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 .
Status of Claims
The following is Office Action on the merits in response to the communication received on 3/4/26.
Claim status:
Amended claims: 1, 5, 7, 9-11, 13, 15 and 17
Canceled claims: 2, 6, 8, 12, 14, and 18
Added New claims: None
Pending claims: 1, 3-5, 7, 9-11, 13, and 15- 17
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, 3-5, 7, 9-11, 13, and 15-17 are rejected under 35 U.S.C. § 101 because the claimed invention is not directed to statutory subject matter. Specifically, the invention of claims 1, 3-5, 7, 9-11, 13, and 15-17 is directed to an abstract idea without significantly more.
Independent claims 1, 7 and 13 are directed to a method (claim 1), a system (claim 7), and a non-transitory computer readable storage medium (claim 13). Therefore on its face, each of claims 1, 7 and 13 is directed to a statutory category of invention under Step 1 of the 2019 PEG. However each of claims 1, 7 and 13, is also directed to an abstract idea without significantly more, under Step 2A (Prong One and Prong Two) and Step 2B of the 2019 PEG, which is a judicial exception to 35 U.S.C. 101, as detailed below. Using the language of independent claim 7 to illustrate the claim recites the limitations of, (i) create a borrower entry based on a plurality of borrower loans for a borrower and a borrower rating, (ii) store the borrower entry, (iii) to poll the plurality of rating agencies for agency borrower ratings by transmitting a borrower identifier to each of the plurality of rating agencies, (iv) to receive agency borrower ratings with a latest update date from the plurality of rating agencies, (v) to determine that one of the agency borrower ratings has changed from a previous agency borrower rating, (vi) by comparing the received agency borrower ratings and the latest update dates against previously stored agency borrower ratings and update dates and disregarding agency borrower ratings for which no change is detected, (vii) to update the pricing grid for the borrower based on the recommended change if the accuracy percentage is above a threshold and store the updated pricing grid, and (viii) to provide the updated pricing grid to a loan platform, wherein the loan platform is configured to implement the pricing grid which changes a payment for at least one of the plurality of borrower loans under the broadest reasonable interpretation (BRI) covers methods of organizing human activity: commercial interactions but for the recitation of generic computers and generic computer components. (Independent claims 1 and 13 recite similar limitations and the analysis is the same).
That is, other than reciting an electronic device, a borrower database, a memory, a pricing database, a network interface, to predict, using a machine learning engine that is trained with historic agency rating changes, a recommended change to a pricing grid for the borrower based on the change in the agency borrower rating, wherein the recommended change includes an accuracy percentage for the recommended change, repeat training the machine learning engine and predicting the recommended change to the pricing grid during a lifetime of each of plurality of borrower loans, wherein the machine learning engine is trained with a plurality of historical borrower rating changes and corresponding pricing grid updates, and wherein the pricing computer program automatically retrains the machine learning engine based on detected borrower rating changes and pricing grid updates during the life of the plurality of borrower loans, wherein, after the loan platform implements the updated pricing grid, actual loan performance data resulting from the implementation is collected and provided to the machine learning engine, and the machine learning engine is retrained using both subsequently received agency borrower ratings from the plurality of rating agencies and the actual loan performance data nothing in the claim precludes the steps from being directed to methods of organizing human activity: commercial interactions. If a claim limitation under its BRI, covers methods of organizing human activity but for the recitation of generic computers, then the limitations fall within the “methods of organizing human activity” grouping of abstract ideas. Therefore, claim 7 recites an abstract idea under Step 2A Prong One of the Revised Patent Subject Matter Eligibility Guidance 84 Fed.Reg 50 (“2019 PEG”).
This “methods of organizing human activity” is not integrated into a practical application under Step 2A prong Two of the 2019 PEG. In particular claim 7 recites the following additional elements of, an electronic device, a borrower database, a memory, a pricing database, a network interface, to predict, using a machine learning engine that is trained with historic agency rating changes, a recommended change to a pricing grid for the borrower based on the change in the agency borrower rating, wherein the recommended change includes an accuracy percentage for the recommended change, repeat training the machine learning engine and predicting the recommended change to the pricing grid during a lifetime of each of plurality of borrower loans, wherein the machine learning engine is trained with a plurality of historical borrower rating changes and corresponding pricing grid updates, and wherein the pricing computer program automatically retrains the machine learning engine based on detected borrower rating changes and pricing grid updates during the life of the plurality of borrower loans, wherein, after the loan platform implements the updated pricing grid, actual loan performance data resulting from the implementation is collected and provided to the machine learning engine, and the machine learning engine is retrained using both subsequently received agency borrower ratings from the plurality of rating agencies and the actual loan performance data. In particular, the claim only recites the additional elements – an electronic device, a borrower database, a memory, a pricing database, a network interface, to predict, using a machine learning engine that is trained with historic agency rating changes, a recommended change to a pricing grid for the borrower based on the change in the agency borrower rating, wherein the recommended change includes an accuracy percentage for the recommended change, repeat training the machine learning engine and predicting the recommended change to the pricing grid during a lifetime of each of plurality of borrower loans, wherein the machine learning engine is trained with a plurality of historical borrower rating changes and corresponding pricing grid updates, and wherein the pricing computer program automatically retrains the machine learning engine based on detected borrower rating changes and pricing grid updates during the life of the plurality of borrower loans, wherein, after the loan platform implements the updated pricing grid, actual loan performance data resulting from the implementation is collected and provided to the machine learning engine, and the machine learning engine is retrained using both subsequently received agency borrower ratings from the plurality of rating agencies and the actual loan performance data.
The electronic device, borrower database, memory, pricing database, network interface, to predict, using a machine learning engine that is trained with historic agency rating changes, a recommended change to a pricing grid for the borrower based on the change in the agency borrower rating, wherein the recommended change includes an accuracy percentage for the recommended change, repeat training the machine learning engine and predicting the recommended change to the pricing grid during a lifetime of each of plurality of borrower loans, wherein the machine learning engine is trained with a plurality of historical borrower rating changes and corresponding pricing grid updates, and wherein the pricing computer program automatically retrains the machine learning engine based on detected borrower rating changes and pricing grid updates during the life of the plurality of borrower loans, wherein, after the loan platform implements the updated pricing grid, actual loan performance data resulting from the implementation is collected and provided to the machine learning engine, and the machine learning engine is retrained using both subsequently received agency borrower ratings from the plurality of rating agencies and the actual loan performance data are recited at a high-level or generality (i.e. as a generic computer performing generic computer functions) such that, they amount to no more than instructions to apply the abstract idea with a computer (see MPEP 2106.05(h)). Accordingly these additional elements 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 Step 2B of the 2019 PEG independent claim 7 does not include additional elements that are sufficient to amount to significantly more than the abstract idea. The claim(s) 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 using an electronic device, a borrower database, a memory, a pricing database, a network interface, to predict, using a machine learning engine that is trained with historic agency rating changes, a recommended change to a pricing grid for the borrower based on the change in the agency borrower rating, wherein the recommended change includes an accuracy percentage for the recommended change, repeat training the machine learning engine and predicting the recommended change to the pricing grid during a lifetime of each of plurality of borrower loans, wherein the machine learning engine is trained with a plurality of historical borrower rating changes and corresponding pricing grid updates, and wherein the pricing computer program automatically retrains the machine learning engine based on detected borrower rating changes and pricing grid updates during the life of the plurality of borrower loans, wherein, after the loan platform implements the updated pricing grid, actual loan performance data resulting from the implementation is collected and provided to the machine learning engine, and the machine learning engine is retrained using both subsequently received agency borrower ratings from the plurality of rating agencies and the actual loan performance data, create a borrower entry based on a plurality of borrower loans for a borrower and a borrower rating, store the borrower entry, to poll the plurality of rating agencies for agency borrower ratings by transmitting a borrower identifier to each of the plurality of rating agencies, to receive agency borrower ratings with a latest update date from the plurality of rating agencies, to determine that one of the agency borrower ratings has changed from a previous agency borrower rating, by comparing the received agency borrower ratings and the latest update dates against previously stored agency borrower ratings and update dates and disregarding agency borrower ratings for which no change is detected, to update the pricing grid for the borrower based on the recommended change if the accuracy percentage is above a threshold and store the updated pricing grid, and to provide the updated pricing grid to a loan platform, wherein the loan platform is configured to implement the pricing grid which changes a payment for at least one of the plurality of borrower loans, amount to instructions to apply the abstract idea with a computer. The claims are not patent eligible.
The dependent claims have been given the full two part analysis including analyzing the additional limitations individually. The Dependent claim(s) when analyzed individually are also held to be patent ineligible under 35 U.S.C. 101 because for the same reasoning as above and the additional recited limitation(s) fail to establish that the claim(s) are not directed to an abstract idea. The additional limitations of the dependent claim(s) when considered individually do not amount to significantly more than the abstract idea. Claims 3-5, 9-11 and 15-17 merely further explain the abstract idea.
When viewed individually the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea. Accordingly claims 1, 3-5, 7, 9-11, 13, and 15-17 are ineligible.
Claim Rejections - 35 USC § 112
The Applicant’s arguments and amendments overcome the 112 Rejections, therefore, the Rejection(s) are moot.
Response to Arguments
Applicant's arguments filed 3/4/26 have been fully considered but they are not persuasive.
35 USC § 101
The Applicant states “claim 1 does not merely recite negotiating/setting prices as a fundamental economic practice or other organizing- human-activity concept” (page 11) and “claim 1 is not directed to an abstract idea because any alleged abstract idea is integrated into a practical application” (page 12). The Examiner disagrees with the sentences because the claims are an improvement of the abstract idea only. It is a business solution to a business problem determining performance-based pricing. The applicant has not shown how the claims improve a computer or other technology, invoke a particular machine, transform matter, or provide more than a general link between the abstraction and the technology, MPEP 2106.05(a)-(c) & (e). The Examiner disagrees that “the ordered combination of additional elements recited in claim 1 provides significantly more than any alleged abstract idea” (page 13). The claims do not use generic and conventional components in a non-conventional manner, it is well-known that a machine learning model may be retrained to optimize performance. The claims make the abstract idea more specific, and determining performance-based pricing is not an unconventional activity. Applicant’s remarks about why these limitations provide a practical application fail to surface any technical improvement identified in the specification and, therefore this is not an inventive concept and significantly more.
35 USC § 112
The Applicant’s arguments and amendments overcome the 112 Rejections, therefore, the Rejection(s) are moot.
Conclusion
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 MARLA HUDSON whose telephone number is (571)272-1063. The examiner can normally be reached M-F 9:30 a.m. - 5:30 p.m. ET.
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/M.H./Examiner, Art Unit 3694
/BENNETT M SIGMOND/Supervisory Patent Examiner, Art Unit 3694