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 .
This is a Non-Final Office Action in response to application 18/239,214 entitled "Distributed, Privacy Preserving, Payments Fraud Detection System" filed on June 13, 2025, with claims 1 to 20 pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
Status of Claims
Claims 1, 8, and 15 have been amended and are hereby entered.
Claims 1-20 are pending and have been examined.
Response to Amendment
The amendment filed June 13, 2025, has been entered. Claims 1-20 remain pending in the application. Applicant’s amendments to the Specification, Drawings, and/or Claims have been noted in response to the Final Office Action mailed April 2, 2025.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on August 29, 2023, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement 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.
Please see MPEP 2106 for additional information regarding Patent Subject Matter Eligibility Guidance.
Claims 1-20 are directed to a system, method/process, machine/apparatus, or composition of matter, which are/is one of the statutory categories of invention. (Step 1: YES).
The claimed invention is directed to an abstract idea without significantly more.
Independent Claim 1 recites:
“…model on a data set;
communicate, …and to a local instance of a decentralized privacy preserving fraud detection system on each payment …system of a plurality of payments … systems, the …model;
monitor, in real-time based on the …model and locally to each of the plurality of payments …systems by the local instance of the decentralized privacy preserving fraud detection system, each of the plurality of payments … systems for an indication of a fraudulent transaction, wherein data privacy is preserved because local data sets do not leave the …and data privacy is further enhanced by selective gradient clipping;
perform, by the local instance of the decentralized privacy preserving fraud detection system utilizing the … model while monitoring real-time payments transactions being performed by each payments … system of the plurality of payments …systems, …on a local dataset;
receive, … and by a centralized model …a gradient update to a first local model utilized by a first instance of a first decentralized privacy preserving fraud detection system associated with on a first-payments … system of the plurality of payments…systems, wherein a gradient comprises differences local to the first payments …system and the first instance of the decentralized privacy preserving fraud detection system processing the …. model and wherein the differences comprise a first fraudulent activity pattern;
…by the centralized model …the model based on aggregated gradients received from each of the plurality of payments … systems and including the gradient update, wherein the…model comprises a capability to identify a pattern of fraudulent transactions in real-time while maintaining privacy, wherein the pattern of fraudulent transactions comprises the first fraudulent activity pattern identified on the first payment …system;
communicate, … and to the plurality of payments …systems,
the …model to each local instance of the decentralized privacy preserving fraud detection system on each payment … system of the plurality of payments … systems, wherein the plurality of payments …systems comprises a plurality of electronic transaction system types; and
… in real-time and based on an identification of a fraudulent transaction by a second instance of a second decentralized privacy preserving fraud detection system associated with a second payments … system of the plurality of payments … systems based on use of the … model… identifying the fraudulent transaction.”
These limitations clearly relate to managing payment transactions. These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. Specific instances include instructing to “communicate… to the plurality of payments … systems, the … model to each of the plurality of payments … systems” recite a fundamental economic principles or practice and/or commercial or legal interactions. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic, commercial, or financial action, principle, or practice then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. (Step 2A-Prong 1: YES. The claims recite an abstract idea).
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of:
[A computing platform comprising: a processor; and memory storing instructions that, when executed by the processor, cause the computing platform] [via a network] [computing] [corresponding local network]:
merely applying computer processing, storage, and networking technology as tools to perform an abstract idea
[to: train an artificial intelligence/machine learning (AI/ML)] [training] [trained] [Nc epochs of gradient descent ][retraining engine][re-train] [re-trained]:
merely applying computer machine learning technology as a tool to perform an abstract idea
[generate…an alert]
insignificant post-solution activity
are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer components and/or electronic processes. For example, the Applicant’s Specification reads:
[30] The application computing systems 108 may be one or more host devices (e.g., a workstation, a server, and the like) or mobile computing devices (e.g., smartphone, tablet).
[32] The user device(s) 110 may be computing devices (e.g., desktop computers, laptop computers) or mobile computing device (e.g., smartphones, tablets)
[17] "Computer machines" can include one or more: general-purpose ...computers... desktop computers... laptop or notebook computers”
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, Claim 1 is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination.
The [generate …an alert] is an additional element, that is merely insignificant extra post solution activity, specifically mere data gathering. Per Outputting data/notifications: MPEP2106.05(g), Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55.
The claim further define the abstract idea that is present and hence are abstract for the reasons presented above. The independent claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the independent claim is directed to an abstract idea. Thus, the claim is not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent Claims recite additional elements.
This judicial exception is not integrated into a practical application. In particular, the recited additional elements of
Claim 2:
“computing”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
Claim 3:
“computing”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
“retrain”: generally linking to machine learning as a tool to perform an abstract idea
Claim 4:
“computing”, “computing network”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
Claim 5:
“computing”, “computing network”, “network”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
Claim 6:
“computing”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
Claim 7:
“computing”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea.
are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer components and/or electronic processes. For example, the Applicant’s Specification reads:
[30] The application computing systems 108 may be one or more host devices (e.g., a workstation, a server, and the like) or mobile computing devices (e.g., smartphone, tablet).
[32] The user device(s) 110 may be computing devices (e.g., desktop computers, laptop computers) or mobile computing device (e.g., smartphones, tablets)
[17] "Computer machines" can include one or more: general-purpose ...computers... desktop computers... laptop or notebook computers”
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, the claim is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Dependent claims further define the abstract idea that is present in their respective independent claims and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, the claims are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Independent Claim 8 recites:
“…cause a fraud detection system to:
…an ….model on a data set;
communicate, …and to a local instance of a decentralized privacy preserving fraud detection system on each payment …system of a plurality of payments … systems, the …model;
monitor, in real-time based on the …model and locally to each of the plurality of payments …systems by the local instance of the decentralized privacy preserving fraud detection system, each of the plurality of payments … systems for an indication of a fraudulent transaction, wherein data privacy is preserved because local data sets do not leave the …and data privacy is further enhanced by selective gradient clipping;
perform, by the local instance of the decentralized privacy preserving fraud detection system utilizing the … model while monitoring real-time payments transactions being performed by each payments … system of the plurality of payments …systems, …on a local dataset;
receive, … and by a centralized model …a gradient update to a first local model utilized by a first instance of a first decentralized privacy preserving fraud detection system associated with on a first-payments … system of the plurality of payments…systems, wherein a gradient comprises differences local to the first payments …system and the first instance of the decentralized privacy preserving fraud detection system processing the …. model and wherein the differences comprise a first fraudulent activity pattern;
…by the centralized model …the model based on aggregated gradients received from each of the plurality of payments … systems and including the gradient update, wherein the…model comprises a capability to identify a pattern of fraudulent transactions in real-time while maintaining privacy, wherein the pattern of fraudulent transactions comprises the first fraudulent activity pattern identified on the first payment …system;
communicate, … and to the plurality of payments …systems,
the …model to each local instance of the decentralized privacy preserving fraud detection system on each payment … system of the plurality of payments … systems, wherein the plurality of payments …systems comprises a plurality of electronic transaction system types; and
… in real-time and based on an identification of a fraudulent transaction by a second instance of a second decentralized privacy preserving fraud detection system associated with a second payments … system of the plurality of payments … systems based on use of the … model… identifying the fraudulent transaction.”
These limitations clearly relate to managing payment transactions. These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. Specific instances include instructing to “cause a fraud detection system to…communicate… to the plurality of payments … systems, the … model to each of the plurality of payments … systems” recite a fundamental economic principles or practice and/or commercial or legal interactions. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic, commercial, or financial action, principle, or practice then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. (Step 2A-Prong 1: YES. The claims recite an abstract idea).
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of:
[Non-transitory computer readable media storing instructions that, when executed by a processor], [computing] [comprising: a processor; and memory storing instructions that, when executed by the processor][via a network] [via the network]:
merely applying computer processing, storage, and networking technology as tools to perform an abstract idea
[to: train an artificial intelligence/machine learning (AI/ML)] [training] [trained] [Nc epochs of gradient descent ][retraining engine][re-train] [re-trained]:
merely applying computer machine learning technology as a tool to perform an abstract idea
[generate…an alert]
insignificant post-solution activity
are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer components and/or electronic processes. For example, the Applicant’s Specification reads:
[30] The application computing systems 108 may be one or more host devices (e.g., a workstation, a server, and the like) or mobile computing devices (e.g., smartphone, tablet).
[32] The user device(s) 110 may be computing devices (e.g., desktop computers, laptop computers) or mobile computing device (e.g., smartphones, tablets)
[17] "Computer machines" can include one or more: general-purpose ...computers... desktop computers... laptop or notebook computers”
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, Claim 8 is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination.
The [generate …an alert] is an additional element, that is merely insignificant extra post solution activity, specifically mere data gathering. Per Outputting data/notifications: MPEP2106.05(g), Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55.
The claim further define the abstract idea that is present and hence are abstract for the reasons presented above. The independent claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the independent claim is directed to an abstract idea. Thus, the claim is not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent Claims recite additional elements.
This judicial exception is not integrated into a practical application. In particular, the recited additional elements of
Claim 9:
“non-transitory computer readable media”, “computing”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
Claim 10:
“non-transitory computer readable media”, “computing”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
“retrain”: generally linking to machine learning as a tool to perform an abstract idea
Claim 11:
“non-transitory computer readable media”, “computing”, “computing network”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
Claim 12:
“non-transitory computer readable media”, “computing”, “computing network”, “network”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
Claim 13:
“non-transitory computer readable media”, “computing”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
Claim 14:
“non-transitory computer readable media”, “computing”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea.
are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer components and/or electronic processes. For example, the Applicant’s Specification reads:
[30] The application computing systems 108 may be one or more host devices (e.g., a workstation, a server, and the like) or mobile computing devices (e.g., smartphone, tablet).
[32] The user device(s) 110 may be computing devices (e.g., desktop computers, laptop computers) or mobile computing device (e.g., smartphones, tablets)
[17] "Computer machines" can include one or more: general-purpose ...computers... desktop computers... laptop or notebook computers”
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, the claim is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Dependent claims further define the abstract idea that is present in their respective independent claims and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, the claims are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Independent Claim 15 recites:
“A method comprising
…an ….an ….model on a data set;
communicating, …and to a local instance of a decentralized privacy preserving fraud detection system on each payment …system of a plurality of payments … systems, the …model;
monitoring, in real-time based on the …model and locally to each of the plurality of payments …systems by the local instance of the decentralized privacy preserving fraud detection system, each of the plurality of payments … systems for an indication of a fraudulent transaction, wherein data privacy is preserved because local data sets do not leave the …and data privacy is further enhanced by selective gradient clipping;
performing, by the local instance of the decentralized privacy preserving fraud detection system utilizing the … model while monitoring real-time payments transactions being performed by each payments … system of the plurality of payments …systems, …on a local dataset;
receiving, … and by a centralized model …a gradient update to a first local model utilized by a first instance of a first decentralized privacy preserving fraud detection system associated with on a first-payments … system of the plurality of payments…systems, wherein a gradient comprises differences local to the first payments …system and the first instance of the decentralized privacy preserving fraud detection system processing the …. model and wherein the differences comprise a first fraudulent activity pattern;
…by the centralized model …the model based on aggregated gradients received from each of the plurality of payments … systems and including the gradient update, wherein the…model comprises a capability to identify a pattern of fraudulent transactions in real-time while maintaining privacy, wherein the pattern of fraudulent transactions comprises the first fraudulent activity pattern identified on the first payment …system;
communicating, … and to the plurality of payments …systems,
the …model to each local instance of the decentralized privacy preserving fraud detection system on each payment … system of the plurality of payments … systems, wherein the plurality of payments …systems comprises a plurality of electronic transaction system types; and
… in real-time and based on an identification of a fraudulent transaction by a second instance of a second decentralized privacy preserving fraud detection system associated with a second payments … system of the plurality of payments … systems based on use of the … model… identifying the fraudulent transaction.”
These limitations clearly relate to managing payment transactions. These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. Specific instances include instructing to “communicate… to the plurality of payments … systems, the … model to each of the plurality of payments … systems” recite a fundamental economic principles or practice and/or commercial or legal interactions. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic, commercial, or financial action, principle, or practice then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. (Step 2A-Prong 1: YES. The claims recite an abstract idea).
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of:
[computing] [comprising: a processor; and memory storing instructions that, when executed by the processor][via a network] [via the network]:
merely applying computer processing, storage, and networking technology as tools to perform an abstract idea
[training an artificial intelligence/machine learning (AI/ML)] [training] [trained] [Nc epochs of gradient descent ][retraining engine][re-train] [re-trained]:
merely applying computer machine learning technology as a tool to perform an abstract idea
[generating…an alert]
insignificant post-solution activity
are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer components and/or electronic processes. For example, the Applicant’s Specification reads:
[30] The application computing systems 108 may be one or more host devices (e.g., a workstation, a server, and the like) or mobile computing devices (e.g., smartphone, tablet).
[32] The user device(s) 110 may be computing devices (e.g., desktop computers, laptop computers) or mobile computing device (e.g., smartphones, tablets)
[17] "Computer machines" can include one or more: general-purpose ...computers... desktop computers... laptop or notebook computers”
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, Claim 15 is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination.
The [generating …an alert] is an additional element, that is merely insignificant extra post solution activity, specifically mere data gathering. Per Outputting data/notifications: MPEP2106.05(g), Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55.
The claim further define the abstract idea that is present and hence are abstract for the reasons presented above. The independent claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the independent claim is directed to an abstract idea. Thus, the claim is not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent Claims recite additional elements.
This judicial exception is not integrated into a practical application. In particular, the recited additional elements of
Claim 16:
“computing”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
Claim 17:
“computing”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
“retraining”: generally linking to machine learning as a tool to perform an abstract idea
Claim 18:
“computing”, “computing network”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
Claim 19:
“computing”, “computing network”, “network”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
Claim 20:
“computing”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer components and/or electronic processes. For example, the Applicant’s Specification reads:
[30] The application computing systems 108 may be one or more host devices (e.g., a workstation, a server, and the like) or mobile computing devices (e.g., smartphone, tablet).
[32] The user device(s) 110 may be computing devices (e.g., desktop computers, laptop computers) or mobile computing device (e.g., smartphones, tablets)
[17] "Computer machines" can include one or more: general-purpose ...computers... desktop computers... laptop or notebook computers”
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, the claim is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Dependent claims further define the abstract idea that is present in their respective independent claims and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, the claims are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hassanzadeh ("PRIVACY-PRESERVING COLLABORATIVE MACHINE LEARNING TRAINING USING DISTRIBUTED EXECUTABLE FILE PACKAGES IN AN UNTRUSTED ENVIRONMENT", U.S. Publication Number: 20220414661 A1), in view of Perez (“PRESERVING PRIVACY AND TRAINING NEURAL NETWORK MODELS”, U.S. Publication Number: 20240311834 A1),in view of Marathe (“USER-LEVEL PRIVACY PRESERVATION FOR FEDERATED MACHINE LEARNING”, U.S. Publication Number: 20230047092 A1).
Regarding Claim 1,
Hassanzadeh teaches,
A computing platform comprising: a processor; and memory storing instructions that, when executed by the processor, cause the computing platform to: train an artificial intelligence/machine learning (AI/ML) model on a training data set;
(Hassanzadeh [Abstract] cooperative training of machine learning (ML) models
Hassanzadeh [0007] machine learning models using distributed executable file packages includes executing, by one or more processors, an executable file package stored at a memory)
communicate, via a network and to a local instance of a decentralized privacy preserving fraud detection system on each payment computing system of a plurality of payments computing systems, the trained model;
(Hassanzadeh [0005] may cause the server and clients to perform cooperative learning, such as federated learning
Hassanzadeh [0006] the client devices to implement and train a local ML model
Hassanzadeh [0023] training of machine learning (ML) models that preserve privacy
Hassanzadeh [Abstract] with multiple clients, particularly for a fraud prediction model for financial transactions.
Hassanzadeh [0065] In federated learning, the server 340 initializes a global ML model (e.g., an initial ML model) and shares copies of the model with participating clients.
Hassanzadeh [0035] to perform other types of predictions for other clients, such as in the health industry, network service providers, government agencies, or any other environment)
monitor, … based on the trained model and locally to each of the plurality of payments computing systems by the local instance of the decentralized privacy preserving fraud detection system, each of the plurality of payments computing systems for an indication of a fraudulent transaction,
(Hassanzadeh [0043] process of sharing output data and gradient data between the server 102 and the first client device 140 may continue until the first ML model and the first server ML model are trained
Hassanzadeh [0001] training of a fraud detection machine learning model by multiple financial institutions using distributed executable file packages.
Hassanzadeh [0006] training the local ML models may include providing output data
Hassanzadeh [0002] to identify and predict anomalies)
wherein data privacy is preserved because local data sets do not leave the corresponding local network and data privacy is further enhanced by [gradient data]
(Hassanzadeh [0079] Each participating client trains a respective local partial ML model using its local dataset of financial transactions data, which may be private or confidential. The training includes providing output data to the server 540 for training of corresponding server-side partial ML models, and receipt of gradient data for training of the local partial ML models
Hassanzadeh [0028] The client devices 140 and 142 may also store client-specific data, which may be private or confidential to the respective client
Hassanzadeh [0031] clients may be competitors, or potential competitors, each client may desire to keep some or all client-specific data private, and thus the system 100 may represent an untrusted environment. ...To preserve privacy, the present disclosure provides techniques for cooperative training and use of ML models by the server 102, the first client device 140, the Nth client device 142, and the user device 150 that do not require client-specific data
Hassanzadeh [0044] the Nth client device 142 may implement a second ML model ... may train the second ML model by providing the Nth client data 146 as training data to the second ML model... The Nth training output 172 may include ...second output data (e.g., second smash data) that includes gradient output)
perform, by the local instance of the decentralized privacy preserving fraud detection system utilizing the trained model while monitoring … payments transactions being performed by each payments computing system of the plurality of payments computing systems, Nc epochs ….on a local dataset; receive, via the network and by a centralized model retraining engine, a gradient update to a first local model utilized by a first instance of a first decentralized privacy preserving fraud detection system associated with on a first-payments computing system of the plurality of payments computing systems, wherein a gradient comprises differences associated with a particular local to the first payments computing system and the first instance of the decentralized privacy preserving fraud detection system processing the trained model
(Hassanzadeh [Abstract] particularly for a fraud prediction model for financial transactions
Hassanzadeh [0050] the splitting, training, and aggregating are repeated for multiple iterations or epochs
Hassanzadeh [0043] any output data and gradient data shared between the server and the first client device 140 may be encrypted. This process of sharing output data and gradient data between the server 102 and the first client device 140 may continue until the first ML model and the first server ML model are trained
Hassanzadeh [0054] the first client device 140, and the Nth client device 142 while preserving privacy of client data used to train the ML models.
Hassanzadeh [0040] the differences between client-side partial ML models of different clients may be similarly based on any desired characteristic or information associated with the clients. Performing individual splits on a client-by-client basis is more flexible and may improve the robustness of a resulting trained ML model as well as improve computing resource utilization across the server 102, the first client device 140, and the Nth client device 142 as compared to performing the same split for all clients, or not splitting the initial ML model)
and wherein the differences …re-train, by the centralized model retraining engine, the model based on aggregated gradients received from each of the plurality of payments computing systems and including the gradient update, wherein the re-trained model comprises a capability to identify a pattern of fraudulent transactions in real-time while maintaining privacy, wherein the pattern of fraudulent transactions comprises … identified on the first payment computing system;
(Hassanzadeh [0050] training, and aggregating are repeated for multiple iterations or epochs
Hassanzadeh [0060] training the local ML models may include providing output data (e.g., smash data) to the server and receiving gradient data from the server
Hassanzadeh [0001] to support collaborative training of a fraud detection machine learning model by multiple financial institutions
Hassanzadeh [0054] privacy of sensitive client data is preserved while enabling computing resource-intensive training to be offloaded to the server 102 (or one or more cloud-based processing systems))
communicate, via the network and to the plurality of payments computing systems, the re-trained model to each local instance of the decentralized privacy preserving fraud detection system on each payment computing system of the plurality of payments computing systems,
(Hassanzadeh [0065] In federated learning, the server 340 initializes a global ML model (e.g., an initial ML model) and shares copies of the model with participating clients.... trained local ML models are collected and aggregated by the server 340 to construct a new global ML model (e.g., an aggregated ML model) for deployment
Hassanzadeh [0006] server may deploy the fraud prevention model, such as providing the fraud prevention model to the clients or implementing the fraud prevention model at an endpoint node for providing fraud prediction services)
wherein the plurality of payments computing systems comprises a plurality of electronic transaction system types;
(Hassanzadeh [0002] When facing distributed networks and highly sensitive datasets (e.g., finance, healthcare, etc.)
Hassanzadeh [0035] ML model may be cooperatively trained to perform other types of predictions for other clients, such as in the health industry, network service providers, government agencies, or any other environment in which data privacy is important or required.)
Hassanzadeh does not teach in real-time; selective gradient clipping; of gradient descent; a first fraudulent activity pattern; generate, in real-time and based on an identification of a fraudulent transaction by a second instance of a second decentralized privacy preserving fraud detection system associated with on a first a second payments computing system of the plurality of payments computing systems based on use of the re-trained model, an alert identifying the fraudulent transaction.
Perez teaches,
in real-time;
(Perez [0005] any applied processing needs to take place at “real-time”, e.g. with millisecond latencies
Perez [0035] a machine learning system is applied in real-time, high-volume transaction processing pipelines to provide an indication of)
a first fraudulent activity pattern;
(Perez [0070] the output of the machine learning system 160 may comprise a label, alert or other indication of fraud, or general malicious or anomalous activity.
Perez [0035] observed and/or predicted patterns of activity or actions, e.g. an indication of whether a transaction or entity is “normal” or “anomalous”. The term “behavioural” is used herein to refer to this pattern of activity or actions)
generate, in real-time and based on an identification of a fraudulent transaction by a second instance of a second decentralized privacy preserving fraud detection system associated with on a first a second payments computing system of the plurality of payments computing systems based on use of the re-trained model, an alert identifying the fraudulent transaction.
(Perez [0070] the output of the machine learning system 160 may comprise a label, alert or other indication of fraud, or general malicious or anomalous activity.
Perez [0005] any applied processing needs to take place at “real-time”, e.g. with millisecond latencies
Perez [0035] a machine learning system is applied in real-time, high-volume transaction processing pipelines to provide an indication of whether a transaction...and/or predicted patterns of activity or actions, e.g. an indication of whether a transaction or entity is “normal” or “anomalous”.
Perez [0073] a third party, such as the payment processor, one or more banks and/or one or more merchants.
Perez [0046] A transaction comprises a series of communications between different electronic systems
Perez [0224] bank may change its model or schema, subject to a partial retrain)
It is prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the distributed edge machine learning model of Hassanzadeh to incorporate the real-time alerts of Perez where “the output of the machine learning system … may comprise a label, alert” (Perez [0070]). The modification would have been obvious, because it is merely applying a known technique (i.e. real-time alerts) to a known concept (i.e. distributed edge machine learning models) ready for improvement to yield predictable result (i.e. “a machine learning system is applied in real-time… to provide an indication of whether a transaction...and/or predicted patterns of activity or actions, e.g. an indication of whether a transaction or entity is “normal” or “anomalous”.” Perez [0035])
Perez does not teach selective gradient clipping; (Nc epochs) of gradient descent.
Marathe teaches,
selective gradient clipping; of gradient descent.
(Marathe [0052] may train using … Stochastic Gradient Descent (SGD). … and then clip the gradients, …such as the global clipping threshold
Marathe [0015] then repeat for a number of training rounds until the model converges or a fixed number of rounds is complete )
It is prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the distributed edge machine learning model of Hassanzadeh to incorporate the gradient descent/clippings of Marathe to “compute parameter gradients …, clip the gradients per a globally prescribed clipping threshold” (Marathe [0029]). The modification would have been obvious, because it is merely applying a known technique (i.e. gradient descent/clippings) to a known concept (i.e. distributed edge machine learning models) ready for improvement to yield predictable result (i.e. “While carefully calibrated noise may be injected in the parameter updates, this noise injection is a distinct operation that may be decoupled from parameter update computation. The resulting two steps in parameter updates may be modeled based on Stochastic Gradient Descent” Marathe [0022])
Regarding Claim 2,
Hassanzadeh, Perez, and Marathe teach the distributed edge machine learning model of Claim 1 as described earlier.
Hassanzadeh teaches,
wherein the instructions cause the computing platform to aggregate gradient updates received from at least two payments computing systems of the payments computing systems.
(Hassanzadeh [Absract] cooperative training includes the clients training respective ML models and the server aggregating the trained ML models
Hassanzadeh [0042] output data (e.g., for split learning or federated split learning implementations) that is output during training of the first ML model at the first client device 140, such as gradient output
Hassanzadeh [0065] trained local ML models are collected and aggregated by the server 340 to construct a new global ML model (e.g., an aggregated ML model) for deployment
Hassanzadeh [0042] first training output 170 may include a trained ML model parameter set (e.g., for federated learning implementations) or output data (e.g., for split learning or federated split learning implementations) that is output during training of the first ML model at the first client device 140, such as gradient output
Hassanzadeh [Abstract] with multiple clients, particularly for a fraud prediction model for financial transactions.
Hassanzadeh [0065] In federated learning, the server 340 initializes a global ML model (e.g., an initial ML model) and shares copies of the model with participating clients.
Hassanzadeh [0035] to perform other types of predictions for other clients, such as in the health industry, network service providers, government agencies, or any other environment)
Regarding Claim 3,
Hassanzadeh, Perez, and Marathe teach the distributed edge machine learning model of Claim 1 as described earlier.
Hassanzadeh teaches,
wherein the instructions cause the computing platform to retrain the model based on aggregated gradient updates received from at least two payments computing systems of the payments computing systems.
(Hassanzadeh [0050] the splitting, training, and aggregating are repeated for multiple iterations or epochs
Hassanzadeh [0042] output data (e.g., for split learning or federated split learning implementations) that is output during training of the first ML model at the first client device 140, such as gradient output
Hassanzadeh [Abstract] with multiple clients, particularly for a fraud prediction model for financial transactions.
[0042] first training output 170 may include a trained ML model parameter set (e.g., for federated learning implementations) or output data (e.g., for split learning or federated split learning implementations) that is output during training of the first ML model at the first client device 140, such as gradient output
Hassanzadeh [0035] to perform other types of predictions for other clients, such as in the health industry, network service providers, government agencies, or any other environment
Hassanzadeh [Abstract] with multiple clients, particularly for a fraud prediction model for financial transactions.)
Regarding Claim 4,
Hassanzadeh, Perez, and Marathe teach the distributed edge machine learning model of Claim 1 as described earlier.
Hassanzadeh teaches,
wherein at least one payments system of the plurality of payments computing system communicates via an enterprise computing network local to the computing platform.
(Hassanzadeh [0065] Each participating client trains a respective local ML model using its local dataset of financial transactions data, which may be private or confidential. The trained local ML models are collected and aggregated by the server 340 to construct a new global ML model (e.g., an aggregated ML model).)
Regarding Claim 5,
Hassanzadeh, Perez, and Marathe teach the distributed edge machine learning model of Claim 1 as described earlier.
Hassanzadeh teaches,
wherein at least one payments system of the plurality of payments computing system communicates via an external computing network communicatively coupled to the network and wherein the network is local to the computing platform.
(Hassanzadeh [0034] communication files or applications may cause a device to communicate data to another device, such as in accordance with one or more formatting requirements, one or more application program interfaces (APIs), one or more cloud communication criteria
Hassanzadeh [0032] The cloud service provider, or a third party that provides cooperative ML training services using external cloud resources
Hassanzadeh [0063] first storage location 312 may be configured to store private client data of the first client 310 that is to be used as training data and the second storage location 314 may be configured to store local ML models.)
Regarding Claim 6,
Hassanzadeh, Perez, and Marathe teach the distributed edge machine learning model of Claim 1 as described earlier.
Hassanzadeh teaches,
wherein the first payments computing system operates using a first messaging protocol and a second computing payments system operates using a second messaging protocol.
(Hassanzadeh [0027] communicatively couple the server 102 to the one or more networks 130 via wired or wireless communication links established according to one or more communication protocols or standards (e.g., an Ethernet protocol, a transmission control protocol/internet protocol (TCP/IP), an Institute of Electrical and Electronics Engineers (IEEE) 802.11 protocol, an IEEE 802.16 protocol, a 3rd Generation (3G) communication standard, a 4th Generation (4G)/long term evolution (LTE) communication standard, a 5th Generation (5G) communication standard, and the like))
Regarding Claim 7,
Hassanzadeh, Perez, and Marathe teach the distributed edge machine learning model of Claim 1 as described earlier.
Hassanzadeh does not teach wherein the first payments computing system operates using a standard messaging protocol and a second payments computing system operates using a proprietary messaging protocol.
Perez teaches,
wherein the first payments computing system operates using a standard messaging protocol and a second payments computing system operates using a proprietary messaging protocol.
(Perez[0086] The request may be made over a proprietary communications channel or as a secure request over public networks (e.g., an HTTPS request over the Internet).)
It is prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the distributed edge machine learning model of Hassanzadeh to incorporate the alert communication protocols of Perez where “the output of the machine learning system … may comprise a label, alert” (Perez [0070]). The modification would have been obvious, because it is merely applying a known technique (i.e. alert communication protocols) to a known concept (i.e. distributed edge machine learning models) ready for improvement to yield predictable result (i.e. “a machine learning system is applied in real-time… to provide an indication of whether a transaction...and/or predicted patterns of activity or actions, e.g. an indication of whether a transaction or entity is “normal” or “anomalous”.” Perez [0035])
Claim 8 is rejected on the same basis as Claim 1.
Claim 9 is rejected on the same basis as Claim 2.
Claim 10 is rejected on the same basis as Claim 3.
Claim 11 is rejected on the same basis as Claim 4.
Claim 12 is rejected on the same basis as Claim 5.
Claim 13 is rejected on the same basis as Claim 6.
Claim 14 is rejected on the same basis as Claim 7.
Claim 15 is rejected on the same basis as Claim 1.
Claim 16 is rejected on the same basis as Claim 2.
Claim 17 is rejected on the same basis as Claim 3.
Claim 18 is rejected on the same basis as Claim 4.
Claim 19 is rejected on the same basis as Claim 5.
Claim 20 is rejected on the same basis as Claim 6.
Response to Remarks
Applicant's arguments filed on June 13, 2025, have been fully considered and Examiner’s remarks to Applicant’s amendments follow.
Response Remarks on Claim Rejections - 35 USC § 101
The Applicant states:
“Claim 1 is not directed to "managing payment transactions" as alleged. Indeed, claim 1 is directed to "securely and uniformly managing how internal computer systems exchange information with external computer systems to provide and/or support different products and services offered by an organization" including "fraud detection for real-time transactions via a distributed federated learning-based platform including privacy protection measures while sharing identified fraudulent activity patterns between regional computing systems."."
Examiner responds:
The act of “securely and uniformly managing how internal … systems exchange information with external … systems” is deemed as gathering, sharing, and manipulation of data which expresses an Abstract Idea [Intellectual Ventures I v. Capital One Fin. Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017) “collecting, displaying, and manipulating data” was considered part of the abstract idea], and Selecting A Particular Data Source or Type Of Data To Be Manipulated [Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)]
The acts “to provide and/or support different products and services offered by an organization" including "fraud detection for real-time transactions via a distributed federated learning-based platform including privacy protection measures while sharing identified fraudulent activity patterns between regional … systems” recite a fundamental economic principles or practice and/or commercial or legal interactions. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic, commercial, or financial action, principle, or practice then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The Applicant states:
“As can be seen, rather than "managing payment transactions" as alleged, claim 1 clearly performing actions to allow for real-time identification of fraudulent transactions while maintaining information security between separate computing systems via instances of a decentralized privacy preserving fraud detection system that improves technological functionality of fraud detection systems in a transaction network."
Examiner responds:
The acts of “real-time identification of fraudulent transactions while maintaining information security …via instances of a decentralized privacy preserving fraud detection system… fraud detection systems” expresses and abstract idea. The major acts, as claimed, of “monitoring… each of the plurality of payments… for an indication of a fraudulent transaction”, “perform, …. gradient descent on a local dataset” (“wherein a gradient comprises differences …and wherein the differences comprise a first fraudulent activity pattern”) also express an abstract idea. Again, both relate to gathering, sharing, and manipulation of data expresses an Abstract Idea [Intellectual Ventures I v. Capital One Fin. Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017) “collecting, displaying, and manipulating data” was considered part of the abstract idea], and Selecting A Particular Data Source or Type Of Data To Be Manipulated [Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)]
The amended claims do recite several new and previously mentioned additional elements (technological components) such as: [to: train an artificial intelligence/machine learning (AI/ML)] [training] [trained] [Nc epochs of gradient descent ][retraining engine][re-train] [re-trained] [generate…an alert]
However, nothing in the claims, understood in light of the specification, requires anything other than “merely applying” off-the-shelf, conventional computer, machine learning/training technology, and alerts for gathering, synthesizing, sending, and presenting the desired information. See MPEP 2106.05(d) well-understood, routine, and conventional.
The focus of the claims is not on an improvement in computers as tools, but on certain independently abstract ideas that use computers as tools.
In the absence of unexpected results, changes or alteration of sequence of components do not make for a patentable invention, see Ex parte Rubin, 128 USPQ 440 (Bd. App. 1959) ; In re Burhans, 154 F.2d 690, 69 USPQ 330 (CCPA 1946); In re Gibson, 39 F.2d 975, 5 USPQ 230 (CCPA 1930)
Therefore, the rejection under 35 USC § 101 remains.
Response Remarks on Claim Rejections - 35 USC § 103
Applicant's amendments required the application of NEW new/additional prior art.
New prior art includes:
Hassanzadeh ("PRIVACY-PRESERVING COLLABORATIVE MACHINE LEARNING TRAINING USING DISTRIBUTED EXECUTABLE FILE PACKAGES IN AN UNTRUSTED ENVIRONMENT", U.S. Publication Number: 20220414661 A1),
Perez (“PRESERVING PRIVACY AND TRAINING NEURAL NETWORK MODELS”, U.S. Publication Number: 20240311834 A1).
Applicant’s remarks regarding the rejection made under 35 USC § 102 are rendered moot by the introduction of additional prior art.
Therefore, the rejection under 35 USC § 103 remains.
Prior Art Cited But Not Applied
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Chhibber (“Federated Machine Learning Management”, U.S. Publication Number: 20220414529 A1) proposes in which a computer system receives, from a plurality of user computing devices, a plurality of device-trained models and obfuscated sets of user data stored at the plurality of user computing devices, where the device-trained models are trained at respective ones of the plurality of user computing devices using respective sets of user data prior to obfuscation. In some embodiments, the server computer system determines similarity scores for the plurality of device-trained models, wherein the similarity scores are determined based on a performance of the device-trained models. In some embodiments, the server computer system identifies, based on the similarity scores, at least one of the plurality of device-trained models as a low-performance model. In some embodiments, the server computer system transmits, to the user computing device corresponding to the low-performance model, an updated model.
Knighton (“System and Method with Federated Learning Model for Medical Research Applications”, U.S. Publication Number: 20210225463 A1) proposes in [0103] The machine learning model...can be deployed on respective edge devices...This model can be trained in a federated manner, beginning with a base model 1051 trained conventionally to produce a model that performs relatively well. This base model is sent to an edge device where it's first used to perform inference on new data collected by the user...The user will be given the option to correct the ... inferences made by the model, so that accurate predictions are known. The participants can provide input whether the ... prediction from the model is correct or incorrect. ...The system can use the input to update the model's gradients or parameters. With this ground truth, the base model is trained to produce an updated model. Each of the participating edge devices similarly produces local updates to the current model. Those local updates are centrally aggregated into a new based model and the process repeats. The updated gradients (or tensors) are then sent to the... server (or Flea Circus). Therefore, the prediction accuracy of the model can be improved without any participant-specific data leaving the respective edge devices of the participants. The clinical trial conductor server can aggregate the participant-specific gradients that can cumulatively improve the model predictions. The updated gradients can then be shared with participants ....to repeat the training.
Balakrishnan (“Federated learning optimizations”, U.S. Publication Number: 20230177349 A1) proposes an edge computing node, a system, a method and a machine-readable medium. The apparatus includes a processor to cause an initial set of weights for a global machine learning (ML) model to be transmitted a set of client compute nodes of the edge computing network; process Hessians computed by each of the client compute nodes based on a dataset stored on the client compute node; evaluate a gradient expression for the ML model based on a second dataset and an updated set of weights received from the client compute nodes; and generate a meta-updated set of weights for the global model based on the initial set of weights, the Hessians received, and the evaluated gradient expression .
Gu (“Privacy-preserving asynchronous federated learning for vertical partitioned data”, U.S. Publication Number: 20220004933 A1) proposes training a federated learning model asynchronously. The system includes a coordinator, an active computing device and a passive computing device in communication with each other. The active computing device has a processor and a storage device storing computer executable code. The computer executable code is configured to: train the federated learning model in the active computing device using dimensions of an instance in the active computing device; and instruct the at least one passive computing device to train the federated learning model in the at least one passive computing device using dimensions of the instance in the at least one passive computing device. The training instances in the active and the at least one passive computing devices do not correspond to each other at the same training time.
Conclusion
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/C.E./Examiner, Art Unit 3695
/CHRISTINE M Tran/Supervisory Patent Examiner, Art Unit 3695
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