Bank of America Corporation

Madhavi Mendu

George Albero

Maharaj Mukherjee

Michael Ogrinz

Elijah Clark

Michael B Jones, Jr

A system is provided for machine-learning based identification and filtering of electronic network communication. In particular, the system may continuously monitor and pull electronic communications data from one or more networked computing systems in an enterprise environment. Based on the electronic communications data, the system may use machine learning algorithms to generate a database of associations between one or more users and one or more topics of interest. The system may then output one or more recommendations to one or more users for transmitting communications associated with the one or more topics of interest. In this way, the system may improve the efficiency of communications received and transmitted within a network.

System for machine-learning based identification and filtering of electronic network communication

A system is provided for encoding user information. The system is configured to generate a random code pattern, assign the random code pattern to a user, assign a personal identifier of the user to the random code pattern, map a combination of the random code pattern and the personal identifier to user information associated with the user, and store an association of the random code pattern and the personal identifier to the user information in a mapping table. The system is further configured to receive from a decoder a query comprising the random code pattern and the personal identifier of the user, wherein the decoder is configured to decode the random code pattern. The system determines based on the mapping table, the user information mapped to the combination of the random code pattern and the personal identifier and transmits the user information to the decoder.

Prevention of unauthorized access to information

A decoder is provided for decoding user information. The decoder obtains a random code pattern from at least one of a card or a device owned by a user. The decoder further obtains a personal identifier from the user. The decoder then queries a mapping table based on a combination of the random code pattern and the personal identifier of the user, wherein the mapping table comprises user information associated with each of a plurality of users mapped to a respective unique combination of a random code pattern and a personal identifier of the user. The decoder obtains user information of the user in response to the query, wherein one or more actions are performed based on the user information.

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Edison, NJ, United States of America

Madhavi Mendu