Office Action Predictor
Last updated: April 15, 2026
Application No. 18/314,113

MODEL SELECTION APPARATUS, MODEL SELECTION METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM

Final Rejection §101
Filed
May 08, 2023
Examiner
PATEL, JIGNESHKUMAR C
Art Unit
2116
Tech Center
2100 — Computer Architecture & Software
Assignee
Yokogawa Electric Corporation
OA Round
2 (Final)
79%
Grant Probability
Favorable
3-4
OA Rounds
2y 9m
To Grant
99%
With Interview

Examiner Intelligence

Grants 79% — above average
79%
Career Allow Rate
346 granted / 439 resolved
+23.8% vs TC avg
Strong +21% interview lift
Without
With
+20.9%
Interview Lift
resolved cases with interview
Typical timeline
2y 9m
Avg Prosecution
28 currently pending
Career history
467
Total Applications
across all art units

Statute-Specific Performance

§101
14.2%
-25.8% vs TC avg
§103
46.9%
+6.9% vs TC avg
§102
19.5%
-20.5% vs TC avg
§112
14.7%
-25.3% vs TC avg
Black line = Tech Center average estimate • Based on career data from 439 resolved cases

Office Action

§101
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 . Status of the Application 2. Claim 1-3, 5, 7, 9-18, and 20 have been amended, claim 6 and 8 have been canceled, claim 21-22 have been added. Claim 1-5, 7, and 9-22 are in pending status. Examiner’s Note: 3. Examiner suggest to add the below limitation or similar limitation at the end of claim 1 and 19-20 in order to overcome the 35 U.S.C 101 abstract idea rejection. “and controlling the controlled object based on the selected object model” Response to Arguments 4. The prior art rejection for claim 1-20 have been withdrawn in light of the amendment to the claim and argument filed on 11/20/25. 5. The 35 U.S.C 101 abstract idea rejection is maintained as the amended claim does not include additional elements, individually or in combination, that are sufficient to amount to significantly more than the judicial exception as explained below. 6. In regard to 35 U.S.C 101 abstract idea rejection, applicant’s main argument is that, “As a practical matter, simulating the operation of equipment as result of applying a plurality of candidate models, which are computer models, while acquiring a plurality of pieces of state data representing the state of the equipment resulting from the use of each of the plurality of candidate models, cannot be performed in the human mind and requires the use of a programmable machine or apparatus. "A claim does not recite a mental process when it contains limitation(s) that cannot practically be performed in the human mind." Response: Examiner does not agree with the above argument as the argument is not persuasive. As previously mentioned that the limitation “selecting an object model” is a abstract idea under mental process. The basic selection of model from the plurality of the model is easily done in human mind. Also acquiring data is a simple data gathering process. The added computer as a hardware is a generic computer element that is not sufficient to amount to more than the judicial exception. Hence the receiving data is a just data receiving activity using the generic computer element that does not provide a technological solution to a problem. Applicant’s second argument is that, “As with the claim in the case of the aforesaid Example 38, the claims here do not recite a mathematical relationship, formula, or calculation. While some of the limitations of the present claims may be based on mathematical concepts, the mathematical concepts are not recited in the claims. As shown above, the claims do not recite mental processes because the steps are not practically performable in the human mind. Finally, the claim does not recite a certain method of organizing human activity such as a fundamental economic concept or commercial and legal interactions. Therefore, the claims should be eligible because the claims do not recite a judicial exception” Response: Examiner does not agree with the above argument as the argument is not persuasive. The claim limitation the model is generated using the machine learning is enumerates a mathematical concept. The filed specification in Para. [0049], [0057], [0061], [0057] disclosed that the model is generated using the machine learning algorithm or AI algorithm. As in general machine learning algorithms are fundamentally mathematical. The core logic and procedures that enable machines to learn from data and make predictions are built entirely on mathematical concepts and statistical methods and AI algorithm is based on the formulas and operate as complex, multi-layered mathematical functions—specifically linear algebra, calculus, probability, and statistics—to analyze data, identify patterns, and make predictions. AI turns data into numerical structures, using mathematical optimization to minimize errors and "learn. Hence the limitation of model generation are pure math and is fall under mathematical concepts category of the abstract idea. 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. 7. Claims 1, 19, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to an abstract idea without significantly more. The claims recite limitation of “selecting an object model for controlling the controlled object from among the plurality of candidate models based on the plurality of indicators.” These claim limitations are abstract idea because the providing, determining prediction and selecting features of the claim can be reasonably performed in human mind and fall under mental processes category of the abstract idea. Claim also recites “a plurality of candidate models each of which is generated by reinforcement learning using, as at least a part of a reward, an output of an evaluation model configured to output an indicator obtained by evaluating a state of equipment and is capable of outputting an action according to the state of the equipment, the evaluation model”. These different model includes the mathematical formula or functions which is fall under mathematical concepts category of the abstract idea. Accordingly, claims 1, 19, and 20 recite an abstract idea because the particular limitations, as briefly outlined above, fall into at least two of the groupings of abstract ideas (see MPEP 2106.04(a)) (Step 2A, Prong One, Yes). The claim further recite additional limitation “ acquiring a plurality of pieces of state data representing the state of the equipment in a case where each of manipulated variables based on outputs of the plurality of candidate models is applied to a controlled object in the equipment; acquiring a plurality of indicators output, and outputting the object model”. These limitations adds only insignificant extra solution activity akin to “amounts to necessary data gathering and outputting” (See MPEP §2106.05(g)). This judicial exception is not integrated into a practical application because the claim limitations are directed to the generality of “model selection apparatus, method and non-transitory medium (e.g., see claim 1, and 19-20). In other words, the claim limitations are not providing meaningful limitations to the method. Finally, the apparatus as recited in claims 1, the method claimed in claim 19, and a non-transitory computer-readable medium as recited in claim 20 is merely the field of use of model selection and do not provide any meaningful limitations to the claims (Step 2A, Prong Two, No). The claims do not include additional elements, individually or in combination, that are sufficient to amount to significantly more than the judicial exception because the computer provided with computer-readable instruction, as recited in claim 1 and 19, a non-transitory computer-readable medium, as recited in claims 20 , are generic elements. Furthermore, the claim limitations are implemented on these generic elements. In other words, the claim limitations are being implemented on these units and are not specifically linked to these elements. Also the court decisions have determined that this additional element as discussed above in step 2A of acquiring a data and output a model to be well-understood, routine, and conventional when claimed in a merely generic manner for output the model (outputting step) (See MPEP § 2106.05(d)(II)(i: 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 (See Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) and Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)) (Step 2B, No). Accordingly, these claims 1, and 19-20 are rejected under 35 U.S.C. 101. 8. Regarding claims 2-18, and 21-22, these claim recite limitations that are directed to abstract idea because limitations recited by these claims can also be performed using the mathematical concepts, and mental process. Claims 2-18, and 21-22 only recites limitations that are abstract idea (Step 2A, Prong One, Yes) under the mental process and mathematical concept. None of the claims 2-22 recite any limitations that can integrate the claims into a practical applications because all these limitations recite is abstract idea. Thus, they fail “PRONG TWO” of the PEG 2019 (Step 2A, PRONG TWO, No). Claims 2-18, and 21-22 further fail to recite any additional element that can amount to significant more than the judicial exception (Step 2B, No). Thus, claims 2-18, and 21-22 are not patent eligible. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Lin (Pub: 2021/0247367) disclose a workflow-based model optimization method for vibrational spectral analysis is provided. The method includes: initializing and determining the evaluation indicator for the model in vibrational spectral analysis and the optimization object of this model, and carrying out permutation and combination on preprocessing methods and multivariate analysis methods to obtain method combinations; determining hyper-parameters within the various method combinations and corresponding hyper-parameter space combinations; inputting the training set into the various method combinations and optimizing hyper-parameters to determine optimal hyper-parameters of the method combinations; using the training set for training to obtain model parameters so as to acquire various combined models; inputting the test set into the various combined models, calculating the evaluation indicator value for the various combined models, and selecting the optimal model (Abstract). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIGNESHKUMAR C PATEL whose telephone number is (571)270-0698. The examiner can normally be reached Monday - Friday, 7:00 AM - 5:00 PM. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kenneth M. Lo can be reached at (571)272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /JIGNESHKUMAR C PATEL/ Primary Examiner, Art Unit 2116
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Prosecution Timeline

May 08, 2023
Application Filed
Jul 25, 2025
Non-Final Rejection — §101
Nov 20, 2025
Response Filed
Feb 09, 2026
Final Rejection — §101
Mar 30, 2026
Response after Non-Final Action

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

3-4
Expected OA Rounds
79%
Grant Probability
99%
With Interview (+20.9%)
2y 9m
Median Time to Grant
Moderate
PTA Risk
Based on 439 resolved cases by this examiner. Grant probability derived from career allow rate.

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