DETAILED ACTION
This office action is responsive to the above identified application filed 2/29/2024. The application contains claims 1-20, all examined and rejected.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The Information Disclosure Statement with references submitted 2/29/2024, has been considered and entered into the file.
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.
Claim 8-14 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim 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 8 recites the limitation "said abbreviated model". There is insufficient antecedent basis for this limitation in the claim. Dependent claims inherit the independent claim deficiency.
Claims 16-17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim 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 16 recite limitations “a module for sending”, “a module for receiving”, “a module for updating” which invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. While the specification describes various modules in terms of software code, the specification is devoid for any explicit definition of the phrase “communications module”. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claim 17 recite limitations “a module for receiving”, “an initialization module configured”, “a module for updating”, “a module for sending” which invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. While the specification describes various modules in terms of software code, the specification is devoid for any explicit definition of the phrase “communications module”. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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 1-7, 10-12, 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 1 is rejected under 35 USC 101 because the claimed inventions are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
While independent claims 1, 8, 17 and 19-20 are each directed to a statutory category, it recites a series of steps which appears to be directed to an abstract idea (mental process).
Claims 1-7, 10-12, 19 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include certain methods of organizing human activities; a mental processes; and mathematical concepts, (2019 PEG)
STEP 1.
Per Step 1, the claims are determined to include process, manufacture, and machine as in independent Claim 1, 8, 17 and 19-20, and in the therefrom dependent claims. Therefore, the claims are directed to a statutory eligibility category.
At step 2A, prong 1, The invention is directed to what is akin to Mental Process (see Alice), As such, the claims include an abstract idea. When considering the limitations individually and as a whole the limitations directed to the abstract idea are:
Claim 1
“at least one partitioning of the set of nodes into at least one cluster of nodes“, “designating at least a first node of said cluster as an aggregation node managing an aggregate model of said at least one cluster of nodes for said federated learning” (Mental process, observation, evaluation and judgment).
Claim 10
determining whether said cluster must be restructured by taking into account a change in the weights of said cluster and/or a change in the weights of the nodes of said cluster” (Mental process, observation, evaluation and judgment).
The claim recites additional elements as
Claim 1
“A method for configuring weights of models of neural networks of the same structure, of nodes from a set of nodes of a communication network, said method including a federated learning of said weights in which said nodes locally train their model of neural networks and share the weights of their model with other nodes of said communication network “ (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h));
“sending, to the nodes of said cluster, information designating said first node as an aggregation node” (insignificant extra-solution activity, MPEP 2106.05(g))
Claim 10
“A learning method implemented by a node from a set of nodes including neural networks having a model of the same structure, of a communication network, said method including, before federated learning of the weights of said models of the neural networks of the nodes of said set, in which said nodes locally train their model of neural networks and share the weights of their model with aggregation nodes of said network” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h));
“receiving, from an entity of said communication network, information designating a first node from said set as an aggregation node managing an aggregate model for said federated learning and, when said node is said first node, identifiers of the nodes of a cluster whose said aggregation node manages said abbreviated model” (insignificant extra-solution activity, MPEP 2106.05(g)).
This judicial exception is not integrated into a practical application. The elements are recited at a high level of generality, i.e. a generic computing system performing generic functions including generic processing of data. Accordingly the additional elements do not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore the claims are directed to an abstract idea. (2019 Revised Patent Subject Matter Eligibility Guidance ("2019 PEG"). Thus, under Step 2A of the Mayo framework, the Examiner holds that the claims are directed to concepts identified as abstract.
STEP 2B.
Because the claims include one or more abstract ideas, the examiner now proceeds to Step 2B of the analysis, in which the examiner considers if the claims include individually or as an ordered combination limitations that are "significantly more" than the abstract idea itself. This includes analysis as to whether there is an improvement to either the "computer itself," "another technology," the "technical field," or significantly more than what is "well-understood, routine, or conventional" (WURC) in the related arts.
The instant application includes in Claim 1 additional steps to those deemed to be abstract idea(s).
When taken the steps individually, these steps are:
Claim 1
“A method for configuring weights of models of neural networks of the same structure, of nodes from a set of nodes of a communication network, said method including a federated learning of said weights in which said nodes locally train their model of neural networks and share the weights of their model with other nodes of said communication network “(merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f));
“sending, to the nodes of said cluster, information designating said first node as an aggregation node” (well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)),
Claim 10
“A learning method implemented by a node from a set of nodes including neural networks having a model of the same structure, of a communication network, said method including, before federated learning of the weights of said models of the neural networks of the nodes of said set, in which said nodes locally train their model of neural networks and share the weights of their model with aggregation nodes of said network” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f));
“receiving, from an entity of said communication network, information designating a first node from said set as an aggregation node managing an aggregate model for said federated learning and, when said node is said first node, identifiers of the nodes of a cluster whose said aggregation node manages said abbreviated model” (well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i))
In the instant case, Claims 1 and 10 are directed to above mentioned abstract idea. Technical functions such as receiving, and extracting are common and basic functions in computer technology. The individual limitations are recited at a high level and do not provide any specific technology or techniques to perform the functions claimed.
In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps does not add "significantly more" by virtue of considering the steps as a whole, as an ordered combination. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments using what is well-understood, routine, and conventional in the related arts. The steps are still a combination made to the abstract idea. The additional steps only add to those abstract ideas using well understood and conventional functions, and the claims do not show improved ways of, for example, an unconventional non-routine functions for analyzing model operations or updating the model that could then be pointed to as being "significantly more" than the abstract ideas themselves.
Moreover, Examiner was not able to identify any "unconventional" steps, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is well-understood, routine, and conventional (WURC) in the related arts.
Further, note that the limitations, in the instant claims, are done by the generically
recited computing devices. The limitations are merely instructions to implement the abstract idea on a computing device that is recited in an abstract level and require no more than a generic computing devices to perform generic functions.
Claim 19 recites a “ A non-transitory computer readable medium” and “at least one processor” configured to perform the same method as set forth in claim 1, the added element of “ A non-transitory computer readable medium” and “at least one processor” do not transform the judicial exception into a practical application because they are tantamount to a mere instruction to apply the judicial exception to a generic computer. The additional elements are also not sufficient to amount to significantly more than the judicial exception because the action of implementing the method on a general purpose computer with at least one processor and at least one memory is tantamount to a mere instruction to apply the judicial exception to a computer.
Claim 19 is therefore rejected according to the same findings and rationale as provided above.
Independent claims 19 are the same analogy and rejected using similar analysis as claim 1.
CONCLUSION
It is therefore determined that the instant application not only represents an abstract idea identified as such based on criteria defined by the Courts and on USPTO examination guidelines, but also lacks the capability to bring about "Improvements to another technology or technical field" (Alice), bring about "Improvements to the functioning of the computer itself" (Alice), "Apply the judicial exception with, or by use of, a particular machine" (Bilski), "Effect a transformation or reduction of a particular article to a different state or thing" (Diehr), "Add a specific limitation other than what is well-understood, routine and conventional in the field" (Mayo), "Add unconventional steps that confine the claim to a particular useful application" (Mayo), or contain "Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment" (Alice), transformed a traditionally subjective process performed by humans into a mathematically automated process executed on computers (McRO), or limitations directed to improvements in computer related technology, including claims directed to software (Enfish).
The dependent claims, when considered individually and as a whole, likewise do not provide "significantly more" than the abstract idea for similar reasons as the independent claim.
claims 2 disclose “ wherein said designation is temporary, the method comprising another designation for at least one other partition of said set of nodes” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)). This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 3 disclose “sending, to the aggregation node of said at least one cluster, a request to learn the weights of the models of the nodes of said cluster with the weights of a global model to the set of nodes; receiving, from the aggregation node of said at least one cluster, the weights of said aggregate model of said cluster resulting from said learning“ (insignificant extra-solution activity, MPEP 2106.05(g) that is well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)), “updating the weights of the global model by aggregation of the received weights of the aggregate model of said at least one cluster” (mental process). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 4 disclose “ partitioning the set of nodes into at least one cluster by taking into account a communication cost between the nodes within said at least one cluster” (mental process). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 5 disclose “partitioning the set of nodes to reorganize said clusters into at least one reorganized cluster, said at least one reorganized cluster being constituted according to a function taking into account a communication cost between the nodes within a reorganized cluster and a similarity of a change in the weights of the models of the nodes within a reorganized cluster” (mental process). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 6 disclose “similarity is determined by: asking said nodes to replace the weights of their model with the weights of the updated global model; asking said nodes to update their model by training their model with their local dataset; and by (insignificant extra-solution activity, MPEP 2106.05(g) that is well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)), “determining a similarity of the changes in the weights of the models of the different nodes” (mental process). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 7 disclose “receiving, from the aggregation node of a first cluster, an identifier of an isolated node of said first cluster” (insignificant extra-solution activity, MPEP 2106.05(g) that is well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)), “reallocating said isolated node to another cluster, by taking into account a proximity between: a direction of a change of the weights of said isolated node when it is trained by local data to the isolated node; and a direction of a change of the weights of the aggregate model of said other cluster, compared to the same reference model” (mental process). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. claims 11 disclose “said node is said aggregation node, in response to a determination that said cluster must be restructured, restructuring said cluster by grouping at least part of the nodes of said cluster into at least one subcluster, said at least one subcluster being constituted according to a function taking into account a communication cost between the nodes within said subcluster and a similarity of a change in the weights of the models of the nodes within one said subcluster” (mental process). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. claims 12 disclose “restructuring of said cluster includes sending, to said entity of said communication network, the identifier of an isolated node of said cluster” (insignificant extra-solution activity, MPEP 2106.05(g) that is well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea.
The dependent claims which impose additional limitations also fail to claim patent eligible subject matter because the limitations cannot be considered statutory. The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1 ; where all claims are directed to the same abstract idea, "addressing each claim of the asserted patents [is] unnecessary." Content Extraction &. Transmission LLC v, Wells Fargo Bank, Natl Ass'n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter. Claims for the other statutory classes are similarly analyzed.
For at least these reasons, the claimed inventions of each of dependent claims 2-7, 11-12,are directed or indirect to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more and are rejected under 35 USC 101.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4, 8-9, 13-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by “E-Tree Learning: A Novel Decentralized Model Learning Framework for Edge AI” Published Jan 2021 hereinafter D1.
With regard to Claim 1,
D1 teach a method for configuring weights of models of neural networks of the same structure, of nodes from a set of nodes of a communication network (P. 2, 2.1, “The upper one is the edge computing layer which includes a set of edge devices (or servers) interconnected with each other via particular networks”, P. 4, 3.2, “Supposed that there are N nodes in the physical network G, and the node IDs is denoted as {0,1,2,...,N − 1}”, 3.3, “we first initialize a model for the root, and the root sends the model to all of the nodes in N2”) , said method including a federated learning of said weights in which said nodes locally train their model of neural networks and share the weights of their model with other nodes of said communication network (P. 4, 3.3, “The leaf node of E-Tree is responsible for training a local model using its own data samples, while an internal node aggregates the models from its children and updates an aggregated model back to the children”, “After the training is finished, the node computes the updates of the model and sends the updates to its parent”), the method including:
at least one partitioning of the set of nodes into at least one cluster of nodes (P. 4, 3.2, “Then we use node clustering algorithms, which are detailed in Section 4.1, to divide the nodes of N1 into K1 groups, and denote the nodes in each cluster as {C1,1,C2,1,...,CK1,1}”, 4.1, “To optimize the structure, E-Tree learning clusters the edge devices before the 1st level of aggregation. It determines which edge devices should be grouped together for model aggregation;
designating at least a first node of said cluster as an aggregation node managing an aggregate model of said at least one cluster of nodes for said federated learning (P. 4, Eq(1), “Next, we find the center node nk1,1 of each cluster Ck1,1 by [Eq 1] After the center nodes of these clusters are found, we use these center nodes as aggregation nodes at the second layer in E-Tree, which are denoted as N2. Each node of N2 is connected to all of the nodes in the corresponding cluster”, 3.3, “Note that the nodes which are chosen to be the center nodes finish model training and aggregation on the same device”), said designation comprising:
sending, to the nodes of said cluster, information designating said first node as an aggregation node (P. 4, 3.2, “Each node of N2 is connected to all of the nodes in the corresponding cluster”, “After the training is finished, the node computes the updates of the model and sends the updates to its parent”); and
sending, to the first node, the identifiers of the nodes of said cluster (P. 4, 3.2, “the node IDs is denoted as {0,1,2,...,N − 1}”, 3.3, “After the parent in N2 receives all the updates from each child, it starts the model aggregation”).
With regard to Claim 2,
D1 teach the method claim 1, wherein said designation is temporary (P. 3, 2.3, “The structure of the aggregation tree including the number of layers and node grouping is dynamically built according to network topology and data distribution.”, P. 6, 4.2, “In order to deal with the network dynamics, E-Tree learning enables the change of structure in the next iteration of aggregation. Thus, E-Tree learning has a planner to dynamically adjust the structure of the aggregation tree”), the method comprising another designation for at least one other partition of said set of nodes (P. 6, Algorithm1, lines 17-18).
With regard to Claim 3,
D1 teach the method of claim 1, during said federated learning:
sending, to the aggregation node of said at least one cluster, a request to learn the weights of the models of the nodes of said cluster with the weights of a global model to the set of nodes (P. 4, 3.3, “we first initialize a model for the root, and the root sends the model to all of the nodes in N2. After nodes in N2 receive the model, they save the model locally and send it to their children without any processing”, P. 3, 3.1, “Similar to existing federate learning, E-Tree learning also requires an initial model to start the training”);
receiving, from the aggregation node of said at least one cluster, the weights of said aggregate model of said cluster resulting from said learning (P. 4, 3.3, “For instance, nodes in N2 send their models to their parents after finishing a2-th aggregation from the children. Similar to the nodes in N2, the root starts the aggregation after receiving all the models from its children”; and
updating the weights of the global model by aggregation of the received weights of the aggregate model of said at least one cluster (P. 4, 3.3, “The aggregation is done by computing the average of all the updates and adding it to the current model in the parent”, “After the aggregation on the root is done, the root sends its new model downwards and the new turn is processed as mentioned above”, 3.1, “The top level aggregation is the root of E-Tree learning. It aggregates the model updates and then sends back the result to all the edge devices in the same routing paths with the aggregation”).
With regard to Claim 4,
D1 teach the method of claim 1, wherein the method comprises partitioning the set of nodes into at least one cluster by taking into account a communication cost between the nodes within said at least one cluster (P. 4, 3.3, “The transmission delay between any two nodes i and j is denoted as di,j”, Eq (1), P. 4-5, 4.1, “Existing network clustering algorithms group the devices according to the physical distance in the network. The devices with low communication cost among each other are grouped together”, P. 5, 4.1.2, “KMA clusters the nodes based on both transmission delay and pre-trained accuracy”, P. 9, 5.1, “The clustering algorithm for grouping takes into account the network distance”).
With regard to Claim 8,
D1 teach learning method implemented by a node from a set of nodes including neural networks having a model of the same structure, of a communication network (P. 3, 3.1, “In the structure, the leaf-nodes at the bottom layer represent the edge devices involved in the learning. The non-leaf nodes represent model aggregation. We name the non-leaf nodes by aggregation nodes.”, P. 4, 3.3, “we first initialize a model for the root, and the root sends the model to all of the nodes in N2”), said method including, before federated learning of the weights of said models of the neural networks of the nodes of said set, in which said nodes locally train their model of neural networks and share the weights of their model with aggregation nodes of said network (P. 4, 3.3, “The leaf node of E-Tree is responsible for training a local model using its own data samples, while an internal node aggregates the models from its children and updates an aggregated model back to the children”, 3.2, “E-Tree is built from the bottom to top”, the tree (aggregation node assignment) is built before training begin):
receiving, from an entity of said communication network, information designating a first node from said set as an aggregation node managing an aggregate model for said federated learning (P. 4, 3.2, “Each node of N2 is connected to all of the nodes in the corresponding cluster”, 3.3, “the node computes the updates of the model and sends the updates to its parent”) and, when said node is said first node, identifiers of the nodes of a cluster whose said aggregation node manages said abbreviated model (P. 4, 3.3, “After the parent in N2 receives all the updates from each child, it starts the model aggregation” , 3.2, “the node IDs is denoted as {0,1,2,...,N − 1}).
With regard to Claim 9,
D1 teach the method of claim 8, the method further comprising, when said node is said aggregation node, receiving, from said entity of said communication network, the weights of a model having said structure (P. 4, 3.3, “root sends the model to all of the nodes in N2. After nodes in N2 receive the model, they save the model locally and send it to their children without any processing”);
upon receipt of a request to learn the weights of an aggregate model of said cluster from said received weights (P. 4, 3.3, “After nodes in N2 receive the model, they save the model locally and send it to their children without any processing. Nodes in N1 then receive the model and begin training on it with their local data”);
initializing the weights of the aggregate model of said cluster and the weights of the models of the nodes of said cluster with said received weights (P. 4, 3.3, “they save the model locally and send it to their children without any processing”, P. 3, 3.1, “Similar to existing federate learning, E-Tree learning also requires an initial model to start the training”);
at least updating the weights of the aggregate model of said cluster, by aggregation of the weights of the models of the nodes of said cluster, trained with local datasets to these nodes, the weights of the models of the nodes of said cluster being replaced by the updated weights of the aggregate model of said cluster after each update (P. 4, 3.3, “After the parent in N2 receives all the updates from each child, it starts the model aggregation. The aggregation is done by computing the average of all the updates and adding it to the current model in the parent. Then the parent sends the updated model to its children for training”, “It means the aggregation node at layer l does one global aggregation to its parent every al times of local aggregation from its children”); and
sending, to said entity of said network, the weights of the aggregate model of said updated cluster (P. 4, 3.3, “nodes in N2 send their models to their parents after finishing a2-th aggregation from the children”, 4.2, “For an aggregation node, it aggregates model updates from the children nodes and then sends the result to its parent node”).
With regard to Claim 13,
D1 teach the method of claims 8, further comprising, when said node is not said aggregation node:
receiving, from said aggregation node, the weights of a model having said structure to initialize the weights of the model of the node (P. 4, 3.3, “After nodes in N2 receive the model, they save the model locally and send it to their children without any processing. Nodes in N1 then receive the model and begin training on it with their local data using Stochastic Gradient Descent (SGD)”);
transmitting, to said aggregation node, the weights of the model of the trained node with a local dataset to said node (P. 4, 3.3, “After the training is finished, the node computes the updates of the model and sends the updates to its parent”, P. 3, 3.1, “Within a group, the edge device generates a model update using its own dataset”).
With regard to Claim 14,
D1 teach the method of claim 8, said method being implemented by a node belonging to a first cluster, wherein said entity of said communication network is:
a coordination entity of the network (P. 4, 3.2, “Finally, we find the center node of N2 in the same way, and use it as the root node of a 3-layer E-Tree. The root node is connected with all of the nodes in N2”); or
a node of said set of nodes playing the role of aggregation node managing an aggregate model of a second cluster of lower level than the level of said first cluster.
With regard to Claim 15,
Claim 15 is similar in scope to claim 1 therefore it is rejected under similar rationale. D1 further teach a coordination entity able to configure weights of models of neural networks of the same structure, of nodes from a set of nodes of a communication network, by federated learning of said weights in which said nodes locally train their models of neural networks and share the weights of their model with other nodes of said network, said coordination entity comprising at least one processor (Fig. 1, P. 4, 3.3, “we first initialize a model for the root, and the root sends the model to all of the nodes in N2”, “The edge device is an abstraction of the device/machine that has certain capability”, P. 2, 3.1, “In industrial IoTs, the edge devices could be smart routers or switches with built-in high performance CPU/GPU cores“, 2.2, “The devices have heterogeneous compute capabilities and are connected by bandwidth-limited and intermittent wireless networks”).
With regard to Claim 16,
Claim 16 is similar in scope to claim 3 therefore it is rejected under similar rationale.
With regard to Claim 17,
Claim 17 is similar in scope to claim 8 therefore it is rejected under similar rationale. D1 further teach comprising at least one processor (Fig. 1, P. 4, 3.3, “we first initialize a model for the root, and the root sends the model to all of the nodes in N2”, “The edge device is an abstraction of the device/machine that has certain capability”, P. 2, 3.1, “In industrial IoTs, the edge devices could be smart routers or switches with built-in high performance CPU/GPU cores“, 2.2, “The devices have heterogeneous compute capabilities and are connected by bandwidth-limited and intermittent wireless networks”).
With regard to Claim 18,
Claim 18 is similar in scope to claim 9 therefore it is rejected under similar rationale.
With regard to Claim 19,
Claim 19 is similar in scope to claim 1 therefore it is rejected under similar rationale. D1 further teach a non-transitory computer readable medium having stored thereon instructions which, when executed by a processor (Fig. 1, P. 4, 3.3, “we first initialize a model for the root, and the root sends the model to all of the nodes in N2”, “The edge device is an abstraction of the device/machine that has certain capability”, P. 2, 3.1, “In industrial IoTs, the edge devices could be smart routers or switches with built-in high performance CPU/GPU cores“, 2.2, “The devices have heterogeneous compute capabilities and are connected by bandwidth-limited and intermittent wireless networks”).
With regard to Claim 20,
Claim 20 is similar in scope to claim 8 therefore it is rejected under similar rationale. D1 further teach comprising at least one processor (Fig. 1, P. 4, 3.3, “we first initialize a model for the root, and the root sends the model to all of the nodes in N2”, “The edge device is an abstraction of the device/machine that has certain capability”, P. 2, 3.1, “In industrial IoTs, the edge devices could be smart routers or switches with built-in high performance CPU/GPU cores“, 2.2, “The devices have heterogeneous compute capabilities and are connected by bandwidth-limited and intermittent wireless networks”).
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.
Claims 5-7 , and 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over “E-Tree Learning: A Novel Decentralized Model Learning Framework for Edge AI” Published Jan 2021 hereinafter D1 in view of Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints” Published 2019 hereinafter D2.
With regard to Claim 5,
D1 teach the method of claim 1, the method further comprising partitioning the set of nodes to reorganize said clusters into at least one reorganized cluster (P. 6, 4.2, “In order to deal with the network dynamics, E-Tree learning enables the change of structure in the next iteration of aggregation. Thus, E-Tree learning has a planner to dynamically adjust the structure of the aggregation tree”), said at least one reorganized cluster being constituted according to a function taking into account a communication cost between the nodes within a reorganized cluster (P. 4-5, 4.1, “Existing network clustering algorithms group the devices according to the physical distance in the network. The devices with low communication cost among each other are grouped together”).
D1 does not teach a similarity of a change in the weights of the models of the nodes within a reorganized cluster
D2 teach partitioning the set of nodes to reorganize said clusters into at least one reorganized cluster (P. 8, Algorithm 5, line 19, cluster is replaced by 2 reorganized clusters), and a similarity of a change in the weights of the models of the nodes within a reorganized cluster ((P. 7, “compute cosine similarities between weight-updates … Eq(44)), P. 8, Algorithm 5, line 16, P. 11, VII, “the cosine similarity between the weight-updates of different clients is highly indicative of the similarity of their data distributions”).
D1 and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of clustering based federated learning. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by D2 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1 as described above to provide flexibility to handle client populations that vary over time and can be implemented in a privacy preserving way and to provide a post-processing method that will always achieve greater or equal performance than conventional FL by allowing clients to arrive at more specialized models (D2, Abstract, “CFL is flexible enough to handle client populations that vary over time and can be implemented in a privacy preserving way. As clustering is only performed after Federated Learning has converged to a stationary point, CFL can be viewed as a post-processing method that will always achieve greater or equal performance than conventional FL by allowing clients to arrive at more specialized models.”).
With regard to Claim 6,
D1-D2 teach the method of claim 5, wherein said similarity is determined by:
asking said nodes to replace the weights of their model with the weights of the updated global model (D1, P. 4, Nodes in N1 then receive the model and begin training on it with their local data”, D2, P. 2, “the clients first synchronize with the server by downloading the latest master model θt”, P. 6, Algorithm 2, line 4, P. 8, Algorithm 5: Clustered Federated Learning with Privacy Preservation and Weight-Updates, line 6-7);
asking said nodes to update their model by training their model with their local dataset (D1, P. 1, “edge devices separately train their own models using the local data”, P. 4, Nodes in N1 then receive the model and begin training on it with their local data”,, D2, P. 1, “performing multiple iterations of stochastic gradient descent with mini-batches sampled from it’s local data Di, resulting in a weight-update vector Eq (1), Algorithm 5: Clustered Federated Learning with Privacy Preservation and Weight-Updates, line 8); and by
determining a similarity of the changes in the weights of the models of the different nodes (D2, P. 9, “In all following experiments we will compute cosine similarities based on weight-updates instead of gradients”, P. 8, Algorithm 5: Clustered Federated Learning with Privacy Preservation and Weight-Updates, line 15). The same motivation to combine for claim 5 equally applies for current claim.
With regard to Claim 7,
D1 teach the method of claim 1, the method further comprising:
receiving, from the aggregation node of a first cluster, an identifier of an isolated node of said first cluster (ALG. 1 lines 13-14, line 17, 4.2, “For an aggregation node, it aggregates model updates from the children nodes and then sends the result to its parent node. The model updates would not be sent back to the edge devices until the model is aggregated to the root node in E-Tree learning”, “3.2, “the node IDs is denoted as {0,1,2,...,N − 1}”); reallocating said isolated node to another cluster (ALG. 1 lines 15-17).
D1 does not explicitly teach taking into account a proximity between: a direction of a change of the weights of said isolated node when it is trained by local data to the isolated node; and a direction of a change of the weights of the aggregate model of said other cluster, compared to the same reference model.
D2 teach reallocating said isolated node to another cluster (P. 8, “When a new client joins the training it can get assigned to a leaf cluster by iteratively traversing the parameter tree from the root to a leaf, always moving to the branch which contains the more similar client updates”), by taking into account a proximity (P. 8, Algorithm4: Assigning new Clients to a Cluster, line 6-7, lines 8-11, Fig. 4, P. 7, “This way the new client can be moved down the tree along the path of highest similarity)between:
a direction of a change of the weights of said isolated node when it is trained by local data to the isolated node (P. 8, Algorithm4: Assigning new Clients to a Cluster, line 5, P. 1, “Every client then proceeds to improve the downloaded model, by performing multiple iterations of stochastic gradient descent with mini-batches sampled from it’s local data Di, resulting in a weight-update vector … Eq(1)”; and
a direction of a change of the weights of the aggregate model of said other cluster (P. 8, “At every edge e (vparent → vchild) the pre-split weight updates of the children clients … Eq(47), P. 5, “CFL is recursively re-applied to each of the two separate groups starting from the stationary solution θ∗”, P. 7, Both cv and the corresponding stationary solution θ∗ v obtained by running the Federated Learning Algorithm 2 on cluster cv are cached at node v”), compared to the same reference model (P. 8, “At every edge e (vparent → vchild) the pre-split weight updates of the children clients … Eq(47), P. 5, “CFL is recursively re-applied to each of the two separate groups starting from the stationary solution θ∗” P. 8, Algorithm4: Assigning new Clients to a Cluster, line 5).
D1 and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of clustering based federated learning. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by D2 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1 as described above to provide flexibility to handle client populations that vary over time and can be implemented in a privacy preserving way and to provide a post-processing method that will always achieve greater or equal performance than conventional FL by allowing clients to arrive at more specialized models (D2, (Abstract, “CFL is flexible enough to handle client populations that vary over time and can be implemented in a privacy preserving way. As clustering is only performed after Federated Learning has converged to a stationary point, CFL can be viewed as a post-processing method that will always achieve greater or equal performance than conventional FL by allowing clients to arrive at more specialized models.”).
With regard to Claim 10,
D1 teach the method of claim 9, further comprising, when said node is said aggregation node (P. 6, 4.2, “In order to deal with the network dynamics, E-Tree learning enables the change of structure in the next iteration of aggregation. Thus, E-Tree learning has a planner to dynamically adjust the structure of the aggregation tree”).
D1 does not explicitly teach determining whether said cluster must be restructured by taking into account a change in the weights of said cluster and/or a change in the weights of the nodes of said cluster.
D2 teach determining whether said cluster must be restructured by taking into account a change in the weights of said cluster and/or a change in the weights of the nodes of said cluster (P. 5, “If on the other hand, criterion (34) is violated, this means that the clients are incongruent and the server computes the pairwise cosine similarities α between the clients’ latest transmitted updates according to equation (13).”, P. 8, Algorithm 5, line 14, P. 5, Eq (32), Eq (33)).
D1 and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of clustering based federated learning. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by D2 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1 as described above to provide flexibility to handle client populations that vary over time and can be implemented in a privacy preserving way and to provide a post-processing method that will always achieve greater or equal performance than conventional FL by allowing clients to arrive at more specialized models (D2, (Abstract, “CFL is flexible enough to handle client populations that vary over time and can be implemented in a privacy preserving way. As clustering is only performed after Federated Learning has converged to a stationary point, CFL can be viewed as a post-processing method that will always achieve greater or equal performance than conventional FL by allowing clients to arrive at more specialized models.”).
With regard to Claim 11,
D1 teach the method of claim 8, further comprising, when said node is said aggregation node, in response to a determination that said cluster must be restructured, restructuring said cluster by grouping at least part of the nodes of said cluster into at least one subcluster (P. 4, 3.4, “Therefore, we can further divide N2 into K2 groups, and the center nodes of these groups form layer 3, denoted as N3”), said at least one subcluster being constituted according to a function taking into account a communication cost between the nodes within said subcluster (P. 4, 3.4, “Transmission delay between any two nodes in the same cluster should be relatively short to make sure there is no straggler”, 4.1, “The devices with low communication cost among each other are grouped together”).
D1 does not explicitly teach a similarity of a change in the weights of the models of the nodes within one said subcluster.
D2 teach when said node is said aggregation node, in response to a determination that said cluster must be restructured, restructuring said cluster by grouping at least part of the nodes of said cluster into at least one subcluster (P. 8, Algorithm5: Clustered Federated Learning with Privacy Preservation and Weight-Updates, lines 14-19 P. 5, “If criterion (39) is satisfied, CFL is recursively re-applied to each of the two separate groups starting from the stationary solution θ∗.”), and a similarity of a change in the weights of the models of the nodes within one said subcluster (P. 3, “In other words, a bi-partitioning is called correct, if clients with the same data generating distribution end up in the same cluster “, Corollary1 Eq. (24), P. 8, Algorithm5: Clustered Federated Learning with Privacy Preservation and Weight-Updates, line 16).
D1 and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of clustering based federated learning. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by D2 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1 as described above to provide flexibility to handle client populations that vary over time and can be implemented in a privacy preserving way and to provide a post-processing method that will always achieve greater or equal performance than conventional FL by allowing clients to arrive at more specialized models (D2, (Abstract, “CFL is flexible enough to handle client populations that vary over time and can be implemented in a privacy preserving way. As clustering is only performed after Federated Learning has converged to a stationary point, CFL can be viewed as a post-processing method that will always achieve greater or equal performance than conventional FL by allowing clients to arrive at more specialized models.”).
With regard to Claim 12,
D1-D2 teach the method of claim 11, wherein restructuring of said cluster includes sending, to said entity of said communication network, the identifier of an isolated node of said cluster (D1, Algorithm1:KMAAlgorithm, lines 13-16, P. 6, 4.2, “In order to deal with the network dynamics, E-Tree learning enables the change of structure in the next iteration of aggregation. Thus, E-Tree learning has a planner to dynamically adjust the structure of the aggregation tree”, 4.3).
Conclusion
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
US Patent Application Publication No. 20240152768 filed by Butt et al. that disclose deciding whether federated-learning training depending on availability of a cluster head (CH) of federated leaning training hosts (TH) and computation and communication costs for a training task is performed. Local model training is locally performed, and a part of the training task is delegated to the cluster head based on the decision by delegating cluster model computation and cluster model communication to a central training host (CTH) configured for global model training and by communicating a local data set for local model training and computed model parameters of a local model to the cluster head through device-to-device or side link communication.
Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED ABOU EL SEOUD whose telephone number is (303)297-4285. The examiner can normally be reached Monday-Thursday 9:00am-6:00pm MT.
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/MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148
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