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 Objections Claims 26-31 are objected to because of the following informalities: They are dependent upon cancelled claims. Appropriate correction is required. Claim Rejections - 35 USC § 102 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis ( i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 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. Claim s 1- 5, 8- 12 , 25- 28, 31 and 32 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Lee, US Pub. No. 2020/0210806 . Regarding claim 1, Lee teaches a data processing apparatus for executing a neural network model (fig. 3) , comprising: a storage circuit configured to store a folding filter of a convolution layer of the neural network model (fig. 3, memory 120) , wherein the folding filter is obtained by dimension folding of an original filter, wherein the dimension folding comprises rearranging data of a width dimension and/or a height dimension to an input channel dimension (fig. 2B, input feature map reads on input channel and width/height dimension; Kernel reads on folding filter) ; and a processing circuit (fig. 3, processor 110) configured to: perform the dimension folding on an input feature map to obtain a folding feature map (figs. 2A-2C, input feature map times Kernel) ; and perform a convolution operation on the folding feature map by using the folding filter to obtain an output feature map (figs. 1-2; convolution, subsampling, to output feature map 230). Regarding claim 2, Lee teaches wherein a size of an input channel dimension of the original filter does not exceed a first threshold A1, and a size of an input channel dimension of the folding filter equals a second threshold Aci , wherein the first threshold A1 is less than the second threshold Aci (fig. 2C and accompanying text). Regarding claim 3, Lee teaches wherein the processing circuit is configured to perform the dimension folding by: determining an overall folding multiple N t otal based on a size Ci of an input channel dimension of to-be-folded multi-dimensional data and the second threshold Aci (figs. 1, 2A-2C) ; splitting the overall folding multiple Ntotal into a width dimension folding multiple Nw and a height dimension folding multiple Nh (figs. 1, 2A-2C, convolution, feature maps) ; determining a width dimension size and a height dimension size of folded multi- dimensional data based on Nw , Nh, and a width dimension size and a height dimension size of the to-be-folded multi-dimensional data (figs. 1, 2A-2C, subsampling, convolution, feature maps) ; and determining a folded convolution stride of the convolution operation based on Nw , Nh, and an original convolution stride of the convolution operation (figs. 1, 2A-2C, convolution, subsampling, feature maps breakdown). Regarding claim 4, Lee teaches wherein the processing circuit is further configured to determine the overall folding multiple Ntotal as follows: Ntotal = Aci /Cia, wherein Cia is a value that Ci is aligned to the nearest value of Aci /2 n , and n is a natural number (fig. 16 and accompanying text). Regarding claim 5, Lee teaches wherein the processing circuit is further configured to split the overall folding multiple Ntotal according to any one of following rules or combinations of the rules: preferentially splitting the overall folding multiple Ntotal to the width dimension; averagely splitting the overall folding multiple Ntotal to the width dimension and the height dimension; splitting the overall folding multiple Ntotal to make a completion amount caused by alignment of folding multiples as small as possible; or splitting the overall folding multiple Ntotal to make a convolution stride in the width dimension be divisible by a folding multiple of the width dimension (figs. 1, 2A-2C, and accompanying text). Regarding claim 8, Lee teaches wherein the second threshold Aci is determined based on an instruction alignment requirement, and the first threshold A1< Aci /2 (figs. 1, 2A-2C and accompanying text). Regarding claim 9, Lee teaches implementing the dimension folding in the width dimension through dimensional recombination; and/or implementing the dimension folding in the height dimension through dimensional transposition (figs. 2A, 2B, and accompanying text). Regarding claim 10, Lee teaches wherein a size of an output channel dimension of the original filter equals a size of an output channel dimension of the folding filter (fig. 2A, elements 211 and 220). Regarding claim 11, Lee teaches wherein the folding filter is generated offline or online ([0072]). Regarding claim 12, it is a chip of claim 1 and is rejected on the same grounds presented above (see [0070-0076] for chip). Regarding claim 25, it has similar limitations to those of claim 2 and is rejected on the same grounds presented above. Regarding claim 26, it has similar limitations to those of claim 3 and is rejected on the same grounds presented above. Regarding claim 27, it has similar limitations to those of claim 4 and is rejected on the same grounds presented above. Regarding claim 28, it has similar limitations to those of claim 5 and is rejected on the same grounds presented above. Regarding claim 31, it has similar limitations to those of claim 8 and is rejected on the same grounds presented above. Regarding claim 32, it has similar limitations to those of claim 9 and is rejected on the same grounds presented above. Allowable Subject Matter Claims 6, 7, 29, and 3 0 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. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Li et al. (US Pub. No. 2019/0163716) teaches folding unfolded feature data provided to a convolutional layer and pre-processing the folded feature data and an original convolution kernel. Li et al. (US Pub. No. 2019/0164045) teaches padding an unfolded feature data provided to a convolutional layer, folding the padded unfolded feature data, folding convolution kernel, and performing a convolution operation. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT KENNETH B LEE JR whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-3147 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Mon - Fri 9am-5pm . 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 William Boddie can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT 571-272-0666 . 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. /KENNETH B LEE JR/ Primary Examiner, Art Unit 2625