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 the Application
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/05/2025 has been entered.
Status of Claims
Claims 1, 4, 8, 11, 15, and 18 were previously canceled.
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.
Adjusting the weight of a node or an edge in a neural network in the present application is not eligible under 35 USC § 101 because the instant claims involve applying generic machine learning techniques to new data. Generically training a machine learning model is incident to the nature of machine learning and does not represent a technological improvement.
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, 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
Any inquiry concerning this communication from the examiner should be directed to Abdallah El-Hagehassan whose telephone number is (571) 272-0819. 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) 273-3734.
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/ABDALLAH A EL-HAGE HASSAN/
Primary Examiner, Art Unit 3623