DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/21/2024 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Allowable Subject Matter
Claims 5, 11, and 13 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.
Claim 18 is not being rejected under prior art, however would be allowable if 101 rejection is overcome and rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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 17-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the phrases "server…" and "client nodes…" are not defined as hardware in the specification and under broadest reasonable interpretation they could be considered as software, making the claims as being directed to software per se.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 6-7, 9 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Dhunay et al (US Published Patent Application No. US 20220210140, "Dhunay"), in view of Agrawal et al (Homomorphic MACs: MAC-Based Integrity for Network Coding, "Agrawal").
In regard of claim 1, Dhunay teaches selecting, from a plurality of client nodes each being provided with a unique identifier id in the system, a first quantity of client nodes, to participate in an i-th iteration of the federated learning, where i is an integer; (Duhany, paragraph 0034, “Training coordinator 114 manages various training processes in federated learning system 100 on behalf of aggregator node 110. For example, training coordinator 114 selects client nodes 150 for participation in a training cycle ( e.g., based on a pool of available nodes and corresponding DIDs provided by trust organization server 10) and is responsible for initiating each training cycle.’”)
sending a list of the selected client nodes to each of the first quantity of client nodes; (Duhany, paragraph 0083, “Trust organization server 10 provides to aggregator node 110 a list of DIDs reflecting a pool of client nodes 150 that can participate in a training cycle.” Examiner would like to point out that the list is being provided to nodes that are interpreted as the plurality of the first client nodes.)
acquiring model training information transmitted by each of a second quantity of client nodes among the first quantity of client nodes, the model training information being transmitted in a form of cypher text, and being obtained by the client nodes through training local models with local training data, (Duhany, paragraph 0083, “Trust organization server 10 provides to aggregator node 110 a list of DIDs reflecting a pool of client nodes 150 that can participate in a training cycle. Aggregator node 110 selects a desired number of participants in the training cycle and sets a target index equal reflective of this number” and paragraph 0086, “Upon receiving the model, each participating client node 150 ( e.g., Clients A, B, and C in FIG. 4) begins training the local model using training data available at the respective client node 150.” and paragraph 0087, “Cryptographic engine 116 of aggregator node 110 generates a private/public key pair. The private key is retained at aggregator node 110, while the public key is provided to each client node 150 participating in the training cycle, e.g., by way of communication interface 112. FIG. 5 depicts an example JSON code listing for a public key data structure 500 provided to each client node 150, which includes an example public key.”)
aggregating the cypher text of the model training information to obtain an aggregate result; and (Duhany, paragraph 0087, “Cryptographic engine 116 of aggregator node 110 generates a private/public key pair. The private key is retained at aggregator node 110, while the public key is provided to each client node 150 participating in the training cycle, e.g., by way of communication interface 112. FIG. 5 depicts an example JSON code listing for a public key data structure [obtain an aggregate result] 500 provided to each client node 150, which includes an example public key.”)
broadcasting a list of the second quantity of client nodes and the aggregate result via a blockchain. (Duhany, paragraph 0104, “To facilitate random selection, the payload data structure [via a blockchain.] may contain a list of DIDs for client nodes 150 and indicators of which client nodes 150 have not yet participated in a current training cycle [broadcasting]. The list may be updated at each successive client node 150.”)
However, Dhunay does not explicitly teach wherein the cypher text is generated by performing homomorphic encryption on the model training information based on pairwise seeds computed for each of the first quantity of client nodes from a symmetric bivariate polynomial and a private seed computed from asymmetric bivariate polynomial;
Agrawal teaches wherein the cypher text is generated by performing homomorphic encryption on the model training information based on pairwise seeds computed for each of the first quantity of client nodes from a symmetric bivariate polynomial and a private seed computed from asymmetric bivariate polynomial; (Agrawal, pg. 295. 2, paragraph 1, “We begin by defining homomorphic MACs and their security. A (q, n,m) homomorphic MAC is defined by three probabilistic, polynomial-time algorithms, (Sign, Verify, Combine). The Sign algorithm computes a tag for a vector space V = span(v1, . . . , vm) [from a symmetric bivariate polynomial and a private seed computed from asymmetric bivariate polynomial; ]by computing a tag for one basis vector at a time. Combine implements the homomorphic property and Verify verifies vector-tag pairs.”)
Dhunay and Agrawal are related to the same field of endeavor (i.e. federated learning). In view of the teachings of Agrawal, it would have been obvious for a person with ordinary skill in the art to apply the teachings of Agrawal to Dhunay before the effective filing date of the claimed invention in order to allow for more intelligent routing of nodes. (Agrawal, pg. 292, 1, paragraph 1, “Network coding [1,2] proposes to replace the traditional ‘store and forward’ paradigm in networks by more intelligent routing that allows intermediate nodes to transform the data in transit.”)
In regard to claim 15, the claim recites similar limitations as corresponding claim 1, and is rejected for similar reasons as claim 1 using similar teachings and rationale.
In regard to claim 2, Dhuany and Agrawal teach the method of claim 1.
Dhunay further teaches for a (i+1)-th iteration, sending a list of a third quantity of client nodes together with an aggregate result of model training information previously transmitted from the second quantity of client nodes, to each of the third quantity of client nodes, for allowing the third quantity of client nodes to reconstruct local models to be used in the (i+1)-th iteration, wherein the third quantity of client nodes are a subset of the second quantity of client nodes. (Duhany, paragraph 0120, “The example architecture of federated learning system 100' is hierarchical in that aggregator node 110 communicates with micro-aggregator nodes 190, e.g., to provide model data, global model parameter updates, a public key, etc., and to receive local model updates therefrom. In turn, each micro-aggregator node 190 communicates with a subset of client nodes 150 and forward to such nodes model data, global model parameter updates, a public key, etc., received from aggregator node 110.”)
Dhunay and Agrawal are combinable for the same rationale as set forth above with respect to claim 1.
In regard to claim 6 and analogous claim 9, Dhuany and Agrawal teach the method of claim 1.
Agrawal further teaches acquiring the cipher text cid and a tag tagid transmitted by each of the second quantity of client nodes, wherein the cipher text cid and the tag tagid are computed by MaskAndMAC (pp, sk, id, xid) -> (cid, tagid) , wherein pp represents a public parameter component available to the server and the client nodes, sk represents secret key secretly shared among the client nodes, and xid represents the model training information of the client node id; (Agrawal, pg. 295, 2, paragraph 1, , “We begin by defining homomorphic MACs and their security. A (q, n,m) homomorphic MAC is defined by three probabilistic, polynomial-time algorithms, (Sign, Verify, Combine). The Sign algorithm computes a tag for a vector space V = span(v1, . . . , vm) by computing a tag for one basis vector at a time. Combine implements the homomorphic property and Verify verifies vector-tag pairs.”)
aggregating respective tags into an aggregate tag; and broadcasting the aggregate tag associated with the aggregate result via the blockchain. (Agrawal, pg. 304, paragraph 1, “In our implementation, we chose m = 5, so the sender sends 5 messages, each a 1 kilobyte vector as described above. Each message is signed with 49 (resp. 121) keys by the sender. An intermediate node (or router) receives 5 messages with 49 (resp. 121) tags each, which it linearly combines to yield an aggregate message and an aggregate tag. We verify that the resultant aggregate tag is valid for the aggregate message.”)
Dhunay and Agrawal are combinable for the same rationale as set forth above with respect to claim 1.
In regard to claim 7 and analogous claim 16, Dhunay teaches receiving, from a server in the system, a list of selected client nodes, wherein the selected client nodes are in a first quantity and are selected to participate in an i-th iteration of the federated learning by the server from a plurality of client nodes in the system, each of the selected client nodes is provided with a unique identifier id, and the first client node is one of the first quantity of the selected client nodes, i is an integer; (Duhany, paragraph 0034, “Training coordinator 114 manages various training processes in federated learning system 100 on behalf of aggregator node 110. For example, training coordinator 114 selects client nodes 150 for participation in a training cycle ( e.g., based on a pool of available nodes and corresponding DIDs provided by trust organization server 10) and is responsible for initiating each training cycle.’” and paragraph 0083, “Trust organization server 10 provides to aggregator node 110 a list of DIDs reflecting a pool of client nodes 150 that can participate in a training cycle. Aggregator node 110 selects a desired number of participants in the training cycle and sets a target index equal reflective of this number”)
acquiring model training information by training a local model with local training data; (Duhany, paragraph 0083, “Trust organization server 10 provides to aggregator node 110 a list of DIDs reflecting a pool of client nodes 150 that can participate in a training cycle. Aggregator node 110 selects a desired number of participants in the training cycle and sets a target index equal reflective of this number” and paragraph 0086, “Upon receiving the model, each participating client node 150 ( e.g., Clients A, B, and C in FIG. 4) begins training the local model using training data available at the respective client node 150.”)
sending the cypher text of the model training information to the server. (Duhany, paragraph 0083, “Trust organization server 10 provides to aggregator node 110 a list of DIDs reflecting a pool of client nodes 150 that can participate in a training cycle. Aggregator node 110 selects a desired number of participants in the training cycle and sets a target index equal reflective of this number” and paragraph 0086, “Upon receiving the model, each participating client node 150 ( e.g., Clients A, B, and C in FIG. 4) begins training the local model using training data available at the respective client node 150.”)
However, Duhany does not explicitly teach generating cypher text of the model training information by performing homomorphic encryption on the model training information based on pairwise seeds computed for each of the first quantity of client nodes from a symmetric bivariate polynomial and a private seed computed from asymmetric bivariate polynomial; and
Agrawal teaches generating cypher text of the model training information by performing homomorphic encryption on the model training information based on pairwise seeds computed for each of the first quantity of client nodes from a symmetric bivariate polynomial and a private seed computed from asymmetric bivariate polynomial; and (Agrawal, pg. 295. 2, paragraph 1, “We begin by defining homomorphic MACs and their security. A (q, n,m) homomorphic MAC is defined by three probabilistic, polynomial-time algorithms, (Sign, Verify, Combine). The Sign algorithm computes a tag for a vector space V = span(v1, . . . , vm) [a symmetric bivariate polynomial and a private seed computed from asymmetric bivariate polynomial; ] by computing a tag for one basis vector at a time. Combine implements the homomorphic property and Verify verifies vector-tag pairs.”)
Dhunay and Agrawal are combinable for the same rationale as set forth above with respect to claim 1.
In regard to claim 17, Dhunay teaches the server is configured to: selecting, from a plurality of client nodes each being provided with a unique identifier id in the system, a first quantity of client nodes, to participate in an i-th iteration of the federated learning, where i is an integer; (Duhany, paragraph 0034, “Training coordinator 114 manages various training processes in federated learning system 100 on behalf of aggregator node 110. For example, training coordinator 114 selects client nodes 150 for participation in a training cycle ( e.g., based on a pool of available nodes and corresponding DIDs provided by trust organization server 10) and is responsible for initiating each training cycle.’”)
sending a list of the selected client nodes to each of the first quantity of client nodes; (Duhany, paragraph 0083, “Trust organization server 10 provides to aggregator node 110 a list of DIDs reflecting a pool of client nodes 150 that can participate in a training cycle.” Examiner would like to point out that the list is being provided to nodes that are interpreted as the plurality of the first client nodes.)
acquiring model training information transmitted by each of a second quantity of client nodes among the first quantity of client nodes, the model training information being transmitted in a form of cypher text; (Duhany, paragraph 0083, “Trust organization server 10 provides to aggregator node 110 a list of DIDs reflecting a pool of client nodes 150 that can participate in a training cycle. Aggregator node 110 selects a desired number of participants in the training cycle and sets a target index equal reflective of this number” and paragraph 0086, “Upon receiving the model, each participating client node 150 ( e.g., Clients A, B, and C in FIG. 4) begins training the local model using training data available at the respective client node 150.” and paragraph 0087, “Cryptographic engine 116 of aggregator node 110 generates a private/public key pair. The private key is retained at aggregator node 110, while the public key is provided to each client node 150 participating in the training cycle, e.g., by way of communication interface 112. FIG. 5 depicts an example JSON code listing for a public key data structure 500 provided to each client node 150, which includes an example public key.”)
aggregating the cypher text of the model training information to obtain an aggregate result; and (Duhany, paragraph 0087, “Cryptographic engine 116 of aggregator node 110 generates a private/public key pair. The private key is retained at aggregator node 110, while the public key is provided to each client node 150 participating in the training cycle, e.g., by way of communication interface 112. FIG. 5 depicts an example JSON code listing for a public key data structure [obtain an aggregate result] 500 provided to each client node 150, which includes an example public key.”)
broadcasting a list of the second quantity of client nodes and the aggregate result via a blockchain; and each of the plurality of client nodes is configured for: (Duhany, paragraph 0104, “To facilitate random selection, the payload data structure [via a blockchain.] may contain a list of DIDs for client nodes 150 and indicators of which client nodes 150 have not yet participated in a current training cycle [broadcasting]. The list may be updated at each successive client node 150.”)
receiving, from a server in the system, the list of selected first quantity client nodes; (Duhany, paragraph 0034, “Training coordinator 114 manages various training processes in federated learning system 100 on behalf of aggregator node 110. For example, training coordinator 114 selects client nodes 150 for participation in a training cycle ( e.g., based on a pool of available nodes and corresponding DIDs provided by trust organization server 10) and is responsible for initiating each training cycle.’” and paragraph 0083, “Trust organization server 10 provides to aggregator node 110 a list of DIDs reflecting a pool of client nodes 150 that can participate in a training cycle. Aggregator node 110 selects a desired number of participants in the training cycle and sets a target index equal reflective of this number”)
acquiring model training information by training a local model with local training data; (Duhany, paragraph 0083, “Trust organization server 10 provides to aggregator node 110 a list of DIDs reflecting a pool of client nodes 150 that can participate in a training cycle. Aggregator node 110 selects a desired number of participants in the training cycle and sets a target index equal reflective of this number” and paragraph 0086, “Upon receiving the model, each participating client node 150 ( e.g., Clients A, B, and C in FIG. 4) begins training the local model using training data available at the respective client node 150.”)
sending the cypher text of the model training information to the server. (Duhany, paragraph 0083, “Trust organization server 10 provides to aggregator node 110 a list of DIDs reflecting a pool of client nodes 150 that can participate in a training cycle. Aggregator node 110 selects a desired number of participants in the training cycle and sets a target index equal reflective of this number” and paragraph 0086, “Upon receiving the model, each participating client node 150 ( e.g., Clients A, B, and C in FIG. 4) begins training the local model using training data available at the respective client node 150.”)
However, Duhany does not explicitly teach generating cypher text of the model training information by performing homomorphic encryption on the model training information based on pairwise seeds computed for each of the first quantity of client nodes from a symmetric bivariate polynomial and a private seed computed from asymmetric bivariate polynomial; and
Agrawal teaches generating cypher text of the model training information by performing homomorphic encryption on the model training information based on pairwise seeds computed for each of the first quantity of client nodes from a symmetric bivariate polynomial and a private seed computed from asymmetric bivariate polynomial; and (Agrawal, pg. 295. 2, paragraph 1, “We begin by defining homomorphic MACs and their security. A (q, n,m) homomorphic MAC is defined by three probabilistic, polynomial-time algorithms, (Sign, Verify, Combine). The Sign algorithm computes a tag for a vector space V = span(v1, . . . , vm) [a symmetric bivariate polynomial and a private seed computed from asymmetric bivariate polynomial; ] by computing a tag for one basis vector at a time. Combine implements the homomorphic property and Verify verifies vector-tag pairs.”)
Dhunay and Agrawal are combinable for the same rationale as set forth above with respect to claim 1.
Claims 3-4, 8, 10, 12, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Dhunay, in view of Agrawal and in further view of Premnath et al (US Published Patent Application No. 20150341326, "Premnath").
In regard to claim 3 and analogous claim 8, Duhany and Agrawal teach the method of claim 1.
However, Duhany and Agrawal do not explicitly teach wherein the symmetric bivariate polynomial denoted by F(x, y) and the asymmetric bivariate polynomial denoted by G(x, y) are selected and sent to the client nodes by a one-time dealer in an initialization process.
Premnath teaches wherein the symmetric bivariate polynomial denoted by F(x, y) and the asymmetric bivariate polynomial denoted by G(x, y) are selected and sent to the client nodes by a one-time dealer in an initialization process. (Premnath, paragraph 0009, “In works related to the field of the present invention, homomorphic encryption is an approach that enables performing computations directly on the encrypted data, without requiring private decryption keys. For example, in the RSA public key system, the product of two ciphertext messages produces a ciphertext corresponding to the product of the underlying plain text messages [Rivest]. Domingo-Ferrer present a homomorphic scheme that represents ciphertext as polynomials, allowing both addition and multiplication operations on the underlying plain text; however, in this scheme, multiplication operations drastically increase the size of the cipher text.” and paragraph 0108, “Let G denote a pseudorandom generator, which on providing a k-bit input seed, outputs a sequence of (2 nk+2) bits, i.e., if lsl=k, then IG(s)1=(2 nk+2). G may represent the output of AES block cipher in output feedback mode [sent to the client nodes by a one-time dealer in an initialization process], for example. Let Go(s) and G 1 (s) denote the first and last (nk+ 1) bits ofG(s), respectively.”)
Dhunay, Agrawal and Premnath are related to the same field of endeavor (i.e. federated learning). In view of the teachings of Premnath, it would have been obvious for a person with ordinary skill in the art to apply the teachings of Premnath to Dhunay and Agrawal before the effective filing date of the claimed invention in order to have an efficient computing system. (Premnath, paragraph 0132, “Therefore, the protocols of Goldreich 2004, Goldreich 1987 and BMR [Beaver; Rogaway] are adapted in a number of ways, discussed below, to build an efficient, secure cloud computing system, that also enables the client to easily verify the outputs of the computation.”)
In regard to claim 4 and analogous claim 10, Duhany, Agrawal and Premnath teach the method of claim 3.
Premnath further teaches wherein for any client node id, the pairwise seed computed for another client node with identifier id' from the symmetric bivariate polynomial is fia(id') = F(id, id'), where F(id, id') = F(id', id). (Premnath, paragraph 0009, “In works related to the field of the present invention, homomorphic encryption is an approach that enables performing computations directly on the encrypted data, without requiring private decryption keys. For example, in the RSA public key system, the product of two ciphertext messages produces a ciphertext corresponding to the product of the underlying plain text messages [Rivest]. Domingo-Ferrer present a homomorphic scheme that represents ciphertext as polynomials, allowing both addition and multiplication operations on the underlying plain text; however, in this scheme, multiplication operations drastically increase the size of the cipher text.” and paragraph 0108, “Let G denote a pseudorandom generator, which on providing a k-bit input seed, outputs a sequence of (2 nk+2) bits, i.e., if lsl=k, then IG(s)1=(2 nk+2). G may represent the output of AES block cipher in output feedback mode, for example. Let Go(s) and G 1 (s) denote the first and last (nk+ 1) bits ofG(s), respectively.”)
Dhunay, Agrawal and Premnath are combinable for the same rationale as set forth above with respect to claim 3.
In regard to claim 12, Duhany, Agrawal and Premnath teach the method of claim 8.
Duhany further teaches acquiring, from the server, a list of a third quantity of client nodes together with an aggregate result of model training information previously transmitted from the second quantity of client nodes; and (Duhany, paragraph 0120, “The example architecture of federated learning system 100' is hierarchical in that aggregator node 110 communicates with micro-aggregator nodes 190, e.g., to provide model data, global model parameter updates, a public key, etc., and to receive local model updates therefrom. In turn, each micro-aggregator node 190 communicates with a subset of client nodes 150 and forward to such nodes model data, global model parameter updates, a public key, etc., received from aggregator node 110.”)
reconstructing a local model to be used in the (i+ 1)-th iteration by using the list of the third quantity of client nodes together with the aggregate result of model training information previously transmitted from the second quantity of client nodes. (Duhany, paragraph 0120, “The example architecture of federated learning system 100' is hierarchical in that aggregator node 110 communicates with micro-aggregator nodes 190, e.g., to provide model data, global model parameter updates, a public key, etc., and to receive local model updates therefrom. In turn, each micro-aggregator node 190 communicates with a subset of client nodes 150 and forward to such nodes model data, global model parameter updates, a public key, etc., received from aggregator node 110.”)
Dhunay, Agrawal and Premnath are combinable for the same rationale as set forth above with respect to claim 3.
In regard to claim 14, Duhany, Agrawal and Premnath teach the method of claim 8.
Agrawal further teaches acquiring, from the block-chain, an aggregate tag associated with the aggregate result of model training information previously transmitted from the third quantity of client nodes, and (Agrawal pg. 304, paragraph 1, “In our implementation, we chose m = 5, so the sender sends 5 messages, each a 1 kilobyte vector as described above. Each message is signed with 49 (resp. 121) keys by the sender. An intermediate node (or router) receives 5 messages with 49 (resp. 121) tags each, which it linearly combines to yield an aggregate message and an aggregate tag. We verify that the resultant aggregate tag is valid for the aggregate message.”)
before reconstructing the local model, verifying correctness of the aggregate result by checking validity of the aggregate tag against the aggregate result, pg. 304, paragraph 1, “In our implementation, we chose m = 5, so the sender sends 5 messages, each a 1 kilobyte vector as described above. Each message is signed with 49 (resp. 121) keys by the sender. An intermediate node (or router) receives 5 messages with 49 (resp. 121) tags each, which it linearly combines to yield an aggregate message and an aggregate tag. We verify that the resultant aggregate tag is valid for the aggregate message.”)
wherein the aggregate tag is aggregated by the server from tags of the client nodes. (Agrawal, pg. 304, paragraph 1, “In our implementation, we chose m = 5, so the sender sends 5 messages, each a 1 kilobyte vector as described above. Each message is signed with 49 (resp. 121) keys by the sender. An intermediate node (or router) receives 5 messages with 49 (resp. 121) tags each, which it linearly combines to yield an aggregate message and an aggregate tag. We verify that the resultant aggregate tag is valid for the aggregate message.”)
Dhunay, Agrawal and Premnath are combinable for the same rationale as set forth above with respect to claim 3.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SKYLAR K VANWORMER whose telephone number is (703)756-1571. The examiner can normally be reached M-F 6:00am to 3:00 pm.
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/S.K.V./Examiner, Art Unit 2146
/USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146
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