DETAILED ACTION
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 .
Claim Rejections - 35 USC § 102
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 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 –
Claim(s) 1, 3, 7-8, 17, 19 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by FU et al. (US Pub No. 2024/0040521).
Claim 1, FU discloses a user equipment (UE) (See Fig. 3A), comprising: one or more memories (Fig. 3A; item 360) storing processor-executable code; and one or more processors coupled with the one or more memories (Fig. 3A; items 340, 360) and individually or collectively operable to execute the code to cause the UE to: receive a set of machine learning parameters associated with wireless communications between the UE and a network entity via one or more repeaters (par [0143] “the repeater uses beam b1 to forward the signal from the base station to the UE, and employs a first set of power control parameters which applied MACHINE Learning ML service support in par [0007]”); select a first subset of the set of machine learning parameters based at least in part on a first state of the one or more repeaters (par [0144] “set of power control parameters to adjust the power of the repeater. In some examples, the power control parameters determined for different beams may be same or different”); and communicate with the network entity (par [0144] “the repeater uses beam b4 to forward the signal from the UE to the base station”), via the one or more repeaters, using one or more communications parameters that are selected based at least in part on the first subset of the set of machine learning parameters (par [0144] “repeater uses beam b2 to forward the signal from the base station to the UE, and employs a second set of power control parameters to adjust the power of the repeater as further disclosed in par [0143]).
Claim 3, 19, FU further discloses the UE of claim 1, wherein the first subset of the set of machine learning parameters is selected based at least in part on a set of available repeater states of the one or more repeaters (par [0111] “forwarding the information by the repeater herein may refer to the repeater directly forwards the radio frequency without decoding the information, while receiving the information by the repeater may refer to the repeater decodes the received information”).
Claim 7, FU further discloses the UE of claim 1, wherein the first subset of the set of machine learning parameters is selected based at least in part on a source of one or more reference signals received at the UE (par [0108] “control signaling and information by reference signals”)
Claim 8, FU further discloses the UE of claim 1, wherein the first subset of the set of machine learning parameters is selected based at least in part on an antenna array configuration of at least a first repeater of the one or more repeaters (par [0063] “applied antenna arrays to support the repeater in par [0143]).
Claim 17, the claim is rejected for the same reasons as set forth in claim 1.
Allowable Subject Matter
Claims 9-16 are allowed.
The following is an examiner’s statement of reasons for allowance: Claim 9, and their dependents thereof, are allowed because the closest prior art, either alone or in combination, fail to disclose of a user equipment (UE), comprising: one or more memories storing processor-executable code; and one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the UE to: receive a set of machine learning parameters associated with wireless communications between the UE and a network entity via one or more repeaters, the set of machine learning parameters including at least a first subset of machine learning parameters associated with a first configuration of the one or more repeaters and a second subset of machine learning parameters associated with a second configuration of the one or more repeaters; and transmit a request to update the one or more repeaters from the first configuration to the second configuration, the request based at least in part on a difference between a first communications parameter and a second communications parameter meeting one or more request criteria, wherein the first communications parameter is determined using the first subset of machine learning parameters and the second communications parameter is determined using the second subset of machine learning parameters.
Claims 2, 4-6, 18, 20 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.
4. The UE of claim 1, wherein the first state of the one or more repeaters is associated with a first repeater that is in an off state and a second repeater that is in an on state, and output from a machine learning algorithm associated with the first repeater is ignored when the one or more repeaters are in the first state.
Claim 2. The UE of claim 1, wherein, to receive the set of machine learning parameters, the one or more processors are individually or collectively operable to execute the code to cause the UE to: receive configuration information that indicates the set of machine learning parameters, a set of states associated with the one or more repeaters, and one or more selection criteria that associates different states of the set of states with different subsets of the set of machine learning parameters, and wherein the set of machine learning parameters include one or more of a set of machine learning algorithms, a set of parameters associated with one or more machine learning algorithms, or any combination thereof.
Claim 18. The method of claim 17, wherein the receiving the set of machine learning parameters comprises: receiving configuration information that indicates the set of machine learning parameters, a set of states associated with the one or more repeaters, and one or more selection criteria that associates different states of the set of states with different subsets of the set of machine learning parameters, and wherein the set of machine learning parameters include one or more of a set of machine learning algorithms, a set of parameters associated with one or more machine learning algorithms, or any combination thereof.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
WANG (US Pub No 2025/0254598) discloses method, devices, and computer readable medium for communication.
LEE et al. (US Pub No. 2023/0371123) discloses method and apparatus for transmission and reception of network-controlled repeater for wireless communication systems.
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/PHUOC H DOAN/ Primary Examiner, Art Unit 2646
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