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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
Claim(s) 1, 3, 7-8, 17, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over by FU et al. (US Pub No. 2024/0040521 in view of Ali et al. (US Pub No. 2023/0170962).
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]). However, Fu unclearly to disclose a set of machine learning parameters associated with wireless communication between the UE and a network entity via one or more repeaters. In the same field of invention Ali clearly discloses a set of machine learning parameters associated with wireless communication between the UE and a network entity via one or more repeaters (Fu disclosed which applied MACHINE Learning ML service support in par [0007] of FU, Ali disclosed with wireless communication between the UE (Fig. 1, item 105) and a network entity (Fig. 1, Interworking Entity 135, Cellular Base Unit Station 121) via one or more repeaters (Fig. 1, Repeater 122) (See par [0139-0141] wireless communication between the UE and a network entity via one or more repeaters by a predefined threshold, then the BS selects the beam with the highest RSRP (or a suitable beam above an RSRP threshold) reported by the repeater for the link between the repeater and BS and configures the repeater to use the beam direction/spatial filter associated with the highest RSRP beam, and also applied such as an operating system or other controller algorithms operating on the network apparatus 800 in par [0170]).
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, Fu, and Ali, 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.
Claim 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHUOC HUU DOAN whose telephone number is (571)272-7920. The examiner can normally be reached 8:00AM - 4:00PM.
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, JEANETTE PARKER can be reached at 571-270-3647. 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.
/PHUOC H DOAN/Primary Examiner, Art Unit 2646
Prosecution Insights