CTNF 18/669,079 CTNF 100286 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. Specification 07-29 AIA The disclosure is objected to because of the following informalities: Paragraph 36, “datastream” should be “data stream” . Appropriate correction is required. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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. 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. 07-15 AIA Claim s 1-2, 4-6, 8-9, 11-12, 14-16, and 18-19 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Zuo et al. (CN112598635B), hereinafter referred to as Zuo . In regards to claim 1, Zuo discloses a method of decoding point cloud data for a point cloud, comprising: decoding a signal indicative of a number of sub-blocks, N1 (Abstract and paragraph 3 of page 5, Abstract discloses both encoding and decoding with paragraph 3 of page 5 disclosing the use of sub-blocks); configuring a neural network to have N1 sub-blocks, wherein each sub-block of the N1 sub-blocks includes an upsampling function and at least a neural network layer (Paragraph 3 of page 5, Discloses that the upsampling is in the sub-blocks with convolution layers that would read upon the blocks being in a neural network), wherein each of the N1 sub-blocks is configured with same neural network parameters; and decoding the point cloud data based on the neural network (Fourth paragraph of page 10 and first paragraph of page 11, Both of these paragraphs explicitly recite that a neural network is used). In regards to claim 2, Zuo discloses wherein the point cloud data uses a voxel-based representation (Fourth paragraph of page 10, Explicitly discloses that it utilizes voxel based methods). In regards to claim 4, Zuo discloses further comprising: decoding another signal indicative of another number of sub-blocks, N2 (Abstract and paragraph 3 of page 5, Abstract discloses both encoding and decoding with paragraph 3 of page 5); and configuring the neural network to have N2 sub-blocks, wherein each sub-block of the N2 sub-blocks includes the upsampling function and the at least a neural network layer (Paragraph 3 of page 5, Discloses that the upsampling is in the sub-blocks with convolution layers that would read upon the blocks being in a neural network), and wherein each of the N2 sub-blocks is configured with the same neural network parameters as for the N1 sub-blocks (Fourth paragraph of page 10 and first paragraph of page 11, Both if these paragraphs explicitly recite that a neural network is used). In regards to claim 5, Zuo discloses a method of encoding point cloud data for a point cloud, comprising: encoding a signal indicative of a number of sub-blocks, N1 (Abstract and paragraphs 10-13 of page 13, The abstract discloses the point cloud and the encoding while the paragraphs 10-13 of page 13 detail the structure of the encoder using subblocks); configuring a neural network to have N1 sub-blocks, wherein each sub-block of the N1 sub-blocks includes a downsampling function and at least a neural network layer (paragraphs 10-13 of page 13, Discloses that the downsampling is in the sub-blocks with convolution layers that would read upon the blocks being in a neural network), wherein each of the N1 sub-blocks is configured with same neural network parameters; and encoding the point cloud data based on the neural network (Fourth paragraph of page 10 and first paragraph of page 11, Both if these paragraphs explicitly recite that a neural network is used). In regards to claim 6, Zuo discloses wherein the point cloud data uses a voxel-based representation (Fourth paragraph of page 10, Explicitly discloses that it utilizes voxel based methods). In regards to claim 8, Zuo discloses further comprising: encoding another signal indicative of another number of sub-blocks, N2 (Abstract and paragraphs 10-13 of page 13, The abstract discloses the point cloud and the encoding while the paragraphs 10-13 of page 13 detail the structure of the encoder using subblocks); and configuring the neural network to have N2 sub-blocks, wherein each sub-block of the N2 sub-blocks includes the downsampling function and the at least a neural network layer (paragraphs 10-13 of page 13, Discloses that the downsampling is in the sub-blocks with convolution layers that would read upon the blocks being in a neural network), and wherein each of the N2 sub-blocks is configured with the same neural network parameters as for the N1 sub-blocks (Fourth paragraph of page 10 and first paragraph of page 11, Both if these paragraphs explicitly recite that a neural network is used). In regards to claim 9, Zuo discloses determining that the neural network is to be adjusted based on at least one of a bitrate, bit depth, and sparsity of content (Paragraph 1 of page 11, Discloses that density is calculated and used to adjust the neural network which would read upon sparsity of content). In regards to claim 11, it is similar to claim 1, and it is similarly rejected. In regards to claim 12, it is similar to claim 2, and it is similarly rejected. In regards to claim 14, it is similar to claim 4, and it is similarly rejected. In regards to claim 15, it is similar to claim 5, and it is similarly rejected. In regards to claim 16, it is similar to claim 6, and it is similarly rejected. In regards to claim 18, it is similar to claim 8, and it is similarly rejected. In regards to claim 19, it is similar to claim 9, and it is similarly rejected . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim s 3 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Zuo et al. (CN112598635B), hereinafter referred to as Zuo, in view of Besson et al. (US 20220122250 A1), hereinafter referred to as Besson . In regards to claim 3, Zuo discloses wherein the at least a neural network layer includes a convolution layer (Paragraph 3 of page 5, This mentions that a convolution layer is used). However, Zuo does not explicitly disclose an activation function. Besson does disclose an activation function (Paragraph 24, Besson discloses that an activation layer can be used in the sub-blocks particularly a ReLu activation function). It would be prima facie obvious to combine the teachings of these two arts as the inclusion of an activation function would lead to a predictable increase in accuracy. Activation functions introduce non-linearity to functions which would allow them to more easily process complex data patterns and more accurately reflect any predicted patterns in the neural network. As such, it would lead to a predictable increase in accuracy. As such, it would be prima facie obvious to combine these arts. In regards to claim 7, it is similar to claim 3, and it is rejected similarly . Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim s 10 and 20 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 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Ahmed et al. (“A Meta-Heuristic Automatic CNN Architecture Design Approach Based on Ensemble Learning”) is pertinent prior art as it is the closest piece of prior art to the aspects recited by the claims 10 and 20. However, it was deemed insufficient to cover those claims as it is directed to a different function of chromosome and gene mapping rather than to a process of encoding or decoding. Any inquiry concerning this communication or earlier communications from the examiner should be directed to CONOR AIDAN O'MALLEY whose telephone number is (571)272-0226. The examiner can normally be reached Monday - Friday 9:00 am. - 5:00 pm. EST. 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, Andrew Moyer can be reached at 5722729523. 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. CONOR AIDAN. O'MALLEY Examiner Art Unit 2675 /CONOR A O'MALLEY/ Examiner, Art Unit 2675 /ANDREW M MOYER/ Supervisory Patent Examiner, Art Unit 2675 Application/Control Number: 18/669,079 Page 2 Art Unit: 2675 Application/Control Number: 18/669,079 Page 3 Art Unit: 2675 Application/Control Number: 18/669,079 Page 4 Art Unit: 2675 Application/Control Number: 18/669,079 Page 5 Art Unit: 2675 Application/Control Number: 18/669,079 Page 6 Art Unit: 2675 Application/Control Number: 18/669,079 Page 7 Art Unit: 2675 Application/Control Number: 18/669,079 Page 8 Art Unit: 2675
