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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/24/2026 has been entered.
Response to Amendment
Applicant’s “Amendment” filed on 02/24/2026 has been considered.
Claims 1 and 7 are amended. Claims 1, 3-7, and 9-12 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.
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, 3-7, and 9-12 are rejected under 35 USC 101. The claimed invention is directed to non-statutory subject matter because claims 1 and 7 are directed to an abstract idea without significantly more. Claims 3-6, and 9-12 fail to remedy these deficiencies.
The claims 1 and 7 recite receiving internal data, receiving external data, training the machine learning model, deploy the trained machine learning model, generating the financial propensity outcomes from the trained machine learning model, integrating the financial propensity outcomes into a payroll instruction platform, visualizing the behavioral data, generating alerts and at least one recommended action, and using the internal data…to fine tune the machine learning model.
The Claims 1 and 7 recite training the machine learning model, deploy the trained machine learning model, generating the financial propensity outcomes from the trained machine learning model, and tuning the machine learning model using the collected data step as drafted, are processes that under broadest reasonable interpretation, cover performance of the limitation by utilizing mathematical algorithms/functions but for the recitation of generic computer components. That is, other than reciting “a non-transitory computer readable medium”, nothing in the claim element precludes the steps from practically being performed by utilizing mathematical algorithms. For example, but for the “a non-transitory computer readable medium” language, in the context of these claims encompasses a user manually applies data to a machine learning model to train the machine learning model, deploys the trained machine model to generate financial propensity outcomes, and tunes/trains/adjust the machine learning model. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by utilizing mathematical algorithms/functions but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Claims 1 and 7 recite integrating to authorize disbursements, visualizing and generating steps as drafted, are processes that under broadest reasonable interpretation, cover performance of managing commercial interactions and fundamental economic practices, but for the recitation of generic computer components. That is, other than reciting “a non-transitory computer readable medium”, nothing in the claim element precludes the steps from practically being performed by organizing human activity for commercial interactions and fundamental economic practices. For example, but for “a non-transitory computer readable medium” in the context of these claims encompasses a person manually authorizes to disburse funds from payroll accounts to custody account based on the financial propensity outcomes, draws/visualizes key metrics and alerts the client with at least one recommended action. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by managing commercial interactions and fundamental economic practices 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.
This judicial exception is not integrated into a practical application because receiving steps are recited at a high level of generality (i.e., as a general means of receiving internal data and external data step) and amounts to mere data gathering, which is a form of insignificant extra-solution activity. 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 receiving, training, deploying, generating, integrating, visualizing, alerting, and tuning in a computer environment. The claimed computer component such as the non-transitory computer readable medium is recited at a high level of generality and is merely invoked as a tool to perform receiving, training, deploying, generating, integrating, visualizing, alerting, and tuning 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 1 and 7 are directed to an abstract idea.
The claims 1 and 7 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 using the ton-transitory computer readable medium to perform receiving, training, deploying, generating, integrating, visualizing, alerting, and tuning steps amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, the claim does not amount to significantly more than the recited abstract idea (Step 2B: NO). The claims 1 and 7 are not patent eligible.
Claims 3-6 and 9-12, disclose insignificant helpful content to further describe content, such as the financial propensity outcomes comprise turnover, engagement, savings, and next best options, which are merely descriptive content to further limit the abstract idea but not make it less abstract. Thus, the claims 3-6 and 9-12 are directed to an abstract idea.
This judicial exception is not integrated into a practical application because descriptive content in claims 3-6 and 9-12 further limit the abstract idea but not make it less abstract. Thus, the claims 3-6 and 9-12 are directed to an abstract idea.
There are no additional claim element limitations recited in the claims 3-6 and 9-12. Therefore, the claim does not amount to significantly more than the recited abstract idea (Step 2B: NO). The claims 3-6 and 9-12 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 1, 3-5, 7, and 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2019/0180358 to Nandan et al., in view of U.S. Patent Application Publication No. 2013/0238475 to Green, and further in view of U.S. Patent Application Publication No. 2014/0379611 to Goldman.
With regard to claims 1 and 7, Nandan discloses a method for generating financial propensity outcomes using a machine learning model, the method comprising:
receiving internal data on a client, the internal data comprises transactional data, behavioral data, demographic data, credit data, and communication data (paragraph 22, The machine learning and predictive analytics system may collect data from a variety of internal and external data sources (e.g., wealth and assets, life style and interests, demographics, macroeconomic factors, etc.) );
receiving external data, the external data comprises publicly available data (paragraphs 22 and 63, The external data source may be a public database and/or a web feed associated with the subject.);
training the machine learning model using historical financial data to model forecasts and predictive analytics (paragraphs 22, 41 and 59, The training set 254 may be generated from historic procurement data. FIG. 4 illustrates the data flow 400 of the machine learning and predictive analytics system 300 of FIG. 3. For example, data may be collected at the input. These may include various financial sources, as well as other personal data from various internal, external, or third part data sources, as described above. The machine learning and predictive analytics system 300 may cluster this data into similar groups for machine learning classification and training. );
deploying the trained machine learning model and providing the internal data and the external data as data input to the trained machine learning model (paragraphs 22, 26, and 59, The data in an example, during a training phase, the training sets 103 are input into the machine learning functions 104. FIG. 4 illustrates the data flow 400 of the machine learning and predictive analytics system 300 of FIG. 3. For example, data may be collected at the input. These may include various financial sources, as well as other personal data from various internal, external, or third part data sources, as described above.); and
generating the financial propensity outcomes associated with the client from the trained machine learning model through forecasts and predictive analytics (abstract, paragraphs 22, 61 and 67, At block 505, a processor of the system 300, for example, may generate a recommendation based on the financial forecast, ratio, index, and/or other calculation. In an example, the recommendation may include one or more financial actions for the subject to take or elect based on the predicted life event. ).
However, Nandan does not disclose integrating the financial propensity outcomes into a payroll instruction platform that automatically distributes funds from employer payroll accounts to partner custody accounts based on client authorizations, thereby improving technical functioning of automated payroll distribution systems; visualizing key metrics and actionable insights in time series based on the internal data to detect noticeable data inconsistencies and sudden behavioral changes; generating, on detection of noticeable data inconsistencies and sudden behavioral changes, alerts and at least one recommended action to the client for selection when noticeable data inconsistencies or sudden changes in behavior exceed a predetermined threshold; and using the internal data, the external data, the financial propensity outcomes, and client- selected action to fine tune the machine learning model.
However, Green teaches visualizing key metrics and actionable insights in time series based on the internal data to detect noticeable data inconsistencies and sudden behavioral changes (Generalized financial object 300 may allow an individual's financial decisions to be optimized to maximize (or minimize) specific success metrics or financial performance metrics (such as the individual's net worth). In an exemplary embodiment, a behavioral pattern in generalized financial object 300 codifies an interrelationship in a dimension other than time in the multidimensional space (although, in other embodiments, the interrelationship may be between variables as a function of time). Note that each of the aforementioned derivatives describes the rate of change within the data and also the rate of change of the user's behavior with respect to historical activity, fig. 3, paragraphs 33, 46-48, and 58-59); generating, on detection of noticeable data inconsistencies and sudden behavioral changes, alerts and at least one recommended action to the client for selection when noticeable data inconsistencies or sudden changes in behavior exceed a predetermined threshold (For example, if the MWWH examined the spend velocity of the user's cash flow model and determined that the spend velocity had increased, and that the rate of increase was accelerating (positive spend acceleration), a user may be alerted to this behavior so that they have the option of correcting it. A user of the MWWH may have an increased spend velocity and a positive spend acceleration. In this example, the MWWH may call the rule-driven software application embedded in a financial object to run structural alterations to a mathematical model with the goal or financial performance metric of turning the spend acceleration negative, thereby slowing down the rate at which the spend velocity is increasing. the electronic device optionally provides a recommendation to the user on how to achieve a desired financial goal based on the comparison. Examiner notes that when the spend acceleration threshold is above zero, i.e., positive, alerts and a recommendation/corrective action is provided, which is considered as “generating, on detection of noticeable data inconsistencies and sudden behavioral changes, alerts and at least one recommended action to the client for selection when noticeable data inconsistencies or sudden changes in behavior exceed a predetermined threshold”. paragraphs 58-59, 93, and 98); and using the internal data, the external data, the financial propensity outcomes, and client- selected action to fine tune the machine learning model (One of these benefits is learning new behaviors or alternatives by examining the financial output values determined using the functional representations of more successful users. If this second process achieves a corresponding testing or financial performance metric, the MWWH may store the successful structural changes (including one or more alternatives) into a child financial object, and may then leverage this child financial object to create a set of alerts or cues to help steer the user toward behavior(s) that follows the results of those optimizations (assuming that the user previously consented to this kind of behavior modification). In addition, feedback module 970 may learn, and thus may modify recommendations made to the user, based on the user's response to the recommendations (as evidenced by the user's subsequent behavior in data 948).,paragraphs 77, 94, 106, and 112).
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 Nandan to include, visualizing key metrics and actionable insights in time series based on the internal data to detect noticeable data inconsistencies and sudden behavioral changes; generating, on detection of noticeable data inconsistencies and sudden behavioral changes, alerts and at least one recommended action to the client for selection when noticeable data inconsistencies or sudden changes in behavior exceed a predetermined threshold; and using the internal data, the external data, the financial propensity outcomes, and client- selected action to fine tune the machine learning model, as taught in Green, in order to provide the functional representation to the user to facilitate financial decision-making (Green, paragraph 11).
However, Goldman teaches integrating the financial propensity outcomes into a payroll instruction platform that automatically distributes funds from employer payroll accounts to partner custody accounts based on client authorizations, thereby improving technical functioning of automated payroll distribution systems (The system may automatically enroll the employee in a retirement plan using the determined contribution level, and it may monitor the plan's value and make automatic adjustments to the contribution amounts to keep the employee on track toward retirement goals. If this is a new employee or an employee who is not yet enrolled in the employer's retirement plan, the system may also transmit an instruction to enroll the employee in a retirement plan. Optionally, the system may provide the employee an ability to opt out of or change the amount of any or all contributions (step 209). If the system receives a request from the employee to opt out of or change any contribution amount, the system may adjust or eliminate the contribution amount in accordance with the employee's instruction (step 211). Abstract, paragraph 40).
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 Nandan to include, integrating the financial propensity outcomes into a payroll instruction platform that automatically distributes funds from employer payroll accounts to partner custody accounts based on client authorizations, thereby improving technical functioning of automated payroll distribution systems, as taught in Goldman, in order to make contributions to a retirement account for the employee in the employer's retirement plan by automatic payroll deduction of the periodic contribution amount for the employee (Goldman, paragraph 10).
With regard to claims 3 and 9, Nandan discloses the financial propensity outcomes comprise turnover, engagement, savings, and next best options (abstract, paragraphs 22, 59-60, 66, and 74, The processor may use machine learning, statistical analysis, simulation, and/or modeling techniques to analyze the data, predict the future life event, and calculate the at least one of a financial forecast, a ratio, and an index, which may represent likelihood of the subject taking a financial action with a financial institution. The processor may also generate a recommendation for the subject to elect the financial action or other product or service based on the predicted life event. For example, the financial action may include applying for, requesting information related to, or securing a loan, a mortgage, a credit card, a line of credit, banking options, crowdfunding, financial savings, investment options, financial planning services, or other products or services. Through financial data, opening banking, real estate assessments, income, savings, debt determinations, etc).
With regard to claims 4 and 10, Nandan discloses the next best options comprise provision of at least one of at least one financial recommendation or at least one financial assistance option to assist the client's financials based on the data input (paragraph 22, The system may also predict probability of distribution of timing of life events and probability distribution of financial impact. The system may employ multiple sets of models in the processing and prediction of these events to generate up-to-date, real-time, or near-real-time outputs. These outputs may include forecasts, ratios, financial well-being indices, as well as recommendations for further actions.).
With regard to claims 5 and 11, Nandan discloses the engagement estimates an amount of time that the client engages a financial specialist or financial assistant for service provision (paragraphs 59-60 and 66, The machine learning and predictive analytics system 300 may implement the machine learning processes and techniques described herein to predict probability of life events, including distribution, timing, and financial impact. The predictions may include estimate date of life event).
Claims 6 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2019/0180358 to Nandan et al., U.S. Patent Application Publication No. 2013/0238475 to Green, and U.S. Patent Application Publication No. 2014/0379611 to Goldman., and further in view of U.S. Patent Application Publication No. 2016/0180264 to Beck et al.
With regard to claims 6 and 12, the combination of references substantially discloses the claimed invention, however, the combination of references not disclose the turnover estimates the likelihood of the client leaving an employer based on the internal data.
However, Beck teaches the turnover estimates the likelihood of the client leaving an employer based on the internal data (A system for retention risk determination is disclosed. In some embodiments, the system for retention risk determination receives a set of employee transaction data (e.g., employee title changes, employee location changes, company division changes, etc.) and creates a model for determining the chance or risk that a given employee will leave the company (e.g., within the next year), paragraph 26).
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 combination of references to include, the turnover estimates the likelihood of the client leaving an employer based on the internal data, as taught in Beck, in order to determine retention risk (Beck, abstract).
Response to Arguments
Applicants' arguments filed on 02/24/2026 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 disclose integrating the financial propensity outcomes into a payroll instruction platform that
automatically distributes funds from employer payroll accounts to partner custody accounts based on client authorizations, thereby improving technical functioning of automated payroll distribution systems”.
Examiner directs Applicants' attention to the office action above.
Applicants remark that “ the amended claims now require integrating the financial propensity outcomes into a payroll instruction platform that automatically distributes funds from employer payroll accounts to partner custody accounts based on client
authorizations. This integration is not a post-solution field of use, but rather creates a bidirectional technical relationship where the platform's transaction data trains the ML model, and the ML outcomes inform decisions within the platform's specific technical architecture. Accordingly, claims 1, 3-7, and 9-12 are directed to patent-eligible subject matter under 35 U.S.C. § 101, and withdrawal of this rejection is respectfully requested”.
Examiner directs Applicants' attention to the office action above.
Conclusion
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/ARIEL J YU/Primary Examiner, Art Unit 3627
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