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 .
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.
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.
Claim(s) 1-11, 13, 16, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ohashi (US 5,949,890) in view of Cha et al. (US 2023/0282193 A1 and hereafter Cha).
Regarding claim 1, Ohashi teaches an active noise control apparatus and waveform transforming apparatus through a neural network (see Ohashi, abstract and figures 2-3 and 13-14). Ohashi teaches a neural network comprising an input layer, a hidden layer, and an output layer (see Ohashi, column 4, lines 3-7 and lines 42-67, and figure 2, units 18-41 and 92). Ohashi does not appear to teach a “deep” neural network.
Cha teaches a method and apparatus for active noise cancellation using deep learning (see Cha, abstract). Herein, Cha teaches that recent developments in deep learning have improved the performance of noise cancellation methods over traditional machine-learning based methods (see Cha, ¶ 0009). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to modify Ohashi with the features of Cha for the purpose of improving the noise cancellation performance (see Ohashi, column 1, lines 14-20 and column 1, line 59 – column 2, line 35, in view of Cha, ¶ 0009).
Therefore, the combination of Ohashi and Cha makes obvious:
“An active noise cancellation system comprising:
an input node for receiving a noise signal” (see Ohashi, column 4, lines 26-35 and figure 2, “input signal Xj”);
“an output node for providing an anti-noise signal” (see Ohashi, column 4, line 63 – column 5, line 3, and figure 2, units 42 and 43); and
“one or more blocks each configured to provide a transfer function between its input and output” by teaching the neural network comprising an input layer, a hidden layer, and an output layer to provide a mapping (i.e., transfer function) of a primary noise path, such as the path between the primary noise sources to the speaker (see Ohashi, column 4, lines 3-7 and lines 42-67, column 5, lines 13-16, and column 6, lines 8-15, and figure 2, units 18-41 and 92), “the block further configured to be capable of having its transfer function estimated by a deep neural network framework” where Cha makes obvious the use of deep learning frameworks to improve the performance of noise cancelling methods (see Cha, ¶ 0009-0010).
Regarding claim 2, see the preceding rejection with respect to claim 1 above. The combination makes obvious the “active noise cancellation system of claim 1 wherein each block includes a filter” by teaching that the input signal is applied to delay elements and non-linear functions and different weighting parameters between the layers of the neural network (see Ohashi, column 4, lines 27-67 and figure 2, units 3-41).
Regarding claim 3, see the preceding rejection with respect to claim 2 above. The combination makes obvious the “active noise cancellation system of claim 2 wherein the filter is configured as a finite impulse response filter or an infinite impulse response filter” because the connected layers provide the equivalent to the finite and/or infinite impulse response filter.
Regarding claim 4, see the preceding rejection with respect to claim 1 above. The combination makes obvious the “active noise cancellation system of claim 1 wherein the block includes a feedforward block” by teaching the connected layers of the neural network (see Ohashi, column 4, lines 42-67, and see Cha, ¶ 0075-0076).
Regarding claim 5, see the preceding rejection with respect to claim 1 above. The combination makes obvious the “active noise cancellation system of claim 1 wherein the block includes a feedback block” by making obvious different types of neural networks using a feedback block (see Ohashi, figure 2, unit 93 and see Cha, ¶ 0097).
Regarding claim 6, see the preceding rejection with respect to claim 5 above. The combination makes obvious the “active noise cancellation system of claim 5 wherein the transfer function of the feedback block is estimated based on linear and ideal speaker conditions” (see Ohashi, column 5, lines 17-56 and column 6, lines 8-20 and figure 2, unit 93).
Regarding claim 7, see the preceding rejection with respect to claim 5 above. The combination makes obvious the “active noise cancellation system of claim 5 wherein the transfer function of the feedback block is estimated with a condition including nonlinearity in a signal path” (see Ohashi, column 5, lines 17-29 and column 6, lines 8-20 and figure 2, units 56-63 and 93).
Regarding claim 8, see the preceding rejection with respect to claim 1 above. The combination makes obvious the “active noise cancellation system of claim 1 wherein the one or more blocks include a feedforward block and a feedback block” (see Ohashi, figure 2, units 92 and 93 and see Cha, ¶ 0075-0076 and 0093).
Regarding claim 9, see the preceding rejection with respect to claim 8 above. The combination makes obvious the “active noise cancellation system of claim 8 wherein each of the feedforward and feedback blocks has its transfer function estimated by a separate deep neural network framework” (see Ohashi, column 6, lines 8-20 and figure 2, units 92 and 93).
Regarding claim 10, see the preceding rejection with respect to claim 8 above. The combination makes obvious the “active noise cancellation system of claim 8 wherein the feedforward and feedback blocks have their transfer functions estimated by a common deep neural network framework” (see Cha, ¶ 0075 and 0102-0103).
Regarding claim 11, see the preceding rejection with respect to claim 1 above. The combination makes obvious the “active noise cancellation system of claim 1 further comprising a computation engine configured to support the deep neural network framework” by teaching the use of the NVIDIA GPU (see Cha, ¶ 0101-0103).
Regarding claim 13, see the preceding rejection with respect to claim 11 above. The combination makes obvious the “active noise cancellation system of claim 11 wherein the computation engine includes a multiply-accumulate engine configured to perform multiply-accumulate computations” by teaching the use of the NVIDIA GPU (see Cha, ¶ 0101-0103).
Regarding claim 16, see the preceding rejection with respect to claim 1 above. Ohashi teaches an active noise control apparatus and waveform transforming apparatus through a neural network (see Ohashi, abstract and figures 2-3 and 13-14). Ohashi teaches a neural network comprising an input layer, a hidden layer, and an output layer (see Ohashi, column 4, lines 3-7 and lines 42-67, and figure 2, units 18-41 and 92). However, Ohashi does not appear to teach a “deep” neural network.
Cha teaches a method and apparatus for active noise cancellation using deep learning (see Cha, abstract). Herein, Cha teaches that recent developments in deep learning have improved the performance of noise cancellation methods over traditional machine-learning based methods (see Cha, ¶ 0009). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to modify Ohashi with the features of Cha for the purpose of improving the noise cancellation performance (see Ohashi, column 1, lines 14-20 and column 1, line 59 – column 2, line 35, in view of Cha, ¶ 0009).
Therefore, the combination of Ohashi and Cha makes obvious:
“A chip comprising:
a substrate” because Cha makes obvious the use of deep learning frameworks to improve the performance of noise cancelling methods (see Cha, ¶ 0009-0010), and Cha teaches the use of GPU (i.e., TESLA K80, NVIDIA with 12GB of available RAM) to implement various different noise cancellation methods including different deep learning network methods, and the GPU makes obvious a chip with a substrate, such as the substrate of the silicon microchip and/or the substrate of the printed circuit board (PCB) used to connect the various components of the GPU (see Cha, ¶ 0101-0103); and
“an active noise cancellation circuit implemented on the substrate, the active noise cancellation circuit including an input node for receiving a noise signal, and an output node for providing an anti-noise signal, the active noise cancellation circuit further including one or more blocks each configured to provide a transfer function between its input and output, the block further configured to be capable of having its transfer function estimated by a deep neural network framework” because Ohashi teaches the neural network comprising an input layer, a hidden layer, and an output layer to provide a mapping (i.e., transfer function) of a primary noise path, such as the path between the primary noise sources to the speaker (see Ohashi, column 4, lines 3-7 and lines 42-67, column 5, lines 13-16, and column 6, lines 8-15, and figure 2, units 18-41 and 92), and Cha makes obvious the active noise cancellation circuit implemented in the GPU, where the block has its transfer function estimated by a deep neural network method (see Cha, ¶ 0009-0010 and 0101-0103).
Regarding claim 20, see the preceding rejection with respect to claim 1 above. Ohashi teaches an active noise control apparatus and waveform transforming apparatus through a neural network (see Ohashi, abstract and figures 2-3 and 13-14). Ohashi teaches a neural network comprising an input layer, a hidden layer, and an output layer (see Ohashi, column 4, lines 3-7 and lines 42-67, and figure 2, units 18-41 and 92). However, Ohashi does not appear to teach a “deep” neural network.
Cha teaches a method and apparatus for active noise cancellation using deep learning (see Cha, abstract). Herein, Cha teaches that recent developments in deep learning have improved the performance of noise cancellation methods over traditional machine-learning based methods (see Cha, ¶ 0009). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to modify Ohashi with the features of Cha for the purpose of improving the noise cancellation performance (see Ohashi, column 1, lines 14-20 and column 1, line 59 – column 2, line 35, in view of Cha, ¶ 0009).
Therefore, the combination of Ohashi and Cha makes obvious:
“An audio device comprising:
an input circuit for receiving an input signal” (see Ohashi, column 4, lines 26-35 and figure 2, “input signal Xj”);
“a speaker for producing sound waves based on an output signal corresponding to the input signal” by teaching the speaker (see Ohashi, column 5, lines 1-3 and lines 13-16 and figure 2, unit 43);
an audio circuit configured to process the electrical signal and generate the output signal” by making obvious that the digital output signal from the neural network is converted to an electrical audio signal for the speaker (see Ohashi, column 4, lines 3-7 and lines 42-67, column 5, lines 13-16, and column 6, lines 8-15, and figure 2, units 18-42 and 92); and
“an active noise cancellation circuit including an input node for receiving a noise signal, and an output node for providing an anti-noise signal, the active noise cancellation circuit further including one or more blocks each configured to provide a transfer function between its input and output, the block further configured to be capable of having its transfer function estimated by a deep neural network framework” by teaching the neural network comprising an input layer, a hidden layer, and an output layer to provide a mapping (i.e., transfer function) of a primary noise path, such as the path between the primary noise sources to the speaker (see Ohashi, column 4, lines 3-7 and lines 42-67, column 5, lines 13-16, and column 6, lines 8-15, and figure 2, units 18-41 and 92), and Cha makes obvious the active noise cancellation circuit implemented in the GPU, where the block has its transfer function estimated by a deep neural network method (see Cha, ¶ 0009-0010 and 0101-0103).
Claim(s) 14-15 and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Ohashi and Cha as applied to claims 11 and 16 above, and further in view of Lim (US 2025/0069577 A1).
Regarding claim 14, see the preceding rejection with respect to claim 11 above. The combination of Ohashi and Cha makes obvious the active noise cancellation system of claim 11, wherein the computation engine is part of a GPU (see Cha, ¶ 0101-0103). The combination does not appear to teach a system-on-chip (SoC) device.
Lim teaches an intelligent active noise control device where deep learning is utilized to improve the generation of cancelling sound for various different noise sources (see Lim, abstract, ¶ 0003-0007, 0010, 0055-0056, and figures 1-10). Herein, Lim teaches an SoC as an alternative to other processing devices (see Lim, ¶ 0089). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to modify the combination of Ohashi and Cha with the teachings of Lim for the purpose of using a different processing device based on a designer’s needs and/or familiarity, and/or for the purpose of cost and/or efficiency provided by an SoC.
Therefore, the combination of Ohashi, Cha, and Lim makes obvious the “active noise cancellation system of claim 11 wherein the computation engine is part of a system-on-chip device” (see Cha, ¶ 0101-0103 in view of Lim, ¶ 0089).
Regarding claim 15, see the preceding rejection with respect to claim 14 above. The combination makes obvious the “active noise cancellation system of claim 14 wherein the system-on-chip device further includes a digital signal processing circuit that includes the one or more blocks” because it would be obvious to include various well-known processing blocks in an SoC design for improved performance by placing the processing blocks on the same silicon in contrast to using discrete processors sharing a data bus (see Cha, ¶ 0101-0103 in view of Lim, ¶ 0089).
Regarding claim 17, see the preceding rejection with respect to claims 11 and 16 above. The combination of Ohashi and Cha makes obvious the chip of claim 16, but does not appear to teach a system-on-chip (SoC) device. Lim teaches an SoC as an alternative to other processing devices (see Lim, ¶ 0089), and it would have been obvious to one of ordinary skill in the art at the time of the effective filing date to modify the combination of Ohashi and Cha with the teachings of Lim for the purpose of using a different processing device based on a designer’s needs and/or familiarity, and/or for the purpose of cost and/or efficiency provided by an SoC.
Therefore, the combination of Ohashi, Cha, and Lim makes obvious the “chip of claim 16 wherein the chip is configured as a system-on-chip” (see Cha, ¶ 0101-0103 in view of Lim, ¶ 0089).
Regarding claim 18, see the preceding rejection with respect to claim 17 above. The combination makes obvious the “chip of claim 17 wherein the system-on-chip further includes a computation engine configured to support the deep neural network framework” (see Cha, ¶ 0101-0103 in view of Lim, ¶ 0089).
Regarding claim 19, see the preceding rejection with respect to claim 18 above. The combination makes obvious the “chip of claim 18 wherein the system-on-chip device further includes a digital signal processing circuit that includes the one or more blocks” because it would be obvious to include various well-known processing blocks in an SoC design for improved performance by placing the processing blocks on the same silicon in contrast to using discrete processors sharing a data bus (see Cha, ¶ 0101-0103 in view of Lim, ¶ 0089).
Allowable Subject Matter
Claim 12 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pepin et al. (US 2023/0129022 A1 and hereafter Pepin) teaches a method and system for reducing noise, where machine learning and/or deep learning processes are utilized (see Pepin, abstract, ¶ 0179-0180, 0192-0193, and 0197, and figures 1-7).
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/Daniel R Sellers/Primary Examiner, Art Unit 2694
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