Prosecution Insights
Last updated: April 19, 2026
Application No. 18/245,544

STATE DETERMINATION DEVICE AND STATE DETERMINATION METHOD

Non-Final OA §101
Filed
Mar 16, 2023
Examiner
KUAN, JOHN CHUNYANG
Art Unit
2857
Tech Center
2800 — Semiconductors & Electrical Systems
Assignee
Fanuc Corporation
OA Round
1 (Non-Final)
72%
Grant Probability
Favorable
1-2
OA Rounds
3y 1m
To Grant
99%
With Interview

Examiner Intelligence

Grants 72% — above average
72%
Career Allow Rate
387 granted / 534 resolved
+4.5% vs TC avg
Strong +47% interview lift
Without
With
+46.9%
Interview Lift
resolved cases with interview
Typical timeline
3y 1m
Avg Prosecution
38 currently pending
Career history
572
Total Applications
across all art units

Statute-Specific Performance

§101
27.9%
-12.1% vs TC avg
§103
31.6%
-8.4% vs TC avg
§102
10.8%
-29.2% vs TC avg
§112
23.5%
-16.5% vs TC avg
Black line = Tech Center average estimate • Based on career data from 534 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 . Claim Objections Claims 1-11 are objected to because of the following informalities: In claim 1, lines 8-9, “an industrial machine” should be --the industrial machine-- to avoid creating another antecedent basis. In claim 1, line 17, “the estimation values” should be --estimation values-- to avoid the issue of lack of antecedent basis. In claim 1, line 19, “the number” should be --a number-- to avoid the issue of lack of antecedent basis. In claim 1, line 22, “a statistical quantity” should be --the statistical quantity-- to avoid creating another antecedent basis. In claim 1, line 24, “uses the calculated statistical quantity” should be --use the calculated statistical quantity-- to correct a grammatical error. In claim 1, line 30, “configured to” should be --is configured to-- to correct a grammatical error. In claim 1, line 38, “a statistical estimation value” should be --the statistical estimation value-- to avoid creating another antecedent basis. In claim 11, lines 6-7, “an industrial machine” should be --the industrial machine-- to avoid creating another antecedent basis. In claim 11, lines 8-9, “based on data acquired” should be --based on the data acquired-- to avoid creating another antecedent basis. In claim 11, line 12, “the estimation values” should be --estimation values-- to avoid the issue of lack of antecedent basis. In claim 11, lines 13-14, “the number” should be --a number-- to avoid the issue of lack of antecedent basis. The other claim(s) not discussed above are objected to for inheriting the issue(s) from their linking claim(s). Appropriate correction is required. 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. MPEP 2106 outlines a two-part analysis for Subject Matter Eligibility as shown in the chart below. PNG media_image1.png 930 645 media_image1.png Greyscale Step 1, the claimed invention must be to one of the four statutory categories. 35 U.S.C. 101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter. Step 2, the claimed invention also must qualify as patent-eligible subject matter, i.e., the claim must not be directed to a judicial exception unless the claim as a whole includes additional limitations amounting to significantly more than the exception. Step 2A is a two-prong inquiry, as shown in the chart below. PNG media_image2.png 681 881 media_image2.png Greyscale Prong One asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. If the claim recites a judicial exception (i.e., an abstract idea enumerated in MPEP § 2106.04(a), a law of nature, or a natural phenomenon), the claim requires further analysis in Prong Two. If the claim does not recite a judicial exception (a law of nature, natural phenomenon, or abstract idea), then the claim cannot be directed to a judicial exception (Step 2A: NO), and thus the claim is eligible at Pathway B without further analysis. Abstract ideas can be grouped as, e.g., mathematical concepts, certain methods of organizing human activity, and mental processes. Prong Two asks does the claim recite additional elements that integrate the judicial exception into a practical application? If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES), and requires further analysis under Step 2B. Claims 1-7 and 9-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Regarding claim 1, Step 1: Is the claim to a process, machine, manufacture or composition of matter? Yes. Step 2A: Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea (judicially recognized exceptions)? Yes (see analysis below). Prong one: Whether the claim recites a judicial exception? (Yes). The claim recites: 1. A state determination device for determining a state of an industrial machine, the state determination device comprising: a data acquirer configured to acquire data related to the industrial machine; a learning model storage configured to store a learning model that learned an operation state of the industrial machine associated with data related to an industrial machine; an estimator configured to, based on the data acquired from the industrial machine by the data acquirer, estimate an estimation value related to the state of the industrial machine by using the learning model stored in the learning model storage; a statistical condition storage configured to, as a condition for calculating a statistical quantity from a plurality of the estimation values estimated by the estimator, store a statistical condition including a statistical function and the number of samples related to calculation of at least the statistical quantity; a statistical data calculator configured to calculate a statistical quantity in accordance with the statistical condition stored in the statistical condition storage and uses the calculated statistical quantity to calculate a statistical estimation value corrected from the estimation value estimated by the estimator; and a determination result output configured to output a result of determination of the state of the industrial machine based on the statistical estimation value, wherein the statistical data calculator configured to calculate a first statistical quantity calculated based on an estimation value estimated by the estimator before an event that occurred in the industrial machine and a second statistical quantity calculated based on an estimation value estimated by the estimator after the event and uses the calculated first statistical quantity and second statistical quantity and a predefined certain correction function to calculate a statistical estimation value corrected from the estimation value estimated by the estimator after the event. The above bold-faced limitations are directed to mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; and/or mental processes – concepts performed in the human mind (or with a pen and paper). Note that the learning model storage and statistical condition storage are recited generally for storing information. They can be in one’s mind or with a pen and paper for keeping the information. Prong two: Whether the claim recites additional elements that integrate the exception into a practical application of that exception? (No). The claim recites additional elements as underline above. The data acquirer is recited generally to collect data necessary for the abstract idea, considered as an insignificant extra-solution activity for the abstract idea. See MPEP 2106.05(g). Note that he learning model storage and statistical condition storage are part of the abstract idea, as discussed above. In case they are implemented as computer data storages, they are simply invoking generic computer components to facilitate the application of the abstract idea. See MPEP 2106.05(f). As a result, the additional elements are insufficient to integrate the abstract idea into a practical application of the abstract idea. Step 2B: Does the claim recite additional elements (other than the judicial exception) that amount to significantly more than the judicial exception? No (see analysis below). The claim does not include additional elements that are sufficient to make the claim significantly more than the judicial exception. As discussed with respect to Step 2A Prong Two above, the additional element(s) in the claim are an insignificant extra-solution activity and/or invoking generic computer components to facilitate the application of the abstract idea. Considered as a whole, the claim does not amount to significantly more than the abstract idea. Claim 11 is similarly rejected by analogy to claim 1. Dependent claims 2-7, 9 and 10 when analyzed as a whole respectively are held to be patent ineligible under 35 U.S.C. 101 because they either extend (or add more details to) the abstract idea or the additional recited limitation(s) (if any) fail(s) to establish that the claim(s) is/are not directed to an abstract idea., as discussed below: there is no additional element(s) in the dependent claims that sufficiently integrates the claims into a practical application of, or makes the claims significantly more than, the judicial exception (abstract idea). The additional element(s) (if any) are mere instructions to apply an except, field of use, and/or insignificant extra-solution activities (applied to Step 2A_Prong Two and Step 2B; see MPEP 2016.05(f)-(h)) and/or well-understood, routine, or conventional (applied to Step 2B; see MPEP 2106.05(d)) to facilitate the application of the abstract idea. On the other hand, claim 8 is eligible because it recites additional elements of “wherein when the state of the industrial machine is determined as abnormal, the determination result output outputs at least any one of a signal to suspend operation of the industrial machine, a signal to decelerate operation of the industrial machine, or a signal to limit drive torque of a motor driving the industrial machine.” This sufficiently transforms the claim into a practical application of controlling specific mechanical operations of the industrial machine in response to the determination of the abnormality. Allowable Subject Matter Claim 8 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The following is a statement of reasons for the indication of allowable subject matter: Regarding claim 8, the closest prior art of record fails to teach the features of claim 1: “wherein the statistical data calculator configured to calculate a first statistical quantity calculated based on an estimation value estimated by the estimator before an event that occurred in the industrial machine and a second statistical quantity calculated based on an estimation value estimated by the estimator after the event and uses the calculated first statistical quantity and second statistical quantity and a predefined certain correction function to calculate a statistical estimation value corrected from the estimation value estimated by the estimator after the event,” in combination with the rest of the claim limitations as claimed and defined by the Applicant. HIRUTA et al. (US 20180059656 A1; cited in IDS) teaches a machine diagnostic device, involving a malfunction diagnostic unit that diagnoses a malfunction of a machine on the basis of acquired sensor data and a normal operation model; and a sensor adjustment unit that, when the sensors are detached and re-attached to the machine, displays discrepancies between the normal operation model prior to the detachment of the sensors and the post sensor attachment sensor data. HIRUTA is dealing with a situation similar to the instant invention (i.e., an event of sensor detachment and re-attachment), but is fundamentally different from the claimed invention in determining a state (i.e., abnormality) of the machine. TAJIMA et al. (US 20180275642 A1) teaches an anomaly detection system, involving adjusting an anomaly score such that the anomaly score for operational data under normal operation falls within a predetermined range based on a deviation of the operational data acquired from the monitoring target device from a prediction result obtained by the predictive model. TAJIMA is similar to the instant invention in adjusting the determination, but is different in the methods of the adjustment. Maya et al. (US 20180225166 A1) teaches an apparatus of abnormality detection of a target, involving calculating a degree of deviation of a second period after a first period on the basis of the state data and the estimated data of the second period; and calculating a degree of abnormality of the second period on the basis of the degree of deviation of the second period. ABE et al. (US 20190278247 A1) teaches a method of detecting abnormality, involving calculating a score value indicating a degree to which feature quantity of the type is to be classified into a class indicated by learning data; comparing the score value with a threshold value indicated by an abnormality detection parameter; and detecting an abnormality when the score value crosses (exceeds) the threshold value. The threshold can be adjusted based on an evaluation result of the validity of the abnormality detection. None of the closest prior art of record teaches or suggests the indicated features as claimed. Prior Art The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. YOSHIDA et al (US 20190354080 A1) teaches an abnormality detector, involving a machine learning model for estimating an operation state of a manufacturing machine based on an observed physical quantity related to an operation of the manufacturing machine. HAYAKAWA (US 20200371858 A1) teaches a fault detection system, involving a machine learning model for outputting a fault index value based on whether or not there is a particular-event occurrence in the operation log information corresponding to a fault occurrence in the fault record information of a prediction target period, and a period-length from the reference time point until the fault-occurrence time point. Takeda et al. (US 20200150159 A1) teaches an anomaly detection device for calculating a degree of anomaly according to a predictive value that is predicted through a machine learning model using data acquired from a target device and a measurement value that is actually measured for the target device. The model is updated when a degree of anomaly according to the current model is higher than a degree of anomaly according to a new model. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN C KUAN whose telephone number is (571)270-7066. The examiner can normally be reached M-F: 9:00AM-5:30PM. 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, Andrew Schechter can be reached at (571) 272-2302. 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. /JOHN C KUAN/Primary Examiner, Art Unit 2857
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Prosecution Timeline

Mar 16, 2023
Application Filed
Sep 08, 2025
Non-Final Rejection — §101 (current)

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

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Prosecution Projections

1-2
Expected OA Rounds
72%
Grant Probability
99%
With Interview (+46.9%)
3y 1m
Median Time to Grant
Low
PTA Risk
Based on 534 resolved cases by this examiner. Grant probability derived from career allow rate.

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