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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitations use a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
"a transceiver unit” in claim 1.
“a data storage unit” in claim 1.
"a federated learning execution unit” in claim 1.
"a large model learning unit” in claims 1, 3, and 11.
"a true label approximate value generation unit” in claims 3, 4, 5, 6, 8, 9, and 10.
"a learning execution unit” in claims 3, and 7.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-13 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 limitation "a transceiver unit”, “a data storage unit”, and "a federated learning execution unit” in claim 1; "a large model learning unit” in claims 1, 3, and 11; "a true label approximate value generation unit” in claims 3, 4, 5, 6, 8, 9, and 10; "a learning execution unit” in claims 3, and 7 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. This disclosure is devoid of any structure that performs the function in the claim. 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.
Claims 2 and 13 recites “wherein the local information comprises a global parameter of a local model, in which the learning was performed, a local latent vector obtained by inputting the local data into a local parameter of the local model, and a local loss value obtained by inputting the local latent vector into the global parameter of the local model”. It is not clear what constitutes as “inputting the local data into a local parameter” and “inputting the local latent vector into the global parameter”. A local parameter is a value and it is not clear how the local data can be inputted into a local parameter. Examiner interprets the claim limitation as “a local latent vector obtained by inputting the local data into the local model” and “a local loss value obtained by inputting the local latent vector into the local model”.
Claim 12 recites the limitation "the local latent vector" in pg. 3, line 10. There is insufficient antecedent basis for this limitation in the claim.
Additionally, “the local latent vector” is indefinite because it is not clear what constitutes as local latent vector. Claim 12 does not explicitly define what is the local latent vector and it is not clear how the local latent vector is different from the local information. Examiner interprets the local latent vector as useful latent representations from each class extracted by the local model and the latent representations serves as a proxy to measure semantic similarity of classes.
Claim 12 recites “a server that receives the extracted local information from each of the plurality of individual devices, stores the local information, generates a federated global parameter for a global model using the local information, uses the local information and the federated global parameter to generate a true label approximate value ...”. The claim limitation recites both “extracted local information” and “local information”, which both terms are understood to be distinct. It is not clear whether the server is processing extracted local information or local information. Examiner interprets the claim as the server is receiving, storing, and using the “local information”. The term “extracted” is redundant because the claims defines “local information” as an extracted portion of information about the learned local model. The term “extracted local information” would mean a second process of extracting would be performed on the data. Applicant should amend the claims for consistency of the claim terminology in the claim limitation.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 1:
Claim 1 recites a system, one of the four statutory categories of patentable subject matter. The subject matter eligibility test is shown below.
Subject Matter Eligibility Analysis Step 2A Prong 1:
“” (a mental process that can be performed in the human mind, i.e. judgement)
“” (a mental process that can be performed in the human mind, i.e. judgement)
Claim 1 therefore recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
"” (This step is directed to data gathering, which is understood to be insignificant extra solution activity - see MPEP 2106.05(g))
“” (This step is directed to storing data in memory, which is understood to be insignificant extra solution activity - see MPEP 2106.05(g))
“a transceiver unit that receives ...; a data storage unit that stores ...; a federated learning execution unit that collects ...; a large model learning unit that generates ...” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“” (This step is directed to data gathering, which is understood to be insignificant extra solution activity - see MPEP 2106.05(g))
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above. Therefore, Claim 1 is directed to the abstract idea.
Subject Matter Eligibility Analysis Step 2B:
"” (This step is directed to transmitting or receiving information, which is understood to be insignificant extra solution activity and well understood, routine and conventional activity of transmitting and receiving data as identified by the court - see MPEP 2106.05(d))
“” (This step is directed to storing data in memory, which is understood to be insignificant extra solution activity and well understood, routine and conventional activity of storing and retrieving information in memory as identified by the court - see MPEP 2106.05(d))
“a transceiver unit that receives ...; a data storage unit that stores ...; a federated learning execution unit that collects ...; a large model learning unit that generates ...” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“” (This step is directed to transmitting or receiving information, which is understood to be insignificant extra solution activity and well understood, routine and conventional activity of transmitting and receiving data as identified by the court - see MPEP 2106.05(d))
The additional elements as disclosed above alone or in combination do not recite significantly more than the abstract idea itself as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above. Therefore, Claim 1 is subject-matter ineligible.
Regarding Claim 2:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein each of the plurality of individual devices uses local data to learn a local model composed of a local parameter and a global parameter” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“wherein the local information comprises a global parameter of a local model, in which the learning was performed, a local latent vector obtained by inputting the local data into a local parameter of the local model, and a local loss value obtained by inputting the local latent vector into the global parameter of the local model” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h))
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“” (a mental process that can be performed in the human mind, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the large model learning unit comprises, a true label approximate value generation unit that generates ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“a learning execution unit that learns the large model using the generated true label approximate value and a preset loss function” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“” (a mental process that can be performed in the human mind with the aide of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the true label approximate value generation unit generates ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“” (a mental process that can be performed in the human mind with the aide of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the true label approximate value generation unit generates ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“” (a mental process that can be performed in the human mind with the aide of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the true label approximate value generation unit determines ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 7:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“” (a mathematical calculation; see pg. 12, lines 2-3 in Specification)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the learning execution unit performs learning of the large model ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 8:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“” (a mathematical calculation; see pg. 12, lines 7-10 in Specification)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the true label approximate value generation unit generates ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 9:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“” (a mental process that can be performed in the human mind with the aide of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the true label approximate value generation unit generates ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 10:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“” (a mental process that can be performed in the human mind with the aide of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the true label approximate value generation unit reversely infers” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 11:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“” (a mental process that can be performed in the human mind with the aide of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the large model learning unit, learns the large model using local information received in a current communication round” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“further learns the large model ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 12:
Subject Matter Eligibility Analysis Step 1:
Claim 12 recites “A system for augmenting knowledge using federated learning information comprising” wherein the claimed system is software per se. The claims recite a system comprising "a plurality of individual devices” and “a server” but these limitations are not specifically disclosed in the specification as being software or hardware elements. Under the broadest reasonable interpretation, these claim elements can be a virtualized environment. Therefore, the claimed system under the broadest reasonable interpretation can include only software elements and therefore software per se.
Claim 12 recites “A system for augmenting knowledge using federated learning information comprising” and is thus a system, one of the four statutory categories of patentable subject matter. The subject matter eligibility test is shown below.
Subject Matter Eligibility Analysis Step 2A Prong 1:
“” (a mental process that can be performed in the human mind, i.e. judgement)
“” (a mental process that can be performed in the human mind, i.e. judgement)
Claim 12 therefore recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
“” (This step is directed to storing data in memory, which is understood to be insignificant extra solution activity - see MPEP 2106.05(g))
“a plurality of individual devices that store ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“a server that ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
"
“” (This step is directed to storing data in memory, which is understood to be insignificant extra solution activity - see MPEP 2106.05(g))
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above. Therefore, Claim 12 is directed to the abstract idea.
Subject Matter Eligibility Analysis Step 2B:
“” (This step is directed to storing data in memory, which is understood to be insignificant extra solution activity and well understood, routine and conventional activity of storing and retrieving information in memory as identified by the court - see MPEP 2106.05(d))
“a plurality of individual devices that store ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“a server that ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
"
“” (This step is directed to storing data in memory, which is understood to be insignificant extra solution activity and well understood, routine and conventional activity of storing and retrieving information in memory as identified by the court - see MPEP 2106.05(d))
The additional elements as disclosed above alone or in combination do not recite significantly more than the abstract idea itself as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above. Therefore, Claim 12 is subject-matter ineligible.
Regarding Claim 13:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the local information comprises a global parameter of a local model, in which the learning was performed, a local latent vector obtained by inputting the local data into a local parameter of the local model, and a local loss value obtained by inputting the local latent vector into the global parameter of the local model” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h))
Regarding Claim 14:
The claim recites a method that performs the same process as the system as described in claim 1. Therefore, claim 14 is rejected for the same reasons as disclosed for claim 1.
Regarding Claim 15:
The claim recites an article of manufacture (“A computer program stored in a computer-readable recording medium”) that is signal per se because the claim is directed to mere information in the form of data. The specification does not provide a clear definition on whether the claimed computer-readable recording medium is limited to statutory or non-transitory elements. Therefore, under the broadest reasonable interpretation, the claim elements are not limited to statutory elements and can be considered as non-statutory. The claimed article of manufacture is signal per se.
The claim recites an article of manufacture that performs the same process as described in claim 14, which is rejected for the same reasons as disclosed for claim 1. Therefore, claim 15 is rejected for the same reasons as disclosed for claim 1.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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 12 is rejected under 35 U.S.C. 103 as being unpatentable over Dai (US20240054350A1) in view of He, “Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge”. He is provided as a reference in the IDS dated 12/10/2025.
Regarding claim 12, Dai teaches:
“A system for augmenting knowledge using federated learning information comprising” (abstract, A federated learning system consists of a central server that updates a global model based on the received updated local model parameters.)
“a plurality of individual devices that store local data for learning a local model composed of a local parameter and a global parameter, learn the local model using the local data, and extract a portion of information about the learned local model as local information” ([0030-0033, 0048, 0063, Figure 2B], A plurality of client devices includes a personalized model that is periodically updated based on a global model from the central server. The client computes the averaged representations that provides a local description of class prototypes for classes that is presented in the local dataset of the client. The client’s local training is executed based on the class prototypes and the averaged representations are sent to the central server as feedback.)
“a server that receives the extracted local information from each of the plurality of individual devices, stores the local information, generates a federated global parameter for a global model using the local information, uses the local information and the federated global parameter to generate a ” ([0033-0034; 0041; 0078-0079; 0082-0083; Fig. 2A], The central server consists of a memory, which consist of a client update submodule that is configured to receive information from client systems. Clients sends local updates to the server. A global update submodule is configured to update the global model based on aggregated feedback from clients. The server receives local updates from the clients and aggregate those local updates to update the global model. The FedNH module receive input training data for training a baseline global model. The server generates the plurality of updated class prototypes of the global model based on the representatives received from the client. The global model may be tested for accuracy during training by computing a metric based on the true label and predicted label.)
Dai does not explicitly disclose an implementation of “true label approximate value”. However, He discloses in the same field of endeavor:
“a server that ... uses the local information and the federated global parameter to generate a true label approximate value for learning a large model, and learns the large model using a prediction result obtained by inputting the local latent vector into the large model and the true label approximate value” ([pg. 1-2, Section 1, par. 2; pg. 4-5, Section 3.3, par. 1-3], The server model generates probabilistic prediction or soft labels (prediction result) based on the SoftMax of logits. Cross entropy loss (true label approximate value) is computed between the predicted values and the ground truth labels. The server model is trained for multiple training iterations to improve its performance. KL divergence loss attempts to bring the soft label and the ground truth close to each other.)
It would be obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of “true label approximate value” from He into the teaching of Dai. Doing so can improve the performance of federated learning on resource constrained edge devices by implementing a group knowledge transfer training algorithm (He, abstract).
Claims 1-5, 7-11 and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Dai (US20240054350A1) in view of He, “Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge” and Zeng, “FedCav: Contribution-aware Model Aggregation on Distributed Heterogeneous Data in Federated Learning”. He is provided as a reference in the IDS dated 12/10/2025.
Regarding claim 1, Dai teaches:
“An apparatus for augmenting knowledge using federated learning information comprising” (abstract, A federated learning system consists of a central server that updates a global model based on the received updated local model parameters.)
“a transceiver unit that receives local information including a global parameter of a local model, a local latent vector, ” ([0033, 0063], The server consists of at least one network interface component that communicate with user device. The client sends both the body parameters (global parameter of a local model) and local prototypes (local latent vector) to the server for aggregation.)
“a data storage unit that stores the local information” ([0033; 0036-0037; 0041; Fig. 2A], The central server consists of a memory, which consist of a client update submodule that is configured to receive information from client systems. Clients sends local updates to the server.)
“a federated learning execution unit that collects a global parameter of the local model and generates a federated global parameter for a global model” ([0033-0034; 0041; Fig. 2A], A global update submodule is configured to update the global model based on aggregated feedback from clients. The server receives local updates from the clients and aggregate those local updates to update the global model.)
“a large model learning unit that generates a inputting the local latent vector into the large model ” ([0041; 0078-0079; 0082-0083], The FedNH module receive input training data for training a baseline global model. The server generates the plurality of updated class prototypes of the global model based on the representatives received from the client. The global model may be tested for accuracy during training by computing a metric based on the true label and predicted label.)
Dai does not explicitly disclose an implementation of “receives local information including ... a local loss value from each of a plurality of individual devices” and “true label approximate value”. However, He discloses in the same field of endeavor:
“a large model learning unit that generates a true label approximate value for learning a large model using the local information and the federated global parameter, and learns the large model using a prediction result obtained by inputting the local latent vector into the large model and the true label approximate value” ([pg. 1-2, Section 1, par. 2; pg. 4-5, Section 3.3, par. 1-3], The server model generates probabilistic prediction or soft labels (prediction result) based on the SoftMax of logits. Cross entropy loss (true label approximate value) is computed between the predicted values and the ground truth labels. The server model is trained for multiple training iterations to improve its performance. KL divergence loss attempts to bring the soft label and the ground truth close to each other.)
It would be obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of “true label approximate value” from He into the teaching of Dai. Doing so can improve the performance of federated learning on resource constrained edge devices by implementing a group knowledge transfer training algorithm (He, abstract).
Dai in view of He does not explicitly disclose an implementation of “receives local information including ... a local loss value from each of a plurality of individual devices”. However, Zeng discloses in the same field of endeavor:
“a transceiver unit that receives local information including ... a local loss value from each of a plurality of individual devices” ([pg. 4, Section 4.1, par. 1-2], The client computes the inference loss, which is the value of the global model loss function on the local data and sends the inference loss to the server with the local updates. It is implied that the server consists of a component to communicate with the client device over a network.)
It would be obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of “receives local information including ... a local loss value from each of a plurality of individual devices” from Zeng into the teaching of Dai in view of He. Doing so can improve the performance of federated learning on heterogenous data by implementing a global loss function with explicit optimization preference on informative local updates (Zeng, abstract).
Regarding claims 2 and 13, Dai in view of He teaches:
“wherein each of the plurality of individual devices uses local data to learn a local model composed of a local parameter and a global parameter” ([Dai, 0017, 0025, 0030, 0046], Each client has a respective personalized local model. The client receives the body parameter and the head parameter from the server and trains the local model with its local training dataset that is not shared with the server. The updated parameters of the body may be shared with the central server.)
“wherein the local information comprises a global parameter of a local model, in which the learning was performed, a local latent vector obtained by inputting the local data into a local parameter of the local model, and a local loss value ” ([Dai, 0017, 0023, 0030-033], The server sends the body parameter to the clients to train the local model using the global parameter. The client computes the averaged representations that provides a local description of class prototypes for classes that is presented in the local dataset of the client. A loss is computed for an optimization process to learn a strong body.)
Dai in view of He does not explicitly disclose an implementation of “wherein the local information comprises ... a local loss value obtained by inputting the local latent vector into the global parameter of the local model”. However, Zeng discloses in the same field of endeavor:
“wherein the local information comprises ... a local loss value obtained by inputting the local latent vector into the global parameter of the local model” ([pg. 4, Section 4.1, par. 1-2], The client computes the inference loss, which is the value of the global model loss function on the local data and sends the inference loss to the server with the local updates.)
It would be obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of “wherein the local information comprises ... a local loss value obtained by inputting the local latent vector into the global parameter of the local model” from Zeng into the teaching of Dai in view of He. Doing so can improve the performance of federated learning on heterogenous data by implementing a global loss function with explicit optimization preference on informative local updates (Zeng, abstract).
Regarding claim 3, Dai in view of He teaches:
“a true label approximate value generation unit that generates the true label approximate value” ([He, pg. 1-2, Section 1, par. 2; pg. 4-5, Section 3.3, par. 1-3], Cross entropy loss (true label approximate value) is computed between the predicted values and the ground truth labels. Under the broadest reasonable interpretation, the generation unit can refer to a specific part of the computer instruction. Dai [par. 39] discloses a processor and memory to implement instructions for using the FedNH module to train a global model.)
“a learning execution unit that learns the large model using the generated true label approximate value and a preset loss function” ([He, pg. 1-2, Section 1, par. 2; pg. 4-5, Section 3.3, par. 1-3], The server and edge models are trained based on the cross-entropy loss between the predicted values and the ground truth labels and the KL divergence function.)
Regarding claim 4, Dai in view of He teaches:
“wherein the true label approximate value generation unit generates the true label approximate value through a softmax temperature-based reverse inference process” ([He, pg. 1-2, Section 1, par. 2; pg. 4-5, Section 3.3, par. 1-3], Cross entropy loss (true label approximate value) is computed between the predicted values and the ground truth labels. The probabilistic prediction of the server model is determined with the softmax of logits and the temperature hyperparameter of the softmax function.)
Regarding claim 5, Dai in view of He and Zeng teaches:
“wherein the true label approximate value generation unit generates a true label approximate value distribution ([He, pg. 1-2, Section 1, par. 2; pg. 4-5, Section 3.3, par. 1-3], Cross entropy loss (true label approximate value) is computed between the predicted values and the ground truth labels. The probabilistic prediction of the server model is determined with the softmax of logits.) by applying a softmax temperature determined based on the local loss value to a prediction probability result obtained by inputting the local latent vector into the global model” ([Zeng, pg. 5-6, Section 4.2.3, par. 5-9], The softmax function is computed based on local loss function value.)
Regarding claim 7, Dai in view of He teaches:
“wherein the learning execution unit performs learning of the large model using a distance-based loss function to follow the true label approximate value distribution” ([He, pg. 1-2, Section 1, par. 2; pg. 4-5, Section 3.3, par. 1-3], The KL divergence function is a distance-based loss function to transfer knowledge from a network to another.)
Regarding claim 8, Dai in view of He teaches:
“wherein the true label approximate value generation unit generates the true label approximate value using a cross-entropy loss function” ([He, pg. 1-2, Section 1, par. 2; pg. 4-5, Section 3.3, par. 1-3], Cross entropy loss (true label approximate value) is computed between the predicted values and the ground truth labels.)
Regarding claim 9, Dai in view of He teaches:
“wherein the true label approximate value generation unit generates the true label approximate value using a loss value set for a prediction result obtained by inputting the local latent vector into the global model” ([He, pg. 4, Section 3.2, par. 2; pg. 4-5, Section 3.3, par. 1-3], Cross entropy loss (true label approximate value) is computed between the predicted values and the ground truth labels. The server model is trained using the feature map output from the feature extractor of the edge-side model as input features.)
Regarding claim 10, Dai in view of He teaches:
“wherein the true label approximate value generation unit reversely infers an element with the closest distance to a true label probability value as a true label approximate value by using elements included in the loss value set and the true label probability value calculated using the local loss value” ([He, pg. 4-5, Section 3.3, par. 1-6], An alternating minimization is proposed to optimize the server model and edge model. In Equation 10, the cross-entropy loss is computed for the edge model and the KL divergence is computed. Knowledge distillation between the edge devices and server is used to effectively capture knowledge from multiple client datasets. The KL divergence loss brings the soft label and the ground truth close to each other. The server model absorbs the knowledge gained from each of the edge models.)
Regarding claim 11, Dai teaches:
“learns the large model using local information received in a current communication round” ([0030, 0033-0034], The server updates the global model using information received from the clients during each communication round.)
“further learns the large model by randomly sampling a portion of the local information stored in the data storage unit” ([0021, 0033-0034, 0041, Figure 2A], The client update submodule receives information from the client systems and stores the information in memory. The central server may update the global model based on aggregating information from a subset of active clients at a given communication interval. Thus, the server may only use a portion of the local information received from all of the active clients.)
Regarding Claim 14:
Claim 14 recites a method that performs the same process as the system as described in Claim 1. Therefore claim 14 is rejected under the same reasons mention for claim 1.
Regarding Claim 15:
Claim 15 recites an article of manufacture that performs the same process as Claim 14, which is a method of the system as described in Claim 1. Therefore claim 15 is rejected under the same reasons mention for claim 1.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Dai (US20240054350A1) in view of He, “Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge”, Zeng, “FedCav: Contribution-aware Model Aggregation on Distributed Heterogeneous Data in Federated Learning”, and Li “FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data”. He is provided as a reference in the IDS dated 12/10/2025.
Regarding claim 6, Dai in view of He and Zeng teaches:
“wherein the true label approximate value generation unit determines the softmax temperature ” ([Zeng, pg. 5-6, Section 4.2.3, par. 5-9], The softmax function is computed based on local loss function value.)
Dai in view of He and Zeng does not explicitly disclose an implementation of “determines the softmax temperature through a relative scale that allows a lot of learning using local information of an individual device that is relatively good at predicting in a current communication round and an absolute scale that determines an amount of learning using local information of each individual device”. However, Li discloses in the same field of endeavor:
“... determines the softmax temperature through a relative scale that allows a lot of learning using local information of an individual device that is relatively good at predicting in a current communication round and an absolute scale that determines an amount of learning using local information of each individual device” ([pg. 4-5, Section 4.3, par. 1-7; pg. 4, Figure 4], Scaling factors are added to softmax operations to define restricted softmax. The use of the restricted softmax allows for pushing and pulling of features (relative scale) that may or may not belong to a particular class. If a client does not observe a class, a pushing force is imposed to that particular missing class. The client selection ratio and the specific class distribution in each client determines the strength (absolute scale) of these forces.)
It would be obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of “determines the softmax temperature through a relative scale that allows a lot of learning using local information of an individual device that is relatively good at predicting in a current communication round and an absolute scale that determines an amount of learning using local information of each individual device” from Li into the teaching of Dai in view of He and Zeng. Doing so can improve the performance of federated learning on heterogenous data by implementing a restricted SoftMax parameter to limit the updates of missing classes (Li, abstract).
Conclusion
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/GARY MAC/Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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