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 amendment filed 5/1/26. Claims 1 and 11 have been amended. Claims 3-10 and 13-20 are cancelled. Claims 21-30 are newly added. Claims 1, 2, 11, 12, and 21-30 are pending.
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, 11, 12, and 21-30 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 26-30 are directed to a method (i.e., a process) and claims 1, 2, and 21-25 are directed to a system (i.e., a machine). Accordingly, claims 1, 2, 11, 12, and 21-30 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 patient data and one or more claims associated with a selected healthcare provider, the patient data comprising patient enrollment data and demographic data associated with patients associated with the one or more claims, 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, a corresponding diagnosis code, a billed amount, and one of a claim service date and a claim submission date;
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;
marking each of the flagged one or more claim lines with an indication of the one or more of the hard and soft rules violated by the flagged claim line; 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 using the one of the claim service date and the claim submission date to generate provider profiling counters for the selected healthcare provider;
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 by 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;
executing a claim evaluator model on the raw claims data, the claim evaluator model comprising a supervised neural network trained on labelled claims data including procedure codes, diagnosis codes, and known claim outcomes,
the supervised neural network generating, for each of the one or more claims, a denial prediction output;
determining, by the claim evaluator model, a second score for the selected healthcare provider by combining, for each of the one or more claims, the 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, the relevancy index score being determined from historic claims data based on one or more of a number of providers, an overall number of claims, and a number of patients that include a procedure-code/diagnosis-code pairing;
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, including generating each edge between a respective pair of the nodes in response to determining, from the raw claims data including the patient data, that a same patient visited two healthcare providers represented by the respective pair of nodes within a thirty (30) day period, each respective edge functioning as a proxy for a provider-to- provider referral;
providing 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, the one or more features comprising provider-level features generated by aggregating claim-level features including overall utilization variables, velocity variables, and domain-specific variables;
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 patient data and one or more claims associated with a selected healthcare provider, the patient data comprising patient enrollment data and demographic data associated with patients associated with the one or more claims, 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, a corresponding diagnosis code, a billed amount, and one of a claim service date and a claim submission date; 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; marking each of the flagged one or more claim lines with an indication of the one or more of the hard and soft rules violated by the flagged claim line; 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 using the one of the claim service date and the claim submission date; 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 by 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; claims data including procedure codes, diagnosis codes, and known claim outcomes, generating, for each of the one or more claims, a denial prediction output; determining a second score for the selected healthcare provider by combining, for each of the one or more claims, the 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, the relevancy index score being determined from historic claims data based on one or more of a number of providers, an overall number of claims, and a number of patients that include a procedure-code/diagnosis-code pairing; constructing a provider-to-provider referral graph; determining, from the raw claims data including the patient data, that a same patient visited two healthcare providers within a thirty (30) day period, each respective edge functioning as a proxy for a provider-to- provider referral; the one or more features comprising provider-level features generated by aggregating claim-level features including overall utilization variables, velocity variables, and domain-specific variables; 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, profiling counters, 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, marking data, generating 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, 12, and 21-30 are ultimately dependent from Claim(s) 1 and 11 and include all the limitations of Claim(s) 1 and 11. Therefore, claim(s) 2, 12, and 21-30 recite the same abstract idea. Claims 2, 12, and 21-30 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; determining a provider entropy index; generating a phantom billing index by segmenting frequently co-occurring diagnosis codes and procedure codes in claims associated with the selected healthcare provider; creating, at a claim level, sequences of diagnosis codes and procedure codes and randomly permuting the diagnosis codes and procedure codes in the sequences to generate multiple sequences; generating the phantom billing index comprising generating final sequences by distributing a healthcare provider identifier for the selected healthcare provider between every two codes of the multiple sequences; generating the phantom billing index comprising passing the final sequences through a word2vec model to obtain procedure and diagnosis representations in a same embedding space and segmenting the procedure and diagnosis representations to generate clusters of co-occurring procedure codes and diagnosis codes. These are all just further describing the abstract idea recited in claims 1 and 11 or “mathematical concepts,” 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, 11, 12, and 21-30 are ineligible under 35 USC §101.
Claim Objections
Claims 1 and 11 are objected to because of the following informalities: change “…trained on historic claims data…” to “…trained on the historic claims data….” Appropriate correction is required.
Claims 23 and 28 are objected to because of the following informalities: change “…sequences of diagnosis codes and procedure codes…“ to “…sequences of the diagnosis codes and procedure codes….” Appropriate correction is required.
Response to Arguments
Applicant's arguments filed 5/1/26 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 5/1/26.
(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 101 rejection above constitute “a mental process” because receiving raw claims data the raw claims data including patient data and one or more claims associated with a selected healthcare provider, the patient data comprising patient enrollment data and demographic data associated with patients associated with the one or more claims, 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, a corresponding diagnosis code, a billed amount, and one of a claim service date and a claim submission date; 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; marking each of the flagged one or more claim lines with an indication of the one or more of the hard and soft rules violated by the flagged claim line; 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 using the one of the claim service date and the claim submission date; 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 by 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; claims data including procedure codes, diagnosis codes, and known claim outcomes, generating, for each of the one or more claims, a denial prediction output; determining a second score for the selected healthcare provider by combining, for each of the one or more claims, the 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, the relevancy index score being determined from historic claims data based on one or more of a number of providers, an overall number of claims, and a number of patients that include a procedure-code/diagnosis-code pairing; constructing a provider-to-provider referral graph; determining, from the raw claims data including the patient data, that a same patient visited two healthcare providers within a thirty (30) day period, each respective edge functioning as a proxy for a provider-to- provider referral; the one or more features comprising provider-level features generated by aggregating claim-level features including overall utilization variables, velocity variables, and domain-specific variables; 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.
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, marking data, generating 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.
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).
Furthermore, the arguments regarding profile counters is not persuasive because there is no active step of generating the profile counters and the Applicant appears to argue features from paragraph 62 that have not been claimed. In addition, the claims do not recite how the claim lines are marked. Applicant’s arguments regarding Example 47 are not persuasive because Example 47’s claims are not analogous to Applicant’s claims. Note that Example 47’s claim 1 was eligible because it did not recite an abstract idea and claim 3 was eligible because it provided specific computer solutions of dropping malicious network packets and blocking future traffic without the need for any action by a network administrator.
Regarding BASCOM, the court agreed that the additional elements were generic computer, network, and Internet components that did not amount to significantly more when considered individually, but explained that the district court erred by failing to recognize that when combined, an inventive concept may be found in the non-conventional and non-generic arrangement of the additional elements, i.e., the installation of a filtering tool at a specific location, remote from the end-users, with customizable filtering features specific to each end user. It is unclear what is non-conventional and non-generic about the arrangement of the additional elements in Applicant’s case.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 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
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