DETAILED ACTION
Status of the Application
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This communication is a non-final action in response to the correspondences filed on 5/13/2026. Claims 1-5, 8-16, 19-22 are currently pending and have been considered 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 5/13/2026 has been entered.
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, 8-16, 19-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claims 1-5, 8-16, 19-22 are determined to be directed to an abstract idea.
The claims 1-5, 8-16, 19-22 are directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea), without a practical application and without providing significantly more.
Regarding Step 1 of the subject matter eligibility test per MPEP 2106.03, Claims 1-5, 8-11 are directed to a method (i.e., process), Claims 12-16, 19-22 are directed to a system (i.e., apparatus/machine), which are directed to one of the four statutory categories of invention.
Regarding Step 2A-Prong 1 of the subject matter eligibility test per MPEP 2106.04, Claims 1 and 12 are directed specifically to the abstract idea of training models/algorithms {to make new task schedules} by: generating a first plurality of simulated training data sets and a second plurality of simulated training data sets for a plurality of different work projects; and training a [model/algorithm] to maximize the specified training objective for each training data set of the plurality of training data sets through trial and error; which include mental processes (i.e., evaluating and analyzing data {work packages, resource data, constraints data, scheduling objectives} and making a judgement and opinion on training algorithm/model to maximize objectives) and certain methods of organizing human activities based on fundamental economic principles/practice (i.e., facilitating/training models/algorithms {for scheduling of resources to work/tasks}), based on managing personal behavior and interactions between people (following rules and instructions to train scheduling models/algorithms to maximize objectives {regarding tasks to resources allocation}). Claims 2-5, 8-11, 13-16, 19-22 are directed to performing the abstract idea of claims 1 and 12 with further details provided for how to perform the abstract idea defined in 1 and 12, which includes mental processes and certain methods of organizing human activity for similar reasons as provided above for claim 1 and 12. After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself.
Regarding Step 2A-Prong 2 of the subject matter eligibility test per MPEP 2106.04(d) and 2106.05, while the claims 1-5, 8-16, 19-22 recite additional elements which are hardware or software elements, such as providing a reinforcement learning engine including a computational agent, a data simulation service, database(s), providing, by the data simulation service, the first plurality of training data sets directly to a JSON file formatter through a first path; storing, by the data simulation service, the second plurality of training data sets as one or more databases in at least one memory and providing the second plurality of training data sets from the at least one memory to the JSON file formatter through a second path separate from the first path; formatting, by the JSON file formatter, the first plurality of training data sets received directly from the data simulation service into a first JSON file and the second plurality of training data sets stored by the data simulation service in the at least one memory into a second JSON file, providing the JSON file to a schedule training service, system comprising: a processor, the processor comprising neural networks; a memory coupled to the processor; a scheduling trainer coupled to the processor, wherein the training scheduler is configured to {perform the functions of the invention}, a network architecture that includes neural networks, the neural network has one input layer, one or more hidden layers having a plurality of neurons, and an output layer, deep reinforcement learning engine, an artificial intelligence, cloud-based micro-services, automatically {generating}, these limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these limitations merely perform instructions of the abstract idea, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological environment and/or field of use which do not provide practical application for an abstract idea (MPEP 2106.05 (f) & (h)). The claims do not amount to "practical application" for the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Regarding Step 2B of the subject matter eligibility test per MPEP 2106.05, while the claims 1-5, 8-16, 19-22 recite additional elements which are hardware or software elements, such as providing a reinforcement learning engine including a computational agent, a data simulation service, database(s), providing, by the data simulation service, the first plurality of training data sets directly to a JSON file formatter through a first path; storing, by the data simulation service, the second plurality of training data sets as one or more databases in at least one memory and providing the second plurality of training data sets from the at least one memory to the JSON file formatter through a second path separate from the first path; formatting, by the JSON file formatter, the first plurality of training data sets received directly from the data simulation service into a first JSON file and the second plurality of training data sets stored by the data simulation service in the at least one memory into a second JSON file, providing the JSON file to a schedule training service, system comprising: a processor, the processor comprising neural networks; a memory coupled to the processor; a scheduling trainer coupled to the processor, wherein the training scheduler is configured to {perform the functions of the invention}, a network architecture that includes neural networks, the neural network has one input layer, one or more hidden layers having a plurality of neurons, and an output layer, deep reinforcement learning engine, an artificial intelligence, cloud-based micro-services, automatically {generating}, these limitations are not enough to qualify as “significantly more” being recited in the claims along with the abstract idea since these limitations are merely invoked as a tool to perform instructions of the abstract idea, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological environment and/or field of use which do not provide significantly more to an abstract idea (MPEP 2106.05(f) & (h)). The claims do not amount to "significantly more" than the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) add a specific limitation other than what is well-understood, routine and conventional in the field; (6) add unconventional steps that confine the claim to a particular useful application; nor (7) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Therefore, since there are no limitations in the claims 1-5, 8-16, 19-22 that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, and looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, the claims are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant’s arguments filed on 5/13/2026 have been fully considered and would not overcome all of the rejections in the most recent Office action. Details are provided below.
Rejections under 35 U.S.C. 101:
Applicant’s arguments are focused on the additional elements. Additional limitations are recited at a high level of generality and are interpreted as “apply it” and/or “generally linking” when considered in combination with the abstract idea. Therefore, the amendments do not provide a practical application or significantly more to the abstract idea.
Conclusion
Closest prior art to the invention includes Mitra et al (US 20200257968 A1), Nasr-Azadani et al (US 20220012089 A1), Swamy et al (US 20190384640 A1), Li et al (US 20220232531 A1), Pang et al (CN 113077091 A) and Yajnanarayana et al (US 20220166676 A1) as applied in the previous Office actions. None of the prior art alone or in combination teaches the claimed invention wherein the novelty is in combination of all limitations and not in a single limitation.
Additional prior art that are not relied upon in the rejections above include:
Mauer (US-11467872-B1), Poole (US-20200104640-A1), and Zbikovski et al (Kamil Żbikowski, Michał Ostapowicz, Piotr Gawrysiak, "Deep Reinforcement Learning for Resource Allocation in Business Processes", https://doi.org/10.48550/arXiv.2104.00541).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MEHMET YESILDAG whose telephone number is (571)272-3257. The examiner can normally be reached M-F 8:30 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MEHMET YESILDAG/Primary Examiner, Art Unit 3624
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