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 application filed on January 5, 2026.
Claims 1-3, 5-6, 8-18, 20-21, and 23-30 were amended.
Claim(s) 1-30 are currently pending and have been examined.
This action is made Non-Final.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 January 5, 2026 has been entered.
Response to Arguments
Applicant argued that Examiner’s 101 rejection was moot due to Applicant’s amendment of the claims. Examiner disagrees. After further examination and consideration of Applicant’s amended claims, Examiner still found the subject matter patent ineligible under 35 USC 101, as explained below. Therefore, Examiner finds Applicant’s argument non-persuasive.
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) 1-30 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim(s) 1-30 are directed to a system, method, or product, which are/is one of the statutory categories of invention. (Step 1: YES).
The Examiner has identified independent system claim 16 as the claim that represents the claimed invention for analysis and is similar to independent method Claim 1. Claim 16 recites the following limitations:
generate a trained machine learning model using training data that includes examples of product category configurations for different product categories, entity configurations for different entities, and offering configurations for different offerings;
identify an offering to be offered by an entity, wherein the offering is part of a product category;
retrieve a predetermined product category configuration associated with the product category;
retrieve a predetermined entity configuration associated with the entity;
generate an offering configuration for the offering by processing the predetermined product category configuration and the predetermined entity configuration [through the trained machine learning model], wherein the offering configuration includes a combination of a product setting from the predetermined product category configuration and an entity setting from the predetermined entity configuration, and wherein the offering configuration includes a setting that differs from both the predetermined product category configuration and the predetermined entity configuration;
customize the offering configuration for a user according to user information from a data source to generate a customized offering, wherein the customized offering is associated with a customized variant of the setting that is adjusted based on the user information;
enroll the user in the customized offering on behalf of the entity and according to the customized variant of the setting, wherein enrolling includes generating a record and storing the record [in a database], and wherein the record maps an entity identifier of the entity to the user information and the customized offering;
process a transaction according to the customized offering, wherein the transaction is between the user and the entity; and
update [the trained machine learning model] based on feedback associated with the customized offering to reduce error for further evaluations.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity because the limitations recite fundamental economic principles or practices as well as commercial or legal interactions. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic principle or practice, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The at least one memory, at least one processor, database, and trained machine learning model in Claim 16 are 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. Claim(s) 1 is also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims recite an abstract idea)
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of at least one memory, at least one processor, a database, and a trained machine learning model. The computer hardware/software is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to 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. Therefore, claim(s) 1 and 16 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 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 computer hardware/software 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. Accordingly, these additional elements do not change the outcome of the analysis when considered separately and as an ordered combination. Thus, claim(s) 1 and 16 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 5-7 and 20-22 include the additional element of a machine learning model. The machine learning model does not integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself because it is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (i.e., a machine learning model). Dependent claims 2-4, 8-15, 17-19, and 23-30 further define the abstract idea that is present in their respective independent claim(s) 1 and 16 and thus correspond to certain methods of organizing human activity and hence are abstract for the reasons presented above. Dependent claims 2-4, 8-15, 17-19, and 23-30 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, dependent claims 2-15 and 17-30 are directed to an abstract idea. Thus, claim(s) 1-30 are not patent-eligible.
Subject Matter Distinguished from Prior Art
The prior art of record fails to expressly teach or suggest, either alone or in combination, the features of claims 1-30. In light of Applicant's remarks, Examiner agrees that the cited reference(s) of Singh, Agbamu, and Leoputera do not disclose, teach, or suggest the claimed invention. Singh teaches a system and method of managing a payment network that includes offering an installment plan to customers. Agbamu teaches a transaction management system and onboarding process for customers. Leoputera teaches a system and method for the optimization of financing programs. However, the prior art of record fails to anticipate or render obvious the claimed invention. Specifically, the prior art of record fails to anticipate or render obvious limitations to compare information associated with the offering to the predetermined product category configuration and the predetermined entity configuration to generate an evaluation of the offering, wherein a trained machine learning model is used for the comparison; and customize the offering configuration for a user according to user information to generate a customized offering, wherein the customized offering is associated with a customized variant of the third plurality of settings in which at least one setting of the third plurality of settings is adjusted based on the user information; as described by the allowed claims. For these reasons, claims 1-30 are deemed to be allowable over the prior art of record.
Conclusion
Pertinent Art
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure. Lash (US 11,568,481) discloses a system and method for intelligently bundling payments.
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/JOHN O PRESTON/Examiner, Art Unit 3693
February 16, 2026
/Mike Anderson/Supervisory Patent Examiner, Art Unit 3693