Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This Office Action is sent in response to Applicant’s Communication received on 06/09/2026 for application number 18/457,960.
Response to Amendments
3. The Amendment filed 06/09/2026 has been entered. Claims 1-3, 5, 8-10, 12, 15, 16, and 19 have been amended. Claims 1-20 remain pending in the application.
Response to Arguments
Applicant argues that the pending claims recite meaningful limitations, beyond generally linking the use of the putative judicial exception to a particular technological environment, that amount to significantly more than the putative judicial exception and that integrate the putative judicial exception into a practical application: network-initiated and network-controlled AI/ML model management by a UE of an AI/ML model used at the UE, based on all of AI/ML model monitoring at the UE, monitoring results reported by the UE, and a request by the UE for a specific AI/ML model management operation (which is then by the network). MPEP § 2106.05(e). The claims are directed to a clear improvement to a technology: AI/ML model use by a UE for operation of the UE. MPEP § 2106.06(b). For those reasons, the claims are directed to patent-eligible subject matter. However, the Examiner respectfully disagrees, the claims are directed to the abstract ideal of monitoring AI/ML model use, reporting monitoring results, requesting and receiving model management/adaptation information, and performing model management/adaptation based on that information. The claims do not recite a particular technical improving UE operation, wireless communication, or operation of the AI/ML model itself. Rather, the claims recite the desired functional result of managing or adapting use of an AI/ML model at a UE. Furthermore, the additional limitations relied upon do not integrate the judicial exception into a practical application. The recited UE and network merely provide the technological environment in which the abstract monitoring/adaptation concept is performed. Accordingly, the claims do not integrate the abstract idea into a practical application under Step 2A, Prong two and also do not recite significantly more than the abstract idea under Step 2B. Therefore, the 101 rejection is maintained.
Applicant argues that Ren does not disclose the amended independent claims 1, 8, and 15. However, the argument is moot since this is a newly presented limitation, thus changes the scope of the claim. However, a newly found reference, Soldati, is applied.
Claim Rejections - 35 USC § 101
4. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the abstract idea without significantly more.
Step 1, the claims are directed to a process and machine.
Step 2A Prong 1, Claims 1, 8, and 15 recite, in part
performing, monitoring operation based on a monitoring configuration forming part of a configuration of use of a model for an operation performed (Mental processes, observing model use according to criteria and determining results).
a request for the model management and adaptation information specifies an action of model management and adaptation for the operation performed (Mental processes, judgment or selection).
Step 2A Prong 2, this judicial exception is not integrated into a practical application.
The additional elements:
a transceiver; and a processor (mere instructions to apply the exception using a generic computer component).
a user equipment (UE), an artificial intelligence/machine learning (AI/ML) (mere instructions to apply the exception using a generic computer component).
reporting, based on the monitoring configuration, AI/ML model assistance information including AI/ML model monitoring results from the AI/ML monitoring operation (mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity).
requesting, by the UE to the network, AI/ML model management and adaptation information based on the AI/ML model monitoring results (mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity).
receiving, at the UE from the network and based on the AI/ML model monitoring results and the specified action of AI/ML model management and adaptation, the request AI/ML model management and adaptation information (mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity).
performing an AI/ML model management and adaptation operation based on the AI/ML model management and adaptation information, the AI/ML model management and adaptation operation relating to management or adaptation of the use by the UE of the AI/ML model (mere instructions to apply the exception).
Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, either alone or in combination.
The additional elements:
a transceiver; and a processor (mere instructions to apply the exception using a generic computer component).
a user equipment (UE), an artificial intelligence/machine learning (AI/ML) (mere instructions to apply the exception using a generic computer component).
reporting, based on the monitoring configuration, AI/ML model assistance information including AI/ML model monitoring results from the AI/ML monitoring operation (mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity).
requesting, by the UE to the network, AI/ML model management and adaptation information based on the AI/ML model monitoring results (mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity).
receiving, at the UE from the network and based on the AI/ML model monitoring results and the specified action of AI/ML model management and adaptation, the request AI/ML model management and adaptation information (mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity).
performing an AI/ML model management and adaptation operation based on the AI/ML model management and adaptation information, the AI/ML model management and adaptation operation relating to management or adaptation of the use by the UE of the AI/ML model (mere instructions to apply the exception).
Claims 2-7, 9-14, and 16-20 provide further limitations to the abstract idea (Mathematical concepts and/or Mental processes) as rejected in claims 1, 8, 15, however, they do not disclose any additional elements that would amount to a practical application or significantly more than an abstract idea (data gathering/insignificant extra-solution activity and/or generic computer component).
Claim Rejections – 35 USC § 103
5. 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 of this title, 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.
6. Claims 1-2, 4-5, 8-9, 11-12, 15-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ren et al. (U.S. Patent Application Pub. No. US 20240147267 A1) in view of Soldati et al. (U.S. Patent Application Pub. No. US 20240243984 A1).
Claim 1: Ren teaches a method, comprising:
performing, at a user equipment (UE), an artificial intelligence/machine learning (AI/ML) monitoring operation (i.e. The UE is also operable to monitor a status of the machine learning model for wireless communication; para. [0008, 0089]) based on a monitoring configuration received from a network and forming part of a configuration by the network (i.e. model information 910 and a model status report configuration 920 are embedded in a model download message received by the UE 810 at block 804 in FIG. 8. In this example, the model status report configuration 920 is associated with one specific machine learning model for wireless communication (e.g., the model information 910). In these aspects of the present disclosure, the model download message is from the network 850 (e.g., the model manager 830) and includes the model information 910 (e.g. the model structure and weights), and the model status report configuration 920. In this example, the model status report configuration 920 includes a method to detect the model status, a content to report, a resource to report, and a timer for the report; para. [0101, 0102]) of use by the UE of an AI/ML model for an operation performed at the UE (i.e. FIG. 8 is a timing diagram illustrating network configuration for model inference of matched machine learning models for wireless communication in a user equipment (UE) and a network; para. [0095-0097, 0101]), UE performing monitoring of the status of a ML model and received model status report configuration;
reporting, based on the monitoring configuration, AI/ML model assistance information including AI/ML model monitoring results from the AI/ML monitoring operation (i.e. During communication, a status of the machine learning model for wireless communication is monitored. This method includes reporting the status of the machine learning model for wireless communication using a predetermined resource according to a predetermined format; para. [0038, 0046, 0101]);
receiving, at the UE from the network and based on the AI/ML model monitoring results and the specified action of AI/ML model management and adaptation, the request AI/ML model management and adaptation information (i.e. The network is also operable to receive a status report of the machine learning model for wireless communication. The network is further operable to indicate a fallback procedure to the UE to maintain wireless communication in response to the status report of the machine learning model indicating a model failure; para. [0009, 0047, 0120, 0129]); and
performing an AI/ML model management and adaptation operation based on the AI/ML model management and adaptation information, the AI/ML model management and adaptation operation relating to management or adaptation of the use by the UE of the AI/ML model (i.e. The UE is also operable to fall back to communicating with a fallback procedure, instead of the machine learning model for wireless communication, to maintain wireless communication with the network in response to the status of the machine learning model indicating a model failure; para. [0008, 0089, 0118-0120]), UE performs fallback and can update/replace the model.
Ren does not explicitly teach requesting, by the UE to the network, AI/ML model management and adaptation information based on the AI/ML model monitoring results, wherein a request for the AI/ML model management and adaptation information specifies an action of AI/ML model management and adaptation for the operation performed at the UE using the AI/ML model.
Howevever, Soldati teaches requesting, by the UE to the network, AI/ML model management and adaptation information based on the AI/ML model monitoring results, wherein a request for the AI/ML model management and adaptation information specifies an action of AI/ML model management and adaptation for the operation performed at the UE using the AI/ML model (i.e. sending a fifth message to the third network node, the fifth message comprising a performance metric associated with the AI/ML model monitored by the first network node. Some such embodiments may provide that the method further comprises determining an indication of whether the AI/ML model fulfills a retraining criterion, based on at least one performance metric associated with the AI/ML model or algorithm to be monitored. The method also comprises sending a fifth message to the third network node, the fifth message comprising a request to retrain the AI/ML model or algorithm … the retraining criterion comprises one or more of a criterion indicating that the AI/ML model should be retrained if at least one model performance metric is less than or exceeds a first threshold; para. [0029, 0030, 0071-0075]); receiving, at the UE from the network and based on the AI/ML model monitoring results and the specified action of AI/ML model management and adaptation, the request AI/ML model management and adaptation information (i.e. the method further comprises receiving a sixth message from the third network node, the sixth message comprising an updated AI/ML model … Send a sixth message to the first network node, the sixth message comprising an updated AI/ML model (based on the previous request or on the indicated model performance feedback); para. [0030, 0075, 0090]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Ren to include the feature of Soldati. One would have been motivated to make this modification because it improves the system by allowing the UE to identify the desired model management action and enabling the network to provide a more targeted corrective action.
Claim 2: Ren and Soldati teach the method of claim 1. Ren further teaches wherein the AI/ML model management and adaptation information includes: an indication of the specified action of AI/ML model management and adaptation (i.e. the network is operable to communicate with a user equipment (UE) having a machine learning model for wireless communication. The network is also operable to receive a status report of the machine learning model for wireless communication. The network is further operable to indicate a fallback procedure to the UE to maintain wireless communication in response to the status report of the machine learning model indicating a model failure; para. [0009]); and parameters that characterize the specified action of AI/ML model management and adaptation (i.e. the fallback procedure comprises receiving, from the network, a new machine learning model or an updated machine learning model; para. [0164]).
Claim 4: Ren and Soldati teach the method of claim 1. Ren further teaches wherein the monitoring configuration includes: resources for monitoring in time or frequency or spatial domain, report quantities, and report types for the operation (i.e. the model status report configuration 920 includes a method to detect the model status, a content to report, a resource to report, and a timer for the report para. [0101-0110]); or conditions that trigger the UE to one of report AI/ML monitoring results or autonomously perform AI/ML model management and adaptation (i.e. The periodic model status reporting described with respect to FIGS. 9A-11B, has multiple options for the UE 810 to determine which/when/whether a model status report is transmitted using a given configured resource. According to a first alternative, the UE 810 transmits the model status report in each configured periodic resource. According to a second alternative, whether a model status report is transmitted using the configured periodic resource is condition-based. For example, the model status report is transmitted by the UE 810 using the configured periodic resource when some conditions are met in some of the machine learning models. That is, the UE 810 may be limited to reporting the status of the models for which the conditions are met using the configured resource. For example, the conditions may be based on a pre-defined threshold; para. [0107, 0111, 0112]).
Claim 5: Ren and Soldati teach the method of claim 1. Ren further teaches comprising one of: autonomously performing, at the UE, the specified action of AI/ML model management and adaptation for the operation when a second condition is fulfilled (i.e. FIG. 14A, at block 1420, the UE 810 dynamically performs a model fallback, without an additional configuration, to maintain the wireless communication link with the network 850. The fallback may be based on a pre-configured rule or a set of pre-defined rules; para. [0137]).
Claim 8 is similar in scope to Claims 1 and is rejected under a similar rationale.
Ren further teaches a transceiver (i.e. the UE (e.g., using the antenna 252, the DEMOD/MOD 254, the MIMO detector 256, the receive processor 258, the transmit processor 264, the TX MIMO processor 266, the controller/processor 280, and/or the memory 282) can communicate with the network based on the machine learning model for wireless communication; para. [0134]); and a processor configured to (i.e. A user equipment (UE) includes a processor and a memory coupled with the processor. The UE also includes instructions stored in the memory; para. [0008]).
Claim 15: Ren teaches a network node (i.e. The base stations 110 may include a model configuration block 150. For brevity, only one base station 110 a is shown as including the model configuration block 150. The model configuration block 150 may provide a predetermined resource and a predetermined format to the UEs 120 for reporting a status of the machine learning model for wireless communication. The model configuration block 150 may provide a fallback procedure to the UEs 120 to maintain wireless communication with a network in response to a status of the machine learning model indicating a model failure; para. [0047]), comprising:
a transceiver configured to (i.e. As shown in FIG. 18, in some aspects, the process 1800 includes communicating with a user equipment (UE) having a machine learning model for wireless communication (block 1802). For example, the base station (e.g., using the DEMOD/MOD 232, the MIMO detector 236, the receive processor 238, the TX MIMO detector 230, the transmit processor 220, the controller/processor 240, and/or the memory 242) can communicate with the UE having the machine learning model for wireless communication; para. [0139]):
transmit, from a base station coupled to the network node to a user equipment (UE), a monitoring configuration for monitoring an artificial intelligence/machine learning (AI/ML) monitoring operation (i.e. The base stations 110 may include a model configuration block 150. For brevity, only one base station 110 a is shown as including the model configuration block 150. The model configuration block 150 may provide a predetermined resource and a predetermined format to the UEs 120 for reporting a status of the machine learning model for wireless communication; para. [0047, 0116]), the monitoring configuration forming part of a configuration by the network node of use by the UE of an AI/ML model for an operation performed at the UE (i.e. FIG. 8 is a timing diagram illustrating network configuration for model inference of matched machine learning models for wireless communication in a user equipment (UE) and a network … FIG. 13 involves network configuration of the UE 810 to generate and transmit a model status report. A network request may indicate a model index to identify the model from which to generate and transmit the model status report. The configuration of the model status report may also include an indication of which resources to use to transmit the model report status. The configuration of the model status report may further include an available timer or a specific timestamp for generating the model status report; para. [0095-0097, 0116]), and
receive, from the UE, AI/ML use assistance information including AI/ML model monitoring results from the AI/ML monitoring operation (i.e. the process 1800 further includes indicating a fallback procedure to the UE to maintain wireless communication in response to the status report of the machine learning model indicating a model failure (block 1806). For example, the base station (e.g., using the antenna 234, the DEMOD/MOD 232, the TX MIMO detector 230, the transmit processor 220, the controller/processor 240, and/or the memory 242) can indicate the fallback procedure to the UE to maintain wireless communication in response to the status report of the machine learning model indicating the model failure. For example, as shown in FIG. 14B, following block 1430, the UE 810 receives a model fallback indication from the network 850. As a result, the UE performs a model fallback procedure according to the fallback indication received from the network 850 at block 1450; para. 0120, 0122, 0141]); and
a processor configured to (i.e. As shown in FIG. 18 , in some aspects, the process 1800 includes communicating with a user equipment (UE) having a machine learning model for wireless communication (block 1802). For example, the base station (e.g., using the DEMOD/MOD 232, the MIMO detector 236, the receive processor 238, the TX MIMO detector 230, the transmit processor 220, the controller/processor 240, and/or the memory 242) can communicate with the UE having the machine learning model for wireless communication; para. [0139]):
evaluate, based on the AI/ML model monitoring results, UE-specific performance of the use of the AI/ML model for the operation (i.e. when the network 850 detects a performance loss, or the network 850 desires a check on the status of the machine learning model at the UE 810, the network 850 triggers a model status report from the UE 810 to the network 850. In this example, the network 850 may determine a large performance loss may be caused by a channel estimate machine learning model failure at the UE 810. In response, the network 850 configures and triggers the UE 810 to transmit a model status report to the network 850 at time t1; para. [0115, 0141]), and
determine the requested AI/ML model management and adaptation information corresponding to the AI/ML model monitoring results and the specified action of AI/ML model management and adaptation for the operation performed at the UE using the AI/ML model (i.e. The base stations 110 may include a model configuration block 150. For brevity, only one base station 110 a is shown as including the model configuration block 150. The model configuration block 150 may provide a predetermined resource and a predetermined format to the UEs 120 for reporting a status of the machine learning model for wireless communication. The model configuration block 150 may provide a fallback procedure to the UEs 120 to maintain wireless communication with a network in response to a status of the machine learning model indicating a model failure … network responses to the model failure including the network improving the model by generating a new machine learning model or an updated machine learning model at the UE 810; para. [0047, 0129, 0130, 0141]), and
transmit, to the UE, the requested AI/ML model management and adaptation information (i.e. Following block 1430, the UE 810 receives a model fallback indication from the network 850. As a result, the UE performs a model fallback procedure according to the fallback indication received from the network 850 at block 1450; para. [0120, 0130, 0141]).
Ren does not explicitly teach a request for AI/ML model management and adaptation information based on the AI/ML model monitoring results, wherein a request for the AI/ML model management and adaptation information specifies an action of AI/ML model management and adaptation for the operation performed at the UE using the AI/ML model.
Howevever, Soldati teaches a request for AI/ML model management and adaptation information based on the AI/ML model monitoring results, wherein a request for the AI/ML model management and adaptation information specifies an action of AI/ML model management and adaptation for the operation performed at the UE using the AI/ML model (i.e. sending a fifth message to the third network node, the fifth message comprising a performance metric associated with the AI/ML model monitored by the first network node. Some such embodiments may provide that the method further comprises determining an indication of whether the AI/ML model fulfills a retraining criterion, based on at least one performance metric associated with the AI/ML model or algorithm to be monitored. The method also comprises sending a fifth message to the third network node, the fifth message comprising a request to retrain the AI/ML model or algorithm … the retraining criterion comprises one or more of a criterion indicating that the AI/ML model should be retrained if at least one model performance metric is less than or exceeds a first threshold; para. [0029, 0030, 0071-0075]); determine the requested AI/ML model management and adaptation information corresponding to the AI/ML model monitoring results and the specified action of AI/ML model management and adaptation for the operation performed at the UE using the AI/ML model (i.e. the method further comprises receiving a sixth message from the third network node, the sixth message comprising an updated AI/ML model … Send a sixth message to the first network node, the sixth message comprising an updated AI/ML model (based on the previous request or on the indicated model performance feedback) … third network node may further determine to retrain the AI/ML model monitored by the first network node based on the information comprised in the fifth message; para. [0030, 0075, 0086-0090]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Ren to include the feature of Soldati. One would have been motivated to make this modification because it improves the system by allowing the UE to identify the desired model management action and enabling the network to provide a more targeted corrective action.
Claims 9, 11, 12, 16, and 20 are similar in scope to Claims 2, 4, 5 and are rejected under a similar rationale.
7. Claims 3, 10, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ren in view of Soldati; and further in view of Deo et al. (U.S. Patent Application Pub. No. US 20190147371 A1).
Claim 3: Ren and Soldati teach the method of claim 2. Ren further teaches wherein:
when the indication of the specified action of AI/ML model management and adaptation comprises an indication of model switch, the method further comprises selecting, at the UE, an AI/ML model from among trained model to be applied at the UE (i.e. FIGS. 16A and 16B illustrate network responses to the model failure including the network improving the model by generating a new machine learning model or an updated machine learning model at the UE 810. FIG. 16A illustrates a first process 1600, in which the network 850 triggers a new model configuration in response to a model failure. In this option, the network 850 transmits a new model in the model download message at block 1610; para. [0129]),
when the indication of the specified action of AI/ML model management and adaptation comprises an indication of model refinement or update, the method further comprises refining, at the UE, the AI/ML model by one or both of using new training data, or using new validation data (i.e. FIG. 16B illustrates a second process 1650, in which the network 850 configures a model update 1660 without a configuration of a new model. For example, the network 850 may transmit a differential update, in other words a difference between a new model and the previous model. In other examples, the model remains the same but the network transmits different parameters for the model. In each case, the network updates the machine learning model with the model update 1660; para. [0130]),
when the indication of the specified action of AI/ML model management and adaptation comprises an indication of model update, the method further comprises one of reconstructing or preparing, at the UE, a new AI/ML model to be applied at the UE (i.e. FIG. 16B illustrates a second process 1650, in which the network 850 configures a model update 1660 without a configuration of a new model. For example, the network 850 may transmit a differential update, in other words a difference between a new model and the previous model. In other examples, the model remains the same but the network transmits different parameters for the model. In each case, the network updates the machine learning model with the model update 1660; para. [0129, 0130]), and
when the indication of the specified action of AI/ML model management and adaptation comprises an indication of model transfer, the method further comprises applying, at the UE, received AI/L model parameters (i.e. FIG. 16B illustrates a second process 1650, in which the network 850 configures a model update 1660 without a configuration of a new model. For example, the network 850 may transmit a differential update, in other words a difference between a new model and the previous model. In other examples, the model remains the same but the network transmits different parameters for the model. In each case, the network updates the machine learning model with the model update 1660; para. [0102, 0130]).
Ren does not explicitly teach selecting, at the UE, an AI/ML model from among trained models; re-training using new training data, or re-validation using new validation data.
However, Deo teaches selecting, at the UE, an AI/ML model from among trained models; re-training using new training data, or re-validation using new validation data (i.e. The validation platform may select a trained model, from the plurality of trained models, based on model metrics and the scores, and may process a training sample, with the trained model, to generate first results. The training sample may be created based on the unbiased training data and production data associated with a production environment in which the trained model is to be utilized. The validation platform may process a production sample, with the trained model, to generate second results, wherein the production sample may be created based on the production data and the training sample. The validation platform may provide the trained model for use in the production environment based on the first results and the second results; para. [0013]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Ren and Soldati to include the feature of Deo. One would have been motivated to make this modification because it improves how the system chooses and validates replacement or fallback models after model failure, thereby making the system more reliable and robust.
Claims 10 and 19 are similar in scope to Claim 3 and are rejected under a similar rationale.
8. Claims 6-7, 13-14, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Ren in view of Soldati, and further in view of Zhu et al. (U.S. Patent Application Pub. No. US 20220360973 A1).
Claim 6: Ren and Soldati teach the method of claim 1. Ren does not explicitly teach comprising: receiving, at the UE, a request for UE capabilities of AI/ML functionality; and transmitting, by the UE, information of the UE capabilities of AI/ML functionality.
However, Zhu teaches comprising: receiving, at the UE, a request for UE capabilities of AI/ML functionality; and transmitting, by the UE, information of the UE capabilities of AI/ML functionality (i.e. the UE 104 may include a UE capability indicator component 198 configured to receive a request to report a UE capability for at least one of an AI procedure or an ML procedure; and transmit, based on the request to report the UE capability, an indication of one or more of an AI capability, an ML capability, a radio capability associated with the at least one of the AI procedure or the ML procedure, or a core network capability associated with the at least one of the AI procedure or the ML procedure; para. [0041, 0077]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Ren and Soldati to include the feature of Zhu. One would have been motivated to make this modification because it improves the suitability, efficiency, and reliability of wireless ML deployment across heterogeneous UEs.
Claim 7: Ren, Soldati, and Zhu teach the method of claim 6. Ren does not explicitly teach wherein the UE capabilities of AI/ML functionality comprise: supported AI/ML-based operations, or supported types or structures of AI/ML models, or supported types of training or inferences, or supported operations for model management and adaptation.
However, Zhu further teaches wherein the UE capabilities of AI/ML functionality comprise: supported AI/ML-based operations, or supported types or structures of AI/ML models (i.e. The UE radio capability may be used by the network to determine whether the UE is configured for an AI/ML model-based function. As such, the UE radio capability may include bits that indicate one or more supported functions F of the UE. The bits may correspond to a list of functions that the UE is configured to perform; para. [0070-0075]), or supported types of training or inferences, or supported operations for model management and adaptation (i.e. The processing capability may include a training processing capability, an inference processing capability, and/or a total processing capability; para. [0073-0076]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Ren and Soldati to include the feature of Zhu. One would have been motivated to make this modification because it improves the suitability, efficiency, and reliability of wireless ML deployment across heterogeneous UEs.
Claims 13, 14, 17, and 18 are similar in scope to Claims 6, 7 and are rejected under a similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Zhu et al. (Pub. No. US 20230075276 A1), At 345, the network may activate the neural network model. To activate the neural network model at the UE 115-b, the UE 115-b may transmit a model activation request message to the other network nodes 315 via the CU-CP 305 requesting activation of machine learning and the other network nodes may send a model activation response message to the UE 115-b via a MAC-CE or RRC signaling activating the machine learning at the UE 115-b.
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 extension fee 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 date of this final action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TAN TRAN whose telephone number is (303)297-4266. The examiner can normally be reached on Monday - Thursday - 8:00 am - 5:00 pm MT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matt Ell can be reached on 571-270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/TAN H TRAN/Primary Examiner, Art Unit 2141
Prosecution Insights