Capital One Services, LLC

Janssen Biopharma, Inc.

Perrigo Pharma International D.a.c.

Anh Truong

Austin Grant Walters

Jeremy Edward Goodsitt

Vincent Pham

Reza Farivar

Fardin Abdi Taghi Abad

Mark Watson

Leonid Beigelman

Galen Rafferty

Kenneth Taylor

Kate Key

Guangyi Wang

Mark Louis Watson

Grant Eden

Ernest Kwak

Alvin Hua

Samuel Sharpe

Christopher Wallace

Brian Chandler Barr

Daniele Andreotti

Karin Ann Stein

Noriaki Tatsumi

Carmela Napolitano

Michael Walters

Fardin Abdi Tagh Abad

Fardi Abdi Taghi Abad

Fardin Abdi Taghi

Haiying He

Fardin Abdi Abad

Andrei W Konradi

R Neitz

Hing L Sham

Roy K Hom

Simeon Bowers

Jennifer Sealy

Gary D Probst

Gergely Toth

Disclosed herein are system, method, and computer program product embodiments for process corruption prevention. An embodiment operates by determining security vulnerabilities for an entity and correlation values for the security vulnerabilities by applying completed processed records of the entity to a machine learning model. Each of the correlation values quantifies a relationship strength between a security vulnerability and fraudulent activity. The embodiment further operates by generating a security vulnerability score for the entity using the correlation values and identifying one or more patches for at least one of the security vulnerabilities. The one or more patches may be ranked and the ranking may be revised using a feedback mechanism after the one or more patches are implemented by the entity.

Patching security vulnerabilities using machine learning

A device may receive a preauthorization associated with missed transaction prevention for a transaction account of a user, wherein the missed transaction prevention involves preventing a missed transaction associated with merchant accounts of the user. The device may monitor a transaction log of the transaction account and identify a transaction pattern associated with a merchant account based on a plurality of historical transactions identified in the transaction log related to the merchant account. The device may determine that a historical transaction of the plurality of historical transactions is not designated for automatic execution and that an execution of an upcoming transaction corresponding to the plurality of historical transactions is not scheduled. The device may cause an account transaction associated with the upcoming transaction to be automatically executed before a transaction period expiration, associated with the merchant account, passes.

Automatic transaction execution based on transaction log analysis

Embodiments include systems and techniques to perform secure transactions including processing registration information for each computing device of a plurality of computing devices having authority over a financial account. In one example, embodiments include receiving a pending transaction against the financial account from a computing device. Embodiments include executing an authentication protocol to determine the computing device is a member of the plurality of computing devices based upon the registration information, and processing data identifying another computing device within a configurable distance from the computing device. Embodiments further include the system to execute the authentication protocol to determine the other computing device is a member of the plurality of computing devices based upon the registration information and bypass a portion of a security procedure for authorizing execution of the pending transaction.

Mobile authentification method via peer mobiles

Systems and methods for censoring text-based data are provided. In some embodiments a censoring system may include at least one processor and at least one non-transitory memory storing application programming interface instructions. The censoring system may be configured to perform operations comprising storing a target pattern type and a computer-based model for identifying a target data pattern corresponding to a target pattern type within text based data. The censoring system may also be configured to receive text-based data by a server, and to retrieve the stored target pattern type to be censored in the text-based data. The censoring system may be configured to identify within the received text-based data, a target data pattern corresponding to the retrieved target pattern type. The censoring system may be configured to censor target characters within the identified target data pattern, and transmit the censored text-based data to a receiving party.

Systems and methods for censoring text inline

Systems and methods for generating synthetic data are disclosed. For example, a system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving a dataset including time-series data. The operations may include generating a plurality of data segments based on the dataset, determining respective segment parameters of the data segments, and determining respective distribution measures of the data segments. The operations may include training a parameter model to generate synthetic segment parameters. Training the parameter model may be based on the segment parameters. The operations may include training a distribution model to generate synthetic data segments. Training the distribution model may be based on the distribution measures and the segment parameters. The operations may include generating a synthetic dataset using the parameter model and the distribution model and storing the synthetic dataset.

Systems and methods for synthetic data generation for time-series data using data segments

Systems and methods of procuring real data items based on user affinity gauged via synthetic data items are disclosed. In one embodiment, an exemplary computer-implemented method may comprise: utilizing a trained machine learning model to generate a synthetic data item based on real user data; presenting the synthetic data item to those users; obtaining indications identifying user responses to the synthetic data item; obtaining user-defined control parameters from the users; configuring a user-defined control mechanism to share a portion of the real user data based thereon; obtaining a subset of the real user data based the user-defined control parameters; providing to a particular third-party data source at least one of: data regarding the synthetic data item, the at least one portion of the real user data, and the indications of the users; and then receiving a second real data item from the particular third-party data source.

Computer-based systems configured for procuring real content items based on user affinity gauged via synthetic content items and methods of use thereof

Methods and systems described herein relate to authenticating users-based on generating images that have modified features. More specifically, the methods and systems generate these images by processing existing training data to identify a common feature in existing photos that may be modified, and then modifying that feature with the use of generative adversarial networks.

Systems and methods for authenticating via photo modification identification

A method for generating a cluster-based machine learning model based on federated learning with cluster feedback includes providing a current machine learning model to a plurality of user devices that train the current machine learning model, receiving respective model states, generating updated model states, causing the plurality of user devices to obtain a respective instance of an updated machine learning model based on the updated model states, receiving an applicability feedback for the updated machine learning model for each of the plurality of user devices, determining a plurality of user clusters including a subset of the plurality of user devices, identifying a first user cluster and a second user cluster, the first user cluster having a higher cluster applicability feedback than the second user cluster, receiving the additional model states from the clusters and updating the updated machine learning model to generate the cluster-based machine learning model.

Systems and methods for federated learning optimization via cluster feedback

Methods and systems for using a generative adversarial network to generate personalized content in real-time as a user accesses original content. The methods and systems perform the generation through the use of an application programming interface ('API') layer. Using the API layer, the methods and systems may generate alternative content as a user accesses original content (e.g., a website, video, document, etc.). Upon receiving this original content, the API layer access the generative adversarial network to create personalized alternative content.

Methods and systems for generating alternative content

Aspects described herein may allow for deploying third-party callback to be performed on a mobile device. For example, the mobile device may configure a third-party callback to be performed after a triggering event is detected by a remote device. The triggering event may relate to a remote device associated with the mobile device. A determination may be made as to whether the mobile device is within a pre-determined proximity of the remote device. If the two devices are in proximity and if the mobile device receives a message that indicates the triggering event has been detected, the mobile device may perform the third-party callback. In this way, a user's need to request services conveniently and accurately is facilitated.

Systems and methods for integrated third-party callbacks

The present disclosure provides systems and methods for synthetic data generation. A recurrent neural network can be trained for synthetic data generation by obtaining a sequence of elements and determining, using a classifier, that the sequence corresponds to a token. In response to the determination, a recurrent neural network configured to use a first vocabulary including the elements can be modified to use a second vocabulary, the second vocabulary including the token and the first vocabulary. The modified recurrent neural network can be trained using the token and the sequence of elements. The trained recurrent neural network can be used to generate synthetic data. A classifier can detect sequences of elements in the synthetic data corresponding to tokens. The tokens can replace the sequences of elements in the generated synthetic data and can be provided to the trained recurrent neural network to continue synthetic data generation.

Systems and methods for synthetic data generation using a classifier

Methods and systems disclosed herein describe using machine learning to lock and unlock a device. Machine learning may be trained to recognize one or more features. Once the device has been trained to recognize one or more features, a user may define an unlock condition for the device using the one or more trained features. After defining the unlock condition, the device may be locked by verifying the one or more features that the user defined as the unlock condition using machine learning. When verification is successful, the device may be unlocked and the user allowed to access the device.

Active locking mechanism using machine learning

Systems and methods for architecture embeddings for efficient dynamic synthetic data generation are disclosed. The disclosed systems and methods may include a system for generating synthetic data configured to perform operations. The operations may include retrieving a set of rules associated with a first data profile and generating, by executing a hyperparameter search, a plurality of hyperparameter sets for generative adversarial networks (GANs) that satisfy the set of rules. The operations may include generating mappings between the hyperparameter sets and the first data profile and storing the mappings in a hyperparameter library. The operations may include receiving a request for synthetic data, the request indicating a second data profile and selecting, from the mappings in the hyperparameter library, a hyperparameter set mapped to the second data profile. The operations may include building a GAN using the selected hyperparameter set and generating, using the GAN, a synthetic data set.

Systems and methods for architecture embeddings for efficient dynamic synthetic data generation

Systems as described herein may label data to preserve privacy. An annotation server may receive a document comprising a collection of text representing a plurality of confidential data from a first computing device. The annotation server may convert the document to a plurality of text embeddings. The annotation server may input the text embeddings into a machine learning model to generate a plurality of synthetic images, and receive a label for each of the plurality of synthetic images from a third-party labeler. Accordingly, the annotation server may send the confidential data and the corresponding labels to a second computing device.

Privacy preserving data labeling

In some embodiments, gaze-tracking-based image downscaling for multi-party video communication may be provided. In some embodiments, a set of gaze locations may be received from a set of receiving devices during concurrent video communication sessions between the receiving devices and a sending device. Different collections of positions may be determined based on the gaze locations, and, for each such collection, a spatial indicator (e.g., a bounded region) may be determined. A first downscaled encoding of the source image (from the sending device) may be generated based on the first spatial indicator, and a second downscaled encoding of the source image may be generated based on the second spatial indicator, etc. The downscaled encodings may then be sent to the respective receiving devices during the concurrent video communication sessions.

Growing community of inventors

Champaign, IL, United States of America

Anh Truong