DETAILED ACTION
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 .
Claims 1-20 are pending.
Claim Objections
Claim 3, 8-9, 16-17 are objected to because of the following informalities: “…functionality-related network configuration (radio resource control (RRC) (Re)configuration) is identified.” should be corrected to remove the limitation in parentheses as it is not an abbreviation. Appropriate correction is 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 (i.e., changing from AIA to pre-AIA ) 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(s) 1-2, 4-7, 15 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Chen Larsson et al. US 20250330373.
Regarding claim 1, A method of identifying artificial intelligence (AI)/machine learning (ML) functionalities/models supported for mobile communication operated in mobile communication systems including a base station and one or more user equipments (UEs) (method for indicating and configuring machine learning (ML) model support in a user equipment (UE) in a network, Abstract, Figure 3), the method comprising: identifying, by at least one of the base station or the one or more UEs, information related to the AI/ML functionalities supportable by the one or more UEs (a UE provides machine learning model support info to a network node, including at least one type or version info of at least one machine learning model associated with a certain functionality such as Channel State Information reporting, Demodulation Reference Signal pattern, beam reporting, para. 0059, the UE sends machine learning model support information to the network, para. 0080, Figure 3); and identifying, by at least one of the base station or the one or more UEs, AI/ML model information supportable by the one or more UEs (a network node uses the machine learning type and version info, and optionally a previous machine learning model ID allocation info in current or other nodes, to determine at least one machine learning model ID, and signals it to the UE, the model ID is used to refer to the model in subsequent model handling-related signaling in downlink and uplink, the network node assigns the model ID in such a way that both the UE and the network are aware that a given ML model ID refers to a given ML model or ML model version, para. 0060, ).
Claim 15 is rejected under the same rationale.
Regarding claim 2, The method of claim 1, wherein, in the identifying of the information related to the AI/ML functionalities supportable by the one or more UEs, the information related to the AI/ML functionalities supportable by the one or more UEs and network configuration information supported for each AI/ML functionality supportable by the one or more UEs are identified together (method performed by a UE for obtaining a configuration for a machine learning model comprises providing a machine learning model support info, such as model type indicator and one or more model version indicators, to a network node, describing a machine learning model available at the UE; and obtaining a machine learning model ID from the network node for the machine learning model available at the UE, para. 0065-0067).
Regarding claim 4, The method of claim 1, wherein the identifying of the information related to the AI/ML functionalities supportable by the one or more UEs includes: requesting, by the base station, a UE capability enquiry to the UE; and reporting, by the UE, UE capability information to the base station in response to the UE capability enquiry (the UE transmits the at least one ML version or other ML capability info of at least one ML model to the network such as the network node as part of the UE capability transfer procedure, included in the UECapabilityInformation message, as one or more Information Element(s) (IEs) and/or fields, this is transmitted by the UE in response to a request from the network e.g., in response to the reception of a UECapabilityEnquire message, para. 0144).
Regarding claim 5, The method of claim 4, wherein, in the reporting of, by the UE, the UE capability information to the base station in response to the UE capability enquiry, the UE capability information including the AI/ML functionalities and the network configuration information supported for each AI/ML functionality is forwarded to the base station (the UE transmits the at least one ML version or other ML capability info of at least one ML model to the network such as the network node as part of the UE capability transfer procedure, included in the UECapabilityInformation message, as one or more Information Element(s) (IEs) and/or fields, this is transmitted by the UE in response to a request from the network e.g., in response to the reception of a UECapabilityEnquire message, he UECapabilityEnquire message may include an indication for the report of a specific ML version or other ML support info associated to the ML model of a specific function, such as an ML model for beam management (BM), or CSI compression. In another example the UE transmits the ML support, such as the Model types in the UECapabilityInformation message as one or more Information Element(s) (IEs) and/or fields e.g. UEModelVersions IE, para. 0144).
Regarding claim 6, The method of claim 5, wherein, in the reporting of, by the UE, the UE capability information to the base station in response to the UE capability enquiry, the UE capability information including at least one of whether the network configuration information supported for each AI/ML functionality is shared for a plurality of AI/ML functionalities or whether the network configuration information is specialized for individual AI/ML functionalities is forwarded to the base station (the UE transmits the at least one ML version or other ML capability info of at least one ML model to the network node as part of the UE capability transfer procedure, included in the UECapabilityInformation message, as one or more Information Element(s) (IEs) and/or fields in response to a request from the network e.g., in response to the reception of a UECapabilityEnquire message, the UECapabilityEnquire message may include an indication for the report of a specific ML version or other ML support info such as associated to the ML model of a specific function, such as an ML model for beam management (BM), or CSI compression, para. 0144, a network node uses the ML type and version info to determine at least one ML model ID, and signals it to the UE, the model ID is assigned in such a way that both the UE and the NW are aware that a given ML model ID refers to a given ML model or ML model version such that the mapping is unique for the UE and the ML-model ID can also be unique within a functional area, para. 0060).
Regarding claim 7, The method of claim 4, wherein, in the reporting of, by the UE, the UE capability information to the base station in response to the UE capability enquiry, general UE capability information and AI/ML functionality-related UE capability information are reported individually or in an integrated process based on a result of decision on whether to report the general UE capability information and the AI/ML functionality-related UE capability information in respective processes or not (the UE individually transmits the at least one ML version or other ML capability info of at least one ML model to the network node as part of the UE capability transfer procedure, included in the UECapabilityInformation message, as one or more Information Element(s) (IEs) and/or fields in response to a request from the network e.g., in response to the reception of a UECapabilityEnquire message, the UECapabilityEnquire message may include an indication for the report of a specific ML version or other ML support info such as associated to the ML model of a specific function, such as an ML model for beam management (BM), or CSI compression, para. 0144).
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 (i.e., changing from AIA to pre-AIA ) 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, 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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 8, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen Larsson in view of Ali et al. US 20240137787.
Regarding claim 8, The method of claim 1, Chen Larsson discloses identifying of the AI/ML model information supportable by the one or more UEs (the model information is requested and/or provided via RAN signaling. e.g., via RRC or MAC CE, using a common IE or separate IEs for parts of the listed elements, para. 0143, the model version info may be provided by the UE to the core network as part of connection establishment or via on-demand Radio Access Network signaling such as Radio Resource Control or MAC control element, para. 0059-0060) but does not disclose forwarding, by the base station, AI/ML-related network configuration (RRC (Re)configuration) to the UE; and reporting, by the UE, at least one of model identifier (model ID) information or model information supportable by the UE to the base station.
Ali discloses a gNB sends a UE capability Enquiry request to UE to inform one or more configurable features of the apparatus potentially causing an anomalous operating condition in the UE, the request may include a filter setting for the UE to limit the configurable features to a specific subset of features, Figure 6, para. 0115. Ali discloses based on the request, the UE compiles a list of several possible problematic capabilities and/or their combinations by executing the the model that provides the prediction and the UE sends a UE capability response message to the network, para. 0116. Ali discloses the UE receiving RRC reconfiguration parameters from the network in a MAC control element; running a model for the RRC reconfiguration parameters; and signaling information whether any of said RRC reconfiguration parameters infers a predetermined likelihood to cause an anomalous operating condition to the network, para. 0119-0120. Before the filing of the invention it would have been obvious to modify Chen Larsson to include Ali’s reconfiguration. One of ordinary skill in the art would be motivated to do so for the UE to dynamically flag features potentially causing an anomalous operating condition based on RRC reconfiguration parameters suggested by the network, thus providing feedback to the network about potentially problematic issues that may arise, para. 0115.
Claim 16 is rejected under the same rationale.
Claim(s) 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen Larsson in view of Ali in view of Sundararajan et al. US 20240267725.
Regarding claim 9, The method of claim 8, Ali discloses UE sends the RRC Reconfiguration Complete message including the labels of the alternative configurations to the network and the network trains a model that allows predictive functionality for detecting possible anomalies before preparing the RRC reconfiguration for a UE, para. 0127. Chen Larsson and Ali do not disclose wherein the AI/ML-related network configuration (RRC (Re)configuration) includes a functionality identifier (functionality ID).
Sundararajan discloses a gNB conveys to one or more UEs information about the different models that are applicable at the network entity and/or are implemented by the network entity via system information (SI) and/or radio resource control (RRC) messages, the information may be in the form of a list of model identifiers (IDs), such as functionality IDs, para. 0025. Before the filing of the invention it would have been obvious to modify Chen Larsson and Ali to include Sundararajan’s information about models to the UE including a list of model identifiers including functionality IDs. One of ordinary skill in the art would be motivated to do so for improving speed and data carrying capacity of communications, improving efficiency of the use of shared communications mediums, reducing power used by transmitters and receivers while performing communications, improving reliability of wireless communications, avoiding redundant transmissions and/or receptions and related processing, improving the coverage area of wireless communications, increasing the number and types of devices that can access wireless communications systems, increasing the ability for different types of devices to intercommunicate, para. 0004.
Regarding claim 10, The method of claim 8, Chen Larsson and Ali do not disclose wherein, in the reporting of, by the UE, the at least one of the model ID information or the model information supportable by the UE to the base station, the UE reports at least one of model ID information or model information supported for each AI/ML functionality-related network configuration, along with functionality ID information supported for each AI/ML functionality-related network configuration. Sundararajan discloses a gNB conveys to one or more UEs information about the different models that are applicable at the network entity and/or are implemented by the network entity via system information (SI) and/or radio resource control (RRC) messages, the information may be in the form of a list of model identifiers (IDs), such as functionality IDs, para. 0025. Before the filing of the invention it would have been obvious to modify Chen Larsson and Ali to include Sundararajan’s information about models to the UE including a list of model identifiers including functionality IDs. One of ordinary skill in the art would be motivated to do so for improving speed and data carrying capacity of communications, improving efficiency of the use of shared communications mediums, reducing power used by transmitters and receivers while performing communications, improving reliability of wireless communications, avoiding redundant transmissions and/or receptions and related processing, improving the coverage area of wireless communications, increasing the number and types of devices that can access wireless communications systems, increasing the ability for different types of devices to intercommunicate, para. 0004.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen Larsson in view of Sundararajan.
Regarding claim 17, A method of managing artificial intelligence (AI)/machine learning (ML) functionalities/models supported for mobile communication operated in mobile communication systems including a base station and one or more user equipments (UEs) (method for indicating and configuring machine learning (ML) model support in a user equipment (UE) in a network, Abstract, Figure 3), the method comprising: forwarding, by the base station, functionality information for identifying AI/ML functionality-related network configurations (RRC (Re)configuration) to the UE (a network node uses the machine learning type and version info, and optionally a previous machine learning model ID allocation info in current or other nodes, to determine at least one machine learning model ID, and signals it to the UE, the model ID is used to refer to the model in subsequent model handling-related signaling in downlink and uplink, the network node assigns the model ID so both the UE and the network are aware that a given ML model ID refers to a given ML model or ML model version, para. 0060, ‘ML functionality’ is used for the same purpose as ‘ML model’ is used, para. 0076, the message in which the UE receives the mapping is an RRC message, such as RRC Reconfiguration and in the case of an RRC Reconfiguration, the model ID is assigned by the gNB node, para. 0169);
forwarding, by the UE, one or more model ID information for identifying supported models for each AI/ML functionality-related network configuration to the base station (a UE provides machine learning model support info to a network node, including at least one type or version info of at least one machine learning model associated with a certain functionality such as Channel State Information reporting, Demodulation Reference Signal pattern, beam reporting, para. 0059, the UE sends machine learning model support information to the network, para. 0080, Figure 3).
and operating, by the base station, at least part of a life cycle management (LCM) process for specific AI/ML functionalities/models using the functionality nformation and the model ID information (the ML model ID framework enables unambiguous referencing of a multitude of ML models implemented or available in a UE for model handling and LCM (life cycle management) purposes, para. 0074).
Chen Larsson does not disclose a functionality identifier information for identifying AI/ML functionality-related network configurations (RRC (Re)configuration) to the UE. Sundararajan discloses a gNB conveys to one or more UEs information about the different models that are applicable at the network entity and/or are implemented by the network entity via system information (SI) and/or radio resource control (RRC) messages, the information may be in the form of a list of model identifiers (IDs), such as functionality IDs, para. 0025. Before the filing of the invention it would have been obvious to modify Chen Larsson to include Sundararajan’s information about models to the UE including a list of model identifiers including functionality IDs. One of ordinary skill in the art would be motivated to do so for improving speed and data carrying capacity of communications, improving efficiency of the use of shared communications mediums, reducing power used by transmitters and receivers while performing communications, improving reliability of wireless communications, avoiding redundant transmissions and/or receptions and related processing, improving the coverage area of wireless communications, increasing the number and types of devices that can access wireless communications systems, increasing the ability for different types of devices to intercommunicate, para. 0004.
Regarding claim 18, The method of claim 17, wherein the LCM process includes at least one of data collection, model training, model inference operation, model deployment, model activation, model deactivation, model selection, model monitoring, or model transfer (the ML-model ID may be used by the network for configuring and/or activating and/or de-activating ML-model(s) within the UE, or by the UE to refer to the model during status reporting or model handling requests, para. 0079).
Allowable Subject Matter
Claims 11-14, 19-20 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MELANIE JAGANNATHAN whose telephone number is (571)272-3163. The examiner can normally be reached M-F 9-5.
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, Marcus Smith can be reached at 571-270-1096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MELANIE JAGANNATHAN/Primary Examiner, Art Unit 2468