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 .
DETAILED ACTION
This Office Action is in response to Applicant’s communication filed on August 04, 2025 for the patent application 19/289,838. Claims 1 – 20 are pending in the application.
Information Disclosure Statement
The Information Disclosure Statement (IDS) submitted on August 21, 2025 was filed in compliance with the provisions of 37 CFR 1.97. Accordingly, this Information Disclosure Statement is being considered by the Examiner.
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.
Claim(s) 1 – 20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 - 20 are either directed to a method or system or computer readable medium, which are statutory categories of invention. (Step 1: YES).
The Examiner has identified method claim 1 as the claim that represents the claimed invention for analysis and is similar to system claim 8 and computer readable claim 15. Claim 1 recites the limitations of:
( A ) determining, by at least one processor, measured card usage data comprising a measured acquisition rate and a measured retention rate for a card management program based on a bundled set of parameter configurations being applied to the card management program;
( B ) determining, by the at least one processor, a loss based on the measured card usage data and a card usage score generated by a machine-learning model according to an estimated acquisition rate and an estimated retention rate for a combination of predetermined card parameter configurations of the bundled set of parameter configurations by:
determining, from the card usage score generated by the machine-learning model and the measured card usage data, a first difference between the estimated acquisition rate and the measured acquisition rate and a second difference between the estimated retention rate and the measured retention rate; and
utilizing a loss function to determine the loss utilizing the first difference and the second difference; and
modifying, by the at least one processor, trained weights of the machine-learning model according to the loss to reduce the first difference between the estimated acquisition rate and the measured acquisition rate and the second difference between the estimated retention rate and the measured retention rate.
These limitations without the bolded limitations above, cover performance of the limitations as certain methods of organizing human activity under their broadest reasonable interpretation.
More specifically, these limitations cover performance of the limitations as a fundamental economic practice.
In summary, if claim 1 limitations, 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. Claims 8 and 15 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract).
The use of the one or more processors or any of the bolded limitations in claim 1 are just applying generic computer components to the recited abstract limitations. Similar arguments apply to claims 8 and 15.
Therefore, the above mentioned judicial exception is not integrated into a practical application by merely applying generic computer components (bolded elements).
Furthermore, the “utilizing” and “modifying” steps are recited at a high level of generality and amounts to mere data gathering/transmitting, which are forms of insignificant extra-solution activity (See MPEP 2106.05(g): CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011); and OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)).
In addition, supported by specification, the computer hardware are 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., see MPEP 2106.05(f), where applying a computer or using a computer is not indicative of a practical application).
Claim 1, limitation ( A ) and ( B ) above in Applicant’s specification para [0113], which discloses “Embodiments of the present disclosure may comprise or utilize a special purpose or general-purpose computer including computer hardware, such as, for example, one or more processors and system memory, as discussed in greater detail below. Embodiments within the scope of the present disclosure also include physical and other computer-readable media for carrying or storing computer-executable instructions and/or data structures. In particular, one or more of the processes described herein may be implemented at least in part as instructions embodied in a non-transitory computer-readable medium and executable by one or more computing devices (e.g., any of the media content access devices described herein). In general, a processor (e.g., a microprocessor) receives instructions, from a non-transitory computer-readable medium, (e.g., a memory, etc.), and executes those instructions, thereby performing one or more processes, including one or more of the processes described herein.“.
Also, claim 1, limitation ( A ) above in Applicant’s specification para [0092], which discloses “As mentioned, FIG. 7 illustrates a graphical user interface for creating a bundled set of parameter configurations. Specifically, FIG. 7 illustrates a client device 700 (e.g., a desktop device) displaying a graphical user interface 702 of a client application associated with card management programs. The client application can include tools for generating and managing card management programs (e.g., developing, deploying, and tracking card management pr-grams). For example, the card parameter bundling system 102 provides a plurality of predetermined card parameter configurations to the client device 700 in connection with generating a card management program.“.
Also, claim 1, limitation ( A) and ( B ) above in Applicant’s specification para [0088], which discloses “In alternative embodiments, the card parameter bundling system 102 utilizes machine-learning to estimate the card usage results for variants of the initial bundled set 600. For example, the card parameter bundling system 102 utilizes one or more machine-learning models to generate estimated card usage scores for the first updated bundled set 608a and the second updated bundled set 608b. The card parameter bundling system 102 can utilize the machine-learning model(s) to estimate performance of the first updated bundled set 608a and the performance of the second updated bundled set 608b with the same or different segments.“. Similar arguments apply to claims 8 and 15.
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.
Therefore, claims 1, 8 and 15 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).
The claims 1, 8 and 15 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 elements (bolded elements above) amount to no more than mere instructions to apply the abstract idea using generic computer components. In conclusion, merely "applying" the exception using generic computer components cannot provide an inventive concept. Therefore, the claims 1, 8 and 15 are not patent eligible under 35 USC 101. (Step 2B: NO. The claims do not provide significantly more).
Dependent Claims
Dependent claims 2 – 7, 9 - 14 and 16 - 20 are also rejected under 35 U.S.C. 101.
Regarding claims 2, 9 and 16, these claims merely recite additional steps that amount to no more than insignificant extra-solution activity. Specifically, claim,2, states that “wherein determining the measured card usage data comprises: determining the measured acquisition rate as a rate at which users presented with an option to obtain a card signed up for the card; and determining the measured retention rate as a rate at which users kept an account for the card for a specified amount of time.”. These steps amount to no more than mere data gathering/analysis, which is a form of insignificant extra- solution activity (See M PEP 2016.05(g): CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011); and GIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)). Such limitations do not integrate the abstract idea into a practical application, or amount to significantly than the abstract idea, because the courts have found the concept of data gathering to be well-understood, routine, and conventional activity (See MPEP 2106.05(d): GIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, (Fed. Cir. 2014)). Similar arguments can be made for claims 9 and 16.
Regarding claims 3, 10 and 17, These claims merely add further description to the process of “generating the bundled set of parameter configurations for applying to the card management program by: generating, utilizing the machine-learning model, card usage scores for combinations of predetermined card parameter configurations corresponding to a plurality of different card parameter categories and indicating usage characteristics of cards in relation to using the cards to engage in payment transactions; and generating the bundled set of parameter configurations based on the card usage score of the combination of predetermined card parameter configurations.”. These limitations amount to no more than mere data gathering/outputting as described in reference to claim 1 (see analysis above). Merely describing the conditions under which the score is generated does not integrate the abstract idea into a practical application, or amount to significantly more than the judicial exception, because it does not impose any meaningful limitations on practicing the abstract idea. Similar arguments can be made for claims 10 and 17.
Regarding claims 4, 11 and 18, these claims merely add further description to the process of “generating the card usage score for the combination of predetermined card parameter configurations for a target segment comprising a group of user accounts according to the trained weights of the machine-learning model; and generating the bundled set of parameter configurations by: comparing the card usage score of the combination of predetermined card parameter configurations to one or more additional card usage scores of one or more additional combinations of predetermined card parameter configurations; and generating the bundled set of parameter configurations in response to the card usage score being higher than the one or more additional card usage scores..”. These limitations amount to no more than mere data gathering/outputting as described in reference to claim 1 (see analysis above). Merely describing the conditions under which the score is generated does not integrate the abstract idea into a practical application, or amount to significantly more than the judicial exception, because it does not impose any meaningful limitations on practicing the abstract idea. Similar arguments can be made for claims 11 and 18.
Regarding claims 5, 12 and 19, these claims merely add further description to the process of “wherein modifying the trained weights of the machine-learning model comprises performing a plurality of training iterations to fine-tune the trained weights of the machine-learning model according to a plurality of sets of card usage scores and a plurality of sets of measured card usage data.”. These limitations amount to no more than mere data gathering/outputting as described in reference to claim 1 (see analysis above). Merely describing the conditions under which the score is generated does not integrate the abstract idea into a practical application, or amount to significantly more than the judicial exception, because it does not impose any meaningful limitations on practicing the abstract idea. Similar arguments can be made for claims 12 and 19.
Regarding claims 6, 13 and 20, these claims merely recite additional steps that amount to no more than insignificant extra-solution activity. Specifically, claim 6 states that “determining a plurality of sets of measured card usage data for a plurality of target segments comprising separate groups of user accounts associated with different combinations of card parameter configurations; and modifying trained weights of a plurality of machine-learning models for the plurality of target segments according to a plurality of losses based on the plurality of sets of measured card usage data and a plurality of sets of card usage scores corresponding to the plurality of target segments.“. These steps amount to no more than mere data gathering/analysis, which is a form of insignificant extra- solution activity (See M PEP 2016.05(g): CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011); and GIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)). Such limitations do not integrate the abstract idea into a practical application, or amount to significantly than the abstract idea, because the courts have found the concept of data gathering to be well-understood, routine, and conventional activity (See MPEP 2106.05(d): GIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, (Fed. Cir. 2014)). Similar arguments can be made for claims 13 and 20.
Regarding claims 7 and 14, these claims merely alter the nature of the abstract idea to include steps for “modifying the trained weights of the machine-learning model to: select the bundled set of parameter configurations; and select a target segment for the bundled set of parameter configurations based on features of user accounts of the target segment, the features comprising demographics and behavior of the user accounts in connection with one or more cards.". This amounts to no more than the user mentally determining, for example, whether to complete the transaction on their mobile phone or their desktop computer. Similar arguments can be made for claim 14.
As a result, such limitations do not overcome the requirements as described above. Therefore, the claims 2 – 7, 9 - 14 and 16 - 20 are not seen to be statutory. Thus, claims 1 - 20 are not patent eligible.
Nonstatutory Double Patenting Rejection
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal-Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1 - 20 are rejected on the ground of nonstatutory double patenting as being unpatentable U.S. Patent No. 12,400,212 (Pat.’212). Although the claims over claims 1 - 20 of at issue are not identical, they are not patentably distinct from each other because Applicant used a slightly different terminology to claim the same or substantially the same invention. See In re Griswold, 150 USPQ 804 (CCPA 1966) and MPEP § 804.02. In fact, a comparison between claims 1 - 20 of this application and claims 1 - 20 of Pat.’212 shows that these claims claim common elements such as “providing, by the one or more processors for display at a client device, the bundled set of parameter configurations as a recommendation for the card management program in connection with providing the card to one or more users; determining measured card usage data comprising a measured acquisition rate and a measured retention rate for the card management program based on the bundled set of parameter configurations being applied to the card management program; determining a loss based on the measured card usage data and a card usage score generated by the machine-learning model for the combination of the subset of the plurality of predetermined card parameter configurations by: determining, from the card usage score generated by the machine-learning model and the measured card usage data, a first difference between an estimated acquisition rate and the measured acquisition rate and a second difference between an estimated retention rate and the measured retention rate; and utilizing a loss function to determine the loss utilizing the first difference and the second difference; and modifying the trained weights of the machine-learning model according to the loss to reduce the first difference between the estimated acquisition rate and the measured acquisition rate and the second difference between the estimated retention rate and the measured retention rate.”. See In AbbVie Inc. On the other hand, Applicant has not shown that the one or more processors; and a memory storing computer-executable instructions, are critical. See In re Aller. To select the non-transitory, computer-readable medium storing instructions claimed in claims 1 - 20 of Pat.’212 as claimed in claims 1 - 20 of this application would have been an obvious choice by performing routine experimentation. KSR and stare decisis above.
It would have been obvious to the person having ordinary skill in the art before the effective filing date of the application to include one or more processors; and a memory storing computer-readable instructions, as claimed in this application as taught or suggested by claims 1 - 20 of Pat.’212 because application claims 1 - 20 would have been obvious over the reference claims 1 - 20 in Pat.’212.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN H. HOLLY whose telephone number is (571)270-3461. The examiner can normally be reached on MON. - FRI 10 AM - 8 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MATTHEW S. GART can be reached on 571-272-3955. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/John H. Holly/Primary Examiner, Art Unit 3696
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