Bank of America Corporation

International Business Machines Corporation

Jpmorgan Chase Bank, N.a.

Globalfoundries Inc.

Globalfoundries U.S. 2 LLC

Eren Kursun

Craig Douglas Widmann

Dharmender Kumar Satija

Brian Daniel Goodman

Gene Fernandez

Alex Berson

Scott Anderson Sims

Andrew DongHo Kim

Philip George Emma

Chen-Yong Cher

Pradip Bose

Jude A Rivers

Sih Lee

Phillip John Restle

Haifeng Qian

Gary Dale Carpenter

Gary William Maier

Maharaj Mukherjee

Paul William Coteus

Alper Buyuktosunoglu

John Edward Sheets, Ii

Hubertus Franke

Stephen McConnell Gates

Alan G Gara

Michael Stephen Floyd

David Paul Paulsen

Robert H Dennard

John Maxwell Cohn

Anna Wanda Topol

Alan J Drake

Shurong Tian

Victor V Zyuban

Robert Louis Franch

Liang-Teck Pang

Brian Andrew Schuelke

Madhusudan K Iyengar

Roger R Schmidt

Claude Basso

Mukta G Farooq

Hendrik F Hamann

Jean Louis Calvignac

Bulent Abali

Wilfried Haensch

Thomas J Brunschwiler

Vijayalakshmi Srinivasan

Hans Mikael Jacobson

Vinod Kamath

Bipin Rajendran

Alan Weger

Moinuddin K Qureshi

Raphael Peter Robertazzi

Kolt Bell

David R Motschman

Michael R Scheuermann

Joel Filliben

Hylton N Van Zyl

William David Kahn

Neil David Zane

Shawn Parris Bench

Margaret A Payne

Lorraine C Edkin

Dana M Pusey-Conlin

Ronnie J Morris

Hongda Shen

A system for event-based peer-to-peer resource transfers. The system may include a controller configured for tracking and confirming resource transfers, the controller being further configured to: generate a resource transfer for transferring a resource from a first user device to a second user device, where the resource transfer includes a conditional event for triggering a transfer of the resource, and where the conditional event is accepted by the first user device and the second user device; receive the resource from the first user device, where the resource is held separate from the first user device and the second user device; determine that the conditional event has been executed by at least one of the first user device and the second user device; and based on the determining that the conditional event has been executed, trigger the transfer of the resource to the second user device.

System for secure peer-to-peer interactions with event-based confirmation triggering mechanism

The present disclosure is directed to a novel system for a self-constructing deep neural network. The system may comprise a hybrid logic library which contains the building structures needed to construct the neural network, which may include both traditional logic and memory structures as well as learning structures. In constructing the neural network from library structures, the system may use an algorithm to iteratively improve the performance of the neural network. In this way, the system may provide a way to generate complex neural networks that become increasingly optimized over time.

System and method for self constructing deep neural network design through adversarial learning

A system for machine learning-derived contract generation is provided. The system comprises: a machine learning engine and a controller configured to: input historical and streaming interaction data into the machine learning engine, wherein the machine learning engine is trained by the historical and streaming interaction data; determine one or more machine learning-derived interaction patterns for a resource transfer between the first user device and the second user device, wherein the one or more machine learning-derived interaction patterns comprise calculated exposure levels for one or more events for completing the resource transfer; based on the machine learning-derived interaction patterns, generate the resource transfer contract for transferring a resource from the first user device to the second user device, wherein the resource transfer contract comprises a sequential flow of the one or more events; and distribute the resource transfer contract to the first user device and the second user device.

Machine learning-based event analysis for customized contract generation and negotiation in enhanced security peer-to-peer interaction applications

A system is typically configured for customizing interconnectivity of one or more layers associated with a neural network architecture, wherein the neural network architecture is associated with an application, customizing functional transformation of the one or more layers associated with the neural network architecture, wherein each of the one or more layers comprises a custom transformation function, and generating a custom neural network architecture based on customizing the interconnectivity and the functional transformation of the one or more layers.

Custom convolutional neural network architectures for exposure detection

Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Unsupervised machine learning system to automate functions on a graph structure

The present disclosure is directed to a novel system for performing online reconfiguration of a neural network. Once a neural network has been implemented into a production environment, the system may use underlying construction logic to perform an in-situ reconfiguration of neural network elements while the neural network is live. The system may accomplish the reconfiguration by modifying the architecture of the neural network and/or performing adversarial training and/or retraining. In this way, the system may provide a way increase the performance of the neural network over time along one or more performance parameters or metrics.

System and method for online reconfiguration of a neural network system

Systems, computer program products, and methods are described herein for de-conflicting data labeling in real-time deep learning systems. The present invention is configured to retrieve one or more dynamically generated expert profiles; and determine an optimal expert mix of experts to classify the transaction into a transaction types, wherein the expert profiles comprises: (i) shared information metrics, (ii) divergence metrics, (iii) characteristics associated with the one or more experts, (iv) a predictive accuracy of the one or more experts, (v) an exposure score associated with the one or more experts, and (vi) information associated with the transaction, wherein the optimal expert mix comprises: (i) a best expert for classifying the transaction, (ii) a combination score from at least the portion of the one or more experts evaluating the transaction simultaneously, and (iii) a sequence of at least the portion of the one or more experts analyzing the transaction.

De-conflicting data labeling in real time deep learning systems

A system for adversarial targeting mitigation is provided, the system generally comprising identifying, using an artificial intelligence and machine learning model engine, a user targeting pattern employed by an entity based on interaction data between the entity and one or more users, based on the identified pattern of targeting, training the machine learning model to identify specific user profile data correlated with specific responses from the entity, identifying, using the machine learning model, a subset of one or more favorable responses from the specific responses, and triggering the one or more favorable responses by altering the user profile data for the one or more users prior to interaction with the specific entity.

System and methods to mitigate adversarial targeting using machine learning

Embodiments of the present invention provide an improvement to conventional machine model training techniques by providing an innovative system, method and computer program product for the generation of synthetic data using an iterative process that incorporates multiple machine learning models and neural network approaches. A collaborative system for receiving data and continuously analyzing the data to determine emerging patterns is provided. The proposed invention involves generating synthetic data clusters to be stored and used for retraining the main model as well as other models. In addition, the invention includes using one or more (subset) of the synthetic data clusters to train or retrain machine learning models, developing and training machine learning models that are trained with emerging synthetic data clusters, and ensembling machine learning models trained with emerging synthetic data clusters.

System and methods for generation of synthetic data cluster vectors and refinement of machine learning models

A system for feature relevance visualization optimization is provided. The system comprises a controller configured for modifying placement of features in a relevance visualization. The controller is further configured to: receive interaction data comprising one or more features positioned in the relevance visualization, wherein the one or more features are defined and measurable properties of the interaction data; construct a logical grouping of the one or more features based on a type of each of the one or more features, wherein similar features are collocated in the relevance visualization; construct a machine learning-based grouping of the one or more features based on relevance calculations for the one or more features; combine the logical grouping and the machine learning-based grouping to generate a combined feature placement, wherein the one or more features are repositioned in the relevance visualization; and output the relevance visualization having the combined feature placement.

System and methods for feature relevance visualization optimization and filtering for explainability in AI-based alert detection and processing systems

Embodiments of the present invention provide an improvement to convention machine model training techniques by providing an innovative system, method and computer program product for the generation of synthetic data using an iterative process that incorporates multiple machine learning models and neural network approaches. A collaborative system for receiving data and continuously analyzing the data to determine emerging patterns is provided. Common characteristics of data from the identified emerging patterns are broadened in scope and used to generate a synthetic data set using a generative neural network approach. The resulting synthetic data set is narrowed based on analysis of the synthetic data as compared to the detected emerging patterns, and can then be used to further train one or more machine learning models for further pattern detection.

System and methods for iterative synthetic data generation and refinement of machine learning models

A system for attention-based layered neural network classification is provided. The system comprises: a sequence of layered neural networks; and a controller configured for controlling data routed through the sequence of layered neural networks, the controller configured to: receive interaction data comprising data features, wherein the data features are distinct characteristics of the interaction data; input data features into the sequence of layered neural networks, wherein each sequential layer of the sequence of layered neural networks comprises a heightened rigor level for at least one of the data features; calculate a relevance score output for at least one of the data features at each layer of the sequence of layered neural networks; and integrate the relevance score output from each layer of the sequence of layered neural networks to generate a total relevance score output.

Attention-based layered neural network architecture for explainable and high-performance AI processing

An artificial intelligence system and method for state-based learning using one or more adaptive response states of the artificial intelligence system are provided. A controller for modifying a neural network engine is configured to monitor a data stream having a data pattern by comparing the data pattern to a trained data pattern; identify a change in the data pattern of the data stream; determine a response state of the neural network learning engine, the state defining one or more neural network parameters for monitoring the data stream with the neural network learning engine; identify a predetermined policy for reconfiguring the neural network learning engine based on the data pattern and the response state; and in response to identifying the change in the data pattern and determining the response state, reconfigure the one or more neural network parameters according to the predetermined policy.

System and method for incremental learning through state-based real-time adaptations in neural networks

A system for analyzing machine learning-derived misappropriation types with an array of shadow models is provided. The system comprises: a controller configured for analyzing an output of a machine learning model, the controller being further configured to: input interaction data into a machine learning model, wherein the interaction data is analyzed using the machine learning model to determine a misappropriation type output associated with the interaction data; identify data features in the interaction data associated with the misappropriation type output; construct an array of shadow models based on the data features, wherein each individual model in the array of shadow models is configured to extract logical constructs from a portion of the data features; and consolidate the logical constructs output by the array of shadow models, wherein consolidating the logical constructs determines a final explanation output for the misappropriation type output determined by the machine learning model.

System and methods for explainability ensembles for neural network architectures in regulated applications

Embodiments of the system, as described herein leverage artificial intelligence, machine-learning, and/or other complex, specific-use computer systems to provide a novel approach for identifying patterns in input data and determine and implement necessary changes to a regulated ML model within the bounds of a regulatory control structure. The system utilizes a collection of machine learning models, either individually or clustered, to process incoming data to determine if specific data should be flagged as irregular or part of the formation of an emerging pattern. The system may intelligently analyze such patterns to determine any regulatory implications that may arise from acting on or adapting to the perceived patterns.

Systems and methods for incremental learning and autonomous model reconfiguration in regulated AI systems

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New York, NY, United States of America

Eren Kursun