DETAILED ACTION
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/20/2026 has been entered.
Status of Claims
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 in reply to the RCE filed on 01/20/2026.
Claims 3, 5, 10, 12, 17, and 19 have been cancelled.
Claims 1, 8, and 15 have been amended.
Claims 1, 2, 4, 6-9, 11, 13-16, 19, and 20 are currently pending and have been examined.
Response to Arguments
Applicant's arguments filed 01/20/2026 with respect to claims 1, 2, 4, 6-9, 11, 13-16, 19, and 20 rejected under 35 USC 101 have been fully considered but they are not persuasive.
Applicant argues #1:
Amended claim 1 is not directed to a "certain method of organizing human activity" or any other judicial exception. Instead, the claim is directed to a computer-implemented technique for training and applying a machine-learned model using an iterative, closed-loop process that incorporates actual usage feedback tied to discrete time intervals, and for configuring operation of an online system based on an output of the resulting trained model.
As amended, claim 1 does not merely recite the abstract goal of predicting a user request or facilitating a financial transaction. Rather, the claim recites specific technical steps governing how a machine-learned model is trained and updated, including:
" generating training vectors associated with discrete time intervals;
" labeling each training vector with an actual usage amount for its respective time interval;
" generating predictions for each time interval using the model;
" iteratively updating model weights for each time interval based on a comparison between
predicted usage and actual usage; and
" using the resulting trained model to configure operation of the online system to control
execution of a computer-implemented network action.
These steps define a particular computational process for training a machine-learned model using real-world feedback across time-segmented data, not a mental process or a fundamental economic practice. The claimed method cannot be performed in the human mind and is not a mere automation of a longstanding commercial activity. Accordingly, the claims are not directed to an abstract idea under Step 2A, Prong One.
Examiners response:
The Examiner respectfully disagrees, with regards to the human mind/mentally test, as an initial matter, just because an idea cannot be performed with a pen and paper or in the human mind does not mean it’s not directed towards an abstract idea and the Examiner did not rely on grouping the claims into the Mental Processes grouping of abstract ideas for the analysis. Furthermore Examiners are directed to continue to use the Mayo Alice framework (as laid out in MPEP 2106 which incorporates Steps 2A and Step 2B of the 2019 PEG) as guidance in evaluating subject matter eligibility, which the Examiner has properly applied. With respect to mere automation of a longstanding commercial activity, the courts have used the phrases "fundamental economic practices" or "fundamental economic concepts" to describe concepts relating to the economy and commerce, such as agreements between people in the form of contracts, legal obligations, and business relations. The term "fundamental" is used in the sense of being foundational or basic, and not in the sense of necessarily being "old" or "well-known." See, e.g., In re Smith, 815 F.3d 816, 818-19, 118 USPQ2d 1245, 1247 (Fed. Cir. 2016) (see MPEP 2106.04(a)(2)). “The Supreme Court’s decisions make it clear that judicial exceptions need not be old or long-prevalent, and that even newly discovered or novel judicial exceptions are still exceptions.” (MPEP 2106.04(I)). With respect to the argued limitations, " generating training vectors associated with discrete time intervals; " labeling each training vector with an actual usage amount for its respective time interval; " generating predictions for each time interval using the model; " iteratively updating model weights for each time interval based on a comparison between predicted usage and actual usage; and " using the resulting trained model to configure operation of the online system to control execution of a computer-implemented network action.” These are akin to Recentive Analytics, Inc. v. Fox Corp., Case No. 2023-2437 (Fed. Cir. Apr. 18, 2025), and fails to render the claims eligible for the same reasons, in which the Courts found that instead of disclosing “a specific implementation of a solution to a problem in the software arts,” Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339 (Fed. Cir. 2016), or “a specific means or method that solves a problem in an existing technological process,” Koninklijke, 942 F.3d at 1150, the only thing the claims disclose about the use of machine learning is that machine learning is used in a new environment and that the requirements that the machine learning model be “iteratively trained” or dynamically adjusted in the Machine Learning Training patents was not a technological improvement in that iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.
Applicant argues #2:
Even if the Examiner were to maintain that some aspect of the claims relates to an abstract idea, claim 1 integrates that idea into a practical application under Step 2A, Prong Two.
The Final Office Action asserts that prior versions of the claims merely recited "generic" training of a machine-learned model followed by "post-solution activity" such as enabling a network action. Claim 1, as amended, clarifies that the machine-learned model be trained through an iterative comparison between predicted user requests and actual usage values associated with specific time intervals, with the model weights being updated for each time interval based on that comparison. This feedback-driven training loop is not a result-oriented instruction to "train a model," but a concrete computational technique that governs how the model evolves based on observed system behavior.
Further, amended claim 1 does not simply recite "enabling" a network action. Instead, the claim requires configuring an operation of the online system based on an output of the updated machine-learned model to control execution of a computer-implemented network action. In this way, the output of the trained model is integrated into the operational behavior of the system itself, rather than being used as an after-the-fact advisory output. This is not simply post-solution activity, but is instead a direct application of the trained model to control how the computer system operates.
Under USPTO guidance, claims that apply a machine-learned model in a manner that affects system operation and that recite specific training techniques tied to real-world feedback qualify as practical applications rather than abstract ideas. The amended claims therefore satisfy Step 2A, Prong Two.
Examiners response:
The Examiner respectfully disagrees, with respect to the training the model, adjusting the weights and using feedback, this does not render the claims eligible for the same reasons as discussed above with respect to Recentive Analytics, Inc. v. Fox Corp., Case No. 2023-2437 (Fed. Cir. Apr. 18, 2025). With respect configuring an operation to be performed between online system and 3rd party system, this does not render the claims eligible, as this akin to Alice, as the network action could be as simple as sending a set of instructions to transfer funds, as describe in fig. 4 of applicant’s drawings, and as shown in MPEP 2106.05(d), and Court decisions cited below, sending and receiving data over a network is WURC. Further, the configuring an operation and in light of the specification, this function is akin to Alice Corp. and ineligible for the same reasons, in which the Court walked through the test and found:
The Court identified the additional elements in the claim, e.g., by noting that the method claims recited steps of using a computer to "create electronic records, track multiple transactions, and issue simultaneous instructions", and that the product claims recited hardware such as a "data processing system" with a "communications controller" and a "data storage unit" (573 U.S. at 224-26, 110 USPQ2d at 1984-85);
The Court considered the additional elements individually, noting that all the computer functions were "‘well-understood, routine, conventional activit[ies]' previously known to the industry," each step "does no more than require a generic computer to perform generic computer functions", and the recited hardware was "purely functional and generic" (573 U.S. at 225-26, 110 USPQ2d at 1984-85);. Additionally, the Examiner’s fails to find any guidance that explicitly states claims that apply a machine-learned model in a manner that affects system operation and that recite specific training techniques tied to real-world feedback qualify as practical applications rather than abstract ideas. Therefor these arguments are not persuasive.
Applicant argues #3:
For at least the same reasons, the claims also satisfy Step 2B. When considered as an ordered combination, the claim elements recite significantly more than any alleged abstract idea. The claimed method does not rely on generic machine learning performed at a high level of generality. Instead, it recites a specific sequence of operations that 1) structures training data by time interval, 2) associates predictions with actual usage values, 3) iteratively updates model weights based on those comparisons, and 4) uses the output of the resulting trained model to configure an operation of an online system.
This ordered combination reflects a non-conventional application of machine learning in which training is constrained by time-segmented real-world usage feedback and is tightly coupled to system configuration. As such, the claims are fundamentally different from claims that merely recite performing generic calculations, sending and receiving data, or applying a machine-learning technique "in a vacuum," as described in the Final Office Action.
Unlike the claims discussed in Recentive Analytics, the amended claims do not merely apply a generic machine-learning technique in a particular business environment. Instead, they recite how the model is trained using iterative comparisons to actual usage values and how the trained model is used to configure system operation, thereby improving the functioning of the computer system itself. Similarly, the claims are unlike the July 2024 Subject Matter Eligibility Examples cited by the Examiner, in which model training is recited only at a high level of abstraction. Here, the training process is defined by specific data relationships (predicted vs. actual usage), temporal segmentation, and iterative weight updates, which meaningfully limit the scope of the claims and provide an inventive concept.
Accordingly, as amended, independent claim 1 and all similar and dependent claims are not directed to an abstract idea, integrate any alleged judicial exception into a practical application, and recite significantly more than any abstract idea. The rejection under 35 U.S.C. § 101 should therefore be withdrawn.
Examiners response:
The Examiner respectfully disagrees, the claims are using a generic machine learning technique in a particular environment, with no inventive concept, as opposed to disclosing “a specific implementation of a solution to a problem in the software arts,” Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339 (Fed. Cir. 2016), or “a specific means or method that solves a problem in an existing technological process,” Koninklijke, 942 F.3d at 1150, the use of AI in the instant application does not provide an inventive concept or significantly more. The only thing the claims disclose about the use of machine learning is that machine learning is used in a new environment, which has been found by the Courts to be ineligible (see Recentive Analytics, Inc. v. Fox Corp., Case No. 2023-2437 (Fed. Cir. Apr. 18, 2025). As shown above and discussed in Recentive Analytics, Inc. v. Fox Corp using machine learning in a new environment and that the requirements that the machine learning model be “iteratively trained” or dynamically adjusted is was not a technological improvement in that iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning, as is the case with particularly claiming the type of data based on the time intervals, associates predictions with actual usage values, and weighting values.
For the reasons above, the 101 rejection is hereby maintained.
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 1, 2, 4, 6-9, 11, 13-16, 19, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more, and fails step 2 of the analysis because the focus of the claims is not on the devices themselves or a practical application but rather directed towards an abstract idea, the analysis is provided below.
Step 1 (Statutory Categories) - The claims pass step 1 of the subject matter eligibility test (see MPEP 2106(III)) as the claims are directed towards a system, method and non-transitory computer-readable medium.
Step 2A – Prong One (Do the claims recite an abstract idea?) - The idea is recited in the claims, in part, by:
training a machine-learned model by:
accessing historical seasonality data from a plurality of time intervals;
generating a plurality of training vectors based on the historical seasonality data, wherein each of the plurality of training vectors is associated with a time interval of the plurality of time intervals, and wherein each respective training vector is associated with a label indicating a usage amount of a respective time interval;
for each of the plurality of training vectors, applying the machine-learned model to the training vector to generate a prediction of a user request for the respective time interval based on the respective training vector;
iteratively updating the weights of the machine-learned model for each time interval of the plurality of time intervals based on a comparison between the predicted user request and; and an actual usage associated with the time interval; and
configuring an operation of based on an output of the updated machine-learned model to control execution of an action between the online system and a third-party computer system.
The steps recited above under Step 2A Prong One of the analysis under the broadest reasonable interpretation covers commercial or legal interactions (including marketing or sales activities or behaviors; business relations) for analyzing seasonality data to generate an action for a time interval but for the recitation of generic computer components. That is other than reciting an online system, a non-transitory computer-readable storage medium, a third-party computer system, and a hardware processor, nothing in the claim elements are directed towards other than commercial or legal interactions. The actions are described in the specification as possibly advances for uncompensated time or employees needing a cash-advance (see [0003-0006]), and there for under broadest reasonable interpretation and in light of the specification describe commercial and legal interactions. If a claim limitation, under its broadest reasonable interpretation, covers commercial or legal interactions, then it falls within the “Certain Methods of Organizing Human Activities” groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A – Prong Two (Does the claim recite additional elements that integrate the judicial exception into a practical application?) - This judicial exception is not integrated into a practical application. In particular, the claims only recite the additional elements of an online system, a non-transitory computer-readable storage medium, a third-party computer system, and a hardware processor. The online system, non-transitory computer-readable storage medium, third-party computer system, and hardware processor are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components and limits the idea to the computer environment. Mere instructions to apply the judicial exception using generic computer components and limiting an idea to a particular environment are not indicative of a practical application (see MPEP 20106.05(f) and MPEP 20106.05(h)). With respect to the configuring an operation computer-implemented to control a network action, a network action could be as simple as sending a set of instructions to transfer funds, as describe in fig. 4 of applicant’s drawings, and as shown in MPEP 2106.05(d), and Court decisions cited below, sending and receiving data over a network is WURC. Further, the configuring an operation to control a network action is akin to Alice Corp. and ineligible for the same reasons, in which the Court walked through the test and found:
The Court identified the additional elements in the claim, e.g., by noting that the method claims recited steps of using a computer to "create electronic records, track multiple transactions, and issue simultaneous instructions", and that the product claims recited hardware such as a "data processing system" with a "communications controller" and a "data storage unit" (573 U.S. at 224-26, 110 USPQ2d at 1984-85);
The Court considered the additional elements individually, noting that all the computer functions were "‘well-understood, routine, conventional activit[ies]' previously known to the industry," each step "does no more than require a generic computer to perform generic computer functions", and the recited hardware was "purely functional and generic" (573 U.S. at 225-26, 110 USPQ2d at 1984-85);
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed towards an abstract idea.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?) - The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, with respect to integration of the abstract idea into a practical application, using the additional elements of an online system, a non-transitory computer-readable storage medium, a third-party computer system, and a hardware processor to perform the steps recited in Step 2A Prong One of the analysis amounts to no more than mere instructions to apply the exception using generic computer components and limits the idea to the computer environment. Mere instructions to apply an exception using generic computer components and limiting an idea to particular environment does not provide an inventive concept. The additional elements have been considered separately, and as an ordered combination as a whole, and do not add significantly more (also known as an “inventive concept”) to the judicial exception. Further, MPEP 2106.05(d)(ii) provides that receiving and transmitting data over a network (see buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), and, Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."), are well-understood routine and conventional, similar to the instant application claims which recites and sending and receiving data over network (accessing historical data), and what amounts analyzing data and performing repetitive calculations for analyzing seasonality data to configure an operation based on the analysis but for the recitation of generic computer components. With respect to the machine learning model and training the model, the training of the model is recited at a generic level, and in view of the new July 2024 Subject Matter Eligibility Examples, which provides additional guidance on Patent Subject Matter Eligibility, including artificial intelligence, the Examiner finds the claims ineligible. Similar to Claim 2 of Example 47 in the July 2024 Subject Matter Eligibility Examples, the training is recited at high level of generality such that it amounts to using a generic computer to perform generic computer functions, akin to using a computer to perform repetitive calculations (See MPEP 2106.05(d)), and therefore amounts to no more than mere instructions to apply the exception using a generic computer (See MPEP 2106.05(f)). Additionally, similar to Recentive Analytics, Inc. v. Fox Corp., Case No. 2023-2437 (Fed. Cir. Apr. 18, 2025), the claims are directed to the abstract idea of using a generic machine learning technique in a particular environment, with no inventive concept in which the Courts found the claims to be ineligible. The claims are not patent eligible.
The dependent claims have been given the full analysis including analyzing the additional limitations both individually and in combination as a whole. For instance, claims 2, 4, 6, 7, 9, 11, 13, 14, 16, 18, and 20 are all steps that fall within the “Certain Methods of Organizing Human Activities” groupings of abstract ideas, further defining the abstract idea, amounting to mere instructions to apply the idea as discussed above. The Dependent claims when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 for the same reasoning as above and the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GREGORY S CUNNINGHAM II whose telephone number is (313)446-6564. The examiner can normally be reached Mon-Fri 8:30am-4pm.
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GREGORY S. CUNNINGHAM II
Primary Examiner
Art Unit 3694
/GREGORY S CUNNINGHAM II/Primary Examiner, Art Unit 3694
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