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 Objections
Claim 1 is objected to because of the following informalities: On line 5, of claim 1, the Acronym “MISO IC” is recited without reciting the description, which is “Multiple Input Single Output Interference Channel”. Appropriate correction is required.
Information Disclosure Statement
3. The information disclosure statements (IDSs) submitted on 11/5/2024 and 7/28/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 6, 8 and 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Hong et al. (US. Pub. No. 2024/0088952 A1) in view of Hong et al. (US. Pub. No. 2022/0029665 A1, referred to as Hong’665).
Regarding claim 1, Hong discloses a method performed by a beamforming vector generation apparatus (See Hong; Fig. 2; Communication Node 200) operated by a processor (See Hong; Fig. 2; Processor 210), the method comprising
an operation of acquiring local channel information for base station k (k is identification information of a base station) among base stations constituting a MISO IC, weight information of base station k, and passive interference information (See Eq. 6, 7 (7c, 7d), 13, 14; FIGS. 6, 9-11 (steps S610-S630)) of other base stations affecting a control signal strength of base station k (See Par. [3]-[4], [83]-[85] of Hong for a reference to that when multiple user data is transmitted simultaneously in space using multiple antennas, interference between users also increases. Local channel gain is obtained. The distributed MIMO system utilizes local channel gain between distributed antennas and users to calculate beamforming vectors that coordinate interference between the users [distributed APs cooperating in beamforming]);
an operation of deriving active interference information of base station k affecting a control signal strength of other base stations based on a first neural network model learned to derive active interference information for generating a beamforming vector of a preset purpose from the local channel information, the weight information, and the passive interference information (See Par. [9], [133]-[135], [151] of Hong for a reference to a bipartite graph neural network (BGNN) includes antenna-message, user-message, and beam-characteristic DNNs, with messages containing reception- and transmission-beam information. user-message generation, antenna-message generation, and beam-characteristic determination operations are identified); and
an operation of deriving a beamforming vector of base station k based on the passive interference information, the active interference information, and the dual variable using a beam recovery function (See Par. [108], [235]-[236] of Hong for a reference to a deep learning beamforming optimizer 400 may include a deep neural network 410 and a beam-recovery block 420. The beamforming optimizer 400 may receive a channel matrix H as input and may output an optimal beamforming matrix. The last learned beam characteristic is used to obtain the final beamforming solution).
Hong does not explicitly disclose an operation of deriving a dual variable of base station k based on a second neural network model trained to derive a dual variable for generating a beamforming vector of a preset purpose from the local channel information, the passive interference information, and the active interference information.
However, Hong’665 discloses an operation of deriving a dual variable of base station k based on a second neural network model trained to derive a dual variable for generating a beamforming vector of a preset purpose from the local channel information, the passive interference information, and the active interference information (See Par. [108], [235]-[236] of Hong’665 for a reference to two node groups output predicted values in an optimal beamforming solution. Predicted p and q values are transformed to beam-direction information, which rely on uplink-downlink beamforming duality).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Hong’665 to Hong. The motivation for combination would be to improve the system’s performance, by providing an efficient beamforming method that can minimize the number of deep learning parameters to be trained, and operate as a single deep neural network even under various power constraints of the radio system. (Hong’665; Par. [50])
Regarding claim 2, Hong discloses the weight information of base station k includes weight information set in advance for base station k among the base stations constituting the MISO IC (See Par. [3]-[4], [83]-[85] of Hong for a reference to that when multiple user data is transmitted simultaneously in space using multiple antennas, interference between users also increases. Local channel gain is obtained. The distributed MIMO system utilizes local channel gain between distributed antennas and users to calculate beamforming vectors that coordinate interference between the users).
Hong does not explicitly disclose wherein the local channel information for base station k includes Channel State Information (CSI) of base station k affecting other receivers.
However, Hong’665 discloses wherein the local channel information for base station k includes Channel State Information (CSI) of base station k affecting other receivers (See Par. [7], [92] of Hong’665 for a reference to obtaining channel information between a base station and K terminals and a transmit-power limit P, and using channel information and the power limit as learning inputs).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Hong’665 to Hong. The motivation for combination would be to improve the system’s performance, by providing an efficient beamforming method that can minimize the number of deep learning parameters to be trained, and operate as a single deep neural network even under various power constraints of the radio system. (Hong’665; Par. [50])
Regarding claim 3, the combination of Hong and Hong’665, specifically Hong discloses wherein the first neural network model and the second neural network model are trained based on an interference temperature constraint configured of scalar information as a feature value reflecting distribution information of base stations constituting the MISO IC, and each of the base stations constituting the MISO IC includes the same first neural network model and second neural network model (See Par. [27], [239], [259], [267] of Hong for a reference to the BGNN uses three types of reusable component DNNs, and random bipartite graphs as training samples. The serial BGNNs may all have the same structure. The BGNN through distributed and parallel implementation configures corresponding DNNs at each AP).
Regarding claim 6, the combination of Hong and Hong’665, specifically Hong discloses wherein the first neural network model is trained in a direction that maximizes an expected value of an achievable transmission rate of the base station according to a predetermined objective function, and includes parameters learned to derive active interference information for generating a beamforming vector of the preset purpose from the local channel information, the weight information, and the passive interference information (See Par. [241]-[245], Eq. 4, 4a, 17, 18 and Fig. 13 of Hong for a reference to BGNN parameters are trained to maximize the channel-averaged utility of achievable rate. total or minimum user data rate can be maximized; train component DNN parameters by SGD to improve aggregated utility).
Regarding claim 8, Hong does not explicitly disclose wherein the second neural network model is trained in a direction that minimizes an expected value of a dual variable according to a predetermined objective function, and includes parameters learned to derive a dual variable for generating a beamforming vector of the preset objective from the local channel information, the passive interference information, and the active interference information.
However, Hong’665 discloses wherein the second neural network model is trained in a direction that minimizes an expected value of a dual variable according to a predetermined objective function, and includes parameters learned to derive a dual variable for generating a beamforming vector of the preset objective from the local channel information, the passive interference information, and the active interference information (See Par. [19], [26], [77]-[86] of Hong’665 for a reference to predicting variables based on uplink-downlink beamforming duality; training the neural network under a rate loss; recovering beam direction from predicted p/q optimization features).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Hong’665 to Hong. The motivation for combination would be to improve the system’s performance, by providing an efficient beamforming method that can minimize the number of deep learning parameters to be trained, and operate as a single deep neural network even under various power constraints of the radio system. (Hong’665; Par. [50])
Regarding claim 10, the combination of Hong and Hong’665, specifically Hong discloses wherein as the first neural network model and the second neural network model are alternately trained in a method of learning the second neural network model after learning the first neural network model for each epoch during the learning process using the same data set of local channel information and data set of weight information, each parameter is updated by the same samples of local channel information and weight information for each epoch (See Par. [14], [39], [241]-[245], [249]-[251] and Fig. 13 of Hong for a reference to constructing mini-batches from training epochs and updates the respective BGNN DNNs. Connecting component networks in series with the same structure; BGNN parameters are trained to maximize the channel-averaged utility of achievable rate. total or minimum user data rate can be maximized; train component DNN parameters by SGD to improve aggregated utility; dividing a training dataset into epoch/mini-batch sets).
Regarding claim 11, the claim is interpreted and rejected for the same reason as set forth
in claim 1, including a beamforming vector generation apparatus (See Hong; Fig. 2; Communication Node 200) comprising: a memory containing instructions (See Hong; Fig. 2; Memory 220); and a processor performing predetermined operations based on the instructions (See Hong; Fig. 2; Processor 210).
Allowable Subject Matter
Claims 4-5, 7 and 9 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
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gandhi et al. (US. Pub. No. 2025/0219685 A1) discloses a method and apparatus for coordinating beamforming sounding across numerous bands of a multi-link operation.
Raghavan et al. (US. Pub. No. 2024/0380448 A1) discloses techniques and apparatuses for switching between polarization and spatial multiple-input-multiple-output (MIMO) based on a multiband antenna.
Haustein et al. (US. Pub. No. 2023/0189315 A1) discloses a method for handling inter-cell interference and cross-link interference.
8. Any inquiry concerning this communication from the examiner should be directed to RASHA FAYED whose telephone number is (571) 270-3804. The examiner can normally be reached on M-F 8:00AM-4:30PM.
If attempts to reach the examiner by telephone are unsuccessful, the supervisory Examiner, Un Cho can be reached on (571)272-7919. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/R.K.F/Examiner, Art Unit 2413
/UN C CHO/Supervisory Patent Examiner, Art Unit 2413
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