like ). It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to add, to the modified user input, an instruction restricting the large language model from generating content that violates a domain specific policy associated with the semantic framework in order to restrict the type of output generated by a generative language model / LLM ( Padmanabhan, ¶49 ). Allowable Subject Matter Claim 20 recites: One or more computer-readable storage media 1 comprising: computer-executable instructions that, when executed by a computing system comprising at least one hardware processor and at least one memory coupled to the at least one hardware processor, cause the computing system to receive, from a user, user input comprising a plurality of tokens; computer-executable instructions that, when executed by the computing system, cause the computing system to analyze at least a portion of the plurality of tokens; computer-executable instructions that, when executed by the computing system, cause the computing system to, based on the analyzing, determine one or more entities of a semantic framework represented in the at least a portion of the plurality of tokens; computer-executable instructions that, when executed by the computing system, cause the computing system to determine one or more triples of the semantic framework for at least a portion of the one or more entities or for associated entities; computer-executable instructions that, when executed by the computing system, cause the computing system to add to the user input to provide modified user input: (1) at least a portion of the one or more triples, or a representation thereof (2) a first verbal instruction, distinct from the one or more triples or the representation thereof and expressed in a natural language format in the modified user input, directing a large language model to consider the at least a portion of the one or more triples, or a representation thereof, as a sole factual basis for generating a response to the user input; and (3) a second verbal instruction, distinct from the one or more triples or the representation thereof and expressed in the natural language format in the modified user input, directing a large language model to treat the added triples, or the representation thereof, as system-provided new information introduced by the computing system; computer-executable instructions that, when executed by the computing system, cause the computing system to submit the modified user input to the large language model; computer-executable instructions that, when executed by the computing system, cause the computing system to process the modified user input using the large language model to provide a response; and computer-executable instructions that, when executed by the computing system, cause the computing system to return the response in response to the receiving the user input. The prior arts combination Bhatia et al. (US 11997056 B2) in view of Ge et al. (US 2020/0380991 A1) does not disclose the ordered combination of limitations. Therefore, claim 20 is allowed over the prior arts. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner Richard Z. Zhu whose telephone number is 571-270-1587 or examiner’s supervisor Hai Phan whose telephone number is 571-272-6338. Examiner Richard Zhu can normally be reached on M-Th, 0730:1700. 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. /RICHARD Z ZHU/Primary Examiner, Art Unit 2654 06/13/2026 Application/Control Number: 18/369,065 Page 2 Art Unit: 2654 Application/Control Number: 18/369,065 Page 3 Art Unit: 2654 Application/Control Number: 18/369,065 Page 4 Art Unit: 2654 Application/Control Number: 18/369,065 Page 5 Art Unit: 2654 Application/Control Number: 18/369,065 Page 6 Art Unit: 2654 Application/Control Number: 18/369,065 Page 7 Art Unit: 2654 Application/Control Number: 18/369,065 Page 8 Art Unit: 2654 Application/Control Number: 18/369,065 Page 9 Art Unit: 2654 Application/Control Number: 18/369,065 Page 10 Art Unit: 2654 Application/Control Number: 18/369,065 Page 11 Art Unit: 2654 Application/Control Number: 18/369,065 Page 12 Art Unit: 2654 Application/Control Number: 18/369,065 Page 13 Art Unit: 2654 Application/Control Number: 18/369,065 Page 14 Art Unit: 2654 Application/Control Number: 18/369,065 Page 15 Art Unit: 2654 Application/Control Number: 18/369,065 Page 16 Art Unit: 2654 Application/Control Number: 18/369,065 Page 17 Art Unit: 2654 1 Specification, US 2025/0094707 A1 at ¶374: “The term computer-readable storage media does not include signals and carrier waves”.
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