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 .
Status of Claims
This Office action is in reply to filing by applicant on 04/13/2026.
Claims 21, 30, and 36 were amended by Applicant.
Claims 22 – 28, 31 – 35, and 37 – 40 were previously presented by Applicant.
Claim 41 is new.
Claims 1 – 20 and 29 were cancelled by Applicant.
Claims 21 – 28 and 30 – 41 are currently pending and have been examined.
The prior 35 USC 101 claim rejections set forth in the Non-Final rejection of 11/12/2025 as to claims 21 – 40 are withdrawn in view of Applicant's arguments and amendments.
There were no prior 35 USC 103 rejections in this matter.
An obviousness type double patenting rejection (ODP) as to all pending claims herein (claims 21 – 28 and 30 - 41) is made as to the reference patent of (US12020257B2) to Laptiev.
THIS ACTION IS MADE FINAL.
Response to Arguments
There are no new grounds of rejection herein as to any of the claims, apart from the obviousness type double patenting rejection of all claims 21 – 28 and 30 – 41 herein, which rejection was previously somewhat unclear (from the initial claims herein). The present rejection of all claims herein is because of the existence of the prior grant of parent patent (US1202025B27, see below analysis). Examiner notes that several attempts (phone / email) to contact Applicant throughout the week of June 22, 2026 in this regard were made (in an effort to obtain a Terminal Disclaimer respecting said parent patent), but examiner received no response. Any other argument made herein by Applicant is moot.
Obviousness Type Double Patenting Rejection
Claims 21 – 28 and 30 - 41 are rejected on the ground of nonstatutory double patenting as being unpatentable over independent claims 1, 10, and 17 of issued parent reference patent (US12020257B2, the “’257 Patent”) to Laptiev, et al., which patent has the same Applicant as the instant application.
The nonstatutory double patenting rejection (“ODP”) is based on a judicially created doctrine grounded in public policy 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.
Although the instant claims 21 – 28 and 30 - 41 at issue herein are not identical to the above noted independent claims 1, 10, and 17 (mirrored) of the ‘257 reference patent of Laptiev, they are not patentably distinct from them because the instant claims herein are all separately anticipated by said ‘257 patent’s independent claims.
Anticipation analysis, independent claims
Regarding independent method (claim 21), independent non-transitory CRM claim 30, and independent system claim 36, of the present 04/13/2026 claims under examination: (claim 36 of the instant application is reprinted immediately below)
A system comprising: at least one processor; and at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to:
receive, from a client device associated with a user account, a digital dispute claim indicating a disputed network transaction associated with the user account;
identify one or more features associated with the digital dispute claim;
provide the one or more features to a fraud detection machine-learning model that was trained using training fraud predictions corresponding to training digital dispute claims,
wherein the fraud detection machine-learning model is trained by comparing the training fraud predictions to fraud action labels using a loss function to generate losses that are used to adjust one or more parameters of the fraud detection machine-learning model;
and generate, utilizing the fraud detection machine-learning model, a fraud prediction indicating a probability that the digital dispute claim is fraudulent.
Note that the above limitations of independent claim 36 (and of 21 / 30) of the instant application are all separately anticipated by and / or would have been obvious over, the below referenced claims 1, 10, and 17 of the ‘257 reference patent of Laptiev, they are:
(claim 1 of the ‘257 Laptiev patent is used below)
access a fraud detection machine-learning model that was trained using one or more training fraud predictions corresponding to training digital claims,
wherein training the fraud detection machine-learning model comprises comparing a training digital claim to fraud action labels using a loss function to generate losses that are used to adjust one or more parameters of the fraud detection machine-learning model;
receive, from a client device associated with a user account, a digital claim indicating that a network transaction associated with the user account was not authorized;
generate, as an output of the fraud detection machine-learning model, a fraud prediction indicating a probability that the digital claim is fraudulent by processing the one or more features associated with the digital claim with the fraud detection machine-learning model; provide, for display in a graphical user interface, a visual indicator of the fraud prediction for the digital claim; receive a fraud action label for the digital claim indicating whether the digital claim was fraudulent or not fraudulent; and update the one or more parameters of the fraud detection machine-learning model using the fraud action label by: comparing the fraud action label to the fraud prediction and one or more additional fraud action labels to one or more additional fraud predictions; determining, based on comparing the fraud action label to the fraud prediction and one or more additional fraud action labels to one or more fraud predictions, that the fraud prediction and the one or more additional fraud predictions do not satisfy a measure of loss; and
updating parameters of the fraud detection machine-learning model.
As seen above, the noted ‘257 reference patent of Laptiev (i.e., its independent claims 1, 10,17) recite(s) at least each and every limitation of (reads on, anticipates) each of the three instant independent claims 21, 30, and 36 (which are also mirrored). No additional limitations of the instant application’s claims need to be added, by an obviousness analysis or otherwise.
All dependent claims of the instant application i.e., claims 22 – 28, 31 – 35, and 37 – 41 specifically depend, respectively, from their instantly rejected (on anticipation grounds as above) independent claims 21, 30, and 36, and those dependent claims are therefore similarly rejected.
Allowable Subject Matter
Claims 21 – 28 and 30 – 41 would be allowable if rewritten or amended to overcome the additional rejection pursuant to the ODP analysis made herein as to those same claims. The following is a statement of reasons for the indication of allowable subject matter: Independently, while the claims' limitations most recently set forth herein may individually be disclosed by the prior art, the claims as a whole are not obvious because the examiner would have to improperly use their separate limitations as a road map to combine them.
CONCLUSION
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.
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form 892.
Beckman (US10872341B1) – Systems and methods for secondary fraud detection during transaction verifications are disclosed. A payment system may transmit a fraud protection notification to a user in response to potential fraud being detected as part of a primary transaction fraud detection process. In response to the user interacting with the fraud protection notification to confirm that the transaction was not fraudulent, the system may capture user device data from the user's device. The system may perform a secondary fraud detection process on the captured user device data to determine whether the verification of the transaction has a risk of being fraudulent.
Kramme (US20210374764A1) - In a computer-implemented method of facilitating a fraud dispute resolution process, types of information historically indicative of fraud (or its absence) may be identified by training a machine learning program using transaction data associated with financial transactions and fraud determinations for those transactions. An indication that fraud is suspected for a first transaction may be received, and transaction data may be retrieved. Based upon at least one of the identified types of information and the transaction data, a first set of one or more queries that are designed to ascertain whether the first transaction was fraudulent may be generated. The first set of queries may be transmitted to a remote computing device for display to the customer, and a first set of one or more customer responses may be received. Based upon the first set of customer responses, it may be determined whether the first transaction was fraudulent.
Ramakrishnan (US20200034842A1) - A system for predicting a non-fraud dispute using an artificial intelligence (AI) based communications system is disclosed. The system may comprise a data access interface to receive instructions historical transaction and disputes data from at least one data source associated with an account issuer. The data access interface may also receive incoming transaction data associated with a transaction from at least one data source associated with an account holder. The system may comprise a processor to predict a likelihood of a non-fraud dispute associated with the transaction by: examining the historical transaction and disputes data; retrieving non-fraud dispute attributes; parsing the incoming transaction data; applying predictive analytics to the incoming transaction data to yield a prediction value; determining that the prediction value meets a predetermined threshold; and generating a prediction for the likelihood of a non-fraud dispute associated with the transaction associated with the account holder to be outputted, via an output interface to a user device.
Kramme (US20210374753A1) - A method of identifying a potential chargeback scenario includes generating or updating chargeback candidate detection rules, at least by training a machine learning program. The machine learning program may be trained using transaction data associated with financial transactions, and using chargeback determinations, for the financial transactions, that were made in accordance with chargeback rules associated with a card network entity. The method also includes receiving an indication that fraud has been confirmed for a financial transaction associated with a merchant and a financial account, and retrieving transaction data associated with the financial transaction. The method may further include determining, by applying the chargeback candidate detection rules, that a chargeback may be warranted for the transaction, and causing an indication of such to be displayed to one or more people via one or more computing device user interfaces.
Pranav (US20220358507A1) - Embodiments provide methods and systems for predicting chargeback behavioral data of an account holder. The method performed by a server system includes accessing payment transaction data associated with the account holder from a transaction database. The payment transaction data includes a set of transaction indicators corresponding to payment transactions performed by the account holder within a predetermined time period. The method further includes generating a set of transaction features based on the set of transaction indicators. Furthermore, the method includes computing, via a chargeback risk prediction model, a set of chargeback risk probability scores corresponding to one or more time intervals associated with the account holder based, at least in part, on the set of transaction features. The method also includes transmitting a notification to an issuer server associated with the account holder based, at least in part, on the set of chargeback risk probability scores.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW COBB whose telephone number is (571) 272-3850. The examiner can normally be reached 9 - 5, M - F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to call examiner Cobb as above, or to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Nolan, can be reached at (571) 270-7016. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at (866) 217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call (800) 786-9199 (IN USA OR CANADA) or (571) 272-1000.
/MATTHEW COBB/Examiner, Art Unit 3661
/PETER D NOLAN/Supervisory Patent Examiner, Art Unit 3661
Prosecution Insights