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 .
Status of the claims
Claims 1 – 20 were pending in the application.
With the amendment filed on June 02, 2026, Applicant have amended claims 1 – 20.
Claims 1 – 20 are pending in the application.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 June 02, 2026 has been entered.
Response to Arguments
Regarding independent claims, Applicant’s arguments directed toward newly amended limitations, which was not present in the previous rejection. Furthermore, newly made amendments have changed the scope of the claims. However, upon further consideration, a new ground(s) of rejection is made in view of Chen et al (US 2025/0233624) in view of Raghavan et al (US 2024/0048198).
With respect all other claims the Applicant makes same argument as the argument applied to claim 1. Therefore, the same response applied to the argument with respect to independent claims above is applied here.
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) 1 – 5, 7, 8, 10 – 12, 15, and 17 – 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al (US 2025/0233624) in view of Raghavan et al (US 2024/0048198).
Regarding claim 1, Chen et al teach one or more processors (see figure 8 and 9), comprising: circuitry to: generate, using one or more neural networks (see figure 1C first and second neural networks) and based on channel state information (CSI) corresponding to wireless signals (see figure 1A Obtaining channel characteristics” and “determining precoding matrices Ft and Wt”) , one or more digital beamforming parameters (see figure 1C, Wt) and one or more analog beamforming parameters (see figure 1C. Ft); and modify one or more beams of the two or more wireless signals based on the one or more digital beamforming parameters and the one or more analog beamforming parameters (see figure 3, determining precoding matrices and Communicating function). Chen et al does not expressly disclose determining analog and digital parameter based two more wireless signal. However in analogous art, Raghavan et al teach using a neural network to generate analog and digital beamforming parameters based on channel state information (CSI) corresponding to two mor more wireless signals (see abstract, paragraph 0085, 0091 “one or more beam weights selected based on the adaptive beam weight-based hybrid beamforming procedure and the one or more reference signals”). Therefore it would have been obvious to an ordinary skilled in the art at the time the invention was filed to use two or more wireless signals for generating channel state information. The motivation or suggestion to do so is to improve the beamforming performance (see paragraph 0091).
Regarding claim 2, which inherits the limitations of claim 1, Chen et al in view of Raghavan et al further teach wherein the circuitry is to identify, using the one or more neural networks, one or more transmit powers to be used by one or more first devices to transmit the two or more wireless signals using the one or more modified beams based, at least in part, on information about one or more beams used by one or more second devices (see Raghavan et al paragraph 0112, CSI-RS SRS and paragraph 0131 “For adaptive beam weight selection, the input values 505 may include reference signal measurements (e.g., a reference signal received power (RSRP), a reference signal received quality (RSRQ), an SNR value) for multiple reference signals received from another device (e.g., a network entity 105), and the output values 545 may include beam weights for communication beams (e.g., a receive beam, a transmit beam, or both).”).
Regarding claim 3, which inherits the limitations of claim 1, Chen et al in view of Raghavan et al further teach wherein the circuitry is to modify the one or more beams use at least in part, on increasing a total data transmission rate of at least a portion of a wireless network (see Raghavan et al, paragraph 0091 “improved beamforming performance”).
Regarding claim 4, which inherits the limitations of claim 1, Chen et al in view of Raghavan et al further teach wherein the circuitry is to modify the one or more beams based, at least in part, on modifying one or more directions of the one or more beams (see Raghavan paragraph 0082 – 0087 “directional beam”).
Regarding claim 5, which inherits the limitations of claim 1, Chen et al in view of Raghavan et al further teach wherein the one or more digital beamforming parameters comprise one or more complex values to be used to transmit the two or more wireless signals (see Chen et al, paragraph 0046, 0069, 0073, 0082 and Raghavan et al paragraph 0131).
Regarding claim 7, which inherits the limitations of claim 1, Chen et al in view of Raghavan et al further teach wherein the circuitry is to modify the one or more beams based, at least in part, on modifying one or more transmit powers of the one or more beams (see Raghavan et al, paragraph 0091, 0149, 0201 “improved beamforming performance”).
Regarding claim 8, Chen et al teach a system comprising: one or more processors to: generate, using one or more neural networks (see figure 1C first and second neural networks) and based on channel state information (CSI) corresponding to wireless signals (see figure 1A Obtaining channel characteristics” and “determining precoding matrices Ft and Wt”) , one or more digital beamforming parameters (see figure 1C, Wt) and one or more analog beamforming parameters (see figure 1C. Ft); and modify one or more beams of the two or more wireless signals based on the one or more digital beamforming parameters and the one or more analog beamforming parameters (see figure 3, determining precoding matrices and Communicating function). Chen et al does not expressly disclose determining analog and digital parameter based two more wireless signal. However in analogous art, Raghavan et al teach using a neural network to generate analog and digital beamforming parameters based on channel state information (CSI) corresponding to two mor more wireless signals (see abstract, paragraph 0085, 0091 “one or more beam weights selected based on the adaptive beam weight-based hybrid beamforming procedure and the one or more reference signals”). Therefore it would have been obvious to an ordinary skilled in the art at the time the invention was filed to use two or more wireless signals for generating channel state information. The motivation or suggestion to do so is to improve the beamforming performance (see paragraph 0091).
Regarding claim 10, which inherits the limitations of claim 8, Chen in view of Raghavan et al further teach wherein the one or more processors generate the one or more digital beamforming parameters and the one or more analog beamforming parameters to modify one or more directions of the one or more beams (see Raghavan paragraph 0082 – 0087 “directional beam”).
Regarding claim 11, which inherits the limitations of claim 8, Chen in view of Raghavan et al further teach wherein the one or more processors generate the one or more digital beamforming parameters and the one or more analog beamforming parameters to modify one or more transmit powers of the one or more beams (see Raghavan et al, paragraph 0091, 0149, 0201 “improved beamforming performance”).
Regarding claim 12, which inherits the limitations of claim 8, Chen in view of Raghavan et al further teach wherein the one or more processors are to jointly generate, using the one or more neural networks, the one or more digital beamforming parameters and the one or more analog beamforming parameters to reduce an interference value between the two or more wireless signals. (see Raghavan et al, paragraph 0091, 0149, 0201 “improved beamforming performance”).
Regarding claim 15, Chen et al teach a method comprising; generating, using one or more neural networks (see figure 1C first and second neural networks) and based on channel state information (CSI) corresponding to two or more wireless signals (see figure 1A Obtaining channel characteristics” and “determining precoding matrices Ft and Wt”), one or more digital beamforming parameters (see figure 1C, Wt) and one or more analog beamforming parameters (see figure 1C. Ft); and modifying one or more beams of the two or more wireless signals based on the one or more digital beamforming parameters and the one or more analog beamforming parameters (see figure 3, determining precoding matrices and Communicating function) Chen et al does not expressly disclose determining analog and digital parameter based two more wireless signal. However in analogous art, Raghavan et al teach using a neural network to generate analog and digital beamforming parameters based on channel state information (CSI) corresponding to two mor more wireless signals (see abstract, paragraph 0085, 0091 “one or more beam weights selected based on the adaptive beam weight-based hybrid beamforming procedure and the one or more reference signals”). Therefore it would have been obvious to an ordinary skilled in the art at the time the invention was filed to use two or more wireless signals for generating channel state information. The motivation or suggestion to do so is to improve the beamforming performance (see paragraph 0091).
Regarding claim 17, which inherits the limitations of claim 15, Chen in view of Raghavan et al further teach wherein generating the one or more digital beamforming parameters and the one or more analog beamforming parameters is based, at least in part, on identifying one or more directions of the one or more beams in relation to one or more transmit powers of the one or more beams used by one or more second devices. (see Raghavan et al, paragraph 0091, 0149, 0201 “improved beamforming performance”).
Regarding claim 18, which inherits the limitations of claim 15, Chen in view of Raghavan et al further teach wherein generating the one or more digital beamforming parameters and the one or more analog beamforming parameters is based, at least in part, on reducing an interference value between the two or more wireless signals being transmitted or received by two or more devices (see Raghavan et al, paragraph 0091, 0149, 0201 “improved beamforming performance”).
Regarding claim 19, which inherits the limitations of claim 15, Chen in view of Raghavan et al further teach wherein generating the one or more digital beamforming parameters and the one or more analog beamforming parameters is based, at least in part, on two or more transmit powers of the two or more wireless signals (see Chen et al, paragraph 0046, 0069, 0073, 0082 and Raghavan et al paragraph 0131).
Claim(s) 6, 9, 13 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al (US 2025/0233624) in view of Raghavan et al (US 2024/0048198) and further in view of Ali et al (US 2022/0294511)
Regarding claim 6, which inherits the limitations of claim 1, Chen et al in view of Raghavan et al does not expressly disclose using one-hot vectors for beamforming parameters. However, in analogous art, Ali et al teach a processor using neural network to determine the beam directions generate one or more hybrid beamforming parameters are based, at least in part, on a representation of one or more phase values using one or more one-hot vectors (see figure 2, and 120 – 124). Therefore, it would have been obvious to ana ordinary skilled in the art at the time the invention was filed to use a hybrid beamforming using one hot vector algorithm. The motivation or suggestion to do so is to greatly improve the performance of machine learning models.
Regarding claim 9, which inherits the limitations of claim 8, Chen et al in view of Raghavan et al further teach, wherein the one or more processors are to generate the one or more digital beamforming parameters and the one or more analog beamforming parameters based, at least in part, on an expected signal-to-noise ratio (SNR) or other quality measurements of the two or more wireless signals corresponding to the modified one or more beams (see Raghavan et al paragraph 0131 “For adaptive beam weight selection, the input values 505 may include reference signal measurements (e.g., a reference signal received power (RSRP), a reference signal received quality (RSRQ), an SNR value) for multiple reference signals received from another device (e.g., a network entity 105), and the output values 545 may include beam weights for communication beams (e.g., a receive beam, a transmit beam, or both).”) . Chen in view of Raghavan et al does not expressly disclose use the quality parameter of SINR. However, using SINR as a quality parameter is well known in the art. Furthermore, Ali teach a beamforming apparatus wherein the one or more processors are to use the one or more neural networks to identify one or more transmit powers to be used by the one or more first devices to transmit the one or more wireless signals based, at least in part, on an expected signal-to-interference-and-noise ratio (SINR) of the one or more wireless signals (see paragraph 0071, 0083 – 0085). Therefore, it would have been obvious to an ordinary skilled in the art at the time the invention was filed to use SINR to determine the beamforming parameter. The motivation or suggestion to do so is to accurately transmit the data.
Regarding claim 13, which inherits the limitations of claim 8, Chen et al in view of Raghavan et al does not expressly disclose using one-hot vectors for beamforming parameters. However, in analogous art, Ali et al teach a processor using neural network to determine the beam directions generate one or more hybrid beamforming parameters are based, at least in part, on a representation of one or more phase values using one or more one-hot vectors (see figure 2, and 120 – 124). Therefore, it would have been obvious to ana ordinary skilled in the art at the time the invention was filed to use a hybrid beamforming using one hot vector algorithm. The motivation or suggestion to do so is to greatly improve the performance of machine learning models.
Regarding claim 16, which inherits the limitations of claim 15, Chen et al in view of Raghavan et al further teach, wherein the one or more processors are to generate the one or more digital beamforming parameters and the one or more analog beamforming parameters based, at least in part, on an expected signal-to-noise ratio (SNR) or other quality measurements of the two or more wireless signals corresponding to the modified one or more beams (see Raghavan et al paragraph 0131 “For adaptive beam weight selection, the input values 505 may include reference signal measurements (e.g., a reference signal received power (RSRP), a reference signal received quality (RSRQ), an SNR value) for multiple reference signals received from another device (e.g., a network entity 105), and the output values 545 may include beam weights for communication beams (e.g., a receive beam, a transmit beam, or both).”) . Chen in view of Raghavan et al does not expressly disclose use the quality parameter of SINR. However, using SINR as a quality parameter is well known in the art. Furthermore, Ali teach a beamforming apparatus wherein the one or more processors are to use the one or more neural networks to identify one or more transmit powers to be used by the one or more first devices to transmit the one or more wireless signals based, at least in part, on an expected signal-to-interference-and-noise ratio (SINR) of the one or more wireless signals (see paragraph 0071, 0083 – 0085). Therefore, it would have been obvious to an ordinary skilled in the art at the time the invention was filed to use SINR to determine the beamforming parameter. The motivation or suggestion to do so is to accurately transmit the data.
Allowable Subject Matter
Claims14 and 20 are 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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JAISON . JOSEPH
Primary Examiner
Art Unit 2633
/JAISON JOSEPH/ Primary Examiner, Art Unit 2633
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