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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/12/2024 has been considered and placed on record in the file.
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.
Claim(s) 13-15 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by KOSTAS et al. (US 2023/0006718 A1, hereinafter, “Kostas”).
Consider claim 13, Kostas teaches a network node, comprising: communication circuitry coupled to a plurality of antennas and configured to establish communications with a wireless device in an environment (see at least figure 1, figure 2 (102), figures 18-19); and a processing system (figure 18 (1820) or figure 19 (1920)) configured to: obtain receive power measurements from the plurality of antennas (see at least figure 18 (1821), paras. 74, 81, 90, figure 16 (160), figure 17 (1702), Kostas teaches obtaining a spherical gain data (power measurements) from plurality of antennas (i.e., wireless node/device)); perform a machine learning-based analysis of the environment based on the receive power measurements (see at least paras. 82, 91-92, figure 16 (1604-1606), figure 17 (1704), Kostas teaches training beam codebooks using machine learning (ML) model and received power measurements (from 1602 or 1702) of the environment); and adapt the communications with the wireless device in accordance with the machine learning-based analysis of the environment (see at least figure 17 (1706) and para. 93).
Consider claim 14, Kostas teaches wherein the communication circuitry comprises a radio frequency (RF) transceiver (see figures 1-2 and description thereof).
Consider claim 15, Kostas teaches wherein the RF transceiver is configured to communicate via at least one of a terahertz (THz) band or a millimeter wave (mm Wave) band (see at least paras. 45, 145 and 147).
Claim Rejections – 35 USC § 103
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 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, 9, 10, 16 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over KOSTAS et al. (US 2023/0006718 A1, hereinafter, “Kostas”) in view of LEE et al. (US 2021/0250068 A1, hereinafter, “Lee”).
Consider claim 1, Kostas teaches a method for intelligently learning a beam codebook for multi-antenna wireless communications (see at least abstract, paras. 48, 58-59 and figure 2), the method comprising: obtaining receive power measurements from a plurality of antennas (see at least paras. 74, 81, 90, figure 16 (160), figure 17 (1702), Kostas teaches obtaining a spherical gain data (power measurements) from plurality of antennas (i.e., wireless node)); and training the beam codebook using machine learning and the receive power measurements (see at least paras. 82, 91-92, figure 16 (1604-1606), figure 17 (1704), Kostas teaches training beam codebooks using machine learning (ML) model and received power measurements (from 1602 or 1702)).
Kostas teaches training the beam codebook using machine learning and the receive power measurements (see above), however, did not particularly teach deep learning. Lee teaches said technique (see at least paras. 58, 66, 161-162, Lee teaches machine learning being deep learning in a neural network which includes of training codebook).
It would have been obvious to one of ordinary skill in the art at the time of the application to modify the invention of Kostas and teach deep learning, as taught by Lee, thereby, allowing efficient wireless communication system.
Consider claim 7, Kostas teaches a neural network for training of a beam codebook for multi-antenna wireless communications (see at least abstract, paras. 48, 58-59 and figure 2), the neural network comprising: a model configured to predict one or more beam patterns for the beam codebook (see at least para. 81, 92, 95, figure 5, figure 16 (1604), Kostas teaches a model training codebooks in an ML model for prediction); and second part of the model configured to evaluate the one or more beam patterns predicted by the model based on receive power measurements of an environment (see at least figure 5, figure 13, figure 16 (1606), paras. 83-85, Kostas teaches a second part of the model (i.e., 1302 in figure 13) determining communication parameters (evaluation) of beams based on the model and RSRP (receive power measurements of the environment)).
Kostas teaches a model and a second part of the model (see above), however, did not particularly teach two networks (actor and critic networks) determining the above-mentioned limitations. Lee teaches said technique (see at least figure 5, figure 6, paras. 66-67, 101-107, and 120-122, Lee teaches two neural network models to evaluate beam patterns (para. 48)).
It would have been obvious to one of ordinary skill in the art at the time of the application to modify the invention of Kostas and teach two networks (actor and critic networks) determining the above-mentioned limitations, as taught by Lee, thereby, allowing efficient wireless communication system.
Consider claim 2, Kostas in view of Lee teaches beamforming wireless communications with a wireless device using the trained beam codebook (see at least figure 17 (1706) in Kostas; see at least figure 6 (s650) in Lee).
Consider claim 3, Kostas in view of Lee teaches initiating the wireless communications with the wireless device using the trained beam codebook (see at least figure 17 (1706) in Kostas; see at least figure 6 (s620) in Lee).
Consider claims 4 and 9, Kostas in view of Lee teaches training of the beam codebook uses the deep learning and the receive power measurements only (see at least paras. 82, 91-92, figure 16 (1604-1606), figure 17 (1704), Kostas teaches training beam codebooks using machine learning (ML) model and received power measurements (from 1602 or 1702); see at least paras. 58, 66, 161-162 in Lee, Lee teaches machine learning being deep learning in a neural network which includes of training codebook).
Consider claims 5 and 10, Kostas in view of Lee teaches training of the beam codebook is achieved without employing channel estimation (see at least para. 106 in Lee).
Consider claim 16, Kostas teaches training of the beam codebook uses the machine learning and the receive power measurements only (see at least paras. 82, 91-92, figure 16 (1604-1606), figure 17 (1704), Kostas teaches training beam codebooks using machine learning (ML) model and received power measurements (from 1602 or 1702)).
Kostas teaches training the beam codebook using machine learning and the receive power measurements (see above), however, did not particularly teach deep learning. Lee teaches said technique (see at least paras. 58, 66, 161-162, Lee teaches machine learning being deep learning in a neural network which includes of training codebook).
It would have been obvious to one of ordinary skill in the art at the time of the application to modify the invention of Kostas and teach deep learning, as taught by Lee, thereby, allowing efficient wireless communication system.
Consider claim 17, Kostas teaches training of the beam codebook (see at least paras. 82, 91-92, figure 16 (1604-1606), figure 17 (1704), however, did not particularly teach training of the beam codebook is achieved without employing channel estimation. Lee teaches said technique (see at least para. 106).
It would have been obvious to one of ordinary skill in the art at the time of the application to modify the invention of Kostas and teach training of the beam codebook is achieved without employing channel estimation, as taught by Lee, thereby, allowing efficient wireless communication system.
Allowable Subject Matter
Claims 6, 8, 11, 12, and 18-20 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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FITWI Y. HAILEGIORGIS
Primary Examiner
Art Unit 2632
/FITWI Y HAILEGIORGIS/Examiner, Art Unit 2632