DETAILED ACTION
Claims 1-8 are presented for examination.
This office action is in response to submission of application on 09-AUGUST-2024
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 03-JULY-2024 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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 4-8 rejected under 35 U.S.C. 101 because the claimed invention is direction to an abstract idea without significantly more.
MPEP 2106.04(a)(2)(Ill) “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide run) to perform the claim limitation.
Regarding claim 4:
Step 2A, Prong 1 will now be evaluated for this claim:
A judicial exception is recited in this claim as it recites a mental process:
designating, by the central server, a temporary master node among the plurality of nodes using the information of the feature set
Designating a temporary master node would mean selecting a particular node to act as one, which would be a mental process as a selection maybe be performed in the human mind.
generating, by the central server, a plurality of local groups comprising the master node and nodes adjacent to the master node
Generating a plurality of local groups would be sorting the nodes into groups, wherein sorting items into groups is performable in the human mind.
Step 2A, Prong 2 will now be evaluated for this claim:
Furthermore, the additional elements:
By the central server
are interpreted as a general purpose computer under MPEP 2106.05(f)
Furthermore, MPEP 2106.05(g) Insignificant Extra-Solution Activity has found mere data gathering and post-solution activity to be insignificant extra-solution activity.
The following steps are mere data gathering:
requesting, by a central server, information of a feature set (C) from a plurality of nodes to participate in federated learning
Requesting the feature set would be a form of gathering data from the nodes for participation in federated learning.
receiving, by the central server, federated learning policy information from nodes constituting the local group through the master node
Receiving policy information is gathering that policy information from the nodes.
The additional elements have been considered both individually and as an ordered combination in order to determine whether they integrate the exception into a practical application. Therefore, no meaningful limits are imposed practicing the abstract idea.
Therefore, the claim is related to an abstract idea.
Step 2B will now be discussed with regards to this claim:
The claim does not provide an inventive concept. There is no additional Insignificant Extra- Solution Activity, as identified in Step 2A Prong Two, that provides an inventive concept.
Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)) does not overcome a rejection.
Generally linking the use of the judicial exception to computer environments, e.g., a claim describing how the abstract idea of creating a contractual relationship that guarantees performance of a transaction be performed using a computer that receives and sends information over a network, as discussed in buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1354, 112 USPQ2d 1093, 1095-96 (Fed. Cir. 2014). (MPEP § 2106.05(h)) does not overcome a rejection.
The additional elements have been considered both individually and as an ordered combination as to whether they whether they warrant significantly more consideration.
The claim is ineligible.
Regarding claim 5, which depends upon claim 4:
This claim further limits the plurality of local groups of claim 4. Further specifying the structure of the plurality of local groups in this manner does not overcome the parent claim’s rejection as it does not negate the presence of a mental process, and the database it teaches is a generic computer function that does not provide an advantage.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Regarding claim 6, which depends upon claim 4:
This claim further limits the information of the feature set of claim 4. Further specifying the information of the feature set in this manner does not overcome the parent claim’s rejection as it merely provides the criteria that a human mind could use to organize the local groups.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Regarding claim 7, which depends upon claim 7:
This claim further limits the trust score of claim 6. Further specifying the trust score in this manner does not overcome the parent claim’s rejection as it continues to be merely an evaluation criterion.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Regarding claim 8, which depends upon claim 6:
This claim further limits the designation of the temporary master node of claim 6. Further specifying this designation in this manner does not overcome the parent claim’s rejection as it only lists what information might be considered by the human mind to make that decision.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
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)(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.
Claims 1-8 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Haraldson et al. (Pub. No. WO 2021008675 A1, published January 21st 2021, hereinafter Haraldson).
Regarding claim 1:
Claim 1 recites:
A federated learning system comprising: at least one central server and a plurality of local groups, wherein each of the plurality of local groups comprises one master node and a plurality of nodes, wherein the plurality of local groups are formed by the central server using information of a feature set (C) for federated learning.
Haraldson anticipates the limitations of claim 1.
Haraldson teaches the use of federated learning (Page 7, line 14 – Page 8, line 37) which would include a federated learning system with at least one central server and a plurality of local nodes wherein these nodes are further clustered into local groups, wherein the local groups comprise a master node and a plurality of nodes, which are determined by a central server. As can be seen in Fig. 1 of Haraldson, a client computing device may also be designated to orchestrate other client devices (Page 5 lines 33 – Page 6, line 6), which would be a master node. Furthermore, these groups are determined using multiple parameters sent to the server device i.e. information of a feature set (Page 7, lines 20-21).
Regarding claim 2, which depends upon claim 1:
Claim 2 recites:
The system of claim 1, wherein each of the plurality of local groups further comprises a participation DB connected to the master node.
Haraldson anticipates claim 1 upon which claim 2 depends. Furthermore, Haraldson anticipates the limitations of claim 2:
Haraldson teaches a database of threshold crossing time sequences (TCTS) i.e. a form of usage data which stores TCTS data for all client computer devices, including each of the local group master nodes (Page 11, lines 20-35). In this manner, the master node of each local group is connected to a participation DB.
Regarding claim 3, which depends upon claim 1:
Claim 3 recites:
The system of claim 1, wherein information of the feature set (C) comprises any one of trust score (T), execution capability (E), availability (A), participation (P), local data quality (Q), and device information (D).
Haraldson anticipates claim 1 upon which claim 3 depends. Furthermore, Haraldson anticipates the limitations of claim 3:
Haraldson teaches an evaluation metric (Page 8, lines 24-29) which would be analogous to a trust score since it describes the reliability of a model.
Haraldson teaches a variety of parameters i.e. the information of a feature set which correspond to the above list (Page 7, line 30 – Page 8, line 5). In this section, Haraldson recites:
Configuration of the network hardware and software elements would be an execution capability, as both define the ability of a particular system based on available resources.
Resource usage would be analogous to availability since both would determine when certain resources are available.
Configuration of hardware and software elements would also include participation, since the configuration of those elements determines if a particular node is participating in federated learning.
Configuration of the network hardware and software elements likewise includes device information.
Furthermore, Haraldson reaches using similarity of data distributions to determine local groupings (Page 7, lines 25-30) wherein this would include local data quality since a distribution would include the number of samples of particular categories.
Regarding claim 4:
Claim 4 recites:
A federated learning control method comprising: (a) requesting, by a central server, information of a feature set (C) from a plurality of nodes to participate in federated learning; (b) designating, by the central server, a temporary master node among the plurality of nodes using the information of the feature set (C); (c) generating, by the central server, a plurality of local groups comprising the master node and nodes adjacent to the master node; and (d) receiving, by the central server, federated learning policy information from nodes constituting the local group through the master node.
Haraldson anticipates federated learning control method comprising: (a) requesting, by a central server, information of a feature set (C) from a plurality of nodes to participate in federated learning:
Haraldson teaches that the central server in a federated learning system orchestrates the shared machine learning model used by node local groups (Page 6, lines 5-10). Orchestration would be include gathering of the information of a feature set from a plurality of nodes to participate in federated learning.
Haraldson anticipates (b) designating, by the central server, a temporary master node among the plurality of nodes using the information of the feature set (C); (c) generating, by the central server, a plurality of local groups comprising the master node and nodes adjacent to the master node:
Haraldson teaches a client computing device may also be designated to orchestrate other client devices (Page 5 lines 33 – Page 6, line 6, Fig. 1), which would be a master node among a plurality of nodes. Since the clustering is performed by the central server using several parameters which would be a feature set, and the master node is part of the cluster (Page 7, lines 22-31), the master node is designated by the central server using information of the feature set. Furthermore, the clustering itself would be generating a plurality of local groups comprising the master node and nodes adjacent to the master node as the client local devices in the cluster may return updated weights via the master node (Page 8, lines 10-15), making them adjacent.
Haraldson anticipates (d) receiving, by the central server, federated learning policy information from nodes constituting the local group through the master node:
Haraldson teaches returning updated weights from the client devices, i.e. the nodes constituting the local group through master node (Page 8, lines 10-15), to the central served, wherein the weights would be federated learning policy information.
Regarding claim 5, which depends upon claim 4:
Claim 5 recites:
The method of claim 4, wherein each of the plurality of local groups further comprises a participation DB connected to the master node in the (c).
Haraldson anticipates claim 4 upon which claim 5 depends. Furthermore, Haraldson anticipates the limitations of claim 5:
Haraldson teaches a database of threshold crossing time sequences (TCTS) i.e. a form of usage data which stores TCTS data for all client computer devices, including each of the local group master nodes (Page 11, lines 20-35). In this manner, the master node of each local group is connected to a participation DB.
Regarding claim 6, which depends upon claim 4:
Claim 6 recites:
The method of claim 4, wherein the information of the feature set (C) comprises any one of trust score (T), execution capability (E), availability (A), participation (P), local data quality (Q), and device information (D).
Haraldson anticipates claim 4 upon which claim 6 depends. Furthermore, Haraldson anticipates the limitations of claim 6:
Haraldson teaches an evaluation metric (Page 8, lines 24-29) which would be analogous to a trust score since it describes the reliability of a model.
Furthermore, Haraldson teaches a variety of parameters i.e. the information of a feature set which correspond to the above list (Page 7, line 30 – Page 8, line 5). In this section, Haraldson recites:
Configuration of the network hardware and software elements would be an execution capability, as both define the ability of a particular system based on available resources.
Resource usage would be analogous to availability since both would determine when certain resources are available.
Configuration of hardware and software elements would also include participation, since the configuration of those elements determines if a particular node is participating in federated learning.
Configuration of the network hardware and software elements likewise includes device information.
Furthermore, Haraldson reaches using similarity of data distributions to determine local groupings (Page 7, lines 25-30) wherein this would include local data quality since a distribution would include the number of samples of particular categories.
Regarding claim 7, which depends upon claim 6:
Claim 7 recites:
The method of claim 6, wherein the trust score (T) is determined by a behavioral characteristic value (B) and a recommendation score (RB) of each of the plurality of nodes.
Haraldson anticipates claim 6 upon which claim 7 depends. Furthermore, Haraldson anticipates the limitations of claim 7:
Haraldson teaches that its evaluation criterion, which in claim 6 is shown to be analogous to the trust score, is determined via a behavioral characteristic data such as classification accuracy and mean squared error, which describes the behavior of the model being evaluating. Furthermore, the model performance would be a recommendation score as it would describe whether a model is performing sufficiently or not (Page 8, lines 30-37).
Regarding claim 8, which depends upon claim 6:
Claim 8 recites:
The method of claim 6, wherein the (b) designates the temporary master node using information of the trust score (T), execution capability (E), participation (P), and availability (A).
Haraldson anticipates claim 6 upon which claim 8 depends. Furthermore, Haraldson anticipates the limitations of claim 8:
Haraldson teaches that the clustering process for forming local groups (Page 7, lines 22-31), which would include the designation of the temporary master node (Page 5 lines 33 – Page 6, line 6, Fig. 1). Therefore, the trust score (T), execution capability (E), participation (P), and availability (A) as seen in parent claim 6 which are used in the clustering process (Page 7, line 30 – Page line 5) would likewise be used in the designation.
Conclusion
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/A.J.M./Examiner, Art Unit 2142 /Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
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