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 .
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 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 mailing date of this final action.
Status of the Application
The following is a Final Office Action in response to Examiner's communication of 01/27/2026, Applicant, on 04/27/2026.
Status of Claims
Claims 2-3, 5-7, 9-10, 12-14, 16-17 and 19-20 are currently pending following this response.
New matter
No new matter has been added to the amended claims.
Examiner’s Arguments - 35 USC § 101
With respect to the use of neural network techniques, it is common practice that such computational models/techniques and algorithms are per se of an abstract mathematical nature, irrespective of whether they can be “trained” based on training data. Hence, a mathematical method may contribute to the technical character of an invention, if it serves as technical purpose or if it regards as specific technical implementation motivated by the internal function of a computer. Elements in the present claims do not solve a technical problem, but an administrative/business method, i.e. predicting the impact of select transaction events on cardholder behavior. Since the mathematical algorithms or models used in the present application do not serve a technical purpose, but a business purpose, and their implementation does not go beyond generic technical implementation, the use of the neural network, do not contribute to a technical character and they are to be part of the abstract idea.
The Examiner submits that the present claims are directed data manipulation of retrieving, enriching, and storing data which are considered routine computer functions. The present claim language is directed to an abstract idea, specifically, mathematical concept/algorithm and organizing human activities wherein training neural networks, calculating similarity scores, and determining distributions are mathematical processes. Further, the core logic of the claims, matching distributions to select non-target transactions, is a method of data filtering that the court views as a method that can reasonably be performed in human mind (Applicant’s arguments pages 10-11).
The present claims use generic computer components like a processor, memory, database, and communication module. Those components are performing generic computer functions and do not provide an inventive concept. It does not appear from the present claimed limitations that the computer function is improved (Arguments page 15).
The two-model workflow as argued by Applicant on page 13 is not persuasive because updating a model with filtered data to improve accuracy or optimize a business result are still viewed abstract by the court. A skilled in the art would not conclude a technical improvement from the two-stage workflow of using two machine learning models.
Because the Examiner has determined that the judicial exception is not integrated into a practical application, the Examiner proceeds to Step 2B of the Eligibility Guidelines (Applicant’s arguments page 16), which asks whether there is an inventive concept. In making this Step 2B determination, the Examiner must consider whether there are specific limitations or elements recited in the claim “that are not well - understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present” or whether the claim “simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, indicative that an inventive concept may not be present.” Eligibility Guidance, 84 Fed. Reg. 56 (footnote omitted). The Examiner must also consider whether the combination of steps perform “in an unconventional way and therefore include an ‘inventive step,’ rendering the claim eligible at Step 2B ” Id. In this part of the analysis, the Examiner considers “the elements of each claim both individually and ‘as an ordered combination’” to determine “whether the additional elements ‘transform the nature of the claim’ into a patent-eligible application.” Alice, 134 S. Ct. at 2354. As discussed above, there is no evidence in the record that the steps of predicting the impact of select transaction events on cardholder behavior are accomplished in a non-conventional way. The Examiner therefore concludes that the claims used generic, conventional, technology to implement the abstract idea of predicting the impact of select transaction events on cardholder behavior and that there is no inventive concept in the present claims.
In 2025, the U.S. Court of Appeals for the Federal Circuit issued a landmark decision, Recentive Analytics, Inc. v. Fox Corp. (April 18, 2025), which significantly impacts the patent eligibility of adjusting neural network weights to refine results.
Based on this decision and subsequent 2025 USPTO guidance, the eligibility of such adjustments depends on whether they are "generic" or "technically transformative."
The court held that claims which merely apply established machine learning methods to a new field are ineligible under Section 101.
"Incident to Nature": The court specifically stated that features such as iterative training, dynamic updates, and real-time adjustments are "incident to the very nature of machine learning".
Abstract Idea: Simply adjusting weights to optimize a schedule or map (refining results) was ruled an abstract idea because it used generic ML tools on conventional computing infrastructure without improving the underlying technology.
Optimization is Not Enough: Even if the adjustment leads to improved accuracy or efficiency, the court ruled this does not automatically transform an abstract idea into eligible subject matter.
In conclusion, similar to Electric Power Group and unlike Enfish, the Examiner determines that the pending claims are not eligible under 35 USC § 101.
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 2-3, 5-7, 9-10, 12-14, 16-17 and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 2-3, 5-7, 9-10, 12-14, 16-17 and 19-20 are directed to an abstract idea without additional elements to integrate the claims into a practical application or to amount to significantly more than the abstract idea.
Claims 2-3, 5-7, 9-10, 12-14, 16-17 and 19-20, even if they were directed to a process, machine, or manufacture (Step 1), the claims are directed to the abstract idea of predicting the impact of select transaction events on cardholder behavior.
With respect to Step 2A Prong One of the frameworks, claim 5 recites an abstract idea. Claim 5 includes limitations for “Retrieve the historical raw transaction data, the historical raw transaction data including a plurality of transactions, wherein each transaction is one of a target transaction or a non-target transaction; enrich, via a data preparation engine, the historical raw transaction data by appending a target transaction identifier to each of the target transactions contained in the historical raw transaction data, the target transactions being related to a predetermined target transaction event; store the enriched historical raw transaction data to a first data table; train a first neural network using the enriched historical raw transaction data stored in the first data table with the target transaction event as a dependent variable; generate a training data classification model based on the training of the first neural network; apply the training data classification model to the enriched historical raw transaction data stored in the first data table; generate a similarity score for each transaction of the enriched historical raw transaction data in the first data table; determine a first similarity score distribution associated with the target transactions and a second similarity score distribution associated with the non-target transactions; select a plurality of non-target transactions whose combined similarity score distribution matches the first similarity score distribution of the target transactions; based on the selection, store the target transactions and the selected plurality of non- target transactions to a second data table; train a second neural network using the target transactions and the selected plurality of non-target transactions stored in the second data table; and determine, prior to storing the enriched historical raw transaction data to the first data table, a ratio of target transactions to non-target transactions contained in the historical raw transaction data, remove one or more non-target transactions from the historical raw transaction data when the ratio of target transactions to non-target transactions is below a predefined threshold value, the removing occurring until the ratio meets or exceeds the predefined threshold value, retrieve the set of customer transaction data, wherein the set of customer transaction data does not include a target transaction; apply the second neural network to the set of customer transaction data; and output a prediction result, the prediction result being indicative of future transactions associated with the cardholder account after an occurrence of a target transaction”
The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the limitations above recite certain methods of organizing human activity associated with managing personal behavior or relationships or interactions between people because the claimed elements describe a process for predicting the impact of select transaction events on cardholder behavior. As a result, claim 5 recites an abstract idea under Step 2A Prong One.
Claims 12 and 19 recite substantially similar limitations to those presented with respect to claim 5. As a result, claims 12 and 19 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claim 5. Similarly, claims 2-3, 6-7, 9-10, 13-14, 16-17 and 20 recite certain methods of organizing human activity associated with managing personal behavior or relationships or interactions between people because the claimed elements describe a process for predicting the impact of select transaction events on cardholder behavior. As a result, claims 2-3, 6-7, 9-10, 13-14, 16-17 and 20 recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 5 does not include additional elements that integrate the abstract idea into a practical application. Claim 5 includes additional elements that do not recite an abstract idea. The additional elements of claim 5 include “A system for training and applying deep learning within a payment network, the system comprising: a database storing historical raw transaction data and a set of customer transaction data associated with a cardholder account; a processor; and a memory storing computer-executable instructions thereon, the computer-executable instructions, when executed by the processor, causing the processor to”, “via a communications module”, “via a data preparation engine”, “via a modeling engine”, “via the training data classification model”, “via the model application engine”, “said computer-executable instructions further causing the processor to”, “by the second neural network”. When considered in view of the claim as a whole, the step of “retrieving” does not integrate the abstract idea into a practical application because “retrieving” is an insignificant extra solution activity to the judicial exception. When considered in view of the claim as a whole, the recited computer elements do not integrate the abstract idea into a practical application because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claim 5 does not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
As noted above, claims 12 and 19 recite substantially similar limitations to those recited with respect to claim 5. Although claim 12 further recites “A computer-implemented method” and claim 19 further recites “A computer-readable storage medium”, when considered in view of the claim as a whole, the recited computer elements do not integrate the abstract idea into a practical application because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claims12 and 19 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2-3, 6-7, 9-10, 13-14, 16-17 and 20 do not include any additional elements beyond those recited by independent claims 1, 12, and 19. As a result, claims 2-3, 6-7, 9-10, 13-14, 16-17 and 20 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, claim 5 does not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 5 includes additional elements that do not recite an abstract idea. The additional elements of claim 5 include “A system for training and applying deep learning within a payment network, the system comprising: a database storing historical raw transaction data and a set of customer transaction data associated with a cardholder account; a processor; and a memory storing computer-executable instructions thereon, the computer-executable instructions, when executed by the processor, causing the processor to”, “via a communications module”, “via a data preparation engine”, “via a modeling engine”, “via the training data classification model”, “via the model application engine”, “said computer-executable instructions further causing the processor to”, “by the second neural network”. The step of “retrieving” does not amount to significantly more than the abstract idea because “retrieving” is well-understood, routine, and conventional computer function in view of MPEP 2106.05(d)(ll). The recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claim 5 does not include additional elements that amount to significantly more than the abstract idea under Step 2B.
As noted above, claims 12 and 19 recite substantially similar limitations to those recited with respect to claim 5. Although claim 12 further recites “A computer-implemented method” and claim 19 further recites “A computer-readable storage medium”, the recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 12 and 19 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 2-3, 6-7, 9-10, 13-14, 16-17 and 20 do not include any additional elements beyond those recited by independent claims 1, 12, and 19. As a result, claims 2-3, 6-7, 9-10, 13-14, 16-17 and 20 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 2-3, 5-7, 9-10, 12-14, 16-17 and 19-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the claim is directed to signals per se. Applicant has claimed A computer-readable storage medium having computer-executable instructions stored thereon and Applicant's specification fails to narrowly define machine-readable medium device to exclude transitory propagating signals. The broadest reasonable interpretation of a claim drawn to a machine-readable medium device includes transitory propagating signals per se in view of the ordinary and customary meaning of machine-readable medium device, which are non-statutory subject matter. As a result, this claim must be rejected under 35 U.S.C. § 101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007). In order to overcome this rejection under 35 U.S.C. 101.
In the present disclosure, the applicant’s specification discloses that the machine-readable medium may include signals, paragraph [0088] recites “Certain embodiments are described herein as including logic or a number
of routines, subroutines, applications, or instructions. These may constitute either software (e.g., code embodied on a machine-readable medium or in a transmission signal) or hardware”. Claiming an article comprising machine-readable medium and deliberately specifying in the specification signals per se is doing the opposite of what the court says.
Claims 16- 17 and 20 are rejected under 35 U.S.C. 101 because they depend on claim 19 and therefore inherent the same rejection for the same reasons mentioned in claim 19 (above).
Conclusion
Applicant's amendments and arguments dated 04/27/2026 necessitated the updating of the 35 USC § 101 rejection of the pending claims presented in the present Office Action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
Any inquiry concerning this communication from the Examiner should be directed to Abdallah El-Hagehassan whose contact information is (571) 272-0819 and Abdallah.el-hagehassan@uspto.gov The Examiner can normally be reached on Monday- Friday 8 am to 5 pm.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Rutao Wu can be reached on (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/ABDALLAH A EL-HAGE HASSAN/
Primary Examiner, Art Unit 3623
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