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. DETAILED ACTION Claims 1-17 received on 12/01/2023 have been examined , of which claims 1, 9, 13 are independent . Claim Rejections - 35 USC § 102 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) 1, 3-5, 7- 9 , 13, 15-17 is/are rejected under 35 U.S.C. 102 (a)(2) as being anticipated by Chai et al. ( US 20240346384 ) . Regarding claim 1, Chai teaches a method of a terminal ( communication method and second node in fig 4; para 117: the first node is an access network device, and the second node is a terminal device ) , comprising: receiving augmentation assistance information from a base station ( fig 4; para 116: S401: t he second node obtains first data and first information from the first node, where the first information indicates a data augmentation manner of the first data ; here, the first information is considered augmentation assistance information ) ; generating augmented channel data by applying the augmentation assistance information to original channel data ( fig 4; para 133: S402: The second node determines a first training dataset of the model based on the first data and the first information ; para 12: the type of the first data include s channel data ; here, the first data is considered original channel data ) ; and training an artificial neural network using the augmented channel data ( fig 4; para 137 : S403: The second node trains the model based on the first training dataset ) . Regarding claim 9, Chai teaches a method of a base station ( communication method and second node in fig 4; para 117: t he first node may be a terminal device, and the second node is an access network device ; the access network device is considered as base station ) , comprising: transmitting augmentation assistance information to a terminal ( fig 4; para 142-14 4 : S404: The second node sends third information to the first node , the performance of the model obtained through training based on the first training dataset ) ; receiving augmented channel data from the terminal ( fig 4; para 145 : S 405: t he first node sends the second data to the second node) , the augmented channel data being generated by applying the augmentation assistance information to original channel data ( para 144-148: the first node may determine, based on the third information, that the model does not meet the performance requirement , the third information includes a first flag bit (or referred to as a first field), and a value of the first flag bit may be predefined, and indicates to request the second data , the second node obtains a small amount of training data and a small quantity of data augmentation manners from the first node, and generates the training data actually used for model training) ; and training an artificial neural network using the augmented channel data ( para 147-148: S406: The second node performs update training on the model based on the second data and the first training dataset, the second node obtains a small amount of training data and a small quantity of data augmentation manners from the first node, and generates the training data actually used for model training ) . Regarding claim 13, Chai teaches a terminal ( communication method and second node in fig 4; para 117: the first node is an access network device, and the second node is a terminal device ; communication apparatus 1300, fig 13 ) comprising a processor ( processor 1310, fig 13 ) , wherein the processor causes the terminal to perform ( para 206: t he processor 1310 may execute the computer program stored in the memory 1320, to complete the method) : receiving augmentation assistance information from a base station ( fig 4; para 116: S401: t he second node obtains first data and first information from the first node, where the first information indicates a data augmentation manner of the first data ; here, the first information is considered augmentation assistance information ) ; generating augmented channel data by applying the augmentation assistance information to original channel data ( fig 4; para 133: S402: The second node determines a first training dataset of the model based on the first data and the first information ; para 12: the type of the first data include s channel data ; here, the first data is considered original channel data ) ; and training an artificial neural network using the augmented channel data ( fig 4; para 137 : S403: The second node trains the model based on the first training dataset ) . Regarding claim 3 and 15, Chai further teaches before the generating of the augmented channel data, receiving channel environment change information from the base station ( para 116: S401: The second node obtains first data and first information from the first node , where the first information indicates a data augmentation manner of the first data ; para 123: for channel data, an available data augmentation manner includes but is not limited to one or more of the following: channel flipping, channel interception, channel scaling, channel shift, noise addition, channel cross-replacement, virtual transmission, or data augmentation performed by using a generative AI model. For ease of understanding, the following describes, with reference to the accompanying drawings, the data augmentation manner applicable to the channel data ) , wherein the augmented channel data is generated by reflecting the received channel environment change information and applying the augmentation assistance information to the original channel data ( para 133-134: S402: the second node determines a first training dataset of the model based on the first data and the first information, the second node determines the first training dataset of the model based on the first data and the data augmentation manner of the first data, the data augmentation manner of the first data corresponds to the data augmentation manner indicated by the first information in S401 ) . Regarding claim 4 and 16 , Chai further teaches wherein the channel environment change information includes at least one of information on a channel bandwidth or information on a number of subbands (para 1 24: in channel flipping dimensions of frequency domains are exchange, FIG. 5A shows a frequency domain channel including 72 subcarriers , t he frequency domain channel in an order of subcarrier indexes from 1 to 72 is flipped in a frequency domain dimension, in other words, a new frequency domain channel shown in FIG. 5B may be generated in an order of subcarrier indexes from 72 to 1 , i n this example, the first data may include the frequency domain channel shown in FIG. 5A, and the first training dataset includes a new frequency domain channel shown in FIG. 5B ) . Regarding claim 5 and 17 , Chai further teaches wherein the augmentation assistance information includes at least one of a correspondence relationship between the original channel data and the augmented channel data ( para 123-124 describes data augmentation manner (first information) as channel flipping by exchanging dimension of subcarriers or frequency domain as shown in fig 5a-5b ) , information on an artificial neural network model for generating the augmented channel data from the original channel data ( para 123: for channel data, an available data augmentation manner includes but is not limited to one or more of the following: channel flipping, channel interception, channel scaling, channel shift, noise addition, channel cross-replacement, virtual transmission, or data augmentation performed by using a generative AI model) , or information on a correspondence relationship between a first antenna configuration related to the original channel data and a second antenna configuration related to the augmented channel data ( para 124: t he channel flipping includes that in one or more dimensions of antenna domain (or space domain), positions of a channel element s are exchanged) . Regarding claim 7, Chai further teaches before the receiving of the augmentation assistance information from the base station ( fig 10; para 180 : S1004: t he first node sends first information to the second node (after request in s1003) ) , requesting transmission of the augmentation assistance information from the base station ( fig 10; para 177: S1003: The second node sends fourth information to the first node, where the fourth information is used to request a data augmentation manner of the first data) . Regarding claim 8, Chai further teaches before the receiving of the augmentation assistance information from the base station ( fig 9, prior to step S902: The first node sends the first data and first information to the second node) , transmitting, to the base station, terminal capability information including information on whether the terminal is able to utilize the augmentation assistance information ( fig 9; para 1 50 -157: S901: t he second node sends second information to the first node , f or a data augmentation manner supported by the second node, the second information may also be understood as capability information of the second node, and indicates the first node to provide the first data and the data augmentation manner that meet the capability of the second node ) ; and receiving the augmentation assistance information transmitted from the base station based on the terminal capability information ( para 158-159: S902: The first node sends the first data and first information to the second node, the first node may determine the to-be-sent first data based on the second information, when the second information includes information that indicates the data augmentation manner supported by the second node, the first node may determine, based on the second information, the data augmentation manner indicated by the first information ) . 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. The factual inquiries set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 6 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Chai et al. (US 20240346384) Regarding claim 6, Chai teaches the limitations of the parent claim. Chai further teaches in claim 5 that the correspondence information between first antenna configuration related to the original channel data and second antenna configuration related to the augmented channel data by teaching the channel flipping that includes exchanging positions of channel elements in one or more dimensions of antenna domains (para 124). Chai does not teach term linear transformation relation between the antennas. However, Chai further teaches in para 123-124 describes data augmentation manner for channel flipping of frequency domain or antenna (space) domain as exchanging channel element pos itions of channel indexes. Para 124 further describes example of switching subcarrier index 1-72 for specific channel gain to index 72-1 as shown in fig 5a and 5b, which is example of a linear transformation . This method of channel flipping example of frequency domain can be applied to antenna domain for the linear transformation between antennas, which teaches a linear transformation relationship between the first antenna configuration and the second antenna configuration . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine frequency domain channel flipping for data augmentation and training model as taught by Chai with antenna domain channel flipping for data augmentation and training model as taught by Chai for the benefit of reducing transmission overheads of training data and improving performance of an artificial intelligence model as taught by Chai in para 4 . Regarding claim 10, Chai teaches the limitations of the parent claim. Chai further teaches before the transmitting of the augmentation assistance information to the terminal (fig 4, step S404) , receiving, from the terminal, a request for transmission (para 116: S401: the second node obtains first data and first information from the first node, where the first information indicates a data augmentation manner of the first data; para 140: the second node may determine whether the performance of the model obtained through training based on the first training dataset meets the performance requirement. When the performance of the model does not meet the performance requirement, the second node obtains the second data from the first node. ) ; and transmitting the augmentation assistance information to the terminal in response to the request for transmission of the augmentation assistance information (fig 4; para 142-144: S404: The second node sends third information to the first node, the performance of the model obtained through training based on the first training dataset). Chai does not teach terminal (first node) sending message in the step s401 specifically requesting the transmission of the augmentation assistance information . However, Chai teaches a transmission ( first information and first data ) from terminal (first node) in s401, that causes the base station (second node) to train the model, determine that performance requirements are not met, and transmit the performance of the model in third information (assistance information) in step s404. Thus , third information in step s404 is considered to be sent in response to receiving first information in steps s401, and transmission of information in step s401 is considered receiving, from the terminal, a request for transmission of the augmentation assistance information . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine sending information and data to train the model as taught by Chai with determining model performance and sending information for updating the model as taught by Chai for the benefit of reducing transmission overheads of training data and improving performance of an artificial intelligence model as taught by Chai in para 4. Claims 2 and 14 are rejected unde r 35 U.S.C. 103 a s being unpatentable over Chai et al. ( US 20240346384 ) in view of Zhou et al. ( US 20220132350 ) Regarding claim 2 and 14 , Chai teaches the limitations of the parent claim. Chai teaches channel flipping by exchanging antenna configuration of channel elements. However, the reference does not teach receiving antenna configuration from the base station. Zhou is directed to downlink reference signal reports for antenna panels. Zhou teaches before the generating of the augmented channel data ( 615, fig 6; 730, fig 7 ; para 100: the UE 120 may transmit, and the base station 110 may receive, a report based at least in part on measuring the at least one first downlink reference signal using the first antenna panel ) , receiving default antenna configuration information from the base station (605, fig 6; 710, fig 7 ; para 91: with reference number 605, the base station 110 may transmit, and the UE 120 may receive, a first indication of at least one first downlink reference signal ; para 93: the first indication may include a panel identifier associated with the first antenna panel (e.g., an alphanumeric, hexadecimal, binary, numeric, and/or string-based identifier assigned to the first antenna panel by the UE 120 and/or the base station 110) ) ; receiving information on measurement resources according to the default antenna configuration information from the base station ( para 128: the first indication associates a CSI-RS resource configuration with a plurality of antenna port groups, where each antenna port group is associated with a corresponding TCI state associated with one of the at least one first downlink reference signal or the at least one second downlink reference signal and a corresponding one of the first antenna panel or the second antenna panel ) ; and generating the original channel data through measurement on the measurement resources ( 61 0 , fig 6; 7 2 0, fig 7 ; para 96: with reference number 610, the base station 110 may transmit, and the UE 120 may measure using the first antenna panel, the at least one first downlink reference signal ) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine AI model training based on channel data and data augmentation information as taught by Chai with antenna configuration and measurement of channel data as taught by Zhou for the benefit of increasing communication reliability and quality as taught by Zhou in para 88 . Claims 11-12 are rejected unde r 35 U.S.C. 103 a s being unpatentable over Chai et al. ( US 20240346384 ) in view of Jeon et al. (US 20220294666) Regarding claim 11, Chai teaches the limitations of the parent claim. Chai further teaches the capability of second node indicated to the first node. However, the reference does not teach terminal capability in the instance when the second node is base station and trains the model. Jeon is similarly directed to AI/ML techniques for channel estimation and mobility enhancements. Jeon further teaches before the transmitting of the augmentation assistance information to the terminal ( step 402, fig 4; para 81: a t operation 402, the BS sends the configuration information to the UE, which can include AI/ML related configuration information) , receiving, from the terminal, terminal capability information including information on whether the terminal is able to utilize the augmentation assistance information ( fig 4; para 81: a t operation 401, a BS receives the UE capability information from a UE, including the support of AI/ML approach for UL channel prediction) ; and transmitting the augmentation assistance information to the terminal based on the terminal capability information ( para 81: a t operation 402, the BS sends the configuration information to the UE, which can include AI/ML related configuration information such as enabling/disabling of ML approach for UL channel prediction) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine AI model training based on channel data and data augmentation information as taught by Chai with capability information of terminal to enable AI approach for UL channel prediction as taught by Jeon for the benefit of improving system performance as taught by Jeon in para 50 . Regarding claim 12, Chai fails to teach, but Jeon further teaches before the augmented channel data is generated ( step 403, fig 4, and step 504, fig 5, BS receives assistance information from UE) , transmitting channel environment change information to the terminal ( para 83: the UE follows the configured ML model and model parameters, and uses local data and/or data sent from the BS, such as estimated DL channel (e.g., based on DL CSI reference signals (CSI-RSs) or demodulation reference signals (DMRSs)), the BS location and/or the UE location, etc., to perform the inference operation ) , wherein the augmented channel data is data augmented from the original channel data by reflecting the channel environment change information ( para 83: At operation 503, the UE performs the inference based on the received configuration information and local data) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine AI model training based on channel data and data augmentation information as taught by Chai with capability information of terminal to enable AI approach for UL channel prediction as taught by Jeon for the benefit of improving system performance as taught by Jeon in para 50 . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT RINA C PANCHOLI whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-2679 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-F 7:30am-4pm . 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, FILLIN "SPE Name?" \* MERGEFORMAT Chirag Shah can be reached on FILLIN "SPE Phone?" \* MERGEFORMAT 571-272-3144 . 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. /RINA C PANCHOLI/ Primary Examiner, Art Unit 2477 12/ 20 /2025