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 .
This action is in response to applicant’s request of Continued Examination (RCE) filed on 04/13/2026 on amendments/arguments filed on 03/18/2026. Claims 1, 12, 24 and 27 have been amended. Currently, claims 1-30 are pending for consideration.
Response to Arguments
Applicant’s arguments/amendments with respect to amended claims 1, 12, 24 and 27 have been considered but are moot in view of the new ground(s) of rejection.
Response to Amendments
Claim Rejections - 35 USC § 103
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 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.
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.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 2, 4, 5, 8, 9, 12, 14, 15 and 18-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 20240349082 A1) in view of Ly et al. (US 20250310214 A1).
Consider claim 1, Li discloses a user equipment (UE) configured for wireless communications, comprising: one or more memories comprising instructions; and one or more processors, individually or collectively, configured to execute the instructions to cause the UE to (read as the UE comprising its processor and memory for storing instructions, figures 4 and 5, par [0121]-[0123] and [0132]-[0134] and [0137]):
receive a configuration for at least one machine learning function name (MLFN) (read as based on the UE capability information 112 transmitted by the UE device 104, the network device 102 may select or generate a ML configuration 114 for the UE device 104, and may send the ML configuration 114 to the UE device with the ML model (in form of identifiers), par [0042]), figures 1 and 2, par [0067]-[0068]; additionally, receiving the configuration associated with at least one ML service identifier, such as machine learning service registration, serviceType and the new SIB that includes machine learning service, par [0030], [0050] and [0062]);
receive machine learning (ML) data associated with the at least one MLFN (read as after receiving the ML model (in form of identifiers, par [0042]) and ML configuration 114 with associated other data transmitted by the network, the UE device 104 may execute the ML model based on the ML configuration 114, figures 1 and 2, par [0067]-[0068], this data includes the necessary inputs for ML operation and training, and that the UE uses the received ML model and configuration with associated other data for execution and training purpose (par [0068]); furthermore, the UE downloading the model according to its interested machine learning service type, the training related information and the MachineLearningModelUpdateResponse with corresponding service type, which constitute receiving ML information from the network side that is associated with a ML service, par [0029], [0043] and [0060]); and
use the ML data as an input for at least one of: operation or training of an ML model associated with the at least one MLFN (read as once the UE device 104 has executed the ML model and generated corresponding outputs, the UE device 104 may generate and send a ML report 118 to the network device 102; the ML report 118 may indicate predictions prior to the ML execution 116, outcomes of the ML execution 116 (e.g., actual outputs of the ML execution 116), and requested or selected actions (e.g., an action space) for the UE device 104 to perform based on the ML execution 116; the ML model and/or configuration may be trained and/or updated by the network device 102 and/or the UE device 104, figures 1 and 2, par [0067]-[0068]; furthermore, the UE starting inference based on the input data, the local machine learning model updated and iterated based on the UE’s own environment and input output, and the UE device executing the ML mode based on the ML configuration, par [0029], [0033] and [0068]).
However, Li discloses the claimed invention above and interest indication and UE can send the MachineLearningModelUpdateRequest to the network, requesting a parameter update to the corresponding machine learning model (par [0017] and [0060]) but does not specifically disclose transmit an indication to a network entity to activate transmission of machine learning (ML) data associated with the at least one MLFN
Nonetheless, Ly discloses UE transmitting the indication for enabling AI or ML communications a request to the network with AMF, par [0004], [0016] and [0092]-[0093].
Therefore, it would have been obvious for a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Ly into the teachings of Li, to modify UE ML signaling to network using Ly’s indication enabling technique for ML communications, in order to allow UE requested activation of ML data delivery.
Consider claim 2, as applied to claim 1 above, Li, as modified by Ly, discloses wherein: the operation of the ML model comprises at least one of: running the ML model, switching the ML model, or monitoring the ML model; and the training of the ML model comprises: labeling the ML data for a particular UE and network entity setting or configuration, and performing the training of the ML model using the labeled ML data (read as once the UE device 104 has executed the ML model and generated corresponding outputs, the UE device 104 may generate and send a ML report 118 to the network device 102; the ML report 118 may indicate predictions prior to the ML execution 116, outcomes of the ML execution 116 (e.g., actual outputs of the ML execution 116), and requested or selected actions (e.g., an action space) for the UE device 104 to perform based on the ML execution 116; the ML model and/or configuration may be trained and/or updated by the network device 102 and/or the UE device 104, figures 1 and 2, par [0067]-[0068])
Consider claim 4, as applied to claim 1 above, Li, as modified by Ly, discloses wherein: the ML data is applicable for one or more other UEs in a network entity coverage or target area (read as the NG-RAN (e.g., a gNB device) may generate and send a ML capability indication to UEs to indicate that the NG-RAN supports ML (e.g., and may facilitate ML operations at the UE), par [0017]), and the ML data indicates at least one of: an ML model identification (ID) or a model structure (MS) ID together with the ML data (read as the ML models can also be in the form of identifiers for UE to download the models; according to different UE capability, NG-RAN may assign models with smaller granularity, par [0042]).
Consider claim 5, as applied to claim 4 above, Li, as modified by Ly, discloses wherein the ML data is received via at least one of: a system information block (SIB) or multicast broadcast service (MBS) over MBS channel (read as the network's ML capability may be indicated by a new SIB, this new SIB (e.g. SIBX) contains information related to machine learning, par [0060]-[0062]).
Consider claim 8, as applied to claim 1 above, Li, as modified by Ly, discloses wherein one or more processors, individually or collectively, configured to execute the instructions to cause the UE to: transmit a request to a network entity for the ML data, wherein the ML data is received by the UE in response to the request (read as identify a service registration, received from the UE device, indicating that the UE device requests machine learning support from the node B device; transmit, to the UE device, a request for information associated with the UE device, the information associated with at least one of hardware capabilities or machine learning capabilities of the UE device; identify the information received from the UE based on the request for information, see abstract par [0067]-[0068]).
Consider claim 9, as applied to claim 8 above, Li, as modified by Ly, discloses wherein the transmit comprises transmit the request via at least one of: UE assistance information (UAI) or a subscribe request (read as identify a service registration, received from the UE device, indicating that the UE device requests (i.e. subscribe request as UE is subscribed to base station) machine learning support from the node B device, see abstract par [0067]-[0068]).
Consider claim 10, as applied to claim 8 above, Li, as modified by Ly, discloses wherein the request indicates at least one of: the at least one MLFN; an ML model identification (ID); a model structure (MS) ID; geographical information comprising at least one of: current geographical area of the UE, a public land mobile network (PLMN), cell information, or a frequency list; a validity time comprising at least one of: a duration time or an interval time at which the ML data has to be provided to the UE; one or more network configurations or settings; or a type of the ML data comprising at least one of: meta data, training data, or inference data (read as UE(s) send their interested service type/ID to the network and requesting such service(s), par [0063]).
Consider claim 12, Li discloses a network entity configured for wireless communications, comprising: one or more memories comprising instructions; and one or more processors, individually or collectively, configured to execute the instructions to cause the UE to (read as the UE comprising its memory and processor, figures 4 and 5, par [0121]-[0123] and [0132]-[0134]):
transmit a configuration for at least one machine learning function name (MLFN) to at least one user equipment (UE) (read as based on the UE capability information 112 transmitted by the UE device 104, the network device 102 may select or generate a ML configuration 114 for the UE device 104, and may send the ML configuration 114 to the UE device, figures 1 and 2, par [0067]-[0068]);
determine machine learning (ML) data associated with the at least one MLFN to be used as an input (read as after receiving the ML model (in form of identifiers, par [0042]) and ML configuration 114 with associated other data transmitted by the network, the UE device 104 may execute the ML model based on the ML configuration 114, figures 1 and 2, par [0067]-[0068], this data includes the necessary inputs for ML operation and training, and that the UE uses the received ML model and configuration with associated other data for execution and training purpose (par [0068])) for at least one of:
operation or training of an ML model associated with the at least one MLFN; and transmit the ML data to the at least one UE (read as once the UE device 104 has executed the ML model and generated corresponding outputs, the UE device 104 may generate and send a ML report 118 to the network device 102; the ML report 118 may indicate predictions prior to the ML execution 116, outcomes of the ML execution 116 (e.g., actual outputs of the ML execution 116), and requested or selected actions (e.g., an action space) for the UE device 104 to perform based on the ML execution 116; the ML model and/or configuration may be trained and/or updated by the network device 102 and/or the UE device 104, figures 1 and 2, par [0067]-[0068]).
However, Li discloses the claimed invention above and interest indication and UE can send a “MachineLearningModelUpdateRequest” to the network, requesting a parameter update to the corresponding machine learning model (par [0017] and [0060]) but does not specifically disclose receive an indication from the at least one UE to activate transmission of the ML data to the at least one UE.
Nonetheless, Ly discloses UE transmitting the indication for enabling AI or ML communications a request to the network with AMF, par [0004], [0016] and [0092]-[0093].
Therefore, it would have been obvious for a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Ly into the teachings of Li, to modify UE ML signaling to network using Ly’s indication enabling technique for ML communications, in order to allow UE requested activation of ML data delivery.
Consider claim 14, as applied to claim 12 above, Li, as modified by Ly, discloses wherein: the at least one UE corresponds to a first UE and a second UE; and the first UE and the second UE requiring same ML data inputs or controls (read as the NG-RAN (e.g., a gNB device) may generate and send a ML capability indication to UEs (i.e. first UE and second UE) to indicate that the NG-RAN supports ML (e.g., and may facilitate ML operations at the UE), par [0017]) associated with at least one of: the at least one MLFN, an ML model identification (ID), or a model structure (MS) ID (read as the ML models can also be in the form of identifiers for UE to download the models; according to different UE capability, NG-RAN may assign models with smaller granularity, par [0042]).
Consider claim 15, as applied to claim 14 above, Li, as modified by Ly, discloses wherein the ML data is transmitted to the first UE and the second UE via at least one of: a system information block (SIB) or a multicast broadcast service (MBS) (read as the network's ML capability may be indicated by a new SIB, this new SIB (e.g. SIBX) contains information related to machine learning, par [0060]-[0062]).
Consider claim 18, as applied to claim 12 above, Li, as modified by Ly, discloses wherein: the at least one UE corresponds to a first UE; the one or more processors, individually or collectively, configured to execute the instructions to cause the UE to: receive a request for the ML data from the first UE, wherein the ML data is transmitted to the first UE in response to the request (read as identify a service registration, received from the UE device, indicating that the UE device requests machine learning support from the node B device; transmit, to the UE device, a request for information associated with the UE device, the information associated with at least one of hardware capabilities or machine learning capabilities of the UE device; identify the information received from the UE based on the request for information, see abstract par [0067]-[0068]).
Consider claim 19, as applied to claim 18 above, Li, as modified by Ly, discloses wherein the request is received via at least one of: UE assistance information (UAI) or a subscribe request (read as identify a service registration, received from the UE device, indicating that the UE device requests (i.e. subscribe request as UE is subscribed to base station) machine learning support from the node B device, see abstract par [0067]-[0068]).
Consider claim 20, as applied to claim 18 above, Li, as modified by Ly, discloses wherein the request indicates at least one of: the at least one MLFN; an ML model identification (ID); a model structure (MS) ID; geographical information comprising at least one of: current geographical area of the first UE, a public land mobile network (PLMN), cell information, or a frequency list; a validity time comprising at least one of: a duration time or an interval time at which the ML data has to be provided to the first UE; one or more network configurations; or a type of the ML data comprising at least one of: meta data, training data, or inference data (read as UE(s) send their interested service type/ID to the network and requesting such service(s), par [0063]).
Consider claim 21, as applied to claim 18 above, Li, as modified by Ly, discloses wherein the one or more processors, individually or collectively, configured to execute the instructions to cause the UE to: receive an indication from the first UE to activate or deactivate transmission of the ML data to the first UE (read as the UE sends UE capability information 112 to base station 102 to cause/activate ML configuration 114 from the base station to UE, par [0067]-[0068]).
Consider claim 22, as applied to claim 18 above, Li, as modified by Ly, discloses wherein the one or more processors, individually or collectively, configured to execute the instructions to cause the UE to: transmit the request to another network entity when one or more conditions are satisfied (read as once RAN accepts the handover request, NG-RAN will send a granted response to the request UE and forward the corresponding UE context (i.e. the UE request) to the target cell, par [0021]).
Claim(s) 24, 25, 27, 28 and 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 20240349082 A1) in view of Garcia Rodriguez et al. (US 20250203401 A1).
Consider claim 24, Li discloses a user equipment (UE) configured for wireless communications, comprising: one or more memories comprising instructions; and one or more processors, individually or collectively, configured to execute the instructions to cause the UE to (read as the UE comprising its memory and processor, figures 4 and 5, par [0121]-[0123] and [0132]-[0134]):
transmit UE capability information to a network entity (read as based on the UE capability information 112 transmitted by the UE device 104, the network device 102 may select or generate a ML configuration 114 for the UE device 104, and may send the ML configuration 114 to the UE device, figures 1 and 2, par [0067]-[0068]);
receive from the network entity a configuration for at least one machine learning function name (MLFN) (read as based on the UE capability information 112 transmitted by the UE device 104, the network device 102 may select or generate a ML configuration 114 for the UE device 104, and may send the ML configuration 114 to the UE device, figures 1 and 2, par [0067]-[0068]) and a request for machine learning (ML) data associated with the at least one MLFN to be used as an input for at least one of: operation or training of an ML model associated with the at least one MLFN, wherein the configuration and the request are based on the UE capability information (read as that the network transmits a UE capability inquiry requesting ML-related capability information from the UE, and the UE responds by transmitting that information (par [0067]), this constitutes the request and response mechanism for ML-related data and the network configures the reporting behavior, specifying what ML information the UE must report (par [0031]), the UE then transmits ML report containing ML-related data, such as prediction results and model updates, in response to this configuration (par [0068 and [0051]);and
transmit the ML data to the network entity in response to the request (read as once the UE device 104 has executed the ML model and generated corresponding outputs, the UE device 104 may generate and send a ML report 118 to the network device 102; the ML report 118 may indicate predictions prior to the ML execution 116, outcomes of the ML execution 116 (e.g., actual outputs of the ML execution 116), and requested or selected actions (e.g., an action space) for the UE device 104 to perform based on the ML execution 116; the ML model and/or configuration may be trained and/or updated by the network device 102 and/or the UE device 104, figures 1 and 2, par [0067]-[0068]).
However, Li discloses the claimed invention above and ML report configuration with reporting types configurations received from NG-RAN (par [0031] and [0051]) but does not specifically disclose receive an indication from the network entity to activate transmission of the ML data associated with the at least one MLFN.
Nonetheless, Garcia Rodriguez discloses the network node 110 transmitting to UE 112 a first signal for triggering activation or deactivation of one or more AI and/or ML models by the UE 112, the UE receiving that first signal and then transmitting a second signal, and the triggering information including activation =information, par [0116], [0317], [0334] and [0336].
Therefore, it would have been obvious for a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Garcia Rodriguez into the teachings of Li, to modify network ML reporting using Garcia Rodriguez’s activation triggering signal technique for ML communications, in order to allow network triggered activation of UE ML data report.
Consider claim 25, as applied to claim 24 above, Li, as modified by Garcia Rodriguez, discloses wherein the configuration further indicates at least one of: an ML model identification (ID) or a model structure (MS) ID (read as the ML models can also be in the form of identifiers for UE to download the models; according to different UE capability, NG-RAN may assign models with smaller granularity, par [0042]).
Consider claim 27, Li discloses a network entity configured for wireless communications, comprising: one or more memories comprising instructions; and one or more processors, individually or collectively, configured to execute the instructions to cause the UE to (read as the UE comprising its memory and processor, figures 4 and 5, par [0121]-[0123] and [0132]-[0134]):
receive user equipment (UE) capability information from a UE (read as based on the UE capability information 112 transmitted by the UE device 104, the network device 102 may select or generate a ML configuration 114 for the UE device 104, and may send the ML configuration 114 to the UE device, figures 1 and 2, par [0067]-[0068]);
transmit to the UE a configuration for at least one machine learning function name (MLFN) and a request for machine learning (ML) data associated with the at least one MLFN (read as that the network configures the reporting behavior to specify what ML information the UE must report (par [0031]), this configuration acts as a request for ML data associated with an ML function and that the UE transmits ML reports to the network, which include ML data such as prediction results, model update, and training-related information, these reports are transmitted in response to the network’s configuration request, par [0051] and [0058]) to be used as an input for at least one of: operation or training of an ML model associated with the at least one MLFN, wherein the configuration and the request are based on the UE capability information; and receive the ML data from the UE in response to the request (read as once the UE device 104 has executed the ML model and generated corresponding outputs, the UE device 104 may generate and send a ML report 118 to the network device 102; the ML report 118 may indicate predictions prior to the ML execution 116, outcomes of the ML execution 116 (e.g., actual outputs of the ML execution 116), and requested or selected actions (e.g., an action space) for the UE device 104 to perform based on the ML execution 116; the ML model and/or configuration may be trained and/or updated by the network device 102 and/or the UE device 104, figures 1 and 2, par [0067]-[0068]).
However, Li discloses the claimed invention above and ML report configuration with reporting types configurations received from NG-RAN (par [0031] and [0051]) but does not specifically disclose transmit an indication to the UE to activate transmission of the ML data associated with the at least one MLFN.
Nonetheless, Garcia Rodriguez discloses the network node 110 transmitting to UE 112 a first signal for triggering activation or deactivation of one or more AI and/or ML models by the UE 112, the UE receiving that first signal and then transmitting a second signal, and the triggering information including activation =information, par [0116], [0317], [0334] and [0336].
Therefore, it would have been obvious for a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Garcia Rodriguez into the teachings of Li, to modify network ML reporting using Garcia Rodriguez’s activation triggering signal technique for ML communications, in order to allow network triggered activation of UE ML data report.
Consider claim 28, as applied to claim 27 above, Li, as modified by Garcia Rodriguez, discloses wherein the configuration further indicates at least one of: an ML model identification (ID) or a model structure (MS) ID (read as the ML models can also be in the form of identifiers for UE to download the models; according to different UE capability, NG-RAN may assign models with smaller granularity, par [0042]).
Consider claim 30, as applied to claim 27 above, Li, as modified by Garcia Rodriguez, discloses wherein the one or more processors, individually or collectively, configured to execute the instructions to cause the UE to: determine the ML data to be reported based on the UE capability information (read as based on the UE capability information 112, the network device 102 may select or generate a ML configuration 114 for the UE device 104, and may send the ML configuration 114 to the UE device; for example, based on any requested service in the service registration 108, the network device 102 may select a ML model and a ML configuration for the ML model for the service based on the UE capability information 112 (e.g., a ML model/configuration using more or fewer resources depending on the UE capability information 112), par [0067]).
Claims 3 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 20240349082 A1) in view of Ly et al. (US 20250310214 A1), and in further view of Wang et al. (US 20230325679 A1).
Consider claim 3, as applied to claim 1 above, Li, as modified by Ly, discloses wherein: the ML data comprises model management data and training data (read as the device may generate/select and transmit, to the UE device, a machine learning configuration and a ML model (e.g., the ML configuration 114 of FIG. 1) based on the information received from the UE device; and the ML model and/or configuration may be trained and/or updated, par [0068] and [0080]) and the training data indicates at least one of: a network load, an event threshold, or layer 1 (L1) and layer 2 (L2) measurements (read as, for example, the reportPeriodicity (Value ms20), see Table 2, par [0050]; or required memory size (e.g., unit: MB/KB) as described in par ]0062]) but does not specifically disclose the model management data indicates at least one of: a number of antennas or a number of retransmissions.
Nonetheless, in related art, Wang discloses a machine learning system between base station and different UEs, wherein the UEs configures/indicates its number of antennas based on ML configuration parameters from base station 120, figure 6, par [0093]-[0094].
Therefore, it would have been obvious for a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Wang into the teachings of Li, which modified by Ly, to design the base station to configure the number of UE antennas in order to accurately configure/control the UE for ML model training.
Consider claim 13, as applied to claim 12 above, Li, as modified by Ly, discloses wherein: the ML data comprises model management data and training data (read as the device may generate/select and transmit, to the UE device, a machine learning configuration and a ML model (e.g., the ML configuration 114 of FIG. 1) based on the information received from the UE device; and the ML model and/or configuration may be trained and/or updated, par [0068] and [0080]) and the training data indicates at least one of: a network load, an event threshold, or layer 1 (L1) and layer 2 (L2) measurements (read as, for example, the reportPeriodicity (Value ms20), see Table 2, par [0050]; or required memory size (e.g., unit: MB/KB) as described in par ]0062]) but does not specifically disclose the model management data indicates at least one of: a number of antennas or a number of retransmissions.
Nonetheless, in related art, Wang discloses a machine learning system between base station and different UEs, wherein the UEs configures/indicates its number of antennas based on ML configuration parameters from base station 120, figure 6, par [0093]-[0094].
Therefore, it would have been obvious for a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Wang into the teachings of Li, which modified by Ly, to design the base station to configure the number of UE antennas in order to accurately configure/control the UE for ML model training.
Claims 6, 7, 10, 11, 16 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 20240349082 A1) in view of Ly et al. (US 20250310214 A1), and in further view of Zeng et al. (US 20230209390 A1).
Consider claim 6, as applied to claim 1 above, Li, as modified by Ly, discloses the claimed invention above but does not specifically disclose wherein the ML data is received via a unicast message when at least one condition is satisfied: the UE connects to a network entity; or the UE is configured with at least one of: the at least one MLFN, an ML model identification (ID), or a model structure (MS) ID.
Nonetheless, in related art, Zeng discloses a UE connected the base station, and the base station would send the artificial intelligence (AI) task to the UE in a unicast form, par [0008]) and abstract.
Therefore, it would have been obvious for a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Zeng into the teachings of Li, as modified by Ly, to design the base station to send the ML configuration (AI task) to the UE for ML training as unicast would ensure reliable one-to-one communication.
Consider claim 7, as applied to claim 6 above, Li, as modified by Ly, discloses the claimed invention above the UE sends UE capability information 112 to base station 102 to cause/activate ML configuration 114 from the base station to UE (par [0067]-[0068]) but does not specifically disclose wherein the one or more processors, individually or collectively, configured to execute the instructions to cause the UE to: transmit an indication using a medium access control (MAC) control element (CE), UE assistance information (UAI), or uplink control information (UCI) to the network entity to activate or deactivate transmission of the ML data to the UE.
Nonetheless, in related art, Zeng discloses the UE reports the UE capability to the network device and UE capability is carried over the MAC CE message, par [0211].
Therefore, it would have been obvious for a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Zeng into the teachings of Li, as modified by Ly, to design the UE to send the UE capability to the base station over MAC CE message as MAC CE offers low latency.
Consider claim 10, as applied to claim 8 above, Li, as modified by Ly, discloses wherein the request indicates at least one of: the at least one MLFN; an ML model identification (ID); a model structure (MS) ID; geographical information comprising at least one of: current geographical area of the UE, a public land mobile network (PLMN), cell information, or a frequency list; a validity time comprising at least one of: a duration time or an interval time at which the ML data has to be provided to the UE; one or more network configurations or settings; or a type of the ML data comprising at least one of: meta data, training data, or inference data (read as the machine learning service type/ID send by UE as a request to the base station, par [0063]).
Consider claim 11, as applied to claim 1 above, Li, as modified by Ly, discloses the claimed invention above the UE sends UE capability information 112 to base station 102 to cause/activate ML configuration 114 from the base station to UE (par [0067]-[0068]) but does not specifically disclose wherein the one or more processors, individually or collectively, configured to execute the instructions to cause the UE to: transmit an indication using a medium access control (MAC) control element (CE), UE assistance information (UAI), or uplink control information (UCI) to the network entity to activate or deactivate transmission of the ML data to the UE.
Nonetheless, in related art, Zeng discloses the UE reports the UE capability to the network device and UE capability is carried over the MAC CE message, par [0211].
Therefore, it would have been obvious for a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Zeng into the teachings of Li, as modified by Ly, to design the UE to send the UE capability to the base station over MAC CE message as MAC CE offers low latency.
Consider claim 16, as applied to claim 12 above, Li, as modified by Ly, discloses claimed invention above and wherein: the at least one UE corresponds to a first UE (read as the UE , par [0067]-[0068] but does not specifically disclose the ML data is transmitted to the first UE via a unicast message.
Nonetheless, in related art, Zeng discloses a UE connected the base station, and the base station would send the artificial intelligence (AI) task to the UE in a unicast form, par [0008]) and abstract.
Therefore, it would have been obvious for a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Zeng into the teachings of Li, as modified by Ly, to design the base station to send the ML configuration (AI task) to the UE for ML training as unicast would ensure reliable one-to-one communication.
Consider claim 17, as applied to claim 16 above, Li, as modified by Ly, discloses the claimed invention above the UE sends UE capability information 112 to base station 102 to cause/activate ML configuration 114 from the base station to UE (par [0067]-[0068]) but does not specifically disclose wherein the one or more processors, individually or collectively, configured to execute the instructions to cause the UE to: transmit an indication using a medium access control (MAC) control element (CE), UE assistance information (UAI), or uplink control information (UCI) to the network entity to activate or deactivate transmission of the ML data to the UE.
Nonetheless, in related art, Zeng discloses the UE reports the UE capability to the network device and UE capability is carried over the MAC CE message, par [0211].
Therefore, it would have been obvious for a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Zeng into the teachings of Li, as modified by Ly, to design the UE to send the UE capability to the base station over MAC CE message as MAC CE offers low latency.
Claims 26 and 29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 20240349082 A1) in view of Garcia Rodriguez et al. (US 20250203401 A1), and in further view of Zeng et al. (US 20230209390 A1).
Consider claim 26, as applied to claim 24 above, Li, as modified by Garcia Rodriguez, discloses the claimed invention above and an indication from the network entity to activate or deactivate transmission of the ML data to the network entity (read as the network node 110 transmitting to UE 112 a first signal for triggering activation or deactivation of one or more AI and/or ML models by the UE 112, the UE receiving that first signal and then transmitting a second signal, and the triggering information including activation =information, par [0116], [0317], [0334] and [0336] of Garcia Rodriguez) but does not specifically disclose wherein the processor is further configured to execute the computer-executable instructions and cause the UE to: receive, via a medium access control (MAC) control element (CE) or a downlink control information (DCI), the indication.
Nonetheless, in related art, Zeng discloses the base station and the UE are using MAC CE to indicate/report collected data to each other, par [0343].
Therefore, it would have been obvious for a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Zeng into the teachings of Li, as modified by Garcia Rodriguez, as modified by Ly, to design the network device to send ML capability indication 106 to the UE device over MAC CE as MAC CE offers low latency.
Consider claim 29, as applied to claim 27 above, Li, as modified by Garcia Rodriguez, discloses the claimed invention above and wherein the one or more processors, individually or collectively, configured to execute the instructions to cause the UE to: transmit an indication to the UE to activate or deactivate transmission of the ML data to the network entity (read as the network node 110 transmitting to UE 112 a first signal for triggering activation or deactivation of one or more AI and/or ML models by the UE 112, the UE receiving that first signal and then transmitting a second signal, and the triggering information including activation =information, par [0116], [0317], [0334] and [0336] of Garcia Rodriguez) but does not specifically disclose transmit the indication via a medium access control (MAC) control element (CE) or a downlink control information (DCI).
Nonetheless, in related art, Zeng discloses the base station and the UE are using MAC CE to indicate/report collected data to each other, par [0343].
Therefore, it would have been obvious for a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Zeng into the teachings of Li, as modified by Garcia Rodriguez, to design the network device to send ML capability indication 106 to the UE device over MAC CE as MAC CE offers low latency.
Allowable Subject Matter
Claim 23 is 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.
Conclusion
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/Junpeng Chen/
Primary Examiner, Art Unit 2645
Prosecution Insights