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 .
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 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.
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.
Claim(s) 1 - 20 are directed to statutory computer-readable mediums under Step 1 of the eligibility analysis. However, the claims are further directed toward a judicial exception under Step 2A Prong One of the eligibility analysis, namely an abstract idea. Under Step 2A Prong Two of the eligibility analysis, the claim(s) does/do not include additional elements to integrate the exception into a practical application of that exception. Under Step 2B of the eligibility analysis, the claims are not sufficient to amount to significantly more than the judicial exception because nothing in the asserted claims purports to improve the functioning of the computer itself or effect an improvement in any other technology or technical field. The claim(s) is/are directed to an apparatus and method. This is “organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721”, (see MPEP 2106.04(a)(2)(I)(A)(iv)). “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation”. (see MPEP 2106.04(a)(2)(I)(C)(v. using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 482, 203 USPQ 812, 813 (CCPA 1979)). Furthermore, the claim(s) fail to amount to significantly more than the abstract idea itself, (see MPEP 2106.05(f)(i). A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 – 7 and 13 – 20 are rejected under 35 U.S.C. 102(a)(1) [as best understood in view of the 35 USC § 101 above] as being anticipated by ARIKAWA (US PgPub No. 20220385374).
Regarding claim 1, ARIKAWA teaches an apparatus for signal processing (0026, 0031, 0058, and 0124; processor in a system), comprising: a finite impulse response (FIR) filter (paragraphs 0006 - 0007, 0014, 0079) configured to process a first input signal to reduce linear distortions in the input signal (paragraphs 0019, 0063, 0117, and 0123; non-linear distortion compensation); and a neural network node configured to implement an activation function to apply a nonlinear activation function to reduce nonlinear distortions in the input signal (paragraphs 0019 - 0020, 0063, 0117 – 0123; neural network and reducing nonlinear distortions).
Regarding claim 2, as mentioned above in the discussion of claim 1, ARIKAWA teaches all of the limitations of the parent claim. Additionally, ARIKAWA teaches wherein the nonlinear activation function is selected from a group consisting of sigmoid, hyperbolic tangent (tanh), Rectified Linear Unit (ReLU), Leaky ReLU, or a combination thereof (paragraphs 0091 - 0092 and 0106; ReLU).
Regarding claim 3, as mentioned above in the discussion of claim 1, ARIKAWA teaches all of the limitations of the parent claim. Additionally, ARIKAWA teaches a cache configured to store precomputed values of the nonlinear activation function (paragraphs 0027, 0029, 0032, and 0051, and 0054 - 0055 also figures 2 - 3; predetermined value and pre-equalization).
Regarding claim 4, as mentioned above in the discussion of claim 3, ARIKAWA teaches all of the limitations of the parent claim. Additionally, ARIKAWA teaches wherein the cache is configured to check for precomputed activation function values during processing (paragraphs 0027, 0029, 0032, and 0051, and 0054 - 0055 also figures 2 - 3; predetermined value and pre-equalization check for precomputed activation function values during processing).
Regarding claim 5, as mentioned above in the discussion of claim 1, ARIKAWA teaches all of the limitations of the parent claim. Additionally, ARIKAWA teaches a summation circuit junction configured to subtract an output of the FIR filter from a second input signal, and to provide an output of the summation circuit junction to the neural network node configured to implement the activation function (paragraphs 0014, 0050 - 0052, 0063, 0066, 0068, 0071; adjusting and adding).
Regarding claim 6, as mentioned above in the discussion of claim 1, ARIKAWA teaches all of the limitations of the parent claim. Additionally, ARIKAWA teaches a summation circuit junction configured to subtract an output of the FIR filter from a second input signal, wherein the neural network node configured to implement the activation function is between the output of the FIR filter and the summation circuit junction (paragraphs 0014, 0050 - 0052, 0063, 0066, 0068, 0071; adjusting and adding).
Regarding claim 7, KE teaches an apparatus for signal processing (0026, 0031, 0058, and 0124; processor in a system), comprising: a neural network configured to process a first input signal for signal equalization and provide an output signal, the output signal representative of signal noise or distortions (paragraphs 0019 - 0020, 0063, 0117 – 0123; neural network and reducing nonlinear distortions); and a summation circuit junction configured to subtract the output from a second input signal (paragraphs 0014, 0050 - 0052, 0063, 0066, 0068, 0071; adjusting and adding).
Regarding claim 13, ARIKAWA teaches a method (paragraph 0002) for signal processing (0026, 0031, 0058, and 0124; processor in a system), comprising: processing an input signal using a finite impulse response (FIR) filter (paragraphs 0006 - 0007, 0014, 0079) to reduce linear distortions in the input signal (paragraphs 0019, 0063, 0117, and 0123; non-linear distortion compensation); and applying a nonlinear activation function to reduce nonlinear distortions in the input signal (paragraphs 0019 - 0020, 0063, 0117 – 0123; neural network and reducing nonlinear distortions).
Regarding claim 14, as mentioned above in the discussion of claim 13, ARIKAWA teaches all of the limitations of the parent claim. Additionally, ARIKAWA teaches storing precomputed values of the nonlinear activation function in a cache (paragraphs 0027, 0029, 0032, and 0051, and 0054 - 0055 also figures 2 - 3; predetermined value and pre-equalization).
Regarding claim 15, as mentioned above in the discussion of claim 14, ARIKAWA teaches all of the limitations of the parent claim. Additionally, ARIKAWA teaches initializing the cache during system startup (paragraph 0091; activation function; also paragraphs 0027, 0029, 0032, and 0051, and 0054 - 0055 also figures 2 - 3; predetermined value and pre-equalization).
Regarding claim 16, as mentioned above in the discussion of claim 14, ARIKAWA teaches all of the limitations of the parent claim. Additionally, ARIKAWA teaches checking the cache for precomputed activation function values during processing (paragraphs 0027, 0029, 0032, and 0051, and 0054 - 0055 also figures 2 - 3; predetermined value and pre-equalization check for precomputed activation function values during processing).
Regarding claim 17, as mentioned above in the discussion of claim 13, ARIKAWA teaches all of the limitations of the parent claim. Additionally, ARIKAWA teaches applying the nonlinear activation function before a summation circuit junction (paragraphs 0014, 0050 - 0052, 0063, 0066, 0068, 0071; adjusting and adding).
Regarding claim 18, as mentioned above in the discussion of claim 13, ARIKAWA teaches all of the limitations of the parent claim. Additionally, ARIKAWA teaches applying the nonlinear activation function after a summation circuit junction (paragraphs 0014, 0050 - 0052, 0063, 0066, 0068, 0071; adjusting and adding).
Regarding claim 19, as mentioned above in the discussion of claim 13, ARIKAWA teaches all of the limitations of the parent claim. Additionally, ARIKAWA teaches wherein the nonlinear activation function is selected from a group consisting of sigmoid, hyperbolic tangent (tanh), Rectified Linear Unit (ReLU), and Leaky ReLU (paragraphs 0091 - 0092 and 0106; ReLU).
Regarding claim 20, as mentioned above in the discussion of claim 13, ARIKAWA teaches all of the limitations of the parent claim. Additionally, ARIKAWA teaches configuring the FIR filter with adjustable filter coefficients (paragraphs 0006 - 0007, 0014, 0079; FIR filter with adjustable filter coefficients).
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:
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 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.
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.
Claims 8 – 12 are rejected under 35 U.S.C. 103 [as best understood in view of the 35 USC § 101 above] as being unpatentable over ARIKAWA (US PgPub No. 20220385374) in view of Mitic (US PgPub No. 2011/0243444).
Regarding claim 8, as mentioned above in the discussion of claim 1, ARIKAWA teaches all of the limitations of the parent claim.
However, ARIKAWA fails to teach wherein the neural network comprises one or more delay neurons, and wherein the neural network is a time delay neural network (TDNN). Mitic, on the other hand teaches wherein the neural network comprises one or more delay neurons, and wherein the neural network is a time delay neural network (TDNN).
More specifically, Mitic teaches wherein the neural network comprises one or more delay neurons, and wherein the neural network is a time delay neural network (TDNN) (paragraph 0028).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to incorporate the teachings of Mitic with the teachings of ARIKAWA because in at least paragraph 0028 Mitic teaches that using the invention improves the classification accuracy, thereby improving the invention of ARIKAWA.
Regarding claim 9, as mentioned above in the discussion of claim 8, ARIKAWA in view of Mitic teach all of the limitations of the parent claim.
Additionally, Mitic teaches wherein the neural network includes one or more hidden layers and an output layer (paragraph 0005; hieroglyphic text segments, and outputs the results to an output component. also, paragraph 0031; The character recognition engine 108 outputs a set of character guesses for each candidate character along with a probability for each guess).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to incorporate the teachings of Mitic with the teachings of ARIKAWA because in at least paragraph 0028 Mitic teaches that using the invention improves the classification accuracy, thereby improving the invention of ARIKAWA.
Regarding claim 10, as mentioned above in the discussion of claim 8, ARIKAWA in view of Mitic teach all of the limitations of the parent claim.
Additionally, Mitic teaches wherein the neural network is configured to process time-series data (paragraphs 0028 time delay neural network (TDNN) being supervised).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to incorporate the teachings of Mitic with the teachings of ARIKAWA because in at least paragraph 0028 Mitic teaches that using the invention improves the classification accuracy, thereby improving the invention of ARIKAWA.
Regarding claim 11, as mentioned above in the discussion of claim 8, ARIKAWA in view of Mitic teach all of the limitations of the parent claim.
Additionally, Mitic teaches wherein the neural network is trained using supervised learning, unsupervised learning, or both (paragraphs 0028 and 0033).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to incorporate the teachings of Mitic with the teachings of ARIKAWA because in at least paragraph 0028 Mitic teaches that using the invention improves the classification accuracy, thereby improving the invention of ARIKAWA.
Regarding claim 12, as mentioned above in the discussion of claim 8, ARIKAWA in view of Mitic teach all of the limitations of the parent claim.
Additionally, Mitic teaches a slicer configured to convert a continuous or discrete-time signal from the summation circuit junction into a discrete-time, discrete-amplitude signal for the neural network (paragraphs 0028; time delay neural network (TDNN) can be user to further improve the classification accuracy. In this approach, instead of simply using the aforementioned features to classify a particular candidate, the values for a set of features for a few (e.g., 1-3) preceding or successive break points may also be used in the classification process).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to incorporate the teachings of Mitic with the teachings of ARIKAWA because in at least paragraph 0028 Mitic teaches that using the invention improves the classification accuracy, thereby improving the invention of ARIKAWA.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
KE (US PgPub No. 20220239407) teaches a system for processing data with machine learning.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Usman A Khan whose telephone number is (571)270-1131. The examiner can normally be reached on M - Th 5:30 AM - 2 PM, F 5:30 AM - Noon.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sinh Tran can be reached on (571)272-7564. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Usman Khan
/USMAN A KHAN/Primary Examiner, Art Unit 2637
08/05/2026
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