Bank of America Corporation

Regina Peyfuss

Michael Ogrinz

Gerard P Gay

Mark Alan Odiorne

Allison Dolores Baker

Jeremiah Wiley Fellows

Siddhesh Vinayak Wadikar

Adam B Richman

David Mortman

Andrew Paul Montgomery

Susan E Teague Rector

Aspects of the disclosure relate to a dynamic virtual reality (VR) coaching computing platform. The dynamic VR coaching computing platform may configure a VR coaching model based on model training data. The dynamic VR coaching computing platform may use the VR coaching model and a user profile to select a plurality of VR modules. The dynamic VR coaching computing platform may configure a first VR module of the plurality of VR modules based on one or more user selections. The dynamic VR coaching computing platform may receive real-time feedback during execution of the first VR module. The dynamic VR coaching computing platform may select a second plurality of VR modules based on the first real-time feedback. The dynamic VR coaching computing platform may configure a second VR module of the second plurality of VR modules based on user selections.

Dynamically providing virtual reality (VR) coaching using real-time feedback loops

A system for customizing media presentation based on user's sentiments is disclosed. The system presents a media item to the user on a platform comprising a website. The system captures a first set of microexpressions of the user reacting to the media item. The system extracts a set of baseline features from the first set of microexpressions. The system determines whether the media item elicits positive or sentiment from the user. If the system determines that the media item elicits positive sentiment from the user, the system classifies the media item into a first class of media items that elicit positive sentiment from the user. The system adjusts contents of the platform to include media items from the first class of media items.

System and method for leveraging a time-series of microexpressions of users in customizing media presentation based on users# sentiments

A system for identifying a user using microexpressions presents training media items to the user. The system captures a first set of microexpressions of the user in reaction to the training media items. The system, based on the first set of microexpressions, determines baseline features indicating reactions of the user to the training media items. The system presents test media items to a person. The system captures a second set of microexpressions of the person in reaction to the test media items. The system, based on the second set of microexpressions, determines test features indicating reactions of the person to the test media items. The system determines whether the person is the same as the user by comparing the baseline features to the test features. The system determines that the person is the same as the user if the test features correspond to the baseline features.

System and method for leveraging microexpressions of users in multi-factor authentication

Systems for enhanced protection or security using eye movement tracking are provided. In some examples, a user may launch an application on a user device. If enhanced protections apply to the application, an image of a user eye may be captured and compared to pre-stored data to ensure the user is a registered user. Additional eye movement data may be captured and analyzed. Analyzing the eye movement data may include detecting a look-away by a user. If a look-away is detected, the application may be closed, data within the application may be obscured, or the like. Eye movement data may also be analyzed to identify portions of an application the user viewed for more than a threshold time period, less than a threshold time period, and the like. This data may be analyzed (e.g., using machine learning) to generate one or more heat maps.

Dynamic enhanced security based on eye movement tracking

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Denver, CO, United States of America

Regina Peyfuss