Notice of Pre-AIA or AIA Status
1. 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 § 103
2. 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.
3. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
4. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over KUMAR et al. (US Pub. No: 2024/0054357 A1) in view of SINGH et al. (US Pub. No: 2025/0008346 A1).
Regarding claim 1, KUMAR et al. teach a user equipment (UE) for wireless communication (see Abstract and Fig.3/UE), comprising: at least one memory (see Fig.3, memory 382); and at least one processor coupled with the at least one memory and configured to cause the UE (see Fig.3 and paragraphs [0060] & [0069]) to: receive an indication of one or more validity criterion (see Fig.7 and para [0106] wherein the network entity transmitting the AI/ML data input to be received by the UE, is mentioned and also see para [0105] wherein the AI/ML data input request including MLFN for which AI/ML data input is requested, is mentioned and also the AI/ML data input request including one or more network settings, is mentioned); and store the indication of the one or more validity criterion as part of learning model data (see para [0096] wherein the AI/ML data being used as an input to run the ML model (e.g., input parameters to inference) or training data being used for training the ML model, is mentioned).
KUMAR et al. teach the above UE comprising receiving an indication of one or more validity criterion as mentioned above, but KUMAR et al. is silent in teaching the above UE comprising receiving an indication of one or more validity criterion for a portion of network context information.
However, SINGH et al. teach a teach a user equipment (UE) for wireless communication (see Abstract and Fig.9/device900/UE) comprising receiving an indication of one or more validity criterion for a portion of network context information (see para [0183] wherein the device/UE obtaining cell-specific parameters of the plurality of cells of a mobile communication network & selecting a subset of the plurality of cells based on obtained cell-specific parameters, is mentioned and also see para [0067] wherein the cell-specific parameters being referred to as “cell configuration”, “cell context information” & “cell environment characteristics”, is mentioned, all of which clearly includes and is equivalent to ‘receiving an indication of one or more validity criterion for a portion of network context information’ and also see para [0105]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the above UE of KUMAR et al. to include receiving an indication of one or more validity criterion for a portion of network context information, disclosed by SINGH et al. in order to provide an effective mechanism of UE for efficiently using trained artificial intelligence or machine learning models (AI/ML) for radio resource management and thereby providing an optimum output used in radio resource management of a plurality of cells in wireless communication system.
Regarding claim 2, KUMAR et al. and SINGH et al. together teach the UE of claim 1.
SINGH et al. further teach the UE of claim 1, wherein the indication of the one or more validity criterion for the portion of the network context information comprises at least one of vendor specific validity information, cell specific validity information, or at least one radio access network (RAN) notification area (see para [0183] wherein the device/UE obtaining cell-specific parameters of the plurality of cells of a mobile communication network & selecting a subset of the plurality of cells based on obtained cell-specific parameters, is mentioned and also see para [0067] wherein the cell-specific parameters being referred to as “cell configuration”, “cell context information” & “cell environment characteristics”, is mentioned and also see para [0100] wherein the device/UE being configured to generate training dataset based on the RAN data 711 and/or the cell data 712, is mentioned) (and the same motivation is maintained as in claim 1).
Regarding claim 3, KUMAR et al. and SINGH et al. together teach the UE of claim 1.
SINGH et al. further teach the UE of claim 1, wherein the indication of the one or more validity criterion for the portion of the network context information comprises one or more nodes for applying a same interpretation of the network context information (see para [0100] wherein the device/UE being configured to generate training dataset based on the RAN data 711 and/or the cell data 712, is mentioned and also see para [0101] wherein the device/UE being configured to apply data fusion techniques to aggregate data & data fusion being considered as a process of integrating and combining data, within this context, by combining the RAN data 711 and/or the cell data 712 of the selected subset cells to obtain a unified dataset representative of the RAN environment, is mentioned) (and the same motivation is maintained as in claim 1).
Regarding claim 4, KUMAR et al. and SINGH et al. together teach the UE of claim 1.
SINGH et al. further teach the UE of claim 1, wherein the at least one processor is configured cause the UE to receive one or more identifiers of the network context information (see para [0183] wherein the device/UE obtaining cell-specific parameters of the plurality of cells of a mobile communication network & selecting a subset of the plurality of cells based on obtained cell-specific parameters and causing the AI/ML to be trained with radio access network (RAN)-related data of the subset of the plurality of cells, is mentioned and also see para [0067]) (and the same motivation is maintained as in claim 1).
Regarding claim 5, KUMAR et al. and SINGH et al. together teach the UE of claim 4.
SINGH et al. further teach the UE of claim 4, wherein the at least one processor is configured cause the UE to one or more of: jointly receive the one or more identifiers of the network context information and the indication of the one or more validity criterion for the portion of the network context information or jointly receive the one or more identifiers of the network context information and a dataset for learning model life cycle management (see para [0183] wherein the device/UE obtaining cell-specific parameters of the plurality of cells of a mobile communication network & selecting a subset of the plurality of cells based on obtained cell-specific parameters and causing the AI/ML to be trained with radio access network (RAN)-related data of the subset of the plurality of cells, is mentioned and also see para [0067]).
Regarding claim 6, KUMAR et al. and SINGH et al. together teach the UE of claim 1.
SINGH et al. further teach the UE of claim 1, wherein the indication of the one or more validity criterion for the portion of the network context information is received aperiodically prior to a received dataset for learning model life cycle management (see para [0183] wherein the device/UE obtaining cell-specific parameters of the plurality of cells of a mobile communication network & selecting a subset of the plurality of cells based on obtained cell-specific parameters, is mentioned and also see para [0187] wherein the device/UE configured to cause the RAN-related data of the at least one or more cells that are not within the subset of the plurality of cells to be sampled intermittently from network access node, is mentioned and also see para [0106]) (and the same motivation is maintained as in claim 1).
Regarding claim 7, KUMAR et al. and SINGH et al. together teach the UE of claim 1.
KUMAR et al. further teach the UE of claim 1, wherein the at least one processor is configured cause the UE to transmit capability information that indicates whether the UE supports learning model-enabled measurement and reporting (see paragraphs [0147] & [0148] wherein the UE transmitting UE capability information to the network entity to determine the AI/ML data input (e.g., per MLFN, ML model ID, or MS ID) based on the UE capability information, is mentioned).
Regarding claim 8, KUMAR et al. teach a network equipment for wireless communication (see Abstract and Fig.3/BS 102), comprising: at least one memory (see Fig.3, memory 342 inside BS and para [0069]); and at least one processor (see processor 340 inside BS 102 and para [0058]) coupled with the at least one memory and configured to cause the network equipment (see para [0058]) to: generate an indication of one or more validity criterion (see Fig.7 and para [0106] wherein the network entity transmitting the AI/ML data input to the UE, is mentioned & also see para [0148] wherein the network entity determining the AI/ML data input (e.g., per MLFN, ML model ID, or MS ID) based on the UE capability information, is mentioned and also see para [0105] wherein the AI/ML data input request including MLFN for which AI/ML data input is requested, is mentioned and also the AI/ML data input request including one or more network settings, is mentioned); and transmit the indication of one or more validity criterion (see Fig.7 and para [0106] wherein the network entity transmitting the AI/ML data input to the UE, is mentioned & also see para [0105]).
KUMAR et al. teach the above network equipment comprising generating an indication of one or more validity criterion as mentioned above, but KUMAR et al. is silent in teaching the above network equipment comprising generating an indication of one or more validity criterion for a portion of network context information.
However, SINGH et al. teach a teach a network equipment (see Abstract and Fig.9/network access node/cell 912/913) comprising generating an indication of one or more validity criterion for a portion of network context information (see para [0131] wherein the network access node generating & transmitting RAN data and cell-specific parameters of a plurality of cells to device/UE, is mentioned and also see para [0183] wherein the device/UE obtaining cell-specific parameters of the plurality of cells of a mobile communication network & selecting a subset of the plurality of cells based on obtained cell-specific parameters, is mentioned and also see para [0067] wherein the cell-specific parameters being referred to as “cell configuration”, “cell context information” & “cell environment characteristics”, is mentioned).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the above network equipment of KUMAR et al. to include generating an indication of one or more validity criterion for a portion of network context information, disclosed by SINGH et al. in order to provide an effective mechanism of UE for efficiently using trained artificial intelligence or machine learning models (AI/ML) for radio resource management and thereby providing an optimum output used in radio resource management of a plurality of cells in wireless communication system.
Regarding claim 9, KUMAR et al. and SINGH et al. together teach the network equipment of claim 8.
SINGH et al. further teach the network equipment of claim 8, wherein the indication of the one or more validity criterion for the portion of network context information comprises at least one of vendor specific validity information, cell specific validity information, or at least one radio access network (RAN) notification area (see para [0183] wherein the device/UE obtaining cell-specific parameters of the plurality of cells of a mobile communication network & selecting a subset of the plurality of cells based on obtained cell-specific parameters, is mentioned and also see para [0067] wherein the cell-specific parameters being referred to as “cell configuration”, “cell context information” & “cell environment characteristics”, is mentioned and also see para [0100] wherein the device/UE being configured to generate training dataset based on the RAN data 711 and/or the cell data 712, is mentioned) (and the same motivation is maintained as in claim 8).
Regarding claim 10, KUMAR et al. and SINGH et al. together teach the network equipment of claim 8.
SINGH et al. further teach the network equipment of claim 8, wherein the at least one processor is configured cause the network equipment to transmit one or more identifiers of the network context information (see para [0183] wherein the device/UE obtaining cell-specific parameters of the plurality of cells of a mobile communication network & selecting a subset of the plurality of cells based on obtained cell-specific parameters and causing the AI/ML to be trained with radio access network (RAN)-related data of the subset of the plurality of cells, is mentioned and also see para [0067]) (and the same motivation is maintained as in claim 8).
Regarding claim 11, KUMAR et al. and SINGH et al. together teach the network equipment of claim 10.
SINGH et al. further teach the network equipment of claim 10, wherein the at least one processor is configured cause the network equipment to one or more of: jointly transmit the one or more identifiers of the network context information and a dataset for learning model life cycle management or jointly transmit the one or more identifiers of the network context information and the indication of one or more validity criterion for the portion of the network context information (see para [0183] wherein the device/UE obtaining cell-specific parameters of the plurality of cells of a mobile communication network & selecting a subset of the plurality of cells based on obtained cell-specific parameters and causing the AI/ML to be trained with radio access network (RAN)-related data of the subset of the plurality of cells, is mentioned and also see para [0067]).
Regarding claim 12, KUMAR et al. and SINGH et al. together teach the network equipment of claim 8.
SINGH et al. further teach the network equipment of claim 8, wherein the at least one processor is configured cause the network equipment to transmit the indication of the one or more validity criterion for the portion of network context information aperiodically prior to a transmitted dataset for learning model life cycle management (see para [0183] wherein the device/UE obtaining cell-specific parameters of the plurality of cells of a mobile communication network & selecting a subset of the plurality of cells based on obtained cell-specific parameters, is mentioned and also see para [0187] wherein the device/UE configured to cause the RAN-related data of the at least one or more cells that are not within the subset of the plurality of cells to be sampled intermittently from network access node, is mentioned and also see para [0106]) (and the same motivation is maintained as in claim 8).
Regarding claim 13, KUMAR et al. and SINGH et al. together teach the network equipment of claim 8.
SINGH et al. further teach the network equipment of claim 8, wherein the network context information comprises one or more of at least one network deployment scenario information, beam shape information, codebook information, antenna array information, transmitter to remote unit mapping, or one or more antenna down tilt angles (see para [0044] wherein the communication device 200 transmitting and receiving wireless signals with antenna system 202 that being a single antenna or may include one or more antenna arrays that each include multiple antenna elements, is mentioned and also see para [0131] wherein the network access node generating & transmitting RAN data and cell-specific parameters of a plurality of cells to device/UE, is mentioned and also see para [0183]) (and the same motivation is maintained as in claim 8).
Regarding claim 14, KUMAR et al. teach a user equipment (UE) for wireless communication (see Abstract and Fig.3/UE), comprising: at least one memory (see Fig.3, memory 382); and at least one processor coupled with the at least one memory and configured to cause the UE (see Fig.3 and paragraphs [0060] & [0069]) to: generate an indication of one or more validity criterion (see para [0088] wherein UE developing/generating one or more machine learning (ML) models, model structures (MSs) and/or parameter sets (PSs) per machine learning function name (MLFN), is mentioned also see para [0089]) and transmit the indication of one or more validity criterion (see para [0089] wherein the UE developing ML models for different UE and network entity settings/configurations and signaling/transmitting setting/configuration of the UE to the network entity, is mentioned).
KUMAR et al. teach the above UE comprising generating an indication of one or more validity criterion as mentioned above, but KUMAR et al. is silent in teaching the above UE comprising generating an indication of one or more validity criterion for a portion of UE context information.
However, SINGH et al. teach a user equipment (UE) for wireless communication (see Abstract and Fig.9/device900/UE) comprising generating an indication of one or more validity criterion for a portion of UE context information (see para [0105] wherein the device/UE generating the training dataset based on the RAN data 711 and/or the cell data 712 of the cells of the selected subset, is mentioned and also see para [0060] wherein the device/UE being configured to operate as a non-real-time RAN intelligent controller (a non-RT RIC) that may implement the trained AI/ML, is mentioned and also see para [0057]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the above UE of KUMAR et al. to include generating an indication of one or more validity criterion for a portion of UE context information, disclosed by SINGH et al. in order to provide an effective mechanism of UE for efficiently using trained artificial intelligence or machine learning models (AI/ML) for radio resource management and thereby providing an optimum output used in radio resource management of a plurality of cells in wireless communication system.
Regarding claim 15, KUMAR et al. and SINGH et al. together teach the UE of claim 14.
SINGH et al. further teach the UE of claim 14, wherein the at least one processor is configured to cause the UE to receive configuration information for data collection of a dataset for learning model life cycle management, wherein the configuration information comprises timing information for a duration of validity of the configuration information for data collection (see Fig.9 and see para [0100] wherein the device/UE being configured to generate training dataset based on the RAN data 711 and/or the cell data 712 and, based on the selected subset cells, the data processing unit of the device preparing the training data to be used in the training of the AI/ML, is mentioned and also see para [0179] wherein the output of the AI/ML 1502 including parameters for handover decisions and thresholds and timing, is mentioned and also see para [0132]) (and the same motivation is maintained as in claim 14).
Regarding claim 16, KUMAR et al. and SINGH et al. together teach the UE of claim 14.
SINGH et al. further teach the UE of claim 14, wherein the indication of the one or more validity criterion for the portion of UE context information comprises one or more of vendor specific validity information or UE specific validity information (see para [0105] wherein the device/UE generating the training dataset based on the RAN data 711 and/or the cell data 712 of the cells of the selected subset (that includes UE specific validity information), is mentioned and also see para [0060]) (and the same motivation is maintained as in claim 14).
Regarding claim 17, KUMAR et al. and SINGH et al. together teach the UE of claim 14.
SINGH et al. further teach the UE of claim 14, wherein the UE context information comprises one or more of antenna port layout information for the UE, UE battery information, UE movement data, or one or more UE hardware specifications (see para [0044] wherein the communication device 200 transmitting and receiving wireless signals with antenna system 202 that being a single antenna or may include one or more antenna arrays that each include multiple antenna elements, is mentioned and also see para [0131] wherein the network access node generating & transmitting RAN data and cell-specific parameters of a plurality of cells to device/UE, is mentioned and also see para [0183]) (and the same motivation is maintained as in claim 8).
Regarding claim 18, KUMAR et al. teach a network equipment for wireless communication (see Abstract and Fig.3/BS 102), comprising: at least one memory (see Fig.3, memory 342 inside BS and para [0069]); and at least one processor (see processor 340 inside BS 102 and para [0058]) coupled with the at least one memory and configured to cause the network equipment (see para [0058]) to: receive an indication of one or more validity criterion (see para [0089] wherein the UE developing ML models for different UE and network entity settings/configurations and signaling/transmitting setting/configuration of the UE to be received by the network entity, is mentioned); and store the indication of the one or more validity criterion as part of learning model data (see para [0089] wherein the UE signaling setting/configuration of the UE to the network entity so that the network entity can label ML data (that includes storing the indication of the one or more validity criterion) for a given UE and network entity setting/configuration, is mentioned and also for ML model operation (e.g., learning/running) phase, input ML data being required for running the ML model, is mentioned).
KUMAR et al. teach the above network equipment for wireless communication comprising receiving an indication of one or more validity criterion as mentioned above, but KUMAR et al. is silent in teaching the above network equipment for wireless communication comprising receiving an indication of one or more validity criterion for a portion of user equipment (UE) context information.
However, SINGH et al. teach a network equipment for wireless communication (see Abstract and Fig.9/network access node/cell 912/913) comprising receiving an indication of one or more validity criterion for a portion of user equipment (UE) context information (see para [0105] wherein the device/UE generating & providing the training dataset based on the RAN data 711 and/or the cell data 712 of the cells of the selected subset, is mentioned and also see para [0060] wherein the device/UE being configured to operate as a non-real-time RAN intelligent controller (a non-RT RIC) that may implement the trained AI/ML, is mentioned).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the above network equipment for wireless communication of KUMAR et al. to include receiving an indication of one or more validity criterion for a portion of user equipment (UE) context information, disclosed by SINGH et al. in order to provide an effective mechanism of UE for efficiently using trained artificial intelligence or machine learning models (AI/ML) for radio resource management and thereby providing an optimum output used in radio resource management of a plurality of cells in wireless communication system.
Regarding claim 19, KUMAR et al. and SINGH et al. together teach the network equipment of claim 18.
SINGH et al. further teach the network equipment of claim 18, wherein the at least one processor is configured to cause the network equipment to transmit configuration information for data collection of a dataset for learning model life cycle management, wherein the configuration information comprises timing information for a duration of validity of the configuration information for data collection (see Fig.9 and see para [0100] wherein the device being configured to generate training dataset based on the RAN data 711 and/or the cell data 712 and, based on the selected subset cells, the data processing unit of the device preparing the training data to be used in the training of the AI/ML, is mentioned and also see para [0179] wherein the output of the AI/ML 1502 including parameters for handover decisions and thresholds and timing, is mentioned and also see para [0132]) (and the same motivation is maintained as in claim 18).
Regarding claim 20, KUMAR et al. and SINGH et al. together teach the network equipment of claim 18.
SINGH et al. further teach the network equipment of claim 18, wherein the at least one processor is configured to cause the network equipment to receive identifiers for the portion of the UE context information (see para [0105] wherein the device generating the training dataset based on the RAN data 711 and/or the cell data 712 of the cells of the selected subset (that includes UE context information), is mentioned and also see para [0181]) (and the same motivation is maintained as in claim 18).
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Esswie (US Pub. No: 2024/0378485 A1) disclose mechanisms relating to a radio access network node, or nodes, that determine learning model configuration information to use to train a learning model corresponding to a user equipment in idle mode in wireless communication system.
KUO et al. (US Pub. No: 2026/0094059 A1) disclose mechanisms relating to an artificial intelligence (AI) agent configured to collect a dataset for training an AI or machine learning (ML) (AI/ML) mode and determine context information for the collected dataset of UE in wireless communication system.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to SRINIVASA R REDDIVALAM whose telephone number is (571)270-3524. The examiner can normally be reached on M-F 10-7 EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, CHIRAG G SHAH can be reached on 571-272-3144. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SRINIVASA R REDDIVALAM/Primary Examiner, Art Unit 2477
6/26/2026
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