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 .
Notice to Applicant
The following is a Non-Final Office action. In response to Examiner’s Final Rejection of 11/17/2025, Applicant, on 2/17/2026, amended claims 1,6, 11, 16, 21 and 24; and claims 5, 7, 13-14 and 18-19 are cancelled. Claims 1, 6, 8-11, 15-16 and 20-25 are pending in this application and have been rejected below.
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has been withdrawn pursuant to 37 CFR 1.114.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
2/17/2026 has been entered.
Response to Arguments
Applicant’s arguments filed February 17, 2026 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed February 17, 2026.
On Pgs. 10-11 of the Remarks, regarding 35 U.S.C. § 101 rejections, Applicant states Applicant respectfully submits that forecasting demand data by regression techniques may not be practically performed in the human mind and gradient boosting is should not be considered a part of the abstract grouping. Applicant further states that improvements to how a machine learning model operates, including training of a machine learning model, represent improvements to computer functionality. Accordingly, even if one assumes for purposes of argument only that previously- presented independent claims 1, 11 and 16 could somehow be construed as reciting an abstract idea, these claims are not directed to an abstract idea for reasons similar to those set forth in Ex Parte Desjardins et al. In response, the training forecasting demand data by regression techniques and gradient boosting techniques fall within the Abstract idea grouping of “Mathematical Concepts” – mathematical calculations. The claims primarily recite the additional element of using computer components to perform each step. The of “computer”, “processing device”, “processor”, “memory”; “component”; “processor-readable storage medium”; program code”, “software programs”; and “apparatus” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f).
Examiner finds Applicants arguments in relation to this matter are not persuasive. Specifically, in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting”, and the claims reflect the improvement identified in the specification. The improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation. Examiner finds no similar improvements to take into consideration here. Examiner maintains the claims are directed to an abstract idea of complex mathematical calculations in which computer components are used as a tool to perform the instructions of the process. Applicant has not presented an argument that alters this analysis. For at least these reasons the claims remain rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
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, 6, 8-11, 15-16 and 20-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 6, 8-11, 15-16 and 20-25 are directed to determining configurable component parameters.
Claim 1 recites a method for determining configurable component parameters, and Claim 11 recites an article of manufacture for determining configurable component parameters and Claim 16 recites an apparatus for determining configurable component parameters, which include training (i) one or more gradient boosting techniques and (ii) one or more regression techniques, for forecasting demand data for at least one component, using at least a first portion of training data related to the at least one component, wherein training the one or more gradient boosting techniques comprises utilizing multiple features associated with the at least one component, and wherein training the one or more regression techniques comprises using one or more regularization techniques in connection with at least a subset of the multiple features; determining one of (i) the one or more trained gradient boosting techniques and (ii) the one or more trained regression techniques for use in at least one forecasting operation by processing outputs generated by the one or more trained gradient boosting techniques and the one or more trained regression techniques in conjunction with at least a second portion of the training data using at least one meta model, wherein the at least a second portion of the training data is at least partially distinct from the at least a first portion of the training data; forecasting demand data for the at least one component in connection with one or more temporal periods by processing at least one input set of component-related data using the determined one of the one or more trained gradient boosting techniques and the one or more trained regression techniques; determining information pertaining to one or more modifications associated with the at least one component; determining, by processing at least a portion of the demand data and at least a portion of the information pertaining to the one or more modifications using at least one genetic algorithm, one or more configurable component parameter values attributed to the at least one component and at least a portion of the one or more modifications, wherein the at least one genetic algorithm utilizes at least one heuristic to generate at least one solution for the one or more configurable component parameter values based at least in part on results from one or more previous evaluated solutions, in conjunction with processing the at least a portion of the demand data and the at least a portion of the information pertaining to the one or more modifications ; and performing one or more automated actions based at least in part on at least one of the one or more configurable component parameter values, wherein performing one or more automated actions comprises automatically updating, in at least one system associated with component-related distribution, at least one workflow operation in accordance with the at least one of the one or more configurable component parameter values; and automatically retraining at least a portion of at least one of the one or more trained gradient boosting techniques and the one or more trained regression techniques using feedback related to the at least one of the one or more configurable component parameter values.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Mental Processes” – evaluation and Mathematical Concepts-“mathematical calculations”. The recitation of “computer”, “processing device”, “processor”, “memory”; “component”; “processor-readable storage medium”; program code”, “software programs”; and “apparatus”, provide nothing in the claim elements to preclude the step from being “Mental Processes”- evaluation and “Mathematical Concepts”- mathematical calculations. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The of “computer”, “processing device”, “processor”, “memory”; “component”; “processor-readable storage medium”; program code”, “software programs”; and “apparatus” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). Furthermore, the claim 1, claim 11, and claim 16, recite using one or more machine learning techniques (gradient boosting techniques; genetic algorithm; heuristics). The specification discloses the machine learning analysis at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning analysis does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses 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 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in data analysis.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “computer”, “processing device”, “processor”, “memory”; “component”; “processor-readable storage medium”; program code”, “software programs”; and “apparatus” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 6, 8-10 and 15 and 10-25 recite wherein determining one or more configurable component parameter values comprises processing the at least a portion of the demand data and the at least a portion of the information pertaining to the one or more modifications using the at least one genetic algorithm in conjunction with one or more predetermined constraints; wherein the one or more configurable component parameter values comprises one or more prices attributed to the at least one component and at least a portion of the one or more modifications, and wherein performing one or more automated actions comprises executing at least one of the one or more prices in connection with at least one component-related offering to one or more users; wherein forecasting demand data for the at least one component further comprises processing component- related data using one or more tree-based models in conjunction with one or more Bayesian optimization techniques to increase model performance; wherein determining information pertaining to one or more modifications associated with the at least one component comprises identifying at least one of one or more hardware upgrades for the at least one component and one or more software upgrades for the at least one component; and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 11 and 16. Regarding Claims, 6, 8-10, 15, 20-25 and the additional elements of “component” it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Regarding claim 6 and the additional element of genetic algorithm - the specification discloses the machine learning at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea.
Reasons Claims are Patentably Distinguishable from the Prior Art
Examiner analyzed Claims 1, 6, 8-11, 15-16 and 20-25 in view of the prior art on record and finds not all claim limitations are explicitly taught nor would one of ordinary skill in the art find it obvious to combine these references with a reasonable expectation of success as discussed below.
In regards to Claim 1 (similarly Claim 11 and Claim 16), the prior art does not teach or fairly suggest:
“… forecasting demand data for the at least one component in connection with one or more temporal periods by processing at least one input set of component-related data using the determined one of the one or more trained gradient boosting techniques and the one or more trained regression techniques; determining information pertaining to one or more modifications associated with the at least one component; determining, by processing at least a portion of the demand data and at least a portion of the information pertaining to the one or more modifications using at least one genetic algorithm, one or more configurable component parameter values attributed to the at least one component and at least a portion of the one or more modifications, wherein the at least one genetic algorithm utilizes at least one heuristic to generate at least one solution for the one or more configurable component parameter values based at least in part on results from one or more previous evaluated solutions, in conjunction with processing the at least a portion of the demand data and the at least a portion of the information pertaining to the one or more modifications; and performing one or more automated actions based at least in part on at least one of the one or more configurable component parameter values, wherein performing one or more automated actions comprises: automatically updating, in at least one system associated with component-related distribution, at least one workflow operation in accordance with the at least one of the one or more configurable component parameter values; and automatically retraining at least a portion of at least one of the one or more trained gradient boosting techniques and the one or more trained regression techniques using feedback related to the at least one of the one or more configurable component parameter values”.
Examiner finds that Sangani et al. "Predicting Zillow Estimation Error Using Linear Regression and Gradient Boosting," 2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), Orlando, FL, USA, 2017, pp. 530-534 teaches using data to train linear regression and gradient boosting models with which predictions are made. (see Abstract). In particular, Sangani discloses examining the effectiveness of several machine learning models and techniques at making property related forecasts. Specifically, using data to train linear regression and gradient boosting models with which we then made predictions about other properties. For the gradient boosting model, we used grid search to fine-tune the model’s hyperparameters and observed the contribution of such tuning to the model’s accuracy (see Abstract).
Kontogiannis et al., "Explainability Analysis of Weather Variables in Short- Term Load Forecasting," 2023 14th International Conference on Information, Intelligence, Systems & Applications (IISA), Volos, Greece, 2023, pp. 1-8 teaches Prominent short - term forecasting approaches aimed at real-world applications utilize interpretable machine learning methods and form standalone, combinatorial and meta-estimation structures for the prediction of the target variable. Simpler approaches rely on linear regressors [2] while more robust approaches utilize more complex tree-based boosting algorithms such as extreme gradient boosting (XGBoost) [3], light gradient boosting machine (LightGBM) [4] and categorical boosting (CatBoost) [5]. (see Introduction).
Makhija et al., US Publication No. 20220180274 A1 teaches a system of the invention is configured to recommend a long-range or a short-range forecast frequency based on external variables where a backend data recommendation script created by a recommendation bot is configured to recommend the frequency. The frequency includes daily, monthly or yearly forecast. Further, the system is configured to recommend whether the external variables depend on the output (consumption value) or vice versa. (see par.0048, 0055).
Although Sangani, Kontogiannis and Makhija teaches the use of model techniques elements of the claim, none of the cited prior art, singularly or in combination, teach or fairly suggest, the combination of, the model analysis.
The dependent claims 6, 8-10, 15, 20-25 are eligible under 35 U.S.C. 102 and 35 U.S.C. 103 because they depend on claim 1 (claim 11 and claim 16) that is determined to be eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Publication No. 20200184494 A1 to Joseph et al.- Abstract-“ Disclosed is a system for forecasting demand for goods and/or services. In at least certain embodiments the system is configurable to select a machine learning model from among multiple different machine learning models for forecasting demand for a dataset that may be continually being updated over time. The models available to the system are each based on different machine learning algorithms (e.g., linear regression, gradient boosting, neural network, etc.) as well as several variations for each algorithm available to the system. The system can monitor changes in the datasets, changes in accuracy of the machine learning results, and external factors, and based thereon, determine whether to initiate a model reselection process or a model retraining process. Each machine learning model can be evaluated against each dataset and can select the best model for the dataset.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
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Applicants are invited to contact the Office to schedule an in-person interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
Sincerely,
/CHESIREE A WALTON/ Examiner, Art Unit 3624
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