DETAILED ACTION
This 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.
Response to Arguments
In the remarks the applicant contends “Thus, Pantelidou fails to disclose or suggest "in case that the AI/ML functionality requires to be suspended, transmitting, to the BS, request for suspending of the AI/ML functionality," "receiving, from the BS, signaling for suspending of the AI/ML functionality, while maintaining the inference configuration," and "receiving, from the BS, signaling indicating recovery of the AI/ML functionality," as recited in amended independent Claims 1, 17, 19, and 23.”
The Examiner respectfully disagrees as Pantelidou et al. address dynamically executing and stopping of the AI/ML functionality by the network after activation of the AI/ML functionality based on the ML State reported by the UE. Disclosed through transmitting, to the BS, report information related to changing a state based on a configuration of a report for the state (Page 11 Lines 10-24 disclose “In addition, according to some example embodiments, the UE indicates to the network a new IE (e.g. “ML State Indication" IE). This “ML State Indication" IE is not static (i.e. the gNB does not interpret this is a constant UE readiness), but reflects the ability of the UE to execute and/or train a ML model at a given state/moment. Unlike static UECapabilitylnformation IE, “ML State Indication” is a time-dependent (dynamic) indication. It is complementing information to the generic UE capabilities (the static ones)….”); in case that the AI/ML functionality requires to be suspended, transmitting, to the BS, request for suspending of the AI/ML functionality (FIG. 9 and Page 16 Lines 11-13 discloses “If the terminal indicated the capability (S10 = yes) and the terminal is in the inability state (S20 = yes), the means for informing 30 informs the network that the terminal is in the inability state (S30).”); and receiving, from the BS, signaling for suspending of the AI/ML functionality, while maintaining the inference configuration (FIG 19 and Page 20-21 Lines 31-34, Lines 1-2 discloses “The means for checking 310 checks if a terminal executes and/or trains a machine learning model (S310). In other terms, the information indicates that the terminal performs the ML model. The means for monitoring 320 monitors if the terminal receives an instruction to stop executing and/or training the machine learning model (S320).”) and receiving, from the BS, signaling indicating recovery of the AI/ML functionality (FIG 15 Page 19 Lines 6-18 disclose “The means for monitoring 170 monitors if an information is received according to which the terminal is in an ability state (S170). In the ability state, the terminal is able to execute and/or train the machine learning model with a predefined performance. The ability state is a dynamic property….If the terminal indicated the capability (S160 = yes) and the terminal is in the ability state (S170 = yes), the means for inhibiting 180 instructs the terminal to execute and/or train the machine learning model (S180), i.e. , if these conditions are fulfilled, the ML model is activated in the UE.” furthermore Page 9 Lines 9-17 discloses UEs ability to dynamically indicate ML ability to the network which drives ML start/stop after the ML model has been activated).
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 and receiving, from the BS, signaling indicating recovery of the AI/ML functionality.
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 (Page 11 Lines 10-24 disclose “In addition, according to some example embodiments, the UE indicates to the network a new IE (e.g. “ML State Indication" IE). This “ML State Indication" IE is not static (i.e. the gNB does not interpret this is a constant UE readiness), but reflects the ability of the UE to execute and/or train a ML model at a given state/moment. Unlike static UECapabilitylnformation IE, “ML State Indication” is a time-dependent (dynamic) indication. It is complementing information to the generic UE capabilities (the static ones)….”); in case that the AI/ML functionality requires to be suspended, transmitting, to the BS, request for suspending of the AI/ML functionality (FIG. 9 and Page 16 Lines 11-13 discloses “If the terminal indicated the capability (S10 = yes) and the terminal is in the inability state (S20 = yes), the means for informing 30 informs the network that the terminal is in the inability state (S30).”); and receiving, from the BS, signaling for suspending of the AI/ML functionality, while maintaining the inference configuration (FIG 19 and Page 20-21 Lines 31-34, Lines 1-2 discloses “The means for checking 310 checks if a terminal executes and/or trains a machine learning model (S310). In other terms, the information indicates that the terminal performs the ML model. The means for monitoring 320 monitors if the terminal receives an instruction to stop executing and/or training the machine learning model (S320).”) and receiving, from the BS, signaling indicating recovery of the AI/ML functionality (FIG 15 Page 19 Lines 6-18 disclose “The means for monitoring 170 monitors if an information is received according to which the terminal is in an ability state (S170). In the ability state, the terminal is able to execute and/or train the machine learning model with a predefined performance. The ability state is a dynamic property….If the terminal indicated the capability (S160 = yes) and the terminal is in the ability state (S170 = yes), the means for inhibiting 180 instructs the terminal to execute and/or train the machine learning model (S180), i.e. , if these conditions are fulfilled, the ML model is activated in the UE.” furthermore Page 9 Lines 9-17 discloses UEs ability to dynamically indicate ML ability to the network which drives ML start/stop after the ML model has been activated).
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 and receiving, from the BS, signaling indicating recovery of the AI/ML functionality., 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.”).
Conclusion
THIS ACTION IS MADE FINAL. 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.
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
/SUJOY K KUNDU/Supervisory Patent Examiner, Art Unit 2471
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