DETAILED ACTION
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 03/17/2025 has been considered by the Examiner. The submission is in compliance with the provisions of 37 CFR 1.97.
Claim status
Claims 1-31 have been canceled. Claims 32-51 presented for the examination and remain pending in the application.
Examiner note
The Examiner has interpreted the later “K” and “N” as agent entities, “Mn” as utility metric and “S” as a subset. Furthermore, the word “entity” has been interpreted as a “device connected to perform the claimed function”.
Claim Objections
Claims 33-41 and 46-49 are objected to because these claims recite different kinds of indexes without providing what they stand for. For example, Dn, an ,“t-1” and etc. Please provide what these symbols stand for. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 32-51 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the applicant regards as the invention.
Claim 32 recites “A method for selecting agent entities to broadcast local model parameter vectors in an iterative learning process,…, wherein the method is performed by a coordinator entity, wherein the iterative learning process pertains to a computational task to be performed by N agent entities for training a machine learning model,..”. Although, the claim recites the above indicated limitations in claim 32, the claim does not recite how the K and N agent entity performs the steps in the claim by using a machine learning model and how the locally computed computational results is computed per each of the K & N agent entities based on its own local training data… It is unclear what these agents entities clearly doing and how the agents entities training a machine learning model for each iteration round of the iterative learning process and therefore, the clamed language does not clearly show how the steps of the claimed limitations can be processed as written now in the application and there is ambiguity or inconsistency in the claimed language.
Regarding claims 40, 45 and 51.
Claims 40, 45 and 51 are substantially similar to claim 32 and thus, the same rationale applies.
Dependent claims 33-39, 44-44 and 46-50 are also rejected under the same rationale.
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 32, 33, 35, 36, 37, 39-41, 45, 46, 48-50 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Utilizing the process described in the 2025 Revised Patent Subject Matter Eligibility Guidance (2025 PEG), claims 32, 40 and 45 satisfy the Step 1 because the claims are a process.
Independent claims 32, 40 and 45.
In Step 2A prong 1, evaluating whether the claim “recites Judicial exception without significantly more.” For example, claim 32 recites “A method for selecting agent entities to broadcast local model parameter vectors in an iterative learning process, wherein the method is performed by a coordinator entity”,
“wherein the iterative learning process pertains to a computational task to be performed by N agent
entities for training a machine learning model”,
“wherein, for each iteration round of the iterative learning process, a local model parameter
vector with locally computed computational results is computed per each of the N agent
entities based on its own local training data and at least one local model parameter
vector received from at least one other of the agent entities”,
“wherein the locally computed computational results are updates of the machine learning model”,
“wherein, for each iteration round of the iterative learning process, less than all of the N agent
entities are to broadcast their local model parameter vector”, and
wherein the method, for each iteration of the iterative learning process, comprises:
“obtaining parameters from the agent entities, wherein the parameters pertain to a utility for
each of the agent entities to broadcast its local model parameter vector for the iteration”;
“selecting K < N agent entities to broadcast their local model parameter vector for the iteration
by applying a selection criterion to the obtained parameters”; and
“sending information that informs the N agent entities of the selected K agent entities.” which, under the broadest reasonable claims interpretation, the limitations are steps that are “a mental process (thinking) that can be performed in the human mind including an observation, evaluation, judgement and opinion" and “Certain methods of organizing human activity for managing interactions between people including social activities, teaching, and following rules or instructions” to be an abstract idea. For example, a human can obtain or gather information about the parameters from agent or another person to transmit or broadcast the parameters for iteration (i.e., rehearsal) and perform a selection process by comparing the K or N agent entities and further sending and informing the N entity the selected K entity. If a claim limitation, under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components, then they fall within the “Mental Processes” and “Certain method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
In Step 2A prong 2, whether the judicial exception is integrated into a practical application. Here the judicial exception in claim 32 is not integrated into a practical application because the method,…, “obtaining parameters from the agent entities, wherein the parameters pertain to a utility for
each of the agent entities to broadcast its local model parameter vector for the iteration”;
“selecting K < N agent entities to broadcast their local model parameter vector for the iteration
by applying a selection criterion to the obtained parameters”; and
“sending information that informs the N agent entities of the selected K agent entities.” of claim 32 is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component.
However, these steps are insignificant extra-solution activity, e.g., mere data gathering or displaying data in conjunction with the abstract idea. These steps are performed to gather data so that the data can be analyzed by an abstract idea mental process and thus, the result of the mental process can be displayed. Adding insignificant extra-solution activity to the judicial exception is not enough to qualify as “significantly more”. The additional elements or steps do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
In Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because “the method of selecting agent entities to broadcast local model parameter vectors in an iterative learning process, for each iteration round of the iterative learning process, a local model parameter vector with locally computed computational results is computed per each of the N agent entities based on its own local training data and at least one local model parameter vector received from at least one other of the agent entities, wherein the locally computed computational results are updates of the machine learning model, wherein, for each iteration round of the iterative learning process, less than all of the N agent entities are to broadcast their local model parameter vector” does not include additional element and they do not provide exceptional extra solution activity and they are well-understood, routine and conventional.
Therefore, the elements recited in claim 32, when considered individually or in an ordered combination, fail to amount to significantly more than the abstract idea. Accordingly, claim 32 is not eligible.
Regarding independent claims 40 and 45.
Claims 40 and 45 are substantially similar to claim 32 and thus, the same rationale applies.
Regarding claim 33.
In Step 2A prong 1, evaluating whether the claim “recites Judicial exception without significantly more.” For example, claim 33 recites “wherein the K agent entities are selected by the coordinator entity evaluating different possible candidate subsets, each composed of K agent entities, by, for each candidate subset, evaluating a metric Us, wherein the metric Us for a given candidate subset is a function of the obtained parameters for the K agent entities of said candidate subset.” which, under the broadest reasonable claims interpretation, the limitations are steps that are “a mental process (thinking) that can be performed in the human mind including an observation, evaluation, judgement and opinion" and “Certain methods of organizing human activity for managing interactions between people including social activities, teaching, and following rules or instructions” to be an abstract idea. For example, a human can obtain or gather information about the parameters from agent or another person for evaluating different possible candidate subsets, each composed of K agent entities, by, for each candidate subset, evaluating a metric Us, wherein the metric Us for a given candidate subset is a function of the obtained parameters for the K agent entities of said candidate subset.”
In Step 2A prong 2, whether the judicial exception is integrated into a practical application. Here the judicial exception in claim 33 is not integrated into a practical application because “wherein the K agent entities are selected by the coordinator entity evaluating different possible candidate subsets, each composed of K agent entities, by, for each candidate subset, evaluating a metric Us, wherein the metric Us for a given candidate subset is a function of the obtained parameters for the K agent entities of said candidate subset.” of claim 33 is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component.
However, these steps are insignificant extra-solution activity, e.g., mere data gathering or displaying data in conjunction with the abstract idea. This step is performed to gather data so that the data can be analyzed by an abstract idea mental process and thus, the result of the mental process can be displayed. Adding insignificant extra-solution activity to the judicial exception is not enough to qualify as “significantly more”. The additional elements or steps do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
In Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Therefore, the elements recited in claim 33, when considered individually or in an ordered combination, fail to amount to significantly more than the abstract idea. Accordingly, claim 32 is not eligible.
Regarding claim 41.
Claim 41 is substantially similar to claim 33 and thus, the same rationale applies.
Regarding claim 35.
In Step 2A prong 1, evaluating whether the claim “recites Judicial exception without significantly more.” For example, claim 35 recites “the parameters for agent entity n at least define a value Mn representing a utility metric for agent entity n, wherein one selection criterion is to select the candidate subset S composed of the K agent entities with largest values of Mn, and wherein the utility metric for agent entity n at iteration t is a function of absolute, or relative, magnitude of the local model parameter vector calculated by agent entity n for iteration t - 1.” “wherein the K agent entities are selected by the coordinator entity evaluating different possible candidate subsets, each composed of K agent entities, by, for each candidate subset, evaluating a metric Us, wherein the metric Us for a given candidate subset is a function of the obtained parameters for the K agent entities of said candidate subset.”, which, under the broadest reasonable claims interpretation, the limitations are steps that are “a mental process (thinking) that can be performed in the human mind including an observation, evaluation, judgement and opinion" and “Certain methods of organizing human activity for managing interactions between people including social activities, teaching, and following rules or instructions” to be an abstract idea. For example, a human can obtain or gather information about the parameters from agent or another person for evaluating different possible candidate subsets, each composed of K agent entities, by, for each candidate subset, evaluating a metric Us, wherein the metric Us for a given candidate subset is a function of the obtained parameters for the K agent entities of said candidate subset.”
In Step 2A prong 2, whether the judicial exception is integrated into a practical application. Here the judicial exception in claim 35 is not integrated into a practical application because “the parameters for agent entity n at least define a value Mn representing a utility metric for agent entity n, wherein one selection criterion is to select the candidate subset S composed of the K agent entities with largest values of Mn, and wherein the utility metric for agent entity n at iteration t is a function of absolute, or relative, magnitude of the local model parameter vector calculated by agent entity n for iteration t-1.”
However, these steps are insignificant extra-solution activity, e.g., mere data gathering or displaying data in conjunction with the abstract idea. This step is performed to gather data so that the data can be analyzed by an abstract idea mental process and a mathematical concept by using a formula and thus, the result of the mental process can be displayed. Adding insignificant extra-solution activity to the judicial exception is not enough to qualify as “significantly more”. The additional elements or steps do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
In Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Therefore, the elements recited in claim 35, when considered individually or in an ordered combination, fail to amount to significantly more than the abstract idea. Accordingly, claim 35 is not eligible.
Regarding claim 48.
Claim 48 is substantially similar to claim 35 and thus, the same rationale applies.
Regarding claim 36.
In Step 2A prong 1, evaluating whether the claim “recites Judicial exception without significantly more.” For example, claim 36 recites “the parameters for agent entity n at least define a value Mn representing a utility metric for agent entity n, wherein one selection criterion is to select the candidate subset S composed of the K agent entities with largest values of Mn, and wherein the value Mn is either obtained by the coordinator entity from agent entity n or computed by the coordinator entity from other parameters obtained from agent entity n.” which, under the broadest reasonable claims interpretation, the limitations are steps that are “a mental process (thinking) that can be performed in the human mind including an observation, evaluation, judgement and opinion" and “Certain methods of organizing human activity for managing interactions between people including social activities, teaching, and following rules or instructions” to be an abstract idea. For example, a human can obtain or gather information about the parameters from agent or another person for evaluating different possible candidate subsets, each composed of K agent entities, by, for each candidate subset, evaluating a metric Us, wherein the metric Us for a given candidate subset is a function of the obtained parameters for the K agent entities of said candidate subset.”
In Step 2A prong 2, whether the judicial exception is integrated into a practical application. Here the judicial exception in claim 35 is not integrated into a practical application because “the parameters for agent entity n at least define a value Mn representing a utility metric for agent entity n, wherein one selection criterion is to select the candidate subset S composed of the K agent entities with largest values of Mn, and wherein the value Mn is either obtained by the coordinator entity from agent entity n or computed by the coordinator entity from other parameters obtained from agent entity n.”
However, these steps are insignificant extra-solution activity, e.g., mere data gathering or displaying data in conjunction with the abstract idea. This step is performed to gather data so that the data can be analyzed by an abstract idea mental process and a mathematical concept by using a formula and thus, the result of the mental process can be displayed. Adding insignificant extra-solution activity to the judicial exception is not enough to qualify as “significantly more”. The additional elements or steps do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
In Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Therefore, the elements recited in claim 36, when considered individually or in an ordered combination, fail to amount to significantly more than the abstract idea. Accordingly, claim 36 is not eligible.
Regarding claim 49.
Claim 49 is substantially similar to claim 36 and thus, the same rationale applies.
Regarding claim 37.
In Step 2A prong 1, evaluating whether the claim “recites Judicial exception without significantly more.” For example, claim 37 recites “wherein the parameters for agent entity n at least define any of a vendor of said agent entity n and a trust level of said agent entity n, and wherein one selection criterion is to select the candidate subset S composed of the K agent entities that are from the same vendor and/or that have a trust level higher than a trust level threshold.” which, under the broadest reasonable claims interpretation, the limitations are steps that are “a mental process (thinking) that can be performed in the human mind including an observation, evaluation, judgement and opinion" and “Certain methods of organizing human activity for managing interactions to compare the trust level of the agents by following rules or instructions” to be an abstract idea. For example, a human can obtain or gather information for comparing the trust based on the criterion which is to select the candidate subset S composed of the K agent entities that are from the same vendor and/or that have a trust level higher than a trust level threshold.
In Step 2A prong 2, whether the judicial exception is integrated into a practical application. Here the judicial exception in claim 37 is not integrated into a practical application because “wherein the parameters for agent entity n at least define any of a vendor of said agent entity n and a trust level of said agent entity n, and wherein one selection criterion is to select the candidate subset S composed of the K agent entities that are from the same vendor and/or that have a trust level higher than a trust level threshold.”
However, these steps are insignificant extra-solution activity, e.g., mere data gathering or displaying data in conjunction with the abstract idea. This step is performed to gather data so that the data can be analyzed by an abstract idea mental process and compare the values to select the higher trust level and thus, the result of the mental process can be displayed. Adding insignificant extra-solution activity to the judicial exception is not enough to qualify as “significantly more”. The additional elements or steps do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
In Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Therefore, the elements recited in claim 37, when considered individually or in an ordered combination, fail to amount to significantly more than the abstract idea. Accordingly, claim 37 is not eligible.
Regarding claim 50.
Claim 50 is substantially similar to claim 37 and thus, the same rationale applies.
Regarding claim 39.
In Step 2A prong 1, evaluating whether the claim “recites Judicial exception without significantly more.” For example, claim 32 recites “wherein the selected K agent entities form a first subset of selected agent entities, and wherein the method further comprises: selecting K' < N further agent entities to broadcast their local model parameter vector for the iteration by evaluating the selection criterion, wherein the further K' agent entities form a second subset of further selected agent entities disjoint from the first subset of selected agent entities, and wherein the further K' agent entities are selected according to an interference criterion with respect to the first subset of selected agent entities”; and “sending information that informs the N agent entities of the further selected K' agent entities.” which, under the broadest reasonable claims interpretation, the limitations are steps that are “a mental process (thinking) that can be performed in the human mind including an observation, evaluation, judgement and opinion" and “Certain methods of organizing human activity for managing interactions between people including social activities, teaching, and following rules or instructions” to be an abstract idea. For example, a human can obtain or gather information about the parameters from agent or another person to transmit or broadcast the parameters for iteration (i.e., rehearsal) and perform a selection process by comparing the K or N agent entities and further sending and informing the N entity the selected K entity. If a claim limitation, under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components, then they fall within the “Mental Processes” and “Certain method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
In Step 2A prong 2, whether the judicial exception is integrated into a practical application. Here the judicial exception in claim 39 is not integrated into a practical application because the method,…, “wherein the selected K agent entities form a first subset of selected agent entities, and wherein the method further comprises: selecting K' < N further agent entities to broadcast their local model parameter vector for the iteration by evaluating the selection criterion,…”; and “sending information that informs the N agent entities of the further selected K' agent entities.”;
However, these steps are insignificant extra-solution activity, e.g., mere data gathering or displaying data in conjunction with the abstract idea. These steps are performed to gather data so that the data can be analyzed by an abstract idea mental process and thus, the result of the mental process can be displayed. Adding insignificant extra-solution activity to the judicial exception is not enough to qualify as “significantly more”. The additional elements or steps do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
In Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because “the method of selecting agent entities to broadcast local model parameter vectors in an iterative learning process, for each iteration round of the iterative learning process, a local model parameter vector with locally computed computational results is computed per each of the N agent entities based on its own local training data and at least one local model parameter vector received from at least one other of the agent entities, wherein the locally computed computational results are updates of the machine learning model, wherein, for each iteration round of the iterative learning process, less than all of the N agent entities are to broadcast their local model parameter vector” does not include additional element and they do not provide exceptional extra solution activity and they are well-understood, routine and conventional.
Therefore, the elements recited in claim 39, when considered individually or in an ordered combination, fail to amount to significantly more than the abstract idea. Accordingly, claim 39 is not eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 32-36, 38-41, 43-49 and 51 are rejected under 35 U.S.C. 103 as being unpatentable over “A Joint Decentralized Federated Learning and Communications Framework for Industrial Networks.”, Stefano Savazzi, Sanaz Kianoush, Vittorio Rampa Consiglio Nazionale delle Ricerche (CNR) IEIIT institute, Milano, (hereinafter Stefano) in view of Balevi et al. U.S. Pub. No. 2023/0297875 A1, (hereinafter Balevi).
Regarding claim 32. Stefano teaches a method for selecting agent entities to broadcast local model parameter vectors in an iterative learning process, wherein the method is performed by a coordinator entity (Stefano’s recited server correspond to the coordinator entity),wherein the iterative learning process pertains to a computational task to be performed by N agent entities for training a machine learning model (Stefano teaches in Fig. 1 a standard Communication and computational model: model averaging, SGD on local data, ML model parameters pruning, compress and forward of parameters on each FL round and further explaining on page 2, on the right column, lines 1-10 by providing a vector formula to achieve the predicted function and also further explains in detail pages 2-5 about the practical example of the collected independently and individually by the devices based on their local observations of the given phenomenon. Therefore, local samples are not representative of the full data distribution by providing a formulas. (See page 2 under section II “decentralized FL and physical communications” each agent entity is characterized by a so-called neighbor set of agent entities. As can be seen from the last paragraph of the same section on page 3 of Stefano the said each agent entity may be instructed by the scheduler to broadcast its local parameter vector to all agent entities of the neighbor set, and further on page 3 under section III “networking framework for decentralized FL”), on page 2 on the right column under section II “A. Gossip and consensus approaches to decentralized Federative Learning”, in lines 1-11, the federative learning is an iterative learning process which includes multiple devices (i.e., entities)), furthermore, see page 4 on the left column under Fig. 2, the first paragraph, lines 1-9 which clearly discloses about the server which is considered as the claimed “coordinator entity” and also on page 4 under section A. “TSCH MAC and communication round scheduling”, lines 1-11 the aforementioned of scheduler performs the selection of different agent entities for different time slots for the iterative learning process),
wherein, for each iteration round of the iterative learning process, a local model parameter
vector with locally computed computational results is computed per each of the N agent entities based on its own local training data and at least one local model parameter vector received from at least one other of the agent entities (Stefano teaches on page 3 on the left column in the first paragraph, lines 1-8 Decentralized FL replaces the centralized fusion of model parameters implemented by the PS server with consensus [6].Every new communication round a device performs a model averaging,..),
wherein the locally computed computational results are updates of the machine learning model (Stefano teaches on page 3 the equation 2 clearly indicates the computing process),
wherein, for each iteration round of the iterative learning process, less than all of the N agent entities are to broadcast their local model parameter vector (Stefano teaches on page 4 on the right column Fig. 2 different agents which include 7 agent entities and 6 of the agent entities characterizes by similar number of different carrier frequencies), and
wherein the method, for each iteration of the iterative learning process, comprises:
selecting K < N agent entities to broadcast their local model parameter vector for the iteration by applying a selection criterion to (Stefano teaches on page 4 on the left column under Fig. 2, the first paragraph, lines 1-9 which clearly discloses about the server which is considered as the claimed “coordinator entity” and also on page 4 under section A. “TSCH MAC and communication round scheduling”, lines 1-11 the aforementioned of scheduler performs the selection of different agent entities for different time slots for the iterative learning process); and
sending information that informs the N agent entities of the selected K agent entities (Stefano teaches on page 4 on the right column lines 6-38 and on page 5 I the left column lines 1-7 and under section III. B of Stefano and thus, these sections can be interpreted as the coordinator entity does not inform every agent entity individually about which other agent entity has been selected and which
one has not. Instead, the coordinator entity informs the set of N different agent entities as a
whole about which other agent entity has been selected and which one has not. The later way of
informing all agent entities). Stefano does not explicitly teach wherein the method, for each iteration of the iterative learning process, comprises: obtaining parameters from the agent entities, wherein the parameters pertain to a utility for each of the agent entities to broadcast its local model parameter vector for the iteration.
However, Balevi teaches wherein the method, for each iteration of the iterative learning process, comprises: obtaining parameters from the agent entities, wherein the parameters pertain to a utility for each of the agent entities to broadcast its local model parameter vector for the iteration (Balevi teaches in para. [0006] a first data aggregation period for training the machine learning model; obtaining a first set of updated model parameters from a first client device and a second set of updated model parameters from a second client device,…, and further, Balevi teaches in para. [0025] wireless communication networks are deployed to provide various communication services, such as voice, video, packet data, messaging, broadcast, and the like).
Therefore, Stefano and Balevi are analogues arts and they are in the same field of endeavor as they both are directed to training machine learning and agent entities to broadcast its local model parameter for the iteration.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the
claimed invention to modify the teachings of using a training the machine learning model for obtaining a first set of updated model parameters from a first client device and a second set of updated model parameters from a second client device and to provide various communication services, such as broadcasting ([0006] and [0025]) as taught, by Balevi into the teachings of Stefano invention. One would have been motivated to do so in order to the small cell base station employing in an unlicensed frequency spectrum, boost coverage to increase capacity of the access network. The operation is continuously repeated, thus resulting in increased accuracy of the neural network over time. The signaling efficiencies is enhanced and latency is reduced compared to current standards.
Regarding claim 33.
Stefano teaches wherein the K agent entities are selected by the coordinator entity evaluating different possible candidate subsets, each composed of K agent entities, by, for each candidate subset, evaluating a metric Us, wherein the metric Us for a given candidate subset is a function of the obtained parameters for the K agent entities of said candidate subset (Stefano teaches on page 4 in fig. 2 on the left column shows how the agent entities can be selected, further, Stefano teaches on page 4 on the left column under Fig. 2, the first paragraph, lines 1-9 which clearly discloses about the server which is considered as the claimed “coordinator entity” and also on page 4 under section A. “TSCH MAC and communication round scheduling”, lines 1-11 the aforementioned of scheduler performs the selection of different agent entities for different time slots for the iterative learning process).
Regarding claim 34.
Stefano teaches wherein one or more of: the parameters for agent entity n at least define a value (n representing how many other of the N agent entities that a broadcast transmission from agent entity n reaches, wherein one selection criterion is to select the candidate subset S composed of the K agent entities with largest values of (n the parameters for agent entity n at least define a value an representing how many iterations that have been performed since agent entity n last broadcast its local model parameter vector, wherein one selection criterion is to select the candidate subset S composed of the K agent entities with largest values of an; the parameters for agent entity n at least define a value bn representing duration in time since agent entity n last broadcast its local model parameter vector, wherein one selection criterion is to select the candidate subset S composed of the K agent entities with largest values of bn; the parameters for agent entity n at least define a value Dn representing data importance of the local model parameter vector for agent entity n, wherein one selection criterion is to select the candidate subset S composed of the K agent entities with largest values of Dn; the parameters for agent entity n at least define a value Ln representing availability of agent entity n to perform broadcasting of its local model parameter vector, wherein one selection criterion is to select the candidate subset S composed of the K agent entities with largest values of Ln; and/or the parameters for agent entity n at least define a value Mn representing a utility metric for agent entity n, wherein one selection criterion is to select the candidate subset S composed of the K agent entities with largest values of Mn (Stefano teaches on page 4 on the left column under section III A. “TSCH MAC and communication round scheduling” I lines 1-12 the time slot…A SF generally consists of a number of consecutive TS and
identifies a transmission session where slots and frequencies can be reserved to individual devices, or shared to broadcast control, synchronization or transmission session information. For each SF, a time-frequency scheduler assigns a transmission, and further on page 4 on the right column in lines 1- until the end of the paragraphs (i.e., lines 1-51). Note that here, the claim lists features in the alternative. While the claim lists a number of optional limitations only one limitation from the list is required and needs to be met by the prior art and thus, the prior art of Stefano addressed the limitation of “the parameters for agent entity n at least define a value Ln representing availability of agent entity n to perform broadcasting of its local model parameter vector, wherein one selection criterion is to select the candidate subset S composed of the K agent entities with largest values of Ln”).
Regarding claim 35.
Stefano teaches the parameters for agent entity n at least define a value Mn representing a utility metric for agent entity n, wherein one selection criterion is to select the candidate subset S composed of the K agent entities with largest values of Mn, and wherein the utility metric for agent entity n at iteration t is a function of absolute, or relative, magnitude of the local model parameter vector calculated by agent entity n for iteration t-1 (Stefano teaches on page 5 on the left column under “section B. FL (Federative Learning) and network simulation”, lines 1-28 the decentralized FL (2) learning parameters have been assigned, the framework runs a real-time simulation of the decentralized FL for a configurable number of communication rounds…iii) the computational time for local SGD on mini-batches).
Regarding claim 36.
Stefano teaches the parameters for agent entity n at least define a value Mn representing a utility metric for agent entity n, wherein one selection criterion is to select the candidate subset S composed of the K agent entities with largest values of Mn, and wherein the value Mn is either obtained by the coordinator entity from agent entity n or computed by the coordinator entity from other parameters obtained from agent entity n (Stefano teaches on page 3 on the left column starting from the first paragraph, lines 1-the end different formulas to calculate the values and to select the candidates based on the by the non-linear operator ϱ before being forwarded to the neighbors for a new consensus round. Finally, a stopping criteria, e.g. on learning loss or convergence time, is then applied to end the diffusion of updates after some rounds).
Regarding claim 38.
Stefano teaches wherein K has a value that is dependent on the iteration round of the iterative learning process (Stefano teaches on page 4 on the right column under equation number 5, in lines 1-22 shows the iteration round produced by the individual devices on each communication round, and thus
the duration of the round).
Regarding claim 39.
Stefano teaches wherein the selected K agent entities form a first subset of selected agent entities, and wherein the method further comprises: selecting K' < N further agent entities to broadcast their local model parameter vector for the iteration by evaluating the selection criterion (Stefano teaches on page 4 on the left column under Fig. 2, the first paragraph, lines 1-9 which clearly discloses about the server which is considered as the claimed “coordinator entity” and also on page 4 under section A. “TSCH MAC and communication round scheduling”, lines 1-11 the aforementioned of scheduler performs the selection of different agent entities for different time slots for the iterative learning process), wherein the further K' agent entities form a second subset of further selected agent entities disjoint from the first subset of selected agent entities, and wherein the further K' agent entities are selected according to an interference criterion with respect to the first subset of selected agent entities (Stefano teaches on page 4 in fig. 2 on the left column shows how the agent entities can be selected, further, Stefano teaches on page 4 on the left column under Fig. 2, the first paragraph, lines 1-9 which clearly discloses about the server which is considered as the claimed “coordinator entity” and also on page 4 under section A. “TSCH MAC and communication round scheduling”, lines 1-11 the aforementioned of scheduler performs the selection of different agent entities for different time slots for the iterative learning process and further, Stefano teaches on page 3 on the left column starting from the first paragraph, lines 1-the end different formulas to calculate the values and to select the candidates based on the by the non-linear operator ϱ before being forwarded to the neighbors for a new consensus round. Finally, a stopping criteria, e.g. on learning loss or convergence time, is then applied to end the diffusion of updates after some rounds and further, Stefano teaches on page 3 on the right column under the signal to noise ratio, in lines 4-10 the formula can be used to assess the spectral efficiency of the link…, receiver-side sensitivity threshold…)); and
sending information that informs the N agent entities of the further selected K' agent entities (Stefano teaches on page 4 on the right column lines 6-38 and on page 5 I the left column lines 1-7 and under section III. B of Stefano and thus, these sections can be interpreted as the coordinator entity does not inform every agent entity individually about which other agent entity has been selected and which
one has not. Instead, the coordinator entity informs the set of N different agent entities as a
whole about which other agent entity has been selected and which one has not. The later way of
informing all agent entities).
Regarding claim 40.
Stefano teaches a method for performing an iterative learning process, wherein the method is performed by an agent entity, wherein the iterative learning process pertains to a computational task to be performed by the agent entity for training a machine learning model (Stefano teaches in Fig. 1 a standard Communication and computational model: model averaging, SGD on local data, ML model parameters pruning, compress and forward of parameters on each FL round and further explaining on page 2, on the right column, lines 1-10 by providing a vector formula to achieve the predicted function and also further explains in detail pages 2-5 about the practical example of the collected independently and individually by the devices based on their local observations of the given phenomenon. Therefore, local samples are not representative of the full data distribution by providing a formulas. (See page 2 under section II “decentralized FL and physical communications” each agent entity is characterized by a so-called neighbor set of agent entities. As can be seen from the last paragraph of the same section on page 3 of Stefano the said each agent entity may be instructed by the scheduler to broadcast its local parameter vector to all agent entities of the neighbor set, and further on page 3 under section III “networking framework for decentralized FL”), on page 2 on the right column under section II “A. Gossip and consensus approaches to decentralized Federative Learning”, in lines 1-11, the federative learning is an iterative learning process which includes multiple devices (i.e., entities)), furthermore, see on page 4 on the left column under Fig. 2, the first paragraph, lines 1-9 which clearly discloses about the server which is considered as the claimed “coordinator entity” and also on page 4 under section A. “TSCH MAC and communication round scheduling”, lines 1-11 the aforementioned of scheduler performs the selection of different agent entities for different time slots for the iterative learning process),
wherein, for each iteration round of the iterative learning process, a local model parameter vector with locally computed computational results is computed by the agent entity based on its own local training data and local model parameter vectors received from other agent entities (Stefano teaches on page 4 on the right column under equation number 5, in lines 1-22 shows the iteration round produced by the individual devices on each communication round, and thus
the duration of the round and further, Stefano teaches on page 4 on the right column lines 6-38 and on page 5 I the left column lines 1-7 and under section III. B of Stefano and thus, these sections can be interpreted as the coordinator entity does not inform every agent entity individually about which other agent entity has been selected and which one has not. Instead, the coordinator entity informs the set of N different agent entities as a whole about which other agent entity has been selected and which one has not. The later way of informing all agent entities), wherein the locally computed computational results are updates of the machine learning model (Stefano teaches on page 3 the equation 2 clearly indicates the computing process), wherein for each iteration round of the iterative learning process the agent entity is only to broadcast its local model parameter vector when informed to do so, and wherein the method, for each iteration of the iterative learning process (Stefano teaches in Fig. 1 a standard Communication and computational model: model averaging, SGD on local data, ML model parameters pruning, compress and forward of parameters on each FL round and further explaining on page 2, on the right column, lines 1-10 by providing a vector formula to achieve the predicted function and also further explains in detail pages 2-5 about the practical example of the collected independently and individually by the devices based on their local observations of the given phenomenon. Therefore, local samples are not representative of the full data distribution by providing a formulas. (See page 2 under section II “decentralized FL and physical communications” each agent entity is characterized by a so-called neighbor set of agent entities. As can be seen from the last paragraph of the same section on page 3 of Stefano the said each agent entity may be instructed by the scheduler to broadcast its local parameter vector to all agent entities of the neighbor set, and further on page 3 under section III “networking framework for decentralized FL”), on page 2 on the right column under section II “A. Gossip and consensus approaches to decentralized Federative Learning”, in lines 1-11, the federative learning is an iterative learning process which includes multiple devices (i.e., entities)), comprises:
providing parameters to a coordinator entity, wherein the parameters pertain to a utility for the agent entity to broadcast its local model parameter vector for the iteration (Stefano teaches on page 4 on the left column under Fig. 2, the first paragraph, lines 1-9 which clearly discloses about the server which is considered as the claimed “coordinator entity” and also on page 4 under section A. “TSCH MAC and communication round scheduling”, lines 1-11 the aforementioned of scheduler performs the selection of different agent entities for different time slots for the iterative learning process and further, Stefano teaches on page 1 under fig. 1 in the first paragraph, lines 1-1-3 cost of Device-to-Device (D2D) communications, in terms of energy, computational power (i.e., utility parameters for each entities), bandwidth and channel uses, is much lower than the cost of a long-range server connection); and
broadcasting the local model parameter vector only when the agent entity is one of the selected K agent entities (Stefano teaches on page 4 on the right column lines 6-38 and on page 5 I the left column lines 1-7 and under section III. B of Stefano and thus, these sections can be interpreted as the coordinator entity does not inform every agent entity individually about which other agent entity has been selected and which one has not. Instead, the coordinator entity informs the set of N different agent entities as a whole about which other agent entity has been selected and which one has not. The later way of
informing all agent entities). Stefano does not explicitly teach receiving information from the coordinator entity that informs the agent entity of which K agent entities that have been selected to broadcast their local model parameter vector for the iteration.
However, Balevi teaches receiving information from the coordinator entity that informs the agent entity of which K agent entities that have been selected to broadcast their local model parameter vector for the iteration (Balevi teaches in para. [0006] a first data aggregation period for training the machine learning model; obtaining a first set of updated model parameters from a first client device and a second set of updated model parameters from a second client device,…, and further, Balevi teaches in para. [0025] wireless communication networks are deployed to provide various communication services, such as voice, video, packet data, messaging, broadcast, and the like).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the
claimed invention to modify the teachings of using a training the machine learning model for obtaining a first set of updated model parameters from a first client device and a second set of updated model parameters from a second client device and to provide various communication services, such as broadcasting ([0006] and [0025]) as taught, by Balevi into the teachings of Stefano invention. One would have been motivated to do so in order to the small cell base station employing in an unlicensed frequency spectrum, boost coverage to increase capacity of the access network. The operation is continuously repeated, thus resulting in increased accuracy of the neural network over time. The signaling efficiencies is enhanced and latency is reduced compared to current standards.
Regarding claim 41.
Stefano teaches wherein one or more of: the parameters for the agent entity at least define a value (n representing how many other of the N agent entities that a broadcast transmission from the agent entity reaches; the parameters for the agent entity at least define a value an representing how many iterations that have been performed since the agent entity last broadcast its local model parameter vector; the parameters for the agent entity at least define a value bn representing duration in time since the agent entity last broadcast its local model parameter vector; the parameters for the agent entity at least define a value Dn representing data importance of the local model parameter vector for the agent entity; the parameters for the agent entity at least define a value Ln representing availability of the agent entity to perform broadcasting of its local model parameter vector; and/or the parameters for the agent entity at least define a value Mn representing a utility metric for the agent entity (Stefano teaches on page 4 on the left column under section III A. “TSCH MAC and communication round scheduling” I lines 1-12 the time slot…A SF generally consists of a number of consecutive TS and identifies a transmission session where slots and frequencies can be reserved to individual devices, or shared to broadcast control, synchronization or transmission session information. For each SF, a time-frequency scheduler assigns a transmission, and further on page 4 on the right column in lines 1- until the end of the paragraphs (i.e., lines 1-51). Note that here, the claim lists features in the alternative. While the claim lists a number of optional limitations only one limitation from the list is required and needs to be met by the prior art and thus, the prior art of Stefano addressed the limitation of “the parameters for agent entity n at least define a value Ln representing availability of agent entity n to perform broadcasting of its local model parameter vector, wherein one selection criterion is to select the candidate subset S composed of the K agent entities with largest values of Ln”).
Regarding claim 43.
Stefano teaches wherein the coordinator entity is provided in a network node, and each of the agent entities is provided in a respective user equipment (Stefano teaches on page 2, under equation formula (1) “A. Gossip and consensus approaches to decentralized FL”, lines 1-11, all network nodes connected with user individual devices).
Regarding claim 44.
Stefano teaches wherein the agent entities are provided in a distributed computing architecture (Stefano teaches on page 2 on the left column, under “Contribution:”, lines 7-12 On the contrary,
the proposed tool real-time simulates the decentralized FL training process using federated datasets of any size as in puts and accounts for hundreds of distributed communication rounds, that involve short range D2D communications over an arbitrary graph structure).
Regarding claim 45.
Claim 45 incorporates substantively all the limitation of claim 32 in a coordinator entity form and is rejected under the same rationale. Furthermore, for the limitation of a coordinator entity, the prior art of record Stefano teaches on page 1-3 and Fig. 1 server as a coordinator entity.
Regarding claim 46.
Stefano teaches wherein the K agent entities are selected by the coordinator entity evaluating different possible candidate subsets, each composed of K agent entities, by, for each candidate subset, evaluating a metric Us, wherein the metric Us for a given candidate subset is a function of the obtained parameters for the K agent entities of said candidate subset (Stefano teaches on page 4 in fig. 2 on the left column shows how the agent entities can be selected, further, Stefano teaches on page 4 on the left column under Fig. 2, the first paragraph, lines 1-9 which clearly discloses about the server which is considered as the claimed “coordinator entity” and also on page 4 under section A. “TSCH MAC and communication round scheduling”, lines 1-11 the aforementioned of scheduler performs the selection of different agent entities for different time slots for the iterative learning process).
Regarding claim 47.
Stefano teaches wherein one or more of: the parameters for agent entity n at least define a value (n representing how many other of the N agent entities that a broadcast transmission from agent entity n reaches, wherein one selection criterion is to select the candidate subset S composed of the K agent entities with largest values of (n; the parameters for agent entity n at least define a value an representing how many iterations that have been performed since agent entity n last broadcast its local model parameter vector, wherein one selection criterion is to select the candidate subset S composed of the K agent entities with largest values of an; the parameters for agent entity n at least define a value bn representing duration in time since agent entity n last broadcast its local model parameter vector, wherein one selection criterion is to select the candidate subset S composed of the K agent entities with largest values of bn; the parameters for agent entity n at least define a value Dn representing data importance of the local model parameter vector for agent entity n, wherein one selection criterion is to select the candidate subset S composed of the K agent entities with largest values of Dn; the parameters for agent entity n at least define a value Ln representing availability of agent entity n to perform broadcasting of its local model parameter vector, wherein one selection criterion is to select the candidate subset S composed of the K agent entities with largest values of Ln; and/or the parameters for agent entity n at least define a value Mn representing a utility metric for agent entity n, wherein one selection criterion is to select the candidate subset S composed of the K agent entities with largest values of Mn (Stefano teaches on page 4 on the left column under section III A. “TSCH MAC and communication round scheduling” I lines 1-12 the time slot…A SF generally consists of a number of consecutive TS and identifies a transmission session where slots and frequencies can be reserved to individual devices, or shared to broadcast control, synchronization or transmission session information. For each SF, a time-frequency scheduler assigns a transmission, and further on page 4 on the right column in lines 1- until the end of the paragraphs (i.e., lines 1-51). Note that here, the claim lists features in the alternative. While the claim lists a number of optional limitations only one limitation from the list is required and needs to be met by the prior art and thus, the prior art of Stefano addressed the limitation of “the parameters for agent entity n at least define a value Ln representing availability of agent entity n to perform broadcasting of its local model parameter vector, wherein one selection criterion is to select the candidate subset S composed of the K agent entities with largest values of Ln”).
Regarding claim 48.
Stefano teaches the parameters for agent entity n at least define a value Mn representing a utility metric for agent entity n, wherein one selection criterion is to select the candidate subset S composed of the K agent entities with largest values of Mn, and wherein the utility metric for agent entity n at iteration t is a function of absolute, or relative, magnitude of the local model parameter vector calculated by agent entity n for iteration t – 1 (Stefano teaches on page 5 on the left column under “section B. FL (Federative Learning) and network simulation”, lines 1-28 the decentralized FL (2) learning parameters have been assigned, the framework runs a real-time simulation of the decentralized FL for a configurable number of communication rounds…iii) the computational time for local SGD on mini-batches).
Regarding claim 49.
Stefano teaches the parameters for agent entity n at least define a value Mn representing a utility metric for agent entity n, wherein one selection criterion is to select the candidate subset S composed of the K agent entities with largest values of Mn, and wherein the value Mn is either obtained by the coordinator entity from agent entity n or computed by the coordinator entity from other parameters obtained from agent entity n (Stefano teaches on page 3 on the left column starting from the first paragraph, lines 1-the end different formulas to calculate the values and to select the candidates based on the by the non-linear operator ϱ before being forwarded to the neighbors for a new consensus round. Finally, a stopping criteria, e.g. on learning loss or convergence time, is then applied to end the diffusion of updates after some rounds).
Regarding claim 51.
Claim 51 incorporates substantively all the limitation of claim 40 in a coordinator entity form and is rejected under the same rationale. Furthermore, for the limitation of a coordinator entity, the prior art of record Stefano teaches on page 1-3 and Fig. 1 server as a coordinator entity.
Claims 37, 42 and 50 are rejected under 35 U.S.C. 103 as being unpatentable over Stefano in view of Balevi further in view of Bartfai-Walcott et al. U.S. Pub. No. 2019/0044949 A1, (hereinafter Bartfai).
Regarding claim 37. Stefano in view of Balevi the teaches method according to claim 33.
Stefano further teaches about wherein the parameters for agent entity n at least define any of a vendor of said agent entity n and and/or that have (Stefano teaches on page 3 on the left column starting from the first paragraph, lines 1-the end different formulas to calculate the values and to select the candidates based on the by the non-linear operator ϱ before being forwarded to the neighbors for a new consensus round. Finally, a stopping criteria, e.g. on learning loss or convergence time, is then applied to end the diffusion of updates after some rounds and further, Stefano teaches on page 3 on the right column under the signal to noise ratio, in lines 4-10 the formula can be used to assess the spectral efficiency of the link…, receiver-side sensitivity threshold…). Stefano in view of Balevi does not explicitly teach wherein the parameters for agent entity n at least a trust level and/or that have a trust level higher than a trust level threshold.
However, Bartfai teaches wherein the parameters for agent entity n at least a trust level and/or that have a trust level higher than a trust level threshold (Bartifai teaches in para. [0014] trust level determination (or trust zone assignment) is based on the amount and type of information that the device can collect or observe prior to interacting with a neighboring/proximate device… For example, a neighboring/proximate device may be assigned to a least trusting (high risk) trust zone if the device is only able to observe the neighboring/proximate device's behavior, further, Bartifai teaches in para. [0043] the values in the approach rate column may be based on different speed (velocity) thresholds. Similarly, the values in the risk level and trust level columns may be based on different risk level thresholds and trust level thresholds, respectively).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the
claimed invention to modify the teachings of trust level determination (or trust zone assignment) is based on the amount and type of information that the device can collect or observe prior to interacting with a neighboring/proximate device that the values in the approach rate column may be based on different speed (velocity) thresholds ([0014] and [0043]) as taught, by Bartifai into the teachings of Stefano in view of Balevi invention. One would have been motivated to do so in order to the trust level determination is based on the amount and type of information that the device can collect or observe prior to interacting with a neighboring/proximate device.
Regarding claim 42. Stefano in view of Balevi the teaches the method according to claim 40.
Stefano in view of Balevi does not explicitly teach wherein the parameters for the agent entity at least define any of a vendor of the agent entity and/or a trust level of the agent entity.
However, Bartfai teaches wherein the parameters for the agent entity at least define any of a vendor of the agent entity and/or a trust level of the agent entity (Bartifai teaches in para. [0014] trust level determination (or trust zone assignment) is based on the amount and type of information that the device can collect or observe prior to interacting with a neighboring/proximate device… For example, a neighboring/proximate device may be assigned to a least trusting (high risk) trust zone if the device is only able to observe the neighboring/proximate device's behavior, further, Bartifai teaches in para. [0043] the values in the approach rate column may be based on different speed (velocity) thresholds. Similarly, the values in the risk level and trust level columns may be based on different risk level thresholds and trust level thresholds, respectively).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the
claimed invention to modify the teachings of trust level determination (or trust zone assignment) is based on the amount and type of information that the device can collect or observe prior to interacting with a neighboring/proximate device that the values in the approach rate column may be based on different speed (velocity) thresholds ([0014] and [0043]) as taught, by Bartifai into the teachings of Stefano in view of Balevi invention. One would have been motivated to do so in order to the trust level determination is based on the amount and type of information that the device can collect or observe prior to interacting with a neighboring/proximate device.
Regarding claim 50.
Claim 50 incorporates substantively all the limitation of claim 37 in a coordinator entity form and is rejected under the same rationale. Furthermore, for the limitation of a coordinator entity, the prior art of record Stefano teaches on page 1-3 and Fig. 1 server as a coordinator entity.
Conclusion
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/BERHANU SHITAYEWOLDETSADIK/Primary Examiner, Art Unit 2455
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