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 .
Allowable Subject Matter
Claims 3-10, and 13-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and if the claim objections are addressed.
Claim Objections
Claims 1 ,11 and 20 are objected to because of the following informalities:
-In claim 1 stated “ AI/ML module”, applicant should define the definition of “AI/ML module”.
-The same rational is applied to claims 11 and 20, respectively.
Appropriate corrections are required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 1 is rejected under 35 U.S.C. 102(a)(2) as being anticipated by Pantelidou US20230297882A1
Regarding claim 1,Pantelidou US20230297882A1 discloses An indication apparatus, comprising: first processor circuitry configured to determine a first AI/ML module in a terminal equipment and/or a second AI/ML module in a network device are/is to be stopped; and a first transmitter configured to transmit to the network device a request for indicating to stop the first AI/ML module and/or the second AI/ML module (fig. 5[0115] discloses that the UE detects 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. . The network acknowledges the request in the De-Activate ML model Response message
(fig. 6 and [0116] discloses the network observes that the current ML model does not perform well, the network sends a De-Activate ML model message to the UE, The UE receiving the De-Activate ML model message reverts to Default Behavior for all the indicated problems and acknowledges the deactivation with an “Accept” Response to the network
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2,11-12 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Pantelidou US20230297882A1 in view of Li et al US20250219898A1.
Regarding claim 2, Pantelidou US20230297882A1 discloses all features with respect to claim 1.
Pantelidou further discloses a first receiver configured to receive a response or
a configuration or a signal for indicating that the first AI/ML module and/or
the second AI/ML module are/is to be stopped; wherein AI/ML module is disabled in the configuration or there is not AI/ML related information in the configuration, or is deactivated
(fig. 5[0115] discloses that the UE detects 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. . The network acknowledges the request in the De-Activate ML model Response message) and (fig. 6 and [0116] discloses the network observes that the current ML model does not perform well, the network sends a De-Activate ML model message to the UE, The UE receiving the De-Activate ML model message reverts to Default Behavior for all the indicated problems and acknowledges the deactivation with an “Accept” Response to the network
Pantelidou US20230297882A1 does not disclose a first receiver configured to receive a response or a configuration or a signal for indicating that the first AI/ML module and/or
the second AI/ML module are/is to be stopped;
wherein AI/ML module is disabled in the configuration or there is not AI/ML related information in the configuration, or is deactivated via media access control (MAC) control element (CE) or downlink control information (DCI).
Li et al US20250219898A1 discloses a first receiver configured to receive a response or
a configuration or a signal for indicating that the first AI/ML module and/or the second AI/ML module are/is to be stopped; wherein AI/ML module is disabled in the configuration or there is not AI/ML related information in the configuration, or is deactivated via media access control (MAC) control element (CE) or downlink control information (DCI) [0077]At the network side, e.g., the network node receiving the report of the one or more indications of the ML-model performance, and/or based on an analysis of the ML-model performance, may take some actions, such as: (Stop using the ML-model): The network node can transmit to the UE (and the UE receives) an indication/configuration for the UE to stop using the ML-model (for which the one or more indication(s) of the ML-model performance has/have been reported), wherein stop using the ML-model comprises i) stop performing predictions/estimations based on the one or more ML-model(s) and/or stop producing outputs from the one or more ML-models(s) and/or stop reporting outputs from the one or more ML-models(s). The indication/configuration may be transmitted in an RRC message (e.g., RRC Reconfiguration), MAC CE or DCI.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pantelidou by including a first receiver configured to receive a response or a configuration or a signal for indicating that the first AI/ML module and/or the second AI/ML module are/is to be stopped; wherein AI/ML module is disabled in the configuration or there is not AI/ML related information in the configuration, or is deactivated via media access control (MAC) control element (CE) or downlink control information (DCI)., as taught by Li, in order to stop using the ML-model (see Li [007]).
Regarding claim 11, Pantelidou US20230297882A1 discloses an indication apparatus, comprising: second processor circuitry, configured to
Pantelidou US20230297882A1 discloses second processor circuitry, configured to determine a first AI/ML module in a terminal equipment and/or a second AI/ML module in a network device are/is to be stopped(fig. 5[0115] discloses that the UE detects 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. . The network acknowledges the request in the De-Activate ML model Response message (fig. 6 and [0116] discloses the network observes that the current ML model does not perform well, the network sends a De-Activate ML model message to the UE, The UE receiving the De-Activate ML model; and
Pantelidou US20230297882A1 does not disclose a second transmitter configured to transmit to the terminal equipment a request or a configuration or a signal for indicating to stop the first AI/ML module and/or the second AI/ML module; wherein AI/ML module is disabled in the configuration or there is not AI/ML related information in the configuration, or is deactivated via media access control (MAC) control element (CE) or downlink control information (DCI).
Li et al US20250219898A1 a second transmitter configured to transmit to the terminal equipment a request or a configuration or a signal for indicating to stop the first AI/ML module and/or the second AI/ML module; wherein AI/ML module is disabled in the configuration or there is not AI/ML related information in the configuration, or is deactivated via media access control (MAC) control element (CE) or downlink control information (DCI)0077]At the network side, e.g., the network node receiving the report of the one or more indications of the ML-model performance, and/or based on an analysis of the ML-model performance, may take some actions, such as: (Stop using the ML-model): The network node can transmit to the UE (and the UE receives) an indication/configuration for the UE to stop using the ML-model (for which the one or more indication(s) of the ML-model performance has/have been reported), wherein stop using the ML-model comprises i) stop performing predictions/estimations based on the one or more ML-model(s) and/or stop producing outputs from the one or more ML-models(s) and/or stop reporting outputs from the one or more ML-models(s). The indication/configuration may be transmitted in an RRC message (e.g., RRC Reconfiguration), MAC CE or DCI,
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pantelidou by including a second transmitter configured to transmit to the terminal equipment a request or a configuration or a signal for indicating to stop the first AI/ML module and/or the second AI/ML module; wherein AI/ML module is disabled in the configuration or there is not AI/ML related information in the configuration, or is deactivated via media access control (MAC) control element (CE) or downlink control information (DCI), as taught by Li, in order to stop using the ML-model (see Li [0077]).
Regarding claim 12, the combination of Pantelidou and Li discloses all features with respect to claim 1. Pantelidou further discloses a second receiver configured to receive a response for indicating that the first AI/ML module and/or the second AI/ML module are/is to be stopped(fig. 5[0115] discloses that the UE detects 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. . The network acknowledges the request in the De-Activate ML model Response message and (fig. 6 and [0116] discloses the network observes that the current ML model does not perform well, the network sends a De-Activate ML model message to the UE, The UE receiving the De-Activate ML model message reverts to Default Behavior for all the indicated problems and acknowledges the deactivation with an “Accept” Response to the network.
Regarding claim 20, Pantelidou US20230297882A1 discloses A network system, comprising:
Pantelidou US20230297882A1 discloses a terminal equipment configured to determine a first AI/ML module in a terminal equipment and/or a second AI/ML module in a network device are/is to be stopped; and transmit to the network device a request for indicating to stop the first AI/ML module and/or the second AI/ML module (fig. 5[0115] discloses that the UE detects 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. . The network acknowledges the request in the De-Activate ML model Response message (fig. 6 and [0116] discloses the network observes that the current ML model does not perform well, the network sends a De-Activate ML model message to the UE, The UE receiving the De-Activate ML model; and
Pantelidou US20230297882A1 does not disclose a network device configured to determine a first AI/ML module in a terminal equipment and/or a second AI/ML module in a network device are/is to be stopped; and transmit to the terminal equipment a request or a configuration or a signal for indicating to stop the first AI/ML module and/or the second AI/ML module.
Li et al US20250219898A1 discloses a network device configured to determine a first AI/ML module in a terminal equipment and/or a second AI/ML module in a network device are/is to be stopped; and transmit to the terminal equipment a request or a configuration or a signal for indicating to stop the first AI/ML module and/or the second AI/ML module. (DCI)0077]At the network side, e.g., the network node receiving the report of the one or more indications of the ML-model performance, and/or based on an analysis of the ML-model performance, may take some actions, such as: (Stop using the ML-model): The network node can transmit to the UE (and the UE receives) an indication/configuration for the UE to stop using the ML-model (for which the one or more indication(s) of the ML-model performance has/have been reported), wherein stop using the ML-model comprises i) stop performing predictions/estimations based on the one or more ML-model(s) and/or stop producing outputs from the one or more ML-models(s) and/or stop reporting outputs from the one or more ML-models(s). The indication/configuration may be transmitted in an RRC message (e.g., RRC Reconfiguration), MAC CE or DCI.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pantelidou by including a network device configured to determine a first AI/ML module in a terminal equipment and/or a second AI/ML module in a network device are/is to be stopped; and transmit to the terminal equipment a request or a configuration or a signal for indicating to stop the first AI/ML module and/or the second AI/ML module, as taught by Li, in order to stop using the ML-model (see Li [0077]).
Conclusion
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/ABDELTIF AJID/ Primary Examiner, Art Unit 2478
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