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 .
Response to Amendment
This Office Action is in response to Application 18/588,856 filed on 02/27/2024.
Claims 1-18 are currently pending and examined below.
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 1-18 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-18 is/are directed towards a statutory category (i.e., a process, machine, manufacture, or composition of matter) (Step 1, Yes).
Claim 1 recites (additional elements underlined):
A system for developing, using artificial intelligence, optimized advertisements 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, and wherein the trained foundation models output one or more foundation model outputs related to an advertisement instruction;
input the foundation model outputs and a plurality of goal inputs into a trained campaign execution model, wherein:
the goal inputs comprise one or more of a plurality of financial institution products, a plurality of financial institution product variants, a plurality of financial institution parameters, a plurality of financial institution regions, a plurality of financial institution growth strategies, a product response, a market response, a plurality of a campaign duration, a campaign budget, or an available campaign channel;
the trained campaign execution model is trained based on the foundation model outputs and the goal inputs;
output, from the trained campaign execution model, a natural-language advertisement response, wherein the advertisement response is based on the goal inputs.
Under the broadest reasonable interpretation, the limitations outlined above that describe or set forth the abstract idea, cover performance of the limitations in the mind but for the recitation of generic computer(s) and/or generic computer component(s). That is, other than reciting the additional elements identified below, nothing in the claim precludes the limitations from practically being performed in the mind. These limitations are considered a mental process because the limitations include an observation, evaluation, judgement, and/or opinion. These limitations are also similar to “collecting information, analyzing it, and displaying certain results of the collection and analysis” and/or “collecting and comparing known information” which were determined to be mental processes in MPEP 2106.04(a)(2)(III)(A). The Examiner notes that “[c]laims can recite a mental process even if they are claimed as being performed on a computer” (see MPEP 2106.04(a)(2)(III)(C)). The mere nominal recitation of the additional elements identified below do not take the claims out of the mental process grouping. Therefore, the claim recite a mental process (Step 2A Prong One, Yes).
The limitations outlined above also describe or set forth an advertising/marketing activity. Advertising/marketing fall within the certain method of organizing human activity enumerated grouping of abstract ideas. The limitations outlined above also describe or set forth a fundamental economic principle or practice because advertising/marketing is related to commerce and economy, a commercial interaction (e.g., advertising, marketing or sales activities or behaviors, business relations), and managing personal behavior or relationships or interactions between people. Therefore, the claim recites a certain method of organizing human activity (Step 2A Prong One, Yes).
In Step 2A Prong Two, these additional element(s) are recited at a high level of generality, and under the broadest reasonable interpretation, are generic computer(s) and/or generic computer component(s) that perform generic computer functions. The additional element(s) are merely used as tools, in their ordinary capacity, to perform the abstract idea. The additional element(s) amount adding the words “apply it” with the judicial exception. Merely implementing an abstract idea on generic computer(s) and/or generic computer component(s) does not integrate the judicial exception similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer. “[T]he use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent eligible subject matter" (see pp 10-11 of FairWarning IP, LLC. v. Iatric Systems, Inc. (Fed. Cir. 2016)). The additional elements also amount to generally linking the use of the abstract idea to a particular technological environment or field of use. The type of information being manipulated does not impose meaningful limitations or render the idea less abstract. Further, the courts have found that simply limiting the use of the abstract idea to a particular environment does not integrate the judicial exception into a practical application. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. The additional elements amount no more than mere instructions to apply the abstract idea using generic computer(s) and/or generic computer component(s). Their collective functions merely provide generic computer implementation. There is no indication that the combination of elements improves the functioning of a computer, improves any other technology or technical field, applies or uses the judicial exception to effect a particular treatment or prophylaxis for disease or medical condition, applies the judicial exception with, or by use of a particular machine, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses 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 claims as a whole is more than a drafting effort designed to monopolize the exception. (Step 2A Prong Two, No).
In Step 2B, the additional elements also do not amount to significantly more for the same reasons set forth with respect to Step 2A Prong Two. The Examiner notes that revised Step 2A Prong Two overlaps with Step 2B, and thus, many of the considerations need not be reevaluated in Step 2B because the answer will be the same. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. The additional elements amount no more than a mere instruction to apply the abstract idea using generic computer(s) and/or generic computer component(s) (Step 2B, No).
Claims 2-9 recite further limitations that also fall within the same abstract ideas identified above with respect to claim 1 (i.e., certain methods of organizing human activities and/or mental processes).
Claim 2 recites the additional elements of “foundation”, “the processing device is further configured to”, and “machine learning”. Claim 3 recites the additional elements of “trained”, “one or more of a logistic regression, a random forest, a gradient boosting, a clustering algorithm, or a deep learning model”, “the processing device is further configured to”, “trained”, and “train the trained”. The Examiner notes that “one or more of logistic regression, a random forest, a gradient boosting, and a clustering algorithm” can also be considered to be mental processes and mathematical concepts. Claim 4 recites the additional elements of “wherein the processing device is further configured to”. Claim 5 recites the additional elements of “trained” and “hybrid recommendation filtering”. Claim 6 recites the additional elements of “wherein the processing device is further configured to” and “trained”. Claim 7 recites the additional elements of “the processing device is further configured to”, “trained”, and “train the trained”. Claim 8 recites the additional elements “wherein the system further comprises a user interface configured to”, “provide the user interface to a user device”, “device on one or more elements of the user interface”, “machine learning”, “trained”, and “foundation”. However, these additional elements also do not integrate the judicial exception into a practical application or amount to significantly more because they amount to adding the words “apply it” with the judicial exception, mere instructions to implement the idea on a computer, merely using a computer as a tool to perform an abstract idea, and generally linking the use of the judicial exception to a particular technological environment or field of use.
Claims 9 does not recite any other additional elements. Therefore, for the same reasons explained above with respect to claim 1, claim 9 also does not integrate the judicial exception into a practical application or amount to significantly more.
Claim 10 recites substantially similar limitations as claim 1. Therefore, for the same reasons explained above with respect to claim 1, claim 10 also recites an abstract idea in Step 2A Prong One (i.e., certain method of organizing human activities, and mental processes). Claim 10 recites the additional elements of “using artificial intelligence”, “using a machine learning algorithm”, “trained”, “foundation”, and “natural-language”. However, for the same reasons explained above with respect to claim 1, these additional elements also do not integrate the judicial exception into a practical application or amount to significantly more.
Claims 11-18 recite further limitations that also fall within the same abstract ideas identified above with respect to claim 1 (i.e., certain methods of organizing human activities and/or mental processes).
Claim 11 recites the additional elements of “foundation”, “the processing device is further configured to”, and “machine learning”. Claim 12 recites the additional elements of “trained”, “one or more of a logistic regression, a random forest, a gradient boosting, a clustering algorithm, or a deep learning model”, “the processing device is further configured to”, “trained”, and “train the trained”. The Examiner notes that “one or more of logistic regression, a random forest, a gradient boosting, and a clustering algorithm” can also be considered to be mental processes and mathematical concepts. Claim 14 recites the additional elements of “trained” and “hybrid recommendation filtering”. Claim 15 recites the additional element of “trained”. Claim 16 recites the additional elements of “trained”, and “train the trained”. Claim 17 recites the additional elements “provide a user interface to a user device”, “device on one or more elements of the user interface”, “machine learning”, “trained”, and “foundation”. However, these additional elements also do not integrate the judicial exception into a practical application or amount to significantly more because they amount to adding the words “apply it” with the judicial exception, mere instructions to implement the idea on a computer, merely using a computer as a tool to perform an abstract idea, and generally linking the use of the judicial exception to a particular technological environment or field of use.
Claims 13 and 18 do not recite any other additional elements. Therefore, for the same reasons explained above with respect to claim 1, claims 13 and 18 also does not integrate the judicial exception into a practical application or amount to significantly more.
Double Patenting
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 filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual 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/apply/applying-online/eterminal-disclaimer.
Claims 1-18 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/589,001 (reference application) and claims 1-20 of copending Application no. 18/588,432 (reference application) Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application is anticipated by the copending applications. The limitations of the instant application are broader than the limitations of the copending applications. Therefore, the limitations of the copending applications are in essence a “species” of the generic invention of the instant application. It has been held that a generic invention is “anticipated” by a “species” within the scope of the generic invention. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993). Therefore, a patent to the examined claim genus would improperly extend the right to exclude granted by a patent to the species or sub-genus should the genus issue as a patent after the species or sub-genus. See MPEP 804(II)(B)(1).
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Prior Art
The Examiner notes that after an exhaustive search on the claims as currently presented, the claims currently overcome prior art. The closest prior art found to date are the following:
Domeniconi (US 2025/0061351 A1) discloses methods, systems, and computer program products for training a transactional event data analysis model and fine-tuning a pre-trained model to predict future transactions. Domeniconi also discloses gathering and extracting data, backpropagation, transfer learning, customer behavior features, financial institution behavior features, large language models, and the concept of the outputs of the foundation model being input into a final machine learning model (¶¶ 151-152). However, the final machine learning model of Domeniconi does not appear to provide sufficient detail regarding the final output. Domeniconi does not appear to input the plurality of goal inputs into a trained campaign execution model and outputting, from the trained campaign execution model, a natural language advertisement response, wherein the advertisement response is based on the goal inputs as claimed.
Jonnalagadda et al. (US 2020/0143247 A1) disclose systems and methods for improved automated conversations with intent and action response generation. Jonnalagadda et al. also discloses the use of multiple machine learning models, responses, goals, budgets, financial products, training, feedback, and customer data. However, the models in Jonnalagadda et al. operate in parallel. Jonnalagadda et al. does not appear to input the foundation model outputs and a plurality of goal inputs into a trained campaign execution model in order to output, from the trained campaign execution model, a natural language advertisement response as claimed.
Bhatt et al. (US 2022/0155926 A1) discloses the use of a machine learning model that is pre-trained based on historical input data and historical output data. However, Bhatt et al. does not use the foundation model and trained campaign execution model as claimed.
Abdelrahman et al. (US 2025/0037107 A1) discloses systems and methods for contextual transaction data collection using large language processing. Abdelrahman et al. also discloses gathering, extracting, and processing of customer behavior features and financial institution behavior features using one or more trained foundation models. However, Abdelrahman et al. does not appear to explicitly input the foundation model output and plurality of goal inputs into a trained campaign execution model in order to output a natural language advertisement response from the trained campaign execution model as claimed.
Eidelman et al. (US 2022/0191155 A1) discloses the concept of machine learning models to create messages for advocacy campaigns. Eidelman et al. also disclose the use of logistic regression, pre-trained models, advocacy responses, demographic data, surveys, and key performance indicators. However, Eidelman et al. does not appear to use the foundation model and the trained campaign execution model as claimed.
Response to Arguments
10. Applicant's arguments filed 02/27/2024 have been fully considered but they are not persuasive.
Argument A: “As an initial matter, the Office does not articulate with specificity the alleged abstract idea, instead citing only a broad category of judicial exceptions. Identifying the abstract idea is necessary to afford Applicant the ability to rebut the Office's contentions, and to identify what could be "significantly more" under the Step 2B analysis. The Office's reliance on generic formulations such as "collecting information, analyzing it, and displaying results" is untethered from the claim language and impermissibly oversimplifies the claims.”
In response, the Examiner respectfully disagrees. The Office Action clearly identifies the limitations that describe or set forth the abstract idea in Prong One and provides the reasons why the limitations describe or set forth a mental process and certain method of organizing human activity. The additional elements identified and addressed in Step 2A Prong Two and Step 2B.
Argument B: “Nor does the Office demonstrate that the claims fall within a recognized category of abstract ideas, such as "hedging, insurance, and mitigating risk." MPEP § 2106.04(a)(2)(II)(A). Absent such identification, the Office has not met its burden at Step 2A, Prong One.”
In response, the Examiner respectfully disagrees. The Office Action explains that the limitations outlined above that describe or set forth the abstract, also describe or set forth an advertising/marketing activity. Advertising/marketing fall within the certain method of organizing human activity enumerated grouping of abstract ideas. The limitations outlined above also describe or set forth a fundamental economic principle or practice because advertising/marketing is related to commerce and economy, a commercial interaction (e.g., advertising, marketing or sales activities or behaviors, business relations), and managing personal behavior or relationships or interactions between people. Therefore, the claims recite a certain method of organizing human activity (Step 2A Prong One, Yes).
Argument C: “These operations are not mental processes and cannot be performed by a human using pen and paper. Humans do not select, execute, and retrain foundation models based on computational selection variables, nor do they programmatically input model outputs into trained execution models operating over large, heterogeneous datasets. Accordingly, the claims do not recite a method of organizing human activity or a fundamental economic practice within the meaning of MPEP § 2106.04(a)(2)(II), and the Office has not established that the claims are directed to an abstract idea under Step 2A, Prong One.”
In response, the Examiner respectfully disagrees. The limitations that exclude the additional elements, can be practically performed in the human mind. The Examiner notes that “[c]laims can recite a mental process even if they are claimed as being performed on a computer” (MPEP 2106.04(a)(2)). Additionally, as explained above, the limitations that exclude the additional elements also describe or set forth certain methods of organizing human activities.
Argument D: “This is a technical solution to a technical problem-namely, how to coordinate multiple trained Al models to generate a natural-language advertising response based on goal inputs. The claimed invention addresses a technical problem unique to AI-driven systems: how to coordinate multiple trained machine learning models to generate adaptive, constrained outputs in an automated and scalable computing environment. Id., ar 92. The solution is a specific multi-model architecture with defined data flows, model-selection logic, and feedback-driven retraining. This is not merely the automation of a business practice. Rather, it is a technical solution that improves how the system itself operates.”
In response, the Examiner respectfully disagrees. The additional elements are recited at a high level of generality, and are merely used as tools, in their ordinary capacity, to perform the abstract idea. “Use of a computer or other machinery in its ordinary capacity for economic or other task (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” (MPEP 2106.05(f)(2)).
Argument E: “The claimed language … improve the functioning of the computer system itself. As updated data and performance information are fed back into the machine learning algorithms and trained models, the system becomes more accurate, responsive, and efficient. These improvements are technical in nature and relate directly to system performance, not merely to the presentation of information. Such improvements constitute a practical application under MPEP § 2106.04(d)(1), because the claims apply any alleged abstract idea in a manner that improves computer functionality rather than merely using a computer as a tool. See also Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB Sept. 26, 2025) (precedential) (holding that claims directed to an improved way of training a machine learning model-addressing "catastrophic forgetting" while optimizing performance, reducing storage needs, and lowering system complexity-integrate a mathematical concept into a practical application by improving the functioning of the Al technology itself). … In particular, the use of foundation model selection variables and feedback loops constitutes meaningful limitations that apply the alleged exception in a technological context, rather than merely linking it to generic environment. See MPEP § 2106.04(d)(I). These elements are not generic-they are specifically configured to improve the accuracy and responsiveness of advertising systems. Accordingly, the claims integrate any alleged abstract idea into a practical application, consistent with the precedential guidance in Ex Parte Desjardins.”
In response, the Examiner respectfully disagrees. Unlike in Enfish in which the claimed invention achieved other benefits over conventional databases such as increased flexibility, faster search times, and smaller memory requirements that provided improvements to the functioning of the computer itself, here looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improve any other technology. Their collective functions merely provide generic computer implementation.
In Ex Parte Desjardins, the Appeal Review Panel (APR) credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational task as technological improvements that were disclosed in the patent application specification. The APR then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of "catastrophic forgetting". Here, the claims and specification are completely silent with regard to such technical improvements. The models here are recited at a high level of generality, and are merely used as tools, in their ordinary capacity, to perform the abstract idea. “Use of a computer or other machinery in its ordinary capacity for economic or other task (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” (MPEP 2106.05(f)(2)).
Argument F: “The ordered combination of limitations-including the interaction between trained foundation models and a separately trained campaign execution model, the use of selection variables, and feedback-driven retraining-reflects a non-conventional system design.”
In response, the Examiner respectfully disagrees. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. The additional elements amount no more than mere instructions to apply the abstract idea using generic computer(s) and/or generic computer component(s). Their collective functions merely provide generic computer implementation. Additionally, the Office Action does not take the position that claimed invention is non-conventional. The Examiner notes that “the relevant inquiry is not whether the claimed invention as a whole is unconventional or non-routine" (see p. 16 of BSG Tech LLC v. BuySeasons, Inc. (Fed. Cir. 2018).
Argument G: “For example, claim 2 further recites that "the foundation model selection variables comprise one or more of the goal inputs" and the "processing device is further configured to: update the gathered data at predetermined times; provide the updated data to the machine algorithm through a first feedback loop; and modify the customer behaviour features, financial institution features, and foundation model outputs based upon information received from the first feedback loop to refine the machine learning algorithm." These limitations impose a specific feedback-driven retraining architecture that dynamically modifies extracted features and foundation model outputs over time. Claim 2 does not merely recite generic "updating" or "learning," but instead requires a defined feedback loop that feeds updated data back into the machine learning pipeline to refine the algorithm itself. This recursive retraining improves the functioning of the AI system by maintaining model accuracy and relevance as underlying data distributions evolve, and is neither a mental process nor a conventional computer operation. … As another example, claim 7 involves the "processing device is further configured to: monitor a plurality of key performance indicators; update the trained campaign execution model based on the key performance indicators in a third party feedback loop; and train the trained campaign execution model based upon information received from the third party feedback loop to refine the trained campaign execution model." These limitations improve the operation of the computer system by increasing the accuracy, responsiveness, and efficiency of the system and further underscore that the claims recite a specific, technologically rooted AI architecture rather than an abstract business practice.”
In response, the Examiner respectfully disagrees. As explained above, the additional elements are recited at a high level of generality, and are merely used as tools, in their ordinary capacity, to perform the abstract idea. “The requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement. … Iterative training using selected training materials and dynamic adjustments based on real-time changes are incident to the very nature of machine learning” (see p. 12 of Recentive Analytics, Inc. v. Fox Corp. (Fed. Cir. 2025)). “Today, we hold that patents that do not more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101” (see. p.18 of Recentive Analytics, Inc. v. Fox Corp. (Fed. Cir. 2025)).
Argument H: “Applicant respectfully traverses and asserts that the pending claims are patentably distinct from those of the '001 and '432 applications. Additionally, Applicant notes that because the pending claims are subject to rejection under 35 U.S.C. § 101, the non-statutory double patenting rejections are premature and moot unless and until the § 101 rejections are overcome.”
The Double Patenting rejection will be maintained until a Terminal Disclaimer is filed.
Conclusion
11. THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
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/SAM REFAI/Primary Examiner, Art Unit 3621