DETAILED ACTION
This is an office action on the merits in response to the communication filed on 12/5/2025.
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 .
Claims’ Status
Claims 1-5 are elected. Claims 1-5 are considered in this office action.
Objection
The subject matter of this application admits of illustration by a drawing to facilitate understanding of the invention. Applicant is required to furnish a drawing under 37 CFR 1.81(c). No new matter may be introduced in the required drawing. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d).
This application claims the subject matter of using large language model (LLM); a plurality of payment processing modules; a plurality of modalities; etc for analyzing fraud activities, however none of the drawings describes the said subject matter. Correction is required.
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 1-4 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) do not fall within at least one of the four categories of patent eligible subject matter because the claimed “a large language model (LLM) based operating system”; “a plurality of payment processing modules integrated into said LLM-based operating system”; and “a plurality of modalities” may be implemented in software and claim 1 is a system claim. As such, claim 1 may be directed to software per se.
Applicant may amend the claim to explicitly include hardware components or a functionality performed by the claimed computing instance that nonetheless requires “a large language model (LLM) based operating system”; “a plurality of payment processing modules integrated into said LLM-based operating system”; and “a plurality of modalities.”
Claims 1-5 are rejected under 35 U.S.C. 101 because the claimed invention is not directed to patent eligible subject matter. The claimed matter is directed to a judicial exception (i.e. an abstract idea not integrated into a practical application) without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture or composition of matter? MPEP 2106.03
Per Step 1, Claims 1-4 are drawn to system claims and claim 5 is drawn to a method claim, which are within the four statutory categories (i.e., a process).
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon MPEP 2106.04.
Claim 5 (claim 1 similar in scope) recites:
Analyzing a context window using a Large Language Model (LLM) based operating system to identify multimodal triggers;
Selecting an appropriate payment processing module from a plurality of modules, each associated with different payment methods, based on the identified multimodal triggers;
Initiating a financial transaction utilizing the selected payment processing module;
Facilitating diverse channels of input and output between the LLM-based operating system and a user through a plurality of modalities.
The limitations, as drafted, constitute a process that, under its broadest reasonable interpretation, covers managing, 1) Managing personal behavior or relationships or interaction between people by following rules or instructions, under the Certain methods of organizing human activity, but for the recitation of generic computer components. The abstract idea, recited above, includes: selecting an appropriate payment processing module from a plurality of modules, each associated with different payment methods, based on the identified multimodal triggers; initiating a financial transaction utilizing the selected payment processing module; facilitating diverse channels of input and output between the LLM-based operating system and a user through a plurality of modalities. If a claim limitation, under its broadest reasonable interpretation, covers performance of managing relationships by following rules, but for the recitation of generic computer components, it falls within the Certain Methods of Organizing Human Activity – 1) Managing personal interactions between people by following rules or instructions, grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
The additional positive elements: “analyzing a context window using a Large Language Model (LLM) based operating system to identify multimodal triggers;” in claim 5, which amounts to linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) or simply “applying it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)
Accordingly, these additional claim elements, alone and in combination do not integrate the abstract idea into a practical application, because (1) they do not effect improvements to the functioning of a computer, or to any other technology or technical field (see MPEP 2106.05(a)); (2) they do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or a medical condition (see the Vanda memo); (3) they do not apply the abstract idea with, or by use of, a particular machine (see MPEP 2106.05(b)); (4) they do not effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)); (5) they do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the identified abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designated to monopolize the exception (see MPEP 2106.05(e) and the Vanda memo). Therefore, per Step 2A, Prong Two, the claim is directed to an abstract idea not integrated into a practical application.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B of the eligibility analysis concludes that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Examiner carries over the analysis from Step 2A related to the generic computing elements being no more than a recitation of the words "apply it" (or an equivalent) to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)). The additional claim elements that are just “applying it” or “generally linking the use of the judicial exception to a particular technological environment or field of use” are mere instructions to implement an abstract idea on a computer, are carried over for further analysis in Step 2B.
When the independent claims are considered as a whole, as a combination, the claim elements noted above do not amount to any more than they amount to individually. The operations appear to merely apply the abstract concept to a technical environment in a very general sense. The most significant elements of the claims, that is the elements that really outline the inventive elements of the claims, are set forth in the elements identified as an abstract idea. Therefore, it is concluded that the elements of the independent claims are directed to one or more abstract ideas and do not amount to significantly more. (MPEP 2106.05)
Further, Step 2B of the analysis takes into consideration all dependent claims as well, both individually and as a whole, as a combination:
Claims 2-4 are further directed to additional abstract ideas because the steps performed are simply narrowing the scope of the abstract idea of respective claim 1 since their individual and combined significance is still not significantly more than the abstract concept at the core of the claimed invention. For example, claim 2 further narrowing the scope of the independent claim 1 by dynamically selecting an appropriate payment processing module; claim 3 further includes presenting transaction options and receiving user preferences; claim 4 further includes employing natural language understanding to interpret user commands; etc, which all of the limitation are narrowing the steps performed in the respective claim 1.
Moreover, the claims in the instant application do not constitute significantly more also because the claims or claim elements only serve to implement the abstract idea using computer components to perform computing functions (Enfish, see MPEP 2106.05(a)). Specifically, the computing system encompasses general purpose hardware and software modules.
The most significant elements of the claims, that is the elements that really outline the inventive elements of the claims, are set forth in the elements identified in the independent claims as an abstract idea. The fact that the associated computing devices are facilitating the abstract concept is not enough to confer statutory subject matter eligibility. In sum, the additional elements do not serve to confer subject matter eligibility to the invention since their individual and combined significance is still not heavier than the abstract concepts at the core of the claimed invention. Therefore, it is concluded that the dependent claims of the instant application do not amount to significantly more either. (see MPEP 2106.05)
In sum, claims 1-5 are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Poupyrev et al. (US20250094454A1) in view of BALAKRISHNAN et al. (US20220207602A1), and further in view of Chen (US20180349092A1).
With respect to claim 1
Poupyrev discloses:
a Large Language Model (LLM) based operating system configured to continuously analyze a context window for multimodal triggers ([0133], FIG. 6 illustrates examples 600 of applying an LLM 150 to process sensor data 120, in accordance with some implementations. For example, the LLM 150 is applied to transcribe, summarize, explain anomalies in, automatically perform a task on, monitor, simulate, predict information from, visualize, perform a semantic search on, and/or classify sensor data 120. In some implementations, the sensor data 120 is converted (602) into an LLM output 130 including textual description of physical events and entities in real time. In some implementations, the server system 106 generates (604) an LLM output 130 including concise textual or visual descriptions of large amounts of spatiotemporal sensor data 120, capturing the most salient physical events or entities. Further, in some implementations, a learning base model represents (606) the real-time status and salient events across multiple sensors distributed over different spatial scales (e.g., house, neighborhood). In some implementations, the server system 106 applies the LLM 150 to detect an anomaly, identify a cause and context behind physical events, and generate the LLM output 130 describing how and why physical events or conditions happen.),
Poupyrev doesn’t explicitly disclose, but BALAKRISHNAN teaches: b. a plurality of payment processing modules integrated into said LLM-based operating system, wherein said payment processing modules include: i. A paper check processing module utilizing LLM processing; ii. A credit card processing module utilizing LLM processing; iii. A mobile payment processing module utilizing LLM processing;
iv. A QR code processing module utilizing LLM processing; and v. A bank transfer processing module utilizing LLM processing ([0024], FIG. 1 is a schematic diagram illustrating an example system 100 for processing a financial transaction, consistent with the disclosed embodiments. For example, the financial transaction processed by system 100 may be in the form of check payments, debit card payments, credit card payments, electronic payment made through the Automated Clearing House (ACH) Network, Real-Time Payment Network, wire transfers, electronic payments, peer-to-peer payments, mobile payments (e.g., Apple Pay®), electronic fund payment (e.g., Zelle®), or the like. Moreover, the payments processed by system 100 may include recurring payments, such as payments of utility bills, providing paychecks to an employee through direct deposits, mortgage payments, or the like. As shown in FIG. 1, system 100 includes transaction processing network 120, financial service provider 130, financial transaction system 140, and transaction cloud 170; see [0031], In another example, POS system 160 may include a mobile payment machine that may receive the account number (e.g., by receiving an NFC tap, scanning a QR code, or the like) from user device 110 that provides a digital wallet (e.g., Apple Pay®, Google Pay®, Samsung Pay®, or the like); see also [0043], Analysis engine 240 can be implemented in any general programming language and may also be implemented using optimized languages for particular tasks such as machine learning or statistical analysis (e.g., the R programming language). Analysis engine can process financial data provided through, for example, input/output device 220 and generate output models, visualizations, or other conclusions based on the input data. In some embodiments, analysis engine 240 may analyze or combine multiple data sets from multiple data sources. In some embodiments, analysis engine can continuously process an input data stream and generate updated analysis as new data is received. Analysis engine 240 may utilize machine learning techniques to improve the analysis of input data. In these embodiments, analysis engine 240 can learn from previous data and analysis to constantly improve its predictive models and analysis as more data is processed.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Poupyrev with the teaching of BALAKRISHNAN as they relate to a system of processing and analysis data using machine learning techniques. One of ordinary skill in the art before effective filing date of the claimed invention was made would have modified the system of analyzing data based on large language model in Poupyrev to include a system of collecting and analyzing financial data using machine learning technique in BALAKRISHNAN for predictable results of using a comprehensive set of dataset in order to enhance artificial intelligence training.
Cella in view of BALAKRISHNAN don’t explicitly disclose, but Chen teaches: c. A plurality of modalities facilitating diverse channels of input and output between the LLM-based operating system and a user (see [0003 and 0062]), said modalities including:
i. Vision modality for computer graphics through a screen (see [0025]);
ii. Audition modality for various audio outputs (see [0026]);
iii. Tactition modality for vibrations or other movement (see [0027]);
iv. Gustation modality for taste; v. Olfaction modality for smell; vi. Thermoception modality for heat; vii. Nociception modality for pain; and viii. equilibrioception modality for balance (see [0028);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Poupyrev / BALAKRISHNAN with the teaching of Chen as they relate to a system of processing and analysis data using machine learning techniques. One of ordinary skill in the art before effective filing date of the claimed invention was made would have modified the combined systems of Poupyrev / BALAKRISHNAN, for example analyzing data based on large language model in Poupyrev, to include a system of collecting multimodal data as taught by Chen for predictable results of using a comprehensive set of dataset in order to enhance artificial intelligence training.
With respect to claim 5
Poupyrev teaches:
a. Analyzing a context window using a Large Language Model (LLM) based operating system to identify multimodal triggers ([0133], FIG. 6 illustrates examples 600 of applying an LLM 150 to process sensor data 120, in accordance with some implementations. For example, the LLM 150 is applied to transcribe, summarize, explain anomalies in, automatically perform a task on, monitor, simulate, predict information from, visualize, perform a semantic search on, and/or classify sensor data 120. In some implementations, the sensor data 120 is converted (602) into an LLM output 130 including textual description of physical events and entities in real time. In some implementations, the server system 106 generates (604) an LLM output 130 including concise textual or visual descriptions of large amounts of spatiotemporal sensor data 120, capturing the most salient physical events or entities. Further, in some implementations, a learning base model represents (606) the real-time status and salient events across multiple sensors distributed over different spatial scales (e.g., house, neighborhood). In some implementations, the server system 106 applies the LLM 150 to detect an anomaly, identify a cause and context behind physical events, and generate the LLM output 130 describing how and why physical events or conditions happen.);
Poupyrev doesn’t explicitly disclose, but BALAKRISHNAN teaches:
b. Selecting an appropriate payment processing module from a plurality of modules, each associated with different payment methods, based on the identified multimodal triggers; c. Initiating a financial transaction utilizing the selected payment processing module ([0024], FIG. 1 is a schematic diagram illustrating an example system 100 for processing a financial transaction, consistent with the disclosed embodiments. For example, the financial transaction processed by system 100 may be in the form of check payments, debit card payments, credit card payments, electronic payment made through the Automated Clearing House (ACH) Network, Real-Time Payment Network, wire transfers, electronic payments, peer-to-peer payments, mobile payments (e.g., Apple Pay®), electronic fund payment (e.g., Zelle®), or the like. Moreover, the payments processed by system 100 may include recurring payments, such as payments of utility bills, providing paychecks to an employee through direct deposits, mortgage payments, or the like. As shown in FIG. 1, system 100 includes transaction processing network 120, financial service provider 130, financial transaction system 140, and transaction cloud 170; see [0031], In another example, POS system 160 may include a mobile payment machine that may receive the account number (e.g., by receiving an NFC tap, scanning a QR code, or the like) from user device 110 that provides a digital wallet (e.g., Apple Pay®, Google Pay®, Samsung Pay®, or the like); see also [0043], Analysis engine 240 can be implemented in any general programming language and may also be implemented using optimized languages for particular tasks such as machine learning or statistical analysis (e.g., the R programming language). Analysis engine can process financial data provided through, for example, input/output device 220 and generate output models, visualizations, or other conclusions based on the input data. In some embodiments, analysis engine 240 may analyze or combine multiple data sets from multiple data sources. In some embodiments, analysis engine can continuously process an input data stream and generate updated analysis as new data is received. Analysis engine 240 may utilize machine learning techniques to improve the analysis of input data. In these embodiments, analysis engine 240 can learn from previous data and analysis to constantly improve its predictive models and analysis as more data is processed.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Poupyrev with the teaching of BALAKRISHNAN as they relate to a system of processing and analysis data using machine learning techniques. One of ordinary skill in the art before effective filing date of the claimed invention was made would have modified the system of analyzing data based on large language model in Poupyrev to include a system of collecting and analyzing financial data using machine learning technique in BALAKRISHNAN for predictable results of using a comprehensive set of dataset in order to enhance artificial intelligence training.
Cella in view of BALAKRISHNAN don’t explicitly disclose, but Chen teaches: d. Facilitating diverse channels of input and output between the LLM-based operating system and a user through a plurality of modalities (see [0003 and 0062]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Poupyrev / BALAKRISHNAN with the teaching of Chen as they relate to a system of processing and analysis data using machine learning techniques. One of ordinary skill in the art before effective filing date of the claimed invention was made would have modified the combined systems of Poupyrev / BALAKRISHNAN, for example analyzing data based on large language model in Poupyrev, to include a system of collecting multimodal data as taught by Chen for predictable results of using a comprehensive set of dataset in order to enhance artificial intelligence training.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Poupyrev et al. (US20250094454A1) in view of BALAKRISHNAN et al. (US20220207602A1) in view of Chen (US20180349092A1), and further in view of Rajeev et al. (US20210392106A1).
With respect to claim 2
The combination of Poupyrev, BALAKRISHNAN, and Chen discloses the limitation of claim 1. The combination doesn’t explicitly disclose, but Rajeev teaches:
wherein said LLM-based operating system is configured to dynamically select an appropriate payment processing module based on the identified multimodal triggers within the context window ([0055], Multimodal communication often involves a combination of different communication modalities. Depending on the purpose of the multimodal communication, entities often weigh each communication modality differently as each communication modality may have varying degree of effectiveness. In the financial industry, customers are often known to communicate with employees (e.g., customer service agents, tellers, administrators, managers, etc.) via a combination of different modalities to execute resource transfers. Such multimodal communications that often include a request from the customer to the employee to execute a transfer of resources.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Poupyrev / BALAKRISHNAN/ Chen with the teaching of Rajeev as they relate to a system of processing and analysis data using machine learning techniques. One of ordinary skill in the art before effective filing date of the claimed invention was made would have modified the combined systems of Poupyrev / BALAKRISHNAN/Chen, for example analyzing data based on large language model in Poupyrev, to include a system of selecting an appropriate payment processing module based on the identified multimodal as taught in Rajeev for predictable results of using a comprehensive set of dataset in order to enhance artificial intelligence training.
Claims 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Poupyrev et al. (US20250094454A1) in view of BALAKRISHNAN et al. (US20220207602A1) in view of Chen (US20180349092A1), and further in view of Xiong et al. (US20240346576A1).
With respect to claim 3
The combination of Poupyrev, BALAKRISHNAN, and Chen discloses the limitation of claim 1. The combination doesn’t explicitly disclose, but Xiong teaches: further comprising a user interface module for presenting transaction options and receiving user preferences through any of said modalities (see [0043 and 0044])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Poupyrev / BALAKRISHNAN/ Chen with the teaching of Xiong as they relate to a system of processing and analysis data using machine learning techniques. One of ordinary skill in the art before effective filing date of the claimed invention was made would have modified the combined systems of Poupyrev / BALAKRISHNAN/Chen, for example analyzing data based on large language model in Poupyrev, to include a method of presenting transaction options and receiving user preferences as taught in Xiong for predictable results of using a comprehensive set of dataset in order to enhance artificial intelligence training.
With respect to claim 4
The combination of Poupyrev, BALAKRISHNAN, and Chen discloses the limitation of claim 1. The combination doesn’t explicitly disclose, but Xiong teaches: wherein said LLM-based operating system is configured to employ natural language understanding to interpret user commands related to financial transactions through any of said modalities (see [0015].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Poupyrev / BALAKRISHNAN/ Chen with the teaching of Xiong as they relate to a system of processing and analysis data using machine learning techniques. One of ordinary skill in the art before effective filing date of the claimed invention was made would have modified the combined systems of Poupyrev / BALAKRISHNAN/Chen, for example analyzing data based on large language model in Poupyrev, to include a system of employing natural language to interpret user commands as taught in Xiong for predictable results of using a comprehensive set of dataset in order to enhance artificial intelligence training.
Conclusion
THIS ACTION IS MADE Non-FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YIN Y CHOI whose telephone number is (571)272-1094 or yin.choi@uspto.gov. The examiner can normally be reached on M-F 7:30 - 5:30pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Neha Patel can be reached on 571-270-1492. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/YIN Y CHOI/Examiner, Art Unit 3699
1/19/2026