DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This non-final office action is in response to the application filed 22 February 2024.
Claims 1-20 are pending. Claims 1, 10, and 19 are independent claims.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 22 February 2024 and 15 January 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Drawings
The examiner accepts the drawings filed 22 February 2024.
Claim Rejections - 35 USC § 112
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.
Claims 9, 18, and 20 are 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.
The term “substantially zero” in claim 9 (line 5), claim 18 (line 4), and claim 20 (line 6) is a relative term which renders the claim indefinite. The term “substantially zero” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
According to Step 1 of the two Step analysis, claims 1-9 are directed toward a non-transitory machine-readable medium (manufacture). Claims 10-18 are directed toward a method (process). Claims 19 and 20 are directed toward a system (machine). Therefore, each of these claims falls within one of the four statutory categories.
Claim 1:
Step 2A, Prong 1:
The claim recites:
computing… a transform of the time-series data (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to define a transform of the data)
performing, with… a complex clipping algorithm, a complex clipping operation of the transform of time-series data to obtain a clipped data representation of the transform (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation, based on a clipping algorithm, to transform the time-series data into a clipped data representation)
classifying... using a classifier, the time-series data based on the clipped data representation (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a judgment to classify the clipped data representation)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional elements:
a non-transitory, machine-readable medium storing instructions, that when executed by one or more processors effectuate operations
a computer system
The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim recites the additional element:
obtaining… time series data
As discussed above, the additional elements of obtaining time series data is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim recites the additional element:
storing… a result of the classifying in memory
The courts have found that limitations regarding data storage and data display, when recited at a high level of generality, denote mere data storage and data output indicative of an insignificant extra-solution activity (see MPEP 2106.05(g)). Wherein “receiving or transmitting data over a network” or “storing and retrieving information in memory” are known to be well-understood, routine, and conventional activities when recited at a high level of generality (see MPEP 2106.05(d)(II)). In addition, specifying the composition of the provider records denotes a field of use (see MPEP 2106.05(h)). As such, these claim limitations do not amount to significantly more than the judicial exception.
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
a non-transitory, machine-readable medium storing instructions, that when executed by one or more processors effectuate operations
a computer system
The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim recites the additional element:
obtaining… time series data
As discussed above, the additional elements of obtaining time series data is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim recites the additional element:
storing… a result of the classifying in memory
The courts have found that limitations regarding data storage and data display, when recited at a high level of generality, denote mere data storage and data output indicative of an insignificant extra-solution activity (see MPEP 2106.05(g)). Wherein “receiving or transmitting data over a network” or “storing and retrieving information in memory” are known to be well-understood, routine, and conventional activities when recited at a high level of generality (see MPEP 2106.05(d)(II)). In addition, specifying the composition of the provider records denotes a field of use (see MPEP 2106.05(h)). As such, these claim limitations do not amount to significantly more than the judicial exception.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 2:
With respect to claim 2, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites the abstract idea identified with respect to claim 1.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
wherein the time series data includes audio data
As discussed above, the additional elements obtaining time-series data, wherein the time-series data includes audio data is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional element:
wherein the time-series data includes audio data
As discussed above, the additional elements obtaining time-series data, wherein the time-series data includes audio data is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 3:
With respect to claim 3, the claim depends upon claim 2. The analysis of claim 2 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites the abstract idea identified with respect to claim 2.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
wherein the audio data includes cough, speech, or breathing audio data
As discussed above, the additional elements obtaining time-series data, wherein the time-series data includes audio data including cough, speech, or breathing audio data is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional element:
wherein the audio data includes cough, speech, or breathing audio data
As discussed above, the additional elements obtaining time-series data, wherein the time-series data includes audio data including cough, speech, or breathing audio data is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 4:
With respect to claim 4, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites the abstract idea identified with respect to claim 1.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
wherein the classifier includes a machine learning algorithm
The machine learning algorithm is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional element:
wherein the classifier includes a machine learning algorithm
The machine learning algorithm is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 5:
With respect to claim 5, the claim depends upon claim 4. The analysis of claim 4 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites the abstract idea identified with respect to claim 4.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
wherein the machine learning algorithm is trained using a training set of clipped data representations
The machine learning algorithm is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional element:
wherein the machine learning algorithm is trained using a training set of clipped data representations
The machine learning algorithm is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 6:
With respect to claim 6, the claim depends upon claim 4. The analysis of claim 4 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites the abstract idea identified with respect to claim 4.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
wherein the machine learning algorithm is trained using one or more graphics processing units, the complex clipping algorithm is executed using one or more central processing units, and the machine learning algorithm is executed using one or more tensor processing unis
The recitation of a graphics processing unit, a central processing unit, and a tensor processing unit, are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional element:
wherein the machine learning algorithm is trained using one or more graphics processing units, the complex clipping algorithm is executed using one or more central processing units, and the machine learning algorithm is executed using one or more tensor processing unis
The recitation of a graphics processing unit, a central processing unit, and a tensor processing unit, are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 7:
With respect to claim 7, the claim depends upon claim 4. The analysis of claim 4 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites the abstract idea identified with respect to claim 4.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
wherein the machine learning algorithm includes a deep neural network
The machine learning algorithm is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional element:
wherein the machine learning algorithm includes a deep neural network
The machine learning algorithm is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 8:
With respect to claim 8, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
wherein the transform is a short-time Fourier transform (STFT) having an imaginary part and a real part (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to transform the data using a short-time Fourier transform)
Step 2A, Prong 2:
The claim does not recite any additional elements considered under Step 2A, Prong 2.
Step 2B:
The claim does not recite any additional elements considered under Step 2B.
Claim 9:
With respect to claim 9, the claim depends upon claim 8. The analysis of claim 8 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
retaining two-dimensional spectrogram points when a magnitude of the imaginary part of the STFT is less than a magnitude of the real part of the STFT (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to determine whether the magnitude of the imaginary part is less than a magnitude of the real part and retaining the points)
setting all other two-dimensional spectrogram points to substantially zero (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation regarding the magnitude of points and setting all other points to substantially zero)
Step 2A, Prong 2:
The claim does not recite any additional elements considered under Step 2A, Prong 2.
Step 2B:
The claim does not recite any additional elements considered under Step 2B.
Claims 10-18:
With respect to claims 10-18, the claims recite the limitations substantially similar to those in claims 1-9, respectively. Claims 10-18 are rejected under similar rationale.
Claims 19-20:
With respect to claims 19-20, the claims recite the limitations substantially similar to those in claims 1 and 9 (including intervening claim 8), respectively. Claims 19-20 are rejected under similar rationale.
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.
Claims 1-5, 7, 10-14, 16, and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Khanzada (US 2022/0037022, published 3 February 2022; provided on IDS filed 22 February 2024).
As per independent claim 1, Khanzada discloses a non-transitory, machine readable medium storing instructions that, when executed by one or more processors, effectuate operations (paragraph 0006) comprising:
obtaining, with a computer system, time-series data (Figure 2, item 110; paragraphs 0014, 0025, 0033, and 0041: Here, voice sounds and image data is obtained using a smartphone. Temporal data analysis is performed on this obtained data, including processing the data as time-series data)
computing, with a computer system, a transform of the time-series data (paragraphs 0016 and 0033: Here, a preprocessing is performed on the raw data inputs including filtering and feature extraction before inferring a classification)
performing, with the computer system and a complex clipping algorithm, a complex clipping operation of the transform of the time-series data to obtain a clipped data representation of the transform (Figure 2, items 106 and 112; paragraphs 0019, 0030, and 0037: Here, the data is partitioned into a plurality of different groups, such as training, validation, and testing datasets. Additionally, the data bay be clipped into a plurality of different channels such as cough, deep breathing, facial video, fingertip video, and biometric images for analysis)
classifying, with the computer system and using a classifier, the time-series data based on the clipped data representation (paragraph 0016: Here, a machine learning model is trained and a classifier is used to classify the clipped time series data)
storing, with the computer system, a result of the classifying in memory (paragraph 0066: Here, the results of the classification bay be stored in the patient’s file or electronic medical record)
As per dependent claim 2, Khanzada disclose wherein the time-series data includes audio data (Figure 2, item 110; paragraphs 0014, 0025, 0033, and 0041: Here, voice sounds (audio data) is obtained using a smartphone. Temporal data analysis is performed on this obtained data, including processing the data as time-series data).
As per dependent claim 3, Khanzada discloses wherein the audio data includes cough, speech, or breathing audio data (paragraphs 0019 and 0070: Here, the audio data may include cough, deep breathing, and speech data).
As per dependent claim 4, Khanzada disclose wherein the classifier includes a machine learning algorithm (paragraphs 0019 and 0022: Here, a trained model for analyzing time-series data may include a machine learning model).
As per dependent claim 5, Khanzada disclose wherein the machine learning algorithm is trained using a training set of clipped data representations (Figure 1, item 108; paragraph 0016: Here, a machine learning model is trained on a training data set).
As per dependent claim 7, Khanzada discloses wherein the machine learning algorithm includes a deep neural network (paragraph 0016: Here, the model may include a deep neural network).
With respect to claims 10-14 and 16, the claims recite the limitation substantially similar to those in claims 1-5 and 7, respectively. Claims 10-14 and 16 are similarly rejected.
With respect to claim 19, the claim recites the limitations substantially similar to those in claim 1. Claim 19 is similarly rejected.
Claim Rejections - 35 USC § 103
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 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.
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.
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Khanzada and further in view of Kadechkar et al. (US 2024/0051390, filed 12 August 2022, hereafter Kadechkar).
As per dependent claim 6, Khanzada discloses the limitations similar to those in claim 4, and the same rejection is incorporated herein. Khanzada discloses wherein the machine learning algorithm is trained using a graphics processing units (paragraph 0077) and the complex clipping algorithm is executed using one or more central processing units (paragraph 0077).
Khanzada fails to specifically disclose wherein the machine learning algorithm is executed using one or more tensor processing units.
However, Kadechkar, which is analogous to the claimed invention because it is directed toward processing audio data, discloses wherein the machine learning algorithm is executed using one or more tensor processing units (paragraph 0043: Here, a tensor processing unit implements the machine learning algorithm).
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Kadechkar with Khanzada, with a reasonable expectation of success, as it would have allowed for implementing the machine learning algorithm in hardware (Kadechkar: paragraph 0043).
With respect to claim 15, the claim recites the limitations substantially similar to those in claim 6. Claim 15 is similarly rejected.
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Khanzada and further in view of Wakeland et al. (US 2025/0191600, filed 23 December 2022).
As per dependent claim 8, Khanzada discloses the limitation similar to those in claim 1, and the same rejection is incorporated herein. Khanzada fails to specifically disclose wherein the transform is a short-time Fourier transform (STFT) having an imaginary part and a real part.
However, Wakeland, which is analogous to the claimed invention because it is directed toward processing audio data, discloses wherein the transform is a short-time Fourier transform (STFT) having an imaginary part and a real part (paragraph 0062: Here, a STFT based noise reduction is performed by processing both the real and imaginary data). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Wakeland with Khanzada, with a reasonable expectation of success, as it would have allowed for performing noise reduction in order to improve the sound signal (Wakeland: paragraph 0062).
With respect to claim 17, the claim recites the limitations substantially similar to those in claim 8. Claim 17 is similarly rejected.
Claims 9, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Khanzada and Wakeland and further in view of Wu et al. (US 2023/0160997, filed 23 November 2021, hereafter Wu).
As per dependent claim 9, Khanzada and Wakeland disclose the limitations similar to those in claim 8, and the same rejection is incorporated herein. Wakeland discloses retaining two-dimensional spectrogram points when a magnitude of the imaginary part of the STFT is less than a magnitude of the real part of the STFT (paragraph 0062).
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Wakeland with Khanzada, with a reasonable expectation of success, as it would have allowed for performing noise reduction in order to improve the sound signal (Wakeland: paragraph 0062).
Khanzada fails to specifically disclose setting all other two-dimensional points to substantially zero. However, Wu, which is analogous to the claimed invention because it is directed toward isolating data, discloses setting all other two-dimensional points to substantially zero (paragraph 0047: Here, spectrogram cells with amplitudes are set to zero based on comparing the amplitudes to a threshold). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Wu with Khanzada-Wakeland, with a reasonable expectation of success, as it would have allowed for isolating contents for analysis (Wu: paragraph 0047).
With respect to claim 18, the claim recites the limitations substantially similar to those in claim 9. Claim 18 is similarly rejected.
With respect to claim 20, the claim recites the limitations substantially similar to those in claim 9, including intervening claim 8. Claim 20 is similarly rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Shankar et al. (US 12531046): Discloses short-time Fourier Transforms for noise reduction and echo suppression (Abstract; column 9, line 63- column 10, line 9
Liang et al. (US 2022/0180207): Discloses using machine learning for time series prediction (Abstract)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE R STORK whose telephone number is (571)272-4130. The examiner can normally be reached 8am - 2pm; 4pm - 6pm.
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, Omar Fernandez Rivas can be reached at 571/272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KYLE R STORK/Primary Examiner, Art Unit 2128
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