Prosecution Insights
Last updated: July 17, 2026
Application No. 18/918,463

QUALITY MANAGEMENT APPARATUS, QUALITY MANAGEMENT METHOD, AND STORAGE MEDIUM

Non-Final OA §103
Filed
Oct 17, 2024
Priority
Oct 31, 2023 — JP 2023-186525
Examiner
GUNBERG, EDWIN C
Art Unit
2884
Tech Center
2800 — Semiconductors & Electrical Systems
Assignee
Konica Minolta Inc.
OA Round
1 (Non-Final)
78%
Grant Probability
Favorable
1-2
OA Rounds
8m
Est. Remaining
84%
With Interview

Examiner Intelligence

Grants 78% — above average
78%
Career Allowance Rate
491 granted / 628 resolved
+10.2% vs TC avg
Moderate +6% lift
Without
With
+6.3%
Interview Lift
resolved cases with interview
Typical timeline
2y 5m
Avg Prosecution
22 currently pending
Career history
656
Total Applications
across all art units

Statute-Specific Performance

§101
1.2%
-38.8% vs TC avg
§103
79.7%
+39.7% vs TC avg
§102
11.1%
-28.9% vs TC avg
§112
4.1%
-35.9% vs TC avg
Black line = Tech Center average estimate • Based on career data from 628 resolved cases

Office Action

§103
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 § 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. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. Claims 1-13 are rejected under 35 U.S.C. 103 as being unpatentable over Fujioka (JP H06 109719A) (cited by Applicant) in view of Shelton et al. (2018/0003689). Regarding claim 1, Fujioka teaches a quality management apparatus for cooked rice coated with a coating agent and conveyed, the quality management apparatus comprising: a multispectral sensor whose wavelength range includes at least a near-infrared region; and a hardware processor, wherein the multispectral sensor captures a multispectral signal of the cooked rice conveyed, wherein the hardware processor acquires and analyzes light absorption information of a plurality of wavelengths related to moisture absorption of the cooked rice from the captured multispectral signal of the cooked rice to calculate information indicating an amount of a moisture content of the cooked rice conveyed. Fujioka lacks explicit teaching of the multispectral sensor is an imaging camera and the resultant signal is a two-dimensional moisture distribution. Shelton teaches multispectral thermal imaging and detection of materials with two-dimensional distributions. (Shelton, abstract and throughout) It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to use the imaging detector of Shelton in the rice quality control moisture detection system of Fujioka in order to analyze a greater volume of material. Regarding claim 2, the combination of Fujioka and Shelton further teaches the hardware processor creates a two-dimensional distribution image as the information indicating the two-dimensional distribution of the moisture content of the cooked rice, and causes a display part to show the two-dimensional distribution image and/or a statistical value of the moisture content in a predetermined region of the two-dimensional distribution image (Shelton, [0115]-[0117]) Regarding claim 3, the combination of Fujioka and Shelton further teaches the hardware processor acquires the light absorption information on the moisture absorption of the cooked rice of the plurality of wavelengths and performs regression analysis. (Fujioka, abstract) Regarding claim 4, the combination of Fujioka and Shelton further teaches a display part which shows a two-dimensional distribution image and/or a statistical value of the moisture content in a predetermined region of the two-dimensional distribution image as the information indicating the calculated two-dimensional distribution. (Fig. 2, display 108, Fig. 7) Regarding claim 5, the combination of Fujioka and Shelton further teaches the coating agent is at least one or more of oil, sugar, protein, and egg. (Fujioka, abstract, final line, detection of amylose, which is a polysaccharide, and therefore includes the claimed material sugar) Regarding claim 6, the combination of Fujioka and Shelton further teaches the two-dimensional distribution image is a heat map colored with a plurality of tone values or a plurality of colors. (Shelton, [0115]) Regarding claim 7, the combination of Fujioka and Shelton further teaches the hardware processor further causes the display part to show a transition of the statistical value. (Shelton, [0115]-[0117]) Regarding claim 8, the combination of Fujioka and Shelton further teaches the statistical value is at least one of an average value, a median value, and a standard deviation. (Shelton, [0066], the claimed mathematical elements are basic functions within spectral analysis) Regarding claim 9, the combination of Fujioka and Shelton further teaches an operation part with which a user can specify the predetermined region of the two-dimensional distribution image. (Shelton, [0082]) Regarding claim 10, the combination of Fujioka and Shelton further teaches the hardware processor further causes the display part to show a two-dimensional distribution image of a moisture content of cooked rice in past or a two-dimensional distribution image of a moisture content of ideal cooked rice. (Shelton, 0079]) Regarding claim 11, the combination of Fujioka and Shelton further teaches the multispectral camera imager includes a hyperspectral camera. (Fujioka, abstract; Shelton, [0032]) Regarding claim 12, the combination of Fujioka and Shelton teaches a quality management method for managing quality of cooked rice coated with a coating agent and conveyed, the method comprising: capturing a multispectral image of the conveyed cooked rice using a multispectral camera imager whose wavelength range including at least a near-infrared region; and acquiring and analyzing light absorption information of a plurality of wavelengths related to moisture absorption of the cooked rice from the captured multispectral image of the cooked rice to calculate information indicating a two-dimensional distribution of a moisture content of the cooked rice conveyed; and performing feedback control on the moisture content of the cooked rice based on the calculated information. (Fujioka, abstract; Shelton, abstract and throughout) Regarding claim 13, the combination of Fujioka and Shelton further teaches a non-transitory computer-readable storage medium storing a program for a computer of a quality management apparatus that manages a quality of cooked rice coated with a coating agent and conveyed, wherein the program causes the computer to: acquire and analyze light absorption information of a plurality of wavelengths related to moisture absorption of the cooked rice from a multispectral image of the cooked rice captured by a multispectral camera imager whose imaging wavelength range includes a near-infrared region to calculate information indicating a two-dimensional distribution of a moisture content of the cooked rice conveyed. (Fujioka, abstract; Shelton, abstract and throughout) Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWIN C GUNBERG whose telephone number is (571)270-3107. The examiner can normally be reached Monday-Friday, 8:30AM-5:00PM. 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, Uzma Alam can be reached at 571-272-3995. 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. /EDWIN C GUNBERG/ Primary Examiner, Art Unit 2884
Read full office action

Prosecution Timeline

Oct 17, 2024
Application Filed
Jun 24, 2026
Non-Final Rejection mailed — §103 (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
78%
Grant Probability
84%
With Interview (+6.3%)
2y 5m (~8m remaining)
Median Time to Grant
Low
PTA Risk
Based on 628 resolved cases by this examiner. Grant probability derived from career allowance rate.

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