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 11/26/2025, Applicant, on 1/26/2026, amended claims 1, 8 and 15. Claims 1-20 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has been withdrawn pursuant to 37 CFR 1.114.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
1/26/2026 has been entered.
Response to Arguments
Applicant’s arguments filed January 26, 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 January 26, 2026.
On Pgs. 9-11 of the Remarks, regarding 35 U.S.C. § 101 rejections, Applicant states the claims are not directed to an abstract idea because, like the claims in Enfish, the computer memory of Enfish was a generic computer element and the claimed features of the invention allow for a processing circuit that can determine improved evaluation results in fewer trials allowing for improved processing by a processing circuit.” In response, Examiner respectfully disagrees. The aforementioned procedures are not improvements to a problem in the software arts, a technology or technological field. The evaluation of scheduling policy is a judicial exception (i.e. abstract idea). The claimed invention is executed by generic computer elements performing generic computer functions (see par. 0079-0081). Enfish recited claims that asserted improvements to the configuration of computer memory in accordance with a self-referential table with sufficient support in the specification that the claims were directed to a specific implementation of a solution to a problem in the software arts. Which shows the claimed invention made improvements in computer-related technology. In contrast, the present claims recite generic computer elements to perform the generic functions, such as a processor 520 may execute software (e.g., a program 540) to control at least one other component (e.g., a hardware or a software component) of the electronic device 501 coupled with the processor 520 and may perform various data processing or computations. (see par. 0082). 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. Examiner asserts, regardless of the complexity of the data analysis and/or processing, without recitation of improvements to the functioning of the technology, technological field and/or computer-related technology (i.e. software), the steps outlined in the claimed invention to train and evaluate scheduling policy amount to no more than mere instructions to implement the idea on a general purpose computer.
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- 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-20 are directed to manufacturing scheduling.
Claim 1 recites a method for manufacturing scheduling, Claim 8 recites a system for manufacturing scheduling and Claim 15 recites a system for manufacturing scheduling, which include training a first scheduling policy based on a first weight and training a second scheduling policy based on a second weight that is different from the first weight; calculating a first evaluation result based on the first scheduling policy; calculating a second evaluation result based on the second scheduling policy; determining a third scheduling policy based on inputting the first evaluation result and the second evaluation result into a policy-combination algorithm; calculating a third evaluation result based on the third scheduling policy; based on determining whether the third evaluation result is improved or unimproved over a previous evaluation result; training the third scheduling policy based on the third weight; and controlling a scheduling process based on the third scheduling policy.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Mental Processes” – evaluation. The recitation of “processing circuit”, “system”, and “memory”, provide nothing in the claim elements to preclude the step from being “Mental Processes”- evaluation. 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 “processing circuit”, “system”, and “memory” 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 8
and claim 15 recite using one or more machine learning techniques (Bayesian optimization and/or inverse reinforcement learning). 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 scheduling.
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 “processing circuit”, “system”, and “memory “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). With regards to “reinforcement learning” and “Bayesian optimization”- it is a tool to perform the abstract idea.
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 2-7,9-14 and 16-20 recite wherein the controlling the scheduling process based on the third scheduling policy comprises one of changing an order of machine operations, selecting different navigation tasks, or changing an order of language model tasks; wherein the third weight is determined based on inverse reinforcement learning based on the third evaluation result indicating the third evaluation result is greater than the first evaluation result and the second evaluation result; the third weight is determined based on Bayesian optimization based on the third evaluation result indicating the third evaluation result is less than or equal to the first evaluation result and the second evaluation result; the policy-combination algorithm comprises at least one of a mixture-of-experts method, an adaptive-learning method, or a meta-learning method; wherein determining the third weight based on inverse reinforcement learning or by Bayesian optimization reduces a number of weight-combination trials for determining the third weight; wherein the first scheduling policy or the second scheduling policy are determined based on inverse reinforcement learning or Bayesian optimization; 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, 8 and 15. Regarding claims 3-7, 10-14 and claim 17-20 and the additional element of “reinforcement learning” and “Bayesian optimization” - 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 1-20 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 8 and Claim 15), the prior art does not teach or fairly suggest:
“… selecting between inverse reinforcement learning and Bayesian optimization to determine a third weight for the third scheduling policy, inverse reinforcement learning being selected, instead of Bayesian optimization, based on determining that the third evaluation result is improved over a previous evaluation result, or Bayesian optimization being selected, instead of inverse reinforcement learning, based on determining the third evaluation result is unimproved over the previous evaluation result”.
Examiner finds that Hubbs et al., US Publication No. 20220027817A1 teaches Methods and apparatus for scheduling production at a production facility are provided. A model of a production facility utilizing one or more input materials to produce products that satisfy product requests can be determined. Each product request can specify a requested product to be available at a requested time. Policy and value neural networks can be determined for the production facility. The policy neural network can represent production actions to be scheduled at the production facility and the value neural network can represent benefits of products produced at the production facility. The policy and value neural networks can use the model of the production facility during training for generating a schedule of the production actions at the production facility that satisfy the product requests over an interval of time and relates to penalties due to late production of the requested products. (see Abstract). In particular, Hubbs discloses ANNs trained using the herein-described deep reinforcement learning techniques to account for uncertainty and achieve online, dynamic scheduling. The trained ANNs can then be used for production scheduling. For example, a computational agent can embody and use two multi-layer ANNs for scheduling: a value ANN representing a value function for estimating a value of a state of a production facility, where the state is based an inventory of products produced at the production facility (e.g., chemicals produced a chemical plant) and a policy ANN representing a policy function for scheduling production actions at the production facility (see par. 0042-0043).
Kanazawa et al., US Publication No. 20240403381A1 teaches In practical business optimization problems, there are multiple Key Performance Indicators (KPIs) that are dependent with each other. Their priorities (or relative importance, or preferences) often change dynamically, implying that just sticking to a single policy is suboptimal. Some of the related art methods require separate independent training of multiple Artificial Intelligences (AIs) each associated with a specific priority. Hence, if one wishes to get 200 different policies, the computational cost is 200 times higher than obtaining a single policy. which is prohibitively hard. Other related art implementations allow unlimited policies by training just a single AI but requires mapping a vector-valued KPIs to a scalar KPI by way of linear weighting. It is theoretically proven that such a linear scalarization method can only access part of the set of all good (e.g., Pareto-dominant) policies, hence it runs the risk of overlooking the optimal policy that best suits the business user's demand. (see par. 0023-0025).
Imani et al. "Scalable Inverse Reinforcement Learning Through Multifidelity Bayesian Optimization," in IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 8, pp. 4125-4132, Aug. 2022 teaches Data in many practical problems are acquired according to decisions or actions made by users or experts to achieve specific goals. For instance, policies in the mind of biologists during the intervention process in genomics and metagenomics are often reflected in available data in these domains, or data in cyber–physical systems are often acquired according to actions/decisions made by experts/engineers for purposes, such as control or stabilization. Quantification of experts’ policies through available data, which is also known as reward function learning, has been discussed extensively in the literature in the context of inverse reinforcement learning (IRL). However, most of the available techniques come short to deal with practical problems due to the following main reasons: 1) lack of scalability: arising from incapability or poor performance of existing techniques in dealing with large systems and 2) lack of reliability: coming from the incapability of the existing techniques to properly learn the optimal reward function during the learning process. Toward this, in this brief, we propose a multifidelity Bayesian optimization (MFBO) framework that significantly scales the learning process of a wide range of existing IRL techniques. The proposed framework enables the incorporation of multiple approximators and efficiently takes their uncertainty and computational costs into account to balance exploration and exploitation during the learning process. The proposed framework’s high performance is demonstrated through genomics, metagenomics, and sets of random simulated problems. (see Abstract).
Although Hubbs, Kanazawa and Imani teach policy scheduling elements of the claim, none of the cited prior art, singularly or in combination, teach or fairly suggest, the combination of, the Bayesian optimization and reinforcement learning selection analysis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Publication No. 20210150417A1 to Argerich et al.- Abstract-“ A method for reinforcement machine learning uses a reinforcement learning system that has an environment and an agent. The agent has a policy providing a mapping between states of the environment and actions. The method includes: determining a current state of the environment; determining, using the policy, a current policy output based on the current state; determining, using a knowledge function, a current knowledge function output based on the current state; determining an action based on the current policy output and the current knowledge function output; applying the action to the environment resulting in updating the current state and determining a reward; and updating the policy based on at least one of the current state and the reward.”
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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/
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