DETAILED ACTION
Status of Claims
This communication is in response to the Request for Continued Examination filed 01/16/2026.
Claims 1, 7-8 and 14-15 have been amended.
Claims 1-20 are currently pending and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/16/2026 has been entered.
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 .
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 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-7 are directed to a method (i.e., a process), claims 8-14 are directed to a system (i.e., a machine) and claim 15-20 are directed to non-transitory computer readable medium (i.e., a manufacture). Accordingly, claims 1-20 are all within at least one of the four statutory categories.
Step 2A - Prong One:
An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Representative independent claim 8 includes limitations that recite an abstract idea. Note that independent claim 8 is the system claim, while claim 1 covers a method claim and claim 15 covers the matching computer readable medium.
Specifically, independent claim 15 recites:
A computer product comprising a non-transitory computer readable medium storing a plurality of instructions for controlling a computer system to perform a method of providing an interactive medical guideline, the method comprising:
accessing a plurality of textual medical records associated with a patient user;
training a machine learning model to identify diagnosed conditions in unstructured attending physician statement documents based on a database comprising the unstructured attending physician statement documents and structured electronic health records corresponding to the unstructured attending physician statement documents retrieved from a set of medical history databases;
training the machine learning model to identify inconsistencies in the plurality of textual medical records by identifying the diagnosed conditions in the plurality of textual medical records and comparing the diagnosed conditions identified in the plurality of textual medical records for differences;
accessing a trained large language model (LLM);
fine-tuning the LLM using a medical database to understand medical terminology in the plurality of textual medical records;
extracting, using the LLM, a set of risk appetite rules and parameters specific to a client system based on an underwriting manual provided by the client system, the set of risk appetite rules and parameters comprising weight values for the plurality of textual medical records for automatic resolution of the inconsistencies;
applying the set of risk appetite rules and parameters to the machine learning model to customize the machine learning model to a risk appetite associated with the client system and improve an accuracy of the machine learning model for the risk appetite associated with the client system;
for each textual medical record of the plurality of textual medical records:
extracting data from the textual medical record;
comparing the extracted data to a database of medical data using the machine learning model to identify the inconsistencies; and
automatically resolving, using the machine learning model, at least one inconsistency of the inconsistencies based on the set of risk appetite rules and parameters by applying a first value from a first textual medical record of the plurality of textual medical records in place of a second value from a second textual medical record, the first textual medical record having a first weight value higher than a second weight value of the second textual medical record;
identifying a set of inconsistencies between the extracted data and the database of medical data that are not automatically resolved by the machine learning model;
based on the identified set of inconsistencies, automatically generating a textual summary of the identified set of inconsistencies using the LLM;
based on an application of the patient user, automatically generating text summaries of the application corresponding with categories of patient information in the application using the LLM;
causing display of an interface comprising a set of selectable elements corresponding with the identified set of inconsistencies, a set of scrollable elements corresponding with the categories of patient information, and the generated textual summary of the identified set of inconsistencies, each scrollable element displaying a scrollable text summary of a corresponding category of patient information generated using the LLM;
in response to a first selection of a first selectable element of the set of selectable elements, causing display of a first textual medical record with a first highlighted portion and a second textual medical record with a second highlighted portion in the interface, the first highlighted portion and the second highlighted portion corresponding with a first identified inconsistency of the identified set of inconsistencies, the first identified inconsistency corresponding with the first selectable element; and
in response to a second selection of a first scrollable element of the set of scrollable elements, causing display of a highlighted portion of the application related to a first category of patient information, the first category of patient information corresponding with the first scrollable element.
The Examiner submits that the foregoing underlined limitations constitute: (a) “certain methods of organizing human activity” because accessing medical records to patients, extracting data from textual medical records, comparing the extracted data to medical data in a database, determining inconsistencies, extracting a set of appetite rules and parameters, improving an accuracy for the set of appetite rules and generating a textual summary of the identified inconsistencies are a part of a medical insurance workflow process, which relate to managing human behavior/interactions between people. Furthermore, these limitations constitute (b) “a mental process” because identifying inconsistencies by highlighting identified inconsistency of the identified set of inconsistencies is an observation/evaluation/analysis that can be performed in the human mind or with a pen and pencil. The foregoing underlined limitations also relate to claim 8 (similarly to claims 1 and 15).
Accordingly, the claim describes at least one abstract idea.
In relation to claims 2, 5, 9, 12, 16 and 19, these claims merely recite specific kinds of input data, such as: claims 2, 9 and 16 - the plurality of textual medical records comprise structured data and unstructured data and claims 5, 12 and 19 - extracting the medical data from the textual medical record using a trained natural language processing machine learning model or a trained optical character recognition machine learning model trained to analyze the textual medical record.
In relation to claims 3-4, 7, 10-11 and 17-18, these claims merely recite being associated or relating to data, such as: claims 3, 10 and 17 - the plurality of textual medical records are accessed from a medical records table based on the application of the patient user, claims 4, 11 and 18 - the identified set of inconsistencies are related to insurance underwriting decisions associated with the application of the patient user and claim 7 - automatically resolving a discrepancy between a third textual medical record and a fourth, textual medical record by prioritizing the third textual medical record based on a first weight value, associated with the third textual medical record being higher than a second weight value associated with the fourth textual medical record.
In relation to claims 5-6, 12 and 19, these claims merely recite determining steps such as: claims 5, 12 and 19 - the method further comprising: extracting the medical data from the textual medical record using a trained machine learning model trained to analyze the textual medical record and claim 6 - first scrollable element of the set of scrollable elements displays a first scrollable text summary of the first category patient information, the first. scrollable text summary generated using the LLM based on the highlighted portion of the application.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The limitations of claims 1, 8 and 15, as drafted is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting a system comprising a processor, a memory storing instructions that, when executed by the processor, an interface and a database to perform the limitations, nothing in the claim elements precludes the steps from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation within a health care environment in the mind but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” and “Mental Process” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the system comprising a processor, a memory storing instructions that, when executed by the processor, interface and a database are recited at high levels of generality (i.e., as generic computer components performing generic computer functions of receiving data/inputs, determining and providing data) such that it amounts no more than mere instructions to apply the exception using the generic computer components.
Regarding the additional limitations “accessing a trained large language model (LLM)”, “training a machine learning model to identify inconsistencies in the plurality of textual medical records using a database comprising unstructured attending physician statement documents, and structured electronic health records retrieved from a set of medical history databases”, “using the machine learning model to identify inconsistencies..” and “the application using the LLM” the Examiner submits that this additional limitation amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)).
Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvements in the functioning of a computer or an improvement to another technology or technical field, apply or us the above-noted implement/use to above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (see 2019 PEG and MPEP §2106.05). Their collective functions merely provide conventional computer implementation.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer component provide an inventive concept. The claims are not patent eligible.
Step 2B:
Regarding Step 2B, in representative independent claim 8, regarding the additional limitations of the system comprising a processor, a memory storing instructions that, when executed by the processor, interface and a database, the Examiner submits that these limitations amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)).
Thus, representative independent claim 8 and analogous independent claims 1 and 15 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
The dependent claims no not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reason discussed above with respect to determining that the dependent claims do not integrate the at least abstract idea into a practical application.
Therefore, claims 1-20 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 112, 1st Paragraph
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 8 and 15, recite “extracting, using the LLM, a set of risk appetite rules and parameters specific to a client system based on an underwriting manual provided by the client system, the set of risk appetite rules and parameters comprising weight values for the plurality of textual medical records for automatic resolution of the inconsistencies;” and “applying the set of risk appetite rules and parameters to the machine learning model to customize the machine learning model to a risk appetite associated with the client system and improve an accuracy of the machine learning model for the risk appetite associated with the client system,” but does not specifically disclose how the machine learning extracts content from an underwriting manual or how risk appetite rules and parameters are applied to a machine learning model, so that a person of ordinary skill in the art could recognize that the applicant had possession of the claimed invention and actually invented how data is trained in a machine learning process. Applicant’s specification only makes broad and general statements about machine learning models to include natural language processing (NLP) models and optical character recognition (OCR) models. The specification must describe the claimed invention in a manner understandable to a person of ordinary skill in the art and show that the inventor actually invented the claimed invention. The specification does not reasonably describe how the physical location of a patient is determined in a manner understandable to a person of ordinary skill in the art and show that the inventor had possession of the claimed invention. For example, the specification in ¶0045-¶0047, talks about the NLP and OCR models can be pre-trained models that are trained on labeled medical records datasets. Using NLP and OCR models does not describe to one of ordinary skill in the art of what algorithms, specific analysis, recognition or extractions are to be used on what specific pre-trained model. The applicant only broadly defines a machine learning process, as claimed.
Claims 2-7 incorporate the deficiencies of claim 1, through dependency, and are therefore also rejected.
Claims 9-14 incorporate the deficiencies of claim 8, through dependency, and are therefore also rejected.
Claims 16-20 incorporate the deficiencies of claim 15, through dependency, and are therefore also rejected.
Response to Arguments
Applicant alleges that the claims merely involve rather than recite an abstract idea and fall into the Example 39 category. see pgs. 11-12 of Remarks – Examiner disagrees.
Applicant has not supplied evidence of such neural network classifiers, trained data sets, criteria for detecting patterns, backpropagation, a gradient of a mathematical loss function to adjust weights in a neural network and any other features that has to do with Example 39 or machine learning. Identifying inconsistencies in attending physician statement notes and textual medical records, extracting rules form underwriting manual, applying underwriting risk rules, resolving inconsistencies and generating a text summary in an insurance underwriting process are healthcare administrative tasks that don’t need machine learning to perform.
Applicant alleges that the claims reflect technological improvements, and avoid the “Apply It” consideration and are close call standard favor eligibility. see pgs. 12-13 of Remarks – Examiner disagrees.
Applicant has not supplied evidence of such building a machine learning model, processing of dynamic and static data, software recognition, a machine learning process where learning or training takes place and the functioning of electronic health record systems in the form of a technical improvement provided by the instant claims, and merely asserts that the amended claims explicitly recite technological improvements. The Examiner also maintains that applying or referring to an appetite rule accustomed to a customer's underwriting manual is a non-technical problem that has already been solved and does not amount to a technical improvement to computer functions. According to paragraph 79 of Applicant’s own specification, hardware component comprises a general-purpose processor configured by software to become a special-purpose processor, the general-purpose processor may be configured as respectively different special-purpose processors (e.g., comprising different hardware components) at different times. Here, the computer processing is accomplished using conventional technology and is not significantly more than an abstract idea.
Applicant alleges that the claims the claims must be analyzed as a whole. see pg. 12-13 of Remarks – Examiner disagrees.
The claimed steps of selecting data, scrolling highlighted information, and prioritizing textual medical records are generally standard data processing tasks that are automated, not innovations in how computers or algorithms operate. When considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Applicant’s amendments and arguments, see page 17-18, filed 01/16/2026, with respect to 103 rejections have been fully considered and are persuasive. The 103 rejections of claims 10-20 have been withdrawn.
Claims 1-20 closely relate to Mariappan (US 2024/0395369 A1), Ginsburg (US 2020/0294640 A1) and Kukreja (US 2021/0334462 A1), which discloses identifying inconsistencies in a medical record, text summary using large language model and resolving the identified inconsistencies. However, Mariappan, Ginsburg and Kukreja do not teach “extracting, using the LLM, a set of risk appetite rules and parameters specific to a client system based on an underwriting manual provided by the client system, the set of risk appetite rules and parameters comprising weight values for the plurality of textual medical records for automatic resolution of the inconsistencies”.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TERESA S WILLIAMS whose telephone number is (571)270-5509. The examiner can normally be reached Mon-Fri, 8:30 am -6:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached at (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/T.S.W./Examiner, Art Unit 3687 06/26/2026
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
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