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 .
Response to Amendment
Applicant's amendment filed on 03/10/2026 has been entered. Claims 17-19, 25-26, 28, 37-39, 41-42, 44 and 48 have been amended. No claims have been added. Claims 27 and 43 have been cancelled. Claims 17-26, 28-29, 37-42 and 44-49 are still pending in this application, with claims 17, 25, 37 and 41, being independent.
Response to Arguments
Applicant’s arguments with respect to claim(s) 17-26, 28-29, 37-42 and 44-49 have been considered but are moot based on new grounds of rejections.
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 (i.e., changing from AIA to pre-AIA ) 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.
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(s) 17-21, 23, 37-40, 44 and 46 are rejected under 35 U.S.C. 103 as being unpatentable over Madadi et al. (US 2022/0286927; hereinafter Madadi) in view of Pantelidou et al. (US 2023/0300686; hereinafter Pantelidou) and Song et al. (US 2024/0422629; hereinafter Song).
Regarding claim 17, Madadi shows a user equipment (UE) (Figure 3 shows a UE performing in part the methods of Figures 4-5.), comprising:
one or more memories storing processor-executable code (Figure 3; Par. 0084; noted instructions stored in memory.); and
one or more processors coupled with the one or more memories and individually or collectively operable to execute the code (Figure 3; Par. 0084-0086; instructions stored in memory executed by a processor to perform the disclosed method.) to cause the UE to:
receive, from a network entity, a first control message indicating for each target network entity of a set of target network entities (Figure 5; Par. 0104, 0112; UE receives, from the BS, the configuration information of neighboring cells, e.g., the enabling/disabling of ML approach, ML model and/or model parameters for handover management of neighboring cells, are indicated as part of the system information, e.g., in MIB, SIB.), a reference configuration and one or more additional configurations, wherein the one or more additional configurations correspond to one or more respective machine learning functionalities or one or more respective machine learning models (Figure 5; Par. 0104, 0107-0111; the configuration information related to ML/AI techniques can include one or multiple of the following information: enabling/disabling of ML approach for handover, the configuration information can include whether ML/AI techniques for handover management use case is enabled or disabled, ML model/algorithm and model parameters, and the configuration information can include which ML/AI model or algorithm to be used for the handover management use case along with the model parameters of ML algorithms.);
determine an applicability of the reference configuration or the one or more additional configurations, or any combination thereof, based at least in part on a handover of the UE to a target network entity (Figure 4; Par. 0103, 0106; the UE performs the inference based on the received configuration information, measurement reporting parameters and local data. For example, the UE follows the configured ML model and model parameters, measurement reporting parameters and uses local data and/or data sent from the BS to perform the inference operation to support handover management using ML/AI techniques.).
Madadi shows all of the elements including the network entity, as discussed above. Madadi does not specifically show determine an applicability of the reference configuration or the one or more additional configurations, or any combination thereof, based at least in part on a completion of a handover of the UE from the source network entity to a target network entity; and transmit, to the target network entity, an indication of one or more applicable configurations based at least in part on the completion of the handover. Madadi also does not specifically show that the network entity is a source network entity.
However, the above-mentioned claim limitations are well-established in the art as evidenced by Pantelidou and Song.
First, Pantelidou shows determine an applicability of the reference configuration or the one or more additional configurations, or any combination thereof, based at least in part on a completion of a handover (Figure 5; Par. 0147-0149; the terminal receives signalling indicating whether or not the terminal should keep the at least one machine learning model that it has available in a training, execution or idle state/mode after the terminal is handed over to the network apparatus.) of the UE from the source network entity to a target network entity (Par. 0141, 0189; noted handover of the UE from source network apparatus to target network apparatus.); and
transmit, to the target network entity, an indication of one or more applicable configurations based at least in part on the completion of the handover (Figure 5; Par. 0147-0149; The terminal may receive a request to provide at least one of the machine learning models to a network apparatus. The terminal may respond to the request with at least one of the requested machine learning models. The terminal may respond to the request with only the requested at least one of the machine learning models. This may mean that less than all of the machine learning models available at the terminal for training and/or execution are indicated in the request (and therefore provided in response to the request).).
In view of the above, having the system of Madadi, then given the well-established teaching of Pantelidou, it would have been obvious before the effective filing date of the claimed invention to modify the system of Madadi as taught by Pantelidou, in order to provide motivation for network resources to be used more efficiently as only desired ML models are kept when handover is completed (Par. 0217 of Pantelidou).
Second, Song shows a source network entity (Figure 2; Par. 0048-0049; noted serving cell of the network-side device.).
In view of the above, having the system of Madadi, then given the well-established teaching of Song, it would have been obvious before the effective filing date of the claimed invention to modify the system of Madadi as taught by Song, in order to provide motivation for shortening a handover delay in the cell handover of the UE and increasing a cell handover success rate (Par. 0129 of Song).
Regarding claim 18, modified Madadi shows determine an applicability of the reference configuration or the one or more additional configurations models, and or both, is based at least in part on determining whether the one or more respective machine learning functionalities or the one or more respective machine learning models satisfy an applicability condition associated with the one or more respective machine learning functionalities or the one or more respective machine learning (Madadi: Figure 5; Par. 0106; Based on the outcome of the inference, the UE sends the measurement report to the BS. The contents of the measurement may or may not include additional supporting information which can also be an outcome of the ML model inference engine. Further, the UE may send the updated A1/ML model parameters based on local training to BS. The model parameters are sent according to the configuration of whether the model parameter updates will be used at the BS to update the global model or not.) and
the applicability condition corresponds to the applicability of the reference configuration or the one or more additional configurations, or any combination thereof (Madadi: Figure 5; Par. 0106; Based on the outcome of the inference, the UE sends the measurement report to the BS. The contents of the measurement may or may not include additional supporting information which can also be an outcome of the ML model inference engine. Further, the UE may send the updated A1/ML model parameters based on local training to BS. The model parameters are sent according to the configuration of whether the model parameter updates will be used at the BS to update the global model or not.).
Regarding claim 19, modified Madadi shows wherein, to apply the reference configuration or the one or more additional configurations, or both, the one or more processors are individually or collectively operable to execute the code to cause the UE to:
apply the one or more additional configurations based at least in part on determining that the one or more additional configurations satisfy an applicability condition associated with the one or more respective machine learning functionalities or the one or more respective machine learning models (Madadi: Figure 5; Par. 0106; Based on the outcome of the inference, the UE sends the measurement report to the BS. The contents of the measurement may or may not include additional supporting information which can also be an outcome of the ML model inference engine. Further, the UE may send the updated A1/ML model parameters based on local training to BS. The model parameters are sent according to the configuration of whether the model parameter updates will be used at the BS to update the global model or not.); and
transmit, to the source network entity, UE assistance information indicating the applied one or more additional configurations (Madadi: Figure 5; Par. 0106; the UE may send the updated A1/ML model parameters based on local training to BS. The model parameters are sent according to the configuration of whether the model parameter updates will be used at the BS to update the global model or not.).
Modified Madadi does not specifically show applying the one or more additional configurations based at least in part on the completion of the handover.
However, the above-mentioned claim limitations are well-established in the art as also evidenced by Pantelidou. Specifically, Pantelidou shows applying the one or more additional configurations based at least in part on the completion of the handover (Figure 5; Par. 0147-0149; the terminal receives signalling indicating whether or not the terminal should keep the at least one machine learning model that it has available in a training, execution or idle state/mode after the terminal is handed over to the network apparatus.).
In view of the above, having the system of Madadi, then given the well-established teaching of Pantelidou, it would have been obvious before the effective filing date of the claimed invention to modify the system of Madadi as taught by Pantelidou, in order to provide motivation for network resources to be used more efficiently as only desired ML models are kept when handover is completed (Par. 0217 of Pantelidou).
Regarding claim 20, modified Madadi shows wherein the one or more processors are individually or collectively further operable to execute the code to cause the UE to:
receive, from the target network entity, a control message activating the one or more respective machine learning functionalities or the one or more respective machine learning models based at least in part on the handover and the applied one or more additional configurations (Pantelidou: Par. 0147; At 502, the terminal receives signalling indicating whether or not the terminal should keep the at least one machine learning model that it has available in a training, execution or idle state/mode after the terminal is handed over to the network apparatus. The signalling may be received directly from the target network apparatus. The signalling may be received indirectly from the target network apparatus (e.g. via the source network apparatus).).
Regarding claim 21, modified Madadi shows a wherein the UE assistance information indicates an identifier corresponding to each of the applied one or more additional configurations (Pantelidou: Par. 0157-0158; Metadata information may comprise (but is not limited to): [0158] Model Descriptor: Metadata for a model descriptor provides some information that it usable for identifying a function of the model.).
Regarding claim 23, modified Madadi shows wherein the one or more processors are individually or collectively further operable to execute the code to cause the UE to: transmit, to the source network entity, a measurement report indicating one or more radio resource management measurements (Madadi: Figure 5; Par. 0106; Based on the outcome of the inference, the UE sends the measurement report to the BS. The contents of the measurement may or may not include additional supporting information which can also be an outcome of the ML model inference engine. Further, the UE may send the updated A1/ML model parameters based on local training to BS. The model parameters are sent according to the configuration of whether the model parameter updates will be used at the BS to update the global model or not.).
Regarding claim 37, Madadi shows a method (Figures 4-5 shows a method performed by the UE of Figure 3.) for wireless communications at a user equipment (UE), comprising:
receiving, from a network entity, a first control message indicating, for each target network entity of a set of target network entities (Figure 5; Par. 0104, 0112; UE receives, from the BS, the configuration information of neighboring cells, e.g., the enabling/disabling of ML approach, ML model and/or model parameters for handover management of neighboring cells, are indicated as part of the system information, e.g., in MIB, SIB.), a reference configuration and one or more additional configurations, wherein the one or more additional configurations correspond to one or more respective machine learning functionalities or one or more respective machine learning models (Figure 5; Par. 0104, 0107-0111; the configuration information related to ML/AI techniques can include one or multiple of the following information: enabling/disabling of ML approach for handover, the configuration information can include whether ML/AI techniques for handover management use case is enabled or disabled, ML model/algorithm and model parameters, and the configuration information can include which ML/AI model or algorithm to be used for the handover management use case along with the model parameters of ML algorithms.);
determine an applicability of the reference configuration or the one or more additional configurations, or any combination thereof, based at least in part on a handover of the UE to a target network entity (Figure 4; Par. 0103, 0106; the UE performs the inference based on the received configuration information, measurement reporting parameters and local data. For example, the UE follows the configured ML model and model parameters, measurement reporting parameters and uses local data and/or data sent from the BS to perform the inference operation to support handover management using ML/AI techniques.).
Madadi shows all of the elements including the network entity, as discussed above. Madadi does not specifically show determine an applicability of the reference configuration or the one or more additional configurations, or any combination thereof, based at least in part on a completion of a handover of the UE from the source network entity to a target network entity; and transmit, to the target network entity, an indication of one or more applicable configurations based at least in part on the completion of the handover. Madadi also does not specifically show that the network entity is a source network entity.
However, the above-mentioned claim limitations are well-established in the art as evidenced by Pantelidou and Song.
First, Pantelidou shows determine an applicability of the reference configuration or the one or more additional configurations, or any combination thereof, based at least in part on a completion of a handover (Figure 5; Par. 0147-0149; the terminal receives signalling indicating whether or not the terminal should keep the at least one machine learning model that it has available in a training, execution or idle state/mode after the terminal is handed over to the network apparatus.) of the UE from the source network entity to a target network entity (Par. 0141, 0189; noted handover of the UE from source network apparatus to target network apparatus.); and
transmit, to the target network entity, an indication of one or more applicable configurations based at least in part on the completion of the handover (Figure 5; Par. 0147-0149; The terminal may receive a request to provide at least one of the machine learning models to a network apparatus. The terminal may respond to the request with at least one of the requested machine learning models. The terminal may respond to the request with only the requested at least one of the machine learning models. This may mean that less than all of the machine learning models available at the terminal for training and/or execution are indicated in the request (and therefore provided in response to the request).).
In view of the above, having the system of Madadi, then given the well-established teaching of Pantelidou, it would have been obvious before the effective filing date of the claimed invention to modify the system of Madadi as taught by Pantelidou, in order to provide motivation for network resources to be used more efficiently as only desired ML models are kept when handover is completed (Par. 0217 of Pantelidou).
Second, Song shows a source network entity (Figure 2; Par. 0048-0049; noted serving cell of the network-side device.).
In view of the above, having the system of Madadi, then given the well-established teaching of Song, it would have been obvious before the effective filing date of the claimed invention to modify the system of Madadi as taught by Song, in order to provide motivation for shortening a handover delay in the cell handover of the UE and increasing a cell handover success rate (Par. 0129 of Song).
Regarding claims 38, 39, 40, 44 and 46, these claims are rejected based on the same reasoning as presented in the rejection of claims 18, 19, 20, 21 and 23, respectively.
Claim(s) 22 and 45 are rejected under 35 U.S.C. 103 as being unpatentable over Madadi in view of Pantelidou, Song and Larsson et al. (US 2025/0330373; hereinafter Larsson).
Regarding claim 22, modified Madadi shows all of the elements except storing the one or more additional configurations at the UE based at least in part on refraining to apply the one or more additional configurations.
However, the above-mentioned claim limitations are well-established in the art as evidenced by Larsson. Specifically, Larsson shows storing the one or more additional configurations at the UE based at least in part on refraining to apply the one or more additional configurations (Par. 0183; UE performs numerous local trainings to arrive at numerous local versions, and each time the UE records the version that's currently active. For a given model ID, the UE can report {UE-ID, model ID, model version} to the gNB, where the model version is currently active. The reporting can be done, e.g., each time the UE changes to a different model version (i.e. previous model is replaced with a different model and refrained from being applied.).).
In view of the above, having the system of Madadi, then given the well-established teaching of Larsson, it would have been obvious before the effective filing date of the claimed invention to modify the system of Madadi as taught by Larsson, in order to provide motivation for improving handover performance (Par. 0089 of Larsson).
Regarding claim 45, this claim is rejected based on the same reasoning as presented in the rejection of claim 22.
Claim(s) 24 and 47 are rejected under 35 U.S.C. 103 as being unpatentable over Madadi in view of Pantelidou, Song and Ali et al. (US 2024/0172080; hereinafter Ali). Note: Subject matter relied upon in the rejection are fully-supported in the provisional application 63/426,104 of Ali.
Regarding claim 24, modified Madadi shows all of the elements except wherein the target network entity is associated with a central unit or a distributed unit.
However, the above-mentioned claim limitations are well-established in the art as evidenced by Ali. Specifically, Ali shows wherein the target network entity is associated with a central unit or a distributed unit (Par. 0086; target base station/RAN node is associated with a CU and a DU.).
In view of the above, having the system of Madadi, then given the well-established teaching of Ali, it would have been obvious before the effective filing date of the claimed invention to modify the system of Madadi as taught by Ali, in order to provide motivation for enabling improved support of AI/ML-based algorithms for enhanced performance and/or reduced complexity/overhead (Par. 0100 of Ali).
Regarding claim 47, this claim is rejected based on the same reasoning as presented in the rejection of claim 24.
Claim(s) 25-26, 29, 41-42 and 49 are rejected under 35 U.S.C. 103 as being unpatentable over Ali in view of Pantelidou.
Regarding claim 25, Ali shows a source network entity (Figure 1 shows RAN node/source base station performing in part the method of Figures 6 and 8.), comprising:
one or more memories storing processor-executable code (Figure 1; program code stored in memory.); and
one or more processors coupled with the one or more memories and individually or collectively operable to execute the code (Figure 1; program code stored in memory executed by processor to perform the disclosed method.) to cause the source network entity to:
output, to a target network entity of a set of target network entities (Par. 0140; configuration may be provided for each target cell of any number of target cells.), a handover request message indicating a request to handover a user equipment (UE) from the source network entity to the target network entity (Figures 5-6 and 8; Par. 0140, 0157, 0172, 0184; the source gNB (610) may transmit, to the target gNB (615), a handover request, which may include the measurement results and/or a ML-Compatibility-Request for handover from the source gNB to the target gNB.);
obtain, from the target network entity, a handover response message indicating a reference configuration and one or more additional configurations, wherein the one or more additional configurations correspond to one or more respective machine learning functionalities or one or more respective machine learning models (Figures 5-6 and 8; Par. 0178; the source gNB receives, from the target gNB, a handover request acknowledgement, which may include an HO configuration with updated ML model, and/or a ML-Compatibility-Response.); and
output the UE assistance information indicating, for each target network entity of the set of target network entities, the applicability of the reference configuration and the one or more additional configurations (Figures 5-6 and 8; Par. 0140, 0179; the source gNB (610) may transmit, to the UE (605), an RRCReconfiguration message, which may include the HO configuration with updated ML model and/or the ML-Compatibility-Response.).
Ali shows all of the elements except obtaining UE assistance information indicating an applicability of the reference configuration or one or more additional configurations based at least in part on a completion of the handover; and output the UE assistance information indicating the applicability of the reference configuration and the one or more additional configurations based at least in part on the completion of the handover.
However, the above-mentioned claim limitations are well-established in the art as evidenced by Pantelidou. Specifically, Pantelidou shows obtaining UE assistance information indicating an applicability of the reference configuration or one or more additional configurations based at least in part on a completion of the handover (Figure 4; Par. 0139-0143; source network apparatus obtains the signaling (i.e. result of determining at step 402) from the target network apparatus for forwarding to the terminal. The signaling indicates information whether or not the terminal should keep the at least one machine learning model that it has available after the terminal is handed over to the network apparatus.); and
output the UE assistance information indicating the applicability of the reference configuration and the one or more additional configurations based at least in part on the completion of the handover (Figure 4; Par. 0139-0143; source network apparatus obtains the signaling (i.e. result of determining at step 402) from the target network apparatus for forwarding to the terminal. The signaling indicates information whether or not the terminal should keep the at least one machine learning model that it has available after the terminal is handed over to the network apparatus.).
In view of the above, having the system of Ali, then given the well-established teaching of Pantelidou, it would have been obvious before the effective filing date of the claimed invention to modify the system of Ali as taught by Pantelidou, in order to provide motivation for network resources to be used more efficiently as only desired ML models are kept when handover is completed (Par. 0217 of Pantelidou).
Regarding claim 26, modified Ali shows the applicability of the one or more additional configurations is based at least in part on whether the one or more additional configurations satisfy an applicability condition associated with the one or more respective machine learning functionalities or the one or more respective machine learning models (Ali: Figure 6; Par. 0180, 0183; the UE (605) may transmit, to the source gNB (610) a UE ML compatibility report, which may include data collection results. UE determines whether the at least one function is compatible with a second model of at least one target base station based, at least partially, on the at least one model compatibility configuration and include the results in the UE ML compatibility report.).
Regarding claim 29, modified Ali shows wherein the target network entity is associated with a central unit or a distributed unit (Ali: Par. 0086; target base station/RAN node is associated with a CU and a DU.).
Regarding claim 41, Ali shows a method (Figures 6 and 8 shows a method performed in part by a RAN node/source base station of Figure 1.) for wireless communications at a source network entity, comprising:
outputting, to a target network entity of a set of target network entities (Par. 0140; configuration may be provided for each target cell of any number of target cells.), a handover request message indicating a request to handover a user equipment (UE) from the source network entity to the target network entity (Figures 5-6 and 8; Par. 0140, 0157, 0172, 0184; the source gNB (610) may transmit, to the target gNB (615), a handover request, which may include the measurement results and/or a ML-Compatibility-Request for handover from the source gNB to the target gNB.);
obtaining, from the target network entity, a handover response message indicating a reference configuration and one or more additional configurations, wherein the one or more additional configurations correspond to one or more respective machine learning functionalities or one or more respective machine learning models (Figures 5-6 and 8; Par. 0178; the source gNB receives, from the target gNB, a handover request acknowledgement, which may include an HO configuration with updated ML model, and/or a ML-Compatibility-Response.); and
outputting the UE assistance information indicating, for each target network entity of the set of target network entities, the applicability of the reference configuration and the one or more additional configurations (Figures 5-6 and 8; Par. 0140, 0179; the source gNB (610) may transmit, to the UE (605), an RRCReconfiguration message, which may include the HO configuration with updated ML model and/or the ML-Compatibility-Response.).
Ali shows all of the elements except obtaining UE assistance information indicating an applicability of the reference configuration or one or more additional configurations based at least in part on a completion of the handover; and outputting the UE assistance information indicating the applicability of the reference configuration and the one or more additional configurations based at least in part on the completion of the handover.
However, the above-mentioned claim limitations are well-established in the art as evidenced by Pantelidou. Specifically, Pantelidou shows obtaining UE assistance information indicating an applicability of the reference configuration or one or more additional configurations based at least in part on a completion of the handover (Figure 4; Par. 0139-0143; source network apparatus obtains the signaling (i.e. result of determining at step 402) from the target network apparatus for forwarding to the terminal. The signaling indicates information whether or not the terminal should keep the at least one machine learning model that it has available after the terminal is handed over to the network apparatus.); and
outputting the UE assistance information indicating the applicability of the reference configuration and the one or more additional configurations based at least in part on the completion of the handover (Figure 4; Par. 0139-0143; source network apparatus obtains the signaling (i.e. result of determining at step 402) from the target network apparatus for forwarding to the terminal. The signaling indicates information whether or not the terminal should keep the at least one machine learning model that it has available after the terminal is handed over to the network apparatus.).
In view of the above, having the system of Ali, then given the well-established teaching of Pantelidou, it would have been obvious before the effective filing date of the claimed invention to modify the system of Ali as taught by Pantelidou, in order to provide motivation for network resources to be used more efficiently as only desired ML models are kept when handover is completed (Par. 0217 of Pantelidou).
Regarding claims 42, this claim is rejected based on the same reasoning as presented in the rejection of claim 26.
Regarding claim 49, modified Ali shows wherein the target network entity is associated with a central unit or a distributed unit (Ali: Par. 0086; target base station/RAN node is associated with a CU and a DU.).
Claim(s) 28 and 48 are rejected under 35 U.S.C. 103 as being unpatentable over Ali in view of Pantelidou and Larsson.
Regarding claim 28, modified Ali shows all of the elements except wherein the UE assistance information indicates an identifier corresponding to each of the one or more additional configurations that are applicable to the UE.
However, the above-mentioned claim limitations are well-established in the art as evidenced by Larsson. Specifically, Larsson shows wherein the UE assistance information indicates an identifier corresponding to each of the one or more additional configurations that are applied by the UE (Figure 3; Par. 0079-0080; At 350, the UE 310 and NW 320 engage in model handling signaling using the model ID.).
In view of the above, having the system of Ali, then given the well-established teaching of Larsson, it would have been obvious before the effective filing date of the claimed invention to modify the system of Ali as taught by Larsson, in order to provide motivation for improving handover performance (Par. 0089 of Larsson).
Regarding claim 48, modified Ali shows all of the elements except wherein the UE assistance information indicates an identifier corresponding to each of the one or more additional configurations that are applicable to the UE.
However, the above-mentioned claim limitations are well-established in the art as evidenced by Larsson. Specifically, Larsson shows wherein the UE assistance information indicates an identifier corresponding to each of the one or more additional configurations that are applied by the UE (Figure 3; Par. 0079-0080; At 350, the UE 310 and NW 320 engage in model handling signaling using the model ID.).
In view of the above, having the system of Ali, then given the well-established teaching of Larsson, it would have been obvious before the effective filing date of the claimed invention to modify the system of Ali as taught by Larsson, in order to provide motivation for improving handover performance (Par. 0089 of Larsson).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20240323771 A1 - WIRELESS COMMUNICATION METHOD AND APPARATUS
US 20240119365 A1 - FEEDBACK FOR MACHINE LEARNING BASED NETWORK OPERATION
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/REDENTOR PASIA/Primary Examiner, Art Unit 2413
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