DETAILED ACTION
This action is responsive to the amendment filed on 06/24/2026. Claims 1-20 are pending in the case. Claims 1, 5, 8, 12, 15, and 18 are currently amended. Claims 1, 8, and 15 are independent claims.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/03/2026 is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1 Statutory Category: Claim 1 is directed to a method, which falls under one of the four statutory categories.
Step 2A Prong 1 Judicial Exception: Claim 1 recites, in part, “determining a respective loss for each of the plurality of local machine learning models”. This limitation, under the broadest reasonable interpretation, covers the recitation of mathematical calculations, as directed to “a claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number”. See MPEP §2106.04(a)(2)(I)(C). Further, the claim recites: “determining that a first client of the plurality of clients is disconnected or otherwise unable to receive the at least portions of the plurality of server-maintained machine learning models from the server during a communication round in which the server broadcasts global weights of at least one of the plurality of server-maintained machine learning models to the plurality of clients”. This limitation is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion), in this case an observation. See MPEP § 2106.04(a)(2)(III).
Step 2A Prong 2 Integration into a Practical Application: This judicial exception is not integrated into a practical application. In particular the claim recites: “training neural networks with federated learning”. This limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Further, the claim recites: “sending at least portions of a plurality of server-maintained machine learning models from a server to a plurality of clients, yielding a plurality of local machine learning models”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the claim recites: “at each client, training the plurality of local machine learning models with locally-stored data that is stored locally at that respective client, wherein the training at each client includes…updating respective weights for each of the plurality of local machine learning models”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the claim recites: “transferring the respective updated weights from each client to the server without transferring the locally-stored data of the clients”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Finally, the claim recites: “at the server, training the plurality of server-maintained machine learning models with the updated weights sent from each of the clients”. This limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §2106.05(f). Alternatively, “at the server, training the plurality of server-maintained machine learning models with the updated weights sent from each of the clients” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the claim recites: “connecting the first client to a neighboring client that is able to communicate with the server”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the claim recites: “sending the portions of the plurality of server-maintained machine learning models from the neighboring client to the first client, wherein sending the portions comprises forwarding, by the neighboring client to the first client, the global weights broadcast by the server and received by the neighboring client during the communication round”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g).
Step 2B Significantly more: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element: “training neural networks with federated learning” generally links the use of the judicial exception to a particular technological environment or field of use. Elements that merely generally link the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Further, the additional elements: “sending at least portions of a plurality of server-maintained machine learning models from a server to a plurality of clients, yielding a plurality of local machine learning models”, “transferring the respective updated weights from each client to the server without transferring the locally-stored data of the clients”, and “sending the portions of the plurality of server-maintained machine learning models from the neighboring client to the first client” amount to adding insignificant extra-solution activity to the judicial exception and further, are directed to receiving or transmitting data over a network which courts have recognized as well-understood, routine, and conventional when they are claimed in a generic manner, see MPEP §2106.05(d)(II). Further, the additional element “at each client, training the plurality of local machine learning models with locally-stored data that is stored locally at that respective client, wherein the training at each client includes…updating respective weights for each of the plurality of local machine learning models” amounts to adding insignificant extra-solution activity to the judicial exception, and further, is well-understood, routine, and conventional as taught by activity supported under Berkheimer Option 2, Ryden, U.S. Patent Application Publication No. 20240155714, Paragraphs 0036-0037, “The FL steps performed in a wireless communication system involving access nodes and wireless devices typically comprise [0037] 1) Local Model Training: After receiving the global/initial model, all UEs perform local training based on the global model to update their local weights”. Further, the additional element “at the server, training the plurality of server-maintained machine learning models with the updated weights sent from each of the clients” amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. Elements that merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process cannot provide an inventive concept. Alternatively, the additional element “at the server, training the plurality of server-maintained machine learning models with the updated weights sent from each of the clients” amounts to adding insignificant extra-solution activity to the judicial exception, and further, is well-understood, routine, and conventional as taught by activity supported under Berkheimer Option 2, Ryden, U.S. Patent Application Publication No. 20240155714, Paragraphs 0036-0038, “The FL steps performed in a wireless communication system involving access nodes and wireless devices typically comprise … 2) Global Model Aggregation: The gNB receives the local weights from each UE and updates the global model weights and then send back the updated global model weights to all the participants in the FL process”. Further, the additional element “connecting the first client to a neighboring client that is able to communicate with the server” amounts to adding insignificant extra-solution activity to the judicial exception. Further, the limitation is well-understood, routine, and conventional as taught by activity supported under Berkheimer Option 2, Iyer et al., U.S. Patent Application Publication No. 20250036959, Paragraph 0024, Lines 31-34, “ Typical electronic devices also include a set of one or more physical network interface(s) (NI(s)) to establish network connections (to transmit and/or receive code and/or data using propagating signals) with other electronic devices”. The claim is not patent eligible.
Regarding claim 2, the rejection of claim 1 is incorporated, and further, the claim recites: “wherein the connecting the first client to a plurality of neighboring clients that are able to communicate with the server”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the limitation is well-understood, routine, and conventional as taught by activity supported under Berkheimer Option 2, Iyer et al., U.S. Patent Application Publication No. 20250036959, Paragraph 0024, Lines 31-34, “ Typical electronic devices also include a set of one or more physical network interface(s) (NI(s)) to establish network connections (to transmit and/or receive code and/or data using propagating signals) with other electronic devices”. Further, the claim recites: “wherein the sending of the portions includes sending the portions of the plurality of server-maintained machine learning models from the plurality of neighboring clients to the first client”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, this limitation is directed to receiving or transmitting data over a network which courts have recognized as well-understood, routine, and conventional when they are claimed in a generic manner, see MPEP §2106.05(d)(II). The claim is not patent eligible.
Regarding claim 3, the rejection of claim 2 is incorporated, and further, the claim recites: “at the first client, performing an interpolation of the portions of the plurality of server-maintained machine learning models received from the plurality of neighboring clients”. This limitation recites mathematical concepts in addition to those identified in the rejection of the parent claim. Thus, the claim recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 4, the rejection of claim 3 is incorporated, and further, the claim recites: “wherein the interpolation is
W
+
=
W
+
∑
{
i
∈
C
b
}
A
i
∙
(
W
i
-
W
)
, wherein
W
+
is an interpolated model for models W received by the plurality of neighboring clients
C
b
, and wherein
A
i
is a linear combination weight for model
W
i
”. This limitation recites mathematical concepts in addition to those identified in the parent claim, in this case a mathematical formula or equation, as directed to “a claim that recites a numerical formula or equation will be considered as falling within the "mathematical concepts" grouping. In addition, there are instances where a formula or equation is written in text format that should also be considered as falling within this grouping”. See MPEP § 2106.04(a)(2)(I)(B).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 5, the rejection of claim 4 is incorporated, and further, the claim recites: “wherein the
A
i
is represented by:
A
i
=
L
b
W
-
L
b
(
W
i
)
W
-
W
i
2
wherein
L
b
is a loss associated with the first client”. This limitation recites mathematical concepts in addition to those identified in the rejection of the parent claim, in this case a mathematical formula or equation, as directed to “a claim that recites a numerical formula or equation will be considered as falling within the "mathematical concepts" grouping. In addition, there are instances where a formula or equation is written in text format that should also be considered as falling within this grouping”. See MPEP § 2106.04(a)(2)(I)(B).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 6, the rejection of claim 1 is incorporated, and further, the claim recites: “selecting the neighboring client to communicate with the first client based upon the neighboring client having already received the at least portions of a plurality of server-maintained machine learning models from the server”. This limitation recites mental processes in addition to those identified in the rejection of the parent claim. Thus, the claim recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 7, the rejection of claim 1 is incorporated, and further, the claim recites: “wherein the neighboring client is a plurality of neighboring clients”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the limitation is well-understood, routine, and conventional as taught by activity supported under Berkheimer Option 2, Iyer et al., U.S. Patent Application Publication No. 20250036959, Paragraph 0024, Lines 31-34, “ Typical electronic devices also include a set of one or more physical network interface(s) (NI(s)) to establish network connections (to transmit and/or receive code and/or data using propagating signals) with other electronic devices”. The claim is not patent eligible.
Regarding claim 8:
Step 1 Statutory Category: Claim 8 is directed to a system, which falls under one of the four statutory categories.
Step 2A Prong 1 Judicial Exception: Claim 8 recites, in part, “determining a respective loss for each of the plurality of local machine learning models”. This limitation, under the broadest reasonable interpretation, covers the recitation of mathematical calculations, as directed to “a claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number”. See MPEP §2106.04(a)(2)(I)(C). Further, the claim recites: “determining that a first client of the plurality of clients is disconnected or otherwise unable to receive the at least portions of the plurality of server-maintained machine learning models from the server during a communication round in which the server broadcasts global weights of at least one of the plurality of server-maintained machine learning models to the plurality of clients”. This limitation is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion), in this case an observation. See MPEP § 2106.04(a)(2)(III).
Step 2A Prong 2 Integration into a Practical Application: This judicial exception is not integrated into a practical application. In particular the claim recites: “a system”, “memory storing instructions”, and “a plurality of processors”. These limitations are additional elements that amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §2106.05(f). Further, the claim recites: “training neural networks with federated learning”. This limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Further, the claim recites: “sending at least portions of a plurality of server-maintained machine learning models from a server to a plurality of clients, yielding a plurality of local machine learning models”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the claim recites: “at each client, training the plurality of local machine learning models with locally-stored data that is stored locally at that respective client, wherein the training at each client includes…updating respective weights for each of the plurality of local machine learning models”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the claim recites: “transferring the respective updated weights from each client to the server without transferring the locally-stored data of the clients”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Finally, the claim recites: “at the server, training the plurality of server-maintained machine learning models with the updated weights sent from each of the clients”. This limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §2106.05(f). Alternatively, “at the server, training the plurality of server-maintained machine learning models with the updated weights sent from each of the clients” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the claim recites: “connecting the first client to a neighboring client that is able to communicate with the server”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the claim recites: “sending the portions of the plurality of server-maintained machine learning models from the neighboring client to the first client, wherein sending the portions comprises forwarding, by the neighboring client to the first client, the global weights broadcast by the server and received by the neighboring client during the communication round”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g).
Step 2B Significantly more: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements: “a system”, “memory storing instructions”, and “a plurality of processors” amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. Elements that merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process cannot provide an inventive concept. Further, the additional element “training neural networks with federated learning” generally links the use of the judicial exception to a particular technological environment or field of use. Elements that merely generally link the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Further, the additional elements: “sending at least portions of a plurality of server-maintained machine learning models from a server to a plurality of clients, yielding a plurality of local machine learning models”, “transferring the respective updated weights from each client to the server without transferring the locally-stored data of the clients”, and “sending the portions of the plurality of server-maintained machine learning models from the neighboring client to the first client, wherein sending the portions comprises forwarding, by the neighboring client to the first client, the global weights broadcast by the server and received by the neighboring client during the communication round” amount to adding insignificant extra-solution activity to the judicial exception and further, are directed to receiving or transmitting data over a network which courts have recognized as well-understood, routine, and conventional when they are claimed in a generic manner, see MPEP §2106.05(d)(II). Further, the additional element “at each client, training the plurality of local machine learning models with locally-stored data that is stored locally at that respective client, wherein the training at each client includes…updating respective weights for each of the plurality of local machine learning models” amounts to adding insignificant extra-solution activity to the judicial exception, and further, is well-understood, routine, and conventional as taught by activity supported under Berkheimer Option 2, Ryden, U.S. Patent Application Publication No. 20240155714, Paragraphs 0036-0037, “The FL steps performed in a wireless communication system involving access nodes and wireless devices typically comprise [0037] 1) Local Model Training: After receiving the global/initial model, all UEs perform local training based on the global model to update their local weights”. Further, the additional element “at the server, training the plurality of server-maintained machine learning models with the updated weights sent from each of the clients” amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. Elements that merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process cannot provide an inventive concept. Alternatively, the additional element “at the server, training the plurality of server-maintained machine learning models with the updated weights sent from each of the clients” amounts to adding insignificant extra-solution activity to the judicial exception, and further, is well-understood, routine, and conventional as taught by activity supported under Berkheimer Option 2, Ryden, U.S. Patent Application Publication No. 20240155714, Paragraphs 0036-0038, “The FL steps performed in a wireless communication system involving access nodes and wireless devices typically comprise … 2) Global Model Aggregation: The gNB receives the local weights from each UE and updates the global model weights and then send back the updated global model weights to all the participants in the FL process”. Further, the additional element “connecting the first client to a neighboring client that is able to communicate with the server” amounts to adding insignificant extra-solution activity to the judicial exception. Further, the limitation is well-understood, routine, and conventional as taught by activity supported under Berkheimer Option 2, Iyer et al., U.S. Patent Application Publication No. 20250036959, Paragraph 0024, Lines 31-34, “ Typical electronic devices also include a set of one or more physical network interface(s) (NI(s)) to establish network connections (to transmit and/or receive code and/or data using propagating signals) with other electronic devices”. The claim is not patent eligible.
Regarding claim 9, the rejection of claim 8 is incorporated, and further, claim 9 is substantially similar to claim 2 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 10, the rejection of claim 9 is incorporated, and further, claim 10 is substantially similar to claim 3 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 11, the rejection of claim 10 is incorporated, and further, claim 11 is substantially similar to claim 4 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 12, the rejection of claim 11 is incorporated, and further, claim 12 is substantially similar to claim 5 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 13, the rejection of claim 8 is incorporated, and further, claim 13 is substantially similar to claim 6 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 14, the rejection of claim 7 is incorporated, and further, claim 14 is substantially similar to claim 7 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 15:
Step 1 Statutory Category: Claim 15 is directed to a method, which falls under one of the four statutory categories.
Step 2A Prong 1 Judicial Exception: Claim 15 recites, in part, “determining that a first client of the plurality of clients is disconnected or otherwise unable to receive the at least portions of the plurality of server-maintained machine learning models during a communication round in which the server broadcasts global weights of at least one of the plurality of server-maintained machine learning models to the plurality of clients”. This limitation is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion), in this case an observation. See MPEP § 2106.04(a)(2)(III). Further, the claim recites: “determining a respective loss for each of the plurality of local machine learning models”. This limitation, under the broadest reasonable interpretation, covers the recitation of mathematical calculations, as directed to “a claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number”. See MPEP §2106.04(a)(2)(I)(C). Further, the claim recites: “determining a loss for each of the plurality of local machine learning models”. This limitation, under the broadest reasonable interpretation, covers the recitation of mathematical calculations, as directed to “a claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number”. See MPEP §2106.04(a)(2)(I)(C).
Step 2A Prong 2 Integration into a practical application: This judicial exception is not integrated into a practical application. In particular the claim recites: “training neural networks with federated learning”. This limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Further, the claim recites: “sending at least portions of a plurality of server-maintained machine learning models from a server to a plurality of clients, yielding a plurality of local machine learning models”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the claim recites: “at each of the plurality of clients other than the first client, training the plurality of local machine learning models with locally-stored data that is stored locally at that respective client, wherein the training at each client includes … updating respective weights for each of the plurality of local machine learning models”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the claim recites: “transferring the respective updated weights from each of the plurality of clients other than the first client to the server without transferring the locally-stored data of the clients”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the claim recites: “connecting the first client to a plurality of neighboring clients of the plurality of clients that are able to communicate with the server”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the claim recites: “sending the portions of the plurality of server-maintained machine-learning models from the neighboring clients to the first client, wherein sending the portions comprises forwarding, by the neighboring client to the first client, the global weights broadcast by the server and received by the neighboring client during the communication round”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the claim recites: “at the first client, training the plurality of local machine learning models with locally-stored data that is stored locally at that first client, wherein the training at the first client includes … updating weights associated with each of the local machine learning models”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the claim recites: “once the first client is re-connected to the server, sending the updated weights from the first client to the server”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Finally, the claim recites: “at the server, training the plurality of server-maintained machine earning models with the updated weights from the first client”. This limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §2106.05(f). Alternatively, this limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g).
Step 2B Significantly more: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element: “training neural networks with federated learning” generally links the use of the judicial exception to a particular technological environment or field of use. Elements that merely generally link the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Further, the additional elements: “sending at least portions of a plurality of server-maintained machine learning models from a server to a plurality of clients, yielding a plurality of local machine learning models”, “transferring the respective updated weights from each of the plurality of clients other than the first client to the server without transferring the locally-stored data of the clients”, “sending the portions of the plurality of server-maintained machine-learning models from the neighboring clients to the first client, wherein sending the portions comprises forwarding, by the neighboring client to the first client, the global weights broadcast by the server and received by the neighboring client during the communication round”, and “once the first client is re-connected to the server, sending the updated weights from the first client to the server” amount to adding insignificant extra-solution activity to the judicial exception and further, are directed to receiving or transmitting data over a network which courts have recognized as well-understood, routine, and conventional when they are claimed in a generic manner, see MPEP §2106.05(d)(II). Further, the additional elements “at each of the plurality of clients other than the first client, training the plurality of local machine learning models with locally-stored data that is stored locally at that respective client, wherein the training at each client includes … updating respective weights for each of the plurality of local machine learning models” and “at the first client, training the plurality of local machine learning models with locally-stored data that is stored locally at that first client, wherein the training at the first client includes … updating weights associated with each of the local machine learning models” amount to adding insignificant extra-solution activity to the judicial exception, and further, are well-understood, routine, and conventional as taught by activity supported under Berkheimer Option 2, Ryden, U.S. Patent Application Publication No. 20240155714, Paragraphs 0036-0037, “The FL steps performed in a wireless communication system involving access nodes and wireless devices typically comprise [0037] 1) Local Model Training: After receiving the global/initial model, all UEs perform local training based on the global model to update their local weights”. Further, the additional element “connecting the first client to a plurality of neighboring clients of the plurality of clients that are able to communicate with the server” amounts to adding insignificant extra-solution activity to the judicial exception. Further, the limitation is well-understood, routine, and conventional as taught by activity supported under Berkheimer Option 2, Iyer et al., U.S. Patent Application Publication No. 20250036959, Paragraph 0024, Lines 31-34, “ Typical electronic devices also include a set of one or more physical network interface(s) (NI(s)) to establish network connections (to transmit and/or receive code and/or data using propagating signals) with other electronic devices”. Further, the additional element “at the server, training the plurality of server-maintained machine earning models with the updated weights from the first client” amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. Elements that merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process cannot provide an inventive concept. Alternatively, the additional element “at the server, training the plurality of server-maintained machine earning models with the updated weights from the first client” amounts to adding insignificant extra-solution activity to the judicial exception, and further, is well-understood, routine, and conventional as taught by activity supported under Berkheimer Option 2, Ryden, U.S. Patent Application Publication No. 20240155714, Paragraphs 0036-0038, “The FL steps performed in a wireless communication system involving access nodes and wireless devices typically comprise … 2) Global Model Aggregation: The gNB receives the local weights from each UE and updates the global model weights and then send back the updated global model weights to all the participants in the FL process”.
Regarding claim 16, the rejection of claim 15 is incorporated, and further, claim 16 is substantially similar to claim 3 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 17, the rejection of claim 16 is incorporated, and further, claim 17 is substantially similar to claim 4 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 18, the rejection of claim 17 is incorporated, and further, claim 18 is substantially similar to claim 5 respectively, and is rejected in the same manner and reasoning applying.
Regarding claim 19, the rejection of claim 15 is incorporated, and further, the claim recites: “selecting the neighboring clients to communicate with the first client based upon the neighboring clients being within a short range of wireless communication with the first client”. This limitation recites mental processes in addition to those identified in the rejection of the parent claim. Thus, the claim recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 20, the rejection of claim 15 is incorporated, and further, the claim recites; “selecting the plurality of server-maintained machine learning models from a pool of machine learning models, wherein the plurality of server-maintained machine learning models are selected from the pool based on resource limits associated with the plurality of clients”. This limitation recites mental processes in addition to those identified in the rejection of the parent claim. Thus, the claim recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
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 1-4, 6-11, 13-17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Bhuyan et al., Multi-Model Federated Learning, 01/07/2022, https://arxiv.org/pdf/2201.02582, hereinafter referred to as "Bhuyan", in view of Yemini et al., Semi-Decentralized Federated Learning with Collaborative Relaying, 05/23/2022, https://arxiv.org/pd, hereinafter referred to as “Yemini” in further view of Lim et al., Decentralized Edge Intelligence: A Dynamic Resource Allocation Framework for Hierarchical Federated Learning, in IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 3, pp. 536-550, 1 March 2022, doi: 10.1109/TPDS.2021.3096076, hereinafter referred to as “Lim”.
Regarding claim 1, Bhuyan teaches A method of training neural networks with federated learning (Bhuyan, Page 1, Abstract, Lines 3-5, “In this paper, we extend federated learning to the setting where multiple unrelated models are trained simultaneously”), the method comprising:
sending at least portions of a plurality of server-maintained machine learning models from a server to a plurality of clients, yielding a plurality of local machine learning models (Bhuyan, Page 1, Section II, Lines 1-4, “We consider the setting where the server trains M unrelated models in a distributed manner using a pool for clients. Each client has a separate dataset for each model. The server maintains a global version of each of the M models”; Bhuyan, Page 1, Section II, Paragraph 2, Lines 1-4, “At the start of each round, the server selects up to K clients to be used for training. K is a fixed parameter given as an input to our system. Each selected client receives global weights of the model it needs to train”);
at each client, training the plurality of local machine learning models with locally-stored data that is stored locally at that respective client, wherein the training at each client includes determining a respective loss for each of the plurality of local machine learning models and updating respective weights for each of the plurality of local machine learning models (Bhuyan, Page 1, Section II, Paragraph 2, Lines 4-7, “The selected clients train the global version of the model allotted to them on the corresponding local training dataset and send the updated model weights to the server”; Bhuyan, Page 2, Section III, Subsection B, Paragraph 3, Line 3, “Local loss, for model i of client k, is denoted by
l
t
(
k
,
i
)
”); Bhuyan, Page 2, Algorithm 1, Step 10, “Update localModel[m,c] by local training”);
transferring the respective updated weights from each client to the server without transferring the locally-stored data of the clients (Bhuyan, Page 1, Section II, Paragraph 2, Lines 4-7, “The selected clients train the global version of the model allotted to them on the corresponding local training dataset and send the updated model weights to the server”; Bhuyan, Page 1, Section I, Lines 4-6, “The key feature in federated learning is that the local client datasets are never shared with each other or with the server”);
at the server, training the plurality of server-maintained machine learning models with the updated weights sent from each of the clients (Bhuyan, Page 1, Section II, Lines 7-10, “The server weights are an average of the corresponding client weights with importance given to each client equal to the proportion of total training samples in it”; see also: Bhuyan, Page 2, Algorithm 1, Steps 13 and 14).
Bhuyan does not explicitly teach determining that a first client of the plurality of clients is disconnected or otherwise unable to receive the at least portions of the plurality of server-maintained machine learning models from the server during a communication round in which the server broadcasts global weights of at least one of the plurality of server-maintained machine learning models to the plurality of clients; connecting the first client to a neighboring client that is able to communicate with the server; and sending the portions of the plurality of server-maintained machine learning models from the neighboring client to the first client nor wherein sending the portions comprises forwarding, by the neighboring client to the first client, the global weights broadcasts by the server and received by the neighboring client during the communication round.
Yemini teaches determining that a first client of the plurality of clients is disconnected or otherwise unable to receive the at least portions of the plurality of server-maintained machine learning models from the server during a communication round in which the server broadcasts global weights of at least one of the plurality of server-maintained machine learning models to the plurality of clients (Yemini, Page 2, Section II, Subsection B, Lines 1-7, “We consider a setting where the uplink connections between the clients and the PS are intermittent. As shown in Fig. 1, we model the connectivity of client i to the PS at round r by the Bernoulli random variable
τ
i
(r) ∼ Bern(
p
i
), where
τ
i
= 1 indicates the presence of an uplink communication opportunity, whereas
τ
i
(r) = 0 indicates a blocked uplink”; see also Yemini, Page 3, Algorithm 2, Step 2, Lines 2-3, “Broadcast
x
(
r
)
to all clients. / Set
τ
i
r
+
1
=
1
o
r
0
depending on connectivity”);
connecting the first client to a neighboring client that is able to communicate with the server (Yemini, Page 3, Algorithm 1 COLREL-CLIENT: Collaborative Relaying, “Neighborhood of client I
N
i
”; see also Yemini, Page 2, Figure 1, “System model with intermittent uplink communication between clients and PS (dotted lines) and reliable communication between neighboring clients (solid lines)”); and
sending the portions of the plurality of server-maintained machine learning models from the neighboring client to the first client… (Yemini, Page 3, Algorithm 1, Steps 6 and 7, “Send
∆
x
i
to every
j
∈
N
i
. Receive
∆
x
j
from every
j
∈
N
i
”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention, to have modified the federated learning method of Bhuyan to include connecting disconnected clients to neighboring clients to receive the server-maintained machine learning models as taught by Yemini. The motivation to do so would have been to prevent client models from becoming stale, and thus improving convergence (Yemini, Page 1, Section I, Lines 13-14, “Stragglers deteriorate the convergence of FL as the computed local updates become stale”; Yemini, Page 1, Section I, Paragraph 3, Lines 8-10, “Using this approach, the PS receives new updates from disconnected clients, which would otherwise become stale and be discarded”).
The proposed combination thus far does not explicitly teach wherein sending the portions comprises forwarding, by the neighboring client to the first client, the global weights broadcasts by the server and received by the neighboring client during the communication round.
Lim teaches wherein sending the portions comprises forwarding, by the neighboring client to the first client, the global weights broadcasts by the server and received by the neighboring client during the communication round (Lim, Page 538, Section 3, Paragraph 3, Lines 1-4, “In HFL, a cluster head j first receives an initial global model, i.e., parameters denoted by the vector w, from a model owner that has chosen its services. It then relays the global model to it’s workers”).
It would be obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to have modified the federated learning method of the proposed combination to include forwarding the global weights from the server to the neighboring client as taught by Lim. The motivation to do so would have been that edge computing solutions further enhance the communication efficiency of federated learning (Lim, Page 538, Section 2, Paragraph 2).
Regarding claim 2, the rejection of claim 1 is incorporated, and further, the proposed combination teaches wherein the connecting includes connecting the first client to a plurality of neighboring clients that are able to communicate with the server, and wherein the sending of the portions includes sending the portions of the plurality of server-maintained machine learning models from the plurality of neighboring clients to the first client (Yemini, Page 3, Algorithm 1 COLREL-CLIENT: Collaborative Relaying, “Neighborhood of client I
N
i
”; Yemini, Page 3, Algorithm 1 COLREL-CLIENT: Collaborative Relaying, Step 7, “Receive
∆
x
j
from every
j
∈
N
i
”; see also Yemini, Page 2, Figure 1, “System model with intermittent uplink communication between clients and PS (dotted lines) and reliable communication between neighboring clients (solid lines)”).
Regarding claim 3, the rejection of claim 2 is incorporated, and further, the proposed combination thus far does not explicitly teach at the first client, performing an interpolation of the portions of the plurality of server-maintained machine learning models received from the plurality of neighboring clients.
Yemini teaches at the first client, performing an interpolation of the portions of the plurality of server-maintained machine learning models received from the plurality of neighboring clients (Yemini, Page 3, Algorithm 1, Step 8; Yemini, Page 3, Section C, Lines 4-7, “Then client i computes a weighted average of its own update and those of its neighbors in
N
i
, i.e.,
∆
x
~
i
(
r
+
1
)
=
∑
j
∈
N
i
∪
{
i
}
α
i
j
∆
x
j
(
r
+
1
)
=
∑
j
∈
N
i
∪
{
i
}
α
i
j
(
x
j
r
,
T
-
x
r
)
, where
α
i
j
is a non-negative importance weight assigned by client i while relaying the client j’s update”).
It would have been obvious to a person of ordinary skill in the art, before the effective
filing date of the invention, to have modified the federated learning method of the proposed
combination to include performing an interpolation of the portions of the plurality of server-
maintained machine learning models received from the neighboring clients as taught by Yemini.
The motivation to do so would have been improved convergence rate and accuracy of the global
models (Yemini, Page 1, Abstract, Lines 11-14, “Numerical simulations substantiate our
theoretical claims and demonstrate settings with intermittent connectivity between the clients and
the PS, where our proposed algorithm shows an improved convergence rate and accuracy in
comparison with the federated averaging algorithm”).
Regarding claim 4, the rejection of claim 3 is incorporated, and further, the proposed combination teaches wherein the interpolation is
W
+
=
W
+
∑
{
i
∈
C
b
}
A
i
∙
(
W
i
-
W
)
, wherein
W
+
is an interpolated model for models W received by the plurality of neighboring clients
C
b
, and wherein
A
i
is a linear combination weight for model
W
i
(Yemini, Page 3, Algorithm 1, Step 8; Yemini, Page 3, Section C, Lines 4-7, “Then client i computes a weighted average of its own update and those of its neighbors in
N
i
, i.e.,
∆
x
~
i
(
r
+
1
)
=
∑
j
∈
N
i
∪
{
i
}
α
i
j
∆
x
j
(
r
+
1
)
=
∑
j
∈
N
i
∪
{
i
}
α
i
j
(
x
j
r
,
T
-
x
r
)
, where
α
i
j
is a non-negative importance weight assigned by client i while relaying the client j’s update”).
Regarding claim 6, the rejection of claim 1 is incorporated, and further, the proposed combination teaches selecting the neighboring client to communicate with the first client based upon the neighboring client having already received the at least portions of a plurality of server-maintained machine learning models from the server (Yemini, Page 3, Algorithm 1 COLREL-CLIENT: Collaborative Relaying, Step 1, “Receive
x
(
r
)
from PS”; Thus, a person of ordinary skill in the art would recognize that only clients that received the “at least portions of a plurality of server-maintained machine learning models” communicate with the first client).
Regarding claim 7, the rejection of claim 1 is incorporated, and further, the proposed combination teaches wherein the neighboring client is a plurality of neighboring clients (Yemini, Page 3, Algorithm 1 COLREL-CLIENT: Collaborative Relaying, “Neighborhood of client I
N
i
”; Yemini, Page 3, Algorithm 1 COLREL-CLIENT: Collaborative Relaying, Step 7, “Receive
∆
x
j
from every
j
∈
N
i
”; see also Yemini, Page 2, Figure 1, “System model with intermittent uplink communication between clients and PS (dotted lines) and reliable communication between neighboring clients (solid lines)”).
Regarding claim 8, Bhuyan teaches A system of training neural networks with federated learning, the system comprising: memory storing instructions; and a plurality of processors that, when executing the instructions stored in the memory (Bhuyan, Page 3, Section IV, Subsection A, Lines 1-3, “The policies were tested on Synthetic(1,1) [15], [16] and Synthetic-IID [15], [16] datasets with logistic regression as the classification model”; Bhuyan, Page 4, Figure 1; A person of ordinary skill in the art would recognize that performing these experiments and determining this collection of results would require the use of a generic computer for each client, providing evidence for a “system”, “memory storing instructions” and “a plurality of processors”), collectively perform:
sending at least portions of a plurality of server-maintained machine learning models from a server to a plurality of clients, yielding a plurality of local machine learning models (Bhuyan, Page 1, Section II, Lines 1-4, “We consider the setting where the server trains M unrelated models in a distributed manner using a pool for clients. Each client has a separate dataset for each model. The server maintains a global version of each of the M models”; Bhuyan, Page 1, Section II, Paragraph 2, Lines 1-4, “At the start of each round, the server selects up to K clients to be used for training. K is a fixed parameter given as an input to our system. Each selected client receives global weights of the model it needs to train”);
at each client, training the plurality of local machine learning models with locally-stored data that is stored locally at that respective client, wherein the training at each client includes determining a respective loss for each of the plurality of local machine learning models and updating respective weights for each of the plurality of local machine learning models (Bhuyan, Page 1, Section II, Paragraph 2, Lines 4-7, “The selected clients train the global version of the model allotted to them on the corresponding local training dataset and send the updated model weights to the server”; Bhuyan, Page 2, Section III, Subsection B, Paragraph 3, Line 3, “Local loss, for model i of client k, is denoted by
l
t
(
k
,
i
)
”); Bhuyan, Page 2, Algorithm 1, Step 10, “Update localModel[m,c] by local training”);
transferring the respective updated weights from each client to the server without transferring the locally-stored data of the clients (Bhuyan, Page 1, Section II, Paragraph 2, Lines 4-7, “The selected clients train the global version of the model allotted to them on the corresponding local training dataset and send the updated model weights to the server”; Bhuyan, Page 1, Section I, Lines 4-6, “The key feature in federated learning is that the local client datasets are never shared with each other or with the server”);
at the server, training the plurality of server-maintained machine learning models with the updated weights sent from each of the clients (Bhuyan, Page 1, Section II, Lines 7-10, “The server weights are an average of the corresponding client weights with importance given to each client equal to the proportion of total training samples in it”; see also: Bhuyan, Page 2, Algorithm 1, Steps 13 and 14).
Bhuyan does not explicitly teach determining that a first client of the plurality of clients is disconnected or otherwise unable to receive the at least portions of the plurality of server-maintained machine learning models from the server during a communication round in which the server broadcasts global weights of at least one of the plurality of server-maintained machine learning models to the plurality of clients; connecting the first client to a neighboring client that is able to communicate with the server; and sending the portions of the plurality of server-maintained machine learning models from the neighboring client to the first client nor wherein sending the portions comprises forwarding, by the neighboring client to the first client, the global weights broadcasts by the server and received by the neighboring client during the communication round.
Yemini teaches determining that a first client of the plurality of clients is disconnected or otherwise unable to receive the at least portions of the plurality of server-maintained machine learning models from the server during a communication round in which the server broadcasts global weights of at least one of the plurality of server-maintained machine learning models to the plurality of clients (Yemini, Page 2, Section II, Subsection B, Lines 1-7, “We consider a setting where the uplink connections between the clients and the PS are intermittent. As shown in Fig. 1, we model the connectivity of client i to the PS at round r by the Bernoulli random variable
τ
i
(r) ∼ Bern(
p
i
), where
τ
i
= 1 indicates the presence of an uplink communication opportunity, whereas
τ
i
(r) = 0 indicates a blocked uplink”; see also Yemini, Page 3, Algorithm 2, Step 2, Lines 2-3, “Broadcast
x
(
r
)
to all clients. / Set
τ
i
r
+
1
=
1
o
r
0
depending on connectivity”);
connecting the first client to a neighboring client that is able to communicate with the server (Yemini, Page 3, Algorithm 1 COLREL-CLIENT: Collaborative Relaying, “Neighborhood of client I
N
i
”; see also Yemini, Page 2, Figure 1, “System model with intermittent uplink communication between clients and PS (dotted lines) and reliable communication between neighboring clients (solid lines)”); and
sending the portions of the plurality of server-maintained machine learning models from the neighboring client to the first client (Yemini, Page 3, Algorithm 1, Steps 6 and 7, “Send
∆
x
i
to every
j
∈
N
i
. Receive
∆
x
j
from every
j
∈
N
i
”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention, to have modified the federated learning method of Bhuyan to include connecting disconnected clients to neighboring clients to receive the server-maintained machine learning models as taught by Yemini. The motivation to do so would have been to prevent client models from becoming stale, and thus improving convergence (Yemini, Page 1, Section I, Lines 13-14, “Stragglers deteriorate the convergence of FL as the computed local updates become stale”; Yemini, Page 1, Section I, Paragraph 3, Lines 8-10, “Using this approach, the PS receives new updates from disconnected clients, which would otherwise become stale and be discarded”).
The proposed combination thus far does not explicitly teach wherein sending the portions comprises forwarding, by the neighboring client to the first client, the global weights broadcasts by the server and received by the neighboring client during the communication round.
Lim teaches wherein sending the portions comprises forwarding, by the neighboring client to the first client, the global weights broadcasts by the server and received by the neighboring client during the communication round (Lim, Page 538, Section 3, Paragraph 3, Lines 1-4, “In HFL, a cluster head j first receives an initial global model, i.e., parameters denoted by the vector w, from a model owner that has chosen its services. It then relays the global model to it’s workers”).
It would be obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to have modified the federated learning method of the proposed combination to include forwarding the global weights from the server to the neighboring client as taught by Lim. The motivation to do so would have been that edge computing solutions further enhance the communication efficiency of federated learning (Lim, Page 538, Section 2, Paragraph 2).
Regarding claim 9, the rejection of claim 8 is incorporated, and further, the proposed combination teaches wherein the connecting includes connecting the first client to a plurality of neighboring clients that are able to communicate with the server, and wherein the sending of the portions includes sending the portions of the plurality of server-maintained machine learning models from the plurality of neighboring clients to the first client (Yemini, Page 3, Algorithm 1 COLREL-CLIENT: Collaborative Relaying, “Neighborhood of client I
N
i
”; Yemini, Page 3, Algorithm 1 COLREL-CLIENT: Collaborative Relaying, Step 7, “Receive
∆
x
j
from every
j
∈
N
i
”; see also Yemini, Page 2, Figure 1, “System model with intermittent uplink communication between clients and PS (dotted lines) and reliable communication between neighboring clients (solid lines)”).
Regarding claim 10, the rejection of claim 9 is incorporated, and further, the proposed combination thus far does not explicitly teach conducting an interpolation of the portions of the plurality of server-maintained machine learning models received from the plurality of neighboring clients.
Yemini teaches conducting an interpolation of the portions of the plurality of server-maintained machine learning models received from the plurality of neighboring clients (Yemini, Page 3, Algorithm 1, Step 8; Yemini, Page 3, Section C, Lines 4-7, “Then client i computes a weighted average of its own update and those of its neighbors in
N
i
, i.e.,
∆
x
~
i
(
r
+
1
)
=
∑
j
∈
N
i
∪
{
i
}
α
i
j
∆
x
j
(
r
+
1
)
=
∑
j
∈
N
i
∪
{
i
}
α
i
j
(
x
j
r
,
T
-
x
r
)
, where
α
i
j
is a non-negative importance weight assigned by client i while relaying the client j’s update”).
It would have been obvious to a person of ordinary skill in the art, before the effective
filing date of the invention, to have modified the federated learning method of the proposed
combination to include performing an interpolation of the portions of the plurality of server-
maintained machine learning models received from the neighboring clients as taught by Yemini.
The motivation to do so would have been improved convergence rate and accuracy of the global
models (Yemini, Page 1, Abstract, Lines 11-14, “Numerical simulations substantiate our
theoretical claims and demonstrate settings with intermittent connectivity between the clients and
the PS, where our proposed algorithm shows an improved convergence rate and accuracy in
comparison with the federated averaging algorithm”).
Regarding claim 11, the rejection of claim 10 is incorporated, and further, the proposed combination teaches wherein the interpolation is
W
+
=
W
+
∑
{
i
∈
C
b
}
A
i
∙
(
W
i
-
W
)
, wherein
W
+
is an interpolated model for models W received by the plurality of neighboring clients
C
b
, and wherein
A
i
is a linear combination weight for model
W
i
(Yemini, Page 3, Algorithm 1, Step 8; Yemini, Page 3, Section C, Lines 4-7, “Then client i computes a weighted average of its own update and those of its neighbors in
N
i
, i.e.,
∆
x
~
i
(
r
+
1
)
=
∑
j
∈
N
i
∪
{
i
}
α
i
j
∆
x
j
(
r
+
1
)
=
∑
j
∈
N
i
∪
{
i
}
α
i
j
(
x
j
r
,
T
-
x
r
)
, where
α
i
j
is a non-negative importance weight assigned by client i while relaying the client j’s update”).
Regarding claim 13, the rejection of claim 8 is incorporated, and further, the proposed combination teaches selecting the neighboring client to communicate with the first client based upon the neighboring client having already received the at least portions of a plurality of server-maintained machine learning models from the server (Yemini, Page 3, Algorithm 1 COLREL-CLIENT: Collaborative Relaying, Step 1, “Receive
x
(
r
)
from PS”; Thus, a person of ordinary skill in the art would recognize that only clients that received the “at least portions of a plurality of server-maintained machine learning models” communicate with the first client).
Regarding claim 14, the rejection of claim 8 is incorporated, and further, the proposed combination teaches wherein the neighboring client is a plurality of neighboring clients (Yemini, Page 3, Algorithm 1 COLREL-CLIENT: Collaborative Relaying, “Neighborhood of client I
N
i
”; Yemini, Page 3, Algorithm 1 COLREL-CLIENT: Collaborative Relaying, Step 7, “Receive
∆
x
j
from every
j
∈
N
i
”; see also Yemini, Page 2, Figure 1, “System model with intermittent uplink communication between clients and PS (dotted lines) and reliable communication between neighboring clients (solid lines)”).
Regarding claim 15, Bhuyan teaches A method of training neural networks with federated learning (Bhuyan, Page 1, Abstract, Lines 3-5, “In this paper, we extend federated learning to the setting where multiple unrelated models are trained simultaneously”), the method comprising:
sending at least portions of a plurality of server-maintained machine learning models from a server to a plurality of clients, yielding a plurality of local machine learning models (Bhuyan, Page 1, Section II, Lines 1-4, “We consider the setting where the server trains M unrelated models in a distributed manner using a pool for clients. Each client has a separate dataset for each model. The server maintains a global version of each of the M models”; Bhuyan, Page 1, Section II, Paragraph 2, Lines 1-4, “At the start of each round, the server selects up to K clients to be used for training. K is a fixed parameter given as an input to our system. Each selected client receives global weights of the model it needs to train”);
…
at each of the plurality of clients other than the first client, training the plurality of local machine learning models with locally-stored data that is stored locally at that respective client, wherein the training at each client includes determining a respective loss for each of the plurality of local machine learning models and updating respective weights for each of the plurality of local machine learning models (Bhuyan, Page 1, Section II, Paragraph 2, Lines 4-7, “The selected clients train the global version of the model allotted to them on the corresponding local training dataset and send the updated model weights to the server”; Bhuyan, Page 2, Section III, Subsection B, Paragraph 3, Line 3, “Local loss, for model i of client k, is denoted by
l
t
(
k
,
i
)
”); Bhuyan, Page 2, Algorithm 1, Step 10, “Update localModel[m,c] by local training”);
transferring the respective updated weights from each of the plurality of clients other than the first client to the server without transferring the locally-stored data of the clients (Bhuyan, Page 1, Section II, Paragraph 2, Lines 4-7, “The selected clients train the global version of the model allotted to them on the corresponding local training dataset and send the updated model weights to the server”; Bhuyan, Page 1, Section I, Lines 4-6, “The key feature in federated learning is that the local client datasets are never shared with each other or with the server”).
Bhuyan does not explicitly teach determining that a first client of the plurality of clients is disconnected or otherwise unable to receive the at least portions of the plurality of server-maintained machine learning models during a communication round in which the server broadcasts global weights of at least one of the plurality of server-maintained machine learning models to the plurality of clients; connecting the first client to a plurality of neighboring clients of the plurality of clients that are able to communicate with the server; sending the portions of the plurality of server-maintained machine-learning models from the neighboring clients to the first client; …wherein sending the portions comprises forwarding, by the neighboring client to the first client, the global weights broadcasts by the server and received by the neighboring client during the communication round; at the first client, training the plurality of local machine learning models with locally-stored data that is stored locally at that first client, wherein the training at the first client includes determining a loss for each of the plurality of local machine learning models and updating weights associated with each of the local machine learning models; once the first client is re-connected to the server, sending the updated weights from the first client to the server; and at the server, training the plurality of server-maintained machine earning models with the updated weights from the first client.
Yemini teaches determining that a first client of the plurality of clients is disconnected or otherwise unable to receive the at least portions of the plurality of server-maintained machine learning models during a communication round in which the server broadcasts global weights of at least one of the plurality of server-maintained machine learning models to the plurality of clients (Yemini, Page 2, Section II, Subsection B, Lines 1-7, “We consider a setting where the uplink connections between the clients and the PS are intermittent. As shown in Fig. 1, we model the connectivity of client i to the PS at round r by the Bernoulli random variable
τ
i
(r) ∼ Bern(
p
i
), where
τ
i
= 1 indicates the presence of an uplink communication opportunity, whereas
τ
i
(r) = 0 indicates a blocked uplink”; see also Yemini, Page 3, Algorithm 2, Step 2, Lines 2-3, “Broadcast
x
(
r
)
to all clients. / Set
τ
i
r
+
1
=
1
o
r
0
depending on connectivity”);
connecting the first client to a plurality of neighboring clients of the plurality of clients that are able to communicate with the server (Yemini, Page 3, Algorithm 1 COLREL-CLIENT: Collaborative Relaying, “Neighborhood of client I
N
i
”; see also Yemini, Page 2, Figure 1, “System model with intermittent uplink communication between clients and PS (dotted lines) and reliable communication between neighboring clients (solid lines)”);
sending the portions of the plurality of server-maintained machine-learning models from the neighboring clients to the first client (Yemini, Page 3, Algorithm 1, Steps 6 and 7, “Send
∆
x
i
to every
j
∈
N
i
. Receive
∆
x
j
from every
j
∈
N
i
”);
at the first client, training the plurality of local machine learning models with locally-stored data that is stored locally at that first client, wherein the training at the first client includes determining a loss for each of the plurality of local machine learning models and updating weights associated with each of the local machine learning models (Yemini, Page 3, Algorithm 1 COLREL-CLIENT: Collaborative Relaying, Step 3, “for
k
←
0
to
T
-
1
do / Compute (stochastic) gradient
g
i
x
i
r
,
k
t
. /
x
i
r
,
k
+
1
=
x
i
r
,
k
-
η
r
g
i
x
i
r
,
k
”; see also Yemini, Page 2, Section II, Paragraph 1);
once the first client is re-connected to the server, sending the updated weights from the first client to the server (Yemini, Page 3, Section D, Lines 1-4, “In our setting, the PS does not explicitly select the subset of clients from which it wants to receive information, rather it receives updates from all communicating clients at the beginning of every round”; see also Yemini, Algorithm 2); and
at the server, training the plurality of server-maintained machine earning models with the updated weights from the first client (Yemini, Page 3, Algorithm 2, Step 3, “Update
x
(
r
+
1
)
=
x
(
r
)
+
1
n
∑
i
∈
[
n
]
τ
i
(
r
+
1
)
∆
x
~
i
r
+
1
” Whichever round the first client is reconnected, it will be used to train the server model).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention, to have modified the federated learning method of Bhuyan to include connecting disconnected clients to neighboring clients to receive the server-maintained machine learning models as taught by Yemini. The motivation to do so would have been to prevent client models from becoming stale, and thus improving convergence (Yemini, Page 1, Section I, Lines 13-14, “Stragglers deteriorate the convergence of FL as the computed local updates become stale”; Yemini, Page 1, Section I, Paragraph 3, Lines 8-10, “Using this approach, the PS receives new updates from disconnected clients, which would otherwise become stale and be discarded”).
The proposed combination thus far does not explicitly teach wherein sending the portions comprises forwarding, by the neighboring client to the first client, the global weights broadcasts by the server and received by the neighboring client during the communication round.
Lim teaches wherein sending the portions comprises forwarding, by the neighboring client to the first client, the global weights broadcasts by the server and received by the neighboring client during the communication round (Lim, Page 538, Section 3, Paragraph 3, Lines 1-4, “In HFL, a cluster head j first receives an initial global model, i.e., parameters denoted by the vector w, from a model owner that has chosen its services. It then relays the global model to it’s workers”).
It would be obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to have modified the federated learning method of the proposed combination to include forwarding the global weights from the server to the neighboring client as taught by Lim. The motivation to do so would have been that edge computing solutions further enhance the communication efficiency of federated learning (Lim, Page 538, Section 2, Paragraph 2).
Regarding claim 16, the rejection of claim 15 is incorporated, and further, the proposed combination thus far does not explicitly teach at the first client, performing an interpolation of the portions of the plurality of server-maintained machine learning models received from the plurality of neighboring clients.
Yemini teaches at the first client, performing an interpolation of the portions of the plurality of server-maintained machine learning models received from the plurality of neighboring clients (Yemini, Page 3, Algorithm 1, Step 8; Yemini, Page 3, Section C, Lines 4-7, “Then client i computes a weighted average of its own update and those of its neighbors in
N
i
, i.e.,
∆
x
~
i
(
r
+
1
)
=
∑
j
∈
N
i
∪
{
i
}
α
i
j
∆
x
j
(
r
+
1
)
=
∑
j
∈
N
i
∪
{
i
}
α
i
j
(
x
j
r
,
T
-
x
r
)
, where
α
i
j
is a non-negative importance weight assigned by client i while relaying the client j’s update”).
It would have been obvious to a person of ordinary skill in the art, before the effective
filing date of the invention, to have modified the federated learning method of the proposed
combination to include performing an interpolation of the portions of the plurality of server-
maintained machine learning models received from the neighboring clients as taught by Yemini.
The motivation to do so would have been improved convergence rate and accuracy of the global
models (Yemini, Page 1, Abstract, Lines 11-14, “Numerical simulations substantiate our
theoretical claims and demonstrate settings with intermittent connectivity between the clients and
the PS, where our proposed algorithm shows an improved convergence rate and accuracy in
comparison with the federated averaging algorithm”).
Regarding claim 17, the rejection of claim 16 is incorporated, and further, the proposed combination teaches wherein the interpolation is
W
+
=
W
+
∑
{
i
∈
C
b
}
A
i
∙
(
W
i
-
W
)
, wherein
W
+
is an interpolated model for models W received by the plurality of neighboring clients
C
b
, and wherein
A
i
is a linear combination weight for model
W
i
(Yemini, Page 3, Algorithm 1, Step 8; Yemini, Page 3, Section C, Lines 4-7, “Then client i computes a weighted average of its own update and those of its neighbors in
N
i
, i.e.,
∆
x
~
i
(
r
+
1
)
=
∑
j
∈
N
i
∪
{
i
}
α
i
j
∆
x
j
(
r
+
1
)
=
∑
j
∈
N
i
∪
{
i
}
α
i
j
(
x
j
r
,
T
-
x
r
)
, where
α
i
j
is a non-negative importance weight assigned by client i while relaying the client j’s update”).
Regarding claim 19, the rejection of claim 15 is incorporated, and further, the proposed combination teaches selecting the neighboring clients to communicate with the first client based upon the neighboring clients being within a short range of wireless communication with the first client (Yemini, Introduction, Paragraph 3, Lines 1-5, “Motivated by our prior works [19]–[21], where client cooperation is used to improve the connectivity to the cloud and to reduce the latency and scheduling overhead, this work proposes a new FEEL paradigm, where the clients cooperate to mitigate the detrimental effects of communication stragglers”; Yemini, Page 2, Col 1, Paragraph 3, Lines 1-3, “Client connectivity is a particularly significant challenge in FEEL, where the clients and the PS communicate over unreliable wireless channels”; Yemini, Page 2, Col 1, Paragraph 2, Final 2 Lines, “Therefore, our solution is compatible with OAC”).
Claims 5, 12, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Bhuyan in view of Yemini in view of Lim in further view of Michael Zhang et al. "PERSONALIZED FEDERATED LEARNING WITH FIRST ORDER MODEL OPTIMIZATION." arXiv:2012.08565v4 [cs.LG] 26 Mar 2021, 17 Pages, hereinafter referred to as “Zhang”.
Regarding claim 5, the rejection of claim 4 is incorporated.
The proposed combination does not explicitly teach wherein the
A
i
is represented by:
A
i
=
L
b
W
-
L
b
(
W
i
)
W
-
W
i
2
wherein
L
b
is a loss associated with the first client.
Zhang teaches wherein the
A
i
is represented by:
A
i
=
L
b
W
-
L
b
(
W
i
)
W
-
W
i
2
wherein
L
b
is a loss associated with the first client (Zhang, Page 4, Efficient personalization with FedFomo, 14-18 and Equation 3, “Following this line of reasoning however, we thus derive an approximation of
w
*
for any client: Given previous local model parameters
θ
i
l
(
t
-
1
)
, set of fellow federating models available to download {
θ
i
l
(
t
)
} and local client objective captured by
L
i
, we propose weights of the form:
w
n
=
L
i
θ
i
l
t
-
1
-
L
i
θ
i
l
t
θ
i
l
(
t
)
-
θ
i
l
(
t
-
1
)
where the resulting federated update
θ
i
l
(
t
)
←
θ
i
l
(
t
-
1
)
+
∑
n
∈
[
N
]
w
n
θ
i
l
t
-
θ
i
l
t
-
1
directly optimizes client
c
i
’s objective up to a first-order approximation of the optimal w*”).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to have modified the interpolation of the proposed combination to include the weighted combination taught by Zhang. The motivation to do so would have been improving the model’s loss and weighing models more heavily that improve performance, thus improving performance more quickly (Zhang, Page 4, Efficient personalization with FedFomo, Lines 21-25, “Here we note that our formulation captures the intuition of federating with client models that perform better than our own model, e.g. have a smaller loss on Li. Moreso, we weigh models more heavily as this positive loss delta increases, or the distance between our current parameters and theirs decreases, in essence most heavily weighing the models that most efficiently improve our performance”).
Regarding claim 12, the rejection of claim 11 is incorporated.
The proposed combination does not explicitly teach wherein the
A
i
is represented by:
A
i
=
L
b
W
-
L
b
(
W
i
)
W
-
W
i
2
wherein
L
b
is a loss associated with the first client.
Zhang teaches wherein the weighted combination
A
i
is represented by:
A
i
=
L
b
W
-
L
b
(
W
i
)
W
-
W
i
2
wherein
L
b
is a loss associated with the first client (Zhang, Page 4, Efficient personalization with FedFomo, 14-18 and Equation 3, “Following this line of reasoning however, we thus derive an approximation of
w
*
for any client: Given previous local model parameters
θ
i
l
(
t
-
1
)
, set of fellow federating models available to download {
θ
i
l
(
t
)
} and local client objective captured by
L
i
, we propose weights of the form:
w
n
=
L
i
θ
i
l
t
-
1
-
L
i
θ
i
l
t
θ
i
l
(
t
)
-
θ
i
l
(
t
-
1
)
where the resulting federated update
θ
i
l
(
t
)
←
θ
i
l
(
t
-
1
)
+
∑
n
∈
[
N
]
w
n
θ
i
l
t
-
θ
i
l
t
-
1
directly optimizes client
c
i
’s objective up to a first-order approximation of the optimal w*”).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to have modified the interpolation of the proposed combination to include the weighted combination taught by Zhang. The motivation to do so would have been improving the model’s loss and weighing models more heavily that improve performance, thus improving performance more quickly (Zhang, Page 4, Efficient personalization with FedFomo, Lines 21-25, “Here we note that our formulation captures the intuition of federating with client models that perform better than our own model, e.g. have a smaller loss on Li. Moreso, we weigh models more heavily as this positive loss delta increases, or the distance between our current parameters and theirs decreases, in essence most heavily weighing the models that most efficiently improve our performance”).
Regarding claim 18, the rejection of claim 17 is incorporated.
The proposed combination does not explicitly teach wherein the
A
i
is represented by:
A
i
=
L
b
W
-
L
b
(
W
i
)
W
-
W
i
2
wherein
L
b
is a loss associated with the first client.
Zhang teaches wherein the
A
i
is represented by:
A
i
=
L
b
W
-
L
b
(
W
i
)
W
-
W
i
2
wherein
L
b
is a loss associated with the first client (Zhang, Page 4, Efficient personalization with FedFomo, 14-18 and Equation 3, “Following this line of reasoning however, we thus derive an approximation of
w
*
for any client: Given previous local model parameters
θ
i
l
(
t
-
1
)
, set of fellow federating models available to download {
θ
i
l
(
t
)
} and local client objective captured by
L
i
, we propose weights of the form:
w
n
=
L
i
θ
i
l
t
-
1
-
L
i
θ
i
l
t
θ
i
l
(
t
)
-
θ
i
l
(
t
-
1
)
where the resulting federated update
θ
i
l
(
t
)
←
θ
i
l
(
t
-
1
)
+
∑
n
∈
[
N
]
w
n
θ
i
l
t
-
θ
i
l
t
-
1
directly optimizes client
c
i
’s objective up to a first-order approximation of the optimal w*”).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to have modified the interpolation of the proposed combination to include the weighted combination taught by Zhang. The motivation to do so would have been improving the model’s loss and weighing models more heavily that improve performance, thus improving performance more quickly (Zhang, Page 4, Efficient personalization with FedFomo, Lines 21-25, “Here we note that our formulation captures the intuition of federating with client models that perform better than our own model, e.g. have a smaller loss on Li. Moreso, we weigh models more heavily as this positive loss delta increases, or the distance between our current parameters and theirs decreases, in essence most heavily weighing the models that most efficiently improve our performance”).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Bhuyan in view of Yemini in view of Lim in further view of Li et al., An Efficient Multi-Model Training Algorithm for Federated Learning, 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, 2021, pp. 1-6, doi: 10.1109/GLOBECOM46510.2021.9685230, hereinafter referred to as “Li”.
Regarding claim 20, the rejection of claim 15 is incorporated, and further, the proposed combination teaches selecting the plurality of server-maintained machine learning models from a pool of machine learning models (Bhuyan, Page 2, Algorithm 1, Step 1, “globalModel[m] ← 0 Ɐ m
∈
{1, 2, …, M}”; “{1, 2, …, M}” is considered to be the “pool of machine learning models”).
The proposed combination thus far does not explicitly teach wherein the plurality of server-maintained machine learning models are selected from the pool based on resource limits associated with the plurality of clients.
Li teaches wherein the plurality of server-maintained machine learning models are selected from the pool based on resource limits associated with the plurality of clients (Li, Page 4, Section IV, Paragraph 3, Lines 1-4, “Algorithm 1 gives the detailed algorithm design of LFMB. In Algorithm 1, lines 1-5 perform initialization, where lines1-4 randomly assign models to all clients iteratively until each client has no spare resource to train any more model”; Li, Page 1, Abstract, Lines 7-9, “The objective is to effectively utilize the heterogeneous resources at clients for parallel multi-model training”).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to have modified the federated learning method of Bhuyan to include selecting models based on resource limits of the clients as taught by Li. The motivation to do so would have been to increase training efficiency (Li, Page 1, Abstract, Lines 7-11, “The objective is to effectively utilize the heterogeneous resources at clients for parallel multi-model training and therefore maximize the overall training efficiency while ensuring a certain fairness among individual models”).
Response to Arguments
Applicant’s arguments regarding the 35 U.S.C. 103 rejections of the claims have been fully considered but are unpersuasive.
Argument 1:
Applicant first argues, on page 8, paragraph 5 and page 9 of the response, that the independent claims clarify that the claimed disconnection concerns a first client being unable to receive a downlink transmission of server-maintained model weights from the server during the communication round, and that the neighboring client forwards to the first client the server-maintained model weights that the neighboring client received from the server.
Examiner’s Response:
Examiner respectfully disagrees. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., "a first ) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Applicant’s arguments with respect to the newly added limitation - the neighboring client forwards to the first client the server-maintained model weights that the neighboring client received from the server - have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Please refer to the updated 35 U.S.C. 103 rejection seen above.
Applicant's arguments regarding the remainder of the claims rely upon the arguments asserted with respect to the independent claims, and are thus unpersuasive.
Applicant’s arguments regarding the 35 U.S.C. 101 rejections of the claims have been fully considered but are unpersuasive.
Argument 1:
Applicant argues, in page 10, paragraphs 1-3 of the response, that the claims are not directed merely to calculating a loss, observing a device is disconnected, or transmitting data over a generic network, but rather the claims recite a specific federated-learning failover process and thus any abstract idea is integrated into a practical application.
Examiner’s Response:
Examiner respectfully disagrees. The “failover process” referenced by the applicant, determining a client is disconnected and then deciding how to solve that problem, covers the interpretation of a person using an observation to determine a device is disconnected and their judgement to determine how to solve that problem, thus reciting a mental process. The action of transmitting data from one client to another is simply transmitting data over a network which the courts have identified as well-understood, routine, and conventional. It is important to note the judicial exception alone cannot provide the improvement, see MPEP 2106.05(a).
Argument 2:
Applicant next argues, page 10, final paragraph – page 11, first paragraph of the response, that the ordered combination of the claim amounts to significantly more than the abstract idea.
Examiner’s Response:
Examiner respectfully disagrees. Simply “determining” the first client cannot receive weights from the server and forwarding the weights by a neighboring client does not constitute a particular solution to a problem or a particular way to achieve a desired outcome as required, see MPEP 2106.05(a). That determination and decision to forward could easily be made using a person’s observation and judgment skills in their head. There are no details of the determination that could be considered a particular solution or particular way to achieve a desired outcome. See the updated 35 U.S.C. 101 rejection above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOLLY CLARKE SIPPEL whose telephone number is (571)272-3270. The examiner can normally be reached Monday - Friday, 7:30 a.m. - 4:30 p.m. ET..
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/M.C.S./ Examiner, Art Unit 2122
/KAKALI CHAKI/ Supervisory Patent Examiner, Art Unit 2122
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