CTNF 18/858,976 CTNF 101538 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 § 102 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15 AIA Claim s 1-10 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Matsuda US20210302155 (included in the IDS filed 12/18/2025)(hereinafter “Matsuda”) . Regarding claim 1, Matsuda teaches an apparatus of learning model-based size measurement, the apparatus comprising (see paragraph 0034, a dimension measuring device using a trained image recognition model) : at least one processor (see paragraph 0039, the computer system includes one or more processors) ; and a memory configured to store at least one instruction executed by the at least one processor, wherein the at least one instruction includes (see paragraph 0039-0041, the system includes a memory which stores a program or instruction for the functions described executed on the processor) : an instruction to acquire an image of interest from an object image, the object image containing a measurement object of which size is to be measured (see figure 4 and paragraph 0068-0069, the first and second region dividing unit, where the first region dividing unit receives the input image which includes the measurement object [object image] and then specifies a target region [image of interest]. See paragraph 0041, the memory stores a dimension management application which includes an instruction of executing the functions described) , and PNG media_image1.png 490 524 media_image1.png Greyscale an instruction to input the image of interest into a pre-trained learning model and to output result data including dimension information of the measurement object (see paragraph figure 4 and paragraphs 084-0086, the dimension measuring unit performs processing of measuring a dimension of the measurement object based on the feature points [defining the measurement target]. The output device may display the measurement results [paragraph 0056]. See paragraph 0041, the memory stores a dimension management application which includes an instruction of executing the functions described) . Regarding claim 2, Matsuda teaches the apparatus of claim 1, wherein the pre-trained learning model is a deep learning based learning model (see paragraph 0020 and 0079, the measuring method may use deep learning) . Regarding claim 3, Matsuda teaches the apparatus of claim 1, wherein the instruction to output result data including dimension information of the measurement object includes an instruction to segment the image of interest into at least one physical object using the pre-trained learning model and an instruction to label a size measurement point of the measurement object (see paragraph 0071-0074, the first region dividing unit estimates a region of interest [from the input image containing an object] using a segmentation model, the estimation result contains array label information for each pixel and a marker for the region) . Regarding claim 4, Matsuda teaches the apparatus of claim 1, wherein the instruction to acquire the image of interest includes an instruction to obtain the image of interest from the object image using a rule-based algorithm (see paragraph 0062, the neural network [the neural network for semantic segmentation, which is used for obtaining the target region] may be a machine learning model such as a decision tree [a form of a rule-based algorithm]) . Regarding claim 5, Matsuda teaches the apparatus of claim 4, wherein the instruction to obtain the image of interest includes an instruction to perform image pre-processing on the object image (see paragraph 0075-0076, the second region dividing unit includes region reduction processing on regions of the image) . Claims 6-10 are analogous method to apparatus claims 1-5, respectively, thus are analyzed and rejected similar to claims 1-5 . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see the attached 892 notice of reference cited . Contact Information Any inquiry concerning this communication or earlier communications from the examiner should be directed to EMILY R. HAUK whose telephone number is (571)272-5966. The examiner can normally be reached M-F 8:00-5:00. 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, Chan Park can be reached at 571-272-7409. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /EMILY ROSE HAUK/Examiner, Art Unit 2669 /JOHN B STREGE/Primary Examiner, Art Unit 2669 Application/Control Number: 18/858,976 Page 2 Art Unit: 2669 Application/Control Number: 18/858,976 Page 3 Art Unit: 2669 Application/Control Number: 18/858,976 Page 4 Art Unit: 2669 Application/Control Number: 18/858,976 Page 5 Art Unit: 2669 Application/Control Number: 18/858,976 Page 6 Art Unit: 2669
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