CTFR 17/977,587 CTFR 98441 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. Response to Arguments 07-37 AIA Applicant's arguments filed 29 October 2025 have been fully considered but they are not persuasive. Specification Applicant’s reply is incomplete due to omitting a response to the new matter objection. The objection is maintained. 35 USC 102 Applicant argues that Serra does not teach a binary matrix as described by the amended claims. However, these details are inherent to the pairwise ranking description of Serra paragraphs 74-82. Since Serra teaches a binary flag as an output of a binary comparison, and details a pairwise ranking process which is a well understood term of art, a binary matrix as claimed is inherent to this implementation. See the attached reference “Pairwise comparisons of a data vector” (Wicklin) for an example of this. Specification 07-28 AIA The amendment filed 12 February 2025 is objected to under 35 U.S.C. 132(a) because it introduces new matter into the disclosure. 35 U.S.C. 132(a) states that no amendment shall introduce new matter into the disclosure of the invention. The added material which is not supported by the original disclosure is as follows: "The captured images or sounds can include gestures 904 made by a user 902" (Paragraph [0100], fifth line). Although the new sentence describes the content of the drawing, the original application does not mention or suggest gestures at all. It is not apparent from the disclosure that "captured sounds" would include gestures, nor would one with ordinary skill in the art assume as such. Applicant claims that the addition is supported by the original application, but does not explain how or where . Applicant is required to cancel the new matter in the reply to this Office Action. 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, 3-5, 7-10, 12-14, 16-18 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Serra et al. (WO 2021259842 A1). (Note: See attached description for paragraph numbers) Regarding claim 1, Serra et al. discloses a method of training a classification model to classify a pleasantness of a sound emitted from a device, the method comprising: receiving a plurality of pleasantness ratings from one or more human jurors, each pleasantness rating corresponding to a respective one of a plurality of sounds emitted by one or more devices; (paragraph 58: obtaining audio quality ratings from human listeners) determining, via first pairwise comparisons, first differences between each of the plurality of pleasantness ratings and every other of the plurality of pleasantness ratings; (paragraphs 74-78: pairwise comparisons between sounds and their respective ratings) converting the determined first differences into binary classification values based on which pleasantness rating is higher for that pairwise comparison wherein each binary classification value represents outcomes of the first pairwise comparisons (paragraph 75, 82: setting a flag indicating which rating is higher as label information), and wherein each binary classification value is stored in a binary matrix that is used as an input training set for the classification model (Abstract: inputting the training set to the neural-network-based system) wherein for each ordered pair of sounds, a first binary value is stored at position (i, i) in a binary matrix indicating whether the pleasantness rating of sound i exceeds that of sound j, and a second binary value is stored at position (j, i) in the binary matrix indicating whether the pleasantness rating of sound j exceeds that of sound i, wherein the binary matrix is a two-dimensional n x n matrix containing 2(n2-n) binary values for all ordered pairs of n sounds minus a diagonal of each pleasantness rating compared to itself, and wherein for each pair (i, i), the binary values at (i, i) and (j, i) are complementary, (Paragraphs 74-82: Pairwise Ranking; The described matrix is a conventional data structure for pairwise ranking and is inherent to the description of pairwise ranking in the cited paragraphs) receiving, from one or more sensors, a plurality of measurable sound qualities, each measurable sound quality associated with a respective one of the plurality of sounds; (paragraphs 80, 116: audio samples are labeled with measurable sound quality metrics such as degradation type/strength) determining, via second pairwise comparisons, second differences between each of the plurality of measurable sound qualities and every other of the plurality of measured sound qualities in pairwise fashion; (paragraphs 74-78, 116-117: calculating second differences of pairs indicating measurable qualities) training a classification model to classify sound pleasantness by comparing the binary values with the second differences; (paragraphs 116-123: training a machine learning model to predict sound quality metric differences using the training data) (paragraphs 116-126: producing a trained model) based upon convergence during the step of training, outputting a trained classification model configured to classify sound pleasantness. Regarding claim 3, which is dependent from claim 1 addressed above, Serra discloses the method further comprising: receiving, from the one or more sensors, at least one measurable sound quality of an unrated sound that has not been rated by the one or more human jurors; (paragraph 126-127: using new input audio and the trained model to predict a new quality rating) via the trained classification model, comparing the at least one measurable sound quality of the unrated sound with each measurable sound quality associated with the respective plurality of sounds. (paragraph 128: generating relative quality metrics between the input sound and the reference samples) Regarding claim 4, which is dependent from claim 3 addressed above, Serra discloses the method further comprising: outputting, from the trained classification model, confidence ratings of the pleasantness of the unrated sound compared to each of the plurality of sounds. (paragraph 128: generating relative quality metrics between the input sound and the reference samples) Regarding claim 5, which is dependent from claim 4 addressed above, Serra discloses the method wherein the confidence ratings are on a scale between the two binary values. (paragraph 99: probability output) Regarding claim 7, which is dependent from claim 1 addressed above, Serra discloses the method wherein each of the first pairwise comparisons includes a comparison between a first pleasantness rating and a second pleasantness rating, and wherein the binary values associated with each of the first differences of each pairwise comparison include (a) a first binary value indicating the first pleasantness rating exceeds the second rating of that pairwise comparison, and (b) a second binary value indicating the second pleasantness rating exceeds the first pleasantness rating of that pairwise comparison. (paragraph 75: flag indicating which metric in the pair is higher) Regarding claim 8, which is dependent from claim 1 addressed above, Serra discloses the method wherein a number of the plurality of sounds rated by the human jurors is equal to n, and a number of the binary values is equal to n'-n. (paragraph 78: forming every pair; the number of all pairs is inherently equal to n'-n) Regarding claim 9, which is dependent from claim 1 addressed above, Serra discloses the method wherein the second differences are not converted into binary values. (paragraph 84: distance based metric for the measurable audio quality) Regarding claims 10, 12-14, 16-18, they are analogous to claims 1, 3-5, and 7-9 as addressed above, and are thus rejected in a similar fashion . Claim Rejections - 35 USC § 103 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. 07-21-aia AIA Claim (s) 2, 6, 11, 15, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Serra et al. in view of Tsunoda et al. (JP 2005037559 A). (Note: See attached description for paragraph numbers) Regarding claim 2, it is dependent on claim 1 addressed above. Serra does not disclose the method wherein the plurality of measurable sound qualities includes at least one of loudness, tonality, and sharpness. However, Tsunoda does disclose the method wherein the plurality of measurable sound qualities includes at least one of loudness, tonality, and sharpness. ([0014]/Means for Solving the Problem: A loudness value, a sharpness value, and a tonality value are obtained from an emitted sound) It would have been obvious to one with ordinary skill in the art before the effective filing date to collect loudness, sharpness, and tonality of a sound because those metrics can be used to help predict subjective quality metrics such as psychological discomfort (Tsunoda [0014]) Regarding claim 6, it is dependent on claim 4 addressed above. Serra does not disclose the method further comprising utilizing a regression model to predict an overall pleasantness of the unrated sound based on the confidence ratings output from the trained classification model. However, Tsunoda does disclose utilizing a regression model to predict an overall pleasantness of the unrated sound based on the confidence ratings output from the trained classification model. ([0045]: logistic regression analysis performed with respect to pairwise comparisons and psychoacoustic parameter differences to generate a discomfort rating) Though Serra discloses concepts relating to outputting a final ranking prediction based on previously computed scores, it does not appear to disclose that this is accomplished with a regression model. Tsunoda does disclose this concept and it would have been obvious to incorporate the method of Tsunoda with Serra because the pairwise predictions are suitable to further train a function that outputs a final ranking (Serra paragraphs 128-129). Regarding claims 11, 15, and 20, they are analogous to claims 2 and 6 as addressed above, and are thus rejected in a similar fashion. Conclusion 07-39 AIA THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALVIN ISKENDER whose telephone number is (703)756-4565. The examiner can normally be reached M-F. 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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. /ALVIN ISKENDER/ Examiner, Art Unit 2654 /HAI PHAN/ Supervisory Patent Examiner, Art Unit 2654 Application/Control Number: 17/977,587 Page 2 Art Unit: 2654 Application/Control Number: 17/977,587 Page 3 Art Unit: 2654 Application/Control Number: 17/977,587 Page 4 Art Unit: 2654 Application/Control Number: 17/977,587 Page 5 Art Unit: 2654 Application/Control Number: 17/977,587 Page 6 Art Unit: 2654 Application/Control Number: 17/977,587 Page 7 Art Unit: 2654 Application/Control Number: 17/977,587 Page 8 Art Unit: 2654
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