Bank of America Corporation

Abhishek Nagpal

Manu Jacob Kurian

Jayachandra Varma

Gopikrishna Nemalikanti

Syed Luqman Ahmed

A data loss prevention device that includes a data loss prevention engine implemented by a processor. The data loss prevention engine is configured to receive data in transit to a target network device and to identify content within the data. The data loss prevention engine is configured to determine the content of the data comprises an image and to determine an image type for the image based on objects within the image, and to determine whether the image type matches a restricted image type from a set of restricted image types. The data loss prevention engine is further configured to block transmission of the data to the target network device in response to determining that the image type matches a restricted image type and forward the data to the target network device in response to determining that the image type does not match a restricted image type.

Data loss prevention using machine learning

Aspects of the disclosure relate to transforming event data associated with an entity using a multicomputer platform and directing a client computing device to perform a determined action based on a detected event characteristic of the event data and a detected current event. With some embodiments, an event tracking server detects the current event from sensed data provided by a sensed data server and identifies a correlation measure between the detected event characteristic and the detected current event. The event tracking server may direct one or more client computing devices to perform one or more actions related to an entity based on the correlation measure.

Event tracking and notification based on sensed data

Systems for dynamically modifying one or more parameters of an event processing device are provided. In some examples, a system may receive data, such as data from a mobile device of a user. The data may include current location information of the mobile device. In some examples, additional data, may also be received. In some examples, one or more machine learning datasets may be used to determine whether a parameter of the event processing device should be modified. If so, an instruction to modify the parameter of the event processing device may be generated and executed. After modifying the parameter, additional data may be received and analyzed to determine whether a triggering event has occurred. If not, the parameter may remain in the modified state. If a triggering event has occurred, the parameter may be further modified.

Growing community of inventors

New Delhi, India

Abhishek Nagpal