DETAILED ACTION
This action is in response to the initial filing filed on September 24, 2024, claims 1-20 have been examined this application.
Information Disclosure Statement
The Information Disclosure Statement (IDS) filed on 09/24/2024 and 4/18/2025 have been acknowledged.
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 § 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 1-3, 6-9, 11-13, and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Sayegh (US 2010/0177678 A1) in view of Hong et al (US 2022/0029665 A1).
Regarding Claim 1, Sayegh teaches beamforming antenna calibration method, adapted to a ground terminal device, comprising [0003 for Ground-based beamforming (GBBF) provides the same or greater flexibility than digital beamforming onboard]:
obtaining an original position information of a target satellite [0056 for the orbital position of the satellite may communicate to the gateway and tracking the satellite];
performing a data pre-processing on the original position information to obtain a processed position information of the target satellite [0056 for matrix of excitation level based on orbital positions];
and controlling the antenna array to generate a target beam used to communicate with the target satellite according to the control parameter of each of the plurality of antenna units [0044 for doppler and oscillator shifts and 0058 for parameters are transmitted to the satellite].
Sayegh fails to explicitly teach according to the processed position information of the target satellite, using a deep neural network model to determine a control parameter of each of a plurality of antenna units in an antenna array.
Hong has a beamforming method using a deep neural network (abstract) and teaches according to the processed position information of the target satellite, using a deep neural network model to determine a control parameter of each of a plurality of antenna units in an antenna array [0007 for using MIMO antennas with neural networks to derive beamforming vectors].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the antenna calibration techniques, as disclosed by Sayegh, further including the neural network calculations as taught by Hong for the purpose of directly outputting a beamforming vector for each terminal (Hong, 0008).
Regarding Claim 11, Sayegh teaches beamforming antenna calibration system, comprising [0003 for Ground-based beamforming (GBBF) provides the same or greater flexibility than digital beamforming onboard also 0063]:
a beamforming module, comprising a transceiver and an antenna array [0058 for calibrating beamforming parameters with return link phase shifts];
and at least one processor, coupled to the beamforming module and configured to [0061]:
obtain an original position information of a target satellite [0056 for the orbital position of the satellite may communicate to the gateway and tracking the satellite];
perform a data pre-processing on the original position information to obtain a processed position information of the target satellite [0056 for matrix of excitation level based on orbital positions];
and control the antenna array to generate a target beam used to communicate with the target satellite according to the control parameter of each of the plurality of antenna units [0044 for doppler and oscillator shifts and 0058 for parameters are transmitted to the satellite].
Sayegh fails to explicitly teach according to the processed position information of the target satellite, use a deep neural network model to determine a control parameter of each of a plurality of antenna units in the antenna array.
Hong has a beamforming method using a deep neural network (abstract) and teaches according to the processed position information of the target satellite, use a deep neural network model to determine a control parameter of each of a plurality of antenna units in the antenna array [0007 for using MIMO antennas with neural networks to derive beamforming vectors].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the antenna calibration techniques, as disclosed by Sayegh, further including the neural network calculations as taught by Hong for the purpose of directly outputting a beamforming vector for each terminal (Hong, 0008).
Regarding Claim 2 and 12, Sayegh teaches the original position information of the target satellite comprises a real-time satellite information, a predetermined satellite trajectory information, and an attitude sensing information, and the processed position information of the target satellite comprises a satellite position information of the target satellite, the satellite position information varying over time [0044 for doppler and oscillator shifts and 0058 for parameters are based on phase shifts of the excitation levels and orbital positions to be transmitted from the satellite].
Regarding Claim 3 and 13, Sayegh teaches the control parameter of each of the plurality of antenna units comprises a phase control parameter and a gain control parameter [0003 for calculating both amplitude and phase values with 0058 for updating matrices parameters periodically for the satellite].
Regarding Claim 6 and 16, Sayegh fails to explicitly teach according to the processed position information of the target satellite, using the deep neural network model to determine the control parameter of each of the plurality of antenna units in the antenna array comprises: inputting the processed position information into the deep neural network model such that the deep neural network model outputs a control parameter matrix, wherein the control parameter matrix comprises the control parameter of each of the plurality of antenna units.
Hong has a beamforming method using a deep neural network (abstract) and teaches according to the processed position information of the target satellite, using the deep neural network model to determine the control parameter of each of the plurality of antenna units in the antenna array comprises [0007 for using deep neural networks with input and output layers for processing beamforming for the antennas]:
inputting the processed position information into the deep neural network model such that the deep neural network model outputs a control parameter matrix, wherein the control parameter matrix comprises the control parameter of each of the plurality of antenna units [0008 for the output layer may include M×K nodes, the M×K nodes may be grouped into K node groups].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the antenna calibration techniques, as disclosed by Sayegh, further including the neural network calculations as taught by Hong for the purpose of directly outputting a beamforming vector for each terminal (Hong, 0008).
Regarding Claim 7 and 17, Sayegh fails to explicitly teach according to the processed position information of the target satellite, using the deep neural network model to determine the control parameter of each of the plurality of antenna units in the antenna array comprises: inputting the processed position information and a noise information into the deep neural network model such that the deep neural network model outputs a satellite status information; and generating the control parameter of each of the plurality of antenna units according to the satellite status information.
Hong has a beamforming method using a deep neural network (abstract) and teaches according to the processed position information of the target satellite, using the deep neural network model to determine the control parameter of each of the plurality of antenna units in the antenna array comprises [0108 for using beamforming control for dep neural networks]:
inputting the processed position information and a noise information into the deep neural network model such that the deep neural network model outputs a satellite status information [0107-0108 for maximizing data rate for user data];
and generating the control parameter of each of the plurality of antenna units according to the satellite status information [0110 for using information on interference and noise for additional input into the neural networks].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the antenna calibration techniques, as disclosed by Sayegh, further including the neural network calculations as taught by Hong for the purpose to minimize the error between the performance value calculated (Hong, 0102).
Regarding Claim 8 and 18, Sayegh fails to explicitly teach the deep neural network model is trained by solving a nonlinear state equation of the target satellite through the deep neural network model.
Hong has a beamforming method using a deep neural network (abstract) and teaches the deep neural network model is trained by solving a nonlinear state equation of the target satellite through the deep neural network model [0102 for non-convex optimization P1 problem through iterative convex approximation to secure input-output mapping information].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the antenna calibration techniques, as disclosed by Sayegh, further including the neural network calculations as taught by Hong for the purpose to minimize the error between the performance value calculated (Hong, 0102).
Regarding Claim 9 and 19, Sayegh fails to explicitly teach the deep neural network model is trained using a training data that comprises a noise interference.
Hong has a beamforming method using a deep neural network (abstract) and teaches the deep neural network model is trained using a training data that comprises a noise interference [0092 for using different types of learning information and suing simulations with mathematical models].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the antenna calibration techniques, as disclosed by Sayegh, further including the neural network calculations as taught by Hong for the purpose to minimize the error between the performance value calculated (Hong, 0102).
Claims 4-5 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Sayegh (US 2010/0177678 A1) in view of Hong et al (US 11742901 B2), as applied to Claim 1 and 11 above, and further in view of Lovato et al (IEEE 2019).
Regarding Claim 4 and 14, Sayegh fails to explicitly teach according to the processed position information of the target satellite, using the deep neural network model to determine the control parameter of each of the plurality of antenna units in the antenna array comprises: by simulating each of the plurality of antenna units as an image pixel, operating the deep neural network model through an image acceleration hardware to determine the control parameter of each of the plurality of antenna units in the antenna array.
Lovato has a novel method of phased antenna array beamforming using a convolutional neural network (page 1247, left column, abstract) and teaches according to the processed position information of the target satellite, using the deep neural network model to determine the control parameter of each of the plurality of antenna units in the antenna array comprises [page 1247, left column, 2nd paragraph for using neural networks and GPU with trained dipoles and performing calculations such as reflections coefficients]:
by simulating each of the plurality of antenna units as an image pixel, operating the deep neural network model through an image acceleration hardware to determine the control parameter of each of the plurality of antenna units in the antenna array [page 1247, left column, last paragraph for detect features in images can be applied to beamforming by extracting features from a desired 2D radiation pattern].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the beamforming techniques, as disclosed by Sayegh, further including the GPU calculations as taught by Lovato for the purpose to detect image features applied to beamforming (Lovato, page 1247, left column, 3rd paragraph).
Regarding Claim 5 and 15, Sayegh fails to explicitly teach the image acceleration hardware comprises a graphics processing unit, a neural network processing unit, a convolutional neural network accelerator, or an artificial intelligence accelerator.
Lovato has a novel method of phased antenna array beamforming using a convolutional neural network (page 1247, left column, abstract) and teaches the image acceleration hardware comprises a graphics processing unit, a neural network processing unit, a convolutional neural network accelerator, or an artificial intelligence accelerator [page 1247, left column, 2nd paragraph for using neural networks and GPU with trained dipoles and performing calculations such as reflections coefficients and last paragraph for detect features in images can be applied to beamforming by extracting features from a desired 2D radiation pattern].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the beamforming techniques, as disclosed by Sayegh, further including the GPU calculations as taught by Lovato for the purpose to detect image features applied to beamforming (Lovato, page 1247, left column, 3rd paragraph).
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sayegh (US 2010/0177678 A1) in view of Hong et al (US 11742901 B2), as applied to Claim 1 above, and further in view of Lavi et al (US 2022/0390618 A1).
Regarding Claim 10 and 20, Sayegh fails to explicitly teach performing the data pre-processing on the original position information to obtain the processed position information of the target satellite comprises: detecting an attitude sensing information of the ground terminal device; and obtaining the processed position information of the target satellite according to the attitude sensing information of the ground terminal device.
Lavi has a method for determining attitude of an end point equipment (abstract) and teaches performing the data pre-processing on the original position information to obtain the processed position information of the target satellite comprises [0029 for using controller for raw measurements such as code phase, carrier phase, and doppler shift]:
detecting an attitude sensing information of the ground terminal device [0031];
and obtaining the processed position information of the target satellite according to the attitude sensing information of the ground terminal device [0032 for attitude of the satellite dish determined by the analysis server].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the beamforming techniques, as disclosed by Sayegh, further including the attitude calculations as taught by Lavi for the purpose to optimize the satellite dish orientation for high communication reception (Lavi, 0032).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kim et al (Scientific Reports, 2023) has a novel phase calibration methodology based on a deep neural network (DNN) architecture to enhance beamforming.
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/SAMARINA MAKHDOOM/
Examiner, Art Unit 3648
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