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
This Office Action is sent in response to Application’s Communication received on 05/31/2023 for application number 18/326885. The Office hereby acknowledges receipt of the following and placed of record in file: Specification, Drawing, Abstract, Oath/Declaration, and Claims.
Claims (1-10) and (11-20) are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 11/07/2024 was filed prior to current Office Action. 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 § 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 of this title, 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 1-20 are rejected under AIA 35 U.S.C. 103(a) as being unpatentable over da silva et al. US Patent Application Publication US 202200129786 A1 (hereinafter da silva) in view of in view of Xu, Ke et al. Foreign Patent Application Publication CN 114881229 A (hereinafter Xu).
Regarding claim 1, da silva teaches A method, comprising: for each federated learning simulation of a plurality of federated learning simulations (Abstract, [0001-0002], [0016-0020], [0042-0043] wherein da silva describes a system that includes a model simulation service that includes a plurality of first computer processors, wherein at least one first computer processor of the plurality of first computer processors is programmed to, for each federated learning simulation of a plurality of federated learning simulations, obtain a simulation configuration for the federated learning simulation, instantiate simulated nodes based on the simulation configuration, emulate learning interactions between the simulated nodes, probe the learning interactions to obtain a simulation analysis, and select, following a completion of the plurality of federated learning simulations, an optimal simulation configuration from a plurality of simulation configurations and based on a plurality of simulation analyses for the plurality of federated learning simulations) defining a machine learning model that is to be used in the federated learning simulation, the defined machine learning model having one or more associated variables, the defined machine learning model being implemented at one or more edge nodes of the federated learning simulation and at a central node of the federated learning simulation (FIG. 1, [0001], [0016-0021], [0042-0043] wherein da. Silva describes as illustrated in FIG. 1, defining a machine learning model for simulation that includes a central node and multiple nodes that represent worker nodes) defining a first variable list that specifies one or more of the associated variables that are to be optimized at the one or more edge nodes of the federated learning simulation (FIG. 2, [0029], [0043] wherein da silva optimizes variables and parameters for nodes with edges as illustrated in FIG. 1) and aggregating at the central node of the federated learning simulation the one or more associated variables that are included in the second variable list and that are provided to the central node of the federated learning simulation by the one or more edge nodes of the federated learning simulation ([0018], [0020], [0033], [0036] wherein da. Silva incorporates a learning aggregator for aggregating the parameters at central node and preforms a local optimization of a shared learning model using their respective local data. Thereafter, updates to the shared learning model, derived differently on each device based on different local data, may subsequently be forwarded to a federated learning coordinator (i.e., central node (104)), which aggregates and applies the updates to improve the shared learning model)
Da. Silva teaches defining a second variable list that specifies one or more of the associated variables that are to be provided by the one or more edge nodes of the federated learning simulation to the central node of the federated learning simulation ([0018], [0029], [0046] wherein da silva describes an execution configuration that refer to a collection of parameters, which may be used to define an execution runtime of a given federation learning simulation. These parameters may include, but are not limited to, a seed used to initialize the behavior of (and thus shared between) simulated nodes of the given federated learning simulation, a number of optimization samples (i.e., feature-target tuples) to reside on each simulated worker node, a batch size or a subset number of optimization samples per batch for training and/or validation purposes, a proportion of optimization samples to be used during a training stage of learning model optimizations, a proportion of optimization samples to be used during a validation stage of learning model optimizations, a proportion of optimization samples on each simulated worker node to be sent to the simulated central node for testing purposes, and a learning rate of the simulated central node and/or simulated worker nodes. An execution configuration is not limited to the aforementioned parameter examples).
Da. Silva does not teach optimizing the one or more associated variables included in the first variable list at the one or more edge nodes of the federated learning simulation.
However in analogous art of federated learning method, Xu teaches optimizing the one or more associated variables included in the first variable list at the one or more edge nodes of the federated learning simulation (Abstract, page. 2, paragraphs 2-4, page. 9, paragraphs 10-12, page. 10, paragraphs 1-2 wherein Xu describes parameters that are directed to edge nodes of the federated learning simulation, wherein the parameters include frozen parameters).
It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Da. Silva with Xu by incorporating the method of optimizing the one or more associated variables included in the first variable list at the one or more edge nodes of the federated learning simulation of Xu into the method of defining a first variable list that specifies one or more of the associated variables that are to be optimized at the one or more edge nodes of the federated learning simulation of Da. Silva for the purpose of a personalized cooperative learning method and device based on parameter gradual freezing, wherein the method comprises: receiving the global model of this communication round time sent by the central server at the beginning of each communication round; splicing the global model and the local model of the previous communication round according to the mask matrix to obtain the local initial model of this communication round time; according to the variable parameter, determining the number of training rounds of this communication round, and training the local initial model of this communication round according to the number of training rounds, sending the local model of the communication round after finishing the training, (Xu: Abstract).
Regarding claim 2, Da. Silva as modified by Xu teaches for each federated learning simulation of the plurality of federated learning simulations: defining an aggregation map that includes an aggregation function that is used by the central node of the federated learning simulation to aggregate the one or more associated variables that are included in the second variable list and that are provided to the central node of the federated learning simulation by the one or more edge nodes of the federated learning simulation ([0042] wherein da silva describes a scenario configuration may refer to a collection of parameters, which may define a given federated learning simulation. These parameters may include, but are not limited to, a simulated central node class, a number of simulated worker nodes to employ, a behavior pattern for the simulated central node and/or worker nodes, an architecture (described below) for the learning model through which federated learning on the simulated central node and/or worker nodes may be performed, chosen datasets for emulating local data (described above) (see e.g., FIG. 2) on the simulated worker nodes, a chosen aggregation function employed by the simulated central node through which learning state from the various simulated worker nodes may be aggregated and used to update a global learning model, and a learning state compression technique to be employed by the simulated worker nodes when forwarding learning state to the simulated central node during the federated learning process. A scenario configuration is not limited to the aforementioned parameter examples)
Regarding claim 3, Da. Silva as modified by Xu teaches wherein the one or more associated variables include one or more model variables that are part of the defined machine learning model and one or more variables that are related to, but are not directly part of, the defined machine learning model ([0042-009] wherein da silva describes a proportion of optimization samples to be used during a training stage of learning model optimizations, a proportion of optimization samples to be used during a validation stage of learning model optimizations, a proportion of optimization samples on each simulated worker node to be sent to the simulated central node for testing purposes, and a learning rate of the simulated central node and/or simulated worker nodes. An execution configuration is not limited to the aforementioned parameter examples).
Regarding claim 4, Da. Silva as modified by Xu teaches wherein the one or more model variables include one or more of a weight variable, a bias variable, or a model statistical variable (claim 7 text, page. 4, paragraphs 2-6, page. 8, paragraphs 8-12, page. 9, paragraphs 1-3, page. 11, paragraph 9-10 wherein Xu teaches model variables that includes weight variable).
Regarding claim 5, Da. Silva as modified by Xu teaches wherein the one or more related variables include one or more of statistical information, a number of samples used in model training, or information that is relevant to a particular edge node ([0042] wherein da silva teaches a number of simulated worker nodes to employ, a behavior pattern for the simulated central node and/or worker nodes, an architecture (described below) for the learning model through which federated learning on the simulated central node and/or worker nodes may be performed, chosen datasets for emulating local data (described above) (see e.g., FIG. 2) on the simulated worker nodes, a chosen aggregation function employed by the simulated central node through which learning state from the various simulated worker nodes may be aggregated and used to update a global learning model, and a learning state compression technique to be employed by the simulated worker nodes when forwarding learning state to the simulated central node during the federated learning process. A scenario configuration is not limited to the aforementioned parameter examples), (claims 1-2, 4, 10-13 text wherein Xu teaches variables for determining the number of training).
Regarding claim 6, Da. Silva as modified by Xu teaches wherein those variables of the one or more associated variables that are included in both the first variable list and the second variable list are standard variables that are optimized at the one or more edge nodes of the federated learning simulation and aggregated at the central node of the federated learning simulation (FIG. 2, [0029], [0043] wherein da silva optimizes variables and parameters for nodes with edges as illustrated in FIG. 1), ([0042] wherein da silva describes a scenario configuration may refer to a collection of parameters, which may define a given federated learning simulation. These parameters may include, but are not limited to, a simulated central node class, a number of simulated worker nodes to employ, a behavior pattern for the simulated central node and/or worker nodes, an architecture (described below) for the learning model through which federated learning on the simulated central node and/or worker nodes may be performed, chosen datasets for emulating local data (described above) (see e.g., FIG. 2) on the simulated worker nodes, a chosen aggregation function employed by the simulated central node through which learning state from the various simulated worker nodes may be aggregated and used to update a global learning model, and a learning state compression technique to be employed by the simulated worker nodes when forwarding learning state to the simulated central node during the federated learning process. A scenario configuration is not limited to the aforementioned parameter examples).
Regarding claim 7, Da. Silva as modified by Xu teaches wherein those variables of the one or more associated variables that are only included in the first variable list are locally optimized variables that are optimized at the one or more edge nodes of the federated learning simulation, but are not aggregated at the central node of the federated learning simulation (FIG. 2, [0029], [0043] wherein da silva optimizes variables and parameters for nodes with edges as illustrated in FIG. 1), ([0042] wherein da silva describes a scenario configuration may refer to a collection of parameters, which may define a given federated learning simulation. These parameters may include, but are not limited to, a simulated central node class, a number of simulated worker nodes to employ, a behavior pattern for the simulated central node and/or worker nodes, an architecture (described below) for the learning model through which federated learning on the simulated central node and/or worker nodes may be performed, chosen datasets for emulating local data (described above) (see e.g., FIG. 2) on the simulated worker nodes, a chosen aggregation function employed by the simulated central node through which learning state from the various simulated worker nodes may be aggregated and used to update a global learning model, and a learning state compression technique to be employed by the simulated worker nodes when forwarding learning state to the simulated central node during the federated learning process. A scenario configuration is not limited to the aforementioned parameter examples).
Regarding claim 8, Da. Silva as modified by Xu teaches wherein those variables of the one or more associated variables that are only included in the second variable list are local information variables that are aggregated at the central node of the federated learning simulation, but are not optimized at the one or more edge nodes of the federated learning simulation (FIG. 2, [0029], [0043] wherein da silva optimizes variables and parameters for nodes with edges as illustrated in FIG. 1), ([0042] wherein da silva describes a scenario configuration may refer to a collection of parameters, which may define a given federated learning simulation. These parameters may include, but are not limited to, a simulated central node class, a number of simulated worker nodes to employ, a behavior pattern for the simulated central node and/or worker nodes, an architecture (described below) for the learning model through which federated learning on the simulated central node and/or worker nodes may be performed, chosen datasets for emulating local data (described above) (see e.g., FIG. 2) on the simulated worker nodes, a chosen aggregation function employed by the simulated central node through which learning state from the various simulated worker nodes may be aggregated and used to update a global learning model, and a learning state compression technique to be employed by the simulated worker nodes when forwarding learning state to the simulated central node during the federated learning process. A scenario configuration is not limited to the aforementioned parameter examples)
Regarding claim 9, Da. Silva as modified by Xu teaches wherein those variables of the one or more associated variables are frozen variables that are not optimized at the one or more edge nodes of the federated learning simulation and are not aggregated at the central node of the federated learning simulation (Abstract, page. 2, paragraphs 2-4, page. 9, paragraphs 10-12, page. 10, paragraphs 1-2 wherein Xu describes parameters that are directed to edge nodes of the federated learning simulation, wherein the parameters include frozen parameters).
Regarding claim 10, Da. Silva as modified by Xu teaches : selecting an optimal one of the federated learning simulations; and deploying the defined machine learning model, the central node, and the one or more edge nodes of the optimal one of the federated learning simulations on a plurality of computing systems (Abstract, [0015], [0020], [0040] wherein da silva facilitates communications between the model simulation service (400) and any other above-mentioned system component (e.g., worker nodes, central node, and/or client device (not shown)). To that extent, the service network interface (402) may include functionality to: enable an administrator to sign-in/login into the model simulation service (400) via the client device and, accordingly, allow the administrator to enter inputs and/or issue commands; relay the entered inputs and/or issued commands to the simulation configurator (404) and/or the simulation executor (406) for processing; obtain comparative visualizations from the simulation analyzer (408) for presentation to the administrator; select (either through artificial intelligence decisions or the administrator) an optimal federated learning configuration based, at least in part, on the comparative visualizations; and deploy at least a portion of the optimal federation learning configuration for real-world applications to the central and worker nodes. Further, one of ordinary skill will appreciate that the service network interface (402) may perform other functionalities without departing from the scope of the invention).
Regarding claim 11, da silva teaches A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising ([0017]). The claim is similar in scope to claim 1 therefore the claim is rejected under similar rationale.
Regarding claim 12, the claim is similar in scope to claim 2 therefore the claim is rejected under similar rationale.
Regarding claim 13, the claim is similar in scope to claim 3 therefore the claim is rejected under similar rationale.
Regarding claim 14, the claim is similar in scope to claim 4 therefore the claim is rejected under similar rationale.
Regarding claim 15, the claim is similar in scope to claim 5 therefore the claim is rejected under similar rationale.
Regarding claim 16, the claim is similar in scope to claim 6 therefore the claim is rejected under similar rationale.
Regarding claim 17, the claim is similar in scope to claim 7 therefore the claim is rejected under similar rationale.
Regarding claim 18, the claim is similar in scope to claim 8 therefore the claim is rejected under similar rationale.
Regarding claim 19, the claim is similar in scope to claim 9 therefore the claim is rejected under similar rationale.
Regarding claim 20, the claim is similar in scope to claim 10 therefore the claim is rejected under similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HASSAN MRABI whose telephone number is (571)272-8875. The examiner can normally be reached on Monday-Friday, 7:30am-5pm. Alt, Friday, EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Viker Lamardo can be reached on 571-270-5871. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HASSAN MRABI/Examiner, Art Unit 2144