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.
Claim(s) 1-7, 9, 11-14, 16-19 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Li (2025/0374098).
Regarding claim 1, Li discloses a network apparatus (Network unit 1100, Figure 11) comprising at least one processor (1102), at least one memory (1104) storing instructions that when executed by the at least one processor, cause the network apparatus to receive a data collection request (Figure 6, 630 – report from UE) from a user equipment (UE 1000, Figure 10), wherein the data collection request comprises an identifier of a task involving an AI/ML model for which data is to be collected, wherein the AI/ML model is provided in the UE (ML model 605). In particular, para 72 discloses the UE uses the ML model in the wireless node for prediction based on identified measurements, para 73 and 092 – note the particular measurements are equated to be an identifier of a task for data collection for the AI/ML model. Li discloses determining a measurement configuration corresponding to the identifier of the task (para 145, evaluating the measured values based on the monitoring as discussed above and processing report 640 and para 116) and transmit a response message to the UE, wherein the response message comprises a measurement identifier corresponding to the measurement configuration (update/activate configuration message 645 which causes adjustments in the UE, para 117 – update refers to parameters and configuration which were measured by the UE earlier, which denotes identification of a model).
Regarding claim 11, Li discloses a user equipment (Figure 10, 1000) comprising at least one processor (1002) and at least one memory (1004) storing instructions (1006) that when executed by the at least one processor cause the UE to transmit a data collection request(Figure 6, 630 – report from UE), wherein the data collection request comprises an identifier of a task involving an AI/ML model for which data is to be collected, wherein the AI/ML model is provided in the UE (ML model 605). In particular, para 72 discloses the UE uses the ML model in the wireless node for prediction based on identified measurements, para 73 and 092 – note the particular measurements are equated to be an identifier of a task for data collection for the AI/ML model. Li discloses receiving a response message, wherein the response message comprises a measurement identifier corresponding to the measurement configuration (update/activate configuration message 645 which causes adjustments in the UE, para 117 – update refers to parameters and configuration which were measured by the UE earlier, which denotes identification of a model) and collecting data based on the measurement configuration, for the task involving the AI/ML model (ML model monitoring 625 which collects the measurement data to be reported back to the network, 630). Note that the process described in Figure 6 is iterative in nature, para 106-115 as well as para 148, order of measurements/reports can be in a different order than disclosed.
Regarding claim 16, Li discloses a method comprising transmitting a data collection request(Figure 6, 630 – report from UE), wherein the data collection request comprises an identifier of a task involving an AI/ML model for which data is to be collected, wherein the AI/ML model is provided in the UE (ML model 605). In particular, para 72 discloses the UE uses the ML model in the wireless node for prediction based on identified measurements, para 73 and 092 – note the particular measurements are equated to be an identifier of a task for data collection for the AI/ML model. Li discloses receiving a response message, wherein the response message comprises a measurement identifier corresponding to the measurement configuration (update/activate configuration message 645 which causes adjustments in the UE, para 117 – update refers to parameters and configuration which were measured by the UE earlier, which denotes identification of a model) and collecting data based on the measurement configuration, for the task involving the AI/ML model (ML model monitoring 625 which collects the measurement data to be reported back to the network, 630). Note that the process described in Figure 6 is iterative in nature, para 106-115 as well as para 148, order of measurements/reports can be in a different order than disclosed.
Regarding claim 2, Li further discloses the instructions when executed, cause the network apparatus to further reconfigure a radio communication link associated with the UE (updated beamforming link) based on the measurement configuration corresponding to the measurement identifier (based on the particular ML model based on the measurements made by the UE) Note para 157 discloses the beam configuration based on ML model and measured data identified by the system.
Regarding claims 12 and 17, Li further discloses wherein the task comprises at least one of training the model, retraining the model, monitoring of the model (para 115- deactivate, retrain the model, or adjust operating parameters, i.e. monitoring of the model).
Regarding claims 3, 13, and 18, Li further discloses wherein the data collection request further comprises at least one of a first set of attributes for the data collection, corresponding to the request (Figure 7 and 9, attributes 720a, 725a, 730a, para 120-124).
Regarding claims 4, 14, and 19, Li further discloses wherein the first set of attributes comprises at least one of : a time instance of initiating or terminating data collection, periodicity of data collection, sensitivity, or a characteristic of a geographical area (periodicity of collection via timer fields 725a, para 122 as well as a characteristic of a geographical area, i.e. – a particular beam coverage for the wireless network, Figure 4)
Regarding claim 5, Li further discloses a second set of attributes (720a, beam directions, para 121 and 730a counter threshold, para 123 wherein the second set of attributes are determined using the first set of attributes (part of the set of BPM-RS configurations which include the periodicity or geographical area).
Regarding claims 6-7, Li discloses retrieving a mapping between a plurality of tasks a plurality of measurement configurations (i.e. – choosing an AI/ML model based on the measurement configuration performed at the UE para 72- one or more ML models utilized and para 76 – mapping performance of the BPM-RS groups based on the measurements) and based on the mapping and identifier of the task, determine the management configuration (determine whether to retrain or indicate failure of the model, para 77). Li discloses a first configuration determined based on an earlier configure radio communication link (steps 605, 610 Figure 6), assessing the first configuration based on the determined measurements whether you can use the first configuration for radio communications para 106-115, evaluating the initial configuration and flagging a ML model for failure if measurements indicate as such.
Regarding claim 9, Li discloses the network apparatus further processes the data collection request, wherein the radio communication link associated with the UE was configured previously (initial configuration 610/615 was previously utilized), based on the previous configuration, the UE determines a measurement configuration (monitoring and reporting steps 625/630), and transmitting the response message back to the UE, comprising the measurement identifier corresponding to the measurement configuration to configure the previously configured link (updating configuration based on the measurements in steps 635-645).
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.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Kumar (2024/0064574).
Regarding claim 8, Li discloses all the particulars of the claim except for explicitly disclose a second configuration if conditions in the first configuration cannot be used for the radio link. However, Kumar teaches in an analogous art, the use of a second configuration (second machine learning 560) if an indication from the UE denotes that the first configuration cannot be used (550, UE indication). Note para 93-99 disclose changing between machine learning models based on communications parameters. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include a second configuration in order provide redundancy in the network.
Claim(s) 10, 15, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Li (2025/0219898).
Regarding claims 10, 15, and 20, Li (’098) discloses all the particulars of the claim except for transmitting a termination request by the UE to reconfigure a radio communication link to terminate the logging of data by the UE. However, Li (‘898) teaches in a machine learning environment, a termination request (para 77, stop using model request) in which data logging by the UE is terminated and in which the radio communication link is reconfigured (para 77 – stop reporting outputs and performing prediction/estimates which is sent via the RRC message). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include a termination request in order to designate a failure of a ML model and prevent further data transmission to it.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jung (2026/0082257) disclose selection of different ML models depending on UE measurements.
Ly (2025/0310214) disclose AI/ML models for 5g communications.
Zhang (2025/0168081) discloses ML configuration based on UE capabilities and feedback.
Mueck (2024/0298194) disclose ML model selection based on geographical locations.
Zhang (2023/0319585) discloses AI based modeling in a wireless network and training based on measured data.
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WILLIAM GEORGE TROST IV
Primary Patent Examiner
Art Unit 2641
/WILLIAM G TROST IV/Primary Patent Examiner, Art Unit 2641
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