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 7/31/26.
Claim status:
Amended claims: 12 and 13
Canceled claims: 1-3, 5-11 and 16-20
Added New claims: None
Pending claims: 4 and 12-15
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 4 and 12-15 are rejected under 35 U.S.C. § 101 because the claimed invention is not directed to statutory subject matter. Specifically, the invention of claims 4 and 12-15 is directed to an abstract idea without significantly more.
Independent claims 12 and 13 are directed to a method (claim 12), and a system (claim 13). Therefore on its face, claims 12 and 13 are directed to a statutory category of invention under Step 1 of the 2019 PEG. However claims 12 and 13 are 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 12 to illustrate the claim recites the limitations of, (i) determining an authorization level upgrade, (ii) providing a source of data, where the data includes financial data for one or more accounts of a client of a bank, and the data further includes a credit history for the client; (iii) identifying one or more objectives for the credit authorization level upgrade, by the client communicating; (iv) determining a reward in the form of a credit authorization level upgrade, along with one or more goals which must be achieved in order to obtain the reward, including analyzing the data and the objectives, where the goals are selected from a group consisting of on- time payment percentage for existing credit, paying off or paying down existing credit, a savings balance exceeding a computed savings threshold, a cash flow exceeding a cash flow threshold amount and time duration, an improvement in a credit score, and a reduction in spending in one or more categories of discretionary spending as indicated by a transaction history, and the reward is selected from a group consisting of approval of a mortgage, approval of a car loan or lease, approval of a new credit card, approval of a credit limit for an existing credit card, and approval of a personal loan, where the goals and the reward are determined based on input elements including the financial data for the client, the credit history for the client, and the objectives defined by the client, and the input elements are evaluated against factors defined in a group consisting of a reward desirability factor for the client, a goal achievability factor for the client, and a financial risk management factor for the bank; (v) displaying the one or more goals and the reward; (vi) monitoring progress toward the goals, using updated values from the source of data, including determining when a milestone toward the goals has been reached; (vii) displaying the progress toward the goals, including sending a push notification when a milestone has been reached, where the push notification appears as a pop-up bubble which communicates that the milestone has been reached, and also includes a positive reinforcement message regarding reaching the milestone, without requiring the client to open or login to an application; and (viii) delivering the reward to the user when the one or more goals are achieved, including displaying the one or more goals which were achieved and displaying the reward which has been obtained, and further including a selectable button with which the client activates the reward; (ix) repeating each of displaying the one or more goals and the reward, monitoring progress toward the goals, displaying the progress toward the goals and delivering the reward to the user under the broadest reasonable interpretation (BRI) covers methods of organizing human activity -- fundamental economic principles or practices, risk mitigation but for the recitation of generic computers and generic computer components. (Independent claim 13 recites similar limitations and the analysis is the same).
That is, other than reciting, a server computer, a processor, memory, a user device, where the goals and the reward are computed using a machine learning system including a neural network which forms nodal and layer connections based on the input elements, the factors, the goals and the reward, initially training the neural network to form the nodal and layer connections using a supervised learning process with pre-classified client data examples including the input elements, manually determined goals and reward, and an actual outcome classified as favorable or unfavorable, and subsequently training the neural network to re-form the nodal and layer connections using a supervised learning update training process with supplemental client data examples including the input elements, the goals and the reward previously determined by the neural network, and an actual outcome classified as favorable or unfavorable, wherein the neural network undergoes recurrent training as clients proceed through the credit authorization level upgrade, and wherein actual inputs and outputs from the credit authorization level upgrade are used as training samples for the supervised learning update training process and redetermining the goals and reward based on changes in the input elements or changes in the nodal and layer connections of the neural network nothing in the claim precludes the steps from being directed to methods of organizing human activity -- fundamental economic principles or practices, risk mitigation or a “commercial or legal interaction”. 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 12 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 the claim recites the following additional elements of, a server computer, a processor, memory, a user device, where the goals and the reward are computed using a machine learning system including a neural network which forms nodal and layer connections based on the input elements, the factors, the goals and the reward, initially training the neural network to form the nodal and layer connections using a supervised learning process with pre-classified client data examples including the input elements, manually determined goals and reward, and an actual outcome classified as favorable or unfavorable, and subsequently training the neural network to re-form the nodal and layer connections using a supervised learning update training process with supplemental client data examples including the input elements, the goals and the reward previously determined by the neural network, and an actual outcome classified as favorable or unfavorable, wherein the neural network undergoes recurrent training as clients proceed through the credit authorization level upgrade, and wherein actual inputs and outputs from the credit authorization level upgrade are used as training samples for the supervised learning update training process and redetermining the goals and reward based on changes in the input elements or changes in the nodal and layer connections of the neural network. This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements – a server computer, a processor, memory, a user device, where the goals and the reward are computed using a machine learning system including a neural network which forms nodal and layer connections based on the input elements, the factors, the goals and the reward, initially training the neural network to form the nodal and layer connections using a supervised learning process with pre-classified client data examples including the input elements, manually determined goals and reward, and an actual outcome classified as favorable or unfavorable, and subsequently training the neural network to re-form the nodal and layer connections using a supervised learning update training process with supplemental client data examples including the input elements, the goals and the reward previously determined by the neural network, and an actual outcome classified as favorable or unfavorable, wherein the neural network undergoes recurrent training as clients proceed through the credit authorization level upgrade, and wherein actual inputs and outputs from the credit authorization level upgrade are used as training samples for the supervised learning update training process and redetermining the goals and reward based on changes in the input elements or changes in the nodal and layer connections of the neural network.
The server computer, processor, memory, user device, where the goals and the reward are computed using a machine learning system including a neural network which forms nodal and layer connections based on the input elements, the factors, the goals and the reward, initially training the neural network to form the nodal and layer connections using a supervised learning process with pre-classified client data examples including the input elements, manually determined goals and reward, and an actual outcome classified as favorable or unfavorable, and subsequently training the neural network to re-form the nodal and layer connections using a supervised learning update training process with supplemental client data examples including the input elements, the goals and the reward previously determined by the neural network, and an actual outcome classified as favorable or unfavorable, wherein the neural network undergoes recurrent training as clients proceed through the credit authorization level upgrade, and wherein actual inputs and outputs from the credit authorization level upgrade are used as training samples for the supervised learning update training process and redetermining the goals and reward based on changes in the input elements or changes in the nodal and layer connections of the neural network 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 general 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 12 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, under Step 2A, Prong 1, the additional elements of using a server computer, a processor, memory, a user device, where the goals and the reward are computed using a machine learning system including a neural network which forms nodal and layer connections based on the input elements, the factors, the goals and the reward, initially training the neural network to form the nodal and layer connections using a supervised learning process with pre-classified client data examples including the input elements, manually determined goals and reward, and an actual outcome classified as favorable or unfavorable, and subsequently training the neural network to re-form the nodal and layer connections using a supervised learning update training process with supplemental client data examples including the input elements, the goals and the reward previously determined by the neural network, and an actual outcome classified as favorable or unfavorable, wherein the neural network undergoes recurrent training as clients proceed through the credit authorization level upgrade, and wherein actual inputs and outputs from the credit authorization level upgrade are used as training samples for the supervised learning update training process and redetermining the goals and reward based on changes in the input elements or changes in the nodal and layer connections of the neural network, determining an authorization level upgrade, providing a source of data, where the data includes financial data for one or more accounts of a client of a bank, and the data further includes a credit history for the client; identifying one or more objectives for the credit authorization level upgrade, by the client communicating; determining a reward in the form of a credit authorization level upgrade, along with one or more goals which must be achieved in order to obtain the reward, including analyzing the data and the objectives, where the goals are selected from a group consisting of on- time payment percentage for existing credit, paying off or paying down existing credit, a savings balance exceeding a computed savings threshold, a cash flow exceeding a cash flow threshold amount and time duration, an improvement in a credit score, and a reduction in spending in one or more categories of discretionary spending as indicated by a transaction history, and the reward is selected from a group consisting of approval of a mortgage, approval of a car loan or lease, approval of a new credit card, approval of a credit limit for an existing credit card, and approval of a personal loan, where the goals and the reward are determined based on input elements including the financial data for the client, the credit history for the client, and the objectives defined by the client, and the input elements are evaluated against factors defined in a group consisting of a reward desirability factor for the client, a goal achievability factor for the client, and a financial risk management factor for the bank; displaying the one or more goals and the reward; monitoring progress toward the goals, using updated values from the source of data, including determining when a milestone toward the goals has been reached; displaying the progress toward the goals, including sending a push notification when a milestone has been reached, where the push notification appears as a pop-up bubble which communicates that the milestone has been reached, and also includes a positive reinforcement message regarding reaching the milestone, without requiring the client to open or login to an application; and delivering the reward to the user when the one or more goals are achieved, including displaying the one or more goals which were achieved and displaying the reward which has been obtained, and further including a selectable button with which the client activates the reward; repeating each of displaying the one or more goals and the reward, monitoring progress toward the goals, displaying the progress toward the goals and delivering the reward to the user, amount to instructions to apply the abstract idea with a computer. The claims are not patent eligible. 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 4 and 14-15 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 4 and 12-15 are ineligible.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 12 and 4 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 12 recites the limitations, the method further including initially training the neural network to form the nodal and layer connections using a supervised learning process with pre-classified client data examples including the input elements, manually determined goals and reward, and an actual outcome classified as favorable or unfavorable, and subsequently training the neural network to re-form the nodal and layer connections using a supervised learning update training process with supplemental client data examples including the input elements, the goals and the reward previously determined by the neural network, and an actual outcome classified as favorable or unfavorable at page 2, last sub-paragraph. The specification does not describe details of the claimed training and learning of the neural network. It just repeats the claim language. The specification is silent as to how the neural network is trained. Therefore, the inventor(s) have not shown possession of the claimed invention. Claim 4 depends from claim 12 and is rejected for this reason.
Response to Arguments
Applicant's arguments filed 7/31/26 have been fully considered but they are not persuasive.
35 USC § 101
The Applicant states that “the claims integrate any such abstract idea into a practical application under Step 2A Prong Two and recite significantly more under Step 2B” (page 1). The Examiner disagrees with the sentence because the claims are an improvement of the abstract idea only. It is a business solution to the business problem of rewarding desired financial behavior. 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 claimed redetermination of goals and rewards based on changes in both the input elements and the nodal and layer connections represents a technical improvement in how the system adapts and refines its outputs” (page 2) and “This iterative learning process, where the system's own outputs become inputs for subsequent training, represents a specific technical implementation that improves the functioning of the machine learning system itself” (page 2). A technical implementation is not a technical improvement. The claims do not provide an improvement over prior systems and only add details to the abstract idea (e.g., the invention takes one result to improve the model to obtain a new result). The claims do not address a problem particular to computer networks and merely apply the abstract idea of rewarding desired financial behavior on general computer components. The Examiner disagrees that the claims are similar to example 35 (page 3). The claims do not use generic and conventional components in a non-conventional manner. Instead, the invention uses conventional components arranged in a conventional manner to perform a conventional process. Rewarding desired financial behavior is not an unconventional activity. The machine learning algorithms are conventional, and are used in a conventional manner. The claims are an improvement of the abstract idea. 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 claims are rejected under 35 USC § 112 as indicated above in the Office action. The claim language is superficial and provides no detail regarding the claimed training and learning of the neural network.
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bennett Sigmond can be reached at (303) 297-4411. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/M.H./Examiner, Art Unit 3694
/BENNETT M SIGMOND/Supervisory Patent Examiner, Art Unit 3694
Prosecution Insights