DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This Office action is in response to Applicant’s communication filed on July 6, 2026. Amendments to claims 1, 11 and 19, and cancellation of claim 23 have been entered, Claims 1, 2, 4-6, 8-12, 14, 16-21, and 24-27 are pending and have been examined. The rejections and response to arguments are stated below.
Claim Rejections - 35 USC § 101
2. 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.
3. Claims 1, 2, 4-6, 8-12, 14, 16-21, and 24-27 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
The claim(s) recite(s) a method for artificial-intelligence based product optimization of products and services for offer, which is considered a judicial exception because it falls under the category of “Certain Methods of organizing human activity” such as fundamental economic practice as well as commercial or legal interactions including agreements as discussed below. This judicial exception is not integrated into a practical application as discussed below. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception as discussed below.
Analysis
Step 1: In the instant case, exemplary claim 1 is directed to a system (apparatus).
Step 2A – Prong One: The limitations of “A system for artificial-intelligence based product optimization of products and services for offer, comprising:
at least one non-transitory memory; and
at least one processing device, the memory containing software code configured to cause the processing device to:
gather data from a plurality of data sources;
extract, using a machine learning algorithm, at least one of a plurality of customer behavior features or a plurality of financial institution behavior features based on the gathered data;
process the customer behavior features and financial institution behavior features using one or more trained foundation models, wherein the trained foundation models are selected based on a plurality of foundation model selection variables, wherein the foundation model selection variables comprise one or more of the goal inputs, and wherein the trained foundation models output one or more foundation model outputs;
wherein the trained foundation models are configured to perform feature-level inference on the customer behavior features and financial institution behavior features to generate the foundation model outputs, and wherein the foundation model selection variables are used to dynamically select the trained foundation models based on data contexts;
input the foundation model outputs and a plurality of goal inputs into a trained product model, wherein:
the goal inputs comprise a plurality of financial institution products and one or more of a plurality of financial institution product variants, a plurality of financial institution parameters, a plurality of financial institution regions, or a plurality of financial institution growth strategies;
the trained product model is trained based on the foundation model outputs and the goal inputs;
output, from the trained product model, a natural-language product response, wherein the product response is based on the goal inputs;
receive customer feedback through one or more customer feedback channels, wherein the gathered data further comprises the customer feedback;
update the goal inputs based on the customer feedback;
monitor the trained product model performance according to one or more trained product model metrics at predetermined times;
refine the trained product model according to the trained product model performance;
update the gathered data at predetermined times;
provide the updated data to the machine learning algorithm through a first feedback loop; and
modify the customer behavior features, financial institution behavior features, and foundation model outputs based upon the updated data received from the first feedback loop to refine the machine learning algorithm” as drafted, when considered collectively as an ordered combination without the italicized portions, is a process that, under the broadest reasonable interpretation, covers the category of “Certain Methods of organizing human activity” such as fundamental economic practice as well as commercial or legal interactions including agreements.
Product optimization of products and services for offer is a fundamental economic practice such as marketing, sales etc.
The steps of “gather data from a plurality of data sources;
extract, using a machine learning algorithm, at least one of a plurality of customer behavior features or a plurality of financial institution behavior features based on the gathered data;
process the customer behavior features and financial institution behavior features using one or more trained foundation models, wherein the trained foundation models are selected based on a plurality of foundation model selection variables, wherein the foundation model selection variables comprise one or more of the goal inputs, and wherein the trained foundation models output one or more foundation model outputs; ……….
modify the customer behavior features, financial institution behavior features, and foundation model outputs based upon the updated data received from the first feedback loop to refine the machine learning algorithm” considered collectively is fulfilling agreements between a business and its customers. Hence, the steps of the claim, considered collectively as an ordered combination without the italicized portions, covers the abstract category of “Certain Methods of organizing human activity”.
That is, other than, at least one non-transitory memory; and at least one processing device, the memory containing software code; a machine learning algorithm; one or more trained foundation models; one or more customer feedback channels; and a trained product model, nothing in the claim precludes the steps from being performed as a method of organizing human activity. If the claim limitations, under the broadest reasonable interpretation, covers methods of organizing human activity but for the recitation of generic computer components, then it falls within the “Certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of at least one non-transitory memory; and at least one processing device, the memory containing software code; a machine learning algorithm; one or more trained foundation models; one or more customer feedback channels; and a trained product model to perform all the steps. A plain reading of Figures 1, 2 and 8 and associated descriptions in at least paragraphs [0047], [0062] – [0069] reveals that the at least one processing device may include generic processors suitably programmed to execute the claimed steps. The at least one non-transitory memory containing the software code may be generic memory suitably programmed to store the associated data/information or software code. The one or more customer feedback channels are broadly interpreted to include suitably programmed generic channels for providing feedback. The machine learning algorithm. one or more trained foundation models, and the trained product model are broadly interpreted to include generic software suitably programmed to perform the claimed steps. Hence, the additional elements in the claims are all generic components suitably programmed to perform their respective functions. The additional elements in all the steps are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Hence, claim 1 is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements (identified above) to perform the claimed steps amounts to no more than mere instructions to apply the exception using a generic computer component. The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, independent claim 1 is not patent eligible. Independent claim 11 is also not patent eligible based on similar reasoning and rationale.
Dependent claims 2, 4-6, 8-12, 14, 16-21, and 24-27, when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations only refine the abstract idea further.
For instance, in claims 2 and 12, the steps “wherein:
the data sources comprise one or more of banking core system, customer relationship management system, credit bureau system, country-level asset data system, survey, broker system, or external sources;
the gathered data comprises one or more of customer profile data, transaction history data, demographic information data, economic indicator data, household asset data, financial institution data, credit report data, or broker data;
the customer behavior features comprise one or more of customer lifetime value, spending patterns, or income levels; and
the financial institution behavior features comprise one or more of financial preferences or regional characteristics” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe data and information used in the intermediate steps of the underlying process.
In claims 4 and 14, the steps “wherein the trained product model comprises one or more of a logistic regression, a random forest, a gradient boosting, a clustering algorithm, or a deep learning model; and
the processing device is further configured to:
update the goal inputs at predetermined times;
provide the updated goal input data to the trained product model in a second feedback loop; and
train the trained product model based upon the updated goal input data received from the second feedback loop to refine the trained product model” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe types of the trained product model and further describe the intermediate steps of the underlying process.
In claim 5, the steps “wherein the processing device is further configured to:
receive customer feedback through one or more customer feedback channels, wherein the gathered data further comprises the customer feedback; and
adjust the goal inputs based on the customer feedback” have already been addressed in the rejection of claim1.
In claims 6 and 16, the steps “wherein the trained product model is further configured for one or more of collaborative filtering, content-based filtering, or hybrid recommendation filtering” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process.
In claim 17, the steps “further comprising:
monitoring the trained product model performance according to one or more trained product model metrics at predetermined times; and
refining the trained product model according to the trained product model performance” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process.
In claims 8 and 18, the steps “wherein the target product output is based on a household segment or a client segment; and comprises a ranking and a likelihood scoring” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the target product output in the intermediate steps of the underlying process.
In claims 9 and 19, the steps “wherein the system further comprises a user interface configured to:
provide the user interface to a user device;
receive an input from the user device on one or more elements of the user interface; and
update the one or more of the data sources, the gathered data, the machine learning algorithm, the customer behavior features, the financial institution behavior features, the trained models, the foundation model selection variables, the goal inputs, or the trained product model in response to the input; and
display an updated product response based on the input” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process. The additional element of a user device is broadly interpreted to include a generic user device, The additional elements of a user interface to a user device is broadly interpreted to include generic software suitably programmed to perform the associate functions. The additional element of a user interface to a user device, performs a traditional function recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components.
In claims 10 and 20, the step “wherein the gathered data comprises customer data and financial institution data” under the broadest reasonable interpretation, is a further refinement of methods of organizing human activity because this step describes gathered data used in the intermediate steps of the underlying process.
In claim 21, the step “wherein: the data sources comprise one or more of banking core system, customer relationship management system, credit bureau system, country-level asset data system, survey, broker system, or external sources” under the broadest reasonable interpretation, is a further refinement of methods of organizing human activity because this step describes gathered data used in the intermediate steps of the underlying process.
In claim 24, the steps “wherein the system further comprises a user interface configured to: display the natural-language product response;
receive an input from a user device modifying one or more of the goal inputs or the foundation model selection variables; and
update the trained product model in real-time based on the input to generate an updated product response” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process. The additional elements of a user interface is broadly interpreted to include generic software suitably programmed to perform the associate functions. The additional element of a user interface, performs a traditional function recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components.
In claim 25, the steps “wherein the target product output comprises a ranking and a likelihood scoring based on a household segment or a client segment, and wherein the trained product model is further configured for one or more of collaborative filtering, content-based filtering, or hybrid recommendation filtering” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process.
In claim 26, the steps “further comprising:
updating the goal inputs at predetermined times based on the customer feedback;
providing the updated goal inputs to the trained product model through a second feedback loop; and
retraining the trained product model based on the updated goal inputs to improve accuracy and responsiveness” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process.
In claim 27, the steps “wherein the one or more trained product model metrics comprise at least one of accuracy, precision, recall, F1 score, area under ROC curve, mean absolute error, mean squared error, or mean absolute percentage error” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process.
In all the dependent claims, the judicial exception is not integrated into a practical application because the limitations are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. Also, the claims do not affect an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer system itself; the claims do not affect a transformation or reduction of a particular article to a different state or thing; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment. In addition, the dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. The claims as a whole, do not amount to significantly more than the abstract idea itself. For these reasons, the dependent claims also are not patent eligible.
Response to Arguments
4. In response to Applicants arguments on pages 13-20 of the Applicant’s remarks that the claims are patent-eligible under 35 USC 101 when considered under MPEP 2106, the Examiner respectfully disagrees.
The fact that the claims are Patent-Ineligible when considered under the MPEP 2106 has already been addressed in the rejection and hence not all the details of the rejection are repeated here.
Response to Applicants’ arguments regarding Step 2A – Prong one:
The claim(s) recite(s) a method for artificial-intelligence based product optimization of products and services for offer, which is considered a judicial exception because it falls under the category of “Certain Methods of organizing human activity” such as fundamental economic practice as well as commercial or legal interactions including agreements as discussed in the rejection.
Product optimization of products and services for offer is a fundamental economic practice such as marketing, sales etc. Artificial Intelligence is simply used as a tool, in its ordinary capacity to apply this fundamental economic practice.
The steps of “gather data from a plurality of data sources; extract, using a machine learning algorithm, at least one of a plurality of customer behavior features or a plurality of financial institution behavior features based on the gathered data; process the customer behavior features and financial institution behavior features using one or more trained foundation models, wherein the trained foundation models are selected based on a plurality of foundation model selection variables, wherein the foundation model selection variables comprise one or more of the goal inputs, and wherein the trained foundation models output one or more foundation model outputs; ……….
modifying the customer behavior features, financial institution behavior features, and foundation model outputs based upon the updated data received from the first feedback loop to refine the machine learning algorithm” considered collectively is fulfilling agreements between a business and its customers. Hence, the steps of the claim, considered collectively as an ordered combination without the italicized portions, covers the abstract category of “Certain Methods of organizing human activity”.
The additional elements (identified in the rejection) are used as tools in their ordinary capacity to apply the abstract idea. The claimed features including those recited on pages 13-14 of the remarks such as “the trained foundation models are configured to perform feature-level inference on the customer behavior features and financial institution behavior features to generate the foundation model outputs, and wherein the foundation model selection variables are used to dynamically select the trained foundation models based on data contexts….. using one or more trained foundation models, wherein the trained foundation models are selected based on a plurality of foundation model selection variables," and providing "the updated data to the machine learning algorithm through a first feedback loop" to "refine the machine learning algorithm” may be characterized as an improvement in the abstract idea, using the additional elements, as tools in their ordinary capacity to apply this the abstract idea. These are intermediate steps used in arriving at the overall abstract idea of a method for artificial-intelligence based product optimization of products and services for offer. An improvement in abstract idea is still abstract (SAP America v. Investpic *2-3 (“We may assume that the techniques claimed are “groundbreaking, innovative, or even brilliant,” but that is not enough for eligibility. Association for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013); accord buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1352 (Fed. Cir. 2014). The additional elements (identified in the rejection) are generic computer components used to apply the abstract idea. It does not involve any improvements to another technology, technical field, or improvements to the functioning of the computer itself. Hence, the claims recite an abstract idea. Therefore, the Applicants’ arguments are not persuasive.
Response to Applicants’ arguments regarding Step 2A – Prong two:
According to MPEP 2106, limitations that are indicative of integration into a practical application include:
* Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
* Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition
* Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
* Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
* Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e).
In the instant case, the judicial exception is not integrated into a practical application, because none of the above criteria is met. The claims only recite the additional elements of at least one non-transitory memory; and at least one processing device, the memory containing software code; a machine learning algorithm; one or more trained foundation models; one or more customer feedback channels; and a trained product model to perform all the steps. A plain reading of Figures 1, 2 and 8 and associated descriptions in at least paragraphs [0047], [0062] – [0069] reveals that the at least one processing device may include generic processors suitably programmed to execute the claimed steps. The at least one non-transitory memory containing the software code may be generic memory suitably programmed to store the associated data/information or software code. The one or more customer feedback channels are broadly interpreted to include suitably programmed generic channels for providing feedback. The machine learning algorithm. one or more trained foundation models, and the trained product model are broadly interpreted to include generic software suitably programmed to perform the claimed steps. Hence, the additional elements in the claims are all generic components suitably programmed to perform their respective functions. The additional elements in all the steps are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Hence, the claims are directed to an abstract idea.
The claimed features including those recited on pages 15-18 of the remarks such as “the trained foundation models are configured to perform feature-level inference on the customer behavior features and financial institution behavior features to generate the foundation model outputs," that "the foundation model selection variables are used to dynamically select the trained foundation models based on data contexts," and that the system is configured to "provide the updated data to the machine learning algorithm through a first feedback loop" and "modify the customer behavior features, financial institution behavior features, and foundation model outputs based upon the updated data received from the first feedback loop to refine the machine learning algorithm …… The foundation model selection variables determine "which trained foundation models 114 are used or in what sequence the trained foundation models 114 are used," and "may depend on a product portfolio, available data, or market landscape." Specification at paragraph [0074]. The models are "selected in an iterative manner," and the system may "iteratively optimize the hyperparameters of the models for the prediction target….. The trained foundation models generate their outputs "based on the data inputted into the trained foundation models 114 (e.g., customer behavior features 110, financial institution behavior features 112), selection variables ..., and the tasks the trained foundation models 114 were configured to perform….. the system "continuously monitors data sources 102 and compares source data to customer data 104 and financial institution data 106….. When the source data differs, the system "provides updated customer data 104 to the machine learning algorithm 108 through a first feedback loop," and modifies the customer behavior features and foundation model outputs "to refine the machine learning algorithm 108….. such feedback loops such feedback facilitate refined learning of machine learning models 108, training of trained foundation models 114, or optimization of trained product model 118 ….. monitor the trained product model performance according to one or more trained product model metrics at predetermined times" and to "refine the trained product model according to the trained product model performance." …… "refining the trained product model 118 involves improving the trained product model's 118 performance, accuracy, or generalization by making adjustments or optimizations." ….monitoring the model "by assessing the trained product model's 118 accuracy, reliability, or effectiveness over time” may be characterized as an improvement in the abstract idea of a method for artificial-intelligence based product optimization of products and services for offer, using the additional elements as tools in their normal capacity. An improvement in abstract idea is still abstract (SAP America v. Investpic *2-3 (“We may assume that the techniques claimed are “groundbreaking, innovative, or even brilliant,” but that is not enough for eligibility. Association for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013); accord buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1352 (Fed. Cir. 2014). Nor is it enough for subject-matter eligibility that claimed techniques be novel and nonobvious in light of prior art, passing muster under 35 U.S.C. §§ 102 and 103. See Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 89–90 (2012); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) (“A claim for a new abstract idea is still an abstract idea). The additional elements (identified in the rejection) are generic computer components used to apply the abstract idea. It does not involve any improvements to another technology, technical field, or improvements to the functioning of the computer itself. The alleged advantages of the Applicant’s invention are due to improvements in the abstract idea of a method for artificial-intelligence based product optimization of products and services for offer, using the additional elements as tools in their normal capacity.
The Examiner does not see the parallel between the Applicant’s claims and those in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB Sept. 26, 2025) (precedential). Therefore, the Applicants’ arguments are not persuasive.
Response to Applicants’ arguments regarding Step 2B:
As discussed in the rejection, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements (identified in the rejection) to perform the claimed steps, amount to no more than mere instructions to apply the exception using a generic computer component. The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, the claims are not patent eligible.
The claimed features including those recited on pages 19-20 of the remarks such as “processing the customer behavior features and financial institution behavior features "using one or more trained foundation models, wherein the trained foundation models are selected based on a plurality of foundation model selection variables," performing "feature-level inference on the customer behavior features and financial institution behavior features to generate the foundation model outputs," using the foundation model selection variables to "dynamically select the trained foundation models based on data contexts," inputting the foundation model outputs and the goal inputs into the trained product model, and refining the machine learning algorithm "through a first feedback loop” may be characterized as an improvement in the abstract idea of a method for artificial-intelligence based product optimization of products and services for offer, using the additional elements as tools in their normal capacity. An improvement in abstract idea is still abstract (SAP America v. Investpic *2-3 (“We may assume that the techniques claimed are “groundbreaking, innovative, or even brilliant,” but that is not enough for eligibility. The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The Applicant’s claims do not recite sufficient subject matter to take them from being in the realm of what is encompassed as an abstract idea into patentable subject matter and fail to add significantly more to “transform” the nature of the claims. In Summary, the computer system is merely a platform on which the abstract idea is implemented. Hence, the claims do not recite significantly more than an abstract idea. Therefore, the Applicants’ arguments are not persuasive.
For these reasons and those discussed in the rejection, the rejections under 35 USC § 101 are maintained.
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
(a) Itzkovich; Yosef Haim (US Pub. 2026/0141395 A1) discloses a system and method of checking compliance and enforcing a financial risk policy of a business, are provided herein. The method may include the following steps: receiving financial data of the business, said financial data comprising distribution of funds of the business; obtaining a financial risk policy relating to distribution of funds of the business; detecting a deviation of distribution of the funds from the policy; and optionally issuing an audit relating to the distribution of the funds of the business in view of the deviation from the policy.
6. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Narayanswamy Subramanian whose telephone number is (571) 272-6751. The examiner can normally be reached Monday-Friday from 9:00 AM to 5:00 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Abhishek Vyas can be reached at (571) 270-1836. The fax number for Formal or Official faxes and Draft to the Patent Office is (571) 273-8300.
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/Narayanswamy Subramanian/
Primary Examiner
Art Unit 3691
July 18, 2026
Prosecution Insights