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) 1-11 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Xu (2024/0381199).
Regarding claim 1, Xu discloses a method for user equipment (UE) using artificial intelligence model in a wireless network (para 7, 157-164) comprising: obtaining by the UE a set of mobility-related data (para 166-168, also para 203-210), feeding the set of mobility-related data to a mobility AI model for UE mobility prediction (para 181-184, and 196-197); and obtaining a UE mobility prediction based on the mobility AI model (para 210-217 and para 225-229).
Regarding claims 2-4, Xu further discloses determining the mobility AI model for the mobility prediction based on one or more selection factors (para 202-217, multiple factors in UE statistics and predictions) as well as the UE being in service or out of service of the wireless network (Figure 12A, determining if the UE is in service or out of service to perform the handover procedure). Xu also discloses the set of mobility-related data is configured based on the one or more selection factors (para 222, use of multiple mobility data is used to train the AI model and update the predictions based on the statistics, para 230—248).
Regarding claim 5-7, Xu discloses the mobility-related data includes one or more UE signal measurements from a serving cell from different antenna (beam level mobility, para 155-157) or a UE serving cell changing time in a period (time slot information para 204-206 as well as tracking area information, para 208). Xu discloses wherein the mobility predication is a range prediction (para 215 – TA or cell group which is predicted) and generates a label (para 214-215, predicted location is a label), and the mobility label is one of a set of characteristic labels (again, predicted location is a characteristic).
Regarding claims 8-11, Xu further discloses wherein the mobility label applies to the AI model (AI model is used to predict mobility/handover, Figures 11-12a) and obtaining mobility feedback from one or more UE applications and fine tuning the mobility AI model based on the mobility feedback (para 290, training the AI model based on mobility information and UE history, which is a UE application). Xu also discloses that the fine tuning could be performed by the UE (para 222, UE can have an AI training model on itself) and that the mobility AI model is trained on device by the UE or obtained from the network (Figures 11, 12a-b).
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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) 12-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu in view of Zhang (2023/319585).
Regarding claim 12, Xu discloses a user equipment (UE) comprising a transceiver (106) that transmits and receives radio signals in a wireless network; a collection module that obtains sets of mobility-related data (processing unit 102). Xu discloses that AI processing of mobility data can take place in the user equipment (para 222) but fails to explicitly disclose the mobility module and prediction module. However, Zhang discloses a mobility that performs UE mobility prediction based on UE mobility-related data and a prediction model that obtains a prediction based on the AI model (Figure 5a, AI execution module 220 takes data and provides a prediction/inference based on the provided data to train the model, also para 112-113 denoting the UE data provided to the model). Zhang also teaches that the UE can contain the AI execution model, para 93. 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 UE with modules for AI prediction and training in order to provide distributed processing of the AI models within the network and end devices.
Regarding claims 13-14, Xu further discloses determining the mobility AI model for the mobility prediction based on one or more selection factors (para 202-217, multiple factors in UE statistics and predictions) as well as the UE being in service or out of service of the wireless network (Figure 12A, determining if the UE is in service or out of service to perform the handover procedure). Xu also discloses the set of mobility-related data is configured based on the one or more selection factors (para 222, use of multiple mobility data is used to train the AI model and update the predictions based on the statistics, para 230—248).
Regarding claim 15-17, Xu discloses the mobility-related data includes one or more UE signal measurements from a serving cell from different antenna (beam level mobility, para 155-157) or a UE serving cell changing time in a period (time slot information para 204-206 as well as tracking area information, para 208). Xu discloses wherein the mobility predication is a range prediction (para 215 – TA or cell group which is predicted) and generates a label (para 214-215, predicted location is a label), and the mobility label is one of a set of characteristic labels (again, predicted location is a characteristic).
Regarding claims 18-20, Xu further discloses wherein the mobility label applies to the AI model (AI model is used to predict mobility/handover, Figures 11-12a) and obtaining mobility feedback from one or more UE applications and fine tuning the mobility AI model based on the mobility feedback (para 290, training the AI model based on mobility information and UE history, which is a UE application). Xu also discloses that the fine tuning could be performed by the UE (para 222, UE can have an AI training model on itself) and that the mobility AI model is trained on device by the UE or obtained from the network (Figures 11, 12a-b).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhu (12628061) discloses UE mobility prediction.
Chen (2026/0032496) discloses that an UE evaluates information for AI/ML mobility assisted network processing.
Kim (2025/0317809) discloses mobility processing in handover.
Chen (WO 2025/035367) discloses UE mobility management with AI/ML models that are used to assist with trajectory and handover information.
Hua (2023/0106566) discloses UE history/mobility information used to update AI/ML models with processing for out of service (non-predicted regions) and using RSSI/QoS.
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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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