DETAILED ACTION
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 .
Priority
2. Acknowledgment is made of the present application claims priority under 35 U.S.C. § 119(a) of a Korean patent application number 10-2023-0042806, filed on March 31, 2023, in the Korean Intellectual Property Office.
Information Disclosure Statement
3. Acknowledgment is made of Applicant’s submission of information disclosure statement (IDS), dated on March 28, 2024 and September 23, 2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Examiner's Notes
4. Applicant is encouraged to submit a written authorization for Internet communications (PTO/SB/439, http://www.uspto.gov/sites/default/files/documents/sb0439.pdf) in the instant patent application to authorize the examiner to communicate with the applicant via email. The authorization will allow the examiner to better practice compact prosecution. The written authorization can be submitted via one of the following methods only: (1) Central Fax which can be found in the Conclusion section of this Office action; (2) regular postal mail; (3) EFS WEB; or (4) the service window on the Alexandria campus. EFS web is the recommended way to submit the form since this allows the form to be entered into the file wrapper within the same day (system dependent). Written authorization submitted via other methods, such as direct fax to the examiner or email, will not be accepted. See MPEP § 502.03.
Application Status
5. Acknowledgment is made of Applicant’s submission of the present application on March 28, 2024. Claims 1-20 are pending. This communication is considered fully responsive and sets forth below.
Claim Rejections - 35 USC § 112
6. The following is a quotation of 35 U.S.C. 112(b):
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
7. Claims 2, 7, 12, and 17 are rejected under 35 U.S.C. 112(b).
Regarding claim 2, it recites, “The method of claim 1, wherein the measurement configuration includes at least one of resources for AI/ML performance monitoring or resources for AI/ML data collection, and
wherein the AI/ML based operation comprises at least one of AI/ML performance monitoring based on the resources for AI/ML performance monitoring or AI/ML data collection based on the resources for AI/ML data collection.”
Claim 2 depends from claim 1. Claim 1 recites, “A method performed by a user equipment (UE) in a communication system, the method comprising:
transmitting, to a base station, capability information indicating a set of artificial intelligence (AI)/machine learning (ML) functionalities;
receiving, from the base station, configuration information associated with an AI/ML inference, wherein the configuration information indicates at least one of a measurement configuration or a reporting configuration;
receiving, from the base station, information to indicate activation of an AI/ML functionality; and
performing an AI/ML based operation based on the configuration information.”
Claim 1 is a method claim that includes transmitting, receiving and performing steps.
For the wherein clause related to the first receiving step, i.e., “wherein the configuration information indicates at least one of a measurement configuration or a reporting configuration” as indicated in italics above, it includes an optional element, i.e., “a measurement configuration” or “a reporting configuration.”
Under the broadest reasonable interpretation, the optional element does not narrow the claim because it can always be omitted. In re Johnston, 435 f.3d 1381, 1384 (Fed. Cir. 2006).
Consequently, claim 2 is rejected since there is a lack of antecedent basis for the usage of the term “the measurement configuration,” as indicated in italics in claim 2 above.
Same rationale applies to the usage of the term “the measurement configuration” in claims 7, 12, and 17.
Claim Rejections - 35 USC § 102
8. 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.
9. 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(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.
10. Claims 1-4, 6-9, 11-14, and 16-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Laddu et al. (US 2026/0032427).
Regarding claim 1, Laddu et al. teach the method performed by a user equipment (UE) in a communication system (paragraph [0089] lines 1-6; Examiner’s Notes: UE, as depicted in FIG. 1 in the prior art teaches the limitation of “a user equipment (UE)” in the instant application), the method comprising:
transmitting, to a base station, capability information indicating a set of artificial intelligence (AI)/machine learning (ML) functionalities (paragraphs [0092] lines 1-4 & [0100] lines 1-4; Examiner’s Notes: gNB/network as depicted in FIG. 1 in the prior art teaches the limitation of “a base station;”
available ML models in the prior art teaches the limitation of “a set of artificial intelligence (AI)/machine learning (ML) functionalities;”
in fact, transmitting, to gNB/network, the capability information including available ML models, as illustrated in S14 in FIG. 1 in the prior art teaches the limitation of “transmitting, to a base station, capability information indicating a set of artificial intelligence (AI)/machine learning (ML) functionalities” in the instant application);
receiving, from the base station, configuration information associated with an AI/ML inference (paragraphs [0093] lines 1-4 & [0094] lines 1-2; Examiner’s Notes: the configuration indicating an available ML model in the prior art teaches the limitation of “configuration information associated with an AI/ML inference;”
in fact, receiving, from gNB/network, the configuration indicating an available ML model, as illustrated in S16 in FIG. 1 in the prior art teaches the limitation of “receiving, from the base station, configuration information associated with an AI/ML inference” in the instant application),
wherein the configuration information indicates at least one of a measurement configuration or a reporting configuration (paragraph [0062] lines 1-9; Examiner’s Notes: the associated measurement configuration in the prior art teaches the limitation of “one of a measurement configuration or a reporting configuration;”
in fact, the configuration indicating associated measurement configuration in the prior art teaches the limitation of “receiving, from the base station, configuration information associated with an AI/ML inference” in the instant application);
receiving, from the base station, information to indicate activation of an AI/ML functionality (paragraph [0085] lines 1-5; Examiner’s Notes: acting/activating the ML model, as illustrated in S18 in the prior art teaches the limitation of “activation of an AI/ML functionality;”
in fact, receiving, from gNM/network, the information to indicating activating the ML model, as illustrated in S18 in the prior art teaches the limitation of “receiving, from the base station, information to indicate activation of an AI/ML functionality” in the instant application); and
performing an AI/ML based operation based on the configuration information (paragraph [0087] lines 1-15; Examiner’s Notes: using/performing a ML model according to the configuration in the prior art teaches the limitation of “performing an AI/ML based operation based on the configuration information” in the instant application).
Regarding claim 2, Laddu et al. further teach the method,
wherein the measurement configuration includes at least one of resources for AI/ML performance monitoring or resources for AI/ML data collection (paragraphs [0062] lines 1-9 & [0087] lines 1-15; Examiner’s Notes: the resources/particular bands regards to monitoring/defining ML specific details, e.g.., input/output & dimensions of the ML model, in the prior art teaches the limitation of “resources for AI/ML performance monitoring;”
in fact, the measurement configuration indicating the resources/particular bands regards to monitoring/defining ML specific details, e.g.., input/output & dimensions of the ML model, in the prior art teaches the limitation of “wherein the measurement configuration includes at least one of resources for AI/ML performance monitoring or resources for AI/ML data collection” in the instant application), and
wherein the AI/ML based operation comprises at least one of AI/ML performance monitoring based on the resources for AI/ML performance monitoring or AI/ML data collection based on the resources for AI/ML data collection (paragraphs [0062] lines 1-9 & [0087] lines 1-15; Examiner’s Notes: the resources/particular bands regards to monitoring/defining ML specific details, e.g.., input/output & dimensions of the ML model, in the prior art teaches the limitation of “resources for AI/ML performance monitoring;”
in fact, the ML model/operation including monitoring ML specific details, e.g.., input/output & dimensions of the ML model, based on the resources/particular bands in the prior art teaches the limitation of “wherein the AI/ML based operation comprises at least one of AI/ML performance monitoring based on the resources for AI/ML performance monitoring or AI/ML data collection based on the resources for AI/ML data collection” in the instant application).
Regarding claim 3, Laddu et al. further teach the method,
wherein the capability information indicates a set of notational model identifications (IDs) associated with the set of AI/ML functionalities (paragraphs [0092] lines 1-4 & [0093] lines 1-4; Examiner’s Notes: the ML model IDs in the prior art teaches the limitation of “a set of notational model identifications (IDs) associated with the set of AI/ML functionalities;”
in fact, the capability information indicating the ML model IDs in the prior art teaches the limitation of “wherein the capability information indicates a set of notational model identifications (IDs) associated with the set of AI/ML functionalities” in the instant application),
wherein the configuration information indicates a notational model ID from the set of notational model IDs (paragraph [0093] lines 1-4; Examiner’s Notes: the configuration indicating an ML model ID in the prior art teaches the limitation of “wherein the configuration information indicates a notational model ID from the set of notational model IDs” in the instant application), and
wherein the notational model ID is associated with an AI/ML functionality from the set of AI/ML functionalities (paragraph [0089] lines 1-6; Examiner’s Notes: the specific ML model scenario from the ML models in the prior art teaches the limitation of “an AI/ML functionality from the set of AI/ML functionalities;”
in fact, the ML model ID associated with the specific ML model scenario from the ML models in the prior art teaches the limitation of “wherein the notational model ID is associated with an AI/ML functionality from the set of AI/ML functionalities” in the instant application).
Regarding claim 4, Laddu et al. teach the method, further comprising:
identifying at least one of a minimum processing time required for an AI/ML functionality activation, a minimum processing time required for an AI/ML functionality inference, or a minimum processing time required for an AI/ML functionality monitoring (paragraph [0062] lines 1-9; Examiner’s Notes: the required warm-up time for the ML model in the prior art teaches the limitation of “a minimum processing time required for an AI/ML functionality activation;”
in fact, identifying/indicating the required warm-up time for the ML model in the prior art teaches the limitation of “identifying at least one of a minimum processing time required for an AI/ML functionality activation, a minimum processing time required for an AI/ML functionality inference, or a minimum processing time required for an AI/ML functionality monitoring” in the instant application).
Regarding claim 6, Laddu et al. teach the user equipment (UE) in a communication system (paragraphs [0089] lines 1-6 & [0111] lines 1-22; Examiner’s Notes: both UE depicted in FIG. 1 and UE/Apparatus 30 depicted in FIG. 3 in the prior art teaches the limitation of “user equipment (UE)” in the instant application), the UE comprising:
a transceiver (paragraph [0111] lines 1-22; Examiner’s Notes: I/O unit 32 in UE/Apparatus 30 depicted in FIG. 3 in the prior art teaches the limitation of “a transceiver” in the instant application); and
at least one processor (paragraph [0111] lines 1-22; Examiner’s Notes: processor/controller 31 in UE/Apparatus 30 depicted in FIG. 3 in the prior art teaches the limitation of “one processor” in the instant application) configured to:
transmit, to a base station, capability information indicating a set of artificial intelligence (AI)/machine learning (ML) functionalities (paragraphs [0092] lines 1-4 & [0100] lines 1-4; Examiner’s Notes: gNB/network as depicted in FIG. 1 in the prior art teaches the limitation of “a base station;”
available ML models in the prior art teaches the limitation of “a set of artificial intelligence (AI)/machine learning (ML) functionalities;”
in fact, transmitting, to gNB/network, the capability information including available ML models, as illustrated in S14 in FIG. 1 in the prior art teaches the limitation of “transmit, to a base station, capability information indicating a set of artificial intelligence (AI)/machine learning (ML) functionalities” in the instant application);
receive, from the base station, configuration information associated with an AI/ML inference (paragraphs [0093] lines 1-4 & [0094] lines 1-2; Examiner’s Notes: the configuration indicating an available ML model in the prior art teaches the limitation of “configuration information associated with an AI/ML inference;”
in fact, receiving, from gNB/network, the configuration indicating an available ML model, as illustrated in S16 in FIG. 1 in the prior art teaches the limitation of “receive, from the base station, configuration information associated with an AI/ML inference” in the instant application),
wherein the configuration information indicates at least one of a measurement configuration or a reporting configuration (paragraph [0062] lines 1-9; Examiner’s Notes: the associated measurement configuration in the prior art teaches the limitation of “one of a measurement configuration or a reporting configuration;”
in fact, the configuration indicating associated measurement configuration in the prior art teaches the limitation of “receiving, from the base station, configuration information associated with an AI/ML inference” in the instant application);
receive, from the base station, information to indicate activation of an AI/ML functionality (paragraph [0085] lines 1-5; Examiner’s Notes: acting/activating the ML model, as illustrated in S18 in the prior art teaches the limitation of “activation of an AI/ML functionality;”
in fact, receiving, from gNM/network, the information to indicating activating the ML model, as illustrated in S18 in the prior art teaches the limitation of “receive, from the base station, information to indicate activation of an AI/ML functionality” in the instant application); and
perform an AI/ML based operation based on the configuration information (paragraph [0087] lines 1-15; Examiner’s Notes: using/performing a ML model according to the configuration in the prior art teaches the limitation of “perform an AI/ML based operation based on the configuration information” in the instant application).
Regarding claim 7, Laddu et al. further teach the UE,
wherein the measurement configuration includes at least one of resources for AI/ML performance monitoring or resources for AI/ML data collection (paragraphs [0062] lines 1-9 & [0087] lines 1-15; Examiner’s Notes: the resources/particular bands regards to monitoring/defining ML specific details, e.g.., input/output & dimensions of the ML model, in the prior art teaches the limitation of “resources for AI/ML performance monitoring;”
in fact, the measurement configuration indicating the resources/particular bands regards to monitoring/defining ML specific details, e.g.., input/output & dimensions of the ML model, in the prior art teaches the limitation of “wherein the measurement configuration includes at least one of resources for AI/ML performance monitoring or resources for AI/ML data collection” in the instant application), and
wherein the AI/ML based operation comprises at least one of AI/ML performance monitoring based on the resources for AI/ML performance monitoring or AI/ML data collection based on the resources for AI/ML data collection (paragraphs [0062] lines 1-9 & [0087] lines 1-15; Examiner’s Notes: the resources/particular bands regards to monitoring/defining ML specific details, e.g.., input/output & dimensions of the ML model, in the prior art teaches the limitation of “resources for AI/ML performance monitoring;”
in fact, the ML model/operation including monitoring ML specific details, e.g.., input/output & dimensions of the ML model, based on the resources/particular bands in the prior art teaches the limitation of “wherein the AI/ML based operation comprises at least one of AI/ML performance monitoring based on the resources for AI/ML performance monitoring or AI/ML data collection based on the resources for AI/ML data collection” in the instant application).
Regarding claim 8, Laddu et al. further teach the UE,
wherein the capability information indicates a set of notational model identifications (IDs) associated with the set of AI/ML functionalities (paragraphs [0092] lines 1-4 & [0093] lines 1-4; Examiner’s Notes: the ML model IDs in the prior art teaches the limitation of “a set of notational model identifications (IDs) associated with the set of AI/ML functionalities;”
in fact, the capability information indicating the ML model IDs in the prior art teaches the limitation of “wherein the capability information indicates a set of notational model identifications (IDs) associated with the set of AI/ML functionalities” in the instant application),
wherein the configuration information indicates a notational model ID from the set of notational model IDs (paragraph [0093] lines 1-4; Examiner’s Notes: the configuration indicating an ML model ID in the prior art teaches the limitation of “wherein the configuration information indicates a notational model ID from the set of notational model IDs” in the instant application), and
wherein the notational model ID is associated with an AI/ML functionality from the set of AI/ML functionalities (paragraph [0089] lines 1-6; Examiner’s Notes: the specific ML model scenario from the ML models in the prior art teaches the limitation of “an AI/ML functionality from the set of AI/ML functionalities;”
in fact, the ML model ID associated with the specific ML model scenario from the ML models in the prior art teaches the limitation of “wherein the notational model ID is associated with an AI/ML functionality from the set of AI/ML functionalities” in the instant application).
Regarding claim 9, Laddu et al. further teach the UE, wherein the at least one processor is further configured to:
identify at least one of a minimum processing time required for an AI/ML functionality activation, a minimum processing time required for an AI/ML functionality inference, or a minimum processing time required for an AI/ML functionality monitoring (paragraph [0062] lines 1-9; Examiner’s Notes: the required warm-up time for the ML model in the prior art teaches the limitation of “a minimum processing time required for an AI/ML functionality activation;”
in fact, identifying/indicating the required warm-up time for the ML model in the prior art teaches the limitation of “identify at least one of a minimum processing time required for an AI/ML functionality activation, a minimum processing time required for an AI/ML functionality inference, or a minimum processing time required for an AI/ML functionality monitoring” in the instant application).
Regarding claim 11, Laddu et al. teach the method performed by a base station in a communication system (paragraph [0089] lines 1-6; Examiner’s Notes: gNB/network as depicted in FIG. 1 in the prior art teaches the limitation of “a base station” in the instant application), the method comprising:
receiving, from a user equipment (UE), capability information indicating a set of artificial intelligence (AI)/machine learning (ML) functionalities (paragraphs [0092] lines 1-4 & [0100] lines 1-4; Examiner’s Notes: UE as depicted in FIG. 1 in the prior art teaches the limitation of “a user equipment (UE);”
available ML models in the prior art teaches the limitation of “a set of artificial intelligence (AI)/machine learning (ML) functionalities;”
in fact, receiving, from UE, the capability information including available ML models, as illustrated in S14 in FIG. 1 in the prior art teaches the limitation of “receiving, from a user equipment (UE), capability information indicating a set of artificial intelligence (AI)/machine learning (ML) functionalities” in the instant application);
transmitting, to the UE, configuration information associated with an AI/ML inference (paragraphs [0093] lines 1-4 & [0094] lines 1-2; Examiner’s Notes: the configuration indicating an available ML model in the prior art teaches the limitation of “configuration information associated with an AI/ML inference;”
in fact, transmitting, to UE, the configuration indicating an available ML model, as illustrated in S16 in FIG. 1 in the prior art teaches the limitation of “transmitting, to the UE, configuration information associated with an AI/ML inference” in the instant application),
wherein the configuration information indicates at least one of a measurement configuration or a reporting configuration (paragraph [0062] lines 1-9; Examiner’s Notes: the associated measurement configuration in the prior art teaches the limitation of “one of a measurement configuration or a reporting configuration;”
in fact, the configuration indicating associated measurement configuration in the prior art teaches the limitation of “receiving, from the base station, configuration information associated with an AI/ML inference” in the instant application); and
transmitting, to the UE, information to indicate activation of an AI/ML functionality for an AI/ML based operation (paragraph [0085] lines 1-5; Examiner’s Notes: acting/activating the ML model, as illustrated in S18 in the prior art teaches the limitation of “activation of an AI/ML functionality;”
in fact, transmitting, to UE, the information to indicating activating the ML model, as illustrated in S18 in the prior art teaches the limitation of “transmitting, to the UE, information to indicate activation of an AI/ML functionality for an AI/ML based operation” in the instant application).
Regarding claim 12, Laddu et al. further teach the method,
wherein the measurement configuration includes at least one of resources for AI/ML performance monitoring or resources for AI/ML data collection (paragraphs [0062] lines 1-9 & [0087] lines 1-15; Examiner’s Notes: the resources/particular bands regards to monitoring/defining ML specific details, e.g.., input/output & dimensions of the ML model, in the prior art teaches the limitation of “resources for AI/ML performance monitoring;”
in fact, the measurement configuration indicating the resources/particular bands regards to monitoring/defining ML specific details, e.g.., input/output & dimensions of the ML model, in the prior art teaches the limitation of “wherein the measurement configuration includes at least one of resources for AI/ML performance monitoring or resources for AI/ML data collection” in the instant application), and
wherein the AI/ML based operation comprises at least one of AI/ML performance monitoring based on the resources for AI/ML performance monitoring or AI/ML data collection based on the resources for AI/ML data collection (paragraphs [0062] lines 1-9 & [0087] lines 1-15; Examiner’s Notes: the resources/particular bands regards to monitoring/defining ML specific details, e.g.., input/output & dimensions of the ML model, in the prior art teaches the limitation of “resources for AI/ML performance monitoring;”
in fact, the ML model/operation including monitoring ML specific details, e.g.., input/output & dimensions of the ML model, based on the resources/particular bands in the prior art teaches the limitation of “wherein the AI/ML based operation comprises at least one of AI/ML performance monitoring based on the resources for AI/ML performance monitoring or AI/ML data collection based on the resources for AI/ML data collection” in the instant application).
Regarding claim 13, Laddu et al. further teach the method,
wherein the capability information indicates a set of notational model identifications (IDs) associated with the set of AI/ML functionalities (paragraphs [0092] lines 1-4 & [0093] lines 1-4; Examiner’s Notes: the ML model IDs in the prior art teaches the limitation of “a set of notational model identifications (IDs) associated with the set of AI/ML functionalities;”
in fact, the capability information indicating the ML model IDs in the prior art teaches the limitation of “wherein the capability information indicates a set of notational model identifications (IDs) associated with the set of AI/ML functionalities” in the instant application),
wherein the configuration information indicates a notational model ID from the set of notational model IDs (paragraph [0093] lines 1-4; Examiner’s Notes: the configuration indicating an ML model ID in the prior art teaches the limitation of “wherein the configuration information indicates a notational model ID from the set of notational model IDs” in the instant application), and
wherein the notational model ID is associated with an AI/ML functionality from the set of AI/ML functionalities (paragraph [0089] lines 1-6; Examiner’s Notes: the specific ML model scenario from the ML models in the prior art teaches the limitation of “an AI/ML functionality from the set of AI/ML functionalities;”
in fact, the ML model ID associated with the specific ML model scenario from the ML models in the prior art teaches the limitation of “wherein the notational model ID is associated with an AI/ML functionality from the set of AI/ML functionalities” in the instant application).
Regarding claim 14, Laddu et al. teach the method, further comprising:
identifying at least one of a minimum processing time required for an AI/ML functionality activation, a minimum processing time required for an AI/ML functionality inference, or a minimum processing time required for an AI/ML functionality monitoring (paragraph [0062] lines 1-9; Examiner’s Notes: the required warm-up time for the ML model in the prior art teaches the limitation of “a minimum processing time required for an AI/ML functionality activation;”
in fact, identifying/indicating the required warm-up time for the ML model in the prior art teaches the limitation of “identifying at least one of a minimum processing time required for an AI/ML functionality activation, a minimum processing time required for an AI/ML functionality inference, or a minimum processing time required for an AI/ML functionality monitoring” in the instant application).
Regarding claim 16, Laddu et al. teach the base station in a communication system (paragraphs [0089] lines 1-6 & [0111] lines 1-22; Examiner’s Notes: both gNB/network depicted in FIG. 1 and gNB/Apparatus 30 depicted in FIG. 3 in the prior art teaches the limitation of “base station” in the instant application), the base station comprising:
a transceiver (paragraph [0111] lines 1-22; Examiner’s Notes: I/O unit 32 in gNB/Apparatus 30 depicted in FIG. 3 in the prior art teaches the limitation of “a transceiver” in the instant application); and
at least one processor (paragraph [0111] lines 1-22; Examiner’s Notes: processor/controller 31 in gNB/Apparatus 30 depicted in FIG. 3 in the prior art teaches the limitation of “one processor” in the instant application) configured to:
receive, from a user equipment (UE), capability information indicating a set of artificial intelligence (AI)/machine learning (ML) functionalities (paragraphs [0092] lines 1-4 & [0100] lines 1-4; Examiner’s Notes: UE as depicted in FIG. 1 in the prior art teaches the limitation of “a user equipment (UE);”
available ML models in the prior art teaches the limitation of “a set of artificial intelligence (AI)/machine learning (ML) functionalities;”
in fact, receiving, from UE, the capability information including available ML models, as illustrated in S14 in FIG. 1 in the prior art teaches the limitation of “receive, from a user equipment (UE), capability information indicating a set of artificial intelligence (AI)/machine learning (ML) functionalities” in the instant application);
transmit, to the UE, configuration information associated with an AI/ML inference (paragraphs [0093] lines 1-4 & [0094] lines 1-2; Examiner’s Notes: the configuration indicating an available ML model in the prior art teaches the limitation of “configuration information associated with an AI/ML inference;”
in fact, transmitting, to UE, the configuration indicating an available ML model, as illustrated in S16 in FIG. 1 in the prior art teaches the limitation of “transmit, to the UE, configuration information associated with an AI/ML inference” in the instant application),
wherein the configuration information indicates at least one of a measurement configuration or a reporting configuration (paragraph [0062] lines 1-9; Examiner’s Notes: the associated measurement configuration in the prior art teaches the limitation of “one of a measurement configuration or a reporting configuration;”
in fact, the configuration indicating associated measurement configuration in the prior art teaches the limitation of “receiving, from the base station, configuration information associated with an AI/ML inference” in the instant application); and
transmit, to the UE, information to indicate activation of an AI/ML functionality for an AI/ML based operation (paragraph [0085] lines 1-5; Examiner’s Notes: acting/activating the ML model, as illustrated in S18 in the prior art teaches the limitation of “activation of an AI/ML functionality;”
in fact, transmitting, to UE, the information to indicating activating the ML model, as illustrated in S18 in the prior art teaches the limitation of “transmit, to the UE, information to indicate activation of an AI/ML functionality for an AI/ML based operation” in the instant application).
Regarding claim 17, Laddu et al. further teach the base station,
wherein the measurement configuration includes at least one of resources for AI/ML performance monitoring or resources for AI/ML data collection (paragraphs [0062] lines 1-9 & [0087] lines 1-15; Examiner’s Notes: the resources/particular bands regards to monitoring/defining ML specific details, e.g.., input/output & dimensions of the ML model, in the prior art teaches the limitation of “resources for AI/ML performance monitoring;”
in fact, the measurement configuration indicating the resources/particular bands regards to monitoring/defining ML specific details, e.g.., input/output & dimensions of the ML model, in the prior art teaches the limitation of “wherein the measurement configuration includes at least one of resources for AI/ML performance monitoring or resources for AI/ML data collection” in the instant application), and
wherein the AI/ML based operation comprises at least one of AI/ML performance monitoring based on the resources for AI/ML performance monitoring or AI/ML data collection based on the resources for AI/ML data collection (paragraphs [0062] lines 1-9 & [0087] lines 1-15; Examiner’s Notes: the resources/particular bands regards to monitoring/defining ML specific details, e.g.., input/output & dimensions of the ML model, in the prior art teaches the limitation of “resources for AI/ML performance monitoring;”
in fact, the ML model/operation including monitoring ML specific details, e.g.., input/output & dimensions of the ML model, based on the resources/particular bands in the prior art teaches the limitation of “wherein the AI/ML based operation comprises at least one of AI/ML performance monitoring based on the resources for AI/ML performance monitoring or AI/ML data collection based on the resources for AI/ML data collection” in the instant application).
Regarding claim 18, Laddu et al. further teach the base station,
wherein the capability information indicates a set of notational model identifications (IDs) associated with the set of AI/ML functionalities (paragraphs [0092] lines 1-4 & [0093] lines 1-4; Examiner’s Notes: the ML model IDs in the prior art teaches the limitation of “a set of notational model identifications (IDs) associated with the set of AI/ML functionalities;”
in fact, the capability information indicating the ML model IDs in the prior art teaches the limitation of “wherein the capability information indicates a set of notational model identifications (IDs) associated with the set of AI/ML functionalities” in the instant application),
wherein the configuration information indicates a notational model ID from the set of notational model IDs (paragraph [0093] lines 1-4; Examiner’s Notes: the configuration indicating an ML model ID in the prior art teaches the limitation of “wherein the configuration information indicates a notational model ID from the set of notational model IDs” in the instant application), and
wherein the notational model ID is associated with an AI/ML functionality from the set of AI/ML functionalities (paragraph [0089] lines 1-6; Examiner’s Notes: the specific ML model scenario from the ML models in the prior art teaches the limitation of “an AI/ML functionality from the set of AI/ML functionalities;”
in fact, the ML model ID associated with the specific ML model scenario from the ML models in the prior art teaches the limitation of “wherein the notational model ID is associated with an AI/ML functionality from the set of AI/ML functionalities” in the instant application).
Regarding claim 19, Laddu et al. further teach the base station, wherein the at least one processor is further configured to:
identify at least one of a minimum processing time required for an AI/ML functionality activation, a minimum processing time required for an AI/ML functionality inference, or a minimum processing time required for an AI/ML functionality monitoring (paragraph [0062] lines 1-9; Examiner’s Notes: the required warm-up time for the ML model in the prior art teaches the limitation of “a minimum processing time required for an AI/ML functionality activation;”
in fact, identifying/indicating the required warm-up time for the ML model in the prior art teaches the limitation of “identify at least one of a minimum processing time required for an AI/ML functionality activation, a minimum processing time required for an AI/ML functionality inference, or a minimum processing time required for an AI/ML functionality monitoring” in the instant application).
Claim Rejections - 35 USC § 103
11. 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.
12. Claims 5, 10, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Laddu et al. (US 2026/0032427) in view of Rodriguez et al. (US 2025/0203401).
Regarding claim 5, Laddu et al. teach the method, further comprising: transmitting, to the base station, a set of conditions associated with the AI/ML based operation (paragraph [0062] lines 1-9; Examiner’s Notes: the conditions, e.g., required warm-up time and fine-tune requirements associated with the ML model in the prior art teaches the limitation of “a set of conditions associated with the AI/ML based operation;”
in fact, transmitting, to the gNB, the conditions, e.g., required warm-up time and fine-tune requirements associated with the ML model in the prior art teaches the limitation of “transmitting, to the base station, a set of conditions associated with the AI/ML based operation” in the instant application).
Laddu et al. teach the method without explicitly teaching receiving, from the base station, a set of additional conditions associated with the AI/ML based operation.
Rodriguez et al. from the same or similar field of endeavor teach implementing fairness of the method, receiving, from the base station, a set of additional conditions associated with the AI/ML based operation (paragraph [0112] lines 1-9; Examiner’s Notes: the conditions indicating the capable UE under LoS channel conditions to activate the ML-based model in the prior art teaches the limitation of “additional conditions associated with the AI/ML based operation;”
In fact, the base station transmitting, to the UE, the conditions indicating the capable UE under LoS channel conditions to activate the ML-based model in the prior art teaches the limitation of “receiving, from the base station, a set of additional conditions associated with the AI/ML based operation” in the instant application).
Thus, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in art to implement the method of Rodriguez et al. in the system of Laddu et al.
The motivation for implementing receiving, from the base station, a set of additional conditions associated with the AI/ML based operation, is to further enhance the mechanism of transmitting, to a base station, information by by a UE, wherein the information indicating an activation or a deactivation of AI and ML models, and the base station informs and suggests modifications in the UE configurations to enhance communication performance and AI/ML model selection.
Regarding claim 10, Laddu et al. teach the UE, further comprising: transmitting, to the base station, a set of conditions associated with the AI/ML based operation (paragraph [0062] lines 1-9; Examiner’s Notes: the conditions, e.g., required warm-up time and fine-tune requirements associated with the ML model in the prior art teaches the limitation of “a set of conditions associated with the AI/ML based operation;”
in fact, transmitting, to the gNB, the conditions, e.g., required warm-up time and fine-tune requirements associated with the ML model in the prior art teaches the limitation of “transmitting, to the base station, a set of conditions associated with the AI/ML based operation” in the instant application).
Laddu et al. teach the method without explicitly teaching receiving, from the base station, a set of additional conditions associated with the AI/ML based operation.
Rodriguez et al. from the same or similar field of endeavor teach implementing fairness of the method, receiving, from the base station, a set of additional conditions associated with the AI/ML based operation (paragraph [0112] lines 1-9; Examiner’s Notes: the conditions indicating the capable UE under LoS channel conditions to activate the ML-based model in the prior art teaches the limitation of “additional conditions associated with the AI/ML based operation;”
In fact, the base station transmitting, to the UE, the conditions indicating the capable UE under LoS channel conditions to activate the ML-based model in the prior art teaches the limitation of “receiving, from the base station, a set of additional conditions associated with the AI/ML based operation” in the instant application).
Thus, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in art to implement the method of Rodriguez et al. in the system of Laddu et al.
The motivation for implementing receiving, from the base station, a set of additional conditions associated with the AI/ML based operation, is to further enhance the mechanism of transmitting, to a base station, information by by a UE, wherein the information indicating an activation or a deactivation of AI and ML models, and the base station informs and suggests modifications in the UE configurations to enhance communication performance and AI/ML model selection.
Regarding claim 15, Laddu et al. teach the method, further comprising: receiving, from the UE, a set of conditions associated with the AI/ML based operation (paragraph [0062] lines 1-9; Examiner’s Notes: the conditions, e.g., required warm-up time and fine-tune requirements associated with the ML model in the prior art teaches the limitation of “a set of conditions associated with the AI/ML based operation;”
in fact, receiving, from the UE, the conditions, e.g., required warm-up time and fine-tune requirements associated with the ML model in the prior art teaches the limitation of “receiving, from the UE, a set of conditions associated with the AI/ML based operation” in the instant application).
Laddu et al. teach the method without explicitly teaching transmitting, to the UE, a set of additional conditions associated with the AI/ML based operation.
Rodriguez et al. from the same or similar field of endeavor teach implementing fairness of the method, transmitting, to the UE, a set of additional conditions associated with the AI/ML based operation (paragraph [0112] lines 1-9; Examiner’s Notes: the conditions indicating the capable UE under LoS channel conditions to activate the ML-based model in the prior art teaches the limitation of “additional conditions associated with the AI/ML based operation;”
In fact, the base station transmitting, to the UE, the conditions indicating the capable UE under LoS channel conditions to activate the ML-based model in the prior art teaches the limitation of “transmitting, to the UE, a set of additional conditions associated with the AI/ML based operation” in the instant application).
Thus, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in art to implement the method of Rodriguez et al. in the system of Laddu et al.
The motivation for implementing transmitting, to the UE, a set of additional conditions associated with the AI/ML based operation, is to further enhance the mechanism of transmitting, to a base station, information by by a UE, wherein the information indicating an activation or a deactivation of AI and ML models, and the base station informs and suggests modifications in the UE configurations to enhance communication performance and AI/ML model selection.
Regarding claim 20, Laddu et al. teach base station, wherein the at least one processor is further configured to:
receive, from the UE, a set of conditions associated with the AI/ML based operation (paragraph [0062] lines 1-9; Examiner’s Notes: the conditions, e.g., required warm-up time and fine-tune requirements associated with the ML model in the prior art teaches the limitation of “a set of conditions associated with the AI/ML based operation;”
in fact, receiving, from the UE, the conditions, e.g., required warm-up time and fine-tune requirements associated with the ML model in the prior art teaches the limitation of “receive, from the UE, a set of conditions associated with the AI/ML based operation” in the instant application).
Laddu et al. teach the base station without explicitly teaching transmitting, to the UE, a set of additional conditions associated with the AI/ML based operation.
Rodriguez et al. from the same or similar field of endeavor teach implementing fairness of the method, transmitting, to the UE, a set of additional conditions associated with the AI/ML based operation (paragraph [0112] lines 1-9; Examiner’s Notes: the conditions indicating the capable UE under LoS channel conditions to activate the ML-based model in the prior art teaches the limitation of “additional conditions associated with the AI/ML based operation;”
In fact, the base station transmitting, to the UE, the conditions indicating the capable UE under LoS channel conditions to activate the ML-based model in the prior art teaches the limitation of “transmitting, to the UE, a set of additional conditions associated with the AI/ML based operation” in the instant application).
Thus, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in art to implement the method of Rodriguez et al. in the system of Laddu et al.
The motivation for implementing transmitting, to the UE, a set of additional conditions associated with the AI/ML based operation, is to further enhance the mechanism of transmitting, to a base station, information by by a UE, wherein the information indicating an activation or a deactivation of AI and ML models, and the base station informs and suggests modifications in the UE configurations to enhance communication performance and AI/ML model selection.
Conclusion
13. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Xiong et al. (US 10,666,334) is cited to show an apparatus of an e-NodeB (eNB) capable to establish a communication connection with a user equipment (UE) in a communication network, the eNB comprising processing circuitry to transmit a downlink (DL) beamforming training reference signal (BF-TRS) to a user equipment (UE) using transmit beamforming weights.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WEI ZHAO whose telephone number is (571)270-5672. The examiner can normally be reached from 8:00AM to 5:00PM Monday through Friday.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JAE Y. LEE can be reached on 571-270-3936. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WEI ZHAO/ Primary Examiner
Art Unit 2479
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