CTNF 18/509,119 CTNF 98827 DETAILED ACTION This Action is responsive to Claims filed 11/14/2023. 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. Priority 02-26 AIA Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Information Disclosure Statement 06-52 The information disclosure statement (IDS) submitted on 11/14/2023 was filed before the mailing date of the first Action on the merits. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Drawings Receipt of Drawings filed 11/14/2023 is acknowledged. These Drawings are acceptable. Status of the Claims Claims 1-14 are currently pending. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claims 1-12 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 1 and 2 seem contradictory because the input data described in Claim 2 seems to be the output of the claimed steps of Claim 1. If the Applicant intends this to indicate some kind of iterative or repeated process, as taught by the prior art and rejected below, the Examiner submits the wording of the claims does not adequately convey such. Further specificity and/or details surrounding the generically recited steps would improve clarity. Claims 3-6 share several similarities and key differences, but the generic verbiage used in the limitations creates ambiguity as to the type of data being manipulated. It is unclear whether there is one input dataset, multiple input datasets, or a divided input dataset. Condensation of these claims or further specificity and/or details surrounding the generically recited steps would improve clarity. Claims 9 and 12 seem contradictory as the input data according to Claim 9 is transformed into high dimensional data, while claim 12 indicates the input data is transformed in lower dimensional data. Further specificity and/or details surrounding the generically recited steps would improve clarity. The claims dependent on claim 1 do not rectify the ambiguity of these claims. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-14 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more; and because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al, 573 U.S. (2014). In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines. (2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.) Step 1: Claims 1-12 recite an information processing apparatus, which falls under the statutory category of a machine. Claim 13 recites an information processing method, which falls under the statutory category of a process. Claim 14 recites a non-transitory computer-readable storing a program, which falls under the statutory category of a manufacture. Step 2A – Prong 1: Claim 1 recites an abstract idea, law of nature, or natural phenomenon. The limitations of “ generate an attention map from input data; ”, “ perform a nonlinear transformation on the input data; ”, and “ obtain, based on the generated attention map and an output obtained based on the nonlinear transformation on the input data, a feature amount map having a channel dimension for storing an element vector and one or more spatial dimensions; ” under the broadest reasonable interpretation, cover a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. Generating a generic attention map on generic input data is practically performed within the human mind or with the aid of pen and paper. Performing a generic nonlinear transformation on generic input data is a series of algorithmic steps practically performed within the human mind or with the aid of pen and paper. Obtaining a generic feature amount map from based on the attention map and output of the nonlinear transformation is practically performed within the human mind or with the aid of pen and paper. Step 2A – Prong 2: The additional elements of claim 1 do not integrate the abstract idea into a judicial exception. The claim recites the additional elements recited in “ An information processing apparatus…the information processing apparatus comprising: one or more processors; and one or more memories that store a computer-readable instruction that, when executed by the one or more processors, configures the information processing apparatus to… ” are recognized as generic computer components recited at a high level of generality. Although they have and execute instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application). The additional elements of “ a neural network ” is recognized as a non-generic computer component, but is recited at a high level of generality and is found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)). The additional elements recited in the limitations “… performing inference or learning using a neural network …” and “ perform an inference or learning process based on the obtained feature amount map. ” are found to be mere instructions to apply the abstract ideas representing the observation and manipulation of the data/attention map (see MPEP 2106.05(f) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application). (The Examiner notes the “ perform …” limitation is recited highly generally, and at very broadest, could also be construed as an abstract idea mental process step, but is being interpreted as instructions to apply the generic neural network to perform an inference, in the context of the claim). Step 2B: The only limitation on the performance of the described method is a limitation reciting “ An information processing apparatus…the information processing apparatus comprising: one or more processors; and one or more memories that store a computer-readable instruction that, when executed by the one or more processors, configures the information processing apparatus to… ” These elements are insufficient to transform a judicial exception to a patentable invention because the recited elements are considered insignificant extra-solution activity (generic computer system, processing resources, links the judicial exception to a particular, respective, technological environment). The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components; mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (see MPEP 2106.05(f)). The additional elements of “ a neural network ” is recognized as a non-generic computer component, but is recited at a high level of generality and is found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)). The additional elements recited in the limitations “… performing inference or learning using a neural network …” and “ perform an inference or learning process based on the obtained feature amount map. ” are found to be mere instructions to apply the abstract ideas representing the observation and manipulation of the data/attention map (see MPEP 2106.05(f) indicating mere instructions to apply an abstract idea does not recite significantly more). (The Examiner notes the “ perform …” limitation is recited highly generally, and at very broadest, could also be construed as an abstract idea mental process step, but is being interpreted as instructions to apply the generic neural network to perform an inference, in the context of the claim). Taken alone or in ordered combination, these additional elements do not amount to significantly more than the above-identified abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. For the reasons above, claim 17 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to independent claims 13 and 14. Claim 13 recites similar limitations to Claim 1, with the exception of “ An information processing method executed by an information processing apparatus performing inference or learning using a neural network, the method comprising: ” (generic computer components, or components generally linking). Claim 14 recites similar limitations to Claim 1, with the exception of “ A non-transitory computer-readable storage medium storing a program for causing a computer of an information processing apparatus performing inference or learning using a neural network to execute a method, comprising: ” (generic computer components, or components generally linking). Dependent Claims: Claim 2 recites refinements to the input data. Claim 3 recites abstract idea mental process steps “ wherein the input data is transformed to third data by nonlinear transformation ” (series of algorithmic steps), and “ calculating, for each spatial position of the third data, an element vector of a spatial position in a surrounding area of the third data based on weighting determined based on the attention map. ” (series of algorithmic steps). Claim 4 recites abstract idea mental process steps “ wherein the input data is converted into third data by nonlinear transformation ” (series of algorithmic steps), and “ calculating, for each spatial position of the third data, an element vector of a spatial position in a surrounding area of the third data based on weighting determined based on the attention map. ” (series of algorithmic steps). Claim 5 recites abstract idea mental process steps “ wherein the input data is transformed to third data by nonlinear transformation ” (series of algorithmic steps), and “ calculating, for each spatial position of the third data, an element vector of a spatial position in a surrounding area of the third data based on weighting determined based on the attention map. ” (series of algorithmic steps). Claim 6 recites refinements to the data. Claim 7 recites abstract idea mental process steps “ calculating a weight in accordance with one or more dimensions from the input data, and wherein the feature amount map is calculated by a product of elements of the attention map and a result of the nonlinear transformation. ” (series of algorithmic steps). Claim 8 recites an abstract idea mental process step “ wherein the input data and the obtained feature amount map are added to obtain output data, ” and instruction to apply step “ the inference or learning process is performed based on the output data ” Claim 9 recites an abstract idea mental process step “ wherein the input data is transformed into high-dimensional data. ” Claim 10 recites an abstract idea mental process step “ wherein the input data is converted for each element vector. ” Claim 11 recites abstract idea mental process steps “ wherein the output data is obtained by applying attention to an output obtained based on the nonlinear transformation and then transforming the output to a same dimension as the input data. ” Claim 12 recites abstract idea mental process steps “ wherein the input data is transformed to a lower dimension, and wherein the attention map is generated from the low-dimensional input data. ” 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. 07-15-aia AIA Claim(s) 1-14 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Woo et al. ( CBAM: Convolutional Block Attention Module , 2018), hereinafter Woo . Examiner’s Note : Given the ambiguity and generic nature of the claim limitations (See the 112(b) Rejection above), the Examiner has interpreted the claim limitations very broadly in light of this cited reference. Further specificity and/or details surrounding the generically recited steps would improve clarity and further distinguish from the cited reference. In regards to Claim 1: The present invention claims: “ An information processing apparatus performing inference or learning using a neural network, the information processing apparatus comprising: one or more processors; and one or more memories that store a computer-readable instruction that, when executed by the one or more processors, configures the information processing apparatus to: ” Woo teaches “We propose Convolutional Block Attention Module (CBAM), a simple yet effective attention module for feed-forward convolutional neural networks.” (Abstract, mapping to a neural network , see also Fig. 3, Page 7 for Woo’s block within a neural network). Woo also conducts experiments in Section 4, which the Examiner submits sufficiently reads on the generic hardware components of the limitation. “ generate an attention map from input data; ” Woo teaches “Given an intermediate feature map, our module sequentially infers attention maps along two separate dimensions, channel and spatial,” (Abstract). See also Fig. 1, Page 3, for the attention maps being formed from an input data. “ perform a nonlinear transformation on the input data; ” Woo teaches using ReLU (a nonlinear activation function (Page 5, Fig. 2, and Equation 2), within the attention block. “ obtain, based on the generated attention map and an output obtained based on the nonlinear transformation on the input data, a feature amount map having a channel dimension for storing an element vector and one or more spatial dimensions; ” See Woo Fig. 2 and Pages 5-6 for the generation of channel and spatial (2D, Page 6, mapping to one or more dimensions ) attention maps. A refined feature map results from the attention maps generation (Fig. 1). “ and perform an inference or learning process based on the obtained feature amount map. ” Woo utilizes their system to make image classifications and the like during their experiments (Pages 9-10, particularly for ImageNet, Fig. 4, Page 12 as well). In regards to Claim 2: The present invention claims: “ wherein the input data is a feature amount map having a channel dimension for storing an element vector and one or more spatial dimensions. ” Woo’s Fig. 1 and Fig. 2 as well as Section 3 (Page 4) teach how the attention block’s input data is an intermediate feature map. In regards to Claim 3: The present invention claims: “ wherein the attention map indicates a relationship between spatial dimensions of first data and second data obtained from the input data, wherein the input data is transformed to third data by nonlinear transformation, and wherein the feature amount map is generated by calculating, for each spatial position of the third data, an element vector of a spatial position in a surrounding area of the third data based on weighting determined based on the attention map. ” See above where Woo teaches calculating a channel and spatial attention map, utilizing a ReLU activation function, and including the use of element-wise summation of feature vectors and MLP weights (Page 5). In regards to Claim 4: The present invention claims: “ wherein the attention map indicates a relationship between spatial dimensions of first data obtained from learning parameters and second data obtained from the input data, wherein the input data is converted into third data by nonlinear transformation, and wherein the feature amount map is generated by calculating, for each spatial position of the third data, an element vector of a spatial position in a surrounding area of the third data based on weighting determined based on the attention map. ” See the Rejection of Claim 3. The Examiner is unclear what “ between spatial dimensions of first data obtained from learning parameters and second data obtained from the input data ” indicates, however, the Examiner reiterates Woo’s method operates on feature data from each convolutional block of the model (Fig. 3, for example), and that data operated on in such a way would reasonably be “obtained” from learning parameters between convolutional blocks. Further specificity and/or details surrounding the generically recited steps would improve clarity and further distinguish from the cited reference. In regards to Claim 5: The present invention claims: “ wherein the attention map indicates a relationship between spatial dimensions of the input data, wherein the input data is transformed to third data by nonlinear transformation, and wherein the feature amount map is generated by calculating, for each spatial position of the third data, an element vector of a spatial position in a surrounding area of the third data based on weighting determined based on the attention map. ” See the Rejection of Claim 3. The Examiner is unclear how Claim 5, which operates on input data, is distinct from Claim 3, which operates on first and second data “obtained” from input data. In either case, Woo teaches their attention block may be run parallel or in sequence (Page 6), which the Examiner submits broadly reads on operating on input data by itself (in sequence), or first and second data “obtained” from the input data (in parallel). Further specificity and/or details surrounding the generically recited steps would improve clarity and further distinguish from the cited reference. In regards to Claim 6: The present invention claims: “ wherein data used to generate the attention map is acquired based on data obtained by dividing the input data in a channel dimension direction, and wherein the third data is acquired based on data obtained by dividing the input data in the channel dimension direction. ” See above where Woo teaches “Given an intermediate feature map, our module sequentially infers attention maps along two separate dimensions, channel and spatial, then the attention maps are multiplied to the input feature map for adaptive feature refinement.” (Abstract) and “we adopt our module to emphasize meaningful features along those two principal dimensions: channel and spatial axes. To achieve this, we sequentially apply channel and spatial attention modules (as shown in Fig. 1), so that each of the branches can learn ‘what’ and ‘where’ to attend in the channel and spatial axes respectively.” (Page 2). In regards to Claim 7: The present invention claims: “ wherein the attention map is generated by calculating a weight in accordance with one or more dimensions from the input data, and wherein the feature amount map is calculated by a product of elements of the attention map and a result of the nonlinear transformation. ” See above how Woo’s attention map(s) are calculated using a ReLU (nonlinear) activation function and the weights of the MLP (Equation 2), before outputting a refined feature map (Fig. 1). In regards to Claim 8: The present invention claims: “ wherein the input data and the obtained feature amount map are added to obtain output data, and wherein the inference or learning process is performed based on the output data. ” See above how Woo conducts experiments using their attention blocks in order to arrive at image classification inferences. See also Section 3 (Page 4), for the attention maps being combined for output. In regards to Claim 9: The present invention claims: “ wherein the input data is transformed into high-dimensional data. ” Based on the Applicant’s specification ([0039], [0043], at least), the Examiner submits Woo’s use of the ReLU activation function within the attention block sufficiently reads on the generic limitation of this claim. In regards to Claim 10: The present invention claims: “ wherein the input data is converted for each element vector. ” Woo teaches “After the shared network is applied to each descriptor, we merge the output feature vectors using element-wise summation.” (Page 5). In regards to Claim 11: The present invention claims: “ wherein the output data is obtained by applying attention to an output obtained based on the nonlinear transformation and then transforming the output to a same dimension as the input data. ” See above how Woo arrives at an attention map after applying ReLU activation with their attention block (Page 5) and “Given an intermediate feature map, our module sequentially infers attention maps along two separate dimensions, channel and spatial, then the attention maps are multiplied to the input feature map for adaptive feature refinement.” (Abstract) In regards to Claim 12: The present invention claims: “ wherein the input data is transformed to a lower dimension, and wherein the attention map is generated from the low-dimensional input data. ” See Woo Fig. 2, for how the input data is Max/AvgPool-ed (lower dimensional, and/or convolved, in order to arrive at the attention map(s). In regards to Claim 13: Claim 13 recites similar limitations to Claim 1, with the exception of “ An information processing method executed by an information processing apparatus performing inference or learning using a neural network, the method comprising: ” therefore; both claims are similarly rejected. In regards to Claim 14: Claim 14 recites similar limitations to Claim 1, with the exception of “ A non-transitory computer-readable storage medium storing a program for causing a computer of an information processing apparatus performing inference or learning using a neural network to execute a method, comprising: ” therefore; both claims are similarly rejected. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRIFFIN T BEAN whose telephone number is (703)756-1473. The examiner can normally be reached M - F 7:30 - 4:30. 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, Li Zhen can be reached at (571) 272-3768. 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. /GRIFFIN TANNER BEAN/ Examiner, Art Unit 2121 /Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121 Application/Control Number: 18/509,119 Page 2 Art Unit: 2121 Application/Control Number: 18/509,119 Page 3 Art Unit: 2121 Application/Control Number: 18/509,119 Page 4 Art Unit: 2121 Application/Control Number: 18/509,119 Page 5 Art Unit: 2121 Application/Control Number: 18/509,119 Page 6 Art Unit: 2121 Application/Control Number: 18/509,119 Page 7 Art Unit: 2121 Application/Control Number: 18/509,119 Page 8 Art Unit: 2121 Application/Control Number: 18/509,119 Page 9 Art Unit: 2121 Application/Control Number: 18/509,119 Page 10 Art Unit: 2121 Application/Control Number: 18/509,119 Page 11 Art Unit: 2121 Application/Control Number: 18/509,119 Page 12 Art Unit: 2121 Application/Control Number: 18/509,119 Page 13 Art Unit: 2121 Application/Control Number: 18/509,119 Page 14 Art Unit: 2121 Application/Control Number: 18/509,119 Page 15 Art Unit: 2121 Application/Control Number: 18/509,119 Page 16 Art Unit: 2121
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