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 .
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, 8, 11, 15-16, 18 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Duan (US 2024/0396811 A1).
Re Claims 1 and 18, Duan discloses a network element registration method, and first network element, comprising a processor and a memory, wherein the memory stores a program or instructions executable on the processor (memory and processor [0146]), and the program or instructions, when executed by the processor, cause the first network element to perform:
sending, by a first network element, a network element registration request to a second network element (NWDAF sends a registration request to the network database function NRF to register its own information (NWDAF profile) [0531]), wherein the network element registration request is used to register information of the first network element to the second network element, and the information of the first network element comprises model training related capability information of the first network element (If NWDAF supports Federated Learning (FL), it can include the following FL-related information: 1) NWDAF's FL capabilities, including specific information about FL supported by NWDAF [0531]-[0532]), wherein
the model training related capability information of the first network element comprises at least one of the following:
model platform information supported by the first network element; model description method information supported by the first network element; vendor information of the first network element; network element object information for which a model of the first network element can be shared; first indication information, wherein the first indication information is used to indicate whether the first network element supports model sharing or model interoperation with other network elements; model training speed information of the first network element (NWDAF's FL capabilities, including specific information about FL supported by NWDAF, for example: for each FL global model that NWDAF can provide, its applicable applications, services or scenarios (such as image recognition, remote control, Internet of Vehicles communications (such as image recognition, remote control, Internet of Vehicles communications) Vehicle to Everything (V2X), etc.), area, time period, model type (such as a specific deep neural network type), and/or, accuracy or confidence level, etc. [0532]).
Re Claim 8, Duan discloses the method according to claim 1, wherein the network element object information comprises at least one of vendor information, batch information, address information, or identification information of the network element object (the information of the first functional entity includes one or more of: identification information, address information and location information of the first functional entity [0049]).
Re Claim 11, Duan discloses a network element registration method, wherein the method comprises:
receiving, by a second network element, a network element registration request sent by a first network element (NWDAF sends a registration request to the network database function NRF to register its own information (NWDAF profile) [0531]), wherein the network element registration request is used to register information of the first network element to the second network element, and the information of the first network element comprises model training related capability information of the first network element (If NWDAF supports Federated Learning (FL), it can include the following FL-related information: 1) NWDAF's FL capabilities, including specific information about FL supported by NWDAF [0531]-[0532]), wherein
the model training related capability information of the first network element comprises at least one of the following:
model platform information supported by the first network element; model description method information supported by the first network element; vendor information of the first network element; network element object information for which a model of the first network element can be shared; first indication information, wherein the first indication information is used to indicate whether the first network element supports model sharing or model interoperation with other network elements; model accuracy information reachable by the first network element; or model training speed information of the first network element (NWDAF's FL capabilities, including specific information about FL supported by NWDAF, for example: for each FL global model that NWDAF can provide, its applicable applications, services or scenarios (such as image recognition, remote control, Internet of Vehicles communications (such as image recognition, remote control, Internet of Vehicles communications) Vehicle to Everything (V2X), etc.), area, time period, model type (such as a specific deep neural network type), and/or, accuracy or confidence level, etc. [0532]).
Re Claim 15, Duan discloses the method according to claim 11, wherein after the receiving, by the second network element, a discovery request sent by a third network element, the method further comprises: determining, by the second network element, the target network element based on the first information in the discovery request (which carries one or more of the above service or application-related federated learning parameters, such as federated learning Application ID, UE ID list or UE Group ID, area, S-NSSAI, DNN, application features (Features), etc., based on which NRF replies to PCF with NF information that supports the federated learning (such as NF type, NF instance ID, etc.). [0576]).
Re Claim 16, Duan discloses the method according to claim 15, wherein the determining, by the second network element, the target network element based on the first information in the discovery request comprises at least one of the following: determining, by the second network element, the target network element based on the algorithm information used for model training by the target network element as required by the third network element, wherein the target network element supports the algorithm information used for model training as required by the third network element; determining, by the second network element, the target network element based on the vendor information of the target network element as required by the third network element, wherein the target network element supports the vendor information required by the third network element; determining, by the second network element, the target network element based on the vendor information of the third network element, wherein network element object information for which a model of the target network element can be shared comprises the vendor information of the third network element; determining, by the second network element, the target network element based on the constraint condition information for sharing a model by the target network element as required by the third network element, wherein the target network element supports sharing model under a constraint condition; determining, by the second network element, the target network element based on the identification information of the third network element, wherein the network element object information for which the model of the target network element can be shared matches the identification information of the third network element; or determining, by the second network element, the target network element based on the indication information for model sharing, wherein the target network element supports model sharing or model interoperation with other network elements (which carries one or more of the above service or application-related federated learning parameters, such as federated learning Application ID, UE ID list or UE Group ID, area, S-NSSAI, DNN, application features (Features), etc., based on which NRF replies to PCF with NF information that supports the federated learning (such as NF type, NF instance ID, etc.). [0576]).
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.
Claim(s) 2-5, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Duan (US 2024/0396811 A1) in view of Kim et al. (US 2023/0254719 A1) (Kim herein after).
Re Claims 2 and 19, Duan discloses the method according to claim 1 and the first network element according to claim 18, except wherein the model training related capability information of the first network element is indicated per analytics identifier (ID).
However, Kim discloses a communication method and system with machine learning model wherein the local NWDAF may receive from the NF (e.g., the AMF) an analytics request message (an analytics ID, a FL execution request indicator, an NF provision data share permission indicator, data distribution information obtained or provided by NF, an analytics service provision time requirement, an analytics service accuracy requirement and so on may be included in the request message) ([0084]).
Therefore, it would have been obvious at the time the invention was made to one of ordinary skill in the art to modify the method and system of Duan, by making use of the technique taught by Kim, in order to improve the data analysis accuracy.
Both references are within the same field of telecommunication, and in particular of machine learning, the modification does not change a fundamental operating principle of Duan, nor does Duan teach away from the modification (Duan merely discloses a preferred embodiment). The combination has a reasonable expectation of success in that the modifications can be made using conventional and well known engineering and/or programming techniques, the machine learning in communication taught by Kim is not altered and continues to perform the same function as separately, and the resultant combination produces the highly predictable result of wherein the model training related capability information of the first network element is indicated per analytics identifier (ID).
Re Claim 3, the combined teachings disclose the method according to claim 2, Kim discloses wherein the model training related capability information corresponding to a first analytics ID indicates that the first network element supports sharing a model corresponding to the first analytics ID with other network elements and indicates a network element object for which the model corresponding to the first analytics ID can be shared (NF provides the FL execution request indicator, or an NF provision data no share indicator (an NF provision data share permission indicator indicating “not permitted”) [0084]).
Re Claim 4, Duan discloses the method according to claim 1, except wherein the information of the first network element further comprises data analytics identification information corresponding to the model training related capability information of the first network element.
However, Kim discloses a communication method and system with machine learning model wherein the local NWDAF may receive from the NF (e.g., the AMF) an analytics request message (an analytics ID, a FL execution request indicator, an NF provision data share permission indicator, data distribution information obtained or provided by NF, an analytics service provision time requirement, an analytics service accuracy requirement and so on may be included in the request message) ([0084]).
Therefore, it would have been obvious at the time the invention was made to one of ordinary skill in the art to modify the method and system of Duan, by making use of the technique taught by Kim, in order to improve the data analysis accuracy.
Both references are within the same field of telecommunication, and in particular of machine learning, the modification does not change a fundamental operating principle of Duan, nor does Duan teach away from the modification (Duan merely discloses a preferred embodiment). The combination has a reasonable expectation of success in that the modifications can be made using conventional and well known engineering and/or programming techniques, the machine learning in communication taught by Kim is not altered and continues to perform the same function as separately, and the resultant combination produces the highly predictable result of wherein the information of the first network element further comprises data analytics identification information corresponding to the model training related capability information of the first network element.
Re Claim 5, Duan discloses the method according to claim 1, except wherein the model training related capability information of the first network element further comprises at least one of the following: data analytics identification information for which the first network element supports model sharing; or model index information for which the first network element supports model sharing.
However, Kim discloses a communication method and system with machine learning model wherein the local NWDAF may receive from the NF (e.g., the AMF) an analytics request message (an analytics ID, a FL execution request indicator, an NF provision data share permission indicator, data distribution information obtained or provided by NF, an analytics service provision time requirement, an analytics service accuracy requirement and so on may be included in the request message) ([0084]).
Therefore, it would have been obvious at the time the invention was made to one of ordinary skill in the art to modify the method and system of Duan, by making use of the technique taught by Kim, in order to improve the data analysis accuracy.
Both references are within the same field of telecommunication, and in particular of machine learning, the modification does not change a fundamental operating principle of Duan, nor does Duan teach away from the modification (Duan merely discloses a preferred embodiment). The combination has a reasonable expectation of success in that the modifications can be made using conventional and well known engineering and/or programming techniques, the machine learning in communication taught by Kim is not altered and continues to perform the same function as separately, and the resultant combination produces the highly predictable result of wherein the model training related capability information of the first network element further comprises at least one of the following: data analytics identification information for which the first network element supports model sharing; or model index information for which the first network element supports model sharing.
Re Claim 20, Duan discloses the first network element according to claim 18, except wherein the model training related capability information corresponding to a first analytics ID indicates that the first network element supports sharing a model corresponding to the first analytics ID with other network elements and indicates a network element object for which the model corresponding to the first analytics ID can be shared.
However, Kim discloses a communication method and system with machine learning model wherein NF provides the FL execution request indicator, or an NF provision data no share indicator (an NF provision data share permission indicator indicating “not permitted”) ([0084]).
Therefore, it would have been obvious at the time the invention was made to one of ordinary skill in the art to modify the method and system of Duan, by making use of the technique taught by Kim, in order to improve the data analysis accuracy.
Both references are within the same field of telecommunication, and in particular of machine learning, the modification does not change a fundamental operating principle of Duan, nor does Duan teach away from the modification (Duan merely discloses a preferred embodiment). The combination has a reasonable expectation of success in that the modifications can be made using conventional and well known engineering and/or programming techniques, the machine learning in communication taught by Kim is not altered and continues to perform the same function as separately, and the resultant combination produces the highly predictable result of wherein the model training related capability information corresponding to a first analytics ID indicates that the first network element supports sharing a model corresponding to the first analytics ID with other network elements and indicates a network element object for which the model corresponding to the first analytics ID can be shared.
Claim(s) 6, 9-10, 12-14, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Duan (US 2024/0396811 A1) in view of Wei et al. (US 2022/0337487 A1) (Wei herein after).
Re Claim 6, Duan discloses the method according to claim 1, except wherein the information of the first network element further comprises at least one of the following: algorithm information for model training used by the first network element; constraint condition information for sharing a model by the first network element; or network element information for storing the model trained by the first network element.
However, Wei discloses a network entity for determining a model for digitally analyzing input data wherein network entity is configured to send and/or receive information on the model with the model request, which includes at least one of the following: model type, machine-leaning training algorithm, Analytics ID, feature sets, input data type particular event ID, area of interest, application ID, information on the model, in particular from the requesting network entity, model ID, in particular model version, a model time, in particular a time stamp ([0036]).
Therefore, it would have been obvious at the time the invention was made to one of ordinary skill in the art to modify the method and system of Duan, by making use of the technique taught by Wei, in order to improve the data analysis accuracy.
Both references are within the same field of telecommunication, and in particular of machine learning, the modification does not change a fundamental operating principle of Duan, nor does Duan teach away from the modification (Duan merely discloses a preferred embodiment). The combination has a reasonable expectation of success in that the modifications can be made using conventional and well known engineering and/or programming techniques, the machine learning in communication taught by Wei is not altered and continues to perform the same function as separately, and the resultant combination produces the highly predictable result of wherein the information of the first network element further comprises at least one of the following: algorithm information for model training used by the first network element; constraint condition information for sharing a model by the first network element; or network element information for storing the model trained by the first network element.
Re Claim 9, the combined teachings disclose the method according to claim 6, wherein the constraint condition information for sharing a model by the first network element comprises at least one of the following: time constraint condition information or area constraint condition information, wherein the time constraint condition information is used to indicate a time when the first network element is allowed to share the model, and the area constraint condition information is used to indicate an area in which the first network element is allowed to share the model (Wei discloses algorithm information for model training used by the first network element).
Re Claim 10, the combined teachings disclose the method according to claim 6, wherein the network element information for storing the model trained by the first network element is used to indicate a network element for storing the model trained by the first network element (Wei discloses algorithm information for model training used by the first network element).
Re Claim 12, Duan discloses the method according to claim 11, wherein after the receiving, by a second network element, a network element registration request sent by a first network element, the method further comprises:
receiving, by the second network element, a discovery request sent by a third network element, wherein the discovery request is used to request to discover a target network element, and the discovery request comprises at least one of the following: model training related capability information required by the third network element or first information (PCF sends an NF discovery request Nnrf_NFDiscovery_Request to NRF, which carries one or more of the above service or application-related federated learning parameters, such as federated learning Application ID, UE ID list or UE Group ID, area, S-NSSAI, DNN, application features (Features), etc., based on which NRF replies to PCF with NF information that supports the federated learning (such as NF type, NF instance ID, etc.) [0576]), wherein
the model training related capability information required by the third network element comprises at least one of the following:
model platform information supported by the target network element as required by the third network element, model description method information supported by the target network element as required by the third network element, model accuracy information reachable by the target network element as required by the third network element, or model training speed information of the target network element as required by the third network element (such as federated learning Application ID, UE ID list or UE Group ID, area, S-NSSAI, DNN, application features (Features), etc., based on which NRF replies to PCF with NF information that supports the federated learning (such as NF type, NF instance ID, etc.) [0576]).
Duan discloses the claimed invention except the first information comprises at least one of the following: algorithm information for model training used by the target network element as required by the third network element, vendor information of the target network element as required by the third network element, constraint condition information for sharing a model by the target network element as required by the third network element, identification information of the third network element, indication information for model sharing, or vendor information of the third network element wherein the constraint condition information for sharing a model by the target network element as required by the third network element comprises at least one of the following: time constraint information or area constraint information.
However, Wei discloses a network entity for determining a model for digitally analyzing input data wherein network entity is configured to send and/or receive information on the model with the model request, which includes at least one of the following: model type, machine-leaning training algorithm, Analytics ID, feature sets, input data type particular event ID, area of interest, application ID, information on the model, in particular from the requesting network entity, model ID, in particular model version, a model time, in particular a time stamp ([0036]).
Therefore, it would have been obvious at the time the invention was made to one of ordinary skill in the art to modify the method and system of Duan, by making use of the technique taught by Wei, in order to improve the data analysis accuracy.
Both references are within the same field of telecommunication, and in particular of machine learning, the modification does not change a fundamental operating principle of Duan, nor does Duan teach away from the modification (Duan merely discloses a preferred embodiment). The combination has a reasonable expectation of success in that the modifications can be made using conventional and well known engineering and/or programming techniques, the machine learning in communication taught by Wei is not altered and continues to perform the same function as separately, and the resultant combination produces the highly predictable result of wherein the first information comprises at least one of the following: algorithm information for model training used by the target network element as required by the third network element, vendor information of the target network element as required by the third network element, constraint condition information for sharing a model by the target network element as required by the third network element, identification information of the third network element, indication information for model sharing, or vendor information of the third network element wherein the constraint condition information for sharing a model by the target network element as required by the third network element comprises at least one of the following: time constraint information or area constraint information.
Re Claim 13, the combined teachings disclose the method according to claim 12, Duan discloses wherein after the receiving, by the second network element, a discovery request sent by a third network element, the method further comprises: determining, by the second network element, the target network element based on the discovery request, wherein model training related capability information of the target network element matches the model training related capability information required by the third network element (which carries one or more of the above service or application-related federated learning parameters, such as federated learning Application ID, UE ID list or UE Group ID, area, S-NSSAI, DNN, application features (Features), etc., based on which NRF replies to PCF with NF information that supports the federated learning (such as NF type, NF instance ID, etc.). [0576]).
Re Claim 14, the combined teachings disclose the method according to claim 13, Duan discloses wherein the determining, by the second network element, the target network element based on the discovery request comprises at least one of the following: determining, by the second network element, the target network element based on the model platform information supported by the target network element as required by the third network element, wherein the target network element supports the model platform information required by the third network element; determining, by the second network element, the target network element based on the model description method information supported by the target network element as required by the third network element, wherein the target network element supports the model description method information required by the third network element; determining, by the second network element, the target network element based on the model accuracy information reachable by the target network element as required by the third network element, wherein the target network element supports the reachable model accuracy information required by the third network element; or determining, by the second network element, the target network element based on the model training speed information of the target network element as required by the third network element, wherein the target network element supports the model training speed information required by the third network element (which carries one or more of the above service or application-related federated learning parameters, such as federated learning Application ID, UE ID list or UE Group ID, area, S-NSSAI, DNN, application features (Features), etc., based on which NRF replies to PCF with NF information that supports the federated learning (such as NF type, NF instance ID, etc.). [0576]).
Re Claim 17, the combined teachings disclose the method according to claim 13, Duan discloses wherein after the determining, by the second network element, the target network element based on the discovery request, the method further comprises: sending, by the second network element, a discovery response to the third network element, wherein the discovery response comprises target information, and the target information comprises at least one of the following: identification information of the target network element, address information of the target network element, the model platform information supported by the target network element, the model description method information supported by the target network element, the model accuracy information reachable by the target network element, the model training speed information of the target network element, the algorithm information for model training used by the target network element, the vendor information of the target network element, the constraint condition information for sharing a model by the target network element, or network element information for storing a model trained by the target network element (which carries one or more of the above service or application-related federated learning parameters, such as federated learning Application ID, UE ID list or UE Group ID, area, S-NSSAI, DNN, application features (Features), etc., based on which NRF replies to PCF with NF information that supports the federated learning (such as NF type, NF instance ID, etc.). [0576]).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Duan (US 2024/0396811 A1) in view of Hong et al. (US 2022/0329493 A1) (Hong herein after).
Re Claim 7, Duan discloses the method according to claim 1, except wherein the network element object information for which the model of the first network element can be shared comprises at least one of the following: information of one or more model inference function network elements or information of one or more model training function network elements.
However, Hong discloses a method and apparatus for data analytics wherein A capability of the NWDAF includes at least one of: data collection, which refers to a capability of acquiring data from various 3GPP network functions NF, OAM, or AF; analytics function exposure, which refers to a capability of the NWDAF exposing its own service capability to an NWDAF service consumer; AI Model Management, which means that the NWDAF may have a capability including at least one of: AI model management: including the ability of acquiring, storing, deleting/updating AI model information locally stored at the NWDAF, the AI model information may include at least one of the following parameters: an AI model catalog, an AI model file, or AI model related parameter information (such as model parameter input/output methods, or the like); or AI model instance management: including at least one of the following parameters: model lifecycle management, running status monitoring (such as a model running state or a model stopped state, a number of model callings, accuracy/precision), or the like; AI Model Exposure, which refers to a capability of exposing a model to another model consumer; AI Inference Engine, which refers to a capability of desired software and/or hardware for running an AI model; or AI Training Engine, which refers to a capability of desired software and/or hardware for training an AI model. ([0142]-[0150]).
Therefore, it would have been obvious at the time the invention was made to one of ordinary skill in the art to modify the method and system of Duan, by making use of the technique taught by Hong, in order to improve the data analysis accuracy.
Both references are within the same field of telecommunication, and in particular of machine learning, the modification does not change a fundamental operating principle of Duan, nor does Duan teach away from the modification (Duan merely discloses a preferred embodiment). The combination has a reasonable expectation of success in that the modifications can be made using conventional and well known engineering and/or programming techniques, the machine learning in communication taught by Hong is not altered and continues to perform the same function as separately, and the resultant combination produces the highly predictable result of wherein the network element object information for which the model of the first network element can be shared comprises at least one of the following: information of one or more model inference function network elements or information of one or more model training function network elements.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Xin et al. (US 2023/0388389 A1) – communication methods and apparatus for service network request
Lee et al. (US 2022/0108214 A1) – management method of machine learning model for network data analytics function device
Guo et al. (US 2024/0414523 A1) – terminal device, first network element and second network element for artificial intelligence service
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNETH T LAM whose telephone number is (571)270-1862. The examiner can normally be reached M-F 8:30-5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hannah S. Wang can be reached at (571) 272-9018. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KENNETH T LAM/Primary Examiner, Art Unit 2631
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