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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/16/2023 and 01/24/2024 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.
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 non-obviousness.
Claims 1-3, 6-8, 11-13, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over PEZESHKI et al (US 20210195462 A1) hereinafter PEZESHKI in view of NPL 3GPP TSG RAN WG1 #110bis-e hereinafter Vivo.
Regarding claim 1,
PEZESHKI teaches a method performed by a user equipment (UE) in a wireless communication system (AI-based CSI feedback method [0054]-[0067]; Figs. 4 and 5), the method comprising: the AI-based CSI feedback mode including at least one of an AI-based channel prediction mode or an AI-based CSI compression mode (AI-based CSI compression [0049]-[0052]) ; receiving, from the base station, an indicator indicating the CSI feedback mode performed by the UE and configuration information corresponding to the CSI feedback mode performed by the UE (receiving from the BS an indication indicating configuration to be used by the UE for feedback compression and an AI module to be used [0031], [0051]-[0052], and [0060]-[0066]); generating CSI, based on the received configuration information and a CSI-reference signal (CSI-RS) (UE generating a codeword based on configuration and CSI reference signal received [0057]-[0067]; elements 506 and 510 of Fig. 5); and transmitting the generated CSI to the base station (UE transmitting the codeword based on configuration and CSI reference signal received [0057]-[0067]; elements 516 of Fig. 5).
PEZESHKI does not explicitly teach transmitting, to a base station, an artificial intelligence (AI)-based channel state information (CSI) feedback mode supported by an AI model of the UE and capability information corresponding to the CSI feedback mode.
Vivo teaches transmitting, to a base station, an artificial intelligence (AI)-based channel state information (CSI) feedback mode supported by an AI model of the UE and capability information corresponding to the CSI feedback mode (UE capability reporting procedure indicating model training capability, storage capacity, and model transfer capability, section 3.1.1, page 3).
It would have been obvious to one having ordinary skill in the art before the effective filing date to add the teachings of Vivo to the teachings of PEZESHKI. One would have been motivated to do so, with a reasonable expectation of success, because it would enhance data collection and training (Vivo, section 3.1.1, page 3).
Regarding claim 2,
PEZESHKI and Vivo teach all the features of claim 1, as outlined above.
PEZESHKI does not explicitly teach the AI-based CSI feedback mode supported by the AI model of the UE comprises the AI-based channel prediction mode, the configuration information corresponding to the CSI feedback mode performed by the UE comprises information on a prediction interval and information on a number of prediction times and prediction steps.
Vivo teaches the AI-based CSI feedback mode supported by the AI model of the UE comprises the AI-based channel prediction mode (AI-based CSI prediction conducted at the UE, section 2.1, page 2, and section 3.2, page 9), the configuration information corresponding to the CSI feedback mode performed by the UE comprises information on a prediction interval and information on a number of prediction times and prediction steps (alignment configuration information including the number and the time ID of historical and future CSIs, section 3.2, page 9).
It would have been obvious to one having ordinary skill in the art before the effective filing date to add the teachings of Vivo to the teachings of PEZESHKI. One would have been motivated to do so, with a reasonable expectation of success, because it would enhance data collection and training (Vivo, page 3, section 3.1.1).
Regarding claim 3,
PEZESHKI and Vivo teach all the features of claim 1, as outlined above.
PEZESHKI further teaches the AI-based CSI feedback mode supported by the AI model of the UE comprises the AI-based CSI compression mode (AI-based CSI compression supported by the UE [0054]-[0067]; Figs. 4 and 5), the configuration information corresponding to the CSI feedback mode performed by the UE comprises information on a report mode indicating at least one of a wideband report or a subband report and information on a compression mode indicating a compression ratio (configuration indicating a compression ratio [0060]-[0066]; Fig. 5).
Claims [6-8] corresponding to the “BS method”, claims [11-13] corresponding to the “UE apparatus”, and claims [16-18] corresponding to “BS apparatus” are rejected under the same reasoning as claims [1-3] “UE method”, respectively.
Claims 4-5, 9-10, 14-15, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over PEZESHKI and Vivo in further view of Ryden et al. (US 20230262448 A1) hereinafter Ryden
Regarding claim 4,
PEZESHKI and Vivo teach all the features of claim 1, as outlined above.
PEZESHKI and Vivo do not explicitly teach transmitting, to the base station, information indicating whether to independently compress channels for which the UE has performed AI-based channel prediction, or to compress the channels in combination.
Ryden teaches transmitting, to the base station, information indicating whether to independently compress channels for which the UE has performed AI-based channel prediction, or to compress the channels in combination (the UE can indicate up to how many AI models can be configured simultaneously [0138]).
It would have been obvious to one having ordinary skill in the art before the effective filing date to add the teachings of Ryden to the teachings of PEZESHKI and Vivo. One would have been motivated to do so, with a reasonable expectation of success, because it can reduce signaling overhead (Vivo, page 3, section 3.1.1).
Regarding claim 5,
PEZESHKI and Vivo teach all the features of claim 1, as outlined above.
PEZESHKI and Vivo do not explicitly teach identifying whether to change the AI-based CSI feedback mode, based on a channel estimation result; and transmitting, to the base station, an AI-based CSI feedback mode preferred by the UE or a parameter corresponding to the AI-based CSI feedback mode preferred by the UE, based on a result of the identification.
Ryden teaches identifying whether to change the AI-based CSI feedback mode, based on a channel estimation result (determining which ML model is to be selected based on radio metrics [0083]-[0085]); and transmitting, to the base station, an AI-based CSI feedback mode preferred by the UE (instruction indicating the selected model [0083]-[0089]) or a parameter corresponding to the AI-based CSI feedback mode preferred by the UE, based on a result of the identification.
It would have been obvious to one having ordinary skill in the art before the effective filing date to add the teachings of Ryden to the teachings of PEZESHKI and Vivo. One would have been motivated to do so, with a reasonable expectation of success, because it can reduce signaling overhead (Vivo, page 3, section 3.1.1).
Claims [9-10] corresponding to the “BS method”, claims [14-15] corresponding to the “UE apparatus”, and claims [19-20] corresponding to “BS apparatus” are rejected under the same reasoning as claims [4-5] “UE method”, respectively.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDUL AZIZ SANTARISI whose telephone number is (703)756-4586. The examiner can normally be reached Monday - Friday 8 AM - 5:00 PM ET. 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, Ayman Abaza can be reached on (571)270-0422. 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.
/ABDUL AZIZ SANTARISI/Examiner, Art Unit 2465
/AYMAN A ABAZA/Primary Examiner, Art Unit 2465