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 .
Response to Amendment
The amendment filed January 7, 2026 has been entered. Claims 1-20 remain pending in the application. Applicant’s amendments to the Claims have overcome each and every objections previously set forth in the Non-Final Office Action mailed October 8, 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under the Step 1 of the Section 101 analysis, Claims 1-10 are drawn to a system which is within the four statutory categories (i.e. a machine), and Claims 11-20 are drawn to a method which is within the four statutory categories (i.e., a process).
Since the claims are directed toward statutory categories, it must be determined if the claims are directed towards a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea). Based on consideration of all of the relevant factors with respect to the claim as a whole, claims 1-20 are determined to be directed to an abstract idea. The rationale for this determination is explained below:
Regarding Claims 1 and 11:
Claims 1 and 11 are drawn to an abstract idea without significantly more. The claims recite “retrieve a plurality of data from one or more data sources; perform, using an adjudication and reasoning engine, adjudication with respect to the plurality of data and using at least one machine learning model; output adjudication results from the at least one machine learning model; generate a validity block based on the adjudication results; append the validity block to a blockchain; receive results of one or more hybrid transactions stored in a transaction repository using the plurality of data; determine, using the adjudication and reasoning engine, a proof of achievement score based on the results of the one or more hybrid transactions; manipulate the plurality of data by at least one of a transformation, a filter, a modification, or a standardization; train the adjudication and reasoning engine using the proof of achievement score and ground truth data associated with the proof of achievement score; perform analysis and adjudication using the transaction repository; and determine and output a proof of acknowledgement result based on the analysis and adjudication.”
Under the Step 2A Prong One, the limitations, as underlined above, are processes that, under its broadest reasonable interpretation, cover Certain Methods Of Organizing Human Activity such as commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). For example, but for the “adjudication and reasoning engine”, “machine learning model”, “block”, “blockchain”, “repository”, and “smart contract” language, the underlined limitations in the context of this claim encompass the human activity or mental processes. The series of steps belong to a typical sales activities or behaviors, because data or information such as adjudication results, validity, and proof of achievement score are exchanged and processed for transactions.
Under the Step 2A Prong Two, this judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – “A system comprising: at least one processing device configured to:”, “A method comprising:”, “adjudication and reasoning engine”, “machine learning model”, “block”, “blockchain”, “repository”, and “smart contract”. The additional elements are recited at a high-level of generality (i.e., performing generic functions of an interaction) such that it amounts no more than mere instructions to apply the exception using a generic computer component, merely implementing an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. Additionally, regarding the specification and claims, there is no improvement in the functioning of a computer or an improvement to other technology or technical field present, there is no applying or using the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition present, there is no implementing the judicial exception with or using the judicial exception in conjunction with a particular machine or manufacture that is integral to the claim present, there is no effecting a transformation or reduction of a particular article to a different state or thing present, and there is no applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment present such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Accordingly, these additional elements, individually or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Under the Step 2B, 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 in the process amounts 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.
Regarding Claims 2-10 and 12-20:
Dependent claims 5 and 15 only further elaborate the abstract idea and do not recite additional elements.
Dependent claims 2-4, 6-10, 12-14, and 16-20 include additional limitations, for example, “processing device” and “digital signature” (Claims 2 and 12); “processing device” and “repository” (Claims 3 and 13); “processing device”, “machine learning model”, and “system” (Claims 4 and 14); “processing device” (Claims 6 and 16); “smart contract information” (Claims 7 and 17); “smart contract information” and “block” (Claims 8 and 18); “processing device” and “link” (Claims 9 and 19); and “processing device”, “repository”, and “smart contract” (Claims 10 and 20), but none of these limitations are deemed significantly more than the abstract idea because, as stated above, they require no more than generic computer structures or signals to be executed, and do not recite any Improvements to the functioning of a computer, or Improvements to any other technology or technical field.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Furthermore, 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 a computer or improves any other technology, and their collective functions merely provide conventional computer implementation or implementing the judicial exception on a generic computer.
Therefore, whether taken individually or as an ordered combination, claims 2-10 and 12-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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.
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.
Claim(s) 1-3, 5-13, and 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wince (US 11748818 B1) in view of Kapur (US 20240214194 A1).
Regarding Claims 1 and 11, Wince teaches A system comprising: at least one processing device configured to (Wince: Col. 22, lines 46-60): A method comprising (Wince: 22/46-60):
retrieve a plurality of data from one or more data sources (Wince: 2/58-65 teach(es) retrieving historical transaction data from the immutable ledger of the PBN); perform, using an adjudication and reasoning engine, adjudication with respect to the plurality of data and using at least one machine learning model (Wince: Abstract; 2/58-65; 3/29-35 & 47-50 teach(es) identifying a smart contract associated with the query and defined to automatically adjudicate the query; training a propensity-to-pay machine learning model using the historical transaction data; identifying, by the first peer, a smart contract associated with the query, the smart contract having an endorsement policy and a chaincode defined by the second organization to automatically adjudicate the query); output adjudication results from the at least one machine learning model (Wince: 3/47-50 teach(es) automatically adjudicating the query by executing the chaincode on a second peer, operating on the required information to assign a value to the determination); generate a validity block based on the adjudication results; append the validity block to a blockchain (Wince: 3/43-57 teach(es) updating the immutable ledger with the determination after validating the satisfaction of the endorsement policy, and sending to the client device the determination); receive results of one or more hybrid transactions stored in a transaction repository using the plurality of data (Wince: 2/36-43; 3/43-50 teach(es) The method for healthcare revenue cycle management includes receiving, from each of the at least one endorsing peers, an endorsed proposed transaction response as a result of the smart contract invocation); determine, using the adjudication and reasoning engine, a proof of achievement … based on the results of the one or more hybrid transactions (Wince: 2/47-50; 3/50-57 teach(es) updating the immutable ledger with the determination after validating the satisfaction of the endorsement policy, as well as sending to the client device the determination); manipulate the plurality of data by at least one of a transformation, a filter, a modification, or a standardization (Wince: 19/53-63; 19/64 ~ 20/10 teach(es) The immutable historical transaction data can provide insights into the healthcare revenue cycle that were previously obscured by the lack of trust and uniformity that prevented anyone from seeing the big picture; the historical transaction data may be passed through some sort of cleansing mechanism (i.e., at least one of a transformation, a filter, a modification, or a standardization), such as a specially configured data aggregator, which obfuscates sensitive private data to remain in compliance with privacy laws); train the adjudication and reasoning engine using the proof of achievement … and ground truth data associated with the proof of achievement … (Wince: 19/53-63 teach(es) the historical transaction data (i.e., proof of achievement and associated ground truth data) may have new applications in training machine learning models, as well as oversight of the revenue cycle itself, and all of the players within); perform analysis and adjudication using the transaction repository; and determine and output a proof of acknowledgement result based on the analysis and adjudication (Wince: Abstract; 2/14-28; 3/1-19 teach(es) identifying a smart contract associated with the query and defined to automatically adjudicate the query. The method also includes invoking the smart contract in at least one endorsing peer, and receiving, from each endorsing peer, a proposed transaction response. The method further includes automatically adjudicating the query by executing chaincode on a second peer, operating on the required information to assign a value to the determination, then updating the immutable ledger).
However, Wince does not explicitly teach achievement score.
Kapur from same or similar field of endeavor teaches achievement score (Kapur: Paragraph(s) 0393-0394, 0402-0404 teach(es) Such proofs may be associated with a reputation score indicating the trustworthiness of the prover, which may include one or more endorsements, a score indicating the rate of complaints, a reference to a bond that parties may obtain payment from when they file a complaint that becomes adjudicated in their favor, etc. Such scores may, additionally or alternatively, be part of a positive proof that refers to the user that generates the proof).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Wince to incorporate the teachings of Kapur for achievement score.
There is motivation to combine Kapur into Wince because Kapur’s teachings of reputation/certainty score would facilitate handling assertions (Kapur: Paragraph(s) 0402-0404).
Regarding Claims 2 and 12, the combination of Wince and Kapur teaches all the limitations of claims 1 and 11 above; and Wince further teaches wherein, to perform the adjudication, the at least one processing device is further configured to obtain a digital signature and associate the digital signature with the adjudication results (Wince: 19/3-14 teach(es) Once the orderer has determined that the smart contract has been fulfilled (i.e. all the organizations indicated by the endorsement policy have endorsed, and the signatures have been validated with the certificate authorities), the orderer puts the transaction in sequence to be added to the ledger).
Regarding Claims 3 and 13, the combination of Wince and Kapur teaches all the limitations of claims 1 and 11 above; and Wince further teaches wherein, to perform the adjudication, the at least one processing device is further configured to: retrieve evaluation metrics from a metrics repository; and apply the evaluation metrics to the adjudication to affect the adjudication results (Wince: 3/60-67 & 43-57 teach(es) Invoking the smart contract on the at least one endorsing peer may result in a first endorsing peer retrieving a data object from a third-party server that is outside of the PBN using a data aggregator to transform the data object into a format compatible with the PBN).
Regarding Claims 5 and 15, the combination of Wince and Kapur teaches all the limitations of claims 1 and 11 and achievement score above; and Wince further teaches wherein the at least one processing device is further configured to: provide based on the proof of achievement …, one or more recommendations (Wince: 19/53-63 teach(es) the historical transaction data may have new applications in training machine learning models, as well as oversight of the revenue cycle itself, and all of the players within).
Regarding Claims 6 and 16, the combination of Wince and Kapur teaches all the limitations of claims 1 and 11 above; however the combination does not explicitly teach wherein the at least one processing device is further configured to: determine, based on the proof of achievement score, that a consensus is achieved; and transmit an achievement notification indicating the achievement of the consensus.
Kapur further teaches wherein the at least one processing device is further configured to: determine, based on the proof of achievement score, that a consensus is achieved; and transmit an achievement notification indicating the achievement of the consensus (Kapur: Paragraph(s) 0100, 0102, 0104 teach(es) any of a variety of consensus mechanisms may be used by public blockchains, including but not limited to Proof of Space mechanisms, Proof of Work mechanisms, Proof of Stake mechanisms, and hybrid mechanisms).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the combination of Wince and Kapur to incorporate the teachings of Kapur for wherein the at least one processing device is further configured to: determine, based on the proof of achievement score, that a consensus is achieved; and transmit an achievement notification indicating the achievement of the consensus.
There is motivation to combine Kapur into the combination of Wince and Kapur because Kapur’s teachings of consensus mechanisms would facilitate validating transactions (Kapur: Paragraph(s) 0104).
Regarding Claims 7 and 17, the combination of Wince and Kapur teaches all the limitations of claims 1 and 11 above; and Wince further teaches wherein the plurality of data from the one or more data sources include smart contract information concerning agreements between one or more parties (Wince: 15/44-54 teach(es) The chain execution of smart contracts is advantageous in that gives each organization more control over how it plays its part in the healthcare revenue cycle. For example, a payer may have defined a smart contract for when, in response to a proposed medication prescribed by a provider, an alternative medication is preferred by the payer, and the payer is seeking agreement from all interested providers (e.g. the hospital, the pharmacy, etc.)).
Regarding Claims 8 and 18, the combination of Wince and Kapur teaches all the limitations of claims 7 and 17 above; and Wince further teaches wherein the smart contract information includes one or more of a healthcare token, a contractual block identifier, or a validity requirement parameter (Wince: 13/61-64 teach(es) the client device may prompt an individual for additional information if the client device is aware of the particulars for the smart contract that will be executed in response to this type of transaction proposal).
Regarding Claims 9 and 19, the combination of Wince and Kapur teaches all the limitations of claims 1 and 11 above; however the combination does not explicitly teach wherein the at least one processing device is further configured to provide incentive alignment using the proof of acknowledgement result, including creating a link between a determined performance metric and one or more rewards and penalties.
Kapur further teaches wherein the at least one processing device is further configured to provide incentive alignment using the proof of acknowledgement result, including creating a link between a determined performance metric and one or more rewards and penalties (Kapur: Paragraph(s) 0265 teach(es) an abuse may have to take place, e.g., by malware, on a device associated with a person performing a biometric authentication attempt, and the risks may be limited to the leakage of sensitive data related to this person's PII. This may align incentives in a way that avoids risks of the type that typically are associated with brute-force attacks on other user's sensitive data, e.g., following a breach).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the combination of Wince and Kapur to incorporate the teachings of Kapur for wherein the at least one processing device is further configured to provide incentive alignment using the proof of acknowledgement result, including creating a link between a determined performance metric and one or more rewards and penalties.
There is motivation to combine Kapur into the combination of Wince and Kapur because Kapur’s teachings of aligning incentives would facilitate avoiding risks of the leakage of sensitive data (Kapur: Paragraph(s) 0265).
Regarding Claims 10 and 20, the combination of Wince and Kapur teaches all the limitations of claims 1 and 11 above; and Wince further teaches wherein the at least one processing device is further configured to: monitor data and transactions related to the transaction repository and at least one smart contract; determine fraudulent activity is detected and issue a fraud warning to a controlling party in response; and provide one or more reports on the data and transactions, wherein the one or more reports include at least one of patterns, trends, and improvements data (Wince: 20/20-33 teach(es) a peer within an organization (here, the manager organization) is communicatively coupled to a database having the ledger as well as to a watchdog system configured to compare the global state with the historical transaction data to identify an unwelcome action. Unwelcome actions can include, but are not limited to, insurance fraud, doctor shopping, over-prescription of a pharmaceutical or class of pharmaceuticals, waste due to poorly defined smart contracts, and the like. In some embodiments, the watchdog system may employ machine learning to identify the unwelcome actions).
Claim(s) 4 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wince (US 11748818 B1) in view of Kapur (US 20240214194 A1), as applied to claims 1 and 11 above, and in further view of Cella (WO 2022133330 A1).
Regarding Claims 4 and 14, the combination of Wince and Kapur teaches all the limitations of claims 1 and 11 above; however the combination does not explicitly teach wherein the at least one processing device is further configured to: perform a determination to train the at least one machine learning model based on expert feedback; retrieve the expert feedback stored in the system; and fine-tune the at least one machine learning model based on the retrieved expert feedback.
Cella from same or similar field of endeavor teaches wherein the at least one processing device is further configured to: perform a determination to train the at least one machine learning model based on expert feedback; retrieve the expert feedback stored in the system; and fine-tune the at least one machine learning model based on the retrieved expert feedback (Cella: Paragraph(s) 0029 teach(es) the machine learning service is trained with training data sets includes human-generated feedback on job content parsing results for a plurality of job requests, robot automation knowledge bases, desired job- specific knowledge bases, technical dictionaries, and content received from job experts).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the combination of Wince and Kapur to incorporate the teachings of Cella for wherein the at least one processing device is further configured to: perform a determination to train the at least one machine learning model based on expert feedback; retrieve the expert feedback stored in the system; and fine-tune the at least one machine learning model based on the retrieved expert feedback.
There is motivation to combine Cella into the combination of Wince and Kapur because Cella’s teachings of feedback would facilitate training of machine learning model (Cella: Paragraph(s) 0029).
Response to Arguments
Applicant's arguments filed January 7, 2026 have been fully considered but they are not persuasive.
Regarding applicant’s argument under Claim Rejections - 35 USC § 101 that “Nothing in Claim 1 pertains to organizing the activity of humans, but, rather, Claim 1 is directed to manipulation and verification of data (adjudication, proof of achievement, and proof of acknowledgement) on a blockchain and training of machine learning models,” examiner respectfully argues that manipulation and verification of data can be performed manually by people, and the additional elements such as blockchain and machine learning models are recited without technical details and contexts enough to provide any improvements to the functioning of the computer or other technology or technical field.
Regarding applicant’s argument under Claim Rejections - 35 USC § 103 that “First, nothing in this cited portion of Wince discloses or suggests manipulating data by at least one of a transformation, a filter, a modification, or a standardization. Moreover, nothing in this cited portion of Wince discloses or suggests that a machine learning model is trained using a proof of achievement score, nor anything regarding use of ground truth data associated with the proof of achievement score. The other cited references do not cure these deficiencies of Wince,” examiner respectfully argues that the combination of Wince and Kapur teaches the features. It is recommended for the applicant to amend the claims with more technical details and contexts of the steps of manipulating and training.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Downs (US 12299751 B1) teaches System And Method For Rules-driven Adjudication, including adjudication and dispute.
Chen (EP 4310763 A1) teaches Secure And Trustworthy Crossing Network For Transferring Assets Outside Of Exchange, including adjudicate, pattern, keys, and trust.
THIS ACTION IS MADE FINAL. 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 CLAY LEE whose telephone number is (571)272-3309. The examiner can normally be reached Monday-Friday 8-5pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Neha Patel can be reached at (571)270-1492. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CLAY C LEE/Primary Examiner, Art Unit 3699
Prosecution Insights