Notice of Pre-AIA or AIA Status
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
Status of the Application
Claims 1-16 are currently pending in this case and have been examined and addressed below. This communication is a Non-Final Rejection in response to the Claims filed on 06/04/2025.
Claim Rejections - 35 USC § 101
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 1-16 are rejected because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-14 fall within the statutory category of a process. Claim 15 falls within the statutory category of an article of manufacture as a computer-readable medium. Claim 16 falls within the statutory category of an apparatus or system.
Step 2A, Prong One
As per Claims 1, 14, 15, and 16, the limitations of , under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “the computing device configured to”, nothing in the claim element precludes the step from practically being performed in the mind. The steps of identifying nutritional elements and generating a viral alleviation program are concepts performed including observation, evaluation, judgement and opinion in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers the performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. The step of calculating a plurality of nutrient amounts including determining an effect of the nutrient amounts on the epidemiological profile and calculating the plurality of nutrient amounts as a function of the effect can also fall into the grouping of mathematical concepts as mathematical relationships and calculations can be used to carry out the calculating step. As per the October 2019 Update on Subject Matter Eligibility, a claim can recite more than one judicial exception and claims which recite a series of steps that recite mental steps which are also mathematical calculations are identified as both. Accordingly, the claims recite an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application because the additional elements and combination of additional elements do not impose meaningful limits on the judicial exception. In particular, the claims recite the additional element – a computing device. The computing device in these steps is recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also recites the additional elements of receiving a viral biomarker relating to a user and retrieving a viral epidemiological profile related to the user which amounts to insignificant extra-solution activity, as in MPEP 2106.05(g), because the steps of receiving a viral biomarker and retrieving a viral epidemiological profile are mere data gathering in conjunction with the abstract idea where the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). Because the additional elements do not impose meaningful limitations on the judicial exception, the claim is directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with the respect to integration of the abstract idea into a practical application, the additional element of a computing device to perform the method of the invention amounts to no more than mere instructions to apply the exception using a generic computing component. The system including the "computing device” are recited at a high level of generality and are recited as generic computer components by reciting any computing device including a microprocessor, mobile device such as mobile phone or smartphone, etc. (Specification, [0008]), which do not add meaningful limitations to the abstract idea beyond mere instructions to apply an exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims also include the additional elements of receiving a viral biomarker relating to a user and retrieving a viral epidemiological profile related to the use which are both elements that are well-understood, routine and conventional computer functions in the field of data management because they are claimed at a high level of generality and include receiving or transmitting data as well as storing and retrieving information from memory, which have been found to be well-understood, routine and conventional computer functions by the Court (MPEP 2106.05(d)(II)(i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added) and (iv) Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves another technology. The claims do not amount to significantly more than the underlying abstract idea.
Dependent Claims 2, 6, 8-10, 12, 16 and 18-20 add further limitations which are also directed to an abstract idea. For example, Claims 2 and 12 include receiving a result of a test which is an additional element which amounts to insignificant extra-solution activity that, similar to the independent claims, is well-understood, routine and conventional in the field of data management because it involves receiving or transmitting data. Claims 6, 9-10, 16 and 19-20 recite calculating the plurality of nutrient amounts as a function of the at least viral biomarker and plurality of effects, generating a viral prevention metric, and calculating a change in incidence of viral infection as a function of adhering to alleviation program which fall into both the mental process and mathematical concepts groupings of abstract idea because it can practically be performed in the human mind but also includes mathematical relationships. These claims further specify or limit the elements of the independent claims, and hence are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1 and 11. Claims 8 and 18 include generating an alleviation program classifier using a nourishment classification machine-learning process and outputting the plurality of nutrition elements, where the generation of a classifier and output of nutrition elements recites a mental process as it can be practically performed in the human mind, similar to the independent claims. The use of a machine-learning process, which is recited at a high-level of generality such that it amounts to mere instructions to apply the exception. As per MPEP 2106.05(f), a claim that recites only the idea of a solution or outcome and fails to recite details of how a solution to a problem is accomplished and use of computers as a tool to perform existing processes such as a commonplace mathematical algorithm applied on a general purpose computer, has been found by the courts to amount to mere instructions to apply the exception and does not integrate the abstract idea into a practical application or provide significantly more. Because the additional elements do not impose meaningful limitations on the judicial exception and the additional elements are well-understood, routine and conventional functionalities in the art, the claims are directed to an abstract idea and are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4-13, 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Yinghao Zhu, et al. (Yinghao Zhu, Changyu Ren, et al.; EMERGE: Enhancing Multimodal Electronic Health Records Predictive Modeling with Retrieval-Augmented Generation; CIKM '24; October 21-25, 2024; p. 1-11), hereinafter Zhu, in view of Deepak et al. (US 2025/0259011 A1), hereinafter Deepak, in view of Jing Miao, et al. (Jing Miao, Charat Thongprayoon, et al.; Chain of Thought Utilization in Large Language Models and Application in Nephrology; Medicina 2024, 60, 148; 13 January 2024; p. 2-19), hereinafter Miao.
As per Claims 1, 15, and 16, Zhu discloses:
receiving one or more health records (Page 3, Col. 2 RAG-Driven Enhancement Pipeline extract data from EHR, Page 4 Fig. 1 Retrieval where entities are extracted from EHR time series and clinical notes);
querying one or more prompts to a large language model (LLM) using a Retrieval Augmented Generation (RAG) process to query data from the one or more health records, additional corpora, and LLM outputs to determine a query response (Abstract use of Retrieval-Augmented Generation; Page 2 use of RAG approach to query data from time-series EHR data, clinical notes and knowledge graph to generate a response when LLM is prompted), and extracting specific FHIR resources to incorporate structured EHR data as an additional context to answer quality abstraction questions (Page 2 Col. 1 integrating structured time-series EHR data to be used in the LLM to answer the prompt and generate health status summaries),
further wherein an individual FHIR resource is flattened to create nodes within a knowledge graph structure representing an underlying clinical data ontology, and provided to the LLM a knowledge graph RAG (KG-RAG) to contextualize the LLM's understanding and ability to answer quality abstraction questions (Page 5, Col. 1, 4.2.2 vector retrieval approach for generating nodes of a knowledge graph, page 6 Col. 1 condense the data to generate concise representation of the data; Page 7, Col. 1 generating compatible embeddings for further analysis tasks of the RAG framework to analyze the EHR data; see Fig. 1 where the RAG driven process including knowledge graph to provide data to the LLM for providing a response of summary of health status); and
generate a hospital quality abstraction report based on the query response (Page 1, Abstract generating a summaries of health status for patients of a hospital based on the extracted EHR data by prompting LLMs; Page 2 Col. 1 prompting an LLM to generate comprehensive summary of patients health status from the EHR data).
However, Zhu may not explicitly disclose the following which is taught by Deepak: a processor ([0022]) and a non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors of a device, cause the device to perform operations ([0031], [0071], [0078]);
dynamically generating or modifying the one or more prompts based on an analysis of historical query responses, user feedback, and/or evolving clinical guidelines ([0047] update the LLM based on the updated or additional data received, fine-tuning the model; [0053] use of user feedback to augment prompts/outputs with additional context to provide better results); and
output the report ([0055-0056] administrative dashboard is a user interface for displaying the results of the model such as topics/queries and the associated performance).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of generating prompts based on user feedback or clinical guidelines from Deepak with the known system of an LLM using RAG process to query an EHR and generate a report of hospital data from Deepak in order to improve and retrain large language models to provide better answers to complex questions (Deepak [0002-0003]).
However, Zhu and Deepak may not explicitly disclose the following which is taught by Miao: querying electronic health record (EHR) data directly in Fast Healthcare Interoperability Resources (FHIR) format (Page 13-14, 6.4 Integration with Existing Clinical Systems and Electronic Health Records the data is obtained from EHRs which adhere to FHIR standard).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of the electronic health record being in a determined standardized format from Miao with the known system of an LLM using RAG process to query an EHR and generate a report of hospital data from Zhu and Deepak in order to enable system interoperability during information exchange of data from a plurality of sources (Miao Page 14).
As per Claim 4, Zhu, Deepak, and Miao teach the limitations of Claim 1. Deepak also teaches the querying includes one or more prompts based on guidelines for determining a hospital quality measure assessment from clinical records ([0036] verification of LLM by using criteria to determine the quality of the LLM to align with guidelines).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of generating prompts based on clinical guidelines from Deepak with the known system of an LLM using RAG process to query an EHR and generate a report of hospital data from Deepak in order to improve and retrain large language models to provide better answers to complex questions (Deepak [0002-0003]).
As per Claim 5, Zhu, Deepak, and Miao teach the limitations of Claim 1. Miao also teaches the health records are stored in a predetermined format (Page 14, EHRs are data which adheres to an FHIR/HL7 Standard).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of the electronic health record being in a determined standardized format from Miao with the known system of an LLM using RAG process to query an EHR and generate a report of hospital data from Zhu and Deepak in order to enable system interoperability during information exchange of data from a plurality of sources (Miao Page 14).
As per Claim 6, Zhu, Deepak, and Miao teach the limitations of Claim 1. Miao also teaches the health records are compliant with a Fast Healthcare Interoperability Resources standard (Page 14, EHRs are data which adheres to an FHIR Standard).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of the electronic health record being in a determined standardized format from Miao with the known system of an LLM using RAG process to query an EHR and generate a report of hospital data from Zhu and Deepak in order to enable system interoperability during information exchange of data from a plurality of sources (Miao Page 14).
As per Claim 7, Zhu, Deepak, and Miao teach the limitations of Claim 1. Miao also teaches the querying is performed within a Health Insurance Portability and Accountability Act (HIPAA) compliant virtual private cloud (Page 14, 6.4 Integration with Existing Clinical Systems and Electronic Health Records the EHRs store patient data that complies with HIPAA regulations).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of a HIPAA compliant data system from Miao with the known system of an LLM using RAG process to query an EHR and generate a report of hospital data from Zhu and Deepak in order to protect data privacy (Miao Page 14).
As per Claim 8, Zhu, Deepak, and Miao teach the limitations of Claim 1. Zhu also teaches determining clinical criteria from the electronic health records, wherein the hospital quality abstraction report is based on the clinical criteria and the query response (Page 6, Col. 1 determine disease definitions and descriptions to enhance comprehension and use to compile health status, see Fig. 4, Page 5 which uses EHR data to determine disease criteria based on definitions).
As per Claim 9, Zhu, Deepak, and Miao teach the limitations of Claim 8. Zhu also teaches the clinical criteria include any construct that may comprise of one or more clinical findings that are chained together via logical operators (such as AND,OR), such as Systemic Inflammatory Response Syndrome (SIRS), sequential organ failure assessment score (SOFA), Laboratory Confirmed Bloodstream Infection (LCBI) criteria, among others (Page 5 Figure 4 the clinical criteria includes, for example, the pulmonary edema is determined based on a list of factors, i.e. OR condition; Examiner notes that the claim does not specifically require SIRS, SOFA, or LCBI).
As per Claim 10, Zhu, Deepak, and Miao teach the limitations of Claim 1. Deepak also teaches receiving feedback from a user regarding the hospital quality abstraction report ([0037] user feedback is received based on the LLM output/responses); and
creating a feedback record based on the received feedback ([0038] analytics and reporting module analyzes feedback, [0050] provide the user feedback in a dashboard/report).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of receiving user feedback and creating a record of the feedback from Deepak with the known system of an LLM using RAG process to query an EHR and generate a report of hospital data from Deepak in order to improve and retrain large language models to provide better answers to complex questions (Deepak [0002-0003]).
As per Claim 11, Zhu, Deepak, and Miao teach the limitations of Claim 1. Miao also teaches the querying includes presenting one or more prompts following a chain-of-thoughts prompting strategy (Page 4-5, 2.3 Chain of Thought Prompting use of chain-of-though prompting to create set of prompts).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of using chain-of-thoughts prompts from Miao with the known system of an LLM using RAG process to query an EHR and generate a report of hospital data from Zhu and Deepak in order to improve the decision-making process while complying with healthcare standards (Miao Page 1, Abstract).
As per Claim 12, Zhu, Deepak, and Miao teach the limitations of Claim 1. Miao also teaches the querying includes presenting one or more prompts following a few-shot prompting strategy (Page 4, 2.2 Few-Shot Prompting presenting small number of prompts in few-shot prompting approach for large language models).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of using few-shot prompts from Miao with the known system of an LLM using RAG process to query an EHR and generate a report of hospital data from Zhu and Deepak in order to improve the decision-making process while complying with healthcare standards (Miao Page 1, Abstract).
As per Claim 13, Zhu, Deepak, and Miao teach the limitations of Claim 1. Zhu also teaches prompts are entered that generate hospital quality assessment report data (Page One, Abstract prompts for LLM used to generate summaries of patients’ health statuses).
However, Zhu and Deepak may not explicitly disclose the following which is taught by Miao: the querying includes presenting one or more prompts selected to elicit responses (Page 4, 2.2 Few Shot Prompting, presenting the main query to result in output of the model; 2.3 Chain-of-Thought-Prompting presents a series of prompts to result in model output).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of presenting prompts from Miao with the known system of an LLM using RAG process to query an EHR and generate a report of hospital data from Zhu and Deepak in order to improve the decision-making process while complying with healthcare standards (Miao Page 1, Abstract).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kresevic et al. (Simone Kresevic, Muro Giuffre, et al.; Optimization of hepatological clinical guidelines interpretation by LLMs: a retrieval augmented generation-based framework; npj digital medicine; 23 April 2024, 7:102; p 1-7) teaches LLM that incorporates RAG and prompt engineering to provide the most accurate output, including use of guidelines for generating output of LLM.
Laugerette (DE102024209358A1) teaches generating query messages with prompts to be input into an LLM to determine information from medical information.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Evangeline Barr whose telephone number is (571)272-0369. The examiner can normally be reached Monday to Friday 8:00 am to 4:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fonya Long can be reached at 571-270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/EVANGELINE BARR/Primary Examiner, Art Unit 3682
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