DETAILED ACTION
This Office Action is in response to the Preliminary Amendments filed May 9, 2025. Claim(s) 10, 11, 13, and 15 have been amended. Claim(s) 1-16 is/are pending and have been considered as follows.
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 .
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.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/30/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-16 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Norrman et al. (US 2022/0294706 A1, hereinafter Norrman).
As to Claim 1, Norrman discloses a machine learning (ML) orchestrator entity for a ML system comprising one or more local learning agents (LLAs) configured to jointly compute an analytics output for an analytics service, wherein the ML orchestrator entity comprises first processing circuitry configured to:
receive an analytics service request for the analytics service from a consumer entity ((Norrman; [0029]), where Norrman discloses network data analytics information is provided by the NWDAF.);
define a ML profile for the analytics service based on the analytics service request ((Norrman; [0032-0035, 0079-0085]), where Norrman discloses the NWDAF initiates and manages all of the local ML models as well as combines their updates.); and
determine ML job information based on the ML profile, wherein the ML job information comprises, for each LLA of the one or more LLAs, a computation operation to be performed by that LLA to compute the analytics output ((Norrman; [0082-0085]), where Norrman discloses the NWDAF selects, deploys and triggers the computations of all the local ML models.).
As to Claim 2, Norrman discloses the ML orchestrator entity according to claim 1, wherein the first processing circuitry is configured to define the ML profile for the analytics service further based on one or more LLA constrains ((Norrman; [0032-0035, 0079-0085]), where Norrman discloses the nodes and local ML models are selected function of selection criteria, which are constrains.).
As to Claim 3, Norrman discloses the ML orchestrator entity according to claim 1, wherein the ML job information comprises interface configuration information, wherein the interface configuration information indicates from which LLA of the one or more LLAs each LLA of the one or more LLAs is configured to receive a partial analytics output, and to which LLA of the one or more LLAs each LLA of the one or more LLAs is configured to send a partial analytics output ((Norrman; [0042-0043]), where Norrman discloses the collaborative learning and the management of the local ML models by the NWDAf network entity implies the claim interface confirmation. Defining how the data from the local ML models is combined in the collaborative learning.).
As to Claim 4, Norrman discloses the ML orchestrator entity according to claim 1, wherein the ML orchestrator entity is further configured to determine if the ML job information for the ML profile already exists, and in response to the ML job information exists, the ML orchestrator entity is configured to retrieve the existing ML job information, and in response to the ML job information does not exist, the ML orchestrator entity is configured to create the ML job information based on the ML profile ((Norrman; [0071-0085]), where Norrman discloses the computational resources are selected function of some selection criteria mapping the necessities of the ML algorithm to computations resources, the determination of the necessity implying determining the ML job information, its existence and allocation.).
As to Claim 5, Norrman discloses the ML orchestrator entity according to claim 1, wherein the ML orchestrator entity is configured to communicate to each LLA of the one or more LLAs information indicating the computation operation to be performed by that LLA according to the ML job information ((Norrman; [0071-0085]), where Norrman discloses the computational resources are selected function of some selection criteria mapping the necessities of the ML algorithm to computations resources, the determination of the necessity implying determining the ML job information, its existence and allocation.).
As to Claim 6, Norrman discloses the ML orchestrator entity according to claim 1, wherein the ML orchestrator entity is configured to compute a ML job graph as the ML job information, wherein the ML job graph defines a set of parameters and corresponding functions related to the computation operation for each LLA of the ML system ((Norrman; [0071-0085]), where Norrman discloses the computational resources are selected function of some selection criteria mapping the necessities of the ML algorithm to computations resources, the determination of the necessity implying determining the ML job information, its existence and allocation.).
As to Claim 7, Norrman discloses the ML orchestrator entity according to claim 1, wherein the ML orchestrator entity is configured to collaborate with the one or more LLAs to compare a performance of the analytics output for the analytics service with an expected performance ((Norrman; [0029, 0063-0064, 0066]), where Norrman discloses performance information such as the ratio of successful handovers to failed handovers, ratio of successful setup sessions, resource usage, etc.).
As to Claim 8, Norrman discloses the ML orchestrator entity according to claim 7, wherein the ML orchestrator entity is configured to determine based on the compared performance whether a training or a retraining of the ML profile is needed, and to redefine the ML profile for the analytics service if the training or the retraining is needed ((Norrman; [0066]), where Norrman discloses training a preliminary model, afterwards the model can be retrained.).
As to Claim 9, Norrman discloses the ML orchestrator entity according to claim 1, wherein the analytics service request from the consumer entity comprises at least one of an analytics ID of the analytics service, or a requested ML model accuracy for the analytics service, or a requested ML technique for the analytics service ((Norrman; [0057, 0066]), where Norrman discloses an indication of the ML algorithm to be used. Metrics can be used to indicate accuracy and/or precision of the model.).
As to Amended Claim 10, Norrman discloses the ML orchestrator entity according to claim 1, wherein the ML orchestrator entity is further configured to:
trigger a training operation or a retraining operation of a ML model of the ML profile at one or more of the one or more LLAs for the analytics service based on at least one of the following information: one or more LLA IDs, each LLA ID indicating a LLA for computing their analytics output for the analytics service; a type of the analytics service; an expected analytics performance based on a requested ML model accuracy; a preferred ML technique for computing the analytics output; or data available at the one or more LLAs ((Norrman; [0084-0085]), where Norrman discloses the NWDAF selects the candidate network entities, initiates/triggers the training of a model using a ML algorithm as part of a collaborative learning process.).
As to Amended Claim 11, Norrman discloses the ML orchestrator entity according to claim 1, wherein the ML orchestrator entity is further configured to register the one or more LLAs in association with an analytics ID of the analytics service, wherein the ML orchestrator entity is configured to receive a registration message from each LLA of the one or more LLAs, the registration message comprising at least one of the following information: a LLA ID of the LLA, data available at the LLA, or one or more constraints of the LLA ((Norrman; [0006-0007, 0015, 0062]), where Norrman discloses network entities register themselves with the Network Function repository Function NRF. Each entity having and ID.).
As to Claim 12, Norrman discloses a local learning agent (LLA), for a machine learning (ML) system, the LLA comprising second processing circuitry configured to:
receive ML job information indicating a computation operation to be performed by the LLA ((Norrman; [0029]), where Norrman discloses network data analytics information is provided by the NWDAF.);
perform the computation operation based on the received ML job information to compute an analytics output for an analytics service ((Norrman; [0032-0035, 0079-0085]), where Norrman discloses the NWDAF initiates and manages all of the local ML models as well as combines their updates.); and
output the analytics output to a ML orchestrator entity or another LLA ((Norrman; [0082-0085]), where Norrman discloses the NWDAF selects, deploys and triggers the computations of all the local ML models.).
As to Amended Claim 13, Norrman discloses the LLA according to claim 12, wherein the LLA is further configured to:
train or retrain a ML model of a ML profile for the analytics service based on at least one of the following information: one or more LLA IDs, each LLA ID indicating a LLA for computing their analytics output for the analytics service; a type of the analytics service; an expected analytics performance based on a requested ML model accuracy; a preferred ML technique for computing the analytics output; or local input available at the one or more LLAs ((Norrman; [0084-0085]), where Norrman discloses the NWDAF selects the candidate network entities, initiates/triggers the training of a model using a ML algorithm as part of a collaborative learning process.).
As to Claim 14, Norrman discloses the LLA according to claim 12, wherein the LLA is configured to determine based on a compared performance whether a retraining of a ML profile defined by the ML orchestrator entity is needed, and inform the ML orchestrator entity accordingly ((Norrman; [0066]), where Norrman discloses training a preliminary model, afterwards the model can be retrained.).
As to Claim 15, Norrman discloses the LLA according to claim 12, wherein the LLA is configured to receive one or more inputs from other LLAs, each input comprising a partial analytics output for the analytics service; or the LLA is configured to compute its partial analytics output further based on one or more local inputs ((Norrman; [0042-0043]), where Norrman discloses the collaborative learning and the management of the local ML models by the NWDAf network entity implies the claim interface confirmation. Defining how the data from the local ML models is combined in the collaborative learning.).
As to Claim 16, Norrman discloses a method for a machine learning (ML) orchestrator entity for a ML system comprising one or more local learning agents (LLAs), configured to compute an analytics output for an analytics service, the method being performed by the ML orchestrator entity and comprising:
receiving an analytics service request for the analytics service from a consumer entity ((Norrman; [0029]), where Norrman discloses network data analytics information is provided by the NWDAF.);
defining a ML profile for the analytics service based on the analytics service request ((Norrman; [0032-0035, 0079-0085]), where Norrman discloses the NWDAF initiates and manages all of the local ML models as well as combines their updates.); and
determining ML job information based on the ML profile, wherein the ML job information comprises, for each LLA of the ML system, a computation operation to be performed by that LLA to compute the analytics output ((Norrman; [0082-0085]), where Norrman discloses the NWDAF selects, deploys and triggers the computations of all the local ML models.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
The examiner also requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENJAMIN M THIEU whose telephone number is (571) 270-7475 and fax number is (571) 270-8475. The examiner can normally be reached Monday - Friday: 8:00 AM - 5:00 PM EST.
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/BENJAMIN M THIEU/Primary Examiner, Art Unit 2446 6.18.2026
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