Bank of America Corporation

Veerandra S Srivastava

Maharaj Mukherjee

Utkarsh Raj

Mehul Jayant Pandya

Prasad G Ravva

Venkat P Ravula

Karthikeyan Natanasabapathy

Esada Qazi

Swetha Guddannagari

Jennifer Cooper

Riazuddin Khaja

Mohanraj Subramaniam

Mayank Sharma

Jigar Shah

Dihong Qiu

Sanjeeb Kumar Sahoo

Systems, computer program products, and methods are described herein for enhanced encryption orchestration and application integration frameworks. The system performs steps of initializing a broker application to act as a data security broker within a computing environment; providing an application programming interface (API) for external application interaction; receiving and processing requests via the API for access to encrypted code and data; containerizing the code and data within an isolated environment, subject to predefined rules; monitoring usage to ensure adherence to these rules; initiating self-destruction of the container upon reaching a predetermined usage limit or condition; and securely erasing all sensitive data and code after usage completion.

System and method for enhanced encryption orchestration and application integration framework

Aspects of the disclosure relate to using machine learning for resource planning. A computing platform may detect an occupancy modification event for a physical space. Based on detecting the occupancy modification event, the computing platform may send commands directing display of a data collection prompt to end user devices, which may prompt for work to be performed by users of the end user devices in the physical space during a first day. Using natural language processing, the computing platform may analyze user input information and other occupancy data to determine whether or not the users of the end user devices have permission to occupy the physical space during the first day. The computing platform may cause the end user devices to display a resource management interface indicating whether or not the users of the end user devices have valid permission to physically occupy the physical space during the first day.

Machine learning platform for real time occupancy forecasting and resource planning

Aspects of the disclosure relate to using machine learning techniques for dynamic occupancy prediction. A computing platform may receive first data that is non-personalized and is associated with a first specific physical space, and may receive second data associated with a second specific physical space. The computing platform may normalize, for a common subspace of the first specific physical space and the second specific physical space, the first data and the second data. Using the normalized data, the computing platform may generate a predicted occupancy value for the common subspace. The computing platform may send commands directing a local HVAC control system, deployed at the common subspace, to perform a resource control action for the common subspace, which may cause the local HVAC control system to perform the resource control action for the common subspace.

Dynamic building occupancy forecasting using non-personal real time data

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Plainsboro, NJ, United States of America

Veerandra S Srivastava