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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10/21/2025 has been entered.
Claim Status
The claims filed 10/21/2025 are examined herein.
Claims 19-38 are pending.
Claims 19, 26, and 33 are currently amended.
Claim 20-25, 27-32, and 34-38 are previously presented.
Claims 19, 26, and 33 are independent.
Response to Arguments
Applicant's arguments filed 10/21/2025 have been considered but they are not fully persuasive.
35 U.S.C. 101
Claims 19-38 remain rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Applicant’s arguments have been considered but are not persuasive.
Applicant argues that the currently amended claims, including limitations drawn to "determin[ing], utilizing the one or more machine learning models in real time, that a withdrawal associated with the user account would impact the credit score of the user of the client device; and automatically deny[ing], in real time, the withdrawal associated with the user account", recite a practical application under Step 2A Prong 2 of the subject matter eligibility framework. More specifically, Applicant, pointing to the Specification [0086] for support, argues that the claimed features provide several technical advantages and benefits over conventional solutions, including informing users and performing actions over digital channels with regard to money movement and management based on the implications of datasets and rules that are too large and complex for real-time evaluation solely by human minds or on pen and paper, and notwithstanding that particular mechanisms that impact credit score may change over time (see Remarks, pp. 12-13).
The argument is not persuasive. The claims are drawn to applying a model to a dataset which includes activity and financial data, identifying an income generation opportunity, providing a recommendation, and denying a withdrawal. The additional limitations drawn to applying machine learning models and providing the recommendation or denying a withdrawal in real-time are not indicative of a technical improvement because they are recited in the claims and described in the specification at a high level of generality in a manner which does not convey a technical improvement to one of ordinary skill in the art.
With regards to the machine-learning aspects of the claimed invention, representative claim 19 recites “apply the one or more machine learning models…”. Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea does not integrate a judicial exception into a practical application, as discussed in MPEP § 2106.05(f).
With regards to the real-time aspects of the claimed invention, Applicant argues that the real-time features of the claimed invention are similar to eligible claim 3 of USPTO Example 47. Applicant argues that the instant claims similarly provide a practical application by acting in real time to proactively identify and recommend at least one income generation application on the client device; and by determining an impact on a credit score and denying a withdrawal in real time. However, the limitations drawn to identifying the income generation application and providing the recommendation and denying a withdrawal in real-time are not indicative of a technical improvement. While the instant claims recite recommending an action and denying a withdrawal in real time, these limitations merely describe using generic computer functions as a tool to implement the abstract idea. This is unlike the cited example in that the example eligible claim involves real-time monitoring that modifies network operation, which does improve technology by preventing harmful network events. The example shows more than just generic implementation of an abstract idea because it also causes a meaningful operational impact to computer networks. Here, the additional elements are merely using a computer as a tool to perform an abstract idea. This does not provide integration into a practical application or significantly more.
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 19-38 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims are analyzed under the Alice/Mayo two-part test as described in MPEP 2106.
Step 1
Claims 19-38 are directed to a machine, process, or product, and thus fall within the statutory categories of invention. (Step 1: YES).
Step 2A - Prong 1
The Examiner has identified independent system claim 19 as the claim that represents the claimed invention for analysis and is similar to independent method claim 26 and independent product claim 33. Claim 19 recites the limitations of:
19. A system comprising:
at least one processor; and
at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to:
train one or more machine learning [formulate] models based on a training set of data comprising historical activity data and historical financial data;
access activity data for a user account associated with a client device;
access financial data for a user of the client device from a third-party server located remotely from the system, the financial data comprising a credit score of the user of the client device;
apply the one or more machine learning models to a dataset comprising the activity data clustered with a portion of the historical activity data based on similarities to user activity history and the financial data clustered with a portion of the historical financial data based on similarities to user financial history to determine a set of potential user activities for improving the credit score of the user, wherein the set of potential user activities comprises an income generation activity;
identify, in real time, at least one income generation application on the client device associated with the user account; and
based on determining the set of potential user activities comprising the income generation activity and identifying the at least one income generation application, generate, in real time, an action recommendation to use the at least one income generation application to achieve further income; and
determine, utilizing the one or more machine learning models in real time, that a withdrawal associated with the user account would impact the credit score of the user of the client device; and
automatically deny, in real time, the withdrawal associated with the user account.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as “Certain Methods of Organizing Human Activity”.
The claim limitations delineated in bold above recite a fundamental economic practice, as they pertain to analyzing activity of an entity, generating action recommendations to achieve further income, determining an impact to a credit score, and denying a withdrawal. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic practice, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. MPEP 2106.04(a)(2).
The claim limitations delineated in bold above also recite managing personal behavior, as they pertain to analyzing activity of an entity, generating action recommendations to achieve further income, determining impact to credit score, and denying a withdrawal. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as managing personal behavior, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. MPEP 2106.04(a)(2).
The processor and non-transitory computer-readable storage medium in claim 19 is just applying generic computer components to the recited abstract limitations. The recitation of generic computer components in a claim does not necessarily preclude that claim from reciting an abstract idea. Claims 26 and 33 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims recite an abstract idea)
Step 2A - Prong 2
This judicial exception is not integrated into a practical application. In particular, the independent claims recite the additional elements of:
Claim 19: processor, non-transitory computer-readable storage medium
Claim 26: computer-implemented method (preamble)
Claim 33: non-transitory computer-readable storage medium
The computer hardware/software is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality.
The computer hardware/software is recited in conjunction with training and applying a machine learning model. These limitations merely invoke the additional element as a tool to perform the abstract idea. The machine learning model is described at a high level of generality in Applicant’s specification without any meaningful detail about its structure or configuration. As demonstrated by the specification, the claimed invention does not improve machine learning itself, but merely describes the features at a high level of generality as an implementation tool for performing the abstract idea, which includes generating action recommendations to achieve income based on an analysis of financial data and activity data. Here, the disclosure does not provide sufficient detail such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement to the functioning of the computer or to any other technology or technical field. MPEP 2106.04(d)(1). Both the specification and claims only recite the idea of a solution or outcome and fail to recite how the solution to the problem is accomplished. Thus, even considering the limitations drawn to training and applying machine learning models, the use of the computer hardware/software still merely amounts to adding the words “apply it” (or an equivalent) with the judicial exception. MPEP 2106.05(f).
Therefore, claims 19, 26, and 33 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware 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. MPEP 2106.05(f).
Furthermore, no additional element or combination of elements are other than what is well-understood, routine, conventional activity in the field. The additional elements simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d). In particular, it is well-understood, routine, conventional activity previously known to the industry to train a machine learning model to correlate data and to predict outcomes given a set of input data, as evidenced by:
[1] US 2019/0129436 A1 [0015] … For example, as well-known, neural networks, or other machine learning systems, can be trained to produce configured output based on training data provided to the neural network or other machine learning system in a training phase. …
[2] US 2005/0225552 A1 [0154] “The techniques for specifying and training a neural net is well known in the art of artificial intelligence (AI)….Typically, a neural net is specified by defining input and output parameters, number of layers, and number of neurons in each layer. A neural net can be thought of as a trainable nonlinear mapping between input parameters and output variables.”
Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Thus, claims 19, 26, and 33, are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent Claims
Dependent claims 20-25, 27-32, and 34-38 further define the abstract idea that is present in their respective independent claims 19, 26, and 33 and thus correspond to “Certain Methods of Organizing Human Activity” and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea without significantly more.
Thus, claims 19-38 are not patent-eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Rana (US 2018/0114163 A1) discloses a system for determining an optimal strategy pertaining to a business opportunity. In order to determine the optimal strategy, initially, a strategy type selection module stores a set of strategies in a system database. In one aspect, each strategy, of the set of strategies, may be associated to one or more business opportunities. Subsequently, the strategy type selection module selects a subset of the set of strategies applicable to a business opportunity of the one or more business opportunities. In one aspect, the subset may be selected based on a set of parameters associated to the business opportunity. Post selection of the subset, a strategy optimizer module analyzes each strategy of the subset by using one or more predefined computational libraries. In one aspect, a strategy, of the subset, may be analyzed to determine an impact of the strategy. After analyzing each strategy, the strategy optimizer module determines one or more strategies, of the subset, to be implemented based on the impact, pertaining to the one or more strategies, and a set of Key Performance Indicators (KPI) associated to the business opportunity.
Burrow (US 8,533,092 B1) discloses quantitatively improving a financial evaluation. A plurality of factor scores based on a plurality of factors is determined. A total score is calculated based on the plurality of factor scores. A plurality of actions is identified that will influence at least a portion of the plurality of factor scores. The actions are ranked based on their total impact on the plurality of factor scores. A most important action of the plurality of actions to improve the total score is determined.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC T WONG whose telephone number is (571)270-3405. The examiner can normally be reached 9am-5pm M-F.
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, Michael W Anderson can be reached at 571-270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ERIC T WONG/Primary Examiner, Art Unit 3693
ERIC WONG
Primary Examiner
Art Unit 3693