CTNF 18/769,761 CTNF 88911 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 This action is in response to the application filed on 07/11/2024. Claims 1-20 have been examined. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 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 – 07-12-aia AIA (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. 07-15-03-aia AIA Claim s 1-8, 11-17, and 20 are rejected under 35 U.S.C. 102(a) (2) as being anticipated by PARK et al. (US 2024/0267183) . As of claim 1 , PARK discloses a method comprising: compressing, by an encoder of a user equipment (UE), channel vectors of a channel state information (CSI) matrix to generate respective compressed vectors (para [0058] discloses discussing AI/ML-based CSI compression methods for compressing channel information and AI/ML-based CSI prediction methods for predicting future channel information. Para [0084] [0085] discloses the encoder receive channel information for M resource regions as input, and compress them into latent vectors (=compressed vectors); generating, by a processor of the UE, a predicted channel vector by performing machine learning (ML)-based CSI prediction on the compressed vectors (para [0058] discloses discussing AI/ML-based CSI compression methods for compressing channel information and AI/ML-based CSI prediction methods for predicting future channel information, para [0143] the encoder compress channel information measured with CSI-RS resources in the past M slots, and the decoder predict channel information for the N future slots); and report, by the UE, a predicted CSI to a base station (BS) based on the predicted channel vector (para [0184] discloses the terminal feedback the predicted CSI to the base station). As of claim 11 , PARK discloses a user equipment (UE) comprising : an encoder configured to compress channel vectors of a channel state information (CSI) matrix to generate respective compressed vectors (para [0095] he encoder be located in the terminal, [0058] discloses discussing AI/ML-based CSI compression methods for compressing channel information and AI/ML-based CSI prediction methods for predicting future channel information. Para [0084] [0085] discloses the encoder receive channel information for M resource regions as input, and compress them into latent vectors (=compressed vectors); and a processor configured to generate a predicted channel vector by performing machine learning (ML)-based CSI prediction on the compressed vectors (para [0058] discloses discussing AI/ML-based CSI compression methods for compressing channel information and AI/ML-based CSI prediction methods for predicting future channel information, para [0143] the encoder compress channel information measured with CSI-RS resources in the past M slots, and the decoder predict channel information for the N future slots), and report a predicted CSI to a base station (BS) based on the predicted channel vector (para [0184] discloses the terminal feedback the predicted CSI to the base station). As of claim 20 , PARK discloses a user equipment (UE) comprising : a processor; and a non-transitory computer readable storage medium storing instruction that, when executed (para [0023] [0049] discloses a terminal comprise a processor), cause the processor to: compress channel vectors of a channel state information (CSI) matrix to generate respective compressed vectors ; (para [0058] discloses discussing AI/ML-based CSI compression methods for compressing channel information and AI/ML-based CSI prediction methods for predicting future channel information. Para [0084] [0085] discloses the encoder receive channel information for M resource regions as input, and compress them into latent vectors (=compressed vectors); generate a predicted channel vector by performing machine learning (ML)-based CSI prediction on the compressed vectors (para [0058] discloses discussing AI/ML-based CSI compression methods for compressing channel information and AI/ML-based CSI prediction methods for predicting future channel information, para [0143] discloses the encoder compress channel information measured with CSI-RS resources in the past M slots, and the decoder predict channel information for the N future slots); and report a predicted CSI to a base station (BS) based on the predicted channel vector (para [0184] discloses the terminal feedback the predicted CSI to the base station). As of claims 2 and 12 , rejection of claims 1 and 11 cited above incorporated herein, in addition PARK discloses the encoder comprises an auto-encoder of the UE (para [0066] discloses the CSI feedback device include the encoder and decoder which form an autoencoder). As of claims 3 and 13 , rejection of claims 1 and 11 cited above incorporated herein, in addition PARK discloses each compressed vector has a smaller number of dimensions than a corresponding channel vector (para [0077]- [0079] discloses the autoencoder perform data compression (or dimensionality reduction(=smaller number of dimensions), the encoder receive channel information as input, and compress them into latent vectors (=compressed vectors) and partition the entire latent vectors by the number of resource region which corresponds to compressed vector has a smaller number of dimensions than a corresponding channel vector). As of claim 4 , rejection of claim 1 cited above incorporated herein, in addition PARK discloses the CSI matrix is partitioned into the channel vectors (para [0077]- [0079] discloses the encoder receive channel information as input, and compress them into latent vectors (=compressed vectors) and partition the entire latent vectors by the number of resource region). As of claims 5 and 14 , rejection of claims 1 and 11 cited above incorporated herein, in addition PARK discloses storing the compressed vectors in a buffer of the UE (para [0049] discloses the processor execute a program stored in at least one of the memories and the storage device). As of claims 6 and 15 , rejection of claims 5 and 14 cited above incorporated herein, in addition PARK discloses the compressed vectors are stored in the buffer over multiple time stamps (para [0098] channel information is collected over several slots in the time domain resource regions correspond to time periods (slots (=multiple time stamps). The type 1 latent vector mean CSI for time periods (slots), and the type 2 latent vector mean CSI for each time period (slot). As of claims 7 and 16 , rejection of claims 1 and 11 cited above incorporated herein, in addition PARK discloses generating the predicted channel vector comprises : performing, by the processor, ML-based CSI prediction using the compressed vectors to generate a latent representation of the predicted channel vector (para [0059] [0086] discloses AI/ML-based CSI compression to compress channel information into CSI, so that the CSI corresponds to latent variables (or codes) in a latent space, the encoder receive channel information for M (e.g. M=5) CSI- reference signal (CSI-RS) resource regions (or time resources) of a past time point as input, and compresses them into latent vectors); and decompressing, by a decoder of the UE, the latent representation to generate the predicted channel vector (para [0086] disclose the decoder receives a portion or all of the latent vectors as input, and reconstruct channel information for CSI-RS resource regions (or time resources) of a future time point). As of claims 8 and 17 , rejection of claims 1 and 11 cited above incorporated herein, in addition PARK discloses generating the predicted channel vector comprises: performing, by the processor, ML-based CSI prediction and decoding on the compressed vectors to generate the predicted channel vector (para [0086] discloses the decoder receive a portion or all of the latent vectors as input, and reconstruct per-resource region channel information CSI-RS resource regions (or time resources) of a future time point) . Claim Rejections - 35 USC § 103 07-20-aia AIA 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 of this title, 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 9-10 and 18-19 are rejected under AIA 35 U.S.C 103 as being unpatentable over PARK et al. (US 2024/0267183) in view of JUNG et al. (US 2026/0082257). As of claims 9 and 18 , rejection of claims 8, and 17 cited above incorporated herein, in addition PARK does not explicitly disclose but JUNG teaches compressing the channel vectors comprises selecting a first ML model from a first set of ML models for encoding and decoding (JUNG, para [0128] discloses the ML-aided/based measurement be related to compressing the measurement results in size, so as to reduce reporting overhead, auto-encoder/DNN/CNN based machine learning can be used to enable compressed CSI reporting by generating compressed CSI information at UE side wherein in para [0148] discloses the UE determine a set of ML models for measurement reporting among the plurality of ML models (= first set of ML models ) configured for the UE), performing ML-based CSI prediction comprises selecting a second ML model from a second set of ML models for CSI prediction (JUNG, para [0140] discloses UE perform ML task such as predictions of measurements based on the configured/trained ML model (=second ML model). Therefore it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of PARK with the teaching of JUNG in order for better performance and low power consumption as taught by JUNG in para [0016]. As of claims 10 and 19 , rejection of claims 1 and 11 cited above incorporated herein, in addition PARK does not explicitly disclose but JUNG teaches compressing the channel vectors and performing ML-based CSI prediction are performed with task-dependent ML models (JUNG, para [0140] discloses UE perform ML task such as predictions of measurements based on the configured/trained ML model). Therefore it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of PARK with the teaching of JUNG in order for better performance and low power consumption as taught by JUNG in para [0016]. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FAHMIDA S CHOWDHURY whose telephone number is (571)272-2547. The examiner can normally be reached M-F 8am to 5pm. 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, Sujoy K Kundu can be reached at 571-272-8586. 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. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /FAHMIDA S CHOWDHURY/ Primary Examiner, Art Unit 2471 Application/Control Number: 18/769,761 Page 2 Art Unit: 2471 Application/Control Number: 18/769,761 Page 3 Art Unit: 2471 Application/Control Number: 18/769,761 Page 4 Art Unit: 2471 Application/Control Number: 18/769,761 Page 5 Art Unit: 2471 Application/Control Number: 18/769,761 Page 6 Art Unit: 2471 Application/Control Number: 18/769,761 Page 7 Art Unit: 2471 Application/Control Number: 18/769,761 Page 8 Art Unit: 2471 Application/Control Number: 18/769,761 Page 9 Art Unit: 2471 Application/Control Number: 18/769,761 Page 10 Art Unit: 2471
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