Bank of America Corporation

Divya Sharma

Sandeep Verma

Nandini Rathaur

Shailendra Singh

Jaya Sindhu Parakala

Sameer Grover

Divya Gaur

Narender Mudgal

During a software development lifecycle of a software application, application library versions are modified and multiple versions are built and packaged to be installed on different computing systems. An intelligent prediction and capture of runtime errors that may be encountered during a runtime of a software application provides a system-agnostic intelligent software code compiler/build system that intelligently identifies runtime errors at a build stage using artificial intelligence (AI) and automatically encapsulates specific software code to auto-heal runtime exceptions caused by updates to library files. The system may optionally also output a solution to the runtime error to the graphical user interface (GUI) of a software developer capable of performing another software application build.

Auto healing of runtime exceptions using intelligent build system and generative AI

Embodiments of the present invention provide a system for facilitating a secure way to allow primary users and secondary users to perform interactions. In particular, the system may be configured to receive an interaction request from a primary user, where the interaction request comprises location of an automated machine, initiate and lock a session between the automated machine and the entity application located on the user device of the primary user, identify a trigger and display a code on the automated machine, wherein the code is scannable by the user device of the primary user or a user device of an authorized secondary user, receive a scanned code from the user device of the primary user or the user device of the authorized secondary user, determine a match between the scanned code and the code displayed on the automated machine, and complete the interaction.

System and method for authenticating secondary users of an entity system

Apparatus and methods for a smart glasses device are provided. The smart glasses device may execute a prediction model on video data captured by the smart glasses device to retrain the prediction model. The prediction model may be retrained in response to a data synchronization between an output of the prediction model and a gesture captured by the smart glasses device.

Smart glasses for property evaluation using AI and ML

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Delhi, India

Divya Sharma