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 .
Detailed Action
The action is responsive to the application filed on July 8th of 2024.
This application is a national stage of PCT/IB2022/056753 filed on 01/13/2022.
Status of Claims
Applicant filed a preliminary amendment on 12/23/2024, where Applicant canceled claims 1-54 and added new claims 55-80. Claims 55-80 remain pending examination.
Drawings
Drawings filed on 07/08/2024 are acknowledged.
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.
Claims 55-58-60, 63--67, 70, 73-76, and 79-80 rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20200050951 A1) in view of Lee et al. (US 20200322821 A1).
Regarding Claim 55, Wang teaches a method of controlling the provision of a Machine Learning (ML) model, by a consumer of the ML model, in a wireless communication network, comprising:
transmitting, to the selected data analytics network function, a service request related to an ML model, wherein the request includes parameters specifying the provisioning of the ML model; (See in Wang, Fig. 5, Elem: 506 and 508, ¶50-51, which teaches where the model requester will send specifications to the edge nodes for them to match) .and receiving from the selected data analytics network function a response to the request, the response including information about the ML model preparation status and an estimated time for providing the requested service related to the ML model. (See in Wang, Fig. 5, Elem: 512, ¶51, which teaches where the model requester receives replies from the edge nodes on how they match the specifications, and ¶54 which teaches the price the model requester needs to pay, the price includes the availability of CPU time for the task)
Wang fails to explicitly teach selecting a data analytics network function capable of providing the ML model and that supports consumer control of the ML model provisioning;
However, Lee is in the same field of invention of data analytics network functions and support analytics (See in Lee, ¶68).
Lee discloses a Network Data Analytics Function (NWDAF) (the claimed data analytics network function) selection function where the consumer NF (the claimed consumer) can use the NRF to discover multiple available NWDAF and select which NWDAF to use. (See in Lee, ¶76)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Wang’s invention to include selecting a data analytics network function. In doing so, it would provide an ease of access for the consumer to determine which data analytics network function they want to use.
Regarding claim 56, Wang fails to disclose the method of claim 55, further comprising, prior to selecting a data analytics network function:
discovering, in the network, one or more data analytics network functions capable of providing the ML model and that supports consumer control of the ML model provisioning;
and selecting the data analytics network function from among those discovered.
However, Lee is in the same field of invention of data analytics network functions and support analytics (See in Lee, ¶68).
Lee discloses where the service consume to can discover which NWDAF to select which NWDAF to utilize based on the available NWDAF information available. (See in Lee, ¶76). One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Wang based on the teachings of Lee in accordance to the rationale given for claim 55.
Regarding claim 57, Wang teaches the method of claim 55, further comprising, after transmitting the request and prior to receiving the response:
receiving from the selected data analytics network function information about sharable artifacts related to the ML model; (See in Wang, Fig. 5, Elem: 506 and 508, ¶50-51, which teaches where the model requester will send specifications to the edge nodes to match).
in response to the sharable artifacts not matching predetermined requirements of the consumer, terminating the request; (See in Wang, ¶55, which teaches where the model requesters determines whether to retain the node at the given price, negotiate the price, or exclude the node)
and in response to the sharable artifacts matching predetermined requirements of the consumer, waiting for the response from the selected data analytics network function. (See in Wang, ¶51, which teaches where the model requester receives a reply from each of the edge node to see if it matches the specification and can participate in the requester’s task).
Regarding claim 58, Wang teaches the method of claim 57, wherein the sharable artifacts comprise one or more of whether the consumer is internal or external to the network, application scenarios, analytics identification, identification of an initial model provider, and local policy. (See in Wang, ¶50 and ¶51, which teaches the specifications including input data format, number of output classes, and an example if a node only has audio data, it cannot participate in a task (the claimed application scenario) for image classification)
Regarding claim 59, Wang fails to teach the method of claim 56, wherein discovering one or more data analytics network functions that supports consumer control of the ML model provisioning comprises sending a discovery request to a network repository function wherein the discovery request includes parameters specifying characteristics of the data analytics network function, the ML model, or the ML model provisioning.
However, Lee is in the same field of invention of data analytics network functions and support analytics (See in Lee, ¶68).
Lee discloses that when NF discovers NWDAF (the claimed data analytics network function), each NWDAF should provide the list of Analytic ID(s) (the claimed characteristics) that it supports, and specific type of analytics it can query to the NRF (the claimed characteristics) (See in Lee, ¶63). One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Wang based on the teachings of Lee in accordance to the rationale given for claim 55.
Regarding claim 60, Wang teaches the method of claim 59, wherein the parameters specifying characteristics of the data analytics network function, the ML model, or the ML model provisioning comprise one or more of a capability of the data analytics network function to support consumer control of ML model training, supported ML model learning architectures, a capability of the data analytics network function to output intermediate results during ML model training, and whether an initial ML model structure is may be provided by the consumer. (See in Wang, ¶50-51, which teaches specifications of a machine learning model, which can include input data format, number of output classes, which nodes can participate in specific tasks, and checking of commutation and communication resources).
Regarding claim 63, Wang teaches a Machine Learning (ML) model consumer apparatus operative in or connected to a wireless communication network, comprising:
communication circuitry; (See in Wang, ¶80, which teaches a communication network) and processing circuitry operatively connected to the communication circuitry, the processing circuitry configured to (See in Wang, ¶80, which teaches a processing devices that are linked through a communication network)
transmit, to a selected data analytics network function capable of providing the ML model and that supports consumer control of the ML model provisioning, a service request related to an ML model, wherein the request includes parameters specifying the provisioning of the ML model (See in Wang, ¶50-51, which teaches a model requester sending out a request with parameters for matching nodes); and receive from the selected data analytics network function a response to the request, the response including information about the ML model preparation status and an estimated time for providing the requested service related to the ML model (See in Wang, ¶51, which teaches the model requester receiving replies from the nodes, whether they match the specification and can participate in the specific request).
Regarding Claim 70, a method, by a data analytics network function operative in a wireless communication network, of providing a Machine Learning (ML) model according to specifications of a consumer of the ML model, comprising:
receiving, from an ML model consumer, a service request related to provisioning of an ML model, the request including parameters specifying the provisioning of the ML model; (See in Wang, ¶50-51, which teaches where the NWDAF receives a request with specifications to match for a ML model)determining a time required to provide the requested service; (See in Wang, ¶54 which teaches the price the model requester needs to pay, the price includes the availability of CPU time for the task)transmitting, to the ML model consumer, the determined time. (See in Wang, ¶54 which then sends the price to the model requester node)
Regarding Claim 73, the method of claim 70, wherein the parameters specifying the requested service related to an ML model comprise a learning architecture being a Distributed Machine Learning/Federated Learning (DML/FL) architecture, and further comprising:
and provisioning the selected client data analytics network functions with one of initial DML/FL parameters and requirement on time window for local model reporting. (See in Wang, ¶65, which teaches the distribution of recent parameters of the ML model to the edge nodes, receiving the updates, and establishing new parameters)
Wang fails to teach discovering one or more client data analytics network function to utilize in DML/FL ML model training; selecting one or more discovered client data analytics network functions;
However, Lee is in the same field of invention of data analytics network functions and support analytics (See in Lee, ¶68).
Lee teaches where the service consume can discover and select an available NWDAF based on the information provided. (See in Lee, ¶76). One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Wang based on the teachings of Lee in accordance to the rationale given for claim 55.
Regarding Claim 74, Wang teaches the method of claim 73, further comprising:
receiving local model information from client data analytics network functions; (See in Wang, ¶51, which teaches where the edge nodes check if it matches the specifications and resources, and returns which nodes match)
performing model aggregation on the received local model information; (See in Wang, ¶57, which teaches collating and aggregating the updated model parameters from each retained edge node).
judging training status; (See in Wang, ¶55, which teaches where the model requesters determines whether to retain the node at the given price, negotiate the price, or exclude the node)
and updating the ML model consumer on ML model training status. (See in Wang, ¶52, which teaches after receiving the latest model, the model requester performs a one-step update)
Regarding claim 75, Wang teaches a network node implementing a data analytics network function in a wireless communication network, comprising:
communication circuitry; (See in Wang, ¶80, which teaches a communications network)
and processing circuitry operatively connected to the communication circuitry, (See in Wang, ¶80, which teaches a processing device connection to the communication network)
the processing circuitry configured to receive, from a Machine Learning (ML) model consumer, a service request related to provisioning of an ML model, the request including parameters specifying the provisioning of the ML model; (See in Wang, ¶50-51, which teaches a model requester sending a request with specifications for a task)
determine a time required to provide the requested service; (See in Wang, ¶54, which teaches where the price is derived from the availability in the CPU time)
transmit, to the ML model consumer, the determined time. (See in Wang, ¶54, which teaches where the price is derived from the availability in the CPU time)
Claims 64-67, 70, 71, 76 79, and 80 are a different statutory category of, and are slight variations of the rejected claims 55-58-60, 63, 70, and 73-75 above. Therefore claims 64, 66-67, 70, 71, 76 79, and 80 are rejected based on the same rationale given for 55-56, 58-60, 63, 70, and 73-75 above.
Claims 61, 68, 77 and 78 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20200050951 A1) in view of Lee et al. (US 20200322821 A1) and further in view of Srinivasan et al. (US 20220237208 A1).
Regarding claim 61, Wang and Lee fails to teach the method of claim 55, wherein the parameters specifying the provisioning of the ML model comprise one or more of a time window for intermediate or final outputs, an accuracy level that can be achieved by the ML model, a learning architecture, output strategy for intermediate results during ML model training, information about an initial ML model provider, an initial ML model structure, an indication whether the learning is online or offline, and information about data source(s).
However, Srinivasan is in the same field of training a machine learning model. (See in Srinivasan, ¶9)
Srinivasan discloses where the parameters can include threshold for accuracy, new relevant variables, and prediction error range for upper and lower limit range, to identify and/or select a candidate ML model (See in Srinivasan, ¶48). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Wang’s invention to include the specification of a threshold for accuracy to identify/select a candidate machine learning model. In doing so, it would provide an ease of access for the data analytics network function to narrow down the choices based on accuracy.
Regarding claim 78, Wang teaches the network node of claim 75, wherein the parameters specifying the requested service related to an ML model (See in Wang, ¶50-51, which teaches where the model requester will send specifications to the edge nodes for them to match)
Wang and Lee fail to teach comprising one or more of a time window for intermediate or final outputs, an accuracy level that can be achieved by the ML model, a learning architecture, output strategy for intermediate results during ML model training, information about an initial ML model provider, an initial ML model structure, an indication whether the learning is online or offline, and information about data source(s).
However, Srinivasan is in the same field of training a machine learning model. (See in Srinivasan, ¶9)
Srinivasan discloses where the parameters can include threshold for accuracy, new relevant variables, and prediction error range for upper and lower limit range, to identify and/or select a candidate ML model (See in Srinivasan, ¶48). One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Wang based on the teachings of Srinivasan in accordance to the rationale given for claim 61.
Claims 68 and 77 are a different statutory category of, and are slight variations of the rejected claim 61 above. Therefore claims 68 and 77 are rejected based on the same rationale given for 61 above.
Claims 62, 69, and 72 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20200050951 A1) in view of Lee et al. (US 20200322821 A1) and further in view of Ali et al. (US 20240028961 A1).
Regarding claim 62, Wang and Lee fail to teach the method of claim 55, further comprising, after receiving the response from the selected data analytics network function, receiving, from the selected data analytics network function, a status update regarding training of the ML model, wherein the status update comprises one or more of an accuracy that can be achieved by the current trained ML model, an indication whether training of the ML model has converged, and an indication of the time or percentage of task to complete training the ML model.
However, Ali is in the same field of invention of federated machine learning and improving their capabilities (See in Ali, ¶15).
Ali discloses a training status request message, there it is shown how much more training the ML model requires before competition, how much the ML model is trained, and a probability or other indication that the ML model training will be completed before a deadline. (See in Ali, ¶113). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Wang’s invention to include the specification of indication of the time or percentage of task to complete the training the ML model. In doing so, it would make monitoring and selecting which ML model to choose easier for the consumer.
Claims 69 and 72 are a different statutory category of, and are slight variations of the rejected claim 62 above. Therefore claims 69 and 72 are rejected based on the same rationale given for 61 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CELINE AYLIN IMANI whose telephone number is (571)270-0247. The examiner can normally be reached 8am-5pm.
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, Ario Etienne can be reached at 571-272-4001. 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.
/CELINE AYLIN IMANI/ 07/22/2026 Examiner, Art Unit 2457
/ARIO ETIENNE/Supervisory Patent Examiner, Art Unit 2457
Prosecution Insights