Bank of America Corporation

John Dinakar Iruvanti

Suki Ramasamy

Mahesh Ganesan

Nithya C

Meenakshi MeenakshiSundaram

Venkatrao Meenavalli

Sanjeeva Karna Kammara

Rajaram Vijayvergiya

Panduranga Dongle

Komuraiah Kannaveni

Madhumitha S

An end-to-end approach for (i) identifying potential vulnerabilities in software applications, including security vulnerabilities, (ii) verifying/confirming the potential vulnerabilities as actual vulnerabilities and (iii) in response, identifying the necessary remedial actions necessary to eliminate or at least mitigate the vulnerabilities. An intelligent agent is implemented that is configured to detect a change to the application's code or computing environment and, as a result of detection of changes to the code or computing environment, identify potential vulnerabilities, verify/confirm the potential vulnerabilities as actual vulnerabilities through determination/creation of vulnerability test cases and automatically identify the necessary remedial actions necessary to eliminate or mitigate the vulnerabilities.

System for software code cyber security based on machine learning vulnerability detection and generation and implementation of vulnerability test

A computing platform may configure a dependency knowledge graph indicating file dependencies for mainframe applications, and an error knowledge graph indicating errors and corresponding solutions for the mainframe applications. The computing platform may receive mainframe source code. The computing platform may analyze, using the knowledge graphs, the mainframe source code to identify potential errors and corresponding solutions. Based on identifying an error in the mainframe source code, the computing platform may cause the mainframe source code to be updated according to the corresponding solution. The computing platform may analyze, using the dependency knowledge graph and the error knowledge graph, the updated mainframe source code to identify remaining errors. Based on identifying an absence of the remaining errors, the computing platform may send, to a mainframe build and deployment engine, the updated mainframe source code, which may cause the mainframe build and deployment engine to automatically execute a build process.

Natural language processing (NLP) enabled continuous integration and continuous delivery (CICD) deployment

Aspects described herein relate to a centralized computing system that interacts with a plurality of data centers, each having an edge server. Each edge server obtains sensor information from a plurality of sensors and processes the sensor information to detect an imminent shutdown and sends emergency data to a centralized processing entity when detected. In order to make a decision, the edge server processes the sensor data based on dynamic sensor thresholds and dynamic prioritizer data by syncing with the centralized computing system. Because of the short time duration to report emergency data before an imminent complete shutdown, an edge server may utilize a quantum data pipeline and quantum data storage as a key medium for all data transfer in a normal condition and at the time of emergency for internally transporting processed sensor data and providing the emergency data to the centralized processing entity.

Quantum dot energized heterogenous multi-sensor with edge fulgurated decision accomplisher

A real-time process for establishing and reestablishing virtual reality (VR) sessions between automated agent and a customer with customer VR equipment without losing session context after a connection loss is disclosed. VR sessions are dynamically created/recreated using artificial intelligence/machine learning (AI/ML) for engaging virtual agents and customers. Real-time capturing and processing of the VR interaction is captured for session context and stored via blockchain ledgers for secure tracking. VR session connectivity is monitored in real-time. If any anomaly or interruption occurs, retry notifications are sent to establish a second session. Prior session context is used for the second VR session to allow the virtual agent and customer interaction to continue without loss of context.

Uninterrupted virtual reality experience during customer and virtual agent interaction

A stand-alone embedded internet of things edge artificial intelligence device ('EIEAC') may be provided. The EIEAC may be configured to operate independent of the computing device and may be configured to monitor and repair failures occurring at the computing device. The EIEAC may receive computer health data from a client agent application running on the computing device. The EIEAC may include a memory configured to store the computer health data and a battery configured to charge the EIEAC. The EIEAC may include a processor configured to process the computer health data to identify one or more failures using tiny machine learning (“tinyML”). When one or more failures are identified by the processor, the tinyML may be configured to execute instructions on the computer health data for repairing the one or more failures. When unsuccessful, the EIEAC may establish an electronic connection with a remote cloud lookup tower for repairing.

Stand-alone IOT device for repairing failures on a computing device

Quantum dot energized heterogeneous multi-sensor with edge fulgurated decision accomplisher

Uninterrupted VR experience during customer and virtual agent interaction

Some aspects disclosed herein are directed to, for example, a system and method comprising a client device receiving an input of at least a portion of a message. The client device may display, on a display of the client device, the at least the portion of the message. The client device may transmit, to a server device, the at least the portion of the message for display via a second client device. The client device may receive, from a user of the client device or a user of the second client device, a request to generate a marker for the at least the portion of the message. In response to the receiving the request to generate the marker, the client device may determine, based on a lexicon, a name for the marker for the at least the portion of the message. The client device may store, at a storage location, the name for the marker for the at least the portion of the message, an identifier for the at least the portion of the message, and an association between the name for the marker for the at least the portion of the message and the identifier for the at least the portion of the message.

Processing system for intelligently linking messages using markers based on language data

Some aspects disclosed herein are directed to, for example, a system and method comprising a client device receiving an input of at least a portion of a message. The client device may transmit, to a server device, the at least the portion of the message for display via a second client device. The client device may determine an identifier for the at least the portion of the message. The client device may determine, based on a lexicon, a marker name for the at least the portion of the message. The client device may generate an association between the marker name for the at least the portion of the message and the identifier for the at least the portion of the message. The client device may store, at a storage location, the marker name for the at least the portion of the message, the identifier for the at least the portion of the message, and the association between the marker name for the at least the portion of the message and the identifier for the at least the portion of the message.

Growing community of inventors

Telegana, India

John Dinakar Iruvanti