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 § 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)(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, 3, 8, 10, 11 and 15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Raghavan et al. (US Pub. 2023/0254837).
Regarding claims 1, 8 and 15, Raghavan teaches a processor comprising: one or more circuits to use one or more neural networks (see “calculate the hybrid beamforming parameters … via a machine learning model” in [0154] and “the learning model 215 may represent an example of a neural net, such as a feed forward (FF) or a deep feed forward (DFF) neural network, a recurrent neural network (RNN), a long/short term memory (LSTM) neural network, or any other type of neural network” in [0103]) to generate one or more hybrid beamforming parameters to be used to transmit one or more wireless signals (see step 515 “Calculate Hybrid Beamforming Parameters” in Figure 5).
Regarding claim 3, Raghavan teaches generation of the one or more hybrid beamforming parameters are based, at least in part, on optimizing one or more signal characteristics of the one or more wireless signals to be transmitted (“optimize or improve at least one metric, such as a reference signal received power (RSRP), a spectral efficiency, or some other metric for communications” in [0098]).
Regarding claim 10, Raghavan teaches generation of the one or more hybrid beamforming parameters is based, at least in part, on optimizing one or more signal-to-noise ratios (SNRs) of the one or more wireless signals to be transmitted (see “highest signal-to-noise ratio (SNR)” in [0092] and “optimize or improve at least one metric, such as a reference signal received power (RSRP), a spectral efficiency, or some other metric for communications” in [0098]).
Regarding claim 11, Raghavan teaches the one or more wireless signals are to be transmitted by a base station of a fifth-generation new radio (5G NR) network (“fifth generation (5G) systems which may be referred to as New Radio (NR) systems” in [0002]).
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 2, 4, 5, 7, 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Raghavan et al. in view of Lizarraga et al. (“Hybrid beamforming algorithm using reinforcement learning for millimeter wave wireless systems,” 2019, pp. 253-258, 2019 XVIII Workshop on Information Processing and Control (RPIC)).
Regarding claim 2, Raghavan teaches the limitations in claim 1 as shown above. Raghavan, however, does not teach the one or more circuits are to use the one or more neural networks to generate the one or more hybrid beamforming parameters by jointly inferring one or more analog beamforming parameters and one or more digital beamforming parameters. Lizarraga teaches the one or more circuits are to use the one or more neural networks to generate the one or more hybrid beamforming parameters by jointly inferring one or more analog beamforming parameters and one or more digital beamforming parameters (“the digital precoder and analog beamformer to be utilized in transmission are jointly determined” in page 253). It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify Raghavan to have the one or more circuits are to use the one or more neural networks to generate the one or more hybrid beamforming parameters by jointly inferring one or more analog beamforming parameters and one or more digital beamforming parameters as taught by Lizarraga in order to maximize the achievable sum data rate (page 253).
Regarding claim 4, Lizarraga teaches the one or more hybrid beamforming parameters are to be used to transmit the one or more wireless signals that exhibit, at a base station of a fifth-generation new radio (5G NR) network (“5G New Radio (NR)” in page 253), one or more signal-to-noise ratios (SNRs) above a threshold value (“SNR is supposed to be 15 dB” in page 257).
Regarding claim 5, Lizarraga teaches the one or more hybrid beamforming parameters comprise one or more complex values to be used to transmit a wireless baseband signal (“the coefficients of the digital precoding matrix … can take any complex number” in page 254).
Regarding claim 7, Lizarraga teaches the one or more hybrid beamforming parameters are to be used to modify operation of one or more analog beamforming components and one or more digital beamforming components of a hybrid beamforming system used to transmit the one or more wireless signals (“Frf and Fbb should be selected from the feasibility set of the analog beamforming and digital precoding matrices” in page 254).
Regarding claim 17, Raghavan teaches the limitations in claim 15 as shown above. Raghavan, however, does not teach generation of the one or more hybrid beamforming parameters is based, at least in part, on a signal-to-noise ratio formula that uses analog beamforming parameters and digital beamforming parameters as inputs. Lizarraga teaches generation of the one or more hybrid beamforming parameters is based, at least in part, on a signal-to-noise ratio formula (see Shannon’s formula (8) in page 255) that uses analog beamforming parameters and digital beamforming parameters as inputs (see “when Fbb and Frf are defined according to (7) and (5)” in page 255 and “Frf and Fbb should be selected from the feasibility set of the analog beamforming and digital precoding matrices” in page 254). It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify Raghavan to have generation of the one or more hybrid beamforming parameters is based, at least in part, on a signal-to-noise ratio formula that uses analog beamforming parameters and digital beamforming parameters as inputs as taught by Lizarraga in order to maximize the achievable data rate (page 256).
Regarding claim 18, Lizarraga teaches training the one or more neural networks is based, at least in part, on maximizing a reward function of a reinforcement learning neural network training process (“aim to maximize the reward” in page 256 and “Reinforcement Learning (RL)” in page 253).
Claims 6, 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Raghavan et al. in view of Lee et al. (“A 5.4 mW G-Band Phase Shifter in 90-nm SiGe HBT With One-Hot Encoding,” 2023, pp. 189-192, IEEE Solid-State Circuits Letters, Vol. 6).
Regarding claim 6, Raghavan teaches the limitations in claim 1 as shown above. Raghavan, however, does not teach the one or more circuits are to use the one or more neural networks to generate the one or more hybrid beamforming parameters to comprise a representation of one or more phase shifters using one or more one-hot vectors. Lee teaches the one or more circuits are to use the one or more neural networks to generate the one or more hybrid beamforming parameters to comprise a representation of one or more phase shifters using one or more one-hot vectors (“LOGIC TABLE OF THE PHASE SHIFTER WITH ONE-HOT ENCODING” in Table 1 in page 190). It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify Raghavan to have the one or more circuits are to use the one or more neural networks to generate the one or more hybrid beamforming parameters to comprise a representation of one or more phase shifters using one or more one-hot vectors as taught by Lee in order to select the phase shift through one of the paths (page 189).
Regarding claim 13, Raghavan teaches the limitations in claim 8 as shown above. Raghavan, however, does not teach the one or more hybrid beamforming parameters are based, at least in part, on representing one or more phase shifters with one or more one-hot vectors. Lee teaches the one or more hybrid beamforming parameters are based, at least in part, on representing one or more phase shifters with one or more one-hot vectors (“LOGIC TABLE OF THE PHASE SHIFTER WITH ONE-HOT ENCODING” in Table 1 in page 190). It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify Raghavan to have the one or more hybrid beamforming parameters are based, at least in part, on representing one or more phase shifters with one or more one-hot vectors as taught by Lee in order to select the phase shift through one of the paths (page 189).
Regarding claim 20, Raghavan teaches the limitations in claim 15 as shown above. Raghavan, however, does not teach the one or more hybrid beamforming parameters include a representation of one or more phase angles using a one or more one-hot vectors. Lee teaches the one or more hybrid beamforming parameters include a representation of one or more phase angles using a one or more one-hot vectors (“LOGIC TABLE OF THE PHASE SHIFTER WITH ONE-HOT ENCODING” in Table 1 in page 190). It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify Raghavan to have the one or more hybrid beamforming parameters include a representation of one or more phase angles using a one or more one-hot vectors as taught by Lee in order to select the phase shift through one of the paths (page 189).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Raghavan et al. in view of Shao et al. (“Extreme-Point Pursuit for Unit-Modulus Optimization,” 2022, pp. 5548-5552, 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)).
Regarding claim 12, Raghavan teaches the limitations in claim 8 as shown above. Raghavan, however, does not teach the one or more hybrid beamforming parameters satisfy a unit modulus constraint. Shao teaches the one or more hybrid beamforming parameters satisfy a unit modulus constraint (see “digital phase shifters” and “unit-modulus constraint” in page 5548). It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify Raghavan to have the one or more hybrid beamforming parameters satisfy a unit modulus constraint as taught by Shao in order to unit-modulus optimization (page 5548).
Claims 14 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Raghavan et al. in view of Tang et al. (US Pub. 2021/0067232).
Regarding claim 14, Raghavan teaches the limitations in claim 8 as shown above. Raghavan, however, does not teach the one or more processors are to use the one or more neural networks to output the one or more hybrid beamforming parameters as a single vector. Tang teaches the one or more processors are to use the one or more neural networks to output the one or more hybrid beamforming parameters as a single vector (“derives the optimal beamforming vector using the Eigen-based single-connection analog beamforming (ESAB) algorithm to improve hybrid beamforming gain” in [0025]). It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify Raghavan to have the one or more processors are to use the one or more neural networks to output the one or more hybrid beamforming parameters as a single vector as taught by Tang in order to improve hybrid beamforming gain [0035].
Regarding claim 19, Raghavan teaches the limitations in claim 15 as shown above. Raghavan, however, does not teach the one or more hybrid beamforming parameters are output as a single vector to be applied to a uniform linear array of sensors. Tang teaches the one or more hybrid beamforming parameters are output as a single vector (“derives the optimal beamforming vector using the Eigen-based single-connection analog beamforming (ESAB) algorithm to improve hybrid beamforming gain” in [0025]) to be applied to a uniform linear array of sensors (“one-dimensional antenna array” in [0009]). It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify Raghavan to have the one or more hybrid beamforming parameters are output as a single vector to be applied to a uniform linear array of sensors as taught by Tang in order to improve hybrid beamforming gain [0035].
Allowable Subject Matter
Claims 9 and 16 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CLEMENCE S HAN whose telephone number is (571)272-3158. The examiner can normally be reached Monday-Friday 8AM-5PM EST.
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/CLEMENCE S HAN/Primary Examiner, Art Unit 2414