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. Election/Restrictions Applicant’s election without traverse of claims 1-6 and 15-20 in the reply filed on December 17, 2025 is acknowledged. Claim Rejections - 35 USC § 102 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 – (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. (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. Claim(s) 1-3, 5-6, 15-17 and 19-20 are rejected under 35 U.S.C. 102 (a)(2) as being anticipated by US Patent 11,568,251 Palkar (hereinafter ‘ Palkar ’) . In regards to claim 1, Palkar teaches a quantization apparatus, comprising: a memory; and a processor that executes at least a part of an operation according to a neural network model stored in the memory, wherein the processor receives one of a plurality of matrices obtained for neural network operation in an artificial neural network as an input matrix, divides the input matrix into a plurality of channels, (See Palkar Col. 9, lines 37-67, Palkar teaches neural network for dividing input data into plurality of layer and activation function.) selects a quantizer for each channel by analyzing the distribution of element values included in each of the plurality of divided channels, and quantizes element values included in the channels using the selected quantizer. (See Palkar Col. 10, lines 1-46, Palkar teaches dynamically quantizing output values based on the statistical analysis.) In regards to claim 2, Palkar teaches wherein the processor selects a quantizer for quantizing the element value of the corresponding channel by checking the maximum value and the minimum value of the element value included in each channel. (See Palkar Col. 10, lines 1-46, Palkar teaches statistical analysis). In regards to claim 3 , Palkar teaches wherein the processor selects a quantizer having a minimum error distance by comparing the maximum and minimum values of element values included in each channel with upper and lower limits according to quantization ranges of each of a plurality of quantizers. (See Palkar Col. 10, lines 1-46, Palkar teaches statistical analysis). In regards to claim 5, Palkar teaches wherein the processor segments and converts the image input to the artificial neural network into a plurality of patches, thereby dividing the obtained input matrix into a plurality of channels according to each patch. (See Palkar Col. 9, lines 55-67, Palkar teaches dividing input image by a filter size.) In regards to claim 6, Palkar teaches wherein the processor converts the image input to the artificial neural network, thereby dividing the obtained input matrix into a plurality of channels according to pixels of the image. (See Palkar Col. 10, lines 1-46.) Claims 15-17 and 19-20 recite limitations that are similar to that of claims 1-3 and 5-6, respectively. Therefore, claims 15-17 and 19-20 are rejected similarly as claims 1-3 and 5-6, respectively. Allowable Subject Matter Claims 4 and 18 are 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: In regards to claims 4 and 18, the applied art does not teach or suggest “ wherein the processor converts an image input to the artificial neural network, thereby dividing the obtained input matrix into a plurality of channels according to color information .” Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT UTPAL D SHAH whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-5729 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-F: 7:30-5:30 . 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, FILLIN "SPE Name?" \* MERGEFORMAT Vu Le can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT (571) 272-7332 . 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. /UTPAL D SHAH/ Primary Examiner, Art Unit 2668