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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/16/2024 was considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,120,776. Although the claims at issue are not identical, they are not patentably distinct from each other.
Present Application 18/886,432
U.S. Patent No. 12,120,776
1. A device, comprising: a processing system including a processor, wherein the processing system is associated with a first radio access network (RAN) that includes a first RAN intelligent controller (RIC); and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising:
1. A device, comprising:
a processing system including a processor, wherein the processing system is associated with a first radio access network (RAN) that includes a first RAN intelligent controller (RIC); and
a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising:
obtaining, from an artificial intelligence (AI) model synchronization system associated with a second RAN, data relating to an AI model deployed by a second RIC of the second RAN, wherein the processing system is communicatively coupled with the AI model synchronization system to facilitate exchanges of deployed AI models, and
1. obtaining, from an artificial intelligence (AI) model synchronization system associated with a second RAN, data relating to an AI model deployed wherein the processing system, the first RAN, and the first RIC are operated by a first network provider, wherein the AI model synchronization system, the second RAN, and the second RIC are operated by a second network provider different from the first network provider, and wherein the processing system is communicatively coupled with the AI model synchronization system to facilitate exchanges of deployed AI models between the first network provider and the second network provider;
by a second RIC of the second RAN,
wherein the data relating to the AI model includes telemetry data, network performance information, network traffic information, information regarding performance of the AI model under one or more operating conditions, information regarding performance of the AI model across one or more configurations of the AI model, or a combination thereof;
4. The device of claim 1, wherein the data relating to the AI model includes metadata, and wherein the metadata includes telemetry data, network performance information, network traffic information, information regarding performance of the AI model under one or more operating conditions, information regarding performance of the AI model across one or more configurations of the AI model, or a combination thereof.
determining, based on the data relating to the AI model and network-related information associated with the first RAN, that the AI model can be leveraged by the first RAN to improve traffic management of the first RAN, throughput of the first RAN, resource allocation of the first RAN, or a combination thereof;
1. determining, based on the data relating to the AI model and network-related information associated with the first RAN, that the AI model can be leveraged by the first RAN to improve traffic management of the first RAN, throughput of the first RAN, resource allocation of the first RAN, or a combination thereof;
responsive to the determining, receiving the AI model from the AI model synchronization system; and
1. responsive to the determining, receiving the AI model from the AI model synchronization system, wherein the receiving the AI model is in accordance with one or more AI model subscriptions with the AI model synchronization system; and
causing the first RIC to deploy the AI model in the first RAN after the receiving the AI model, wherein the first RIC comprises a RIC message router (RMR) message bus, wherein the AI model is configured with a Representational State Transfer (REST) interface, and wherein the AI model is deployed in a container along with an AI microservice adapter that facilitates intra-container function calls for providing protocol conversion between the REST interface and the RMR message bus.
1. causing the first RIC to deploy the AI model in the first RAN after the receiving the AI model, wherein the first RIC comprises a RIC message router (RMR) message bus, wherein the AI model is configured with a Representational State Transfer (REST) interface, and wherein the AI model is deployed in a container along with an AI microservice adapter that facilitates intra-container function calls for providing protocol conversion between the REST interface and the RMR message bus.
2. The device of claim 1, wherein at least one of the first RAN and the second RAN is a virtualized RAN (vRAN).
2. The device of claim 1, wherein at least one of the first RAN and the second RAN is a virtualized RAN (vRAN).
3. The device of claim 1, wherein the network-related information includes telemetry data, network performance information, network traffic information, or a combination thereof.
3. The device of claim 1, wherein the network-related information includes telemetry data, network performance information, network traffic information, or a combination thereof.
4. The device of claim 1, wherein the processing system, the first RAN, and the first RIC are operated by a first network provider, and wherein the AI model synchronization system, the second RAN, and the second RIC are operated by a second network provider different from the first network provider.
1. obtaining, from an artificial intelligence (AI) model synchronization system associated with a second RAN, data relating to an AI model deployed wherein the processing system, the first RAN, and the first RIC are operated by a first network provider, wherein the AI model synchronization system, the second RAN, and the second RIC are operated by a second network provider different from the first network provider, and wherein the processing system is communicatively coupled with the AI model synchronization system to facilitate exchanges of deployed AI models between the first network provider and the second network provider;
by a second RIC of the second RAN,
5. The device of claim 1, wherein the determining comprises comparing the data relating to the AI model and the network-related information associated with the first RAN.
5. The device of claim 1, wherein the determining comprises comparing the data relating to the AI model and the network-related information associated with the first RAN.
6. The device of claim 1, wherein the receiving the AI model involves utilizing one or more application programming interfaces (APIs).
6. The device of claim 1, wherein the receiving the AI model involves utilizing one or more application programming interfaces (APIs).
7. The device of claim 1, wherein the receiving the AI model is conducted over an interface between the processing system and the AI model synchronization system.
7. The device of claim 1, wherein the receiving the AI model is conducted over an interface between the processing system and the AI model synchronization system.
8. The device of claim 1, wherein the AI microservice adapter is configured to run as an xApp.
8. The device of claim 1, wherein the AI microservice adapter is configured to run as an xApp.
9. The device of claim 1, wherein the AI model and the AI microservice adapter are executed in the container as separate processes.
9. The device of claim 1, wherein the AI model and the AI microservice adapter are executed in the container as separate processes.
10. The device of claim 1, wherein the AI microservice adapter is configured to adapt the AI model based on configuration data, and wherein the configuration data includes:
information regarding functions relating to an AI microservice;
information regarding inputs;
information regarding outputs;
identification of messages, to detect for on a bus associated with the first RIC, that call or utilize the functions;
identification of messages to deliver on the bus relating to the functions;
information regarding how messages are to be translated into calls;
information regarding how responses to messages from an AI microservice are to be translated and placed onto the bus;
information regarding endpoints of an AI microservice;
information regarding a location at which an AI microservice is running; or
a combination thereof.
10. The device of claim 1, wherein the AI microservice adapter is configured to adapt the AI model based on configuration data, and wherein the configuration data includes:
information regarding functions relating to an AI microservice;
information regarding inputs;
information regarding outputs;
identification of messages, to detect for on a bus associated with the first RIC, that call or utilize the functions;
identification of messages to deliver on the bus relating to the functions;
information regarding how messages are to be translated into calls;
information regarding how responses to messages from an AI microservice are to be translated and placed onto the bus;
information regarding endpoints of an AI microservice;
information regarding a location at which an AI microservice is running; or
a combination thereof.
18. The method of claim 16, wherein the AI model provides one or more RAN-based microservices.
18. The method of claim 16, wherein the AI model provides one or more RAN-based microservices.
19. The method of claim 16, wherein receiving the AI model from the second AI model synchronization system is in accordance with one or more AI model subscriptions with the second AI model synchronization system.
1. responsive to the determining, receiving the AI model from the AI model synchronization system, wherein the receiving the AI model is in accordance with one or more AI model subscriptions with the AI model synchronization system; and
20. The method of claim 16, wherein the first AI model synchronization system, the first RAN, and the first RIC are operated by a first network provider, and wherein the second AI model synchronization system, the second RAN, and the second RIC are operated by a second network provider different from the first network provider.
1. obtaining, from an artificial intelligence (AI) model synchronization system associated with a second RAN, data relating to an AI model deployed wherein the processing system, the first RAN, and the first RIC are operated by a first network provider, wherein the AI model synchronization system, the second RAN, and the second RIC are operated by a second network provider different from the first network provider, and wherein the processing system is communicatively coupled with the AI model synchronization system to facilitate exchanges of deployed AI models between the first network provider and the second network provider;
by a second RIC of the second RAN,
Claims 11-15 are rejected under substantially the same rationale as claims 1-5.
Claims 16-17 are rejected under substantially the same rationale as claims 1-2.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bega et al. (US 20240046148) discloses to train the AI/ML model that is being sent to the AI/ML model consumer for inference (AI/ML inference model, also mentioned as machine learning inference model). If the ground truth data (i.e., real outputs of respective inputs) is not available to evaluate the performance of the inference model, the 5GS is also responsible to train another AI/ML model (AI/ML monitoring model, also mentioned as machine learning monitoring model) to profile the dataset used to train the AI/ML inference model.
Wang et al. (US 20220247634) discloses a Non-real time RAN intelligent controller (Non-RT RIC) is a logical entity built into SMO, which supports non-real-time control and optimization of RAN network elements and resources, artificial intelligence (AI)/machine learning (ML) model establishing, reasoning and updating, RAN is optimized through data analysis and AI/ML training/reasoning, and it provides strategic guidance and ML model management to the near-real Time RAN intelligent controller (Near-RT RIC) through the AI interface.
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/A.L.L/Examiner, Art Unit 2413
/UN C CHO/Supervisory Patent Examiner, Art Unit 2413
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