Prosecution Insights
Last updated: April 19, 2026
Application No. 18/702,596

LEARNING DEVICE AND NEUTRON MEASUREMENT DEVICE

Non-Final OA §101§103§112
Filed
Apr 18, 2024
Examiner
TAYLOR, WILLIAM LAURENCE
Art Unit
2884
Tech Center
2800 — Semiconductors & Electrical Systems
Assignee
Mitsubishi Electric Corporation
OA Round
1 (Non-Final)
Grant Probability
Favorable
1-2
OA Rounds
1y 9m
To Grant

Examiner Intelligence

Grants only 0% of cases
0%
Career Allow Rate
0 granted / 0 resolved
-68.0% vs TC avg
Minimal +0% lift
Without
With
+0.0%
Interview Lift
resolved cases with interview
Fast prosecutor
1y 9m
Avg Prosecution
9 currently pending
Career history
9
Total Applications
across all art units

Statute-Specific Performance

§101
5.1%
-34.9% vs TC avg
§103
51.3%
+11.3% vs TC avg
§102
7.7%
-32.3% vs TC avg
§112
35.9%
-4.1% vs TC avg
Black line = Tech Center average estimate • Based on career data from 0 resolved cases

Office Action

§101 §103 §112
DETAILED ACTION 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 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 1-4 are rejected under 35 U.S.C. 101 because the claimed inventions are directed to non-statutory subject matter. Regarding claim 1, the claim does not fall within at least one of the four categories of patent eligible subject matter because “using a pulse measurement method” and “using the training data” recite a use without reciting the relevant steps. See MPEP 2173.05(q). Regarding claims 2-4, the claims do not fall within at least one of the four categories of patent eligible subject matter for the same rationale as the rejection of claim 1. The following limitations are problematic: “using the training data” in Claim 2 “using a pulse measurement method” and “using a trained model for inferring” in Claim 3 “using the trained model for inferring the pulse origin from the pulse signal and the acquisition time” in Claim 4 Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claims 1-4 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Regarding claim 1, the claim is unclear as it recites language drawn to both a device as well as a method. Claim 1 recites “a learning device”, implying a device, and “using a pulse measurement method”, implying a method. Furthermore, the language “a learning device that obtains a trained model”, “a training data acquisition circuitry which acquires training data”, and “a model generation circuitry which generates the trained model” imply an apparatus and a method of using the apparatus. MPEP 2173.05(p)II explains that “A single claim which claims both an apparatus and the method steps of using the apparatus is indefinite under 35 U.S.C. 112(b)”. Therefore, claim 1 is rejected under 35 U.S.C. 112(b) as being indefinite. The following claims are rejected under 35 U.S.C. 112(b) according to the same rationale as the rejection of claim 1: Claim 2: “the training data acquisition circuitry acquires the training data” and “the model generation circuitry generates the trained model” Claim 3: “a detection data acquisition circuitry which acquires a pulse signal”, “an inference circuitry which… outputs the pulse origin”, and “a counting device which outputs a count” Claim 4: “the detection data acquisition circuitry acquires an acquisition time”, “the inference circuitry outputs the pulse origin”, and “the counting device outputs a count of the pulse signals” For examination purposes, the claim limitations are interpreted where each previously identified step is preceded by “configured to” language. Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claims 1 and 2 are rejected under 35 U.S.C. 103 as being unpatentable over Anshuman (US 20190244056 A1). Regarding claim 1, as best understood, Anshuman teaches a learning device that obtains a trained model for inferring a pulse origin which is an origin of a pulse signal in a neutron measurement device using a pulse measurement method, the learning device comprising: a training data acquisition circuitry which acquires training data (Paragraph [0280]: The neural net is pre-trained with known and experimental data generated during sensor testing and characterization) including the pulse signal outputted from a detector (Paragraphs [0280-1]: pulses from PIN diode sensor); a model generation circuitry which generates the trained model for inferring the pulse origin from the pulse signal outputted from the detector, using the training data (Paragraph [0280]: “This training process provides a trained model for fast real-time neutron and gamma classification via pattern recognition”). Anshuman does not teach the detector being an ionization chamber or a proportional counter. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply a known technique (using an ionization chamber or a proportional counter for detecting radiation and outputting a pulse signal) to a known method ready for improvement (the method of Anshuman for training and generating a neutron detection model) to yield predictable results (acquiring pulse signal training data from an ionization chamber or proportional counter and generating an inference model based on the training data for determining whether a pulse signal is from a neutron source or a gamma ray; with the benefit of using an ionization chamber or proportional counter being that they provide more information about the shape of the pulse useful for discrimination between pulse origins). See KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007). Regarding claim 2, as best understood, Anshuman teaches the training data acquisition circuitry acquires the training data including time information which is a time at which the training data acquisition circuitry has acquired the pulse signal outputted from the detector (Paragraph [0283]: “The signal out of the diode, dependent on amplitude and time, is initially sorted with an analog discriminator, then fed into an analog-to-digital (ADC) converter. Readout of the PIN diode results in a series of time and amplitude dependent pulses. As shown in both of the images in FIG. 24A, typically, gamma photons will produce a wider and shorter pulse 2440 than the pulse 2420 generated by neutron particles.”), and the model generation circuitry generates the trained model for inferring the pulse origin from the pulse signal and the time information, using the training data (Paragraph [0283]: “the raw data is saved and input into the reasoning model 2350 for real-time, accurate particle counting”). Claims 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Anshuman (US 20190244056 A1). Regarding claim 3, as best understood, Anshuman teaches a neutron measurement device using a pulse measurement method, comprising: a detector (Paragraph [0280]: PIN diode sensor and Paragraph [0285]: CMOS sensor); a detection data acquisition circuitry which acquires a pulse signal outputted from the detector (Paragraph [0278] and Fig. 23A describe the output of the sensors being fed into a neural network, so a circuitry for acquiring that data from the sensors is implicitly involved); an inference circuitry which, using a trained model for inferring, from the pulse signal, a pulse origin which is an origin of the pulse signal, outputs the pulse origin on the basis of the pulse signal acquired by the detection data acquisition circuitry (Paragraph [0279]: “sensor information from the PIN diode sensors is analyzed using an analog pulse neural network 2230 while sensor information from the CMOS sensors 2320 are analyzed using a digital pattern neural network 2340. Neutron and gamma counts are determined in real time and output from both types of neural networks and passed through a reasoning module 2350 (which may be programmed onto compute modules 2130A-2130D) to determine total neutron and gamma counts”); and a counting device which outputs a count of the pulse signals of which the pulse origins are a neutron (Paragraph [0281] describes identifying and counting neutrons based on the results of applying AI procedures to CMOS and PIN diode data). Anshuman does not teach the detector being an ionization chamber or a proportional counter. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply a known technique (using an ionization chamber or a proportional counter for detecting radiation and outputting a pulse signal) to a known method ready for improvement (the method of Anshuman for outputting radiation pulses from a detector to a model for determining the origin of the pulse and counting the origins) to yield predictable results (acquiring pulse signal detection data from an ionization chamber or proportional counter and inputting the signal to a trained model that infers the origin of the pulse, with the benefit of using an ionization chamber or proportional counter being that they provide more information about the shape of the pulse useful for discrimination between pulse origins). See KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007). Regarding claim 4, as best understood, Anshuman teaches: the detection data acquisition circuitry acquires an acquisition time which is a time at which the detection data acquisition circuitry has acquired the pulse signal outputted from the detector (Paragraph [0283] describes obtaining time-dependent pulse data from the PIN diode), using the trained model for inferring the pulse origin from the pulse signal and the acquisition time, the inference circuitry outputs the pulse origin on the basis of the pulse signal and the acquisition time acquired by the detection data acquisition circuitry (Paragraph [0279] describes using neural networks and a reasoning model to identify neutrons and gammas from information obtained by both types of sensors, which implicitly includes pulse signal and timing information obtained from the PIN diode), and the counting device outputs a count of the pulse signals of which the origins are a neutron or a pile-up phenomenon including a neutron (Paragraph [0281] describes identifying and counting neutrons based on the results of applying AI procedures to CMOS and PIN diode data. Paragraph [0286] describes using the CMOS to count neutrons and gammas even when they pile up in high gamma background conditions. Therefore, the overall system utilizes both types of sensors and the reasoning model to count neutrons even when they are part of a pile-up with gammas). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM L TAYLOR whose telephone number is (571)272-8389. The examiner can normally be reached Mon-Fri, 8am-4pm. 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, David Makiya can be reached at (571) 272-2273. 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. /WILLIAM LAURENCE TAYLOR/Examiner, Art Unit 2884 /DAVID J MAKIYA/Supervisory Patent Examiner, Art Unit 2884
Read full office action

Prosecution Timeline

Apr 18, 2024
Application Filed
Feb 10, 2026
Non-Final Rejection — §101, §103, §112 (current)

Precedent Cases

Applications granted by this same examiner with similar technology

Patent 12523622
X-RAY ANALYZER WITH MOVABLE SLIT BETWEEN SAMPLE AND ANALYZING CRYSTAL
2y 5m to grant Granted Jan 13, 2026
Study what changed to get past this examiner. Based on 1 most recent grants.

AI Strategy Recommendation

Get an AI-powered prosecution strategy using examiner precedents, rejection analysis, and claim mapping.
Powered by AI — typically takes 5-10 seconds

Prosecution Projections

1-2
Expected OA Rounds
Grant Probability
1y 9m
Median Time to Grant
Low
PTA Risk
Based on 0 resolved cases by this examiner. Grant probability derived from career allow rate.

Sign in with your work email

Enter your email to receive a magic link. No password needed.

Personal email addresses (Gmail, Yahoo, etc.) are not accepted.

Free tier: 3 strategy analyses per month