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 office action is in response to Applicant’s response filed February 23, 2026 in which claims 1, 6, 9, 10, 15, 18 and 19 have been amended. Claims 5, 7, 14 and 16 were previously cancelled. Claim 21 is new. Thus, claims 1-4, 6, 8-13, 15 and 17-21 are pending in the application.
Claim Rejections - 35 USC § 101
2. 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 1-4, 6, 8-13, 15 and 17-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The Examiner has identified independent system Claim 10 as the claim that represents the claimed invention for analysis and is similar to independent Claims 1 and 19.
The claims 1-4, 6, 8-9 and 19-20 are directed to a method and claims 10-13, 15, 17-18 and 21 are directed to a system which are one of the statutory categories of invention (Step 1: YES).
The claim 10 recites : a first server comprising a memory; and one or more processors wherein one or more of the processors include a hardware accelerated computer system, the one or more processors configured to: determine one or more combinations of one or more items, wherein a cost for each of the determined one or more combinations equals a cost for a transaction to take place at a target website, wherein the target website is operated by a second server, the second server different from the first server and connected to the first server over a communication network, wherein the determining of one or more of the combinations comprises recursively solving a subset sum problem using the hardware accelerated computer system, and wherein the determining of the one or more combinations is performed based on the cost for the transaction to take place at the target website not being found in a white list of transaction costs, the white list implemented with hash tables or caching; for each of the items in each determined combination, predict, by a machine learning model, a likelihood value for a quantity of the item in the determined combination, wherein the predicted likelihood value is indicative of whether the quantity of the item in the determined combination is common in historical data, wherein the predicting of the likelihood value comprises inputting a tag describing the item into the machine learning model, wherein the machine learning model comprises a multilayer perceptron neural network trained using a categorical cross-entropy loss function, wherein the tag is determined by a second machine learning model, wherein the second machine learning model is trained using information linking item names to tags;
generate a probability of fraud for the transaction to take place at the target website based on one or more of the predicted likelihood values; adjust one or more detection thresholds in real time, the adjusting based on a number of transactions identified as fraudulent within a predetermined time period; and cause the second server to block or enable the transaction to take place at the target website by sending a signal to the second server based on the generated probability of fraud and the one or more adjusted detection thresholds. These limitations (with the exception of italicized portions), under their broadest reasonable interpretation, is a process that covers Certain methods of organizing human activity such as fundamental economic principles or practices (including insurance, mitigating risk, and hedging). Detecting fraud in a transaction is a way of mitigating risk and mitigating risk is a Fundamental Economic Practice. In addition, these limitations can also be classified under Mathematical concepts. Recursively solving a subset sum problem and predicting a likelihood value is a mathematical concept. The claim also recites a first server, memory, processors, a hardware accelerated computer system, website, a second server, a communication network, hash tables, caching, a machine learning model and “the machine learning model comprises a multilayer perceptron neural network trained using a categorical cross-entropy loss function, wherein the tag is determined by a second machine learning model, wherein the second machine learning model is trained using information linking item names to tags” which do not necessarily restrict the claim from reciting an abstract idea. That is, other than, a first server, memory, processors, a hardware accelerated computer system, website, a second server, a communication network, hash tables, caching, a machine learning model and “the machine learning model comprises a multilayer perceptron neural network trained using a categorical cross-entropy loss function, wherein the tag is determined by a second machine learning model, wherein the second machine learning model is trained using information linking item names to tags” nothing in the claim precludes the steps from being performed as a method of organizing human activity. If the claim limitations, under the broadest reasonable interpretation, covers methods of organizing human activity but for the recitation of generic computer components, then it falls within the “Certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim 10 recites an abstract idea (Step 2A: Prong 1: YES).
This judicial exception is not integrated into a practical application. The additional elements of a first server, memory, processors, a hardware accelerated computer system, website, a second server, a communication network, hash tables, caching and a machine learning model result in no more than simply applying the abstract idea using generic computer elements. “The machine learning model comprises a multilayer perceptron neural network trained using a categorical cross-entropy loss function, wherein the tag is determined by a second machine learning model, wherein the second machine learning model is trained using information linking item names to tags” amounts to generic computer implementation. A Multilayer Perceptron (MLP) is a foundational class of neural network. Hence, a multilayer perceptron neural network is an intrinsic part of a neural network. A cross-entropy loss function is a standard performance metric used in machine learning and deep learning for classification tasks. Hence, a cross-entropy loss function is a fundamental mathematical concept. The specification describes the additional elements of a first server, memory, processors, a hardware accelerated computer system, website, a second server, a communication network, hash tables, caching and a machine learning model to be generic computer elements (see Fig. 1-2, Fig. 4, [0018-0019], [0076], [0100]). Hence, the additional elements in the claim are generic components suitably programmed to perform their respective functions. The additional elements are recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computer arrangement. The presence of a generic computer arrangement is nothing more than mere instructions to implement the abstract idea on a computer (MPEP 2106.05(f)). 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. Hence, the claims as a whole are not integrated into a practical application. Therefore, the claim 10 is directed to an abstract idea (Step 2A - Prong 2: NO).
The claim 10 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)). The additional elements, when considered separately and as an ordered combination, does not add significantly more (also known as an “inventive concept”) to the exception. The additional elements of the instant underlying process, when taken in combination, together do not amount to significantly more than the sum of the functions of the elements when each is taken alone. Thus, claim 10 is not patent eligible (Step 2B: NO).
Similar analysis can he extended to other independent claims 1 and 19 and hence the claims 1 and 19 are rejected on similar grounds as claim 10.
The dependent claims have been given the full two-part analysis including analyzing the additional limitations both individually and in combination. Dependent claims 2-4, 6, 8-9, 11-13, 15, 17-18 and 20-21 are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations narrow the abstract idea further and thus correspond to Certain Methods of Organizing Human Activity and hence are abstract for the reasons presented above. Dependent claims 6, 9, 15, 18 and 21 recite new additional elements that are not present in independent claim 1 and require further analysis under Prong Two of Step 2A and Step 2B.
Claims 6 and 15 recite the additional element of a second machine learning (ML) model. A second machine learning (ML) model, recited in the claims, is recited at a high level of generality and amounts to generic computer implementation. Hence, it does not integrate the abstract idea into a practical application or provide significantly more than the abstract idea when considered individually and as an ordered combination.
Claims 9 and 18 recite the additional elements of a convolutional neural network (CNN) which simply describes the technological environment further. A convolutional neural network (CNN), recited in the claims, are recited at a high level of generality and amounts to generic computer implementation. Hence, it does not integrate the abstract idea into a practical application or provide significantly more than the abstract idea when considered individually and as an ordered combination.
Claim 21 recites the additional element of a long short-term memory (LSTM) network which simply describes the technological environment further. A long short-term memory (LSTM) network, recited in the claims, are recited at a high level of generality and amounts to generic computer implementation. Hence, it does not integrate the abstract idea into a practical application or provide significantly more than the abstract idea when considered individually and as an ordered combination.
Viewing the claim limitations as an ordered combination does not add anything further than looking at the claim limitations individually. When viewed either individually, or as a combination, the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea. Accordingly, claim(s) 1-4, 6, 8-13, 15 and 17-21 are ineligible.
Prior art
3. The prior art rejection was withdrawn in the Final Rejection dated June 26, 2025 based on the claim amendments. An updated search was conducted but does not result in a prior art rejection at this time.
Response to Arguments
4. Applicant's arguments filed February 23, 2026 have been fully considered but they are not persuasive due to the following reasons:
5. With respect to Step 2A, Prong 2 and Step 2B, Applicant argues that (pages 12-16), “the claims would integrate any alleged abstract idea into a practical application; and would amount to significantly more than any alleged abstract idea.”
The Examiner respectfully disagrees. The Examiner would like to point out that according to 2019 Patent Eligibility Guidelines (2019 PEG), limitations that are indicative of integration into a practical application include:
• Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
• Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition - see Vanda Memo
• Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
• Effecting a transformation or reduction of a particular article to a different state or thing -see MPEP 2106.05(c)
• Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
In the instant case, the judicial exception is not integrated into a practical application, because none of the above criteria is met. The amendments to the claims only further define the data being used however a specific abstract idea is still an abstract idea. The limitations of the claims do not result in computer functionality improvement or technical/technology improvement when the underlying abstract idea is implemented using technology. All the features in the Applicant’s claims can at best be considered an improvement in the abstract idea.
Adjusting detection thresholds based on a number of transactions is an improvement in the abstract idea. A machine learning model (LLM) is simply being used as a tool to implement the abstract idea of identifying fraud in a transaction. Claim 10 as amended does not provide specific improvements to technology or computer functionality. It limits an abstract idea of predicting the likelihood of different quantities of items being included in a single transaction to a particular technological environment of machine learning. The advantages over conventional systems are directed towards improving the abstract idea.
The specification describes the additional elements of a first server, memory, processors, a hardware accelerated computer system, website, a second server, a communication network, hash tables, caching and a machine learning model to be generic computer elements (see Fig. 1-2, Fig. 4, [0018-0019], [0076], [0100]). “The machine learning model comprises a multilayer perceptron neural network trained using a categorical cross-entropy loss function, wherein the tag is determined by a second machine learning model, wherein the second machine learning model is trained using information linking item names to tags” amounts to generic computer implementation. A Multilayer Perceptron (MLP) is a foundational class of neural network. Hence, a multilayer perceptron neural network is an intrinsic part of a neural network. A cross-entropy loss function is a standard performance metric used in machine learning and deep learning for classification tasks. Hence, a cross-entropy loss function is a fundamental mathematical concept. Hence, the additional elements in the claims are all generic components suitably programmed to perform their respective functions. The additional elements are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. 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. Hence, the claims as a whole are not integrated into a practical application. The additional elements of the instant underlying process, when taken in combination, together do not amount to substantially more than the sum of the functions of the elements when each is taken alone. Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more (See MPEP 2106.05(f) (2)).
6. Applicant further states that (pages 12-15), “As in Ex parte Guillaume Desjardins, the present Specification details these improvements to technology with sufficient detail.”
The Examiner does not see the parallel between the claims of the instant case and the claims in Desjardins. The invention in Desjardins improved the operation of the machine learning model. Hence, when considered as a whole, independent claim 1 integrated an abstract idea into a practical application. In Desjardins, “when evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation.” Unlike Desjardins, claims here are not directed to "an improvement in computer functionality" or an "improvement to how the machine learning models itself operates" that would make them patent eligible (see page 17). Using a machine learning model to predict a likelihood value for a quantity of the item is an improvement in the business process, not a technical improvement. The claims do not purport to improve the machine learning model. Hence, Desjardins is not applicable.
7. Applicant states that (pages 14-17), the claims in the instant application is similar to the claims in Cosmokey.
Examiner respectfully disagrees. CosmoKey’s claims “recite[d] an improved method for overcoming hacking by ensuring that the authentication function is normally inactive, activating only for a transaction, communicating the activation within a certain time window, and thereafter ensuring that the authentication function is automatically deactivated.” CosmoKey, 15 F.4th at 1099 (“The specification explains that these features in combination with the other elements of the claim constitute an improvement that increases computer and network security, prevents a third party from fraudulently identifying itself as the user, and is easy to implement and can be carried out even with mobile devices of low complexity.”). “[T]he claims recite[d] an inventive concept by requiring a specific set of ordered steps that go beyond the abstract idea identified by the district court.” By contrast, as we found above, both amended claim 1 and claim 1 prior to the amendment recites no technical solution in networks and computers. Hence, CosmoKey is not applicable.
8. Applicant further states that (pages 14-17) claim 2 of Office Example 35 are similar to the instant claims.
Examiner respectfully disagrees.
Claim 2 of Example 35 aims to solve the problem of fraud by impersonation at the ATM by employing a sequence of nonconventional technical steps. Whereas the conventional verification process at an ATM involved entering a PIN, the limitations of Claim 2 depart from this conventional process by having the ATM generate random code, the mobile device generating an image with encrypted code data in response to random code and then making the determination on whether transaction should process. This constituted significantly more than entering PIN on a keypad.
In contrast, the current invention does not describe any problems similar to fraud by impersonation at an ATM. Nor do the amended claims describe any technical steps such as generating an image with encrypted code data to overcome fraudulent impersonators. The only technological element recited in the claims is a generic server. The only purpose that the server is used for in the current claims is to detect fraud or service agreement violations, by recognizing transactions unlikely to be taking place and executing a web scraping process to scan or scrape merchants' websites. The improvement recited in the claim does not improve the server. Instead, the alleged improvement recited in the claims is an abstract idea. Hence, the claimed subject matter does not amount to significantly more than the abstract idea and is not subject matter eligible.
9. Applicant further argues that (pages 14-17), “claim 10 as amended is clearly technological and improves the functioning of a computer, and is more technological and less abstract-as well as more specific and less generic-than claims the Office has previously found patent-eligible, such as those in McRO.”
The Examiner does not see the parallel between the claims of the instant case and those of McRo (McRo, Inc. v. BandaiNamco Games Am., 2015-1080 (Fed. Cir. Sept. 13, 2016)). In McRo, the patents relate to “automating part of existing 3D animations of a character’s facial expressions and synchronize those expressions to the actual speech”, which were to be done manually before the issuance of the patent. The claims were directed to a patentable technological improvement over the existing, manual 3D animation techniques by using “limited complex set of rules specifically designed to achieve an improved technological result” thus providing "unconventional" practices than used in a conventional industry practice. Hence the claims in McRo were patent eligible because they recited significantly more than an abstract idea. Whereas the Applicants’ invention is a business solution, using computers, to a problem rooted in an abstract idea. In McRo, the application of the limited complex set of rules resulted in an improvement of the 3D animation technology. By relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible (See Alice, 134 S. Ct. at 2359 (use of a computer to create electronic records, track multiple transactions, and issue simultaneous instructions is not an inventive concept). Looking at the amended limitations of Applicant’s claimed invention, there is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Hence, the arguments are not persuasive.
For these reasons and those stated in the rejection above, the rejection of pending claims under 35 U.S.C. 101 is hereby maintained by the Examiner.
Examiner Request
10. The Applicant is request to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
Conclusion
11. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. 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).
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 lee 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.
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/B.D.S./Examiner, Art Unit 3694
May 18, 2026
/BENNETT M SIGMOND/Supervisory Patent Examiner, Art Unit 3694
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