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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06 May 2025 has been entered.
Response to Arguments
Applicant's arguments filed 06 May 2025 have been fully considered but they are not persuasive.
35 USC 101
Applicant argues that steps such as transforming subjective human ratings into a structured binary matrix is not an abstraction or mental process. However, the organizing of data in this manner is well understood to be a human-performable task; writing the result of a comparison down onto a spreadsheet or table is essentially the same activity.
Though transforming training data could be a practical application under Step 2A Prong Two, the claimed incorporation of a well-understood, fundamental way of arranging data does not necessarily constitute one.
Specification
Applicant argues that one of ordinary skill in the art would understand that gestures would fall within the scope of “captured images or sounds”. Gestures are body movements that carry some sort of meaning. That captured images could encompass this scope is not unreasonable. However, it is not apparent how captured sounds would include gestures to one with ordinary skill in the art, and the rest of the disclosure doesn’t clarify this.
35 USC 102
Applicant argues that Serra does not teach claimed binary classification values from pairwise comparisons used as an input training set for the classification model. However, the cited flag used to read on this limitation is label information that is part of the training data. In paragraph 116, the method receives “as input, at least one training set comprising audio samples”; “each of the second type of audio samples is labelled with information indicative of a respective audio quality metric relative to that of a reference audio sample (e.g., relative to that of another audio sample in the training set)”. Paragraph 75 also refers to this flag as label information. That the values are stored in a matrix is inherent to “inputting the training set to the neural-network-based system” (Serra Abstract), as this training process involves matrix multiplication of the data.
Specification
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 .
Applicant is required to cancel the new matter in the reply to this Office Action.
Claim Rejections - 35 USC § 101
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 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s):
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; (mental process: collecting and compiling a dataset)
determining, via first pairwise comparisons, first differences between each of the plurality of pleasantness ratings and every other of the plurality of pleasantness ratings; (mental process/mathematical computation: subtraction)
converting the determined first differences into binary values based on which pleasantness rating is higher for that pairwise comparison, wherein each binary classification value represents outcomes of the first pairwise comparisons, and wherein each binary classification value is stored in a binary matrix that is used as an input training set for the classification model; (mental process: human judgement or a simple heuristic can be used to convert the differences into binary values; binary matrices or tables are a known method of organizing such data)
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; (mental process: collecting and compiling data)
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; (mental process/mathematical computation: subtraction, comparison)
training a classification model to classify sound pleasantness by comparing the binary values with the second differences; (mental process: comparison)
based upon convergence during the step of training, outputting a trained classification model configured to classify sound pleasantness. (mental process/mathematical computation: iterative computations)
wherein the plurality of measurable sound qualities includes at least one of loudness, tonality, and sharpness. (narrowing the human-interpretable data)
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; (mental process: collecting data)
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. (mental process/mathematical computation: comparison, mathematical evaluation)
outputting, from the trained classification model, confidence ratings of the pleasantness of the unrated sound compared to each of the plurality of sounds. (mental process/mathematical computation: mathematical evaluation, human judgement/analysis)
wherein the confidence ratings are on a scale between the two binary values. (narrowing the human-interpretable data)
utilizing a regression model to predict an overall pleasantness of the unrated sound based on the confidence ratings output from the trained classification model. (mental process/mathematical computation: evaluation of a statistical formula, human judgement/prediction)
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. (narrowing human-interpretable data)
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. (mathematical concept: n2-n is the inherent number of possible ordered pairs in a set)
wherein the second differences are not converted into binary values. (narrowing human-interpretable data)
This judicial exception is not integrated into a practical application because additional elements such as the receipt of data from sensors constitute data gathering steps that do not add a meaningful limitation to the method as they are an insignificant extra-solution activity. Additional elements such as outputting data or performing the method with a processor and a memory simply amount to implementing the abstract idea on a computer. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because outputting data and retrieving data from sensors and memory are well-understood, conventional computer functions recited at a high level generality. Processors and memory are generic computing components that .
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.
Claim(s) 1, 3-5, 7-10, 12-14, 16-19 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)
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, 16-19, 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
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.
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
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/ALVIN ISKENDER/ Examiner, Art Unit 2654
/HAI PHAN/ Supervisory Patent Examiner, Art Unit 2654