COMMUNICATION METHOD AND APPARATUS

DETAILED ACTION Claims 1-20 are pending. 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 . Priority Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Information Disclosure Statement The information disclosure statement (IDS) submitted on 10/07/26 and 04/24/26 has been acknowledged and considered by the examiner. Examiner Comment The examiner recommends filing a written authorization for Internet communication in response to the present action. Doing so permits the USPTO to communicate with Applicant using Internet email to schedule interviews or discuss other aspects of the application. Without a written authorization in place, the USPTO will not respond via Internet email to any Internet correspondence which contains information subject to the confidentiality requirement as set forth in 35 U.S.C. 122. The preferred method of providing authorization is by filing form PTO/SB/439, available at: https://www.uspto.gov/patent/forms/forms. See MPEP § 502.03 for other methods of providing written authorization. Specification The title of the invention is not descriptive. In particular, “Communication Method and Apparatus” covers a large number and wide scope of subjects and areas. As such, a new title is required that is clearly indicative of the invention to which the claims are directed. Claim Interpretation Regarding claims 4, 5, 11, 12, 18 and 19, the claims recite alternative language, i.e. using the term “one or more of”, and as such, the Examiner interprets certain features to not be required due to the claim language listing the features in the alternative. The rejection below specifies the particular limitations. 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. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over LI (U.S. 2024/0007890 A1) in view of Zhu et al. (U.S. 2025/0253921 A1). Regarding claims 1, 8 and 15, LI discloses a communication method and communication apparatus, comprising: measuring a reference signal (RS) from a network device by using a first RS resource set, to obtain a measurement result of the RS (LI; paragraphs 0035 and 0040; LI discloses first configuration information, sent by a network device, i.e. “from a network device”, is used to configure a first reference signal resource set which is further used for a beam measurement, i.e. “measure a reference signal (RS)…using a first RS resource set”); and inputting the measurement result into a model, to determine a target RS resource set (see LI; paragraphs 0037, 0038, 0040 and 0081; LI discloses a first number of a reference signal resource set and measurement results that are measured and obtained are inputted into a first model, i.e. “inputting the measurement result into a model”, to obtain a second number of reference signal resource set and measurement results. Based on the second number, determining a third number, i.e. “to determine a target RS resource set”. The first model is an AI model), wherein the target RS resource set is determined from a candidate RS resource set, the candidate RS resource set is determined by the model based on the measurement result, and the candidate RS resource set belongs to a second RS resource set (see LI; paragraphs 0037-0040 and 0081; LI discloses a measurement report including a third number, i.e. “target RS resource set”, of a reference signal set and reference signal resource measurement results is sent to a network device. The third number of reference signal resource set and measurement results are a subset of the second number of reference signal resource set, i.e. “wherein the target RS resource set is determined from a candidate RS resource set”). While LI discloses “inputting the measurement result into a model”, as discussed above, and further a second number and third number of reference signal resource sets and measurement results (see LI: paragraph 0040), LI does not explicitly disclose inputting the measurement result into a neural network model, to determine a target RS resource set. In analogous art, Zhu discloses inputting the measurement result into a neural network model, to determine a target RS resource set (see Zhu; paragraphs 0053 and 0056; Zhu discloses acquiring, i.e. “inputting…”, a beam measurement result, i.e. “…the measurement result”, where a beam prediction model, i.e. “…into a neural network model”, such as, a convolutional or recursive neural network model, is used and determining a first beam set, i.e. “determine a target RS resource set”). One of ordinary skill in the art would have been motivated to combine LI and Zhu because they both disclose features of beam measurement, and as such, are within the same environment. Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the feature of a neural network for determining a beam set as taught by Zhu into the system of LI in order to provide the benefit of scalability by allowing the AI model used for measurement results (LI; paragraphs 0040 and 0081) to include different types of neural network models for deep learning (see Zhu; paragraph 0053) and thus improving efficiency. Further, LI discloses the additional limitations of claim 8, at least one processor, and at least one memory storing instructions for execution by the at least one processor (see LI; paragraph 0008; LI discloses one or more processors; and one or more memories for storing instructions executable by the one or more processors). Further, LI discloses the additional limitations of claim 15, at least one processor, and at least one memory storing instructions for execution by the at least one processor (see LI; paragraph 0008; LI discloses one or more processors; and one or more memories for storing instructions executable by the one or more processors); send a reference signal (RS) to a terminal by using a first RS resource set (see LI; paragraphs 0033 and 0040; LI discloses a terminal measures reference signal resources in a reference signal resource set); and receive a measurement result that is of the RS and that is fed back by the terminal (see LI; paragraph 0040; LI discloses the terminal feeds back, i.e. “fed back by the terminal”, the reference signal resource measurement results with the strongest signal strength among the second number of reference signal resource measurement results to the network device). Regarding claims 2, 9 and 16, LI and Zhu disclose all the limitations of claims 1, 8 and 15, as discussed above, and further the combination of LI and Zhu clearly discloses wherein the target RS resource set is determined from the candidate RS resource set based on a first threshold and information about the candidate RS resource set, and the first threshold is based on the information about the candidate RS resource set (see LI; paragraphs 0037-0040 and 0065; LI discloses the third number of reference signal resource set, i.e. “target RS resource set”, and measurement results are a subset of the second number of reference signal resource set, i.e. “candidate RS resource set”. One or more of the reference signal sets, i.e. “target RS resource set”, is based on a first precision threshold, i.e. “based on a first threshold…”). Regarding claims 3, 10 and 17, LI and Zhu disclose all the limitations of claims 2, 9 and 16, as discussed above, and further the combination of LI and Zhu clearly discloses wherein that the candidate RS resource set is determined by the neural network model based on the measurement result is as follows: the information about the candidate RS resource set is determined by the neural network model based on the measurement result (see LI; paragraphs 0037-0040, 0065 and 0081 ; LI discloses the third number of reference signal resource set, i.e. “target RS resource set”, and measurement results are a subset of the second number of reference signal resource set, i.e. “candidate RS resource set”. The measurement results that are measured and obtained are inputted into an AI model, i.e. “determined by the model based on the measurement result”. Further, Zhu discloses acquiring a measurement result where a neural network model is used to determine a beam set; see Zhu; paragraphs 0053 and 0056). The prior art used in the rejection of the current claim is combined using the same motivation as was applied in claims 1, 8 and 15. Regarding claims 4, 11 and 18, LI and Zhu disclose all the limitations of claims 3, 10 and 17, as discussed above, and further the combination of LI and Zhu clearly discloses wherein the information about the candidate RS resource set comprises one or more of a probability that each RS resource in the candidate RS resource set is an optimal RS resource, signal quality of each RS resource in the candidate RS resource set (see LI; paragraphs 0040, 0047; LI discloses terminal feeds back the reference signal resource measurement results with the strongest signal strength, i.e. “signal quality…”, among the second number of reference signal resource measurement results, i.e. “of each RS resource in the candidate RS resource set”, to the network device), and an angle of each RS resource in the candidate RS resource set, wherein the angle of each RS resource in the candidate RS resource set is an angle difference between a transmission beam and a reception beam that correspond to the RS resource (The claim list features in the alternative. While the claim lists a number of optional features only one feature from the list is required and needs to be met by the prior art. The Examiner has chosen the “signal quality…” alternative). Regarding claims 5, 12 and 19, LI and Zhu disclose all the limitations of claims 4, 11 and 18, as discussed above, and further the combination of LI and Zhu clearly discloses wherein information about the target RS resource set and the first threshold meet one or more of the following relationships: a probability that an RS resource in the target RS resource set is an optimal RS resource is greater than a probability represented by the first threshold, signal quality of an RS resource in the target RS resource set is greater than signal quality represented by the first threshold (see LI; paragraphs 0037-0040 and 0065; LI discloses the terminal feeds back the reference signal resource measurement results with the strongest signal strength, i.e. “signal quality…”, among the second number of reference signal resource measurement results, i.e. “of each RS resource in the candidate RS resource set”, to the network device the third number of reference signal resource set, i.e. “target RS resource set”, and measurement results are a subset of the second number of reference signal resource set, i.e. “candidate RS resource set”. One or more of the reference signal sets, i.e. “target RS resource set”, is greater based on a first precision threshold, i.e. “greater than signal quality represented by the first threshold”), and an angle of each RS resource in the RS resource set is less than an angle defined by the first threshold (The claim list features in the alternative. While the claim lists a number of optional features only one feature from the list is required and needs to be met by the prior art. The Examiner has chosen the “signal quality…” alternative). Regarding claims 6 and 13, LI and Zhu disclose all the limitations of claims 2, 9 and 16, as discussed above, and further the combination of LI and Zhu clearly discloses receiving indication information from the network device, wherein the indication information indicates the first threshold (see LI; paragraphs 0037-0040 and 0065; LI discloses the third number of reference signal resource set and measurement results are a subset of the second number of reference signal resource set. One or more of the reference signal sets is based on a first precision threshold, i.e. “indicates the first threshold”). Regarding claims 7, 14 and 20, LI and Zhu disclose all the limitations of claims 2, 9 and 16, as discussed above, and further the combination of LI and Zhu clearly discloses wherein the first threshold is pre-configured in the neural network model (see LI; paragraphs 0062, 0065, 0082 and 0108; LI discloses the model is pre-trained with the precision threshold; Further, Zhu discloses acquiring a measurement result where a neural network model is used to determine a beam set; see Zhu; paragraphs 0053 and 0056). The prior art used in the rejection of the current claim is combined using the same motivation as was applied in claims 1, 8 and 15. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Chavva et al. (U.S. 2021/0351885 A1) discloses beam selection and beam scheduling based on reference signal received power prediction using a neural network. Pezeshki et al. (U.S. 2026/0197245 A1) discloses predicting one or more beam parameters based on reference signals with use of a neural network. Zhang et al. (U.S. 2021/0337406 A1) measurement reference signal of a predetermined type includes at least one of a measurement reference signal for beam failure detection, a candidate reference signal resource. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM A COONEY whose telephone number is (571)270-5653. The examiner can normally be reached M-F 7:30am-5:00pm (every other Fri off). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Umar Cheema can be reached at 571-270-3037. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /A.A.C/Examiner, Art Unit 2458 09/28/26 /UMAR CHEEMA/Supervisory Patent Examiner, Art Unit 2458