DETAILED ACTION
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 .
Notice to Applicant
This communication is in response to the Request for Continued Examination (RCE) filed 1/20/26. Claims 1 and 11 have been amended. Claims 3, 5-10, 13, and 15-20 are cancelled. Claims 1, 2, 4, 11, 12, and 14 are pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/16/25 has been entered.
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, 2, 4, 11, 12, and 14 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 11, 12, and 14 are directed to a method (i.e., a process) and claims 1, 2, and 4 are directed to a system (i.e., a machine). Accordingly, claims 1, 2, 4, 11, 12, and 14 are all within at least one of the four statutory categories.
Step 2A - Prong One:
Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts.
Representative independent claim 11 includes limitations that recite at least one abstract idea. Specifically, independent claim 11 recites:
11. A computer-implemented method performed by a server, the method comprising:
receiving, via an online application-programming interface, raw claims data from one or more data sources, the raw claims data including one or more claims associated with a selected healthcare provider, each of the one or more claims including one or more claim lines, each of the one or more claim lines including a procedure code and a corresponding diagnosis code;
storing the raw claims data in a database managed by the server;
executing an SQL-based decision enhancer model on the raw claims data, comprising:
identifying, for the selected healthcare provider, each of the one or more claim lines that violate one or more of the following: a hard rule including National Correct Coding Initiative (NCCI) edits to detect mutually exclusive procedures and medically unlikely services; and a soft rule including one or more of upcoding and lab unbundling coding patterns;
flagging each of the identified one or more claim lines based on whether it violates one or more of the hard and soft rules; and
aggregating the flagged one or more claim lines with the one or more claims associated with the selected healthcare provider over a moving time window;
determining, by the decision enhancer model for the moving time window, a first score for the selected healthcare provider by: aggregating the flagged one or more claim lines;
aggregating the one or more claims lines; and comparing the aggregated flagged one or more claim lines to the aggregated one or more claims during the moving time window;
executing a claim evaluator model on the raw claims data, the claim evaluator model comprising a supervised neural network trained on labelled claims data to predict denial risk, the neural network consuming procedure codes, diagnosis codes, and known claim outcomes;
determining, by the claim evaluator model, a second score for the selected healthcare provider by combining, for each of the one or more claims, a denial prediction output of the claim evaluator model with a relevancy index score and aggregating the combinations to a provider level denial risk value, the relevancy index score based on a relevancy of the procedure code of the claim to the diagnosis code of the claim;
constructing, in system memory of the server, a provider-to-provider referral graph having nodes that represent respective healthcare providers and between two respective nodes, the edges functioning as a proxy for provider-to-provider referrals;
provide the provider-to-provider referral graph to a provider anomaly measure model comprising an unsupervised machine learning algorithm trained on historic claims data and one or more features of the historic claims data;
executing the provider anomaly measure model on the raw claims data;
determining, by the provider anomaly measure model, a third score for the selected healthcare provider based on the one or more claims, the third score including combining an autoencoder-based anomaly detection output, an isolation forest machine learning model output, and a generative adversarial network (GAN) based anomaly detection output; and
determining a final provider-level risk score for the selected healthcare provider by combining the first, second, and third scores.
The Examiner submits that the foregoing underlined limitations constitute “a mental process” because receiving raw claims data, the raw claims data including one or more claims associated with a selected healthcare provider, each of the one or more claims including one or more claim lines, each of the one or more claim lines including a procedure code and a corresponding diagnosis code; identifying, for the selected healthcare provider, each of the one or more claim lines that violate one or more of the following: a hard rule including National Correct Coding Initiative (NCCI) edits to detect mutually exclusive procedures and medically unlikely services; and a soft rule including one or more of upcoding and lab unbundling coding patterns; flagging each of the identified one or more claim lines based on whether it violates one or more of the hard and soft rules; and aggregating the flagged one or more claim lines with the one or more claims associated with the selected healthcare provider over a moving time window; determining for the moving time window, a first score for the selected healthcare provider by: aggregating the flagged one or more claim lines; aggregating the one or more claims lines; and comparing the aggregated flagged one or more claim lines to the aggregated one or more claims during the moving time window; to predict denial risk, consuming procedure codes, diagnosis codes, and known claim outcomes; determining a second score for the selected healthcare provider by combining, for each of the one or more claims, a denial prediction output of the claim evaluator model with a relevancy index score and aggregating the combinations to a provider level denial risk value, the relevancy index score based on a relevancy of the procedure code of the claim to the diagnosis code of the claim; constructing a provider-to-provider referral graph; determining a third score for the selected healthcare provider based on the one or more claims, the third score including combining outputs; and determining a final provider-level risk score for the selected healthcare provider by combining the first, second, and third scores amount to observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind or via pen and paper.
Accordingly, the claim recites at least one abstract idea.
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. 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 and 11, 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 server, one or more data sources, a server system, a processor, a memory element, a database, system memory, and an online application-programming interface used 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 in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the server, one or more data sources, server system, processor, memory element, database, system memory, and online application-programming interface are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of receiving data, storing data, executing models, identifying data, flagging data, aggregating data, comparing data, determining data, constructing data, providing data, and performing calculations) such that it amounts no more than mere instructions to apply the exception using generic computer components. The limitations regarding nodes, a supervised neural network trained, an unsupervised machine learning algorithm trained, and outputs from an isolation forest machine learning model and generative adversarial network (GAN) are merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). These limitations merely confine the use of the abstract idea to a particular technological environment (e.g., neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). 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.
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 adds 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 improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the 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 other 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 MPEP § 2106.05). Their collective functions merely provide conventional computer implementation.
Claims 2, 4, 12, and 14 are ultimately dependent from Claim(s) 1 and 11 and include all the limitations of Claim(s) 1 and 11. Therefore, claim(s) 2, 4, 12, and 14 recite the same abstract idea. Claims 2, 4, 12, and 14 describe further limitations regarding the hard rule being devised by the Center for Medicare and Medicaid Services (CMS) and the soft rule not being explicitly devised by the CMS; and dividing a total billed amount for the aggregated flagged one or more claim lines in the moving time window by a total billed amount of the one or more claims associated with the selected healthcare provider in the moving time window. These are all just further describing the abstract idea recited in claims 1 and 11, without adding significantly more.
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 integration of the abstract idea into a 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 a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Step 2B:
Regarding Step 2B, independent claims 1 and 11 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 reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Regarding the additional limitations directed to the processor receiving raw claims data from one or more data sources via an interface, storing data in the database, and models determining scores, all of which the Examiner submits merely add insignificant extra-solution activity to the abstract idea or are claimed in a merely generic manner (e.g., at a high level of generality), the Examiner further submits that such steps are not unconventional as they merely consist of receiving and transmitting data over a network, storing and retrieving information in memory, and performing repetitive calculations. See MPEP 2106.05(d)(II).
The dependent claims 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 dependent claims do not integrate the at least one abstract idea into a practical application.
Therefore, claims 1, 2, 4, 11, 12, and 14 are ineligible under 35 USC §101.
Response to Arguments
Applicant's arguments filed 12/16/25 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 12/16/25.
(1) Applicant respectfully requests withdrawal of the 35 U.S.C. § 101 rejections.
(A) As per the first argument, see 101 rejection above. The Examiner submits that the foregoing underlined limitations in the rejection above constitute “a mental process” because receiving raw claims data, the raw claims data including one or more claims associated with a selected healthcare provider, each of the one or more claims including one or more claim lines, each of the one or more claim lines including a procedure code and a corresponding diagnosis code; identifying, for the selected healthcare provider, each of the one or more claim lines that violate one or more of the following: a hard rule including National Correct Coding Initiative (NCCI) edits to detect mutually exclusive procedures and medically unlikely services; and a soft rule including one or more of upcoding and lab unbundling coding patterns; flagging each of the identified one or more claim lines based on whether it violates one or more of the hard and soft rules; and aggregating the flagged one or more claim lines with the one or more claims associated with the selected healthcare provider over a moving time window; determining for the moving time window, a first score for the selected healthcare provider by: aggregating the flagged one or more claim lines; aggregating the one or more claims lines; and comparing the aggregated flagged one or more claim lines to the aggregated one or more claims during the moving time window; to predict denial risk, consuming procedure codes, diagnosis codes, and known claim outcomes; determining a second score for the selected healthcare provider by combining, for each of the one or more claims, a denial prediction output of the claim evaluator model with a relevancy index score and aggregating the combinations to a provider level denial risk value, the relevancy index score based on a relevancy of the procedure code of the claim to the diagnosis code of the claim; constructing a provider-to-provider referral graph; determining a third score for the selected healthcare provider based on the one or more claims, the third score including combining outputs; and determining a final provider-level risk score for the selected healthcare provider by combining the first, second, and third scores amount to observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind or via pen and paper. Accordingly, the claim recites at least one abstract idea. Furthermore, Applicant appears to argues features that have not been claimed (e.g., large scale data ingestion, time windowed aggregation across volumes of claim lines, etc.).
This judicial exception is not integrated into a practical application. In particular, the server, one or more data sources, server system, processor, memory element, database, system memory, and online application-programming interface are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of receiving data, storing data, executing models, identifying data, flagging data, aggregating data, comparing data, determining data, constructing data, providing data, and performing calculations) such that it amounts no more than mere instructions to apply the exception using generic computer components. 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. Applicant’s argument regarding improving the functioning of a claims processing system is not persuasive because the paragraphs of the Specification pointed to by Applicant do not explain a technical improvement. Note that paragraph 61, one of the paragraphs pointed to by Applicant, states that “the final provider-level risk score is a simple average of the three (3) scores.” In addition, a “more accurate, multi-dimensional risk assessment,” as argued, is not a technical improvement. As such, it is unclear how the ”functioning” of the system is improved.
Ex Parte Desjardins claims were directed to improvements in the machine learning technology itself and additionally included data structure elements reciting adjustments in values to plurality of performance parameters while preserving prior values. In Step 2A Prong Two, the ARP determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Applicant's limitations are not analogous because Applicant's limitations regarding nodes, a supervised neural network trained, an unsupervised machine learning algorithm trained, and outputs from an isolation forest machine learning model and generative adversarial network (GAN) are merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). These limitations merely confine the use of the abstract idea to a particular technological environment (e.g., neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LENA NAJARIAN whose telephone number is (571)272-7072. The examiner can normally be reached Monday - Friday 9:30 am-6 pm.
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/LENA NAJARIAN/Primary Examiner, Art Unit 3687