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
This action is in reply to the response to non-final filed on 7/16/2025, wherein:
Claims 1-22 remain as original; and
Claims 1-22 are currently pending and have been examined.
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 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a system for predicting an account balance which is considered a judicial exception because it falls under Certain Methods of Organizing Human Activity such as fundamental economic principles or practices, including agreements in the form of contracts and business relations. This judicial exception is not integrated into a practical application as discussed below and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as discussed below.
This rejection follows the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed Reg 4, January 7, 2019, pp. 50-57 (“2019 PEG”)(MPEP 2106).
Analysis
Step 1 (Statutory Categories) – 2019 PEG pg. 53 (See MPEP 2106.03)
Claims 1-22 are directed to the statutory category of a process.
Step 2A, Prong 1 (Do the claims recite an abstract idea?) – 2019 PEG pg. 54 (See MPEP 2106.04(a)-(c))
For independent claim 1, the claim recites an abstract idea of: predicting an account balance. The steps of independent claim 1 recite the abstract idea (in bold below) of: A system comprising: one or more processors; and a memory storing programming instructions that, when executed by the one or more processors, cause the system to: retrieve historical account activity data for a plurality of accounts, the historical account activity data comprising historical inflow data and historical outflow data; construct a training data set based on the historical account activity data; generate a combined prediction model configured to forecast future inflow activity and future outflow activity for a multi-step time horizon; train the combined prediction model based on the training data set, said training comprising optimizing an objective function of the prediction model by adjusting at least one weight parameter of the objective function based on an accuracy of one or more predictions generated by the combined prediction model during said training, the predictions comprising a combination of projected inflow predictions, projected outflow predictions and differences between the projected inflow predictions and the projected outflow predictions; receive, from one or more third-party systems, current inflow activity data, current outflow activity data, and current balance information associated with one or more target accounts; generate, by the combined prediction model, a future inflow forecast and a future outflow forecast for the one or more target accounts based on a combination of the current inflow activity data, the current outflow activity data, and the current balance information, the future inflow forecast comprising an inflow forecast for each of a plurality of time steps in the multi-step time horizon, and the future outflow forecast comprising an outflow forecast for each of a plurality of time steps in the multi-step time horizon; determine, based on the future inflow forecast and the future outflow forecast, a future balance forecast for the one or more target accounts that comprises a balance forecast for each of a plurality of time steps in the multi-step time horizon; and retrain the combined prediction model by: evaluating a performance of the combined prediction model based on one or more performance metrics, determining whether at least one among the one or more performance metrics falls below a predetermined threshold based on the evaluating, adjusting the at least one weight parameter based on said determining, and optimizing the objective function based on the adjusted at least one weight parameter. Independent claim 1, as drafted, is a process that, under the broadest reasonable interpretation, covers Certain Methods of Organizing Human Activity, since they recite fundamental economic principles or practices including agreements in the form of contracts and business relations. If the claim limitations, under the broadest reasonable interpretation, covers methods of organizing human activity but for the recitation of additional elements including generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Other than reciting the abstract idea, the independent claim recites additional elements including generic computer components such as “A system comprising one or more processors and a memory storing programming instructions executed by the one or more processors, a combined prediction model, and one or more third-party systems”, and nothing in the claim precludes the steps from being performed as a method of organizing human activity. Accordingly, the independent claim recites an abstract idea.
Dependent claims 2-22 recite similar limitations as independent claim 1; and when analyzed as a whole are held to be patent ineligible under 35 U.S.C 101 because the additional recited limitations only refine the abstract idea further. Other than reciting the abstract idea, the dependent claims recite similar additional elements including generic computer components as the independent claims, such as “the system, the programming instructions executed by the one or more processors, the combined prediction model, a graphical user interface (GUI), one or more user devices, and one or more client devices”. If a claim limitation, under its broadest reasonable interpretation, covers commercial or legal interactions, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Step 2A, Prong 2 (Does the claim recite additional elements that integrate the judicial exception into a practical application?) – 2019 PEG pg. 54 (See MPEP 2106.04(d)-(c))
This judicial exception is not integrated into a practical application. In particular, independent claim 1 only recites the additional elements of “A system comprising one or more processors and a memory storing programming instructions executed by the one or more processors, a combined prediction model, and one or more third-party systems”. A plain reading of the Figures and associated descriptions in the specification reveals that generic processors may be used to execute the claimed steps. The additional elements are recited at a high level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)) and limits the judicial exception to a particular environment (See MPEP 2106.05(h)). Mere instructions to apply an exception using a generic computer component and limiting the judicial exception to a particular environment doesn’t integrate the abstract idea into a practical application in Step 2A. 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. Hence, independent claim 1 is directed to an abstract idea.
Dependent claims 2-22, recite similar additional elements as the independent claim including generic computer components, such as “the system, the programming instructions executed by the one or more processors, the combined prediction model, a graphical user interface (GUI), one or more user devices, and one or more client devices”. The judicial exception is not integrated into a practical application because the additional elements in the dependent claims are also recited at a high-level of generality such that it amounts to more no more than mere instructions to apply the exception using generic computer components. Therefore, the additional elements do not integrate the abstract idea into a practical application because they also do not impose any meaningful limits on practicing the abstract idea. Also, the claims do not affect an improvement to another technology or technical field; the claims do not amount to an improvement of the functioning of a computer system itself; the claims do not effect a transformation or reduction of a particular article to a different state or thing; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?) – 2019 PEG pg. 56 (See MPEP 2106.05)
Independent claim 1 does 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 recited additional elements amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)) and limits the judicial exception to the particular environment of computers (See MPEP 2106.05(h)). The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the function of the elements when each is taken alone. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept in Step 2B.
In addition, the dependent claims 2-22 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 dependent claims to perform the claimed limitations, amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)). Similar to the independent claim, mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Also, for the same reasoning as the independent claim, the additional elements of the limitations of the dependent claims, when considered individually and as an ordered combination, together do not offer significantly more than the sum of the functions of the elements when each is taken alone and the dependent claims as a whole, do not amount to significantly more than the abstract idea itself. For these reasons, the dependent claims also are not patent eligible.
Subject Matter Overcoming 35 USC §102/§103
Claims 1-22 would be allowable if rewritten to overcome the rejections under 35 U.S.C. 101 set forth in this Office Action. Examiner’s statement of reasons for subject matter of independent claim 1 overcoming the prior art rejections was previously given in the Non-Final rejection dated 2/26/2025 and are hence not repeated here.
Response to Arguments
Applicant’s arguments with respect to claims 1-22 have been fully considered by the Examiner. Applicant’s arguments with respect to the rejection of claims 1-22 under 35 USC 101 have been fully considered by the Examiner. However, the Examiner does not find the Applicant’s arguments persuasive, and therefore the rejections of claims 1-22 under 35 USC 101 are maintained.
The Applicant argues that under Step 2A of Prong 2, the claimed combined prediction model represents a practical application of machine learning technology in the field of forecasting technology that represents a technical improvement by modeling inflow, outflow, balance and/or any number of other variables together in a combined model, for any number of time steps, and for any number of users, all at once which goes beyond merely applying abstract ideas on computers.
The Applicant also argues that their claims under Step 2B recite a specific combination of steps that operate in an unconventional way to provide a technological solution to a technological problem similar to claims 2 and 3 of Example 35; and amount to significantly more than merely implementing an abstract idea using conventional computer components. Applicant further states in their Remarks that the ordered combination of claimed steps for training and retraining the combined prediction models using specific types of data and processing techniques addresses unique problems in forecasting account balances and overcomes the drift problem in conventional forecasting systems by combining one or more machine learning models trained to forecast inflow data and outflow data separately into a single model to limit the error in the balance forecast. Applicant further states that the claims recite a specific implementation of a deep learning model with particular features that is configured to learn from historical data from multiple accounts, intake multiple variable inputs, and generate multi-variable, multi-step forecasts for multiple entities, all at once which: reduces drift of forecasted values from actual balances, decreases computational resources needed through efficient model design utilizing a combined prediction model, enables real-time processing of multiple accounts simultaneously, and improves scalability of the forecasting system.
Examiner respectfully disagrees with Applicant’s argument that the claimed limitations are indicative of integration into a practical application under Prong 2 of Step 2A of the PEG. Using a computer to: train a prediction model using historical inflow and outflow account data; adjust weight parameters; receive current inflow, outflow, and account balance data; forecast future inflow, outflow, and account balance data; and retrain the combined prediction model; is nothing more than executing instructions to apply the exception to a computer. This is interpreted by the Examiner as using a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)). The additional elements of “A system comprising one or more processors and a memory storing programming instructions executed by the one or more processors, a combined prediction model, and one or more third-party systems” are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). There is no improvement to the claimed computer elements, the prediction model, or to any other technology or technical field. The only improvements identified in the specification are generic speed and efficiency improvements inherent in applying the use of a computer to any task. Therefore, the claimed limitations do not meet the criteria or considerations as indicative of integration into a practical application.
Examiner respectfully disagrees with Applicant’s further argument under Step 2B of the PEG, that the amended claim limitations recite a specific combination of steps that operate in an unconventional way that amount to an inventive concept that renders the claims patent eligible because the claims provide for improvements to the technical field. As stated previously, using a computer to: train a prediction model using historical inflow and outflow account data; adjust weight parameters; receive current inflow, outflow, and account balance data; forecast future inflow, outflow, and account balance data; and retrain the combined prediction model; is nothing more than executing instructions to apply the exception to a computer. The additional elements amount 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. Examiner respectfully disagrees with Applicant’s further argument that their claims are similar to Example 35. The combination of the steps in Example 35 of “the ATM providing a random code, the mobile communication device’s generation of the image having encrypted code data in response to the random code, the ATM’s decryption and analysis of the code data, and the subsequent determination of whether the transaction should proceed based on the analysis of the code data” was a non-generic way to ensure that the customer’s identity is verified in a secure manner that is more than the conventional verification process employed by the ATM alone”. The combination of obtaining information from the mobile communications device instead of the ATM keypad, and using the image instead of a PIN to verify a customer’s identity describes a process that differs from the routine and conventional sequence of events normally conducted by ATM verification using a PIN; and the combination of steps set up a sequence of events that addressed unique problems associated with bank cards and ATMs. Unlike claim 35, the Applicant’s claimed combination of elements is not different from the routine and conventional sequence of events normally conducted for training a machine learning model to perform a calculation and Applicant’s claimed limitations also do not provide a technical solution to a technical problem. In regards to Applicant’s arguments regarding training and retraining the combined prediction model, Applicant’s claims are similar to claim 2 of Example 47 which included receiving continuous training data, using the computer to discretize the continuous training data to generate input data, training the artificial neural network using the input data, and detecting anomalies using the trained artificial neural network; which represented mere instructions to implement an abstract idea on a computer. Furthermore, the Federal Circuit in Recentive Analytics, Inc., v. Fox Corp., Appeal No. 2023-2437 (Fed. Cir. Apr. 18, 2025), held that “patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101”. Similar to Recentive Analytics, Inc., v. Fox Corp., Applicants claims fail to disclose an improvement to the machine learning model. In addition, as indicated further in the final rejection above, the claimed limitations under MPEP 2106.05(d)(ii) amount to well-understood routine and conventional activities of: receiving and transmitting data over a network, the performance of repetitive calculations, electronic recordkeeping, storing and retrieving information in memory, and presenting results of an analysis. Therefore, the rejections of the claims pursuant to 35 USC 101 are maintained.
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.
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/PAUL S SCHWARZENBERG/Examiner, Art Unit 3695 9/9/2025