DETAILED ACTION
This action is in reply to the submission filed on 5/22/2026.
Status of Claims
Applicant’s cancellation of claims 2, 3, 5 and 14-16, and amendments to claims 1, 4, 7-9, 11, 13 and 17 are acknowledged.
Claims 1, 4, 7-13 and 17-20 are currently pending and have been examined.
Response to Remarks
Examiner thanks Applicant for the status of the claims. Applicant's remarks filed 5/22/2026 have been fully considered and have been found not persuasive in full.
Regarding page 9 of remarks, the judicial exception is directed to receiving training data, selecting the desired model, receiving input for model, predicting a need for computer resources corresponding to compute performance, and increasing said computer resources corresponding to prediction. This is directed to the abstract idea, or judicial exception, of organizing human activity, in particular fundamental economic activity relating to inventory management. Allocating resources based on demand is seen as relating to inventory management. The limitations concerning training the models, and using the claimed model for the data analysis are the additional elements, not the judicial exception. These elements do not integrate the abstract idea into a practical application because training machine learning models and using a ML model to perform data analysis is seen as using computing technology in its ordinary capacity as a tool to perform the exception of the claimed resource demand allocation analysis.
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, 4, 7-13 and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: the claims fall under statutory categories of processes and/or machines.
Step 2A Prong 1: the claims recite: receiving historical resource availability data, historical resource consumption data, and historical resource reordering data for sub-resources of the resource, the resource comprising computer memory, central processing unit cycles, or virtual machines in a computer system; receiving current resource availability data, current computer resource demand data, and current resource reordering data; selecting a model that predicts a most constraining reorder point; predicting a dynamic computer resource reordering point for the computer resource based on the current computer resource availability data, the current resource demand data, and/or the current resource reordering data; and increasing an amount of the computer resource in the computer system in response to threshold for the dynamic resource reordering point being met. These limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers certain methods of organizing human activity, specifically fundamental economic behavior, including inventory management.
Step 2A Prong 2: Said judicial exception is not integrated into a practical application because the claims as a whole, looking at the additional elements: a resource management computer program executed on an electronic device; training machine learning engines to predict dynamic computer resource reordering points using the historical data of sub-resources, wherein each machine learning model is trained for a different timescale, individually and in combination, merely use a computer (see MPEP 2106.05f.) The claims use these machines in their ordinary capacity for the purpose of applying the abstract idea(s). Using multiple ML engines and selecting the most appropriate one is seen as a mix of using computing technology ordinarily and a mental process. Therefore, these limitations are invoking computers or other machinery merely as a tool to perform an existing process, such that it amounts to no more than mere instructions to apply the exception. Then, these 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, and the claim is directed to an abstract idea.
Step 2B: Said claims recite additional elements as listed above, which are not sufficient to amount to significantly more than the judicial exception because, as mentioned in Step 2A Prong 2, they use computers or other machinery to perform an abstract idea in such a way that amounts to no more than mere instructions to apply the exception using computers or other machinery. Mere instructions to apply an exception using computers or other machinery cannot provide an inventive concept. Therefore, the claim is not patent eligible.
Claim 4 recites the current availability is received from the computer resource. Sending and receiving data from computers is seen as using computing technology in its ordinary capacity.
Claim 7 recites receiving external data impacting supply or demand. Claim 8 recites receiving supply related data. Claim 9 recites the current availability data is received as machinery telemetry. Specifying the data content and receiving data is seen as part of the abstract idea.
Claim 10 recites training the ML engine using supervised learning or training a neural network. This is seen as narrowing the ML engine to a specific type, but still using technology in its ordinary capacity to perform the abstract idea of managing resources in a fundamental economic activity.
Claim 11 recites monitoring current data for changes to dynamic reordering point and predicting updated point based on said data, using the ML engine. See analysis of claim 1. Claim 12 recites the point comprises a date, time of data, minimum availability threshold, demand velocity, and/or occurrence of demand event. Narrowing the definition of the reordering point does not change subject matter eligibility analysis.
Claim 17 recites the customer service level goal and the current level are based on a time to make the resource available. Claims 18 and 19 recite the action comprises raising or lowering a reorder point in response to the current level being below or above the goal. Narrowing the claim scope in this manner does not change the abstract idea(s) enumerated in the independent claim(s).
Claim 20 recites the level goal or cost goal is based on a simulation. This is seen as including mental processes.
For these reasons the claims are not subject matter eligible.
Reasons why the Claims Would be Allowable over Prior Art
The following is a statement of reasons for the indication of allowable subject matter:
No prior art or non-patent literature has been found that teaches the claimed limitations of training multiple machine learning engines to predict reordering points for a resource using historical reordering data for the sub resources, and identifying a most constraining machine learning engine of determining reordering points, in combination with the other limitations found within the independent claim(s). Applicant’s remarks filed 12/3/2025, pages 14 and 15 are persuasive when positing reasons commensurate in scope with the claim limitations for allowability over prior art.
The closest non-patent literature that reads on the Application is Meisheri, Scalable multi-product inventory control with lead time constraints using reinforcement learning. This paper teaches using machine learning for determining reorder times for inventory. The closest prior art that reads on the claims are as follows: Ha (US 2023/0214773) teaches prediction reorder points for resources using machine learning and historical data. Ohlsson (US 2020/0143313) teaches using historical data and various models to predict reordering points, taking into account a bill of materials/components of items, as well as incorporating adjustable constraints in the predictions. Mimassi (US 2022/0391830) teaches using machine learning to determine a dynamic point of reorder for items. Neither reference alone or in combination teach choosing, or identifying a most constraining machine learning engine from multiple engines that predict a dynamic reordering point of a resource using historical data from sub resources, in combination with the rest of the independent claim limitations. In summation, Applicant' s claims are distinct from the closest prior art and non-patent literature. For these reasons, the 103 rejections are overcome.
The examiner notes the cited limitations above in combination with the other limitations found within the independent claim(s) are found to be allowable over the prior art of record. Independent claims recite the quoted allowable subject matter or substantially similar language. Accordingly, the claims and their dependent claims are allowable over the prior art for the reasons identified.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this 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).
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.
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/AARON TUTOR/Primary Examiner, Art Unit 3627
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