DETAILED ACTION
1. The present application, filed on or after March 13, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
2.. An Amendment was filed November 13, 2025 (hereinafter “Amendment”) and has been entered into the record and fully considered. The Amendment was filed in response to a Non-Final Rejection dated August 13, 2025.
Despite the Amendment to the Claims and Applicant’s remarks, the Rejections under §101 and §103 as set forth in the Non-Final Rejection are hereby maintained. However, the Rejections to Claims 16 and 19 are on NEW GROUNDS necessitated by the Amendment.
An explanation of the maintained Rejections and a response to Applicant’s arguments are set forth below. Please see the “Conclusion” section of this Action below for important information regarding responding to this Action.
The IDS filed December 12, 2025 has been considered.
Status of the Claims:
Claims 1, 3 – 19, and 21 are pending in this Application.
Claims 2 and 20 have been cancelled.
Cancelled Claim 2 was incorporated into independent Claim 1.
Independent Claims 16 and 19 which is different from Claim 1.
The dependent Claims were not amended or only amended in a trivial manner.
Therefore, the following explanation of the maintained rejections with regard to Claims 1, 16, and 19 are considered explanatory of the Rejection as a whole.
With regard to the Amendment:
Claim 1 was amended as follows:
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Claim 16 was amended as follows:
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Claim 19 was amended as follows:
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Summary of the Amendment and Broadest Reasonable Interpretation:
Claim terminology is to be given its plain and ordinary meaning to a person of ordinary skill in the art, consistent with the specification. This is true, unless the terms are given a special meaning. See MPEP §2111.01
Here, no special meaning is detected. As noted in the Amendment, the
changes to Claim 1 relate generally to:
A training step
The training data comprises classifications (i.e. labelled training data) of historical travel-related transactions; and
Analysis notes
Claim 16 has been amended to include the generating of a chatbot to assist an analyst.
Claim 19 has been amended to include both of the additional features added to Claim 1 and the chatbot of Claim 16.
These terms appear – subject to further consideration – to be defined in the specification based on their plain and ordinary meaning.
With regard to §101:
Respectfully, while the Office recognizes the good faith attempt to render the Claims eligible, the amendments to still lack the specificity required to incorporate a practical application into the abstract idea recited in the claims. Thus, the Claims remain directed to an abstract idea.
With regard to Claim 1, the training step relates to a mental process. Selecting data for training is not a computerized function. The training step simply identifies the data that someone has selected to carry out the training. Thus, the MPEP states:
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
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In contrast, claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include:
• a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016);
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In other words, besides the abstract idea of reciting a method of organizing human activity (detecting fraud) the claim also recites a mental process. The additional limitations do not alter this conclusion. They were analyzed in the Non-Final Rejection.
Accordingly, the Rejection of Claim 1 under §101 must be maintained,
A similar analysis applies to Claim 16. It recites – at an extremely high level – a second ML model for providing a chatbot for providing an analyst additional information. This is merely a broad conceptual idea. A chatbot is a common and generic function of computers. It is a very common computerized function. These limitations are recited at a very high level of generality.
The Claim provides no specificity in terms of how the chatbot is implemented or trained. Only the mere outcome or result of additional information is recited. No special functionality is recited. No new computerized components are recited.
These limitations recite results or “outcome” of computer processing without specifying “how” a technical problem is solved. That is, the solution of a technical problem is not reflected in the Claim.
Claim 19 suffers from both of the weaknesses discussed above.
Taking the claim elements separately, the function performed by the computer elements at each step of the process is purely typical of processing identifiers for authentication purposes. Without greater specificity as to “how” certain functions solve a technical problem, the currently recited limitations can be achieved by any general purpose computer without special programming. In short, each step does no more than require a generic computer to perform generic computer functions. Considered as an ordered combination, the computer components of the Claim add nothing that is not already present when the steps are considered separately.
The Claims do not, for example, purport to improve the functioning of the computer elements nor do the claims reflect how an improvement in any other technology or technical field is achieved. Thus, the Claims amount to nothing significantly more than instructions to “apply” the abstract idea of detecting fraud using broadly recited machine learning or chatbot techniques. Such is not sufficient to integrate a practical application in the abstract idea.
Accordingly, the Rejection is maintained.
With regard to §103:
With regard to Claim 1:
The Rejection must be maintained because Edwards teaches the use of classified or “labelled” training data. First, Edwards clearly teaches the correlation between travel and fraud, which is a concept well understood by persons of ordinary skill in the art. That is, when transactions occur far from a cardholder’s residence or normal geographic location, it appears to be a fraudulent use. If the cardholder is traveling, there is a greatly reduced risk of fraud. Thus, Edwards teaches as follows:
“[0010] A user that is traveling has many things to take care of, such as packing, planning flights, planning a rental car, planning hotel accommodations, and/or the like. However, if the user is traveling on a trip with a transaction card or a transaction application, the user should notify a transaction card issuer (e.g., a financial institution) about the trip to ensure that any transactions made using the transaction card or the transaction application do not get declined for suspected fraud. Mistakenly declining valid transactions wastes computing resources (e.g., processing resources, memory resources, and/or the like), networking resources, and/or the like associated with conducting transactions that will be declined due to suspected fraud, contacting a financial institution while traveling about the declined transactions, associating a travel indicator with the transaction account, reconducting the declined transactions before and/or after the travel indicator is associated with the transaction account, and/or the like.” (Emphasis Added)
Secondly, Edwards is replete with teachings regarding machine learning. See Summary. Furthermore, the model of Edwards is trained on historical transactions and other historical travel data:
“[0002] According to some implementations, a method may include receiving historical transaction data associated with transactions conducted via transaction accounts associated with users, and receiving historical travel data indicating whether the users were traveling during times associated with the transactions identified in the historical transaction data. The method may include training a machine learning model with the historical transaction data and the historical travel data to generate a trained machine learning model, and receiving transaction data associated with one or more transactions conducted via a transaction account associated with a user. The method may include processing the transaction data, with the trained machine learning model, to determine a confidence score that provides an indication of whether the user is traveling, and determining whether the confidence score satisfies a confidence threshold. The method may include determining that the user is traveling when the confidence score satisfies the confidence threshold, and performing one or more actions based on determining that the user is traveling.” (Emphasis Added)
It is respectfully submitted that “historical travel data” is considered to constitute the recited term “analysis notes” because it “indicat(es) whether the users were traveling during times associated with the transactions identified in the historical transaction data.”
It is also clear from the teachings of Edwards that the training data may be “classified” - i.e. labelled – as either fraudulent or nonfraudulent:
“[0028] Additionally, or alternatively, the travel prediction platform may train the machine learning model using a supervised training procedure that includes receiving input to the machine learning model from a subject matter expert, which may reduce an amount of time, an amount of processing resources, and/or the like to train the machine learning model relative to an unsupervised training procedure.” (Emphasis Added)
It is well known that a supervised training procedure involves labelled training data; that is, it is labelled and the training is “supervised” by a “subject matter expert.”
Thus, In supervised learning, labeled data is the foundation that enables models to learn the relationship between input features and target outputs. Each data point in a labeled dataset contains both the features (independent variables) and the label (dependent variable or ground truth). The model uses these examples to identify patterns and correlations – such as fraud. , which it later applies to predict outcomes for unseen data.
Finally, the claimed “analysis notes” relate to exactly the types of transaction data and locations – relating to travel – as described in Edwards:
“[0098] In a first implementation, the historical transaction data may include data identifying one or more of: one or more transactions associated with purchases at airports; one or more transactions associated with checking in at airports; one or more transactions associated with purchases at gas stations near an international border; one or more transactions associated with purchases at rest stops; one or more transactions associated with purchasing airline tickets; one or more transactions associated with withdrawing funds from automated teller machines located at airports or rest stops; one or more transactions associated with hotels; one or more transactions associated with wireless access purchases on airplanes; one or more transactions associated with currency exchange at airports; or one or more transactions associated with purchases of items at train stations.” (Emphasis Added)
Thus, the rejection of Claim 1 is maintained on the current grounds.
With regard to Claims 16 and 19, a NEW GROUNDS of rejection is asserted:
Claims 16 and 19 are rejected under 35 U.S.C. §103 as being unpatentable over U.S. Patent Publication No. 2021/0182830 to Edwards et al. (hereinafter “Edwards) in view of U.S. Patent Publication No. 2025/0045735 to Araujo et al. (hereinafter “Araujo”) and further in view of Non-Patent Literature to anonymous, “ChatGPT -AI Chat bot – A Complete Guide,” The Encrypt, December 2022 (hereinafter “ChatGPT”).
The publication to ChatGPT explains in simple terms the training process that allows a chatbot to be trained using historical data and human feedback. The chatbot can then provide additional information to a human analysis in detecting fraud. As described in ChatGPT, such chatbots are a typical use of LLM’s.
Therefore, it would have been obvious to one of ordinary skill in the relevant art at the time of filing the claimed invention to have modified the combined ML fraud detection system of Edwards in view of Araujo, which teaches an LLM narrative output, to add the chatbot teachings of ChatGPT. The motivation to do so comes from Edwards. As quoted above and in the Non-Final Rejection, Edwards teaches the use of neural networks. An LLM is a form of neural network. It would greatly enhance the efficiency and accuracy of the system of Edwards to use the chatbot teachings of Araujo.
Therefore, the Rejection of these Claims is also maintained.
Response to Arguments
3. Applicant's arguments set forth in the Remarks section of the Amendment have been fully considered but they are not persuasive.
With regard to section 101 rejection, Applicant argues as follows:
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It is the mere “identifying” that renders Claim 1 ineligible. There is no recitation of “how” this is accomplished. No special functionality in identifying the training data is recited. Indeed, it is usually done by a human, or a “subject matter expert” as taught by Edwards. No computerized improvement to the field of travel-related fraud is recited.
Claims 16 and 19 suffer from the same defect as noted above.
Applicant has referred to extensive sections of the specification. The Office does not deny that the detection of fraud can involve eligible subject matter. But it must be recited with specificity and the Claim must “reflect” the technical improvement.
Pertinent to this point: Applicant argues the applicability of Desjardins. The Office respectfully disagrees. Even in that case it was critical that the technical improvement be “reflected” in the claim.
Paragraph 21 of the Specification, which the Appellant cites, identifies improvements in training the machine learning model itself. Of course, such an assertion in the Specification alone is insufficient to support a patent eligibility determination, absent a subsequent determination that the claim itself reflects the disclosed improvement. See MPEP § 2106.05(a) (citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316 (Fed. Cir. 2016)).
Such is not the case here. The alleged technical improvements are not reflected in the claim with specificity.
With regard to §103:
The clear teachings of Edwards mandate the maintenance of the Rejection of Claim 1. Applicant’s arguments with respect to Claims 16 and 19 are moot in view of the new grounds of Rejection.
A follow up interview is encouraged to discuss the merits of this Application.
Conclusion
4. Applicant should carefully consider the following in connection with this Office Action:
A. Finality
THIS ACTION IS MADE FINAL. Applicant’s amendments to Claims 16 and 19 necessitated the new grounds of Rejection. See MPEP § 706.07. 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.
B. Search and Prior Art
The search conducted in connection with this Office Action, as well as any previous Actions, encompassed the inventive concepts as defined in the Applicant’s specification. That is, the search(es) included concepts and features which are defined by the pending claims but also pertinent to significant although unclaimed subject matter. Accordingly, such search(es) were directed to the defined invention as well as the general state of the art, including references which are in the same field of endeavor as the present application as well as related fields (e.g. use of machine learning to detect fraud.). Indeed, there is a plethora of prior art in these fields.
Therefore, in addition to prior art references cited and applied in connection with this and any previous Office Actions, the following prior art is also made of record but not relied upon in the current rejection:
U.S. Patent Publication No. 2021/0390629 to Bildner et al. This reference relates to the concept of labelled training data.
U.S. Patent Publication No. 2021/0304204 to Ramesh et al. This reference relates to the concept of natural language processing.
U.S. Patent Publication No. 2017/0262852 to Florimond et al. This reference relates to the concept of training a model to detect fraud.
B. Responding to this Office Action
In view of the foregoing explanation of the scope of searches conducted in connection with the examination of this application, in preparing any response to this Action, Applicant is encouraged to carefully review the entire disclosures of the above-cited, unapplied references, as well as any previously cited references. It is likely that one or more such references disclose or suggest features which Applicant may seek to claim. Moreover, for the same reasons, Applicant is encouraged to review the entire disclosures of the references applied in the foregoing rejections and not just the sections mentioned.
C. Interviews and Compact Prosecution
The Office strongly encourages interviews as an important aspect of compact prosecution. Statistics and studies have shown that prosecution can be greatly advanced by way of interviews. Indeed, in many instances, during the course of one or more interviews, the Examiner and Applicant may reach an agreement on eligible and allowable subject matter that is supported by the specification.
Interviews are especially welcomed by this examiner at any stage of the prosecution process. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool (e.g. TEAMS). To facilitate the scheduling of an interview, the Examiner requests either a phone call at the number set forth below or the use of the AIR form as follows:
USPTO Automated Interview Request http://www.uspto.gov/interviewpractice.
Other forms of interview requests filed in this application may result in a delay in scheduling the interview because of the time required to appear on the Examiner's docket. Thus, the use of the AIR form is strongly encouraged.
D. Communicating with the Office
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM BUNKER whose telephone number is (571)272-0017. The examiner can normally be reached on M - F 8:30AM - 5:30PM, Pacific.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abhishek Vyas, can be reached at 571-270-1836. Information regarding the status of an application, whether published or unpublished, may be obtained from the “Patent Center” system. For more information about the Patent Center system, see https://patentcenter.uspto.gov/
/William (Bill) Bunker/
U.S. Patent Examiner
AU 3691
(571) 272-0017 - office
william.bunker@uspto.gov
February 5, 2026
/ABHISHEK VYAS/Supervisory Patent Examiner, Art Unit 3691