DETAILED ACTION
The following is a Non-Final Office Action per the Response to the Election/Restriction Requirement received on 6 October 2025. Claims 4, 8, 9, and 13 have been withdrawn. Claims 1-13 are pending in this application. Claims 1-3, 5-7, and 10-12 have been examined on their merits.
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 .
Election/Restrictions
Claims 4, 8, 9, and 13 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected Invention II, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 6 October 2025.
Examiner’s Note: The Examiner notes claims 11 and 12 were inadvertently excluded from Invention I in the Election/Restriction Requirement mailed on 27 August 2025. Claims 11 and 12 have been examined on their merits as drawn to elected Invention I.
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 1-3, 5-7, and 10-12 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 5:
At step 5, the claim recites “(an) information processing device” comprising of concrete devices (i.e. a memory and processor), and therefore is a machine, which is a statutory category of invention.
At step 2A, prong one, the claim recites “… by performing inverse reinforcement learning by using the manipulation data and the measurement data, generate a reward function that includes evaluation indices for the manipulated variable distribution information and coefficient distribution information that represents distribution of the values of coefficients of the evaluation indices”.
The limitation of “… by performing inverse reinforcement learning by using the manipulation data and the measurement data, generate a reward function that includes evaluation indices for the manipulated variable distribution information and coefficient distribution information that represents distribution of the values of coefficients of the evaluation indices” (U.S. Patent Publication No. 2023/0266719 A1: pg. 5, par. [0087]), as drafted, is a process, under its broadest reasonable interpretation covers performing the limitation by use of a mathematical calculation(s) (MPEP 2106.04(a)(2)(I)(C): “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation.”).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitations per use of mathematical calculations, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
At step 2A, prong two, the judicial is not integrated into a practical application. In particular, the claim recites “a memory”; and “a processor coupled to the memory and the processor configured to: acquire manipulation data generated based on manipulated variable distribution information that represents distribution of values of manipulated variables, and measurement data measured when a control object device is controlled based on the manipulation data”.
The limitations of “a memory” and “a processor coupled to the memory and the processor configured to: …” are recited at a high level of generality and recited so generically that they represent no more than mere instructions to apply the judicial exception on a computer component (see MPEP 2106.05(f)).
The limitation of “… acquire manipulation data generated based on manipulated variable distribution information that represents distribution of values of manipulated variables, and measurement data measured when a control object device is controlled based on the manipulation data” represents mere data gathering. The limitation of “acquire” is recited at a high level of generally and recited so generically it represents no more than an insignificant extra-solution activity of gathering data (see MPEP 2106.05(g)).
Accordingly, these additional elements neither individually nor in combination integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
At step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As previously discussed with respect to the integration of the abstract idea into a practical application, the addition of the elements of “a memory” and “a processor coupled to the memory and the processor configured to: …”, amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. See MPEP 2106.05(d)(II), “Courts have held computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).”
The limitation of “… acquire manipulation data generated based on manipulated variable distribution information that represents distribution of values of manipulated variables, and measurement data measured when a control object device is controlled based on the manipulation data”, as discussed above, amounts to no more than mere data gathering. In addition, the limitation is well-understood, routine and conventional; wherein the courts have found limitations directed to obtaining data, recited at high level of generality, to be well-understood, routine and conventional. See MPEP 2106.05(d)(II), “storing and retrieving information in memory”.
Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible.
Claim 6:
The limitations “the manipulated variable distribution information”, “the manipulation data”, “the measurement data”, “distribution of values of a specific manipulated variables”, “the reward function”, “the coefficient distribution information”, and “distribution of values of a specific coefficient” in claim 6 further details the limitations of “manipulated variable distribution information”, “manipulation data”, “measurement data”, “distribution of values of manipulated variables”, “a reward function”, “coefficient distribution information”, and “distribution of the values of coefficients of the evaluation indices” in claim 5, respectively; and the claim stands rejected for the same rational as set forth above in claim 5.
Claim 7:
The limitations “the control object is an engine”, “each of the plurality of manipulated variables is a fuel injection quantity, a fuel injection pressure, a fuel injection timing, an exhaust gas recirculation opening, a turbo opening, or an intake valve opening”, and “each of the plurality of measurement object variables is rotational speed, torque, a boost pressure, an intake air flow rate, or concentration of substances contained in exhaust gas” in claim 7 further details the limitations of “a control object” in claim 5, “manipulation data” in claims 5 and 6, and “measurement data” in claims 5 and 6; and the claim stands rejected for the same rational as set forth above in claims 5 and 6, respectively.
Claim 1:
Claim 1 represents an equivalent a non-transitory computer-readable recording medium claim to claim 5 and is rejected under 35 U.S.C. 101 for the same rationale as set forth in claim 5.
Claim 2:
Claim 2 represents an equivalent a non-transitory computer-readable recording medium claim to claim 6 and is rejected under 35 U.S.C. 101 for the same rationale as set forth in claim 6.
Claim 3:
Claim 3 represents an equivalent a non-transitory computer-readable recording medium claim to claim 7 and is rejected under 35 U.S.C. 101 for the same rationale as set forth in claim 7.
Claim 10:
Claim 10 represents an equivalent method claim to claim 5 and is rejected under 35 U.S.C. 101 for the same rationale as set forth in claim 5.
Claim 11:
Claim 11 represents an equivalent method claim to claim 6 and is rejected under 35 U.S.C. 101 for the same rationale as set forth in claim 6.
Claim 12:
Claim 12 represents an equivalent method claim to claim 6 and is rejected under 35 U.S.C. 101 for the same rationale as set forth in claim 6.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The following references are cited to further show the state of the art with respect to machine leaning.
U.S. Patent Publication No. 2018/0089589 A1 discloses a machine learning device learns an operation condition of a robot that stores a plurality of objects disposed on a carrier device in a container using a hand for grasping the objects
U.S. Patent Publication No. 2021/0213977 A1 discloses determining, by an autonomous driving system, an intent of a nearby driver, in order to act to avoid a potential collision.
U.S. Patent Publication No. 2022/0343180 A1 discloses a reward function estimation unit estimates a reward function by multiple importance sampling using samples of a decision-making history of a subject and of a decision-making history generated based on a sampling policy.
U.S. Patent Publication No. 2022/0390909 A1 discloses a learning unit includes an input unit, a reward function estimation unit, and a temporal logic structure estimation unit.
U.S. Patent Publication No. 2023/0040914 A1 discloses a learning device, a learning method, and a learning program capable of improving an estimation accuracy of the model when learning hierarchical mixtures of experts by inverse reinforcement learning.
U.S. Patent Publication No. 2024/0211767 A1 discloses a learning device, a learning method, and a learning program that performs inverse reinforcement learning.
Non-Patent Literature Publication “Maximum Entropy Inverse Reinforcement Learning” discloses modeling of route preferences, as well as, inferring destinations and routes based on partial trajectories.
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/JENNIFER L NORTON/Primary Examiner, Art Unit 2117