CTFR 18/811,404 CTFR 98243 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. DETAILED ACTION In the amendments filed 04 February 2026: Claims 1-3,5, 9-11,13-15 and 17 are amended Claims 1-20 are pending 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. Claims 1, 9, 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 The claim recites a computer-implemented method, system and product, which are within a statutory category. Step 2A1 The limitations of: Claims 1, 9 and 13 (Claim 9 being representative) accessing a communication that is associated with a subject, the communication including one or more selection criteria; determining, based on the communication, a context of the communication and/or one or more clinical conditions; determining, based on the clinical conditions one or more clinical concepts, defines semantics, constraints, and relationships between a plurality of terms; querying a health record using the one or more clinical concepts; receiving, in response to the query, corresponding to the one or more clinical concepts; generating, a prompt, based at least in part on the one or more selection criteria received with the communication; combining the prompt with information corresponding to generate an augmented prompt; generating an inference request, wherein the inference request comprises the augmented prompt that includes the information corresponding to the set of records; executing the inference request to generate an output, wherein the output comprises a summary, a trend, or a categorization of information; and updating with the output , as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to generate patient record summaries (see Spec. Para. 0001 describing clinical summaries as a human activity) in the manner described in the identified abstract idea, supra . The rules or instructions are the claimed steps of “determining, querying, receiving, generating and executing” as indicated supra . Other than reciting generic computer components (discussed infra ), i.e., a system implemented by a data processor (computer), the claimed invention amounts to managing personal behavior or interaction between people. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Step 2A2 This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of a data processor and non-transitory computer readable storage medium that implements the identified abstract idea. The data processor and non-transitory computer readable storage medium are not described by the applicant and is recited at a high-level of generality (i.e., a generic server performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. The claim further recites the additional element of using a generative AI model to summarize patient records. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp. , No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). Alternatively, or in addition, the implementation of the generative AI model to summarize patient records merely confines the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use and thus fails to add an inventive concept to the claims. The claims further recite the additional elements of an (1) an interface, (2) an electronic health record, (3) natural language processing and (4) an ontological knowledge graph. The (1) an interface, (2) an electronic health record, (3) natural language processing and (4) an ontological knowledge graph merely generally link the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Accordingly, even in combination, this additional element does not integrate the abstract idea into a practical application. Step 2B The claims does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a data processor and non-transitory computer readable storage medium to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the generative AI model to summarize patient records was found to represent mere instructions to implement the abstract idea on a generic computer and/or confine the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. See also Recentive Analytics, Inc. v. Fox Corp. , No. 2023-2437 at 17 (Fed. Cir. April 18, 2025) (finding that applying machine learning to an abstract idea does not transform a claim into something significantly more). Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of an (1) an interface, (2) an electronic health record, (3) natural language processing and (4) an ontological knowledge graph was determined to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. As such the claim is not patent eligible. Claims 1-8,10-12,14-20 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim(s) 2, 10, 14 merely describe(s) the set of records, which further defines the abstract idea. Claim(s) 3, 11, 15 merely describe(s) including data into the GenAI model, which further defines the abstract idea. Claim(s) 4, 12, 16 merely describe(s) the type of GenAI model used, which further defines the abstract idea. Claim(s) 5, 17 merely describe(s) the selection criteria, which further defines the abstract idea. Claim(s) 6-8, 18-20 merely describe(s) the output, which further defines the abstract idea. Claim Rejections - 35 USC § 103 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The Examiner notes that the rejection will reference the translated documents (attached) corresponding to any foreign documents recited in the rejection. 07-21 AIA Claim s 1-20 is/are rejected under 35 U.S.C. 103(a) as being unpatentable over MCCALLIE et al (US Publication No. 20120290328) in view of Sheffer et al (US Publication No. 20200126667) in view of Giannulli et al (US Publication No. 20230316095) in view of Gardner et al (US Publication No. 12008332) . Regarding Claim 1 MCCALLIE teaches a computer-implemented method comprising: accessing a communication that is associated with a subject via an interface, the communication including one or more selection criteria [MCCALLIE at Para. 0017 teaches embodiments of the present invention allow a user to search for information in an electronic medical record (“EMR”). An EMR is a collection of information describing the medical history of a patient. The EMR may be managed by a variety of sources including a clinical facility, such as a hospital, and the patient. In one embodiment, the EMR is personal health record. The EMR for a single patient may contain combinations of database entries and electronic documents that are related to the patient's medical history. The database entries may be created by filling out an electronic form presented in a user interface] ; querying an electronic health record using the one or more clinical concepts [MCCALLIE at Para 0084 teaches at step 520, one or more uses of the clinical concept from the search query are identified in the electronic medical record] ; receiving, in response to the query, a set of records corresponding to the one or more clinical concepts from the electronic health record [MCCALLIE at Para 0051 teaches search-engine component 250 receives a search query and retrieves documents or components of documents from the electronic medical record that are responsive to the search query] ; and updating the interface with the output [MCCALLIE at Para 0077 teaches FIG. 6 illustrates an interface 600 for the user to select the matching criteria. The matching interface includes a text-only mode 652, a clinical concept mode 654, and related concepts mode 656. In one embodiment, the search results are dynamically updated as the user toggles between different matching criteria; MCCALLIE at Para 0079 teaches at step 440, search results that communicate information describing each of the one or more matching components are displayed] . MCCALLIE does not teach determining, using natural language processing and based on the communication, a context of the communication and/or one or more clinical conditions; determining, based on the clinical conditions and by using natural language processing and an ontological knowledge graph, one or more clinical concepts, wherein the ontological knowledge graph defines semantics, constraints, and relationships between a plurality of terms; generating, using natural language processing, a prompt, based at least in part on the set of records and/or the one or more selection criteria received with the communication; combining the prompt with information corresponding to the set of records, to generate an augmented prompt; generating an inference request for a generative artificial intelligence (GenAI) model, wherein the inference request comprises the augmented prompt that includes the information corresponding to the set of records: executing the inference request using the GenAI model to generate an output based on the set of records, wherein the output comprises a summary of the set of records, a trend, or a categorization of information of the set of records; Sheffer teaches determining, using natural language processing and based on the communication, a context of the communication and/or one or more clinical conditions [Sheffer at Para. 0089 teaches the NLP engine can also be used to generate queries (step 84) describing the markers. The queries can be provided with evidence (step 85) describing one or more of the clinical indicators, the associated contextual information, or both (interpreted as clinical conditions) ] ; generating, using natural language processing, a prompt, based at least in part on the set of records and/or the one or more selection criteria received with the communication [Sheffer at Para. 0076 teaches in some examples, NLP system 50 may be utilized as part of a clinical document improvement (CDI) initiative. In these examples, NLP processor 52 can also provide real-time processing capability, so that effective queries can be developed to prompt a physician, lab professional, pharmacy worker, or other provider 60 to verify and update medical record 30 during a patient visit or hospital stay, or during a service call at a pharmacy or other facility (interpret to combine with medical records of MCCALLIE) ] ; It would have been prima facie obvious skill in the art, at the time of effective filing, to combine records of MCCALLIE with the nlp of Sheffer with the motivation to improve care within a patient stay and during follow up [Sheffer at Para. 0031]. MCCALLIE/Sheffer do not teach determining, based on the clinical conditions and by using natural language processing and an ontological knowledge graph, one or more clinical concepts, wherein the ontological knowledge graph defines semantics, constraints, and relationships between a plurality of terms; combining the prompt with information corresponding to the set of records, to generate an augmented prompt; generating an inference request for a generative artificial intelligence (GenAI) model, wherein the inference request comprises the augmented prompt that includes the information corresponding to the set of records: executing the inference request using the GenAI model to generate an output based on the set of records, wherein the output comprises a summary of the set of records, a trend, or a categorization of information of the set of records; Giannulli teaches determining, based on the clinical conditions and by using natural language processing and an ontological knowledge graph, one or more clinical concepts, wherein the ontological knowledge graph defines semantics, constraints, and relationships between a plurality of terms [Giannulli at Para. 0015 teaches in an Example 11, the method of Example 10, further comprising using at least one of: machine learning and natural language processing to map the raw data of the clinical encounter to the knowledge graph of the clinical information; Giannulli at Para. 0056 teaches in certain embodiments, the knowledge graph 112 includes a set of nodes and relationships that each have specific attributes that form a clinical reference for a given clinical concern within the context of a clinical encounter. As an example, the nodes of the knowledge graph 112 are organized around a given clinical concern and/or encode the expected clinical concepts that are expressed within an encounter, their relevance, related ontology and/or codification, hierarchy, sequence and/or related expression with respect to the encounter document; Giannulli at Para. 0083 teaches according to certain embodiments, the method 200 includes associating one or more concept nodes with one or more of the other nodes (e.g., the nodes 302-310H) of the knowledge graph 300. In some embodiments, concept nodes represent a particular clinical concept, or other information, including, for example, specific diagnoses, procedures, medications, allergies, demographic, or clinically relevant social information (interpret to combine with clinical conditions of Sheffer) ] ; It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of MCCALLIE, Sheffer with the knowledge graph of Giannulli with the motivation to improve physicians' lives with an ambient solution to automate clinical documentation [Giannulli at Para. 0039] . MCCALLIE/Sheffer/Giannulli do not teach generating an inference request for a generative artificial intelligence (GenAI) model, wherein the inference request comprises the augmented prompt that includes the information corresponding to the set of records: executing the inference request using the GenAI model to generate an output based on the set of records, wherein the output comprises a summary of the set of records, a trend, or a categorization of information of the set of records; Gardner teaches generating an inference request for a generative artificial intelligence (GenAI) model, wherein the inference request comprises the augmented prompt that includes the information corresponding to the set of records [Gardner at Para. 24 teaches a prompt is automatically engineered for providing to the one or more LLMs. The prompt includes a reference to the first content item and the level of the abstraction for the first content item. A response to the prompt is received from the LLM. The response includes a second content item. The second content item includes a representation of the first content item that is generated by the LLM. The representation omits or simplifies one or more of the set of sub-content items based on the level of abstraction. The representation is used to control an output that is communicated to a target device; Gardner at Para. 713 teaches relevant external information is retrieved from databases, knowledge graphs and APIs to augment the context for summarization beyond what is contained directly in the source document (data interpreted as not being part of the training data of the GenAI model) ] : executing the inference request using the GenAI model to generate an output based on the set of records, wherein the output comprises a summary of the set of records, a trend, or a categorization of information of the set of records [Gardner at Para. 4 teaches FIG. 3 is a block diagram illustrating an example method for generating abstractive summaries of content items using a large language model (LLM); Gardner at Para. 247 teaches medical record summaries emphasize diagnostic details over patient background] ; It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of MCCALLIE/Sheffer/Giannulli with the model of Gardner with the motivation to improve human-computer interaction around content summarization [Gardner at Para. 15] . Regarding Claim 2 MCCALLIE/Sheffer/Giannulli/Gardner teach the computer-implemented method of claim 1, MCCALLIE/Sheffer/Giannulli/Gardner further teach wherein the information corresponding to the set of records comprises the set of records, or an identifier for each record of the set of records [MCCALLIE at Para. 0058 teaches the Electronic medical record data store 270 contains the electronic medical records for one or more patients. An EMR is a collection of information describing the medical history of a patient. In addition, the EMR data store 270 may include electronic medical records from one or more clinical facilities (interpreted as the set of records) ] . Regarding Claim 3 MCCALLIE/Sheffer/Giannulli/Gardner teach the computer-implemented method of claim 1, MCCALLIE/Sheffer/Giannulli/Gardner further teach wherein the set of records were not included in training data of the GenAI [Gardner at Para. 24 teaches A prompt is automatically engineered for providing to the one or more LLMs. The prompt includes a reference to the first content item and the level of the abstraction for the first content item. A response to the prompt is received from the LLM. The response includes a second content item. The second content item includes a representation of the first content item that is generated by the LLM. The representation omits or simplifies one or more of the set of sub-content items based on the level of abstraction. The representation is used to control an output that is communicated to a target device; Gardner at Para. 713 teaches relevant external information is retrieved from databases, knowledge graphs and APIs to augment the context for summarization beyond what is contained directly in the source document (data interpreted as not being part of the training data of the GenAI model) ] . Regarding Claim 4 MCCALLIE/Sheffer/Giannulli/Gardner teach the computer-implemented method of claim 1, MCCALLIE/Sheffer/Giannulli/Gardner further teach wherein the GenAI model includes a retrieval-augmented generation (RAG) model, a fine-tuned transformer model, or a domain-specific model that is trained on domain-specific data [Gardner at Para. 463 teaches select extractive and abstractive models from a model catalog containing options like BERT, GPT-3, T5, BART and other encoder-decoder networks fine-tuned for summarization (interpreted as a fine-tuned transformer model) ] . Regarding Claim 5 MCCALLIE/Sheffer/Giannulli/Gardner teach the computer-implemented method of claim 1, MCCALLIE/Sheffer/Giannulli/Gardner further teach wherein the one or more selection criteria comprises one or more filters corresponding to a time interval, an encounter type, a record type, or an author of a record and wherein the method further comprises [MCCALLIE at Para. 0081 teaches the result interface 700 also includes a filter interface 785. The filter interface 785 allows the clinician to filter by year 786, encounter locations 787, or document class 788. The number of search results that match a particular filter criteria are displayed in parentheses adjacent to the suggested filter criteria] : querying the electronic health record using the one or more clinical concepts and the one or more selection criteria [MCCALLIE at Para. 0057 teaches in one embodiment, the filter component 267 suggests a filter criteria for the user to select along with an indication of how many of the search results match the filtered criteria. For example, the interface could indicate that 20 search results are in the document class “physician authored.” In one embodiment, the search results may be filtered by clinical concepts found within the search results. Related clinical concepts may be aggregated into a general filter option that would present search results that include any of the related clinical concepts. The filter options could be presented with the clinical concepts having the highest aggregation of clinical-importance scores. These filter examples are not meant to be exhaustive, other filters based on factors store in the index are within the scope of this disclosure] ; receiving, in response to the query, a second set of records corresponding to the one or more clinical concepts and the one or more selection criteria [MCCALLIE at Para. 0057] ; and generating a second output based on the second set of records by using the GenAI model [MCCALLIE at Para. 0077 (see Claim 1 for explanation; interpret to combine with model of Gardner) ] . Regarding Claim 6 MCCALLIE/Sheffer/Giannulli/Gardner teach the computer-implemented method of claim 1, MCCALLIE/Sheffer/Giannulli/Gardner further teach wherein the output comprises a problem-oriented summary or a disease-oriented summary based on the set of records [Gardner at Para. 247 teaches medical record summaries emphasize diagnostic details over patient background (interpreted as disease-oriented summary) ] . Regarding Claim 7 MCCALLIE/Sheffer/Giannulli/Gardner teach the computer-implemented method of claim 1, MCCALLIE/Sheffer/Giannulli/Gardner further teach wherein the output comprises the trend of symptoms by using temporal analysis of data from the set of records. The prior art of Gardner in Claim 1 recites the use of GenAI model summarizing a set of records. Since the other options are not necessary to occur, this Claim does not need to occur. Regarding Claim 8 MCCALLIE/Sheffer/Giannulli/Gardner teach the computer-implemented method of claim 1, MCCALLIE/Sheffer/Giannulli/Gardner further teach wherein the output comprises categorized information of the set of records corresponding to a plurality of sections, wherein the plurality of sections includes symptoms, medications, lab tests, and diagnosis. The prior art of Gardner in Claim 1 recites the use of GenAI model summarizing a set of records. Since the other options are not necessary to occur, this Claim does not need to occur. Regarding Claim 9 MCCALLIE teaches a system comprising: accessing a communication that is associated with a subject via an interface, the communication including one or more selection criteria [MCCALLIE at Para. 0017 (see Claim 1 for explanation) ] ; querying an electronic health record using the one or more clinical concepts [MCCALLIE at Para. 0084 (see Claim 1 for explanation) ] ; receiving, in response to the query, a set of records corresponding to the one or more clinical concepts from the electronic health record [MCCALLIE at Para. 0051 (see Claim 1 for explanation) ] ; and updating the interface with the output [MCCALLIE at Para. 0077, 0079 (see Claim 1 for explanation) ] . MCCALLIE does not teach one or more data processors; and a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform a set of operations including: determining, using natural language processing and based on the communication, a context of the communication and/or one or more clinical conditions; determining, based on the clinical conditions and by using natural language processing and an ontological knowledge graph, one or more clinical concepts, wherein the ontological knowledge graph defines semantics, constraints, and relationships between a plurality of terms; generating, using natural language processing, a prompt, based at least in part on the set of records and/or the one or more selection criteria received with the communication; combining the prompt with information corresponding to the set of records, to generate an augmented prompt; generating an inference request for a generative artificial intelligence (GenAI) model, wherein the inference request comprises the augmented prompt that includes the information corresponding to the set of records; executing the inference request using the GenAI model to generate an output based on the set of records, wherein the output comprises a summary of the set of records, a trend, or a categorization of information of the set of records; Sheffer teaches determining, using natural language processing and based on the communication, a context of the communication and/or one or more clinical conditions [Sheffer at Para. 0089 (see Claim 1 for explanation) ] ; generating, using natural language processing, a prompt, based at least in part on the set of records and/or the one or more selection criteria received with the communication [Sheffer at Para. 0076 (see Claim 1 for explanation) ] ; It would have been prima facie obvious skill in the art, at the time of effective filing, to combine records of MCCALLIE with the nlp of Sheffer with the motivation to improve care within a patient stay and during follow up [Sheffer at Para. 0031]. MCCALLIE/Sheffer do not teach one or more data processors; and a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform a set of operations including: determining, based on the clinical conditions and by using natural language processing and an ontological knowledge graph, one or more clinical concepts, wherein the ontological knowledge graph defines semantics, constraints, and relationships between a plurality of terms; combining the prompt with information corresponding to the set of records, to generate an augmented prompt; generating an inference request for a generative artificial intelligence (GenAI) model, wherein the inference request comprises the augmented prompt that includes the information corresponding to the set of records; executing the inference request using the GenAI model to generate an output based on the set of records, wherein the output comprises a summary of the set of records, a trend, or a categorization of information of the set of records; Giannulli teaches one or more data processors; and a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform a set of operations including [Giannulli at Para. 0098 teaches in some examples, the processor 404 includes one or more general purpose microprocessors. n some examples, the main memory 412 (e.g., random access memory (RAM), cache and/or other dynamic storage devices) is configured to store information and instructions to be executed by the processor 404. In certain examples, the main memory 412 is configured to store temporary variables or other intermediate information during execution of instructions to be executed by processor 404] : determining, based on the clinical conditions and by using natural language processing and an ontological knowledge graph, one or more clinical concepts, wherein the ontological knowledge graph defines semantics, constraints, and relationships between a plurality of terms [Giannulli at Para. 0015, 0056, 0083 (see Claim 1 for explanation) ] ; It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of MCCALLIE, Sheffer with the knowledge graph of Giannulli with the motivation to improve physicians' lives with an ambient solution to automate clinical documentation [Giannulli at Para. 0039] . MCCALLIE/Sheffer/Giannulli do not teach combining the prompt with information corresponding to the set of records, to generate an augmented prompt [Gardner at Para. 24, 713 (see Claim 1 for explanation) ] ; generating an inference request for a generative artificial intelligence (GenAI) model, wherein the inference request comprises the augmented prompt that includes the information corresponding to the set of records [Gardner at Para. 24, 713 (see Claim 1 for explanation) ] ; executing the inference request using the GenAI model to generate an output based on the set of records, wherein the output comprises a summary of the set of records, a trend, or a categorization of information of the set of records [Gardner at Para. 4, 247 (see Claim 1 for explanation) ] ; It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of MCCALLIE/Sheffer/Giannulli with the model of Gardner with the motivation to improve human-computer interaction around content summarization [Gardner at Para. 15] . Regarding Claim 10 Claim(s) 10 is/are analogous to Claim(s) 2, thus Claim(s) 10 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 2. Regarding Claim 11 Claim(s) 11 is/are analogous to Claim(s) 3, thus Claim(s) 11 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 3. Regarding Claim 12 Claim(s) 12 is/are analogous to Claim(s) 4, thus Claim(s) 12 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 4. Regarding Claim 13 MCCALLIE teaches a computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform a set of operations comprising: accessing a communication that is associated with a subject via an interface, the communication including one or more selection criteria [MCCALLIE at Para. 0017 (see Claim 1 for explanation) ] ; querying an electronic health record using the one or more clinical concepts [MCCALLIE at Para. 0084 (see Claim 1 for explanation) ] ; receiving, in response to the query, a set of records corresponding to the one or more clinical concepts from the electronic health record [MCCALLIE at Para. 0051 (see Claim 1 for explanation) ] ; and updating the interface with the output [MCCALLIE at Para. 0077, 0079 (see Claim 1 for explanation) ] . MCCALLIE does not teach determining, using natural language processing and based on the communication, a context of the communication and/or one or more clinical conditions; determining, based on the clinical conditions and by using natural language processing and an ontological knowledge graph, one or more clinical concepts, wherein the ontological knowledge graph defines semantics, constraints, and relationships between a plurality of terms; generating, using natural language processing, a prompt, based at least in part on the set of records and/or the one or more selection criteria received with the communication; combining the prompt with information corresponding to the set of records, to generate an augmented prompt; generating an inference request for a generative artificial intelligence (GenAI) model, wherein the inference request comprises the augmented prompt that includes the information corresponding to the set of records; executing the inference request using the GenAI model to generate an output based on the set of records, wherein the output comprises a summary of the set of records, a trend, or a categorization of information of the set of records; Sheffer teaches determining, using natural language processing and based on the communication, a context of the communication and/or one or more clinical conditions [Sheffer at Para. 0089 (see Claim 1 for explanation) ] ; generating, using natural language processing, a prompt, based at least in part on the set of records and/or the one or more selection criteria received with the communication [Sheffer at Para. 0076 (see Claim 1 for explanation) ] ; It would have been prima facie obvious skill in the art, at the time of effective filing, to combine records of MCCALLIE with the nlp of Sheffer with the motivation to improve care within a patient stay and during follow up [Sheffer at Para. 0031]. MCCALLIE/Sheffer does not teach determining, based on the clinical conditions and by using natural language processing and an ontological knowledge graph, one or more clinical concepts, wherein the ontological knowledge graph defines semantics, constraints, and relationships between a plurality of terms; combining the prompt with information corresponding to the set of records, to generate an augmented prompt; generating an inference request for a generative artificial intelligence (GenAI) model, wherein the inference request comprises the augmented prompt that includes the information corresponding to the set of records; executing the inference request using the GenAI model to generate an output based on the set of records, wherein the output comprises a summary of the set of records, a trend, or a categorization of information of the set of records; Giannulli teaches determining, based on the clinical conditions and by using natural language processing and an ontological knowledge graph, one or more clinical concepts, wherein the ontological knowledge graph defines semantics, constraints, and relationships between a plurality of terms [Giannulli at Para. 0015, 0056, 0083 (see Claim 1 for explanation) ] ; It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of MCCALLIE, Sheffer with the knowledge graph of Giannulli with the motivation to improve physicians' lives with an ambient solution to automate clinical documentation [Giannulli at Para. 0039] . MCCALLIE/Sheffer/Giannulli do not teach combining the prompt with information corresponding to the set of records, to generate an augmented prompt; generating an inference request for a generative artificial intelligence (GenAI) model, wherein the inference request comprises the augmented prompt that includes the information corresponding to the set of records; executing the inference request using the GenAI model to generate an output based on the set of records, wherein the output comprises a summary of the set of records, a trend, or a categorization of information of the set of records; Gardner teaches combining the prompt with information corresponding to the set of records, to generate an augmented prompt [Gardner at Para. 24, 713 (see Claim 1 for explanation) ] ; generating an inference request for a generative artificial intelligence (GenAI) model, wherein the inference request comprises the augmented prompt that includes the information corresponding to the set of records [Gardner at Para. 24, 713 (see Claim 1 for explanation) ] ; executing the inference request using the GenAI model to generate an output based on the set of records, wherein the output comprises a summary of the set of records, a trend, or a categorization of information of the set of records [Gardner at Para. 4, 247 (see Claim 1 for explanation) ] ; It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of MCCALLIE/Sheffer/Giannulli with the model of Gardner with the motivation to improve human-computer interaction around content summarization [Gardner at Para. 15] . Regarding Claim 14 Claim(s) 14 is/are analogous to Claim(s) 2, thus Claim(s) 14 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 2. Regarding Claim 15 Claim(s) 15 is/are analogous to Claim(s) 3, thus Claim(s) 15 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 3. Regarding Claim 16 Claim(s) 16 is/are analogous to Claim(s) 4, thus Claim(s) 16 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 4. Regarding Claim 17 Claim(s) 17 is/are analogous to Claim(s) 5, thus Claim(s) 17 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 5. Regarding Claim 18 Claim(s) 18 is/are analogous to Claim(s) 6, thus Claim(s) 18 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 6. Regarding Claim 19 Claim(s) 19 is/are analogous to Claim(s) 7, thus Claim(s) 19 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 7. Regarding Claim 20 Claim(s) 20 is/are analogous to Claim(s) 8, thus Claim(s) 20 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 8. Response to Arguments Rejection under 35 U.S.C. § 101 Regarding the rejection of Claims 1-20, the Examiner has considered the Applicant’s arguments; however the arguments are not persuasive. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons. Applicant argues: claim 1 is at least in part directed towards usage of natural language processing and ontological knowledge graph to determine clinical concepts, querying an electronic health record using the clinical concepts, generating and augmenting a prompt, generating an inference request, and executing the inference request. These features define a machine-specific data processing pipeline involving structured knowledge representation (e.g., ontological graph semantics), automated EHR data retrieval, construction and augmentation of model-consumable prompt, and execute an inference request. These are not processes that can be practically performed in the human mind. Humans do not traverse an ontological knowledge graph with defined semantic constraints, nor do they construct structured augmented prompts for execution by a generative model. Accordingly, the claims are directed to a specific technological solution for computer-assisted clinical record analysis, not a mental process or method of organizing human activity. These elements function together as a single technical pipeline that transforms unstructured communication into structured, machine-actionable representations that control both what data are retrieved from a database, and how those data are formatted for model inference. Thus, claim 1 does not merely use a computer to perform an abstract idea. Rather, claim 1 define how multiple computer technologies (e.g., NLP, knowledge graphs, database querying, and generative model prompting) are technically combined, such that the output of one stage becomes the structured input to the next. This defines a technological manner of operating a computer system and a generative model in conjunction with domain-specific data sources. Thus, features claim 1 are directed towards a technological improvement and a practical application, such as a manner in which computer systems generate inference requests for generative models. The Office Action alleges that any additional elements (e.g., interface, electronic health record (EHR), natural language processing (NLP), ontological knowledge graph, and GenAI model do not integrate the alleged abstract idea into a practical application, as these are generic computer components that merely apply the abstract idea on a computer. This analysis considers the elements in isolation rather than as an ordered combination, as required by the eligibility framework. These elements function together as a single technical pipeline that transforms unstructured communication into structured, machine-actionable representations that control both what data are retrieved from a database, and how those data are formatted for model inference. Thus, claim 1 does not merely use a computer to perform an abstract idea. Rather, claim 1 define how multiple computer technologies (e.g., NLP, knowledge graphs, database querying, and generative model prompting) are technically combined, such that the output of one stage becomes the structured input to the next. This defines a technological manner of operating a computer system and a generative model in conjunction with domain-specific data sources. Thus, features claim 1 are directed towards a technological improvement and a practical application, such as a manner in which computer systems generate inference requests for generative models. Regarding (a) , the Examiner respectfully disagrees. Humans perform these actions using tools like software. Humans interacting with a computer to perform these functions recite rules or instructions for a huma to follow. Furthermore, humans generate prompts all the time by constructing questions. The augmented prompt recited in the claims is not well-defined to one of ordinary skill in the art. There is no support in the claim or specification that specifies what the augmented prompt is or how it is formulated. The claims merely recite the augmented prompt including information corresponding to a set of records. By broadest reasonable interpretation, the augmented prompt could merely be interpreted as a question about a particular aspect of a set of health records. Regarding (b) , the Examiner respectfully disagrees. The Examiner considered the additional elements in in combination. The additional elements are merely tools to perform the steps in the claim. There is no improvement to the computer and technological environment, in particular the general field of communication because the problem is not caused by the computer or technological environment. By applicant’s own admittance, the problem of summarizing patient health records, is one that was performed by humans (See Spec. Para 0001). Therefore, no practical application can be found. Regarding (c) , the Examiner respectfully disagrees. The Examiner considered the additional elements in combination and the combination merely improved the abstract idea. The Applicant has not provided evidence to suggest why the combination of additional elements makes the claims eligible. Rejection under 35 U.S.C. § 103 Regarding the rejection of Claims 1-20, the Examiner has considered the Applicant’s arguments; however, these arguments are moot given the new grounds of rejection as necessitated by amendment. Conclusion The prior art made of record and not relied upon in the present basis of rejection are noted in the attached PTO 892 and include: Mirhaji et al (US Publication No. 10423633) discloses methods and systems for informatics systems. Sun et al (US Publication No. 20240045994) discloses prompt-based language models for generating multi-modal electronic records 07-39 AIA THIS ACTION IS MADE FINAL, as necessitated by amendment. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN C EDOUARD whose telephone number is (571)270-0107. The examiner can normally be reached M-F 730 - 430. 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, Robert Morgan can be reached on (571) 272 - 6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /JONATHAN C EDOUARD/Examiner, Art Unit 3683 /JASON S TIEDEMAN/Primary Examiner, Art Unit 3683 Application/Control Number: 18/811,404 Page 2 Art Unit: 3683 Application/Control Number: 18/811,404 Page 3 Art Unit: 3683 Application/Control Number: 18/811,404 Page 4 Art Unit: 3683 Application/Control Number: 18/811,404 Page 5 Art Unit: 3683 Application/Control Number: 18/811,404 Page 6 Art Unit: 3683 Application/Control Number: 18/811,404 Page 7 Art Unit: 3683 Application/Control Number: 18/811,404 Page 8 Art Unit: 3683 Application/Control Number: 18/811,404 Page 9 Art Unit: 3683 Application/Control Number: 18/811,404 Page 10 Art Unit: 3683 Application/Control Number: 18/811,404 Page 11 Art Unit: 3683 Application/Control Number: 18/811,404 Page 12 Art Unit: 3683 Application/Control Number: 18/811,404 Page 13 Art Unit: 3683 Application/Control Number: 18/811,404 Page 14 Art Unit: 3683 Application/Control Number: 18/811,404 Page 15 Art Unit: 3683 Application/Control Number: 18/811,404 Page 16 Art Unit: 3683 Application/Control Number: 18/811,404 Page 17 Art Unit: 3683 Application/Control Number: 18/811,404 Page 18 Art Unit: 3683 Application/Control Number: 18/811,404 Page 19 Art Unit: 3683 Application/Control Number: 18/811,404 Page 20 Art Unit: 3683 Application/Control Number: 18/811,404 Page 21 Art Unit: 3683 Application/Control Number: 18/811,404 Page 22 Art Unit: 3683 Application/Control Number: 18/811,404 Page 23 Art Unit: 3683 Application/Control Number: 18/811,404 Page 24 Art Unit: 3683 Application/Control Number: 18/811,404 Page 25 Art Unit: 3683 Application/Control Number: 18/811,404 Page 26 Art Unit: 3683 Application/Control Number: 18/811,404 Page 27 Art Unit: 3683
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