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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 03/10/2026 has been entered.
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.
Claims 1-6, 10-12, 16, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over O’Shea et al. (hereinafter, referred to as O’Shea) (US 2020/0343985) and Hermann et al. (hereinafter, referred to as Hermann) (DE 102010056158), further in view of Bai et al. (hereinafter, referred to as Bai) (CN 116232812).
As to claims 1 and 16, O’Shea discloses a method/apparatus for processing, using one or more first models (see Fig. 1, block 120 and Fig. 2, block 212, see paragraphs 0003, 0007, 0022-0023, 0036, 0038, 0046, 0054, 0060, and 0079), a digital representation of an analog received (RX) signal received via a communication channel (see Figs. 1 and 2, paragraphs 0004, 0006-0007, 0015, 0022-0023, 0032, 0035, 0046, and 0062-0063) to obtain one or more channel loss metrics for one or more reference frequencies, the one or more channel loss metrics representative of a difference between the RX signal and a transmitted (TX) signal at one or more reference frequencies (see the abstract, paragraphs 0003, 0008, 0011-0012, 0017, 0038, 0044, 0046, 0052, 0054, 0058-0059, 0068, 0074-0075, 0080, 0082, and 0126 and Fig. 1, channel estimation block 121 and Fig. 1, see the structure of the “Input Data”); obtaining, using the one or more channel loss metrics, one or more settings for equalization (EQ) (see paragraphs 0032, 0037-0038, 0046, 0058, 0068, 0072, 0074-0075, 0086, 0094, and 0127, and Fig. 1, block 123); iteratively modifying the one or more settings for EQ (see paragraphs 0013, 0068-0069, 0071-0072, 0097, and 0109, see Fig. 2A, the updates for the weights of Machine Learning Network (including EQ parameters/settings) is generated based on the output of Machine Learning Network 212, therefore setting of the EQ are modified iteratively); and configuring, using the one or more settings, EQ circuitry to equalize at least one of the RX signal, the TX signal, or a channel signal (see Figs. 1 and 2A, paragraphs 0012, 0038, 0058, 0105, 0108, and 0115, the system disclosed by O’Shea generates equalized data symbols based on coefficients of the Machine Learning Network that provide the best performance), wherein the channel signal comprises the TX signal modified during propagation through at least a portion of the communication channel (see paragraphs 0044, 0053, 0058 and 0084). O’Shea discloses all the subject matters claimed in claims 1 and 16, except for iteratively modifying, during operations of the communication channel, the one or more EQ circuitry parameters/settings. O’Shea also does not disclose that the equalization is hardware equalization separate from the model used for obtaining the channel loss metrics. Hermann, in the same field of endeavor, discloses a receiver, where the receiver adapts corresponding reception parameters to the newly determined radio channel properties, that is to say the reception device while the reception of a data packet is in progress can carry out a channel estimation while receiving a data packet (i.e., during operation of the communication channel), for example a channel equalizer of the receiver adapts continuously (i.e., iteratively modifying EQ circuitry parameters/settings) (see paragraph 0027). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention was made to modify the teachings of O’Shea, as suggested by Hermann, in order to increase the performance of the equalizer and recover the received signal more accurately. O’Shea and Hermann disclose all the subject matters claimed in claims 1 and 16, except that the equalization is hardware equalization separate from the model used for obtaining the channel loss metrics. Bai, in the same field of endeavor, discloses a communication system (see Fig. 1). Bai discloses that the system comprises a channel estimation module (see Fig. 1) and an equalizer module (see Fig. 1, the equalization module). Bai shows that the channel estimation module is using a DNN model and also shows that the equalizer module is separate from the DNN model and the channel estimation module (see Fig. 1). Bai does not expressly disclose that the equalizer module is hardware equalizer, however O’Shea discloses that hardware equalizers are well-known in the art (see O’Shea paragraph 0038). Making the components of a circuit separable is not sufficient by itself to patentably distinguish over an otherwise old device unless there are new or unexpected results (see MPEP 2144.04 V.C). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention was made to modify the teachings of O’Shea and Hermann, as suggested by Bai, and make the equalizer module separate from the channel estimation module in order to increase the flexibility and reliability of the communication system.
As to claim 2, O’Shea discloses that the communication channel comprises at least one of: a wire cable or an optical fiber (see paragraph 0003).
As to claim 3, O’Shea discloses that the one or more channel loss metrics are representative of an amplitude difference between the TX signal and the RX signal at one or more frequencies (see paragraphs 0058, 0068, and 0075).
As to claim 4, O’Shea discloses that the one or more channel loss metrics are further representative of a phase difference between the TX signal and the RX signal at the one or more frequencies (see paragraphs 0058, 0068, 0075, and 0084).
As to claim 5, O’Shea discloses that the one or more first models comprise a neural network model (see paragraphs 0016, 0057, and 0064).
As to claim 6, O’Shea discloses that the one or more first models comprise a statistical model (see paragraphs 0081 and 0102).
As to claim 10, O’Shea discloses that the one or more EQ circuits comprise one or more filters of an RX device connected to the TX device via the communication channel, wherein the RX device receives the TX signal (see Fig.1, block 123 (equalization is filtering) and Fig. 2A that shows the equalizer see block 212 is connected to the TX device 201 via communication channel 207 and see paragraphs 0046 and 0060).
As to claims 11 and 19, O’Shea discloses that obtaining the first set of the one or more EQ parameters/settings comprises: applying the one or more channel loss metrics to one or more second models (see paragraphs 0032, 0038-0039,0046, 0054, 0074-0075, 0079, and 0118, the Machine Learning Network is using the channel loss metrics to determine the EQ parameters/settings), wherein the one or more second models comprise a neural network (see paragraphs 0016, 0057, and 0064).
As to claim 12, O’Shea discloses that the one or more first models are trained to estimate the one or more channel loss metrics for a plurality of training communication channels (see Fig. 1 and paragraphs 0012, 0036, 0038, 0046, 0052-0053, 0061, and 0104).
As to claim 20, O’Shea discloses that the processing device is comprised in at least one of: a system implemented at least partially using cloud computing resources (see paragraph 0079).
Claims 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over O’Shea, in view of Bai.
As to claim 13, O’Shea discloses a method comprising: obtaining a plurality of training inputs, wherein an individual training input of the plurality of training inputs comprises: a digital representation of a received (RX) signal received via a respective training communication channel of a plurality of training communication channels (see the abstract, Figs. 1 and 2, and paragraphs 0004, 0006-0007, 0010, 0012, 0015, 0022-0023, 0032, 0035-0038, 0046, 0061-0063, and 0104), one or more measured channel loss metrics at one or more reference frequencies for the respective training communication channel (see the abstract, paragraphs 0003, 0012-0013, 0017, 0046, 0051, 0068-0069, and claim 6), wherein the one or more measured channel loss metrics are representative of a difference between the RX signal and a transmitted (TX) signal at the one or more reference frequencies transmitted via the respective training communication channel (see paragraphs 0003, 0008, 0011-0012, 0017, 0038, 0044, 0046, 0054, 0058-0059, 0068, 0074-0075, 0080, and 0126 and Fig. 1, channel estimation block 121); one or more ground truth equalization (EQ) settings for EQ circuitry, the one or more ground truth EQ settings associated with equalization of the RX signal of the respective training communication channel (see paragraphs 0012, 0032, 0037-0038, 0046, 0058, 0068-0069, 0072, 0074-0075, 0086, 0094-0095, and 0127, and Fig. 1, block 123), training, using the plurality of training inputs, a model to output using the digital representation of the RX signal, one or more estimated channel loss metrics that approximates one or more measured channel loss metrics of the plurality of training communication channels (see paragraphs 0008, 0011-0013, 0032, 0036, 0038, 0044, 0046, 0051-0054, 0061, 0068, 0080, 0082-0083, and 0104) and a model to process at least the one or more measured channel loss metric or the one or more estimated channel loss metrics, to output EQ settings that approximate the ground truth EQ settings (see paragraphs 0032, 0037-0038, 0046, 0058, 0068, 0072, 0074-0075, 0086, 0094, 0124, and 0127, and Fig. 1, block 123); and causing the one or more trained models to be deployed in association with one or more equalization circuits of at least one of a RX device or a TX device communicating with the RX device via a communication channel (see paragraphs 0032, 0037-0038, 0046, 0058, 0068, 0072, 0074-0075, and 0127, and Fig. 1, block 123). O’Shea discloses all the subject matters claimed in claim 13, except that the equalization is hardware equalization separate from the model used for obtaining the channel loss metrics. Bai, in the same field of endeavor, discloses a communication system (see Fig. 1). Bai discloses that the system comprises a channel estimation module (see Fig. 1) and an equalizer module (see Fig. 1, the equalization module). Bai shows that the channel estimation module is using a DNN model and also shows that the equalizer module is separate from the DNN model and the channel estimation module (see Fig. 1). Bai does not expressly disclose that the equalizer module is hardware equalizer, however O’Shea discloses that the hardware equalizers are well-known in the art (see O’Shea paragraph 0038). Making the components of a circuit separable is not sufficient by itself to patentably distinguish over an otherwise old device unless there are new or unexpected results (see MPEP 2144.04 V.C). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention was made to modify the teachings of O’Shea, as suggested by Bai, and make the equalizer module separate from the channel estimation module in order to increase the flexibility and reliability of the communication system.
As to claim 14, O’Shea discloses that the plurality of training communication channels comprises at least one of: a wire cable, or an optical fiber (see paragraph 0003).
As to claim 15, O’Shea discloses that the digital representation of the RX signal comprises data output by an analog-to-digital converter (ADC) (see Fig. 2A and paragraph 0063), wherein an input into the ADC comprises the RX signal (see Fig. 2A the input of block 209), and wherein the one or more models comprise at least one of: a neural network (see paragraphs 0016, 0057, and 0064).
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over O’Shea, Hermann, and Bai, further in view of Lin et al. (hereinafter, referred to as Lin) (US 8,654,830).
As to claims 8 and 17, O’Shea discloses iteratively modifying one or more EQ settings comprises: starting from the first set of the one or more EQ settings (see paragraphs 0032, 0037-0038, 0046, 0058, 0068, 0072, 0074-0075, and 0127, and Fig. 1, block 123), performing one or more iterations, wherein an individual iteration of the one or more iterations comprises: updating a current set of the one or more EQ settings (see paragraphs 0013, 0068-0069, 0072, 0097, and 0109-0110, see Fig. 2A, the updates for the Machine Learning Network weights (including EQ parameters), wherein the updates use one or more iterations of periodic adaptation (see paragraph 0078). O’Shea, Hermann, and Bai disclose all the subject matters claimed in claims 8 and 17, except for determining, using one or more channel quality metrics representative of a signal quality of the RX signal, whether to keep the one or more updated EQ settings. Lin, in the same field of endeavor, discloses a receiver that is optimized by adapting settings of a linear equalizer component within the receiver (see the abstract). Lin further discloses determining, using one or more channel quality metrics representative of a signal quality of the RX signal, whether to keep a one or more updated EQ settings (see Fig. 9, blocks 902, 903, and 904 and see column 7, lines 63-67 – column 8, line 3, at step 904 if the bit error rate (a channel quality metric) is minimized, the system will keep the one or more updated EQ settings calculated at step 903, otherwise it will go through another iteration and calculate a new set of DFE taps). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention was made to modify the teachings of O’Shea, Hermann, and Bai, as suggested by Lin, in order to increase the performance of the communication system.
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over O’Shea, Hermann, and Bai, further in view of Carter et al. (hereinafter, referred to as Carter) (US 2022/0199103).
As to claims 9 and 18, O’Shea, Hermann, and Bai, disclose all the subject matters claimed in claims 9 and 18, except that the one or more EQ settings comprise: a gain for one or more frequencies. Carter, in the same field of endeavor, discloses a system comprising an equalizer, wherein the equalizer configured to be programmed by multiple equalizer parameter sets (see the abstract). Carter further discloses that the gains (214A-214N) for the frequency bands (212A-212N) are set by an equalizer parameterization controller 220. The equalizer parameterization controller 220 may operate using multiple equalizer parameter sets (222A-222M). One of the multiple equalizer parameter sets (222A-222M) may be chosen to parameterize the equalizer. The parameterization may be performed by setting the gains (214A-214N) according to the chosen equalizer parameter set. Thus, each of the equalizer parameter sets (222A-222M) includes gain values for the gains (214A-214N) (see paragraph 0031). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention was made to modify the teachings of O’Shea, Hermann, and Bai, as suggested by Carter, in order to enhance the transmission quality (see paragraph 0032).
Allowable Subject Matter
Claim 7 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
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LEILA . MALEK
Examiner
Art Unit 2632
/LEILA MALEK/Primary Examiner, Art Unit 2632
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