DETAILED ACTION
This Non-Final Office Action is in response to application number 18,098,955 filed on January 19th 2023. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed on April 13th,2023
Information Disclosure Statements
The Information Disclosure Statements (IDS), submitted on June 9th 2025 and June 12th 2023, are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/17/2020 has been entered.
Claim Rejections - 35 USC § 103
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 may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 1,3-4,17,19,21-23 and 25-28 are rejected under 35 U.S.C. 103 as being unpatentable over Parichehrehteroujeni et al. (WO 2022013104 A1) in view of Pantelidou et al. WO2022008037).
Regarding claims 1,17,19 and 23, Parichehrehteroujeni et al. disclose a method performed by a user equipment (UE) in a wireless communication system, the method comprising: receiving, from a base station (BS), request about UE capability information: transmitting, to the BS, information related to supported artificial intelligence/machine learning (AI/ML) functionality on the UE capability information: (WO2022013104 Page 7 Lines 2-12 and FIG 2A (202) disclose “In one example, obtaining information at step 202 comprises obtaining (for example requesting and receiving) information about a capacity of the wireless device to execute an ML model in step 202a. The information may be requested and received from the wireless device itself, or from another network node (a master node, secondary node, previous serving node, current serving node etc.) Information about a capacity of the wireless device to execute an ML model may for example include the maximum available memory that can be consumed by an ML model, floating point support, wireless device computational capabilities, (number of operations per second, type and number of processors, etc.), types of ML model supported, maximum supported computational cost for executing a model or a particular type of model, etc. In some examples, at least a part of the capability information may be implicitly provided via provision by the wireless device of its make and model.” Additionally Page 17 Line 15-30) receiving from the BS, inference configuration in response to the information related to supported AI/ML functionality (WO2022013104 Page 7 Lines 20-24, FIG 2A (210-220) and Page 9 Lines 33-34 respectively disclose “In step 210, the RAN node determines, on the basis of the information about an operating environment of the wireless device, configuration information for an ML model to be executed by the wireless device. If the RAN node has obtained capability information at step 202a, consideration of such information is included in the determining step 210.” And “Referring still to Figure 2a, in step 220, the RAN node sends, to the wireless device, the determined configuration information.” ); activating an (AI/ML) functionality based on the inference configuration;(WO2022013104 Page 10 Lines 8-10 and Fig. 2b (230,270) disclose “Referring now to Figure 2b, the RAN node may receive, from the wireless device, information based on an output of the ML model executed by the wireless device in accordance with the determined configuration information.” Page 21 Lines 24-27 disclose “The UE can report the model output or a derivative thereof when one of its output values changes, or when the model one or more outputs are either above, below, or equal to a certain threshold for a specified duration (for example similar to a time-to-trigger).”).
Parichehrehteroujeni et al. fail to explicitly disclose and transmitting, to the BS, report information related to changing a state based on a configuration of a report for the state; in case that the AI/ML functionality requires to be suspended, transmitting, to the BS, request for suspending of the AI/ML functionality; and receiving, from the BS, signaling for suspending of the AI/ML functionality, while maintaining the inference configuration.
However in an analogous art Pantelidou et al. teaches and transmitting, to the BS, report information related to changing a state based on a configuration of a report for the state; in case that the AI/ML functionality requires to be suspended, transmitting, to the BS, request for suspending of the AI/ML functionality; and receiving, from the BS, signaling for suspending of the AI/ML functionality, while maintaining the inference configuration (Page 14 Lines 19-28 disclose “In the example of Fig. 5, activation of an ML model and detecting the UE state change are the same as in the example of Fig. 4. However, differently than in Fig. 4, if the UE detects its state change (inability to execute and/or train the ML model), the UE requests from the network (with a De-Activate ML model Request message) to be switched to a different operation. Optionally, the UE may additionally send an ML State Indication message to the network to inform the network about updating its Default Behavior for the problem p.sub.m. This can be the case when the UE detects it is not capable for full ML processing for the current state. The network acknowledges the request in the De-Activate ML model Response message. With this message the UE can be switched to its Default Behavior for a given problem p.sub.m. UE may have a different Default Behavior per problem.” Additionally and more importantly Page 9 Line 1-17 discloses “In the present application, inability of the UE includes not only the case that the UE is not able to execute and/or train the ML model at all, but also a case that the UE is able to execute and/or train the ML model, but with a performance below a predefined (desired) performance.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Parichehrehteroujeni et al. to incorporate the teachings of Pantelidou et al., to implement requesting suspending of the AI/ML functionality: and receiving, from the network entity, signaling for suspending of the AI/ML functionality, while maintaining the inference configuration, in order to facilitate for state changes and achieve system efficiency and stability.
Regarding claims 3,21,25 and 27 Parichehrehteroujeni et al. disclose the method of claim 1, further comprising transmitting, to the BS. evaluation of performance of the AI/ML functionality (WO2022013104 Page 16 lines 31-35 disclose “Feedback in the form of information on the ML model performance ….”).
Regarding claims 4,22,26 and 28 Parichehrehteroujeni et al. disclose the method of claim 1, further comprising: transmitting, to the BS. information related to preference for data collection. (WO2022013104 Page 15 Line 1-3 disclose “In the present example, the model may have been trained by the network, either using synthetic data or using data collected by user devices camping in radio cells in the network.”).
Response to Arguments
Applicant's arguments filed on 02/17/2026 have been fully considered but they are not persuasive.
In the remarks, applicant contends that “Parichehrehteroujeni, however, does not teach or suggest a configuration in which a change in a state of the AI/ML functionality itself is defined as an independent management target and such state change is reported.”
The Examiner respectfully disagrees as Pantelidou Page 11 Lines 35-36 and Page 12 Lines 1-7 discloses “Once the UE detects internally constrains and limitations to continue with the previously declared “ML State Indication”, e.g. due to demanding processing operations on running ML model training, UE may send a different value of its ability to gNB. In line with the change of the “ML State Indication” value, the UE may also update its Default Behavior for a given task/problem and inform the network thereabout. The Default Behavior may not be a unique behavior through the course of time of UE operation and may depend on the UE state. For instance, a UE, at times when its memory is full, can indicate to the network that its default behaviour is to “run non-ML algorithm” for a certain task but if later on in time its state changes it can indicate that it is ready to “run light ML algorithm” for the task.” Furthermore Pantelidou Page 14 Lines 14-17 discloses “At some point in time, UE detects a State Change that affects its ability to execute and/or train the ML model. In this situation, the UE can declare to the network it is not able for full ML processing (for instance using Option a or Option b), and UE autonomously falls back to Default Behavior.” The citations listed above disclose detection of state changes that are reported to the network.
In the remarks, applicant contends that “Parichehrehteroujeni does not teach or suggest a stepwise control structure between the UE and the base station (BS) for suspension of the AI/ML functionality. In other words, a configuration in which the UE transmits a request for suspending the AI/ML functionality and performs the suspension in response to signaling from the network is not disclosed in Parichehrehteroujeni. Moreover, Parichehrehteroujeni neither discloses nor suggests maintaining the existing inference configuration even when the AI/ML functionality is suspended.” Applicant also states, “Pantelidou relates to an error-handling technique for situations in which ML processing becomes impossible or fails and does not assume control based on a state change occurring during normal operation of an ML functionality. In contrast, the present claims assume a situation in which the AI/ML functionality is temporarily suspended as needed during normal operation.”
The Examiner respectfully disagrees as Pantelidou Page 14 Lines 19-28 discloses “In the example of Fig. 5, activation of an ML model and detecting the UE state change are the same as in the example of Fig. 4. However, differently than in Fig. 4, if the UE detects its state change (inability to execute and/or train the ML model), the UE requests from the network (with a De-Activate ML model Request message) to be switched to a different operation. Optionally, the UE may additionally send an ML State Indication message to the network to inform the network about updating its Default Behavior for the problem p.sub.m. This can be the case when the UE detects it is not capable for full ML processing for the current state. The network acknowledges the request in the De-Activate ML model Response message. With this message the UE can be switched to its Default Behavior for a given problem p.sub.m. UE may have a different Default Behavior per problem.” Here the structure consisting of a request from the UE followed by a response from the network to suspend the ML functionality is disclosed. Pantelidou Page 12 Lines 9-14 discloses “Option b: Define a new time-varying ML UE Ability IE. According to some example embodiments, UE may provide a radio capabilities ML UE Ability IE. UE may provide this IE separately from the UECapability procedure discussed with respect to option a. It indicates the (time-dependent) ability of the UE to execute and/or train an ML model. This IE may be tailored to specific problems/algorithms/ML models that the UE is expected to execute and/or train. Option b is illustrated in Figure 3.” Here the fact that option b provides a time varying ML UE Ability IE separate from option a UECapability procedure indicates that ML functionality can be activated and deactivated temporarily as during normal operation as stated in the argument. Additionally and more importantly Pantelidou Page 9 Line 1-17 discloses “In the present application, inability of the UE includes not only the case that the UE is not able to execute and/or train the ML model at all, but also a case that the UE is able to execute and/or train the ML model, but with a performance below a predefined (desired) performance.” This indicates that ML is operational or functional even under the suspension as stated in the argument.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Samuel Dilan Rutnam whose telephone number is 703-756-1374. The examiner can normally be reached between 8:30am-5:00pm Mon-Fri.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sujoy Kundu can be reached on 571-272-8586.
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/Samuel Dilan Rutnam/
Patent Examiner, Art Unit 2471
/MOHAMMAD S ADHAMI/Primary Examiner, Art Unit 2471
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