DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This final office action is in response to the amendment filed 21 August 2026.
Claims 1-20 are pending. Claims 1, 13, and 20 are independent claims.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
According to Step 1 of the two Step analysis, claims 1-12 are directed toward a system (machine). Claims 13-19 are directed toward a method (process). Claim 20 is directed toward a non-transitory processor-readable storage medium (manufacture). Therefore, each of these claims falls within one of the four statutory categories.
Claim 1:
Step 2A, Prong 1:
The claim recites, in part:
using, at the central node, an algorithm to classify the edge nodes into a number ‘k’ of node groups by clustering the edge nodes based on the feature importances (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to classify nodes, using an algorithm, based upon feature importance)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional elements:
at least one processing device including a processor coupled to a memory
at least one processing device being configured to implement the following steps
These claim further recites the additional elements at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim recites the additional elements:
receiving, at a central node, relative importances for a plurality of features of input into a machine learning (ML) models usable at edge nodes, thereby defining a plurality of importances, the central node being configured to communicate with the edge nodes
deploying, at the central node, the shared model to each edge node in the node group
These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data gathering and data output (see MPEP 2106.05(g)). The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim recites the additional elements:
an ML algorithm
generating, at the central node, an ML shared model using the featured importances associated with a selected subset of nodes in the node group
These elements are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional elements:
at least one processing device including a processor coupled to a memory
at least one processing device being configured to implement the following steps
These claim further recites the additional elements at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim recites the additional elements:
receiving, at a central node, relative importances for a plurality of features of input into a machine learning (ML) model usable at an edge node, thereby defining a plurality of importances, the central node being configured to communicate with the edge nodes
deploying, at the central node, the shared model to each edge node in the node group
These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data gathering and data output (see MPEP 2106.05(g)). The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim recites the additional elements:
an ML algorithm
generating, at the central node, an ML shared model using the featured importances associated with a selected subset of nodes in the node group
These elements are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 2:
With respect to claim 2, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites, in part:
wherein the feature importances are determined using in-training feature extraction (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to classify nodes, using an algorithm, based upon feature importance, the feature importance determined (judgement) using in training feature extraction)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 3:
With respect to claim 3, the claim depends upon claim 2. The analysis of claim 2 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites, in part:
wherein the in-training feature extraction is performed using header matrices (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to classify nodes, using an algorithm, based upon feature importance, the feature importance determined using in training feature extraction performed using header matrices)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 4:
With respect to claim 4, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites, in part:
wherein the importances are encrypted (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation of the importances to encrypt the data using an encryption protocol)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
wherein the at least one processing device is further configured to implement the following steps: receiving, at the central node, model gradients
These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data gathering and data output (see MPEP 2106.05(g)). The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional element:
wherein the at least one processing device is further configured to implement the following steps: receiving, at the central node, model gradients
These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data gathering and data output (see MPEP 2106.05(g)). The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 5:
With respect to claim 5, the claim depends upon claim 4. The analysis of claim 4 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites, in part:
wherein the feature importances are encrypted using homomorphic encryption (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation of the importances to encrypt the data using a homomorphic encryption)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 5:
With respect to claim 5, the claim depends upon claim 4. The analysis of claim 4 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites, in part:
wherein the feature importances are encrypted using homomorphic encryption (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation of the importances to encrypt the data using a homomorphic encryption)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 6:
With respect to claim 6, the claim depends upon claim 4. The analysis of claim 4 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites, in part:
wherein the feature importances are encrypted using secure aggregation (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation of the importances to encrypt the data using a secure aggregation)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 7:
With respect to claim 7, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim is directed toward the same abstract idea identified with respect to claim 1.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional elements:
wherein the ML algorithm is k-means clustering
These elements are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional elements:
wherein the ML algorithm is k-means clustering
These elements are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 8:
With respect to claim 8, the claim depends upon claim 7. The analysis of claim 7 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites, in part:
determining the number ‘k’ of feature groups using an Elbow test (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation using a specific text to determine the number of groups)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 9:
With respect to claim 9, the claim depends upon claim 7. The analysis of claim 7 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites, in part:
determining the number ‘k’ of feature groups using a Silhouette test (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation using a specific text to determine the number of groups)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 10:
With respect to claim 10, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites, in part:
wherein the subset of nodes is selected based on measuring a correlation between the feature importances for a given node and a set of feature importances for the node group (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to select a subset of nodes based on a correlation of features)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 11:
With respect to claim 11, the claim depends upon claim 10. The analysis of claim 10 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites, in part:
wherein the correlation is measured using a rank based overlap test (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to select a subset of nodes based on a correlation of features determined using a specific test)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 12:
With respect to claim 12, the claim depends upon claim 10. The analysis of claim 10 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites, in part:
wherein the correlation is measured using a Kendall Ranking Correlation Coefficient test (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to select a subset of nodes based on a correlation of features determined using a specific test)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claims 13-19:
With respect to claims 13-19, the claims recite the limitations substantially similar to those in claims 1-7, respectively. The analysis of claims 1-7 are incorporated herein by reference.
Claim 20:
With respect to claim 20, the claim recites the limitations substantially similar to those in claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim is directed toward the same abstract idea identified with respect to claim 1.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional elements:
a non-transitory processor-readable storage medium having stored thereon program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform the following steps
These claim further recites the additional elements at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional elements:
a non-transitory processor-readable storage medium having stored thereon program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform the following steps
These claim further recites the additional elements at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 13-14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Nambiar et al. (US 2025/0013872, filed 5 July 2023, hereafter Nambiar) and further in view of Nascimento Da Silva et al. (US 2025/0045592, filed 3 August 2023, hereafter Nascimento Da Silva).
As per in dependent claim 1, Nambiar discloses a system, comprising:
at least one processing device including a processor coupled to memory (Figures 1A-1C; paragraphs 0032-0033: Here, a network includes a plurality of nodes (Figure 1A, item 110). These nodes include management nodes (Figure 1A, item 12; Figure 1B) and edge nodes (Figure 1A, item 10; Figure 1C). Each of the management node and edge nodes includes processors (Figure 1B, item 20; Figure 1C, item 50) coupled to memory (Figure 1A, item 40; Figure 1B, item 70)
at least one processing device (Figures 1A-1C) being configured to implement the following steps:
receiving, at a central node, relative importances for a plurality of features input into a machine learning (ML) models usable at an edge nodes (paragraph 0032: Here, a management node receives input data, include machine learning data, such as feature data (paragraph 0015), from edge nodes for use in swarm learning), thereby defining a plurality of feature importances, the central node being configured to communicate with the edge nodes (paragraphs 0025-0026: Here, management nodes communicate with edge nodes to provide configuration and updates to the edge nodes (paragraphs 0027-0028)
using, at the central node, an ML algorithm to classify the edge nodes into a number ‘k’ of node groups based on feature importance (paragraph 0070: Here, node in the swarm learning network may enroll or register (paragraph 0068). Based upon a determination that a threshold number of iterations has been reached or a threshold number of nodes are ready to share parameters, a merge may be performed. This separates the edge nodes into at least two distinct classifications, those nodes having reached a threshold number of iterations and/or ready to share parameters, and those nodes not having reached a threshold number of iterations and/or ready to share parameters)
for each node group among the ‘k’ node groups:
generating, at the central node, an ML shared model using the feature importances associated with a selected subset of nodes in the node group (paragraph 0071: Here, parameter sharing is performed by exporting parameters to a swarm learning API. This begins by merging parameters derived after local training on the common ML algorithm at each of the nodes)
deploying, at the central node, the shared model to each edge node in the node group (paragraphs 0072-0073: Here, the merged parameters are shared to update the local versions of the common models with the merged parameters)
Nambiar fails to specifically disclose clustering the edge nodes based on the feature importance. However, Nascimento Da Silva, which is analogous to the claimed invention because it is directed toward finding distribution cliques for edge nodes, discloses clustering the edge nodes based on feature importance (paragraph 0069: Here, clusters of edge nodes are grouped into cliques based upon feature importance). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Nascimento Da Silva with Nambiar, with a reasonable expectation of success, as it would have allowed for clustering edge nodes based upon feature importance to improve training of models (Nascimento Da Silva: paragraphs 0002 and 0069).
As per dependent claim 2, Nambiar and Nascimento Da Silva disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Nambiar discloses wherein the feature importances are determined using in-training feature extraction (paragraphs 0015 and 0019: Here, feature weights convey the importance of a corresponding feature (paragraph 0015). During training of the local nodes in a distributed ML system, each node identifies the corresponding parameters/weights during training (paragraph 0019)).
With respect to claims 13-14, the claim recites the limitations substantially similar to those in claims 1-2, respectively. Claims 13-14 are similarly rejected.
With respect to claim 20, the claim recites the limitations substantially similar to those in claim 1. Claim 20 is similarly rejected.
Further, Nambiar discloses a non-transitory process-readable storage medium having stored thereon program code for one or more software programs, wherein the program code, when executed by at least one processing device, causes the at least one processing device to perform steps (paragraph 0085).
Claims 3 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Nambiar and Nascimento Da Silva and further in view of Stewart et al. (US 2021/0327431, published 21 October 2021, hereafter Stewart).
As per dependent claim 3, Nambiar and Nascimento Da Silva disclose the limitations similar to those in claim 2, and the same rejection is incorporated herein. Nambiar fails to specifically disclose wherein feature extraction is performed using header matrices.
However, Stewart, which is analogous to the claimed invention because it is directed toward training a neural network, discloses wherein feature extraction is performed using header matrices (Figure 7; paragraphs 0180-0189: Here, feature extraction is performed using a feature matrix header file). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Stewart with Nambiar, with a reasonable expectation of success, as it would have allowed for extracting features for translation to use in training a machine learning model (Stewart: paragraph 0189).
With respect to claim 15, the claim recites the limitations substantially similar to those in claim 3. Claim 15 is similarly rejected.
Claims 4-6 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Nambiar and Nascimento Da Silva and further in view of Quintana et al. (US 2025/0006381, filed 39 June 2023, hereafter Quintana) and further in view of Parsa et al. (US 2025/0111265, filed 28 September 2023, hereafter Parsa).
As per dependent claim 4, Nambiar and Nascimento Da Silva disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Nambiar fails to specifically disclose:
wherein the feature importances are encrypted
wherein the at least one processing device is further configured to implement the following steps, receiving, at the central node, model gradients
However, Quintana, which is analogous to the claimed invention because it is directed toward training a machine learning model, discloses wherein the features are encrypted (paragraph 0058: Here, extracted features are encrypted using homomorphic encryption). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Quintana with Nambiar, with a reasonable expectation of success, as it would have allowed encrypting data to increase data privacy (Quintana: paragraph 0058).
Additionally, Parsa, which is analogous to the claimed invention because it is directed toward federated learning, discloses receiving, at the central node, model gradients (paragraph 0092: Here, gradients descent algorithms are used to optimize the model). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Parsa with Nambiar-Quintana, with a reasonable expectation of success, as it would have allowed for using gradients of the differentiable functions for training a federated learning model (Parsa: paragraph 0092).
As per dependent claim 5, Nambiar, Nascimento Da Silva, Quintana, and Parsa disclose the limitations similar to those in claim 4, and the same rejection is incorporated herein. Quintana discloses wherein the features are encrypted using homomorphic encryption (paragraph 0058: Here, extracted features are encrypted using homomorphic encryption). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Quintana with Nambiar, with a reasonable expectation of success, as it would have allowed encrypting data to increase data privacy (Quintana: paragraph 0058).
As per dependent claim 6, Nambiar, Nascimento Da Silva, Quintana, and Parsa disclose the limitations similar to those in claim 4, and the same rejection is incorporated herein. Nambiar discloses aggregation (paragraph 0081). However, Nambiar fails to specifically disclose wherein the features importances are encrypted using a secure method.
Quintana discloses wherein the features are encrypted using a secure method (paragraph 0058: Here, extracted features are encrypted using homomorphic encryption). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Quintana with Nambiar, with a reasonable expectation of success, as it would have allowed encrypting data to increase data privacy (Quintana: paragraph 0058).
With respect to claims 16-18, the claim recites the limitations substantially similar to those in claims 4-6, respectively. Claims 16-18 are similarly rejected.
Claim 7 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Nambiar and Nascimento Da Silva and further in view of Parsa.
As per dependent claim 7, Nambiar and Nascimento Da Silva disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Nambiar fails to specifically disclose wherein the ML algorithm is k-means clustering.
However, Parsa, which is analogous to the claimed invention because it is directed toward federated learning, discloses wherein the ML algorithm is k-means clustering (paragraph 0091: Here, the algorithm is k-means clustering). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Parsa with Nambiar, with a reasonable expectation of success, as it would have allowed for using gradients of the differentiable functions for training a federated learning model (Parsa: paragraph 0092).
With respect to claim 19, the claim recites the limitations substantially similar to those in claim 7. Claim 19 is similarly rejected.
Claim 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Nambiar, Nascimento Da Silva, and Parsa and further in view of Deodhar et al. (WO 2021/033207, published 25 February 2021, hereafter Deodhar).
As per dependent claim 8, Nambiar, Nascimento Da Silva, and Parsa disclose the limitations similar to those in claim 7, and the same rejection is incorporated herein. Nambiar fails to specifically disclose determining the number of feature groups using an elbow test. However, Deodhar, which is analogous to the claimed invention because it is directed toward machine learning discloses using an elbow test for clustering (paragraph 063). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Deodhar with Nambiar-Parsa, with a reasonable expectation of success, as it would have allowed for implementing a known clustering method for clustering (Deodhar: paragraph 063).
As per dependent claim 9, Nambiar, Nascimento Da Silva, and Parsa disclose the limitations similar to those in claim 7, and the same rejection is incorporated herein. Nambiar fails to specifically disclose determining the number of feature groups using a silhouette test. However, Deodhar, which is analogous to the claimed invention because it is directed toward machine learning discloses using a silhouette test for clustering (paragraph 063). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Deodhar with Nambiar-Parsa, with a reasonable expectation of success, as it would have allowed for implementing a known clustering method for clustering (Deodhar: paragraph 063).
Claims 10 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Nambiar and Nascimento Da Silva and further in view of Ghelichi et al. (US 2024/0412078, published 12 December 2024, hereafter Ghelichi).
As per dependent claim 10, Nambiar and Nascimento Da Silva disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Nambiar fails to specifically disclose wherein the subset of nodes are selected based on measuring a correlation between the feature importances for a given node and a set of feature importances for the node group.
However, Ghelichi, which is analogous to the claimed invention because it is directed toward determining correlation between features, discloses wherein the subset are selected based on measuring a correlation between the feature importances and a set of feature importances (paragraph 0095: here, the rank correlation module correlates the importances of features and the relative importances of the features. It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Ghelichi with Nambiar, with a reasonable expectation of success, as it would have allowed for identifying the correlation between features and their relative importance (Ghelichi: paragraph 0095).
As per dependent claim 12, Nambiar, Nascimento Da Silva, and Ghelichi disclose the limitations similar to those in claim 10, and the same rejection is incorporated herein. Ghelichi discloses wherein the correlation is measured using a Kendall Ranking Correlation Coefficient test (paragraph 0096). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Ghelichi with Nambiar, with a reasonable expectation of success, as it would have allowed for identifying the correlation between features and their relative importance (Ghelichi: paragraph 0095).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Nambiar, Nascimento Da Silva, and Ghelichi and further in view of Lee et al. (US 9122995, patented 1 September 2015, hereafter Lee).
As per dependent claim 11, Nambiar, Nascimento Da Silva, and Ghelichi disclose the limitations similar to those in claim 10, and the same rejection is incorporated herein. Nambiar fails to specifically disclose wherein the correlation is measured using a rank biased overlap test.
However, Lee, which is analogous to the claimed invention because it is directed toward identifying correlations in classifying data, discloses using a rank biased overlap test (Figure 6; column 14, lines 29-44: Here, a nearest-neighbor strategy identifies a threshold based on ranked overlap data). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Lee with Nambiar-Ghelichi, with a reasonable expectation of success, as it would have allowed for grouping items using a nearest-neighbor strategy (Lee: column 14, lines 29-44).
Response to Arguments
Applicant's arguments filed 21 August 2026 with respect to the rejection of claims under 35 USC 101 have been fully considered but they are not persuasive.
The applicant argues that “clustering edge nodes based on feature importances along with generating and deploying shared ML models” is a practical application and not a mere mental process (page 8). The examiner respectfully disagrees.
While the examiner agrees that claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743. In this instance, the recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". (Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015)).
Specifically, the claim relies upon “using…an ML algorithm to classify the edge nodes into a number ‘k’ of node groups by clustering the edge nodes based on the feature importances (lines 8-10).” Any “ML algorithm” may be applied to classify the edge nodes with no restriction to how the result is accomplished and no description of the mechanism for accomplishing the result. For this reason, this argument is not persuasive.
The applicant further argues that the claims recite “a technical combination including receiving relative importances for a plurality of features input into ML models usable at edge nodes; using an ML algorithm to classify the edge nodes into a number ‘k’ of node groups by clustering the edge nodes based on the feature importances, generating an ML shared model using the feature importances associated with a selected subset of nodes in the node group; and deploying the shared model to each edge node in the node group” and the “combination is not well-understood, routine, or conventional activity in the field of ML model sharing (page 8).”
However, the examiner never alleges that “receiving relative importances for a plurality of features input into ML models usable at edge nodes; using an ML algorithm to classify the edge nodes into a number ‘k’ of node groups by clustering the edge nodes based on the feature importances, generating an ML shared model using the feature importances associated with a selected subset of nodes in the node group; and deploying the shared model to each edge node in the node group” is well-understood, routine, and conventional activity.
Instead, with respect to the additional elements:
receiving, at a central node, relative importances for a plurality of features of input into a machine learning (ML) models usable at edge nodes, thereby defining a plurality of importances, the central node being configured to communicate with the edge nodes
deploying, at the central node, the shared model to each edge node in the node group
These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data gathering and data output (see MPEP 2106.05(g)). The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). For this reason, this argument is not persuasive.
Applicant’s arguments with respect to the rejections of claims under 35 USC 102 and 35 USC 103 have been fully considered and are persuasive in view of the amended claim limitations. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Nambiar and Nascimento Da Silva.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Tamilselvam et al. (US 11782704): Discloses identifying feature importances and clustering nodes based upon feature importance (Abstract)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE R STORK whose telephone number is (571)272-4130. The examiner can normally be reached 8am - 2pm; 4pm - 6pm.
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, Omar Fernandez Rivas can be reached at 571/272-2589. 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.
/KYLE R STORK/Primary Examiner, Art Unit 2128
