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 .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/09/2026 has been entered.
The present application was filed on 12/03/2020. This action is in response to RCE filed on 02/09/2026, amendments and remarks filed on 01/05/2026. In the current amendments, claims 1-5, 7-9, 11, 13-15, 21-22, 26, 30, 33, 35-36 and 39-41 have been amended. Claims 1-41 are pending and have been examined. Claims 1, 13, 22, 30, 33, 36, 39, 40 and 41 are independent claims.
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.
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.
Claims 1-3, 11-13, 22, 30-32, 36 and 39-41 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Li (US20220038349A1, The applied Li reference was filed as U.S. Patent Application 17/504,711 on 10/19/2021. Li claims priority to U.S. provisional patent application 63,093,666 filed on 10/19/2020, and this date is before the earliest effective filing date of this application, i.e., 12/03/2020. Therefore, Li constitutes prior art under 35 U.S.C. 102(a)(2)).
Claim 1:
Li discloses a first user equipment (UE) for wireless communication, comprising (Para [0020] “the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105. The ProSe interface 105 may alternatively be referred to as a sidelink (SL) interface comprising one or more logical channels, including but not limited to a Physical Sidelink Control Channel (PSCCH), a Physical Sidelink Shared Channel (PSSCH), a Physical Sidelink Discovery Channel (PSDCH), a Physical Sidelink Broadcast Channel (PSBCH), and a Physical Sidelink Feedback Channel (PSFCH)” discloses user equipment for wireless communication):
one or more memories; and one or more processors coupled to the one or more memories, configured to cause the first UE (See e.g. UE 101 in Figure 1) to (Para [0045] “The communication device 200 may include a hardware processor (or equivalently processing circuitry) 202 (e.g., a central processing unit (CPU), a GPU, a hardware processor core, or any combination thereof), a main memory 204 and a static memory 206” discloses memories and processor):
receive, from a second UE (See e.g. UE 102 in Figure 1), a sidelink communication in a sidelink control channel or a sidelink shared channel, wherein the sidelink communication includes a first local update associated with a machine learning component (Para [0020] “the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105. The ProSe interface 105 may alternatively be referred to as a sidelink (SL) interface comprising one or more logical channels, including but not limited to a Physical Sidelink Control Channel (PSCCH), a Physical Sidelink Shared Channel (PSSCH), a Physical Sidelink Discovery Channel (PSDCH), a Physical Sidelink Broadcast Channel (PSBCH), and a Physical Sidelink Feedback Channel (PSFCH)” and Para [0058] “transmitting the model to the UEs (local nodes in federated learning), and aggregating parameters from local nodes and updating the trained model. The UEs act as the "local node", responsible for sending a training model request, receiving a trained model from the RAN (central server in federated learning), training the model locally with its own data and reporting updated parameters to the gNB-DU/gNB-CU or LMF” discloses receiving communication on the sidelink control channel wherein communication includes a local update with a federated learning and receiving communication to cause the UE (i.e. or the second UE) to receive update);
and transmit an aggregated local update based at least in part on the first local update associated with the machine learning component and a second local update associated with the machine learning component (Para [0069] “Each local node reports its training model updated parameters to the central node via RRC signaling, and the central node aggregates all collected information and starts updating the general model. FIG. 3D illustrates Local Model Parameter Reporting and General Model Update in accordance with some aspects. In some cases, the central node may average the information to arrive at a particular updated AI/ML model” and Figure 3D discloses transmitting aggregated local update based on the associated federated learning for the UEs),
wherein the aggregated local update comprises an aggregation of the first local update and the second local update (Para [0059] “federated learning between the RAN (central server) and UEs (local nodes) (or LMF and UEs for positioning) in which the central server can aggregate parameters trained at different UEs experiencing different channel status, and different environment” and Para [0020] “In an aspect, the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105” discloses wherein the aggregated local update comprises aggregation of UEs).
Claim 2.
Li discloses the first UE of claim 1,
Li further discloses wherein the one or more processors are further configured to cause the first UE to determine the second local update associated with the machine learning component based at least in part on training the machine learning component (Para [0068]- [0070] “Operation 3: Each local node (e.g., UE) starts local training based on its own data, and no information exchange is expected during this period for AI/ML model training purposes. FIG. 3C illustrates Local Training in accordance with some aspects. The local training at each UE may eventually be different. Operation 4: Each local node reports its training model updated parameters to the central node via RRC signaling, and the central node aggregates all collected information and starts updating the general model. FIG. 3D illustrates Local Model Parameter Reporting and General Model Update in accordance with some aspects. In some cases, the central node may average the information to arrive at a particular updated AI/ML model. Operation 0 can be treated as an initial phase of the AI/ML service/model subscription. Operation 2 to Operation 4 can be repeatedly performed after the UE receives the deployed model from the RAN” discloses determine the local update using machine learning on the local node (UE)).
Claim 3.
Li discloses the first UE of claim 1,
Li discloses discloses the sidelink communication from a second UE (See e.g. UEs 102) , and wherein the one or more processors are further configured to cause the first UE to receive the second local update from a third UE (See e.g. Figure 3c comprising more than two UEs ) (Para [0069] “Each local node reports its training model updated parameters to the central node via RRC signaling, and the central node aggregates all collected information and starts updating the general model. FIG. 3D illustrates Local Model Parameter Reporting and General Model Update in accordance with some aspects. In some cases, the central node may average the information to arrive at a particular updated AI/ML model” and Para [0020] “In an aspect, the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105. The ProSe interface 105 may alternatively be referred to as a sidelink (SL) interface comprising one or more logical channels, including but not limited to a Physical Sidelink Control Channel (PSCCH), a Physical Sidelink Shared Channel (PSSCH), a Physical Sidelink Discovery Channel (PSDCH), a Physical Sidelink Broadcast Channel (PSBCH), and a Physical Sidelink Feedback Channel (PSFCH” and Fig 3D discloses receiving sidelink communication to cause the UE (i.e. or the second UE) to receive update).
Claim 11.
Li discloses the first UE of claim 1, wherein the one or more processors,
Li further discloses to transmit the aggregated local update, are configured to cause the first UE to transmit the aggregated local update to a third UE (Para [0069] “Operation 4: Each local node reports its training model updated parameters to the central node via RRC signaling, and the central node aggregates all collected information and starts updating the general model. FIG. 3D illustrates Local Model Parameter Reporting and General Model Update in accordance with some aspects. In some cases, the central node may average the information to arrive at a particular updated AI/ML model. Operation 0 can be treated as an initial phase of the AI/ML service/model subscription. Operation 2 to Operation 4 can be repeatedly performed after the UE receives the deployed model from the RAN” and Figure 3d discloses aggregated information that received from the local nodes (UEs)).
Claim 12.
Li discloses the first UE of claim 1,
Li further discloses wherein the first local update comprises an additional aggregated local update (Para [0069]-[0070] “Operation 4: Each local node reports its training model updated parameters to the central node via RRC signaling, and the central node aggregates all collected information and starts updating the general model. FIG. 3D illustrates Local Model Parameter Reporting and General Model Update in accordance with some aspects. In some cases, the central node may average the information to arrive at a particular updated AI/ML model. Operation 0 can be treated as an initial phase of the AI/ML service/model subscription. Operation 2 to Operation 4 can be repeatedly performed after the UE receives the deployed model from the RAN” discloses model update comprises an additional aggregated update by repeating performing steps).
Claim 13.
Li discloses a user equipment (UE) for wireless communication, comprising (Para [0020] “the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105. The ProSe interface 105 may alternatively be referred to as a sidelink (SL) interface comprising one or more logical channels, including but not limited to a Physical Sidelink Control Channel (PSCCH), a Physical Sidelink Shared Channel (PSSCH), a Physical Sidelink Discovery Channel (PSDCH), a Physical Sidelink Broadcast Channel (PSBCH), and a Physical Sidelink Feedback Channel (PSFCH)” discloses user equipment for wireless communication):
receive a machine learning component (Para [0020] “the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105. The ProSe interface 105 may alternatively be referred to as a sidelink (SL) interface comprising one or more logical channels, including but not limited to a Physical Sidelink Control Channel (PSCCH), a Physical Sidelink Shared Channel (PSSCH), a Physical Sidelink Discovery Channel (PSDCH), a Physical Sidelink Broadcast Channel (PSBCH), and a Physical Sidelink Feedback Channel (PSFCH)” and Para [0058] “transmitting the model to the UEs (local nodes in federated learning), and aggregating parameters from local nodes and updating the trained model. The UEs act as the "local node", responsible for sending a training model request, receiving a trained model from the RAN (central server in federated learning), training the model locally with its own data and reporting updated parameters to the gNB-DU/gNB-CU or LMF” discloses receiving communication on the sidelink control channel wherein communication includes a local update with a federated learning);
transmit, to an additional UE, a sidelink communication in a sidelink control channel or a sidelink shared channel, wherein the sidelink communication that includes a first local update associated with the machine learning component (Para [0069] “Each local node reports its training model updated parameters to the central node via RRC signaling, and the central node aggregates all collected information and starts updating the general model. FIG. 3D illustrates Local Model Parameter Reporting and General Model Update in accordance with some aspects. In some cases, the central node may average the information to arrive at a particular updated AI/ML model” and Figure 3D and Para [0020] “In an aspect, the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105” discloses transmitting aggregated local update based on the associated federated learning for the UEs).
Claim 22.
Li discloses a memory; and one or more processors coupled to the memory, the memory and the one or more processors configured to cause network entity to (Para [0020] “the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105. The ProSe interface 105 may alternatively be referred to as a sidelink (SL) interface comprising one or more logical channels, including but not limited to a Physical Sidelink Control Channel (PSCCH), a Physical Sidelink Shared Channel (PSSCH), a Physical Sidelink Discovery Channel (PSDCH), a Physical Sidelink Broadcast Channel (PSBCH), and a Physical Sidelink Feedback Channel (PSFCH)” and Para [0032] “UE 102 can be in communication with RAN 110 as well as one or more other 5GC network entities” discloses user equipment for wireless communication):
transmit a machine learning component to a set of user equipment (UEs) (Para [0069] “Each local node reports its training model updated parameters to the central node via RRC signaling, and the central node aggregates all collected information and starts updating the general model. FIG. 3D illustrates Local Model Parameter Reporting and General Model Update in accordance with some aspects. In some cases, the central node may average the information to arrive at a particular updated AI/ML model” and Figure 3D discloses transmitting aggregated local update based on the associated federated learning for the UEs);
and receive an aggregated local update, wherein the aggregated local update is based at least in part on a first local update associated with the machine learning component and a second local update associated with the machine learning component, wherein first local update is communicated in a sidelink control channel or a sidelink shared channel (Para [0020] “the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105. The ProSe interface 105 may alternatively be referred to as a sidelink (SL) interface comprising one or more logical channels, including but not limited to a Physical Sidelink Control Channel (PSCCH), a Physical Sidelink Shared Channel (PSSCH), a Physical Sidelink Discovery Channel (PSDCH), a Physical Sidelink Broadcast Channel (PSBCH), and a Physical Sidelink Feedback Channel (PSFCH)” and Para [0058] “transmitting the model to the UEs (local nodes in federated learning), and aggregating parameters from local nodes and updating the trained model. The UEs act as the "local node", responsible for sending a training model request, receiving a trained model from the RAN (central server in federated learning), training the model locally with its own data and reporting updated parameters to the gNB-DU/gNB-CU or LMF” discloses receiving communication on the sidelink control channel wherein communication includes a local update with a federated learning),
and wherein the aggregated local update comprises an aggregation of the first local update and the second local update (Para [0059] “federated learning between the RAN (central server) and UEs (local nodes) (or LMF and UEs for positioning) in which the central server can aggregate parameters trained at different UEs experiencing different channel status, and different environment” and Para [0020] “In an aspect, the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105” discloses wherein the aggregated local update comprises aggregation of UEs).
Claim 30.
Li discloses a method of wireless communication performed by a first user equipment (UE), comprising (Para [0020] “the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105. The ProSe interface 105 may alternatively be referred to as a sidelink (SL) interface comprising one or more logical channels, including but not limited to a Physical Sidelink Control Channel (PSCCH), a Physical Sidelink Shared Channel (PSSCH), a Physical Sidelink Discovery Channel (PSDCH), a Physical Sidelink Broadcast Channel (PSBCH), and a Physical Sidelink Feedback Channel (PSFCH)” discloses user equipment for wireless communication):
receiving, from a second UE, a sidelink communication in a sidelink control channel or a sidelink shared channel, wherein the sidelink communication includes a first local update associated with a machine learning component (Para [0020] “the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105. The ProSe interface 105 may alternatively be referred to as a sidelink (SL) interface comprising one or more logical channels, including but not limited to a Physical Sidelink Control Channel (PSCCH), a Physical Sidelink Shared Channel (PSSCH), a Physical Sidelink Discovery Channel (PSDCH), a Physical Sidelink Broadcast Channel (PSBCH), and a Physical Sidelink Feedback Channel (PSFCH)” and Para [0058] “transmitting the model to the UEs (local nodes in federated learning), and aggregating parameters from local nodes and updating the trained model. The UEs act as the "local node", responsible for sending a training model request, receiving a trained model from the RAN (central server in federated learning), training the model locally with its own data and reporting updated parameters to the gNB-DU/gNB-CU or LMF” discloses receiving communication on the sidelink control channel wherein communication includes a local update with a federated learning and receiving communication to cause the UE (i.e. or the second UE) to receive update);
and transmitting an aggregated local update based at least in part on the first local update associated with the machine learning component and a second local update associated with the machine learning component (Para [0069] “Each local node reports its training model updated parameters to the central node via RRC signaling, and the central node aggregates all collected information and starts updating the general model. FIG. 3D illustrates Local Model Parameter Reporting and General Model Update in accordance with some aspects. In some cases, the central node may average the information to arrive at a particular updated AI/ML model” and Figure 3D discloses transmitting aggregated local update based on the associated federated learning for the UEs),
wherein the aggregated local update comprises an aggregation of the first local update and the second local update (Para [0059] “federated learning between the RAN (central server) and UEs (local nodes) (or LMF and UEs for positioning) in which the central server can aggregate parameters trained at different UEs experiencing different channel status, and different environment” and Para [0020] “In an aspect, the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105” discloses wherein the aggregated local update comprises aggregation of UEs).
Claim 31.
Li discloses the method of claim 30.
Li further discloses determining the second local update associated with the machine learning component based at least in part on training the machine learning component (Para [0067] “Operation 3: Each local node (e.g., UE) starts local training based on its own data, and no information exchange is expected during this period for AI/ML model training purposes. FIG. 3C illustrates Local Training in accordance with some aspects. The local training at each UE may eventually be different. Operation 4: Each local node reports its training model updated parameters to the central node via RRC signaling, and the central node aggregates all collected information and starts updating the general model. FIG. 3D illustrates Local Model Parameter Reporting and General Model Update in accordance with some aspects. In some cases, the central node may average the information to arrive at a particular updated AI/ML model” discloses determine update UEs based on the training).
Claim 32.
Li discloses a method of claim 30.
Li further discloses receiving the second local update from a third UE (Para [0069] “Each local node reports its training model updated parameters to the central node via RRC signaling, and the central node aggregates all collected information and starts updating the general model. FIG. 3D illustrates Local Model Parameter Reporting and General Model Update in accordance with some aspects. In some cases, the central node may average the information to arrive at a particular updated AI/ML model” and Para [0020] “In an aspect, the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105. The ProSe interface 105 may alternatively be referred to as a sidelink (SL) interface comprising one or more logical channels, including but not limited to a Physical Sidelink Control Channel (PSCCH), a Physical Sidelink Shared Channel (PSSCH), a Physical Sidelink Discovery Channel (PSDCH), a Physical Sidelink Broadcast Channel (PSBCH), and a Physical Sidelink Feedback Channel (PSFCH” and Fig 3D discloses receiving sidelink communication to cause the UE to receive update).
Claim 36.
Li discloses a method of wireless communication performed by a network entity, comprising (Para [0020] “the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105. The ProSe interface 105 may alternatively be referred to as a sidelink (SL) interface comprising one or more logical channels, including but not limited to a Physical Sidelink Control Channel (PSCCH), a Physical Sidelink Shared Channel (PSSCH), a Physical Sidelink Discovery Channel (PSDCH), a Physical Sidelink Broadcast Channel (PSBCH), and a Physical Sidelink Feedback Channel (PSFCH)” discloses user equipment for wireless communication):
transmitting a machine learning component to a set of user equipment (UEs) (Para [0020] “the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105. The ProSe interface 105 may alternatively be referred to as a sidelink (SL) interface comprising one or more logical channels, including but not limited to a Physical Sidelink Control Channel (PSCCH), a Physical Sidelink Shared Channel (PSSCH), a Physical Sidelink Discovery Channel (PSDCH), a Physical Sidelink Broadcast Channel (PSBCH), and a Physical Sidelink Feedback Channel (PSFCH)” and Para [0058] “transmitting the model to the UEs (local nodes in federated learning), and aggregating parameters from local nodes and updating the trained model. The UEs act as the "local node", responsible for sending a training model request, receiving a trained model from the RAN (central server in federated learning), training the model locally with its own data and reporting updated parameters to the gNB-DU/gNB-CU or LMF” discloses receiving communication on the sidelink control channel wherein communication includes a local update with a federated learning);
and receiving an aggregated local update, wherein the aggregated local update is based at least in part on a first local update associated with the machine learning component and a second local update associated with the machine learning component, wherein first local update is communicated in a sidelink control channel or a sidelink shared channel (Para [0069] “Each local node reports its training model updated parameters to the central node via RRC signaling, and the central node aggregates all collected information and starts updating the general model. FIG. 3D illustrates Local Model Parameter Reporting and General Model Update in accordance with some aspects. In some cases, the central node may average the information to arrive at a particular updated AI/ML model” and Figure 3D discloses transmitting aggregated local update based on the associated federated learning for the UEs),
and wherein the aggregated local update comprises an aggregation of the first local update and the second local update (Para [0067] “Operation 3: Each local node (e.g., UE) starts local training based on its own data, and no information exchange is expected during this period for AI/ML model training purposes. FIG. 3C illustrates Local Training in accordance with some aspects. The local training at each UE may eventually be different. Operation 4: Each local node reports its training model updated parameters to the central node via RRC signaling, and the central node aggregates all collected information and starts updating the general model. FIG. 3D illustrates Local Model Parameter Reporting and General Model Update in accordance with some aspects. In some cases, the central node may average the information to arrive at a particular updated AI/ML model” discloses determine update UEs based on the training).
Claim 39.
Li discloses a non-transitory computer-readable medium storing one or more instructions for wireless communication, the one or more instructions comprising (Para [0020] “the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105. The ProSe interface 105 may alternatively be referred to as a sidelink (SL) interface comprising one or more logical channels, including but not limited to a Physical Sidelink Control Channel (PSCCH), a Physical Sidelink Shared Channel (PSSCH), a Physical Sidelink Discovery Channel (PSDCH), a Physical Sidelink Broadcast Channel (PSBCH), and a Physical Sidelink Feedback Channel (PSFCH)” discloses user equipment for wireless communication):
one or more instructions that, when executed by one or more processors of a first user equipment (UE), cause the one or more processors to (Para [0045] “The communication device 200 may include a hardware processor (or equivalently processing circuitry) 202 (e.g., a central processing unit (CPU), a GPU, a hardware processor core, or any combination thereof), a main memory 204 and a static memory 206” and Para [0020] “the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105” discloses memories and processor)
receive a sidelink communication in a sidelink control channel or a sidelink shared channel, wherein the sidelink communication includes a first local update associated with a machine learning component (Para [0020] “the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105. The ProSe interface 105 may alternatively be referred to as a sidelink (SL) interface comprising one or more logical channels, including but not limited to a Physical Sidelink Control Channel (PSCCH), a Physical Sidelink Shared Channel (PSSCH), a Physical Sidelink Discovery Channel (PSDCH), a Physical Sidelink Broadcast Channel (PSBCH), and a Physical Sidelink Feedback Channel (PSFCH)” and Para [0058] “transmitting the model to the UEs (local nodes in federated learning), and aggregating parameters from local nodes and updating the trained model. The UEs act as the "local node", responsible for sending a training model request, receiving a trained model from the RAN (central server in federated learning), training the model locally with its own data and reporting updated parameters to the gNB-DU/gNB-CU or LMF” discloses receiving communication on the sidelink control channel wherein communication includes a local update with a federated learning);
and transmit an aggregated local update based at least in part on the first local update associated with the machine learning component and a second local update associated with the machine learning component (Para [0069] “Each local node reports its training model updated parameters to the central node via RRC signaling, and the central node aggregates all collected information and starts updating the general model. FIG. 3D illustrates Local Model Parameter Reporting and General Model Update in accordance with some aspects. In some cases, the central node may average the information to arrive at a particular updated AI/ML model” and Figure 3D discloses transmitting aggregated local update based on the associated federated learning for the UEs),
wherein the aggregated local update comprises an aggregation of the first local update and the second local update (Para [0059] “federated learning between the RAN (central server) and UEs (local nodes) (or LMF and UEs for positioning) in which the central server can aggregate parameters trained at different UEs experiencing different channel status, and different environment” and Para [0020] “In an aspect, the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105” discloses wherein the aggregated local update comprises aggregation of UEs).
Claim 40.
Li discloses a non-transitory computer-readable medium storing one or more instructions for wireless communication, the one or more instructions comprising (Para [0020] “the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105. The ProSe interface 105 may alternatively be referred to as a sidelink (SL) interface comprising one or more logical channels, including but not limited to a Physical Sidelink Control Channel (PSCCH), a Physical Sidelink Shared Channel (PSSCH), a Physical Sidelink Discovery Channel (PSDCH), a Physical Sidelink Broadcast Channel (PSBCH), and a Physical Sidelink Feedback Channel (PSFCH)” discloses user equipment for wireless communication):
one or more instructions that, when executed by one or more processors of a user equipment (UE), cause the one or more processors to: receive a machine learning component (Para [0045] “The communication device 200 may include a hardware processor (or equivalently processing circuitry) 202 (e.g., a central processing unit (CPU), a GPU, a hardware processor core, or any combination thereof), a main memory 204 and a static memory 206” and Para [0020] “the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105” and Para [0069] “Each local node reports its training model updated parameters to the central node via RRC signaling, and the central node aggregates all collected information and starts updating the general model. FIG. 3D illustrates Local Model Parameter Reporting and General Model Update in accordance with some aspects. In some cases, the central node may average the information to arrive at a particular updated AI/ML model” and Figure 3D discloses memories and processor, receive machine learning component);
and transmit, to an additional UE, a sidelink communication in a sidelink control channel or a sidelink shared channel, wherein the sidelink communication includes a first local update associated with the machine learning component (Para [0020] “the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105. The ProSe interface 105 may alternatively be referred to as a sidelink (SL) interface comprising one or more logical channels, including but not limited to a Physical Sidelink Control Channel (PSCCH), a Physical Sidelink Shared Channel (PSSCH), a Physical Sidelink Discovery Channel (PSDCH), a Physical Sidelink Broadcast Channel (PSBCH), and a Physical Sidelink Feedback Channel (PSFCH)” and Para [0058] “transmitting the model to the UEs (local nodes in federated learning), and aggregating parameters from local nodes and updating the trained model. The UEs act as the "local node", responsible for sending a training model request, receiving a trained model from the RAN (central server in federated learning), training the model locally with its own data and reporting updated parameters to the gNB-DU/gNB-CU or LMF” discloses receiving communication on the sidelink control channel wherein communication includes a local update with a federated learning, transmitting the model to the UEs);
Claim 41.
Li discloses a non-transitory computer-readable medium storing one or more instructions for wireless communication, the one or more instructions comprising (Para [0020] “the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105. The ProSe interface 105 may alternatively be referred to as a sidelink (SL) interface comprising one or more logical channels, including but not limited to a Physical Sidelink Control Channel (PSCCH), a Physical Sidelink Shared Channel (PSSCH), a Physical Sidelink Discovery Channel (PSDCH), a Physical Sidelink Broadcast Channel (PSBCH), and a Physical Sidelink Feedback Channel (PSFCH)” discloses user equipment for wireless communication):
one or more instructions that, when executed by one or more processors of a network entity, cause the one or more processors to (Para [0020] “the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105. The ProSe interface 105 may alternatively be referred to as a sidelink (SL) interface comprising one or more logical channels, including but not limited to a Physical Sidelink Control Channel (PSCCH), a Physical Sidelink Shared Channel (PSSCH), a Physical Sidelink Discovery Channel (PSDCH), a Physical Sidelink Broadcast Channel (PSBCH), and a Physical Sidelink Feedback Channel (PSFCH)” and Para [0032] “UE 102 can be in communication with RAN 110 as well as one or more other 5GC network entities” and Para [0045] “The communication device 200 may include a hardware processor (or equivalently processing circuitry) 202 (e.g., a central processing unit (CPU), a GPU, a hardware processor core, or any combination thereof), a main memory 204 and a static memory 206” and Para [0020] “the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105” discloses processor of network entity):
transmit a machine learning component to a set of user equipment (UEs) (Para [0045] “The communication device 200 may include a hardware processor (or equivalently processing circuitry) 202 (e.g., a central processing unit (CPU), a GPU, a hardware processor core, or any combination thereof), a main memory 204 and a static memory 206” and Para [0020] “the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105” discloses memories and processor)
receive an aggregated local update, wherein the aggregated local update is based at least in part on a first local update associated with the machine learning component and a second local update associated with the machine learning component, wherein the first local update is communicated in a sidelink control channel or a sidelink shared channel (Para [0020] “the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105. The ProSe interface 105 may alternatively be referred to as a sidelink (SL) interface comprising one or more logical channels, including but not limited to a Physical Sidelink Control Channel (PSCCH), a Physical Sidelink Shared Channel (PSSCH), a Physical Sidelink Discovery Channel (PSDCH), a Physical Sidelink Broadcast Channel (PSBCH), and a Physical Sidelink Feedback Channel (PSFCH)” and Para [0058] “transmitting the model to the UEs (local nodes in federated learning), and aggregating parameters from local nodes and updating the trained model. The UEs act as the "local node", responsible for sending a training model request, receiving a trained model from the RAN (central server in federated learning), training the model locally with its own data and reporting updated parameters to the gNB-DU/gNB-CU or LMF” discloses receiving communication on the sidelink control channel wherein communication includes a local update with a federated learning);
and wherein the aggregated local update comprises an aggregation of the first local update and the second local update (Para [0059] “federated learning between the RAN (central server) and UEs (local nodes) (or LMF and UEs for positioning) in which the central server can aggregate parameters trained at different UEs experiencing different channel status, and different environment” and Para [0020] “In an aspect, the UEs 101 and 102 may further directly exchange communication data via a ProSe interface 105” discloses wherein the aggregated local update comprises aggregation of UEs).
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.
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 factual inquiries 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.
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.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Li (US20220038349A1) in view of Chen (US 20200252989 A1).
Li teaches the UE of claim 13.
Li does not appear to explicitly teach determine that a channel quality associated with an uplink channel fails to satisfy a quality threshold, wherein the one or more processors, when transmit, to the additional UE, the sidelink communication, are configured to transmit, to the additional UE, the sidelink communication based at least in part on determining that the channel quality associated with the uplink channel fails to satisfy the quality threshold.
However. in the same field, analogous art Chen teaches determine that a channel quality associated with an uplink channel fails to satisfy a quality threshold, wherein the one or more processors, when transmit, to the additional UE, the sidelink communication, are configured to transmit, to the additional UE, the sidelink communication based at least in part on determining that the channel quality associated with the uplink channel fails to satisfy the quality threshold (Para [0031] “some standards may define and use multiple types of channels, e.g., different channels for uplink or downlink and/or different channels for different uses such as data, control information, etc.” and Para [0076] “FIG. 7 is a communication flow diagram illustrating one possible approach to performing the radio link monitoring. In the illustrated example, when the sidelink radio link quality is worse than a certain threshold (“Q.sub.out”), the physical layer (UE lower layer 702) in the wireless device may indicate to the RRC layer (UE higher layer 704) that the wireless device is out-of-sync; otherwise, if the sidelink radio link quality is better than a certain threshold (“Q.sub.in”), the physical layer in the wireless device may indicate to the RRC layer that the wireless device is in-sync” teaches determine the uplink channel quality greater than the threshold).
Li and Chen are analogous art because they are directed to wireless sidelink communication between the nodes.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Chen to the disclosed invention of Li.
One of ordinary skill in the arts would have been motivated to make this modification in order to identifying the link “quality is poor” and technology “improve” in the “traffic flow”; “identifying a specific person” etc. (Chen Para [0119] and [00128]).
Allowable Subject Matter
5. Claims 4-10, 14-20, 23-29, 33-35 and 37-38 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.
Response to Arguments
6. Applicant's arguments filed on 01/05/2026 with respect to 35 U.S.C. 102 rejections of claims 1-3, 11-13, 22, 30-32, 36, and 39-41 have been fully considered but they are not persuasive.
With apparent reference to the 35 U.S.C. 102 rejections of claims 1, 13, 22, 30, 33, 36, 39, 40 and 41, applicant asserts, “Rather, Li explicitly teaches that the "UEs act as the "local node ", responsible for... reporting updated parameters to the gNB-DU/gNB-CU or LMF," and not to "receive, from a second UE, a sidelink communication in a sidelink control channel or sidelink shared channel, wherein the sidelink communication includes a first local update associated with a machine learning component." That is, based on the explicit disclosure and teachings of Li, the training model updated parameters for each UE (local node) is reported by each UE to the central node via RRC signaling-and not to a first UE, much less "in a sidelink communication in a sidelink channel or a sidelink shared channel."… See Li, par [0069] referring to Figure 3D, reproduced below. As clearly illustrated in the cited Figure 3D, in Li's architecture, the UEs do not report the local model parameters updates to another UE; instead, each UE reports its local model parameters updates only to the central server. As such, the cited portions of Li fail to disclose or teach a first UE that is configured to "receive, from a second UE, a sidelink communication in a sidelink control channel or a sidelink shared channel, wherein the sidelink communication includes a first local update associated with a machine learning component," as recited in amended independent claim 1, and similarly recited in amended independent claims 30 and 39…Similarly, amended claim 13 recites "transmit, to an additional UE, a sidelink communication in a sidelink control channel or a sidelink shared channel, wherein the sidelink communication includes a first local update associated with the machine learning component." As discussed in more detail above, Li explicitly discloses or teaches that each lE (local node) transmits its local model parameters updates to the central server via RRC signaling, and not to an additional lE via sidelink. Likewise, claims 22, 36, and 41, as amended, recite "receive an aggregated local update, wherein the aggregated local update is based at least in part on a first local update associated with the machine learning component and a second local update associated with the machine learning component, wherein the first local update is communicated in a sidelink control channel or a sidelink shared channel, and wherein the aggregated local update comprises an aggregation of the first local update and the second local update. the first local update is communicated in a sidelink control channel or a sidelink shared channel." To reiterate, Li explicitly discloses or teaches that each lE (local node) transmits its local model parameters updates to the central server via RRC signaling, and not "in a sidelink control channel or a sidelink shared channel."” (Remarks Pg.15-17).
Examiner Response:
The examiner respectfully disagrees. with Applicant’s above-noted assertions paragraph [0020] of Li explicitly states that UEs can communicate directly with each other using a sidelink (Prose). In other words, one UE to transmit data directly to another UE. Paragraph [0058] explicitly states that the system uses machine learning (federated learning), where UEs generate local model updates and send/receive those updates for training and aggregation. Paragraph [0069] of Li further discloses that UEs send updates to a central node for aggregation. When these teachings are considered together, Li shows that UEs both generate model updates and have the ability to communicate directly with other UEs. Thus, it is reasonable to conclude that the model updates generated by a UE can be transmitted to another UE using the disclosed sidelink communication. The fact that Li also describes sending updates to a central server does not exclude UE-to-UE transmission. Rather, these disclosures are part of the same system and are not mutually exclusive. Accordingly, Li at least suggests the claimed limitation of transmitting or receiving a local update via a sidelink, and applicant’s argument is not persuasive. Therefore, Li anticipates claim 1 and similarly all other independent claims, claims 13, 22, 30, 33, 36, 39, 40 and 41.
With apparent reference to the 35 U.S.C. 102 rejections of claims 1, 13, 22, 30, 33, 36, 39, 40 and 41, applicant asserts, “Applicant respectfully submits that this assertion is incorrect as contrary to the Office's assertion, these disclosures are not "directly related to each other by the teachings" of Li itself to form the claimed arrangement. Specifically, the rejection has not established how the UEs communicating directly with each other over a sidelink, as discussed in paragraph [0020] of Li, has been "directly related" by the disclsoure1 and teachings of Li, with a system using machine learning where a central unit manages model training and sends or receives model updates from UEs, as discussed in paragraph [0058] of Li, to allegedly disclose the above-referenced claimed feature. Rather, in rejecting this feature, the Office has used the claims as a roadmap to piece together disparate teachings of Li to arrive at the claimed subject matter…Without using the claims of the subject application as a road map to piece together the disparate teachings in paragraphs [0020] and [0058] of Li, which is prohibited under Net MoneylN and Arkley, Li fails to provide any disclosure or teachings that are directly related by Li to support the Final Office Action's rejection of this claimed feature. Indeed, using a sidelink communication by a lE to provide its local model parameters updates to another lE instead of sending its local model parameters updates to the control node via RRC signaling as described in Li's federated learning system would fundamentally change Li's architecture. Li's federated learning system depends on each local model parameters updates to be sent to the central server by each respective local node. Li's system relies on the central server receiving local model parameters updates from each ULE via RRC to aggregate all the collected information. See Li, paragraph [0069]. In fact, using sidelink for local model parameters updates would fundamentally alter Li's architecture and contradicts stated benefits (e.g., centralized aggregation across users and data protection. See Id. pars. [0058], [0071] Additionally, without conceding the merits of the rejection of claims 1, 30, 39, 22, 36, and 41 under 35 U.S.C. § 102-and solely to expedite compact prosecution-Applicant has amended independent claims 1, 30, 39 as well as independent claims 22, 36, and 41” (Remarks Pg. 17-19).
Examiner response:
The examiner respectfully disagrees with Applicant’s above-noted assertions. Applicant argues that the sidelink communication in paragraph [0020] and the machine learning disclosures in paragraphs [0058] and paragraph [0069] of Li are not related. However, these paragraphs describe different aspects of the same system. Regarding the amendments, Applicant generally asserts that Li does not disclose a UE transmitting or receiving an “aggregated local update”, and that only the central node performs aggregation. While Li does describe aggregation at the central node, it also disclose that multiple UEs generate updates and that updates are aggregated. Li further disclose that UEs can directly communicate with each other. Li explicitly allows UE-to-UE sidelink communication. Therefore, Li anticipates claim 1 and similarly all other independent claims, claims 13, 22, 30, 33, 36, 39, 40 and 41.
Applicant's arguments filed on 01/05/2026 with respect to 35 U.S.C. 103 rejection of claim 21 has been fully considered but they are not persuasive.
With respect to the 35 U.S.C. 103 rejection of claim 21, applicant asserts, “As described in detailed with respect to the rejection of claim 13 under 35 U.S.C. § 102, Li fails to disclose each and every feature of independent claim 13. Furthermore, the Office action has not relied upon Chen to cure the above-referenced deficiencies in Li. As such, it is respectfully submitted the claim 21 is allowable over the combination of Li and Chen for at least the same reasons as independent claim 13 and further for reciting additional distinguishing features. Accordingly, it is respectfully requested that rejection of claim 21 under 35 U.S.C. § 103 be withdrawn” (Remarks Pg. 21)
Examiner response:
The examiner respectfully disagrees. As stated above, claim 13 rejected under 35 U.S.C. 102. Therefore, claim 21 remains rejected under 35 U.S.C. 103.
Conclusion
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Lokesha Patel whose telephone number is (571)272-6267. The examiner can normally be reached 8 AM - 4 PM.
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/LOKESHA PATEL/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
1 [sic – disclosure]
Prosecution Insights