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, 2, 4-10,13, 14, 19, 20, 28-30, 37, 38, 45 and 46 are pending per amendment.
Response to Arguments
Applicant’s arguments, see page 12, filed 5/22/26, with respect to claims 1, 2, 4-10, 13, 14, 19, 20, 28-30, 37, 38, 45 and 46 have been fully considered and are persuasive. The 101 rejection of above claims has been withdrawn.
Applicant's arguments filed 5/22/26 with respect to claim 1 not teaching amended feature “by using signaling which includes formatted requests and responses relating to AI/ML model” have been fully considered but they are not persuasive. Examiner relies upon at least paragraphs 0092, 101 and 109 (in addition to previously cited paragraph 0061) for their explicit teaching of RIC and other related entities exchanging policy-based guidance and notifications related to AI/ML model training, inferences, updates, and malfunctions. As such, Wu does anticipate signaling which includes “formatted requests and responses related to AI/ML model.”
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
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.
Claims 4-10, 13, 14, 19, 20, 28-30, 37 and 38 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4 recites the limitation "controlling…repository configured to store the AI/ML m” (emphasis added). There is insufficient antecedent basis for the limitation the AI/ML Muin the claim.
Claim Rejections - 35 USC § 102
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.
Claims 1, 2, 4-10, 13, 14, 19, 20, 37, 38, 45 and 46 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Applicant disclosed Wu (US 20210184989).
For claim 1, Wu discloses:
A system for supporting artificial intelligence/machine learning (AI/ML) model functions using a service-based architecture in a radio access network (RAN) intelligent controller (RIC) (par. 0077: RIC designed to support AI/ML applications), the system comprising:
one or more memories storing instructions (par. 0053); and one or more processors (par. 0053) configured to execute the instructions to implement:
a first function configured to manage AI/ML functions, and expose management and exposure services for the AI/ML functions (par. 0061: RAN service-based architecture with management plane functions; par. 0096: RIC Or22 accesses feedback on model performance. RIC may scale ML model instances as needed by observing resource utilization);
a second function configured to provide services for deploying the AI/ML models in the one RIC (par. 0095: “The non-RT RIC may operate one or more ML engines, which are packaged software executable libraries that provide methods, routines, data types, etc., used to run ML, models.”); and
a repository configured to store the AI/ML models (par. 0095: Multiple ML catalogs made discoverable by the non-RT RIC: a design-time catalog, a training/deployment-time catalog, a run-time catalog.),
wherein the first function, the second function, and the repository are connected with the service-based architecture by using signaling which includes formatted requests and responses relating to AI/ML model (par. 0061 & Fig. 3: Service-based architecture for managing network functions: control plane, user plane, management plane, data plane, compute plane, network exposure functions, application functions; par. 0101: Exchange of policy-based guidance and enrichment information between RIC entities disclosed; par. 0092:.O-RAN RT RIC Or214 taught as including AI/ML workflows that include model training, inferences and updates; par.0109: ML performance monitoring executing on RIC may trigger ML model updating and notify SMO about malfunctioning models via an internal interface).
For claim 2, Wu discloses:
The system according to claim 1, wherein the one or more processors are further configured to execute the instructions to implement at least one of: a third function configured to provide services for training the AI/ML models; a fourth function configured to provide services for certifying the AI/ML models; a fifth function configured to provide services for registering the AI/ML models; a sixth function configured to provide providing services for performing AI/ML model inference using an AI/ML model; and/or a seventh function configured to provide providing data management services for training the AI/ML models (par. 0093: AI/ML workflows include model training, inferences, updates).
For claim 4, Wu discloses:
A method of providing artificial intelligence/machine learning AI/ML services to a service consumer using a system including one or more memories storing instructions (par. 0053), one or more processors (par. 0053), and a radio access network (RAN) intelligent controller (RIC) (Fig 14 non-RT RIC), the method comprising:
controlling the one or more processors to execute the instructions to implement a first function configured to manage AI/ML functions, and expose management and exposure services for the AI/ML functions (par. 0061: RAN service-based architecture with management plane functions; par. 0096: RIC Or22 accesses feedback on model performance. RIC may scale ML model instances as needed by observing resource utilization);
controlling the one or more processors to execute the instructions to implement a second function configured to provide services for deploying the AI/ML models in the RIC (par. 0095: “The non-RT RIC may operate one or more ML engines, which are packaged software executable libraries that provide methods, routines, data types, etc., used to run ML, models.”); and
controlling the one or more processors to execute the instructions to implement a repository configured to store the AI/ML (par. 0095: Multiple ML catalogs made discoverable by the non-RT RIC: a design-time catalog, a training/deployment-time catalog, a run-time catalog.)
receiving, from a service consumer, a message for requesting the first function to perform at least one of: model training; certification; registration; and/or deployment for an AI/ML model (par. 0111: “The SMO internal interface termination may receive ML model deployment from SMO and route the trained ML model to the responding rApps”); and initiating, by the first function, a procedure to perform the at least one of: model training; certification; registration; and/or deployment for the AI/ML model (par. 0111).
wherein the first function, the second function, and the repository are connected with a service-based architecture by using signaling which includes formatted requests and responses relating to AI/ML model (par. 0061 & Fig. 3: Service-based architecture for managing network functions: control plane, user plane, management plane, data plane, compute plane, network exposure functions, application functions; par. 0101: Exchange of policy-based guidance and enrichment information between RIC entities disclosed; par. 0092:.O-RAN RT RIC Or214 taught as including AI/ML workflows that include model training, inferences and updates; par.0109: ML performance monitoring executing on RIC may trigger ML model updating and notify SMO about malfunctioning models via an internal interface).
For claim 5, Wu discloses:
The method according to claim 4, wherein the message, is for requesting to perform the model training and includes: a parameter indicating an AI/ML identity (ID) for identifying the AI/ML model; a parameter indicating an application type; a parameter indicating an application identity for identifying an application; a parameter indicating a destination that hosts a target application; a parameter indicating that the AI/ML model is a new AI/ML model; a parameter indicating an existing AI/ML model identity (ID) in a case where there is an existing AI/ML model; a parameter indicating a version number for indicating a version of the AI/ML model; and a list of input parameters for model training for indicating input data for training the AI/ML model; a list of output parameters for model training for indicating output data for AI/ML model training for AI/ML model training; and/or at least one parameter indicating a performance criteria for model training for use in measuring a performance of the model training (par. 0100, 0103: ML host generates output produced from training data).
For claim 6, Wu discloses:
The method according to claim 5, wherein the message includes the parameter indicating the application type, and the parameter indicates the application type to be a non-real-time RIC (Non-RT RIC) application (rApp) or a near-real-time RIC (Near-RT RIC) application (xApp) (par. 0106, 0110: RApps and XApps disclosed).
For claim 7, Wu discloses:
The method according to claim 5, wherein the message includes the parameter, and the parameter indicating the destination indicates the destination to be a non-real-time RIC (Non-RT RIC) or a near-real-time RIC (Near-RT RIC) (par. 0092, 0093: Non-RT RIC and near-RT RIC disclosed).
For claim 8, Wu discloses:
The method according to claim 5, wherein the input data for training the AI/ML model includes at least one of the following: measurement data from an open radio access network (O-RAN) central unit (OCU), an O-RAN distributed unit (O-DU), and/or an open RAN remote unite (O-RU); analytical data from at least one non-real-time RIC (Non-RT RIC) application (rApp); analytical data from at least one near-real-time RIC (Near-RT RIC) application (xApp); and/or enrichment information (EI) data from at least one external source (par. 0094, 0111: ML training data sources disclosed).
For claim 9, Wu discloses:
The method according to claim 5, wherein the output data for AI/ML model training includes at least one of the following: analytical data from at least one non-real-time RIC (Non-RT RIC) application (rApp); analytical data from at least one near-real-time RIC (Near-RT RIC) application (xApp); and/or data indicating an accuracy of model training (par. 0103, RApps store ML output data).
For claim 10, Wu discloses:
The method according to claim 5, wherein the performance criteria for model training includes at least one of the following: an accuracy threshold for indicating whether or not a target accuracy for model training has been successfully achieved; and/or an execution time for the trained AI/ML model (par. 0096: ML model prediction accuracy feedback disclosed).
For claim 13, Wu discloses:
The method according to claim 4, wherein the message is for requesting at least AI/ML model deployment and includes: a parameter indicating an AI/ML identity (ID) for identifying the AI/ML model; a parameter indicating an application type; a parameter indicating an application identity for identifying an application; a parameter indicating a destination that hosts a target application; a parameter indicating that the AI/ML model is a new AI/ML model; a parameter indicating an existing AI/ML model identity (ID) in a case where there is an existing AI/ML model; a parameter indicating a version number for indicating a version of the AI/ML model; and/or at least one deployment parameter for use in model deployment (par. 0095: ML model catalogs disclosed; further discovery mechanism if a particular ML model can be executed in a target ML inference host, and what number and type of ML models can be executed in the ML interface host (i.e., deployment parameters)).
For claim 14, Wu discloses:
The method according to claim 13, wherein the at least one deployment parameter includes at least one of the following: at least one parameter indicating at least one deployment option; the parameter indicating the application identity for identifying the application; a parameter indicating the destination that hosts the target application; a parameter indicating an application type; a parameter indicating a target application identity (ID); at least one parameter indicating required resources related to each deployment option; at least one configuration parameter; at least one parameter indicating a runtime environment; and/or at least one parameter indicating a version number (par. 0095:Discovery mechanism parameters include: if a particular ML model can be executed in a target ML inference host, and what number and type of ML models can be executed in the ML interface host).
For claim 19, Wu discloses:
The method according to claim 4, further comprising:
controlling the one or more processors to execute the instructions to implement:
a third function configured to provide services for training the AI/ML models (par. 0061: RAN service-based architecture with management plane functions; par. 0096: RIC Or22 access feedback on model performance. RIC may scale ML model instances as needed by observing resource utilization);
a seventh function configured to provide providing data management services for training the AI/ML models (par. 0093: AI/ML workflows include model training, inferences, updates);
by the first function, instructing the third function to train the AI/ML model (par. 0061: RAN service-based architecture with management plane functions; par. 0096: RIC Or22 access feedback on model performance. RIC may scale ML model instances as needed by observing resource utilization);
by the third function requesting the seventh function to provide data to train the AI/ML model (par. 0095: Multiple ML catalogs made discoverable by the non-RT RIC: a design-time catalog, a training/deployment-time catalog, a run-time catalog.);
the third function receiving, from the seventh function, the data to train the AI/ML model (par. 0094: “…the non-RT RIC Or212 may request or trigger ML model training in the training hosts regardless of where the model is deployed and executed. ML models may be trained and not currently deployed.”); and
by the third function performing model training for the AI/ML model based on the data, storing the trained AI/ML model at the -repository, and informing the first function (par. 0095: “…the non-RT RIC Or212 may provide a query-able catalog for an ML designer/developer to publish/install trained ML models (e.g., executable software components)”.
For claim 20, Wu discloses:
The method according to claim 19, further comprising, by the first function, instructing the third function to train the AI/ML model using a message that includes at least one of: a parameter indicating an AI/ML identity (ID) for identifying the AI/ML model; a parameter indicating an application type; a parameter indicating an application identity for identifying an application; a parameter indicating a destination that hosts a target application; a parameter indicating that the AI/ML model is a new AI/ML model; a parameter indicating an existing AI/ML model identity (ID) in a case where there is an existing AI/ML model; a parameter indicating a version number for indicating a version of the AI/ML model; and a list of input parameters for model training for indicating input data for training the AI/ML model; a list of output parameters for model training for indicating output data for AI/ML model training; and/or at least one parameter indicating a performance criteria for model training for use in measuring a performance of the model training (par. 0094: “…the non-RT RIC Or212 may request or trigger ML model training in the training hosts regardless of where the model is deployed and executed. ML models may be trained and not currently deployed.”).
For claim 37, Wu discloses:
The method according to claim 4, further comprising:
controlling the one or more processors to execute the instructions to implement a fifth function configured to provide services for registering the AI/ML models (par. 0093: AI/ML workflows include model training, inferences, updates);
by the first function, instructing the fifth function to register a trained AI/ML model (par. 0093: AI/ML workflows include model training, inferences, updates)); and by the fifth function, registering the trained AI/ML model for discovery by a service consumer (par. 0095: query-able catalog for ML models disclosed).
For claim 38, Wu discloses:
The method according to claim 37, further comprising, by the first function, instructing the fifth function to register the trained AI/ML model using a message including at least one of: a parameter indicating an AI/ML identity (ID) for identifying the AI/ML model; a parameter indicating an application type; a parameter indicating an application identity for identifying an application; a parameter indicating a destination that hosts a target application; a parameter indicating that the AI/ML model is a new AI/ML model; a parameter indicating an existing AI/ML model identity (ID) in a case where there is an existing AI/ML model; a parameter indicating a version number for indicating a version of the AI/ML model; and a list of input parameters for model training for indicating input data for training the AI/ML model; a list of output parameters for model training for indicating output data for AI/ML model training; and/or at least one parameter indicating a performance criteria for model training for use in measuring a performance of the model training (par. 0095:Discovery mechanism parameters include: if a particular ML model can be executed in a target ML inference host, and what number and type of ML models can be executed in the ML interface host).
For claim 45, Wu discloses:
A method of deploying an AI/ML model in a radio access network (RAN) intelligent controller (RIC), using the system of claim 1, the method comprising: the first function instructing the second function to deploy an AI/ML model; and the second function instructing a network function orchestrator to deploy the AI/ML model, whereby the network function orchestrator deploys the AI/ML model on an open-cloud (par. 0096: RIC Or22 access feedback on model performance. RIC may scale ML model instances as needed by observing resource utilization).
For claim 46, Wu discloses:
The method according to claim 45, the first function instructs the second function to deploy the AI/ML model using a message including at least one of the following: a parameter indicating an AI/ML identity (ID) for identifying the AI/ML model; a parameter indicating an application type; a parameter indicating an application identity for identifying an application; a parameter indicating a destination that hosts a target application; a parameter indicating that the AI/ML model is a new AI/ML model; a parameter indicating an existing AI/ML model identity (ID) in a case where there is an existing AI/ML model; a parameter indicating a version number for indicating a version of the AI/ML model; and/or at least one deployment parameter for use in model deployment (par. 0096: RIC Or22 access feedback on model performance. RIC may scale ML model instances as needed by observing resource utilization; par. 0095: ML model catalogs disclosed; further discovery mechanism if a particular ML model can be executed in a target ML inference host, and what number and type of ML models can be executed in the ML interface host (i.e., deployment parameters)).
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 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.
Claims 28, 29 and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Wu (US 20210184989), in view of Ren (US 20240040404).
For claim 28, Wu discloses:
The method according to claim 19, but fails to explicitly disclose: “further comprising by the third function performing evaluation and validation of the trained AI/ML model prior to storing the trained AI/ML model at the repository.”
However, in a related field, Ren disclosed methods of a machine learning preparation procedure that including training, validation and testing stages (par. 0121). It would have been obvious to one of ordinary skill before effective filing of claimed invention to have introduced Ren’s teachings alongside Wu. The motivation to combine would have been to ensure the machine learning model is performing as expected based on the training data collection stage (Ren, par. 0121).
For claim 29, Wu-Ren discloses:
The method according to claim 4, further comprising:
controlling the one or more processors to execute the instructions to implement a fourth function configured to provide service for certifying the AI/mL models (Ren, par. 0121: ML training set validation following data training disclosed); by the first function, instructing the fourth function to verify and certify a trained AI/ML model stored at the repository (Ren, par. 0121: ML training set validation following data training disclosed); by the fourth function, verifying and certifying the trained AI/ML model stored at the repository and labelling the trained AI/ML model as a certified model (Ren, par. 0121: ML training set validation following data training disclosed).
Moreover, to extent Wu-Ren does not explicitly disclose a multiple level process for a fourth function, responsive to instruction from a first function, to verify and certifying the training AI/ML model and labelling the model as certified or labeling or indicating through parameter a model is “certified”, it would have been obvious to one of ordinary skill, apprised of the relevant art, to have done as much. The motivation would have been to utilize well-known “flagging” or other electronic indicators/metadata to achieve a well-known outcome of recording state/status of data.
For claim 30, Wu-Ren discloses:
The method according to claim 29, further comprising, by the first function instructing the fourth function to verify and certify the trained AI/ML model using a message including at least one of: a parameter indicating an AI/ML identity (ID) for identifying the AI/ML model; a parameter indicating an application type; a parameter indicating an application identity for identifying an application; a parameter indicating a destination that hosts a target application; a parameter indicating that the AI/ML model is a new AI/ML model; a parameter indicating an existing AI/ML model identity (ID) in a case where there is an existing AI/ML model; a parameter indicating a version number for indicating a version of the AI/ML model; and/or at least one certification parameter for use in model certification (Ren, par. 0121: ML training set validation following data training disclosed).
Moreover, to extent Wu-Ren does not explicitly disclose labeling or indicating through parameter a model is “certified”, it would have been obvious to one of ordinary skill, apprised of the relevant art, to have done as much. The motivation would have been to utilize well-known “flagging” or other electronic indicators/metadata to achieve a well-known outcome of recording state/status of data.
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 CLAYTON R WILLIAMS whose telephone number is (571)270-3801. The examiner can normally be reached M-F 10:00am - 6:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nicholas Taylor can be reached at 571-272-3889. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CLAYTON R WILLIAMS/Primary Examiner, Art Unit 2443
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