DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, 18/153,703, was filed on Jan. 12, 2023, and does not claim foreign priority or domestic benefit to any other application.
The effective filing date is after the AIA date of March 16, 2013, and so the application is being examined under the “first inventor to file” provisions of the AIA .
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.
Status of the Application
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/02/2025 has been entered.
This Non-Final Office Action is in response to Applicant’s communication of 12/02/2025.
Claims 1-20 are pending, of which claims 1, 11, and 17 are independent.
Claims 1, 11, and 17 are currently amended.
All pending claims have been examined on the merits.
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 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to an abstract idea, without “significantly more”.
The abstract idea elements in independent claim 17 are shown in italic font. The “additional elements” and “extra solution steps” are shown in underlined font:
17. (Currently Amended) 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:
transmit a first request call to a first data source, from multiple data sources, for at least one of historical transaction activity or deposit transaction source information;
transmit a second request call to a second data source, from the multiple data sources, for at least one of client device data or user activity times on one or more applications for a user account from the multiple data sources;
receive, via the first request call to multiple data source and the second request call to the second data source, the historical transaction activity and the deposit transaction source information from the first data source and the client device data and the user activity times on the one or more applications for the user account from the second data source;
generate, for the user account, a predicted deposit transaction activity time, a predicted deposit transaction activity amount, and a machine learning confidence score for the predicted deposit transaction activity time and the predicted deposit transaction activity amount by utilizing a machine learning deposit transaction predictor model to analyze the historical transaction activity, the deposit transaction source information, the client device data, and the user activity times, wherein the machine learning deposit transaction predictor model is trained utilizing comparisons between predictions generated for historical user account activities of user accounts and ground truth deposit transaction data for the user accounts;
update a universally accessible deposit transaction prediction data source with the predicted deposit transaction activity time, the predicted deposit transaction activity amount, and the machine learning confidence score;
based on receiving a first data request for prediction data stored in the universally accessible deposit transaction prediction data source from a first downstream application corresponding to the first data source,
provide the predicted deposit transaction activity time and the predicted deposit transaction activity amount from the universally accessible deposit transaction prediction data source to the first downstream application to cause the first downstream application to display a selectable option to select a pre-deposit transaction amount based on an available deposit balance determined from the predicted deposit transaction activity time and the predicted deposit transaction activity amount; and
providing the machine learning confidence score from the universally accessible deposit transaction prediction data source to the first downstream application to cause the first downstream application to provide access to the available deposit balance based on the machine learning confidence score; and
based on receiving a second data request for the prediction data stored in the universally accessible deposit transaction prediction data source from a second downstream application corresponding to the second data source, provide the predicted deposit transaction activity time and the predicted deposit transaction activity amount from the universally accessible deposit transaction prediction data source to the second downstream application to cause the second downstream application to display the predicted deposit transaction activity time and the predicted deposit transaction activity amount within a chatbot service application, wherein the second data source is different from the first data source.
More specifically, claims 1-20 recite an abstract idea: “Certain Methods of Organizing Human Activity", specifically “Commercial or Legal Interactions (Including Agreements in the form of Contracts; Legal Obligations; Advertising, Marketing, or Sales Activities or Behaviors; Business Relations)”, as discussed in MPEP §2106(a)(2) Parts (I) and (II), and in the 2019 Revised Patent Subject Matter Eligibility Guidance.
The “Commercial or Legal Interactions” elements include:
“generate, for the user account, time-based deposit prediction data and time-based deposit prediction data by utilizing machine learning deposit transaction predictor model to analyze the historical transaction activity, deposit transaction source information, client device data, and user activity times”.
“providing the machine learning confidence score from the universally accessible deposit transaction prediction data source to the first downstream application to cause the first downstream application to provide access to the available deposit balance based on the machine learning confidence score”
The Examiner interprets that the generation of “time-based deposit prediction data” and “time-based deposit prediction data”, based on analysis of “historical transaction activity, deposit transaction source information, client device data, and user activity times” is a “method of organizing human activity", specifically “Commercial or Legal Interactions”.
Moreover, claims 1-20 recite “Mathematical Concepts", specifically “Mathematical Relationships”, “Mathematical Formulas or Equations”, and “Mathematical Calculations”, as discussed in MPEP §2106.04(a)(2) Part (IV), and in the 2019 Revised Patent Subject Matter Eligibility Guidance.
The mathematic elements include:
“generate, for the user account, a predicted deposit transaction activity time, a predicted deposit transaction activity amount, and a machine learning confidence score for the predicted deposit transaction activity time and the predicted deposit transaction activity amount by utilizing a machine learning deposit transaction predictor model to analyze the historical transaction activity, the deposit transaction source information, the client device data, and the user activity times,” and
“wherein the machine learning deposit transaction predictor model is trained utilizing comparisons between predictions generated for historical user account activities of user accounts and ground truth deposit transaction data for the user accounts”.
The Examiner interprets that the generation of “time-based deposit prediction data” and “time-based deposit prediction data”, by utilizing a generic “machine learning deposit transaction predictor model”, and “and a machine learning confidence score” to analyze “historical transaction activity, deposit transaction source information, client device data, and user activity times” is a “mathematical concept", specifically “Mathematical Calculations”, even if the mathematics used to generate the output data (based on the input data) is not specifically recited in the claims.
The Examiner interprets that the mere “utilizing” of a generic “machine learning deposit transaction predictor model” is not “substantially more” than an abstract idea of using a machine learning model.
The “additional elements” include: “at least one processor”, and “at least one non-transitory computer-readable storage medium storing instructions”.
The “additional extra-solution elements” include:
“transmit a first request call to a first data source, from multiple data sources, for at least one of historical transaction activity or deposit transaction source information”,
“transmit a second request call to a second data source, from the multiple data sources, for at least one of client device data or user activity times on one or more applications for a user account from the multiple data sources”,
“receive, via the first request call to multiple data source and the second request call to the second data source, the historical transaction activity, the deposit transaction source information, the client device data, and the user activity times on the one or more applications for the user account”,
“update a universally accessible deposit transaction prediction data source with the predicted deposit transaction activity time, the predicted deposit transaction activity amount, and the machine learning confidence score”,
“receiving a first data request for prediction data stored in the universally accessible deposit transaction prediction data source from a first downstream application corresponding to the first data source”,
“provide the predicted deposit transaction activity time and the predicted deposit transaction activity amount from the universally accessible deposit transaction prediction data source to the first downstream application”,
“display a selectable option to select a pre-deposit transaction amount based on an available deposit balance determined from the predicted deposit transaction activity time and the predicted deposit transaction activity amount”,
“receiving a second data request for the prediction data stored in the universally accessible deposit transaction prediction data source from a second downstream application corresponding to the second data source”,
“provide the predicted deposit transaction activity time and the predicted deposit transaction activity amount from the universally accessible deposit transaction prediction data source to the second downstream application”,
“display the predicted deposit transaction activity time and the predicted deposit transaction activity amount within a chatbot service application, wherein the second data source is different from the first data source”.
This abstract idea is not integrated into a practical application, because:
The claim recites an abstract idea with additional generic computer elements. The generically recited computer elements (“at least one processor” and “at least one non-transitory computer-readable storage medium storing instructions”) do not add a meaningful limitation to the abstract idea, because they amount to simply implementing the abstract idea on a computer. The claim amounts to adding the words "apply it" (or an equivalent) with the abstract idea, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea.
The extra-solution activities (“transmit a first request call”, “transmit a second request call”, “receive … data”, “update … a data source”, “receiving … a data request”, “provide … data”, and “display a selectable option”) do not add a meaningful limitation to the method, as they are insignificant extra-solution activity;
The combination of the abstract idea with the additional elements (generically recited computer elements), and/or with the extra-solution activities, does not integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea, because:
When considering the elements "alone and in combination" (“at least one processor” and “at least one non-transitory computer-readable storage medium storing instructions”), they do not add significantly more (also known as an "inventive concept") to the exception, because they amount to simply implementing the abstract idea on a computer. Instead, they merely add the words "apply it" (or an equivalent) with the abstract idea, or mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea.
In regards to the extra solution activities “transmit”, “receive … data”, “update … a data source”, “receiving … a data request”, “provide … data”, “display a selectable option”, and “display the predicted deposit transaction activity time and the predicted deposit transaction activity amount”, these are well-understood, routine, conventional computer functions recognized by the court decisions listed in MPEP § 2106.05(d).
More specifically, in regards to the “update a … data source” step (which is equivalent to a “storing” step), see the court cases Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) (storing and retrieving information in memory); and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (storing and retrieving information in memory).
More specifically, in regards to the “transmit a first request call”, “transmit a second request call”, “receive … data”, “receiving … a data request”, and “provide … data” steps (which are equivalent to “communicating” steps), see the court cases OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network) and (presenting offers and gathering statistics), OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93; buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Moreover, in regards to the “display a selectable option”, and “display the predicted deposit transaction activity time and the predicted deposit transaction activity amount” steps, see Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 120 U.S.P.Q.2d 1844 (Fed. Cir. 2016) (Holding that the claimed menu graphic user interface is an abstract idea under 35 USC §101, because claimant "[did] not claim a particular way of programming or designing the software to create menus that have these features, but instead merely claims the resulting systems").
Moreover, in regards to “apply it”, according to MPEP § 2106.05(f)(2):
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
The Examiner holds that the independent claims “use a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data)” or “simply add a general purpose computer or computer components after the fact to an abstract idea”.
Independent claims 1 and 11 are rejected on the same grounds as independent claim 17. Independent claim 11 is also rejected on the grounds that it recites a non-transitory computer-readable medium, which is merely another generic computer component.
In regards to claim 2,
2. (Currently Amended) The computer-implemented method of claim 1, further comprising:
receiving, via the second request call to the second data source, geo-location data from a client device corresponding to the user account; and
generating, for the user account, the predicted deposit transaction activity time and the predicted deposit transaction activity amount by utilizing the machine learning deposit transaction predictor model to analyze the geo-location data.
The “receiving … data” step is mere extra-solution activity, and the “generating … prediction data” step is merely an abstract idea on a general purpose computer (and using a generic machine learning model on a generic computer).
In regards to claim 3,
3. (Previously Presented) The computer-implemented method of claim 1,
wherein the second data sources comprise a client device corresponding to the user account, a computer network corresponding to a deposit transaction source, or a user account transaction activity data repository.
This feature merely further limits the source of the extra-solution activity (“receiving … data” step).
In regards to claim 4,
4. (Currently Amended) The computer-implemented method of claim 1, further comprising
utilizing the machine learning deposit transaction predictor model with user account data to determine one or more predicted dates for one or more predicted deposit transactions or a predicted frequency for the one or more predicted deposit transactions as the predicted deposit transaction activity time.
The “utilizing the machine learning deposit transaction predictor model with user account data to determine one or more predicted dates” step is merely “apply it” of an abstract idea (“determine one or more predicted dates”) on a general purpose computer (using a machine learning model).
In regards to claim 5,
5. (Currently Amended) The computer-implemented method of claim 1, further comprising:
receiving, via the first request call to the first data source, a first set of partial data comprising the historical transaction activity and the user activity times on the one or more applications for the user account;
receiving, via the second request call to the second data source, a second set of partial data comprising the deposit transaction source information and the client device data; and
based on receiving the first data request corresponding to the first data source and utilizing the first set of partial data, providing the predicted deposit transaction activity time and the predicted deposit transaction activity amount from the universally accessible deposit transaction prediction data source.
The “utilizing the machine learning deposit transaction predictor model with user account data to determine one or more deposit transaction monetary amounts” step is merely “apply it” of an abstract idea (“determine one or more deposit transaction monetary amounts”) on a general purpose computer (using a machine learning model).
In regards to claim 6,
6. (Currently Amended) The computer-implemented method of claim 1, further comprising:
utilizing the machine learning deposit transaction predictor model with user account data to determine a predicted deposit transaction rate based on the predicted deposit transaction activity time and the predicted deposit transaction activity amount; and
determining the available deposit balance for the user account utilizing the predicted deposit transaction rate and a historical deposit transaction date from the user account.
The “utilizing the machine learning deposit transaction predictor model with user account data to determine a predicted deposit transaction rate” step is merely “apply it” of an abstract idea (“determine a predicted deposit transaction rate”) on a general purpose computer (using a machine learning model).
The “determining an available deposit balance for the user account as the value-based deposit prediction data” step is merely “apply it” of an abstract idea (“determining an available deposit balance”) on a general purpose computer (using a machine learning model).
In regards to claim 7,
7. (Currently Amended) The computer-implemented method of claim 1, further comprising training the machine learning deposit transaction predictor model by:
adjusting one or more parameters of a deposit transaction time predictor model to determine one or more deposit transaction date patterns based on comparisons between the predicted deposit transaction activity time to historical deposit transaction dates of the user account; and
adjusting one or more parameters of a deposit transaction value predictor model to determine one or more deposit transaction amount patterns based on comparisons between the predicted deposit transaction activity amount to historical deposit transaction amounts of the user account and outlier detection logic.
The “adjusting one or more parameters of a deposit transaction time predictor model” step is merely “apply it” of an abstract idea (“adjusting one or more parameters of a … predictor model”) on a general purpose computer (using a machine learning model).
In regards to claim 8,
8. (Currently Amended) The computer-implemented method of claim 1, further comprising:
based on receiving the first data request for prediction data stored in the universally accessible deposit transaction prediction data source from the first downstream application corresponding to the first data source from the multiple data sources, providing the machine learning confidence score from the universally accessible deposit transaction prediction data source to the first downstream application to cause the first downstream application to provide access to the available deposit balance based on the machine learning confidence score.
The “generating a confidence score” step is merely “apply it” of an abstract idea (“generating a confidence score”) on a general purpose computer (using a machine learning model).
The “updating the universally accessible deposit transaction prediction data source” and “providing the confidence score” steps are merely extra-solution activity.
In regards to claim 9,
9. (Currently Amended) The computer-implemented method of claim 1, further comprising receiving the first data request and the second data request for the predicted deposit transaction activity time and the predicted deposit transaction activity amount through an application programming interface (API) for the universally accessible deposit transaction prediction data source.
The “receiving the first data request and the second data request” step is merely extra-solution activity.
In regards to claim 10,
10. (Currently Amended) The computer-implemented method of claim 1, further comprising based on receiving a third data request for the prediction data stored in the universally accessible deposit transaction prediction data source from a third downstream application corresponding to a third data source, providing the predicted deposit transaction activity time and the predicted deposit transaction activity amount from the universally accessible deposit transaction prediction data source to the third downstream application to cause the third downstream application to display the predicted deposit transaction activity time and the predicted deposit transaction activity amount within a payment scheduler application.
The “providing the time-based deposit prediction data or the value-based deposit prediction data” step is merely extra-solution activity, as is “to cause the first downstream application to display”.
In regards to claim 11, it is rejected on the same grounds as claim 1.
In regards to claim 12, it is rejected on the same grounds as claim 2.
In regards to claim 13, it is rejected on the same grounds as claim 2.
In regards to claim 14, it is rejected on the same grounds as claim 4.
In regards to claim 15, it is rejected on the same grounds as claim 5.
In regards to claim 16, it is rejected on the same grounds as claim 6.
In regards to claim 17, it is rejected on the same grounds as claim 1.
In regards to claim 18,
18. (Previously Presented) The system of claim 17, further comprising instructions that, when executed by the at least one processor, cause the system to receive, via the second request call to the second data source, geo-location data from a client device corresponding to the user account.
The “receive, via the request calls to the multiple data sources, geo-location data” step is merely extra-solution activity.
In regards to claim 19, it is rejected on the same grounds as claim 4.
In regards to claim 20, it is rejected on the same grounds as claim 7.
20. (Currently Amended) The system of claim 17, further comprising instructions that, when executed by the at least one processor, cause the system to utilize the machine learning deposit transaction predictor model to:
receive, via the first request call to the first data source, a first set of partial data comprising the historical transaction activity and the user activity times on the one or more applications for the user account;
receive, via the second request call to the second data source, a second set of partial data comprising the deposit transaction source information and the client device data; and
based on receiving the first data request corresponding to the first data source and utilizing the first set of partial data, provide the predicted deposit transaction activity time and the predicted deposit transaction activity amount from the universally accessible deposit transaction prediction data source.
The “utilize a deposit transaction time predictor model to determine one or more deposit transaction date patterns” and “utilize a deposit transaction value predictor model to determine one or more deposit transaction amount patterns” steps are merely “apply it” of an abstract idea (“utilize a deposit transaction time predictor model to determine … patterns”) on a general purpose computer (using a machine learning model).
Response to Amendment
Re: Claim Rejections - 35 USC § 101
The 35 USC § 101 rejection has been amended, as necessitated by Applicant’s amendments to the claims. More specifically, the Examiner interprets that newly amended features to the independent claim 1, 11, and 17 (previously recited in dependent claim 8) merely further describe the abstract idea:
providing the machine learning confidence score from the universally accessible deposit transaction prediction data source to the first downstream application to cause the first downstream application to provide access to the available deposit balance based on the machine learning confidence score.
Re: Claim Rejections - 35 USC § 103
The 35 USC § 103 rejection has been withdrawn, as necessitated by Applicant’s amendments to the independent claims. More specifically, the 35 USC § 103 rejection of the independent claim 1, 11, and 17 is withdrawn, because none of the cited references (either individually or in combination) disclose or suggest the following features previously recited in dependent claim 8:
providing the machine learning confidence score from the universally accessible deposit transaction prediction data source to the first downstream application to cause the first downstream application to provide access to the available deposit balance based on the machine learning confidence score.
Conclusion
Applicants are invited to contact the Office to schedule an in-person interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
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.
Any inquiry concerning this communication or earlier communications should be directed to Examiner Ayal Sharon, whose telephone number is (571) 272-5614, and fax number is (571) 273-1794. The Examiner can normally be reached from Monday to Friday between 9 AM and 6 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christine M Behncke can be reached on (571) 272-8103. The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
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Sincerely,
/Ayal I. Sharon/
Examiner, Art Unit 3695
February 3, 2026