Bank of America Corporation

Rahul Uniyal

Madhusudhanan Krishnamoorthy

Srikanth Vemula

Mohit Dhingra

Systems, methods, and apparatus are provided for generating dynamic metaverse accommodations using artificial intelligence. A user device may initiate a metaverse session. Deep learning networks may derive a user profile based on data from the user device and generate a set of samples from the user profile. The generated samples may include user characteristics and avatar features associated with the user characteristics. The generated samples may be used to train a multitask machine learning model. Deep learning networks may generate a metaverse accommodation targeted to the user profile. The accommodation may be rendered in the metaverse environment. The accommodation may be transmitted to an actuator at the user device. The accommodation may be stored in association with user characteristics in a distributed ledger.

Dynamic metaverse accommodations

A system, computer program product, and method for training users in phishing prevention may challenge a user with gamified user-specific phishing attacks for a user to encounter in a temporal pattern. Phishing data, including data relating to phishing techniques and actual and non-actual (fake) phishing attacks, may be generated or obtained and input to the training engine. User-related data, including user information, one or more social engineering vectors, or organization information relating to an organization, may be obtained and digitally manipulated. A sequential cross model training engine may process the phishing data and digitally manipulate the user-related data to generate training activities that include test phishing attacks. A gamification engine may generate gamified user-specific testing that includes two or more of the test phishing attacks in a sequence in real time. The test phishing attacks, which may be in a non-question format, may target the user in an XR environment.

Gamification of training for phishing prevention in metaverse

Localized incident resolution with reconstruction is disclosed. Fresh (i.e. non-generic) resolutions for IT incidents that include local user-specific attributes are generated. N-possible robotic process automation ('RPA') workflows are generated for applications at issue or cosine similar ones. IT incidents are reconstructed in safe environments. Modeling is performed to determine which of the N-possible RPA workflows have a sufficient probability of resolving the IT incident. Select RPA workflows are executed on the reconstructed IT incident in the safe environment to identify which workflow(s), if any, resolve the IT incident. RPA workflows demonstrated to work can be localized based on the user-specific attributes and deployed automatically. Detailed feedback and instructions specific to the user and the user's IT incident can be provided to identify what steps to take if a complete automated solution was not identified and successfully tested.

Localize incident resolution generator with reconstruction

A navigation prediction tool tracks and analyzes a user's navigation through a software application to predict the interfaces that are most desired by the user. The tool then dynamically changes the presentation of the software application to present these interfaces to the user. The tool may further conserve resources by predicting how the user will fill certain fields in the interface.

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Dehradun, India

Rahul Uniyal