DETAILED ACTION
Response to Amendment
The amendment filed September 10, 2026 has been entered.
Claim 1, 8, 11, and 18 are amended.
Claim 7 and 17 are cancelled
Claims 1-6, 8-16, and 18-20 are pending this application
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, 8-13, 16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over McCormick (US 11881914 B1) in view of Hong et al (US 2022/0029665 A1) and Gupta (US 2021/0341625 A1).
Regarding Claim 1, McCormick teaches a beamforming antenna calibration method, adapted to a ground terminal device, comprising [figure 1 for satellites and ground devices]:
obtaining an original position information of a target satellite [col 19, lines 25-35 for current satellite ephemeris and GPS data e.g., the current position of the user equipment];
performing a data pre-processing on the original position information to obtain a processed position information of the target satellite [col 19, lines 50-60 for resulting unit pointing vector is rotated by the current or initial)panel orientation estimate qeceJ2body to form the antenna unit pointing vector];
according to the processed position information of the target satellite, [col 19, lines 55-65 for compute steering angles component 1215 can receive the GPS data, satellite ephemeris, and panel orientation estimates as inputs];
and controlling the antenna array to generate a target beam used to communicate with the target satellite according to the control parameter [col 8, lines 45-55 for Transceiver is dynamically configured in order for the user equipment's and col 19, lines 1-5 for satellite ephemeris pointing direction in ECEF coordinates to the actual phased array antenna],
wherein according to the processed position information of the target satellite [col 19, lines 55-65 for panel orientation estimates as inputs, and produce the antenna unit pointing vector as an output],
wherein the processed position information of the target satellite comprises relative position information of the target satellite with respect to the ground terminal device [col 20, lines 5-10 for tracking perturbations to be added around the antenna unit pointing vector];
and generating the control parameter of each of the plurality of antenna units according to the satellite status information [col 19, lines 40-55 for pointing vector -J is calculated from the ECEF position of the user equipment].
McCormick fails to explicitly teach using a deep neural network model to determine a control parameter of each of a plurality of antenna units in an antenna array and using the deep neural network model to determine the control parameter of each of the plurality of antenna units in the antenna array comprises.
Hong has a beamforming method using a deep neural network (abstract) and teaches a control parameter of each of a plurality of antenna units in an antenna array [0070 for K node groups each of which is composed of M nodes may output beamforming vectors]
and using the deep neural network model to determine the control parameter of each of the plurality of antenna units in the antenna array comprises [0098 for derive beamforming vectors in real time through simple linear matrix product operations in the deep neural network, 0110 for having interference and noise in DNN by individual terminals].
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 McCormick, further including the neural network calculations as taught by Hong for the purpose to minimize the error between the performance value calculated (Hong, 0102).
McCormick fails to explicitly teach 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.
Gupta has a first global navigation satellite systems signal data is obtained from a set of GNSS satellites (abstract) and teaches 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 [0055 for process input GNSS signal data to generate output satellite state data, such as orbit data and/or clock data].
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 McCormick, further including the satellite status calculations as taught by Gupta for the purpose to generate a set of parameters that define a clock error of one or more GNSS satellites (Gupta, 0055).
Regarding Claim 11, McCormick teaches a beamforming antenna calibration system, comprising [figure 1 for satellites and ground devices]:
obtaining an original position information of a target satellite [col 19, lines 25-35 for current satellite ephemeris and GPS data e.g., the current position of the user equipment]:
a beamforming module, comprising a transceiver and an antenna array [col 19, lines 50-60 for resulting unit pointing vector is rotated by the current or initial)panel orientation estimate qeceJ2body to form the antenna unit pointing vector];
and at least one processor, coupled to the beamforming module and configured to: obtain an original position information of a target satellite [col 19, lines 50-60 for resulting unit pointing vector is rotated by the current or initial)panel orientation estimate qeceJ2body to form the antenna unit pointing vector];
perform a data pre-processing on the original position information to obtain a processed position information of the target satellite [col 19, lines 55-65 for compute steering angles component 1215 can receive the GPS data, satellite ephemeris, and panel orientation estimates as inputs];
according to the processed position information of the target satellite [figure 12 for computing steering angle];
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 [col 8, lines 45-55 for Transceiver is dynamically configured in order for the user equipment's and col 19, lines 1-5 for satellite ephemeris pointing direction in ECEF coordinates to the actual phased array antenna],
wherein the processed position information of the target satellite comprises relative position information of the target satellite with respect to the ground terminal device [col 20, lines 5-10 for tracking perturbations to be added around the antenna unit pointing vector];
and generating the control parameter of each of the plurality of antenna units according to the satellite status information [col 19, lines 40-55 for pointing vector -J is calculated from the ECEF position of the user equipment].
McCormick fails to explicitly teach 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 use a deep neural network model to determine a control parameter of each of a plurality of antenna units in the antenna array [0098 for derive beamforming vectors in real time through simple linear matrix product operations in the deep neural network, 0110 for having interference and noise in DNN by individual terminals].
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 McCormick, further including the neural network calculations as taught by Hong for the purpose to minimize the error between the performance value calculated (Hong, 0102).
McCormick fails to explicitly teach wherein the at least one processor is configured to: 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.
Gupta has a first global navigation satellite systems signal data is obtained from a set of GNSS satellites (abstract) and teaches 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 [0055 for process input GNSS signal data to generate output satellite state data, such as orbit data and/or clock data].
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 McCormick, further including the satellite status calculations as taught by Gupta for the purpose to generate a set of parameters that define a clock error of one or more GNSS satellites (Gupta, 0055).
Regarding Claim 2 and 12, McCormick teaches the original position information of the target satellite comprises a real-time satellite information [col 6, lines 35-45 for single satellite can send downlink messages to the user equipment containing its updated ephemeris],
a predetermined satellite trajectory information, and an attitude sensing information [col 6, lines 25-35 for becomes outdated, since the exact satellite trajectories are subject to change depending on a number of factors such as, but not limited to, the condition and health of constituent],
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 [col 8, lines 25-35 for detect changes in pitch, roll, and/or yaw of user equipment with col 19, lines 45-50 for satellite ECEF position (provided from downlink satellite ephemeris and eq (10)].
Regarding Claim 3 and 13, McCormick fails to explicitly teach the control parameter of each of the plurality of antenna units comprises a phase control parameter and a gain control parameter.
Hong has a beamforming method using a deep neural network (abstract) and teaches the control parameter of each of the plurality of antenna units comprises a phase control parameter and a gain control parameter [0070 for K node groups each of which is composed of M nodes may output beamforming vectors and 0078 for beam direction recovery may be finally combined with pk].
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 McCormick, 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 6 and 16, McCormick 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: [0098 for derive beamforming vectors in real time through simple linear matrix product operations in the deep neural network, 0110 for having interference and noise in DNN by individual terminals ]
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 [0070 for K node groups each of which is composed of M nodes may output beamforming vectors and 0078 for beam direction recovery may be finally combined with pk].
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 McCormick, 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, McCormick 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.
Gupta has a first global navigation satellite systems signal data is obtained from a set of GNSS satellites (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 [0006 for non-linear dynamical system can be highly sensitive to small effects at an earlier state, 0060-0063 for generate the satellite state model by training the satellite state mode].
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 McCormick, further including the satellite status calculations as taught by Gupta for the purpose to generate a set of parameters that define a clock error of one or more GNSS satellites (Gupta, 0055).
Regarding Claim 9 and 19, McCormick 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 McCormick, 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 10 and 20, McCormick teaches performing the data pre-processing on the original position information to obtain the processed position information of the target satellite comprises [col 19, lines 30-40 for orientation tracking component is configured to calculate the current antenna unit pointing vector for the phased array antenna of the user equipment]:
detecting an attitude sensing information of the ground terminal device [col 8, lines 25-30 for IMU configured to detect pitch, roll, and yaw];
and obtaining the processed position information of the target satellite according to the attitude sensing information of the ground terminal device [col 19, lines 50-60 for unit pointing vector is rotated by current panel orientation].
Claims 4-5 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over McCormick (US 11881914 B1) in view of Hong et al (US 2022/0029665 A1) and Gupta (US 2021/0341625 A1), as applied to Claim 1 and 11 above, and further in view of Lovato et al (IEEE 2019).
Regarding Claim 4 and 14, McCormick 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.
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 [0098 for derive beamforming vectors in real time through simple linear matrix product operations in the deep neural network, 0110 for having interference and noise in DNN by individual terminals].
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 McCormick, further including the neural network calculations as taught by Hong for the purpose to minimize the error between the performance value calculated (Hong, 0102).
McCormick fails to explicitly teach 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 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 McCormick, 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, McCormick 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 McCormick, 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).
Response to Arguments
Applicant’s arguments with respect to claims 1-6, 8-16, and 18-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
In applicant’s arguments page 8, last paragraph of applicant’s arguments, the applicant states that Sayegh does not teach the matrix excitation levels. The examiner thanks the applicant for the amendments, new reference McCormick teaches calculating azimuth and elevation [McCormick, col 8, lines 28-35].
In applicant’s arguments page 9, third paragraph of applicant’s arguments, the applicant states that Hong does not teach the deep neural network for generating control parameters for antennas. The examiner respectfully disagrees: Hong’s deep neural network outputs a separate beamforming setting for each of the M antennas [Hong, 0070 for K node groups each of which is composed of M nodes may output beamforming vectors].
In applicant’s arguments page 10, second paragraph of applicant’s arguments, the applicant states that Hong does not teach the deep neural network for position and noise information. The examiner respectfully disagrees: Hong feeds noise information straight into its DNN input layer [Hong, 0110, σ1 2, . . . , σK 2 may be used as interference and noise information measured by individual user terminals] and Gupta’s neural network takes in satellite signal data and outputs the satellite’s orbit and attitude [Gupta, 0055 generate output satellite state data, such as orbit data and 0057 for satellite attitude data].
The examiner acknowledges that this is a broader interpretation than Applicant’s. However, examiners are not only allowed to apply broad interpretations, but are required to do so, as it reduces the possibility that the claims, once issued, will be interpreted more broadly than is justified. MPEP §2111. Patentability is determined by the “broadest reasonable interpretation
consistent with the specification” (MPEP §2111), not the narrowest reasonable interpretation. And Applicant does not have an explicit lexicographical statement in line with MPEP §2111.01
subsection IV requiring a specific interpretation of the relevant phrases which forces the examiner to interpret them only one way.
The express, implicit, and inherent disclosures of a prior art reference may be relied upon in the rejection of claims under 35 U.S.C. 102 or 103. "The inherent teaching of a prior art reference, a question of fact, arises both in the context of anticipation and obviousness." In re Napier, 55 F.3d 610, 613, 34 USPQ2d 1782, 1784 (Fed. Cir. 1995).
For applicant’s benefit, portions of the cited reference(s) have been cited to aid in the review of the rejection(s). While every attempt has been made to be thorough and consistent within the rejection it is noted that the PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, including disclosures that teach away from the claims. See MPEP 2141.02 VI.
“The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments. Merck & Co. v.Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005) See MPEP 2123.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMARINA MAKHDOOM whose telephone number is (703)756-1044. The examiner can normally be reached Monday – Thursdays from 8:30 to 5:30 pm eastern time.
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/SAMARINA MAKHDOOM/
Examiner, Art Unit 3648
