DETAILED ACTION
Claims 1-19 and 21 are examined and 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 .
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.
Claims 1, 6, 8 and 21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al. (U.S. 2025/0184768 A1, hereinafter “Wang”).
As to claims 1 and 21, Wang discloses a method for communication, comprising:
receiving, at a terminal device, at least one first set of Reference Signals (RSs) from a network device, the network device being deployed with at least one Artificial Intelligence / Machine Learning (AI/ML) model, and each of the at least one first set of RSs corresponding to one of the at least one AI/ML model (para. [0109]; discloses network device perform training on the AI/ML model by using the uplink Reference signal);
calculating, at the terminal device, at least one similarity based on the at least one first set of RSs (para. [0113]-[0115]; discloses the loss function is calculated based on signal similarity);
determining, at the terminal device, at least one similarity information based on the at least one calculated similarity, and at least one model information corresponding to the at least one similarity information, the at least one model information indicating an index of at least one AI/ML model (para. [0115]; discloses a loss function may be set based on signal similarity, and the loss function may satisfy a performance requirement by using iterative training. When a model has performance that satisfies needs after being trained, verified, and tested, the network side may further configure the terminal side to deactivate or terminate previous SRS training-specific configuration, or legacy high-density SRS configuration. This citation shows that the loss function dependent on signal similarity when a model determines a performance requirement); and
transmitting, to the network device, at least one of: the at least one determined similarity information or the at least one model information (para. [0116]-[0117]; discloses model update information is transmitted via RRC signaling to the terminal equipment).
As to claim 6, Wang discloses the method of claim 1, wherein the similarity information comprises a second indication indicating a similarity level corresponding to the calculated similarity (para. [0115]; discloses a loss function may be set based on signal similarity, and the loss function may satisfy a performance requirement by using iterative training.).
As to claim 8, Wang discloses the method of claim 1, wherein the similarity information comprises a third indication indicating the numeric value of the calculated similarity (para. [0115]; discloses a loss function may be set based on signal similarity, and the loss function may satisfy a performance requirement by using iterative training.).
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.
Claims 2, 7, 9 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Cohen et al. (U.S. 2024/0007423 A1, hereinafter “Cohen”).
As to claim 2, Wang discloses the method of claim 1, however Wang does not disclose the method wherein payload size of the model information is determined based on the number of the at least one AI/ML model.
In an analogous art, Cohen discloses the payload size of the model information is determined based on the number of the at least one AI/ML model (para. [0059]; discloses machine-learning model that analyzes historical user interaction that monitor similarities may include size of the payload included in the asset).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang by incorporating a size of the payload in the similarities as taught by Cohen in order to help determine any devices or users that are facing the adverse conditions.
As to claim 7, Wang-Cohen discloses the method of claim 6, wherein payload size of the similarity information is determined based on the number of the similarity levels ( Cohen, para. [0038]; discloses similarities may include the size, value, or type of payload included in the asset. Similarities may include locations or paths being taken by assets through a supply chain. ).
As to claim 9, Wang-Cohen discloses the method of claim 8, wherein payload size of the similarity information is determined based on the number of the scales of the similarity (Cohen, para. [0059]; discloses payload size is included in the asset that are determined by similarities) .
Allowable Subject Matter
Claims 3, 4, 5, 10-19 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ryu et al. (U.S. 2021/0328630 A1) discloses devices for wireless communications are described in which a base station may develop a number of different predictive models for each of a number of different functions. The different functions may be used to determine various beamforming parameters for beamformed communications between a user equipment (UE) and a base station. The base station may provide the models to a UE, and the UE may then use such models to determine values for one or more beamforming parameters.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOE CHACKO whose telephone number is (571)270-3318. The examiner can normally be reached Monday-Friday 7am-5pm.
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/JOE CHACKO/Primary Examiner, Art Unit 2457
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