Office Action Predictor
Application No. 17/181,313

SYSTEMS AND METHODS FOR ANALYZING FINANCIAL PRODUCT UTILIZATION

Non-Final OA §101
Filed
Feb 22, 2021
Examiner
DONLON, RYAN D
Art Unit
3692
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Fidelity Information Services, LLC
OA Round
5 (Non-Final)
9%
Grant Probability
At Risk
5-6
OA Rounds
5y 11m
To Grant
20%
With Interview

Examiner Intelligence

9%
Career Allow Rate
17 granted / 196 resolved
Without
With
+11.6%
Interview Lift
avg trend
5y 11m
Avg Prosecution
23 pending
219
Total Applications
career history

Statute-Specific Performance

§101
32.0%
-8.0% vs TC avg
§103
33.4%
-6.6% vs TC avg
§102
9.0%
-31.0% vs TC avg
§112
22.7%
-17.3% vs TC avg
Black line = Tech Center average estimate • Based on career data

Office Action

§101
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 RCE filed on 11/10/2025. Claims 1 and 11 have been amended. New claims 21 and 22 have been added. Claims 1-8, 10-18 and 20-22 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 1-8, 10-18 and 20-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. A Section 101 analysis is below. Step 1 – are the claims directed to a process, machine, manufacture or composition of matter. The system of claim 1 and method of claim 11 are within the statutory categories of invention. For the purposes of this analysis, representative claim 1 is addressed. Step 2A, prong one – do the claims recite a judicial exception, which is an abstract idea enumerated in MPEP 2106, a law of nature, or a natural phenomenon. Abstract ideas are in bold below, and represent the abstract idea of certain methods of organizing human activity of the commercial or legal interaction of processing financial data to determine BIN utilization for the purpose, as described in the specification, of determining fees. The background section of the Applicant’s specification, paragraphs [0004]-[0007], describes the that financial institutions buy the right to use a BIN from a payment processor, and then the payment processors may charge “hefty fees for BINs that are under-utilized”. In the U.S. only chartered banks may own a BIN. However, this is a contractual relationship between the financial institution and the payment processor and clearly a commercial interaction under the “rent-a-BIN” scheme. Please see MPEP 2106.04(a)(2)(II)(B) which gives “managing a stable value protected life insurance policy via performing calculations” as an example of subject matter where the commercial or legal interaction is an agreement in the form of contracts. 1. A computer-implemented system comprising: a non-transitory computer-readable medium configured to store instructions; and at least one processor configured to execute the instructions to perform operations comprising: obtaining, from at least one data store, at least one historical data record associated with a financial institution; aggregating the at least one historical data record based on related attributes in the at least one historical data record; normalizing the at least one historical data record to a predetermined format; training a predictive model comprising a linear regression model to predict future bank identification number (BIN) utilization, wherein training the predictive model comprises: providing the at least one historical data record as input to the linear regression model; determining features of the at least one historical data record; analyzing how each of the features contributes to the future BIN utilization; determining multicollinear input variables of the linear regression model; reducing a number of inputs to the linear regression model based on the determined multicollinear input variables; and predicting the future BIN utilization based on analyzing how each of the features contribute to the future BIN utilization; calculating a ratio of active and issued payment cards associated with one or more BINs owned by the financial institution; calculating a ratio of inactive and issued payment cards associated with one or more BINs owned by the financial institution; using the at least one historical data record, the ratio of active and issued payment cards associated with one or more BINs, and the ratio of inactive and issued payment cards associated with one or more BINs, for training the predictive model; obtaining, from at least one data store, at least one non-historical data record associated with the financial institution; determining, by applying the predictive model to the at least one non-historical data record, one or more BINs that do not meet a predetermined utilization; and generating a visualization of at least one of the at least one non-historical or at least one historical data record, wherein the visualization comprises: an indication of BINs that do not meet the predetermined utilization; an indication of utilization of one or more BINs owned by the financial institution based on the ratio of active and issued payment cards associated with the one or more BINs; and an indication of utilization of one or more BINs owned by the financial institution based on the ratio of inactive and issued payment cards associated with the one or more BINs. Step 2A, prong two – do the claims recite additional elements that integrate the judicial exception into a practical application. Integration of the judicial exception into a practical application requires an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The additional elements are considered as follows: The “non-transitory computer-readable medium”, “at least one processor”, and “at least one data store”. Referring to MPEP 2106.05(f), the preceding recited additional elements are no more than mere instructions to implement an abstract idea or other exception on a computer. The computer components are recited at a high-level of generality (e.g., to receive, store, or transmit data) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, the 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. Please see MPEP 2106.05(f)(1) discussing when the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished this does not show integration into a practical application. Please see MPEP 2106.05(f)(2) discussing when the claim invokes computers or other machinery merely as a tool to perform an existing process including use of a computer or other machinery for economic tasks this does not show integration into a practical application. The “predictive model comprising a linear regression model”. Referring to MPEP 2106.05(g), the preceding recited additional element is no more than insignificant extra-solution activity amounting to no more than manipulating gathered information. Step 2B – do the claims recited additional elements that 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 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. The computer components implementing the abstract idea appear to be generic in view of at least Applicant’s specification, [0039]. Focusing on the additional element of “predictive model comprising a linear regression model” identified as extra solution activity above, evidence that this element is well understood, routine and conventional can be found in Guven Kaya (US 2015/0294246) ([0040], [0041]); and Basch (US 6,119,103) (11:51-12:2). In view of the above analysis, independent claims 1 and 11 are not patent eligible. Dependent claims 2-8, 10, 12-18 and 20-22 do not cure the deficiencies in their respective base claims. Specifically, claims 2-8, 10, 12-18 and 20-22 merely refine the abstract idea (2A1) by invoking a computer as a tool to perform an existing process (2A2, 2B). Regarding the further additional elements in the dependent claims including the GUI (claims 2, 12); and network (claims 3, 13), please see MPEP 2106.05(f)(2) discussing when the claim invokes computers or other machinery merely as a tool to perform an existing process including use of a computer or other machinery for economic tasks this does not show integration into a practical application or provide significantly more. With respect to the machine learning additional elements, please also see Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 (Fed. Cir. Apr. 18, 2025), which affirmed the District of Delaware’s dismissal of Recentive’s infringement suit on the ground that the asserted patents were directed to ineligible subject matter under 35 U.S.C. § 101. The decision reinforces the courts’ view that applying generic machine learning techniques to known problems — without technical innovation in the machine learning methods themselves — is insufficient for patent eligibility. Response to Arguments Applicant's arguments filed 11/10/2025 have been fully considered and are addressed below. Regarding the rejections under 35 U.S.C. 112(a) and 35 U.S.C. 112(b), these rejections have been withdrawn in view of the PTAB decision dated 9/11/2025. Regarding the rejection under 35 U.S.C. 101, Applicant’s arguments have been fully considered but they are not persuasive. Regarding Step 2A, prong one, no arguments were presented regarding Step 2A, prong one. Regarding Applicant’s arguments regarding Step 2A, prong two, integration into a practical application requires an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. Limitations that are indicative of integration into a practical application include improvements to the functioning of a computer, applying the judicial exception with a particular machine, effecting transformation of a particular article to a different state or thing or applying the judicial exception in some other meaningful was beyond generally linking the use of the judicial exception to a particular technological environment. The Applicant argues “The technical limitations directed to actual improvements to computational function include: (1) having the predictive model comprise a linear regression model, (2) determining multicollinear input variables of the linear regression model, and (3) reducing the number of inputs to the linear regression model based on the determined multicollinear input variables. These additional functions are not a ‘mere arguable advance,’ but instead provide an actual improvement to computer function. See specification as-filed, §] [0056] (“analysis engine 420 can define a new variable equal to a linear combination of the highly correlated variables and define a new regression equation, using the new variable in place of the highly correlated variables” and “reduce the data inputs to the linear regression model”) (emphasis added). Notably, reducing the inputs to the linear regression model—thereby making the computer more efficient—constitutes a specific, technical improvement to the way a computer operates, rather than simply an improved user experience or an abstract idea implemented on a generic computer.” The Examiner respectfully disagrees. The cited features constitute insignificant extra-solution activity amounting to no more than manipulating gathered information. Please see MPEP 2106.05(g). Computer efficiency is not recited in the claims or discussed in the specification. No arguments were presented regarding Step 2B. However, Step 2B is directed to whether the claim recites additional elements that amount to an inventive concept (AKA “significantly more”) than the judicial exception. It is respectfully submitted that the features cited by the Applicants, in Step 2A, prong two, “(1) having the predictive model comprise a linear regression model, (2) determining multicollinear input variables of the linear regression model, and (3) reducing the number of inputs to the linear regression model based on the determined multicollinear input variables.” constitute WURC activity as evidenced by Egan (US 2018/0365370) ([0109]); Jiang (US 2006/0161403) ([0078], [0083]); and Maughan (US 2017/0372232) ([0035], [0084]). MPEP 2106.05(d) gives examples recognized by the courts of known computer functions which specifically include “determining an estimated outcome and setting a price”. Further examples include “receiving or transmitting data over a network”, “performing repetitive calculations”, “electronic recordkeeping”, “storing and retrieving information in memory” and “arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price”, all of which directly correspond to the generically claimed operations of the present claims, which claim determining and visualizing BIN utilization based on historical data. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure includes: US 10984482; US 20180365370; WO 2018013080; US 20170372232; US 20060161403; US 6119103; and US 3576976. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Gregory Harper whose telephone number is (571)272-5481. The examiner can normally be reached M-Th 7am-5pm. 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, Calvin Hewitt II can be reached on (571) 272-6709. 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. /GREGORY HARPER/Examiner, Art Unit 3692
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Prosecution Timeline

Feb 22, 2021
Application Filed
Apr 28, 2022
Non-Final Rejection — §101
Aug 02, 2022
Interview Requested
Aug 11, 2022
Examiner Interview Summary
Aug 11, 2022
Applicant Interview (Telephonic)
Aug 26, 2022
Response Filed
Sep 12, 2022
Final Rejection — §101
Nov 15, 2022
Response after Non-Final Action
Nov 17, 2022
Response after Non-Final Action
Nov 17, 2022
Applicant Interview (Telephonic)
Dec 16, 2022
Request for Continued Examination
Dec 19, 2022
Response after Non-Final Action
Mar 30, 2023
Non-Final Rejection — §101
Sep 05, 2023
Response Filed
Sep 21, 2023
Final Rejection — §101
Mar 27, 2024
Notice of Allowance
Mar 27, 2024
Response after Non-Final Action
Apr 10, 2024
Response after Non-Final Action
Aug 26, 2024
Response after Non-Final Action
Aug 26, 2024
Response after Non-Final Action
Sep 06, 2024
Response after Non-Final Action
Sep 12, 2024
Response after Non-Final Action
Sep 13, 2024
Response after Non-Final Action
Sep 13, 2024
Response after Non-Final Action
Sep 26, 2024
Response after Non-Final Action
Nov 25, 2024
Response after Non-Final Action
Dec 02, 2024
Response after Non-Final Action
Dec 03, 2024
Response after Non-Final Action
Dec 03, 2024
Response after Non-Final Action
Sep 10, 2025
Response after Non-Final Action
Nov 10, 2025
Request for Continued Examination
Nov 18, 2025
Response after Non-Final Action
Dec 02, 2025
Non-Final Rejection — §101
Apr 03, 2026
Response Filed

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Prosecution Projections

5-6
Expected OA Rounds
9%
Grant Probability
20%
With Interview (+11.6%)
5y 11m
Median Time to Grant
High
PTA Risk
Based on 196 resolved cases by this examiner