DETAILED ACTION
This action is in reply to the submission filed on 12/3/2025.
Status of Claims
Applicant’s amendments to claims 13 are acknowledged.
Claims 1-5 and 7-20 are currently pending and have been examined.
Request for Continued Examination
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/3/2025 has been entered.
Response to Remarks
Applicant's remarks filed 12/3/2025 have been fully considered and have been found not persuasive in full.
Applicant’s remarks on pages 14 and 15 are persuasive when positing reasons the claims are distinct from the prior art of record. Then, the art rejections are overcome. Further, no additional prior art has been found that teaches the claimed limitations concerning 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.
The claims are directed to using machine learning techniques of training models and choosing the best-fit model for data analysis of inventory replenishment. This is directed to the judicial exception of fundamental economic activity of managing inventory or goods. Using machine learning techniques in their ordinary capacity for the embodiment of predicting a dynamic resource reordering point is seen as using computing technology to apply said exception. Requesting resources as claimed, according to the identified reorder point, is seen as part of said fundamental economic activity, unlike the feed dispenser in example 46 of the October 2019 PEG.
Claim Interpretation
“Sub-resource” is not directly seen in present disclosure. Rather than being new matter, the claimed sub-resources are interpreted as including a type of resource, distinguished from that of a smaller group of resources within the claimed resources. Support for this comes from the specification’s teachings of types of resources, including disclosed subassemblies as a type of resource in paragraph 38, and sub-widgets as an example of a resource in paragraph 33. Paragraph 47 teaches a computing resource including CPU, memory and storage, and analyzing the components individually. This also is included in the scope of a sub-resource.
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 1-5 and 7-20 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; receiving current resource availability data, current resource demand data, and current resource reordering data; selecting a model that predicts a most constraining reorder point; predicting a dynamic resource reordering point for the resource based on the current resource availability data, the current resource demand data, and/or the current resource reordering data; and requesting additional resources 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 resource reordering points using the historical data, the program trains a plurality of machine learning engines, 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.
Claims 2 and 14 recite the resource is a physical good or service. Claims 3 and 15 recite the resource is a computer resource. Claims 5 and 16 recite the resource is a human resource. The content of the data does not change the abstract idea of the claims. 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. Claim 18 recites 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
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/AARON TUTOR/Primary Examiner, Art Unit 3627