DETAILED ACTION
This action is responsive to communications filed on July 18, 2025. This action is made Non-Final.
Claims 1-17 are pending in the case.
Claims 1, 7, and 12 are independent claims.
Claims 1-3, 7, 8, and 12-14 are rejected.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statement (IDS(s)) submitted on 02/26/2025 is/are in compliance with the provisions of 37 C.F.R. 1.97. Accordingly, the IDS(s) is/are being considered by the examiner.
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.
Claim(s) 1, 2, 7, 8, 12, and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hemantharaja, US Publication 2023/0292117 (“Hemantharaja”), and further in view of Chen, Qian, et al. "PPT: A privacy-preserving global model training protocol for federated learning in P2P networks." arXiv:2105.14408v2 [cs.CR] 9 Aug 2021 (“Chen”).
Claim 1:
Hemantharaja teaches or suggests a communication method, wherein the method comprises:
receiving, by a first terminal, training information, wherein the training information comprises a global model in a previous round and identifiers of at least two second terminals that participate in a current round of training, the first terminal is any one of the at least two second terminals (see Fig. 3-7, 9; para. 0045 - the updated global machine learning models may be shared with each of the first vehicle 410 and the second vehicle 412, so that the updated global machine learning model replaces the local machine learning model(s). may continue to train and update their local machine learning models, so that the process continues, providing continuous, collaborative updates to the machine learning models for each vehicle; para. 0052 - message M may be signed with the digital signature a, the digital signature a generated by the vehicle based on the gsk. As such, each vehicle may generate a distinct and private digital signature. CM may accept vehicles to participate in the CML network if the digital signature a of the message M is verified by the CM using the gpk; para. 0063 - the collaborator may incorporate the contents of M into an updated global machine learning in model, which may in turn be distributed to each vehicle GV. For example, the updated machine learning model may incorporate details from each vehicle in GV.); and
sending, by the first terminal, a local model of the first terminal, wherein the local model is obtained based on the global model in the previous round and a shared key, the shared key is generated based on a private key of the first terminal and a public key (see Fig. 3-7, 9; para. 0045 -may continue to train and update their local machine learning models, so that the process continues, providing continuous, collaborative updates to the machine learning models for each vehicle; para. 0059 - the GM may take details of the vehicles in the group as an argument and may proceed with the following mathematical procedure in order to generate a gpk, a gmsk, and a secret key for each user. group public key is gpk= (g1, g2, h, u, v, w), the group private key is gmsk=(s1 s2) and the secret key of each user is their tuple gsk[i]=(Ai, xi). each vehicle receives the gpk for the group, and each vehicle receives a distinct gsk that identifies the vehicle; para. 0060 - each vehicle in the group of vehicles sharing the local model with the CM. For example, for the group signature scheme, each vehicle (V;) may sign a message (M) containing details of the vehicle's local machine learning model. each vehicle signs their message M with a distinct digital signature a (e.g., generated with the gsk) and transmits the signed message.).
Chen further teaches or suggests of a third terminal, and the third terminal is any one of the at least two second terminals other than the first terminal (see Fig. 3; II, C - A third-party client assigns a path-key to the pairs of clients that are connected; IV, A - third-party client distributes the keys that haven’t been used to the two clients. Then, they choose some keys independently to obtain shared-keys and generate communication keys for themselves. client will never choose all the received keys from the third-party client as its shared-keys. Otherwise, it will lead to privacy leakage, as the third-party client will calculate their communication key. Therefore, the communication keys are established for all potential clients.).
Accordingly, it would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the system and method, taught in Hemantharaja, to include of a third terminal, and the third terminal is any one of the at least two second terminals other than the first terminal for the purpose of efficiently assigning keys for other clients to produce further keys, improving model security, as taught by Chen (IV, Fig. 3).
Claim(s) 12:
Claim(s) 12 corresponds to Claim 1, and thus, Hematharaja and Chen teach or suggest the limitations of claim(s) 12 as well.
Claim 2:
Hemantharaja further teaches or suggests wherein, before the sending, by the first terminal, a local model of the first terminal, the method further comprises: by the first terminal, an initial local model of the first terminal … of the shared key, to obtain the local model of the first terminal (see Fig. 3-7, 9; para. 0045 -may continue to train and update their local machine learning models, so that the process continues, providing continuous, collaborative updates to the machine learning models for each vehicle; para. 0059 - the GM may take details of the vehicles in the group as an argument and may proceed with the following mathematical procedure in order to generate a gpk, a gmsk, and a secret key for each user. group public key is gpk= (g1, g2, h, u, v, w), the group private key is gmsk=(s1 s2) and the secret key of each user is their tuple gsk[i]=(Ai, xi). each vehicle receives the gpk for the group, and each vehicle receives a distinct gsk that identifies the vehicle; para. 0060 - each vehicle in the group of vehicles sharing the local model with the CM. For example, for the group signature scheme, each vehicle (V;) may sign a message (M) containing details of the vehicle's local machine learning model. each vehicle signs their message M with a distinct digital signature a (e.g., generated with the gsk) and transmits the signed message.).
Chen further teaches or suggests scrambling, … by using a random vector (see Fig. 3; I - PPT generates a random noise for local model disturbance to guarantee the privacy; IV, B - protect u0’s privacy, u0 generates a noise s to disturb X0, shown as X0 +s. Note that s has the same dimension as X0. Subsequently, the leader client u0 can execute the transmission and aggregation process.).
Accordingly, it would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the system and method, taught in Hemantharaja, to include scrambling, … by using a random vector for the purpose of efficiently adding noise to data to protect the data, improving model security, as taught by Chen (IV, Fig. 3).
Claim(s) 13:
Claim(s) 13 corresponds to Claim 2, and thus, Hematharaja and Chen teach or suggest the limitations of claim(s) 13 as well.
Claim 7:
Hemantharaja discloses a communication method, wherein the method comprises: sending, by a server, training information wherein the training information comprises a global model in a previous round and identifier of at least two second terminals that participate in a current round of training (see Fig. 3-7, 9; para. 0045 - the updated global machine learning models may be shared with each of the first vehicle 410 and the second vehicle 412, so that the updated global machine learning model replaces the local machine learning model(s). may continue to train and update their local machine learning models, so that the process continues, providing continuous, collaborative updates to the machine learning models for each vehicle; para. 0052 - message M may be signed with the digital signature a, the digital signature a generated by the vehicle based on the gsk. As such, each vehicle may generate a distinct and private digital signature. CM may accept vehicles to participate in the CML network if the digital signature a of the message M is verified by the CM using the gpk; para. 0063 - the collaborator may incorporate the contents of M into an updated global machine learning in model, which may in turn be distributed to each vehicle GV. For example, the updated machine learning model may incorporate details from each vehicle in GV.);
recieving, by the server, local models of the at least two second terminals, wherein the local models are obtained based on the global model in the previous round and a shared key (see Fig. 3-7, 9; para. 0045 - the updated global machine learning models may be shared with each of the first vehicle 410 and the second vehicle 412, so that the updated global machine learning model replaces the local machine learning model(s). may continue to train and update their local machine learning models, so that the process continues, providing continuous, collaborative updates to the machine learning models for each vehicle; para. 0052 - message M may be signed with the digital signature a, the digital signature a generated by the vehicle based on the gsk. As such, each vehicle may generate a distinct and private digital signature. CM may accept vehicles to participate in the CML network if the digital signature a of the message M is verified by the CM using the gpk; para. 0063 - the collaborator may incorporate the contents of M into an updated global machine learning in model, which may in turn be distributed to each vehicle GV. For example, the updated machine learning model may incorporate details from each vehicle in GV.); and aggregating, by the server, the local models of the at least two second terminals based on the shared key …, to obtain an updated global model (see para. 0045 -may continue to train and update their local machine learning models, so that the process continues, providing continuous, collaborative updates to the machine learning models for each vehicle; para. 0059 - the GM may take details of the vehicles in the group as an argument and may proceed with the following mathematical procedure in order to generate a gpk, a gmsk, and a secret key for each user. group public key is gpk= (g1, g2, h, u, v, w), the group private key is gmsk=(s1 s2) and the secret key of each user is their tuple gsk[i]=(Ai, xi). each vehicle receives the gpk for the group, and each vehicle receives a distinct gsk that identifies the vehicle; para. 0060 - each vehicle in the group of vehicles sharing the local model with the CM. For example, for the group signature scheme, each vehicle (V;) may sign a message (M) containing details of the vehicle's local machine learning model. each vehicle signs their message M with a distinct digital signature a (e.g., generated with the gsk) and transmits the signed message.).
Hemantharaja does not explicitly disclose between the at least two second terminals; between the at least two second terminals.
Chen teaches or suggests between the at least two second terminals; between the at least two second terminals (see II, C - shared-key discovery: Each client broadcasts the lists of identifiers in the key ring to discover the same keys, called shared-keys, with its neighbor clients; IV, A - clients execute the above interactions mutually to obtain the same keys with each other, which are called shared-keys. to compute their communication key;
Accordingly, it would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the system and method, taught in Hemantharaja, to include between the at least two second terminals; between the at least two second terminals for the purpose of efficiently determining keys clients to produce further keys for encryption purposes, improving model security, as taught by Chen (IV, Fig. 3).
Claim 8:
Hemantharajan further teaches or suggests wherein the local models of the at least two second terminals are obtained … of the shared key, and the aggregating, by the server, the local models of the at least two second terminals based on the shared key … to obtain an updated global model in the current round comprises: aggregating, by the server, the local models of that least two second terminals, … of the shared key … to obtain the global model (see Fig. 3-7, 9; para. 0045 - the updated global machine learning models may be shared with each of the first vehicle 410 and the second vehicle 412, so that the updated global machine learning model replaces the local machine learning model(s). may continue to train and update their local machine learning models, so that the process continues, providing continuous, collaborative updates to the machine learning models for each vehicle; para. 0052 - message M may be signed with the digital signature a, the digital signature a generated by the vehicle based on the gsk. As such, each vehicle may generate a distinct and private digital signature. CM may accept vehicles to participate in the CML network if the digital signature a of the message M is verified by the CM using the gpk; para. 0063 - the collaborator may incorporate the contents of M into an updated global machine learning in model, which may in turn be distributed to each vehicle GV. For example, the updated machine learning model may incorporate details from each vehicle in GV.); and aggregating, by the server, the local models of the at least two second terminals based on the shared key …, to obtain an updated global model (see para. 0045 -may continue to train and update their local machine learning models, so that the process continues, providing continuous, collaborative updates to the machine learning models for each vehicle; para. 0059 - the GM may take details of the vehicles in the group as an argument and may proceed with the following mathematical procedure in order to generate a gpk, a gmsk, and a secret key for each user. group public key is gpk= (g1, g2, h, u, v, w), the group private key is gmsk=(s1 s2) and the secret key of each user is their tuple gsk[i]=(Ai, xi). each vehicle receives the gpk for the group, and each vehicle receives a distinct gsk that identifies the vehicle; para. 0060 - each vehicle in the group of vehicles sharing the local model with the CM. For example, for the group signature scheme, each vehicle (V;) may sign a message (M) containing details of the vehicle's local machine learning model. each vehicle signs their message M with a distinct digital signature a (e.g., generated with the gsk) and transmits the signed message.).
Chen further teaches or suggests by scrambling initial local models of the at least two second terminals by using a random vector … between the at least two second terminals … and eliminating the random vector … between the at least two second terminals (see Fig. 3; I - PPT generates a random noise for local model disturbance to guarantee the privacy; II, C - shared-key discovery: Each client broadcasts the lists of identifiers in the key ring to discover the same keys, called shared-keys, with its neighbor clients; IV, A - clients execute the above interactions mutually to obtain the same keys with each other, which are called shared-keys. to compute their communication key; IV, B - protect u0’s privacy, u0 generates a noise s to disturb X0, shown as X0 +s. Note that s has the same dimension as X0. Subsequently, the leader client u0 can execute the transmission and aggregation process.).
Accordingly, it would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the system and method, taught in Hemantharaja, to include between the at least two second terminals; between the at least two second terminals for the purpose of efficiently determining keys clients to produce further keys for encryption purposes and adding noise to data to protect the data, improving model security, as taught by Chen (IV, Fig. 3).
Claim(s) 3 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hemantharaja, in view of Chen, and further in view of Wu et al, US Publication 2021/0326762 (“Wu”).
Claim 3:
Hemantharaja teaches or suggests of the shared key, to obtain the local model of the first terminal, and before sending, by the first terminal, a local model of the first terminal, the method further comprises: performing, by the first terminal (see Fig. 3-7, 9; para. 0045 - the updated global machine learning models may be shared with each of the first vehicle 410 and the second vehicle 412, so that the updated global machine learning model replaces the local machine learning model(s). may continue to train and update their local machine learning models, so that the process continues, providing continuous, collaborative updates to the machine learning models for each vehicle; para. 0052 - message M may be signed with the digital signature a, the digital signature a generated by the vehicle based on the gsk. As such, each vehicle may generate a distinct and private digital signature. CM may accept vehicles to participate in the CML network if the digital signature a of the message M is verified by the CM using the gpk; para. 0063 - the collaborator may incorporate the contents of M into an updated global machine learning in model, which may in turn be distributed to each vehicle GV. For example, the updated machine learning model may incorporate details from each vehicle in GV.); and aggregating, by the server, the local models of the at least two second terminals based on the shared key …, to obtain an updated global model (see para. 0045 -may continue to train and update their local machine learning models, so that the process continues, providing continuous, collaborative updates to the machine learning models for each vehicle; para. 0059 - the GM may take details of the vehicles in the group as an argument and may proceed with the following mathematical procedure in order to generate a gpk, a gmsk, and a secret key for each user. group public key is gpk= (g1, g2, h, u, v, w), the group private key is gmsk=(s1 s2) and the secret key of each user is their tuple gsk[i]=(Ai, xi). each vehicle receives the gpk for the group, and each vehicle receives a distinct gsk that identifies the vehicle; para. 0060 - each vehicle in the group of vehicles sharing the local model with the CM. For example, for the group signature scheme, each vehicle (V;) may sign a message (M) containing details of the vehicle's local machine learning model. each vehicle signs their message M with a distinct digital signature a (e.g., generated with the gsk) and transmits the signed message.).
Chen further teaches or suggests wherein after the scrambling an initial local model of the first terminal by using a random vector (see Fig. 3; I - PPT generates a random noise for local model disturbance to guarantee the privacy; II, C - shared-key discovery: Each client broadcasts the lists of identifiers in the key ring to discover the same keys, called shared-keys, with its neighbor clients; IV, A - clients execute the above interactions mutually to obtain the same keys with each other, which are called shared-keys. to compute their communication key; IV, B - protect u0’s privacy, u0 generates a noise s to disturb X0, shown as X0 +s. Note that s has the same dimension as X0. Subsequently, the leader client u0 can execute the transmission and aggregation process.).
Accordingly, it would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the system and method, taught in Hemantharaja, to include wherein after the scrambling an initial local model of the first terminal by using a random vector for the purpose of efficiently determining keys clients to produce further keys for encryption purposes and adding noise to data to protect the data, improving model security, as taught by Chen (IV, Fig. 3).
Wu further teaches or suggests modulo division on the local model (see para. 0047 - for an initial model with a relatively large parameter scale, the executing body may divide the parameter of the initial model into parts of a target number based on a preset method. The target number represents the number of parameter servers in the distributed parameter server; and each parameter server in the distributed parameter server sequentially stores one part of parameter of the target number of parameter; para. 0048 - each parameter is correspondingly provided with a parameter identifier represented by a number, and by performing a modulo operation based on a value represented by the target number on the parameter identifier, the parameter of the initial model may be divided into parts of the target number. In this implementation, each parameter server in the distributed parameter server correspondingly stores the parts of the target number of parameter, so that the present disclosure is suitable for scenarios with a large model parameter scale.).
Accordingly, it would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the system and method, taught in Hemantharaja, to include modulo division on the local model for the purpose of efficiently breaking a model into parts for improved transmission and storage and enabling large model parameter scale, as taught by Wu (0048).
Claim(s) 14:
Claim(s) 14 corresponds to Claim 3, and thus, Hematharaja, Chen, and Wu teach or suggest the limitations of claim(s) 14 as well.
Allowable Subject Matter
Claims 4-6, 9-11, and 15-17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew T McIntosh whose telephone number is (571)270-7790. The examiner can normally be reached M-Th 8:00am-5:30pm.
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/ANDREW T MCINTOSH/Primary Examiner, Art Unit 2144
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