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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/26/26 has been entered.
Status of Claims
Claims 1-20 are pending in this application.
Examiner’s Comments Relating to Prior Art
In the office action dated 1/26/26, the examiner rejected claims 1-3, 5-6, 8-13, 15-16, and 18-20 were rejected under AIA 35 U.S.C. 103 as being unpatentable over Eastwood (20160196610) in view of Ruan (20250117797) and Hong (20240393262). Claims 4, 7, 14, and 17 were rejected under AIA 35 U.S.C. 103 as being unpatentable over Eastwood in view of Ruan, and Hong, further in view of and Alfaras (20240281419). In response, the applicant substantially narrowed down the claim scope in the amendments dated 3/26/26. Specifically, the independent claims 1, 11 and 20 now recite the limitations of: “determining, using the updated trained AI model, the period of time based on a predicted length of time of the threat; and after initiating the remedial action and prior to completion of the period of time, removing an amount associated with the transaction from an account associated with the user”.
This is in addition to the other disclosed elements for managing transactional risk by training an software algorithm on historical data and using the algorithm to process data to identify geopolitical risk previously disclosed and currently existing in the claim language. While the idea is fairly simple, the steps are complicated and precise – including how the algorithm determines a predicted length of time of the threat, and using this predicted period (i.e., before the expiration of this period but after initiating a remedial action) to settle the transaction (i.e., removing transaction amount from the user account). See steps 14-15 below. This is in addition to all the other steps. The specific steps are:
1) receiving user’s transaction history;
2) receiving previous third-party data;
3) training an AI model based on transaction history and getting predicted results;
4) receiving actual effects of previous transaction;
5) comparing predicted results with actual;
6) updating the weights of the AI model to minimize error;
7) receiving a transaction request;
8) receiving 3rd party data;
9) identifying geopolitical events based on the 3rd party data;
10) determining a threat associated with the transaction;
11) determining the severity of the threat;
12) initiating a remedial action;
13) remedial action includes deny, delay, or requiring user verification;
14) determining the time based on a predicted length of time of the threat; and
15) after initiating the remedial action and prior to completion of the period of time, removing an amount associated with the transaction from an account associated with the user.
In summary, the newly added elements – in combination with the other claim elements – overcome the prior art previously found and currently searched. The prior art rejections are withdrawn.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-20 are directed to a system, method, or product, which are/is one of the statutory categories of invention. (Step 1: YES).
The Examiner has identified independent method claim 11 as the claim that represents the claimed invention for analysis and is similar to independent system claim 1 and product claim 20. Claim 11 recites the limitations of managing transactional risk by training an software algorithm on historical data and using the algorithm to process data to identify geopolitical risk.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. Receiving user’s transaction history; receiving previous third-party data; training an AI model based on transaction history and getting predicted results; receiving actual effects of previous transaction; comparing predicted results with actual; updating the weights of the AI model to minimize error; receiving a transaction request; receiving 3rd party data; (using machine learning) identifying geopolitical events based on the 3rd party data; determining a threat associated with the transaction; determining the severity of the threat; initiating a remedial action; remedial action includes deny, delay, or requiring user verification;
determining the time based on a predicted length of time of the threat; and after initiating the remedial action and prior to completion of the period of time, removing an amount associated with the transaction from an account associated with the user – specifically, the claim recites “receiving a transaction history associated with a user, the transaction history comprising one or more previous transactions; receiving previous third-party data; training an artificial intelligence (AI) model based on the transaction history and the previous third-party data to output predicted results associated with predicted effects of the previous third-party data on the one or more previous transactions; receiving actual results associated with actual effects of the previous third-party data on the one or more previous transactions: comparing the predicted results and the actual results to determine an error signal; updating weights of the trained Al model based on the error signal to minimize a difference between the predicted results and the actual results; receiving, from a user device associated with the user, a transaction request comprising transaction data; receiving third-party data from one or more third-party data sources; identifying… one or more geopolitical events based on the third-party data; determining… a threat associated with the transaction request based on the one or more identified geopolitical events; determining… a severity of the threat; and initiating a remedial action in response to the transaction request based on the severity of the threat, wherein the remedial action comprises delaying the transaction request for a period of time; determining… the period of time based on a predicted length of time of the threat; and after initiating the remedial action and prior to completion of the period of time, removing an amount associated with the transaction from an account associated with the user”, recites a fundamental economic practice, directed to mitigating risk.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic practice or commercial or legal interactions, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The “a provider computing system”, “a processing circuit”, “one or more processors”, “one or more memory devices”, “an artificial intelligence (AI) model”, “the trained Al model”, “a user device”, “one or more third-party data sources”, and “the updated trained Al model”, in claim 1; and the additional technical element of “a non-transitory computer-readable medium” in claim 20, are just applying generic computer components to the recited abstract limitations. The recitation of generic computer components in a claim does not necessarily preclude that claim from reciting an abstract idea. Claims 11 and 20 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims recite an abstract idea)
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of: a computer such as a provider computing system, a processing circuit, one or more processors, and a user device; a storage unit such as one or more memory devices and one or more third-party data sources; and software module and algorithm such as an artificial intelligence (AI) model, the trained Al model, and the updated trained Al model. The computer hardware/software is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The examiner notes that although the claim recites “an artificial intelligence (AI) model”, “the trained Al model”, and “the updated trained Al model”, they are the same model over time and they are recited at a high level. See claims 1, 11 and 20. For example, the claims simply state what these, after training, will do in the claimed business process – i.e. based on data input, calculates the geopolitical risk. Similarly the specification recites “trained artificial intelligence (AI) model”, at a high level – see, for examples, paragraph 50, “the AI model 204 may be trained to identify transaction threats caused by geopolitical events based on the training inputs 202 and the actual outputs”. These are nominal recitations. The examiner notes that the applicant is not improving artificial intelligence technology. Rather the applicant is using artificial intelligence in a business process. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, claims 1, 11, and 20 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Thus, claims 1, 11, and 20 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims further define the abstract idea that is present in their respective independent claims 1, 11, and 20 and thus correspond to Certain Methods of Organizing Human Activity, and hence are abstract for the reasons presented above.
Dependent claim 2 discloses the limitation of identifying, using the updated trained AI model, at least one common parameter between the transaction data and contextual information related to the one or more geopolitical events, which further narrows the abstract idea. Note that the technical element “the updated trained AI model” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 3 discloses the limitation of at least one common parameter comprises at least one of a geographical location, a currency, one or more parties, a transaction method, or a transaction purpose, which further narrows the abstract idea.
Dependent claim 4 discloses the limitation of delaying the transaction request for a period of time, wherein the period of time is a first period of time, and wherein the operations further comprise: identifying, based on the determined threat associated with the transaction request, one or more additional transaction requests, wherein the one or more additional transaction requests comprise the at least one common parameter; generating a batch of affected transactions comprising the transaction request and the one or more additional transaction requests; and at least one of: approving the batch of affected transactions; denying the batch of affected transactions; delaying the batch of affected transactions for a second period of time; or requiring a user-verification of the batch of affected transactions, which further narrows the abstract idea.
Dependent claim 5 discloses the limitation of the AI model is trained using a backpropagation algorithm configured to propagate the error signal, which further narrows the abstract idea. Note that the technical element “the AI model” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 6 discloses the limitation of the AI model is trained until the error signal is within a predetermined threshold, which further narrows the abstract idea. Note that the technical element “the AI model” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 7 discloses the limitation of the AI model is a generative AI model, and wherein the training dataset further comprises the determination of the threat associated with the transaction request, which further narrows the abstract idea. Note that the technical elements “the AI model” and “is a generative AI model” are recited at a high level of generality. They do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Dependent claim 8 discloses the limitation of the remedial action is determined based on a comparison of the determined severity of the threat associated with the transaction request to a predefined threshold severity scale, which further narrows the abstract idea.
Dependent claim 9 discloses the limitation of the period of time for which the transaction request is delayed is based on the determined severity, which further narrows the abstract idea.
Dependent claim 10 discloses the limitation of generating a display comprising the response to the transaction request; and presenting the display via a user interface of a user device in real-time relative to receiving the transaction request, which further narrows the abstract idea. Note that the technical elements “a display” and “a user interface of a user device” are recited at a high level of generality. They do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Dependent claim 12 discloses the limitation of identifying, by the provider computing system using the updated trained AI model, at least one common parameter between the transaction data and contextual information related to the one or more geopolitical events, which further narrows the abstract idea. Note that the technical elements “the provider computing system” and “the updated trained AI model” are recited at a high level of generality. They do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Dependent claim 13 discloses the limitation of at least one common parameter comprises at least one of a geographical location, a currency, one or more parties, a transaction method, or a transaction purpose, which further narrows the abstract idea.
Dependent claim 14 discloses the limitation of identifying, by the provider computing system and based on the determined threat associated with the transaction request, one or more additional transaction requests, wherein the one or more additional transaction requests comprise the at least one common parameter; generating, by the provider computing system, a batch of affected transactions comprising the transaction request and the one or more additional transaction requests; and at least one of: approving, by the provider computing system, the batch of affected transactions; denying, by the provider computing system, the batch of affected transactions; delaying, by the provider computing system, the batch of affected transactions for a second period of time; or requiring, by the provider computing system, a user-verification of the batch of affected transactions, which further narrows the abstract idea. Note that the technical element “the provider computing system” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 15 discloses the limitation of predicting, using the updated trained AI model, the one or more geopolitical events based on the third-party data, which further narrows the abstract idea. Note that the technical element “the updated trained AI model” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 16 discloses the limitation of the AI model is trained using a backpropagation algorithm configured to propagate the error signal, and wherein the method further comprises training the AI model until the error signal is within a predetermined threshold, which further narrows the abstract idea. Note that the technical element “the AI model” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 17 discloses the limitation of the AI model is a generative AI model, and wherein the training dataset further comprises the determination of the threat associated with the transaction request, which further narrows the abstract idea. Note that the technical elements “the AI model” and “a generative AI model” are recited at a high level of generality. They do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Dependent claim 18 discloses the limitation of the remedial action is determined based on a comparison of the determined severity of the threat associated with the transaction request to a predefined threshold severity scale, which further narrows the abstract idea.
Dependent claim 19 discloses the limitation of generating, by the provider computing system, a display comprising the response to the transaction request; and presenting, by the provider computing system, the display via a user interface of a user device in real-time relative to receiving the transaction request, which further narrows the abstract idea. Note that the technical elements “the provider computing system”, “a display”, and “a user interface of a user device”, are recited at a high level of generality. They do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Thus, the dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, the claims 1-20 are not patent-eligible.
Response to Arguments
Applicant's arguments filed 3/26/26 have been fully considered but they are not persuasive.
The applicant’s 35 USC 103 arguments are moot because the prior art rejections are withdrawn. The examiner is withdrawing the prior art rejections because the amended claims contain new scope narrowing elements which, in combination with the existing elements, sufficiently narrow the claimed scope to overcome the existing prior art and additional art searched. See Examiner Comment Relating to Prior Art above.
In response to applicant's argument that:
“35 U.S.C. § 101… Applicant has amended independent claim 1… (reciting the amended claim 1)… Applicant submits that the features of amended claim 1 integrate the alleged judicial exception into a practical application of the alleged abstract idea,”
the examiner respectfully disagrees. In comparison to the prior version, the added elements (see underlined) and deleted elements (if any, struck out with a line) are essentially:
(1) “determining, using the updated trained AI model, the period of time based on a predicted length of time of the threat”; and
(2) “after initiating the remedial action and prior to completion of the period of time, removing an amount associated with the transaction from an account associated with the user”.
These changes are not sufficient to overcome the 35 U.S.C. § 101 rejections because: for 101 analysis purpose, this is just stating (corresponding to the numberings above):
processing data to determine the answer according to an algorithm. This is a process and an abstract idea; and
settle a transaction (remove/transfer) amount at a particular period (before expiration date, but after initiating a remedial action).
These are abstract ideas. There is nothing technical about it.
Note that the technical element “the updated trained AI model” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
In response to applicant's argument that:
“the claims integrate the judicial exception into a practical application because the claims recite a technical improvement to "systems and methods for detecting and responding to transaction threats caused by geopolitical events,”
the examiner respectfully disagrees. As stated in the prior office action:
“The applicant essentially adds the process for training machine learning algorithm – i.e., using historical data to train a predictive algorithm, then check if the algorithm by measuring it against actual outcome, then correct the algorithm if there are any errors. It is a summary of how machine algorithms are usually trained. As such, these additional element does not help the claim to overcome the 35 U.S.C. § 101 rejections because they do not improve machine learning technology (or any technology).”
“The examiner notes that although the claim recites “an artificial intelligence (AI) model”, “the trained Al model”, and “the updated trained Al model”, they are the same model over time and they are recited at a high level. See claims 1, 11 and 20. For example, the claims simply state what these, after training, will do in the claimed business process – i.e. based on data input, calculates the geopolitical risk. Similarly the specification recites “trained artificial intelligence (AI) model”, at a high level – see, for examples, paragraph 50, “the AI model 204 may be trained to identify transaction threats caused by geopolitical events based on the training inputs 202 and the actual outputs”. These are nominal recitations. The examiner notes that the applicant is not improving artificial intelligence technology. Rather the applicant is using artificial intelligence in a business process. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK H GAW whose telephone number is (571)270-0268. The examiner can normally be reached Mon-Fri: 9am -5pm.
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/MARK H GAW/Examiner, Art Unit 3693
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