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
Applicant’s “Amendment” filed on 11/24/2025 has been considered.
Claims 22-23, 25, 31-32, 34, and 39-40 are amended. Claims 22-25, 31-34, and 39-41 remain pending in this application and an action on the merits follow.
Applicant’s response by virtue of amendment to claims has not overcome the Examiner’s rejection under 35 USC § 101.
Applicant’s response by virtue of amendment to claims has overcome the Examiner’s rejection under 35 USC § 112.
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 22-25, 31-34, and 39-41 are rejected under 35 USC 101. The claimed invention is directed to non-statutory subject matter because claims 22, 31, and 39 are directed to an abstract idea without significantly more. Claims 23-25, 32-34, and 40-41 fail to remedy these deficiencies.
Claims 22, 31,and 39 recite aggregating data, scrubbed the aggregated data, splitting the dataset, constructing, by the micro-loan system via execution of machine learning on the training dataset, a model, determining based on the human capital management information, a plurality of integrity scores, receiving a request for a loan transaction from, determining a risk score for the loan transaction, identifying, by the micro-loan system, a subset of the set of predicted lender-users, providing a template to receive an initial entry from the loan transaction, receiving one or more alternations to the transaction terms, generating a cryptographic signature for the loan transaction, recording the loan transaction and the transaction terms in a smart contract on a blockchain maintained by a network of computers, and transmitting a push event generated by the smart contract to a URL stored on the blockchain, the push event having instructions to automatically remit loan funds to the subset of the set of predicted lender-users in accordance with the transaction terms by: i) subtracting funds from one or more scheduled payroll payments to the first borrower-user, and ii) adding the subtracted funds to one or more scheduled payroll payments to the subset of the set of lender-users by the external service.
The limitation of determining a plurality of integrity scores, determining a risk score, and identifying a subset of the set of lender-users based on the integrity scores and the risk score steps as drafted, are processes that under broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a micro-loan system comprising a hardware processor and a storage media” and “a network of computers”, nothing in the claim element precludes the steps from practically being performed in the mind. For example, but for the “a micro-loan system comprising a hardware processor and a storage media” and “a network of computers” language, “determining integrity scores and a risk score, and identifying a plurality of lenders” in the context of these claims encompasses a user/person manually evaluates collected information to determine/update a integrity score and/or a risk score and judges a set of lender-users. 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.
The limitation of scrubbing the aggregated data, splitting the dataset, receiving a request for a loan transaction, providing, receiving, generating, recording a smart contract, transmitting a push event including an instruction to subtract funds from the payroll payment and add the subtracted fund the lenders steps as drafted, are processes that under broadest reasonable interpretation, cover performance by prescribed human activities but for the recitation of generic computer components. That is, other than reciting “a micro-loan system comprising a hardware processor and a storage media”, “a network of computers”, and “a client device”, nothing in the claim element precludes the steps from practically being performed by organized human activity, such as commercial or legal interactions including agreements in form of contracts. For example, but for the “a micro-loan system comprising a hardware processor and a storage media”, a network of computers” and “a client device”, language, in the context of these claims encompasses a user/person manually processes/scrubs/removes/splits the collected data, receives a template/form to receive an initial entry, receives alternations, generates a cryptograph signature for the loan transaction, records/creates a smart contract with the transaction terms based on the cryptograph signature, and transmits a push event that includes instructions to transfer fund/subtract fund/add fund executable by the external service associated with the URL information stored on the blockchain. Notably the claim language referencing “blockchain”, acts as descriptive material to the data exchanged/processed or the defining the communication recipient, and fails to represent significantly more than the abstract idea in the patentability analysis. The cryptographic signature is abstract, and the smart contract (with URL) is generic computer implementation. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by organized human activity but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The limitation of constructing, by the micro-loan system via execution of machine learning on the training dataset, a model to predict step as drafted, is process that under broadest reasonable interpretation, cover performance of the limitation by utilizing mathematical algorithms/modeling but for the recitation of generic computer components. That is, other than reciting “a micro-loan system comprising a hardware processor and a storage media, a network of computers, and a client device”, nothing in the claim element precludes the step from practically being performed by utilizing mathematical algorithms/modeling. In other words, the claimed method simply describes the concept of constructing a machine learning model/algorithms by reciting steps of aggregating training data and organizing information thru mathematical relationships to identify predicted borrowers/lenders. In particular, the courts have found mathematical algorithms to be abstract ideas (e.g., a mathematical procedure for converting one form of numerical representation to another in Benson). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by utilizing mathematical algorithms/modeling but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Training a machine learning model and apply this trained model is generic data process. They do not describe any particular improvement in the manner a computer functions. Instead, the claim amounts to nothing significantly more than using machine learning techniques on a computer to identify predicted borrowers/lenders and apply those determinations to efficiently manage loan contracts to minimize risk. Under our precedents, that is not enough to transform an abstract idea into a patent-eligible invention.
This judicial exception is not integrated into a practical application because aggregating data step amount to mere data gathering, which is a form of insignificant extra-solution activity. This judicial exception is not integrated into a practical application. This judicial exception is not integrated into a practical application because the claims as a whole merely describe how to generally “apply” the concept of aggregating, scrubbing, splitting, constructing, determining, receiving, identifying, providing, receiving, generating, recording, transmitting, subtracting, and adding steps in a computer environment. The claimed computer components such as the micro-loan system comprising the hardware processor and the storage media, the network of computers, and the client device are recited at a high level of generality and are merely invoked as tools to perform aggregating, scrubbing, splitting, constructing, determining, receiving, identifying, providing, receiving, generating, recording, transmitting, subtracting, and adding steps. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims 22, 31, and 39 are directed to an abstract idea.
The claims 22, 31, and 39 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 of the micro-loan system comprising the hardware processor and the storage media, the network of computers, and the client device to perform aggregating, scrubbing, splitting, constructing, determining, receiving, identifying, providing, receiving, generating, recording, transmitting, subtracting, and adding steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Claims 23-24, 32-33 and 40-41, disclose the limitation of constructing a machine learning model further comprising performing iterative analysis, generating integrity scores, ranking ordering the users, determining an error rate, comparing the error rate with a threshold, changing a hyperparameter, and retraining the machine learning model steps as drafted, are processes that under broadest reasonable interpretation, cover performance of the limitation by utilizing mathematical algorithms/modeling but for the recitation of generic computer components. That is, other than reciting “a micro-loan system”, nothing in the claim element precludes the steps from practically being performed by utilizing mathematical algorithms/modeling. In other words, the claimed method simply describes the concept of constructing a machine learning model/algorithms by reciting steps of performing iterative analysis, generating integrity scores, ranking ordering the users, determining an error rate, comparing the error rate with a threshold, changing a hyperparameter, and retraining the machine learning model thru mathematical relationships to generate a modified integrity score. In particular, the courts have found mathematical algorithms to be abstract ideas (e.g., a mathematical procedure for converting one form of numerical representation to another in Benson). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by utilizing mathematical algorithms/modeling but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims only recite additional elements – “a micro-loan system” to perform steps. The micro-loan system in the claims is recited at a high-level of generality (i.e. a generic computer device functioning a generic computer function of processing data to update a model), such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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.
The claims 23-24, 32-33 and 40-41 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 element of “a micro-loan system” to perform performing, generating, ranking, determining, comparing, changing, and retraining steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Claims 25 and 34, disclose insignificant helpful content to further describe content, such as the process of remitting loan fund by subtracting fund and adding the subtracted fund which are merely descriptive content to further limit the abstract idea but not make it less abstract. Thus, the claims 25 and 34 are directed to an abstract idea.
This judicial exception is not integrated into a practical application because descriptive content in claims 25 and 34 further limit the abstract idea but not make it less abstract. Thus, the claims 25 and 34 are directed to an abstract idea.
There are no additional claim elements limitations recited in the claim 25 and 34. Therefore, the claims do not amount to significantly more than the recited abstract idea. The claims 25 and 34 are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 22, 23, 25, 31, 32, 34, 39, and 40 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent No. 8,447,667 to Dinamani et al., in view of U.S. Patent Application Publication No. 2029/0102835 to Bjonerud et al., in view of U.S. Patent Application Publication No. 2018/0075527 to Nagla et al., and further in view of U.S. Patent Application Publication No. 2019/0114706 to Bell et al.
With regard to claims 22, 31, and 39, Dinamani discloses a method for facilitating blockchain-based micro transactions via machine learning, comprising:
aggregating, by a micro-loan system comprising a hardware processor, data regarding a plurality of factors associated with human capital management information and a plurality of users (col. 8, lines 22-25, col. 9, lines 8-17, the system analyzes the borrower's income tax return data, employment history, existing loans, and/or credit ratings obtained from credit reporting bureaus or credit rating agencies, and determines a credit score for the borrower loan request);
scrubbing, by the micro-loan system, the aggregated data to generate a refined dataset (col. 8, lines 22-25, col. 9, lines 8-17, it’s a well-known data processing step to further aggregate filtered data);
splitting, by the micro-loan system, the refined dataset into a training dataset and a testing dataset (col. 8, lines 22-25, col. 9, lines 8-17, it’s a well-known data processing step to further aggregate specific datasets);
determining, by the micro-loan system, based on the human capital management information, a first plurality of integrity scores for the predicted borrower-users and a second plurality of integrity scores for the predicted lender-users (col. 3, lines 17-18, col. 11, lines 59-col. 12, lines 8, At step 130, The financial services system analyzes the tax return data to assess the credit worthiness of the borrower. the system may also include a user feedback system which allows users, including both lenders and borrowers, to give ratings and provide comments. The lender and borrower ratings and comments may be made available to other users of the financial services system, so that they can use them to make decisions on whether to enter into loans with certain lenders and borrowers. it’s a well-known technique that the credit worthiness of users (borrowers and lenders) can be generated based on financial data (i.e., income, transaction history, and tax return).);
receiving, by the micro-loan system, a request for a loan transaction from a first borrower-user of the predicted borrower-users (col. 8, lines 22-23, At step 106, the financial services system receives a loan request from the borrower);
determining, by the micro-loan system, a risk score for the loan transaction based at least on an integrity score of the first plurality of integrity scores associated with the first borrower- user (claim 1 and col. 6, lines 42-44, the computerized financial services system determining a credit score of the borrower based at least in part on an analysis of the income tax return data of the borrower. The system uses the income tax return data of the borrower to assess the loan risk and credit worthiness of the borrower);
identifying, by the micro-loan system, a subset of the predicted lender-users based on the risk score and integrity scores of the second plurality of integrity scores associated with the subset of the predicted lender-users (col. 10, lines 29-30, claims 8 and 10, the computerized financial services system presents to the lender a list of a plurality of loan requests which meet the lender's desired loan criteria including the loan request from the borrower. determining that the loan terms and credit score satisfy the second lender's desired loan criteria and presenting the loan request to the second lender. Examiner notes that the lender and the second lender can be identified to present all options that meets the loan criteria, which is considered as “identifying … a subset of the set of lender-users based on the risk score and integrity scores of the second plurality of integrity scores associated with the subset of the set of lender-users”);
providing, by the micro-loan system, to a client device corresponding to at least one of the subset of the predicted lender-users, a template configured to receive an initial entry establishing transaction terms for the loan transaction (Fig. 1b, col. 9, lines63-col. 10, lines 14, At step 120a, the system receives desired loan criteria from the lender. This can be done by any suitable method, such as by displaying to the lender various loan criteria and selections for each loan criterion. For example, the system may display: loan amounts and a selection of loan amounts or loan amount ranges, such as $500-$1000, $1000-$2000, $2000-$5000, $5000-$10000, etc.; loan periods and a selection of loan periods, such as 3-6 months, 6 months--one year, 1-2 years, 3-5 years, etc.; loan purpose, such as pay-off other loan, make purchase, finance a business, etc.; geographic location of the borrower, such as state, or region, or the like; etc. );
receiving, by the micro-loan system, from a client device of the first borrower-user, responsive to the initial entry of the transaction terms, one or more alterations to the transaction terms established by the initial entry (col. 3, lines 65-col. 4, lines 1, The loan is executed according to loan terms agree upon by the borrower and the lender, such as the terms in the loan request, or as modified by mutual agreement of the borrower and lender.);
recording, by the micro-loan system responsive to finalization of the transaction terms, the loan transaction and the transaction terms into electronic copies of loan documents (col. 11, lines 4-15, In one way, the system may send electronic copies of loan documents, including a loan agreement, to the lender and borrower for execution by the lender and borrower); and
transmitting , by the micro-loan system responsive to recording the loan transaction and the transaction terms in the loan documents, an instruction to execute an external service corresponding to the human capital management information, the external service to automatically remit loan funds to the predicted lender-users in accordance with the transaction terms by: i) subtracting funds from one or more scheduled payroll payments to the first borrower-user, and ii) adding the subtracted funds to one or more scheduled payroll payments to the predicted lender-users (col. 4, lines 56-63, col. 11, lines 46-58, the financial services system may include a bill pay system that is used to enable the repayment of the loan by the borrower to the lender. The bill payment system can be set up to transfer automatic payments from an account of the borrower to an account of the lender (or just send a check funded from the borrower's account) according to a repayment schedule that is part of the loan terms. Then, the system can set up automatic loan payments from an account of the borrower to an account of the lender, or as a payroll deduction from the borrower's payroll payments).
Dinamani discloses a peer-to-peer microloan system which is compatible with a payroll system. However, Dinamani does not disclose constructing, by the micro-loan system via execution of machine learning on the training dataset, a model configured to identify predicted borrower-users from the plurality of users and predicted lender-users from the plurality of users.
However, Bjonerud teaches constructing, by the micro-loan system via execution of machine learning on the training dataset, a model configured to identify predicted borrower-users from the plurality of users and predicted lender-users from the plurality of users (The extracting and autonomously matching includes the extraction of borrower data that includes financial, industry operational and business data. The instructions also include the extracting of lender data from prospective lenders that includes financial data, a target profile, and historical deal data for each prospective lender. The instructions then generate, using computer based artificial intelligence, an autonomous ranked match of prospective lenders, wherein the artificial intelligence is used to identify relationships between the borrower data and the prospective lender data to generate the ranked match based on a preference of the borrower and a correlation between the identified relationships. Constantly learning matching algorithms are used to identify lending opportunities for lenders and borrowers that would be otherwise inaccessible. For example, a borrower may have closed on a loan with defined terms six months ago and not be seeking additional or replacement financing, but the system has the ability to identify a lender that is now able to offer better terms to the borrower (based on more recent lending data, borrower data, and/or other data points), thereby creating an opportunity for the lender to provide financing where they would have otherwise been unable to and enabling the borrower to put financing in place with more favorable terms. To anonymously match borrowers and lenders, the system must be able to predict a lender's willingness and ability to provide financing for a borrower's loan request. Paragraphs 12,15, 47, and 56).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the peer-to-peer microloan system of Dinamani to include, constructing, by the micro-loan system via execution of machine learning on the training dataset, a model configured to identify predicted borrower-users from the plurality of users and predicted lender-users from the plurality of users, as taught in Bjonerud, in order to intelligent autonomous match lenders and borrowers (Bjonerud, paragraph 10).
Dinamani discloses a loan transaction and loan terms are recorded on electronic copies of loan documents. However, Dinamani does not disclose electronic copies of loan documents comprises a smart contract on a blockchain maintained by a network of computers and the transaction terms recorded on the blockchain based on the cryptographic signature generated for the loan transaction.
However, Nagla teaches generating, by the micro-loan system, responsive to finalization of the transaction terms, a cryptographic signature for the loan transaction, the cryptographic signature corresponding to a loan coordinator of the loan transaction and generated using a cryptographic key ( transmit a notification of the selected loan offer to the creditor; and receive an acceptance of the selected loan offer from the creditor. The machine learning unit 320 configures A smart contracts middleware layer to generate a smart contract with the selected loan terms. The smart contract being linked to an identifier of the set of identifiers and the selected creditor, the smart contract having an electronic signature and transaction terms. using suitable encryption and cryptographic techniques (e.g., public/private key pairs, hashing, “proof of work” generation); among others. Credit data may be verified, for example, by configuring the system such that the first user signs the transferred data with a private encryption key. A creation of a block in the blockchain for that transferred data may allow the transfer to occur. Each block may be created in accordance with specific secure protocols typically by one or more computers on a public distributed network. Paragraphs 112,154, and 164); recording based on the cryptographic signature generated for the loan transaction, the loan transaction and the transactions terms in a smart contract on a blockchain maintained by a network of computers (generate a smart contract with the selected loan terms, and record a new block on the distributed ledger, the new block having the smart contract, the identifier and the selected creditor, the smart contract being linked to an identifier of the set of identifiers and the selected creditor, the smart contract having an electronic signature and transaction terms. the system has a smart contract middleware application configured to generate a smart contract, and record a new block on the distributed ledger, the new block having the smart contract, the smart contract including an electronic debtor signature, an electronic creditor signature, the transaction terms, and an identifier of the set of identifiers, paragraphs 17 and 21).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the peer-to-peer microloan system of Dinamani to include, generating, by the micro-loan system, responsive to finalization of the transaction terms, a cryptographic signature for the loan transaction, the cryptographic signature corresponding to a loan coordinator of the loan transaction and generated using a cryptographic key; and recording based on the cryptographic signature generated for the loan transaction, the loan transaction and the transactions terms in a smart contract on a blockchain maintained by a network of computers, as taught in Nagla, in order to provide a credit score platform using blockchain technology (Nagla, abstract).
Dinamani discloses a system can enable the repayment of the loan by the borrower to the lender based on the loan documents. However, Dinamani does not disclose transmitting via a web protocol, a push event generated by the smart contract to a uniform resource locator (URL) stored on the blockchain, the URL associated with an external service, the push event having instructions that, when executed by the external service, cause the external service to transfer/remit/subtract/add funds.
However, Bell teaches transmitting via a web protocol, a push event generated by the smart contract to a uniform resource locator (URL) stored on the blockchain, the URL associated with an external service, the push event having instructions that, when executed by the external service, cause the external service to transfer/remit/subtract/add funds (the loan agreement terms including repayment terms for a loan collateralized by a digital asset, broadcasting an oracle initialization transaction to a blockchain network having a set of consensus rules, the oracle initialization transaction including the loan agreement terms and further including oracle code executable on the blockchain network. Participants in the system 200 may sign a transaction and transmit the signed transaction to other participants, who can also sign the transaction. Once at least three of the participants has signed the transaction with their respective private keys, then the transaction may be broadcast to the blockchain network to move funds out of the collateral wallet 210. For example, if repayment of a loan is complete, the digital asset collateral is released back to the borrower under the terms of the loan agreement by broadcasting a signed transaction to the blockchain network to transfer the digital asset collateral from the collateral wallet 210 to a wallet address controlled by the buyer 204 (e.g., to a non-multisig wallet address for which the borrower 204 holds the private key). The loan manager 208 and the arbiter 212 may further determine which addresses are appropriate to receive any funds moved from the collateral wallet 210. For example, if a maximum LTV is breached under the terms of the loan agreement due to falling digital asset collateral prices, then the terms of the loan agreement may permit funds to be moved to a digital asset exchange for liquidation. The digital asset exchange may be an approved destination for funds under the loan agreement. In some blockchains that support executable on-chain code, such as blockchain 302, the on-chain smart contract code is dormant until a status transaction “pokes” the smart contract. As such, a status transaction may be sent to the oracle 306 to periodically “wake up” the oracle and/or request that the oracle 306 perform certain functions relating to the maintenance of the loan collateralized by digital assets. A status update transaction may be sent by any system participant or there may be a whitelist of only certain participants who are authorized to send a status transaction to the oracle 306. For example, the smart contract code of the oracle 306 may include a whitelist of ethereum network addresses that are authorized to call functions on the smart contract or to send status transactions to the oracle 306. Any transactions from ethereum addresses not on the whitelist may be rejected by the oracle 306. , paragraphs 7, 38, 42, and 46).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the peer-to-peer microloan system of Dinamani to include, transmitting via a web protocol, a push event generated by the smart contract to a uniform resource locator (URL) stored on the blockchain, the URL associated with an external service, the push event having instructions that, when executed by the external service, cause the external service to transfer/remit/subtract/add funds, as taught in Bell, in order to manage loans collateralized by a digital assets (Bell, paragraph 4).
With regard to claims 23, 32, and 40, the combination of references discloses performing, by the micro-loan system, iterative analysis on the training dataset using the machine learning to construct the model, wherein the machine learning comprises at least one of a regression, a decision tree, k-nearest neighbors, neural networks, or a support vector machine (Bjonerud, paragraph 14, machine learning algorithms); generating, by the micro-loan system, the first plurality of integrity scores and the second plurality of integrity scores over a specified time period to create indices of integrity scores (Dinamani, col. 8, lines 60-col. 9, lines 17, Examiner notes that the numerical credit scores estimated based on collected information for different people can be converted into different credit scales/category (e.g., excellent, very good, good, fair, poor, etc.), which is considered as “converting the predicted integrity scores… into integrity scores for the plurality of users…to create indices of integrity scores”); generating, by the micro-loan system, the plurality of integrity scores for the plurality of users over a specified time period (Examiner notes that the credit history application is configured to compute a credit score based on the credit history record of the individual and generate a credit score notification indicating the credit score and the credit event, which is considered as “the integrity scores are generated for the plurality of users over a specified time period”, Nagla, paragraph 31); and rank ordering, by the micro-loan system, the predicted borrower-users and the predicted lender- users based on the indices of integrity scores (Dinamani, col. 3, lines 51-56, it’s obvious that a list of a plurality of loan requests which meet the lender's desired loan criteria can be updated based on the updated credit scores).
With regard to claims 25 and 34, Dinamani discloses transmitting, by the micro-loan system, a second instruction to execute the external service to automatically remit loan funds to the first borrower- user in accordance with the transaction terms by: i) subtracting funds from one or more scheduled payroll payments to one or more of the subset of the predicted lender-users and ii) adding the subtracted funds to one or more scheduled payments to the first borrower-user (col. 4, lines 56-63, col. 11, lines 46-58 and col. 11, lines 427, In one way, the system may send electronic copies of loan documents, including a loan agreement, to the lender and borrower for execution by the lender and borrower. The lender loan amount is transferred to the borrower.).
Claims 24, 33, and 41 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent No. 8,447,667 to Dinamani et al., U.S. Patent Application Publication No. 2029/0102835 to Bjonerud et al., and U.S. Patent Application Publication No. 2018/0075527 to Nagla et al., and U.S. Patent Application Publication No. 2019/0114706 to Bell et al., and further in view of Alpaydin, Introduction to Machine Learning, 2nd ed., Cambridge, the MIT press (2010) (hereinafter Alpaydin).
With regard to claims 24, 33, and 41, the combination of references discloses deploying, by the micro-loan system, the model re-trained with the changed hyperparameter to determine the first plurality of integrity scores and the second plurality of integrity scores (Dinamani, col. 11, lines 59-col. 12, lines 8, Examiner notes that a credit rating/score can be updated based on updated feedback/comments/financial information), however, Dinamani does not disclose machine learning modeling procedures in the scope of the claims.
However, Alpaydin teaches machine learning modeling procedures by determining, by the micro-loan system, an error rate of an initial model generated via machine learning using the training dataset (pp. 37-40, p. 40 “We simulate this by dividing the training set we have into two parts. We use one part for training (i.e., to fit a hypothesis), and the remaining validation set part is called the validation set and is used to test the generalization ability”);
comparing, by the micro-loan system, the error rate of the initial model with a threshold (pp. 80-84, p. 80 “The error on the validation set decreases up to a certain level of complexity, then stops decreasing or does not decrease further significantly”);
changing, by the micro-loan system responsive to the error rate greater than or equal to the threshold, a hyperparameter used to generate the initial model (p. 83 “That is, we look for Wi that both decrease error and are also as close as possible to 0 … That is, having such a prior is equivalent to forcing parameters to be close to 0”);
retraining, by the micro-loan system, the initial model with the changed hyperparameter to construct the model (p. 40 “if we collect data once more, we will get a slightly different dataset, the fitted h will be slightly different and will have a slightly different validation error”);
determining, by the micro-loan system, a second error rate of the model is less than the threshold (p. 40 “if we collect data once more, we will get a slightly different dataset, the fitted h will be slightly different and will have a slightly different validation error”); and
deploying, by the micro-loan system, the model re-trained with the changed hyperparameter to predict the categories for the plurality of users (p. 40).
It would have been obvious to one of ordinary still in the art to include in the peer-to-peer microloan system of Dinamani the ability to utilize a machine learning technique to train a prediction model as taught by Alpaydin since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Response to Arguments
Applicants' arguments filed on 11/24/2025 have been fully considered but they are not fully persuasive especially in light of the new art used in the rejections.
Applicants remark that “the combination of references does not teach or suggest generating, by the micro-loan system, responsive to finalization of the transaction terms, a cryptographic signature for the loan transaction, the cryptographic signature corresponding to a loan coordinator of the loan transaction and generated using a cryptographic key; and recording based on the cryptographic signature generated for the loan transaction, the loan transaction and the transactions terms in a smart contract on a blockchain maintained by a network of computers; transmitting via a web protocol, a push event generated by the smart contract to a uniform resource locator (URL) stored on the blockchain, the URL associated with an external service, the push event having instructions that, when executed by the external service, cause the external service to transfer/remit/subtract/add funds”.
Examiner directs Applicants' attention to the office action above.
Applicants remark that “the proposed amendment limitation mentioned above can overcome 101 rejection”.
Examiner directs Applicants' attention to the office action above.
Conclusion
Please refer to form 892 for cited references.
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 extension fee 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 from the examiner should be directed to Ariel Yu whose telephone number is 571-270-3312. The examiner can normally be reached on Monday-Friday 9:00am-5:00pm EST.
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/ARIEL J YU/Primary Examiner, Art Unit 3627