Bank of America Corporation

Nithya C

Meenakshi MeenakshiSundaram

John Dinakar Iruvanti

Madhusudhanan Krishnamoorthy

Suki Ramasamy

Govinda Rajulu Nelluri

Srinivasa Rao Dakshinyam

Sivasankari Abirami Karthikeyan

Meenakshi Meenakshishisundaram

Madhumitha S

Rajaram Vijayvergiya

Systems and methods for using digital triplets to generate a bundle of remediation scripts for healing a system failure is provided. The systems may include a stand-alone embedded internet of things edge artificial intelligence computing device ('EIEAC') coupled to a computing device and a remote emulator. The EIEAC may send to the remote emulator a file including configuration details of the computing device at a time of a failure of the computing device and computer health data. The remote emulator may feed the file to a machine learning algorithm to generate remedial scripts for healing the system failure. The remote emulator may create a plurality of digital triplets to test the remedial scripts.

Virtual pre-solution manifester

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

Systems, methods and apparatus are provided for a reusable, client-server based ecosystem designed to support content-aware, LiFi-powered transfer of large-scale, semi-structured data files. Containerized client-side applications may include a LiFi communication engine (LCE), a job control engine (JCE), and an execution hub that is configured to interface with the JCE, the LCE, job stakeholders and downstream applications. A central server may include a server-side LCE configured for two-way communication with the client-side LCE. Each LCE may be configured to cluster semi-structured data into data packets, broadcast data packets using an LED array, receive data packets using an array of photoreceptors and synchronize received data packets.

LiFi-powered content-aware large-scale data processing factory

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

Deep learning in the form of a deepfakes fabricator can be utilized to analyzed proposed documents, images, webpages etc. in order to ensure compliance with UI/UX standards. A rover component can scan and compartmentalize detected objects. A deepfakes fabricator can identify source datasets corresponding to detected objects and transform the objects so that they comply with standards stored in a web integrant. Resulting objects may be combined into an integrated image and an operational synthetic webpage may be created. Similarly, the disclosed deep-learning and other processes may operate in reverse and utilize an approved document, image, website, etc. in order automatically create (in whole or in part) a set of UI/UX standards.

Compliance fabric for enterprise UI/UX standards

Various systems, methods, and computer program products are provided for generating and monitoring dynamic identifiers for data processing security. The method includes generating a dynamic identifier for a user request. The dynamic identifier changes based on a receiving node of the dynamic identifier. The method also includes updating the dynamic identifier in an instance in which the user request is transmitted from a first node to a second node. The method further includes generating a dynamic identifier change log. The dynamic identifier change log includes at least one historical dynamic identifier from a previous receiving node. The method still further includes determining whether the dynamic identifier change log matches an expected dynamic identifier change log. The method also includes causing a transmission of a dynamic identifier verification upon determining that the dynamic identifier change log matches the expected dynamic identifier change log.

System and method for generating and monitoring dynamic identifiers for data processing security

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

Systems, computer program products, and methods are described herein for dynamic deployment of discerner nodes in a non-monitored distributed network. The present invention is configured to deploy the node crawler on the distributed network based on at least receiving the user input; determine, using the node crawler, node-level network behavior of the one or more nodes; determine that the node-level behavior of a first subset of nodes do not meet one or more security controls; generate a discerner node based on at least the node-level network behavior of the one or more nodes; deploy the discerner node on the first subset of nodes; and determine that the node-level behavior of the first subset of nodes meets the one or more security controls based on at least deploying the discerner node.

Growing community of inventors

Chennai, India

Nithya C