Bank of America Corporation

Raja Arumugam Maharaja

Ankit Singh Negi

Varun Sasi Pakiteeri

Sreekanth Raghuveer Maringanti

Vidhya Vaithiyanathan

Khushbu Rani

Vijay Singh Shekhawat

Aarthi Gupta

Shahid Khan Gorewal

Mohan Krishna Mylavaram

Pankaj Kumar

Sonali Tiwari

Anil Kumar Arora

A system comprises a memory communicatively coupled to at least one processor. The at least one processor is configured to receive a request to store received data, determine whether the received data comprises unstructured data, evaluate the unstructured data of the received data in accordance with a machine learning algorithm in response to determining that the received data comprises the unstructured data, and perform an analysis operation to identify datapoints in the received data in response to evaluating the unstructured data of the received data. The datapoints may be a signature representation of the unstructured data of the received data. Further, the at least one processor may be configured to generate a roadmap to store the datapoints and store the datapoints following the roadmap. The roadmap is a plan to store the datapoints in the memory in accordance with one or more quantum random number generator (QRNG) operations.

System and method to dynamically abbreviate data

Systems, computer program products, and methods are described herein for intelligent database modelling. The present invention is configured to capture, using the augmented reality application, a drawing made by the user on a medium; initiate a first machine learning model on the drawing; determine that the user is attempting to design a database model; extract at least a first entity from the drawing; determine, using the first machine learning model, one or more attributes for the first entity; initiate, via the augmented reality application, a first push notification for display on the computing device of the user; electronically receive, via the augmented reality application, a user selection of a subset of attributes from the one or more attributes; generate, using the augmented reality application, a conceptual database model based on at least the first entity and the subset of attributes.

System for intelligent database modelling

A device configured to provide access to a digital document to a user device and to receive an access request for a first masked data element within the digital document. The device is further configured to generate a first blockchain transaction that identifies a machine learning model that is stored in a blockchain. The device is further configured to publish the first blockchain transaction in a blockchain ledger for the blockchain and to receive a second blockchain transaction from the machine learning model in response to publishing the blockchain transaction in the blockchain ledger. The second transaction indicates whether the user is approved for accessing the masked data element. The device is further configured to provide access to the first masked data element on the user device for the user in response to determining that the user is approved for accessing the masked data element.

Data access control for user devices using a blockchain

A system for improving memory resource allocation in database blocks of a database server using blockchain receives a request to allocate a particular amount of memory resources to a particular database block of the database server to perform a task. The system communicates a blockchain transaction to a block in the blockchain. the system approves the blockchain transaction based on historical memory resource allocations to perform the first task. A new block is generated in the blockchain in response to the blockchain transaction being approved. The system performs the task using the particular amount of memory resources. The system determines whether any portion of memory resources already allocated to any of the database blocks is unutilized. If it is determined that a portion of the memory resources already allocated to any of the database blocks is unutilized, the system reallocates the unutilized memory resources to the particular database block.

System and method for improving memory resource allocations in database blocks using blockchain

A system for implementing a secured data transmission includes a sending device and a light fidelity (LiFi) device. The sending device determines that a subset of data is confidential based on historical indications. The device encrypts the subset of data. The device receives a message that indicates that a user of a second device initiated a request to receive the subset of data. The device communicates the encrypted data to the LiFi device. The LiFi device includes at least one light source that is configured to emit light. The LiFi device communicates the encrypted data via the light emitted from the at least one light source to the receiving device.

System and method for secured data transmission using LiFi and holochain network

Aspects of the disclosure relate to intelligently managing invoice processing using blockchain and mixed reality applications. The computing platform may receive invoices and may analyze the invoices to determine a priority order in which the invoices should be processed. The computing platform may use an auto-matching algorithm to identify errors within the invoice and to generate proposed solutions to fix the errors. The computing platform may transmit the invoice to an enterprise organization for modifications and may receive, from the enterprise organization, confidential data including a modified invoice. The computing platform may receive, from an entity associated with a transaction indicated on the invoice, a request to access the modified invoice using a mixed reality application. The computing platform may receive, from the entity and based on the entity's review of the modified invoice using the mixed reality application, approval to execute the modified invoice.

Intelligently managing invoice processing using blockchain and mixed reality applications

Embodiments of the invention provide a system for performing dynamic execution of data processes in distributed server systems. The system is configured for identifying initiation of at least one data process that is scheduled to be executed on at least one server of distributed server systems associated with an entity, communicating with a metrics node comprising a metrics machine learning model to gather information associated with the at least one server, determining that the at least one server will experience degradation of performance while performing at least one step of the at least one data process based on communicating with the metrics node, and routing the at least one step of the at least one data process to at least one new server and execute the at least one step on the at least one new server.

System and method for performing dynamic execution of data processes in distributed server systems

A system comprises a light fidelity (LiFi) device. The LiFi device receives, from a receiving device, a first request to provide confidential data. The LiFi device masks the confidential data and modulates light with masked confidential data. The LiFi device emits the light modulated with the masked confidential data toward the receiving device. The LiFi device receives, from a virtual reality device, a second request to unmask the confidential data. The LiFi device communicates a blockchain data entry to a blockchain network, where the blockchain data entry indicates to determine whether a user is authorized to access unmasked confidential data. In response to the user being authorized, the LiFi device receives a confirmation message that indicates the user is authorized to access the unmasked confidential data. The LiFi device modulates light with the unmasked confidential data and emits the modulated light toward the virtual reality device.

Provisioning secured data access to authorized users through light fidelity (LiFi) data transmission and a virtual reality device

An intelligent precursory systematized approach for verifying the identity (i.e., authentication) of a resource recipient when conducting an online or mobile application resource event. Machine learning (ML) models are used to determine whether the resource provider has been in contact with the resource provider and, if so, the currency and frequency of such contacts. Contacts are determined from accessing the resource providers call history as well as comparing GPS data obtained from both the resource provider's and resource recipient's mobile communication device. Based on the contact determination the ML models can determine whether to authenticate the resource recipient, require further authentication or provide the resource provider options to authenticate the resource recipient or require further authentication. Further authentication may come in the form of determining that the resource provider and resource recipient match in terms of a purpose for a pending resource event and/or a resource provider-generated OTP.

Intelligent precursory systematized authentication

An authentication system includes a light fidelity (LiFi) device and a receiving device. The LiFi device includes at least one light source that is configured to emit light. The LiFi device communicates encrypted data via the light emitted from the at least one light source to the receiving device. The receiving device receives the encrypted data and obtains the confidential data from the encrypted data. The receiving device accesses an authentication level of the user that indicates to which one or more portions of the confidential data the user is authorized to access. The receiving device determines that the user of the receiving device is authorized to access a first subset of the confidential data based on the authentication level of the user. The receiving device grants, the user of the receiving device, access to the first subset of the confidential data.

System and method for authenticating the receiving end of data transmission via LiFi and holochain network

Systems, methods, and apparatus are provided for migrating data using an MR application. A smart glasses device may interface with a source database having a first schema. The smart glasses device may include an MR application. Using machine learning, the MR application may identify transfer data in a source database display for migration and convert a captured image of the transfer data to machine-encoded text. The smart glasses device may render the transfer data in the MR environment. The smart glasses device may connect to a target database having a second schema. Using machine learning, the MR application may derive the second schema and apply it to the transfer data. The MR environment may enable the user to modify the transfer data and replicate the modified data to generate a new sample set. The MR application may populate the target database with values from the new sample set.

Intelligent data migration via mixed reality

Systems, computer program products, and methods are described herein for mitigating data loss in an edge computing environment using machine learning and distributed ledger techniques. The present invention is configured to receive an indication that one or more edge computing nodes is processing one or more portions of a transaction; retrieve dark data associated with each of the one or more edge computing nodes; initiate a machine learning algorithm on the dark data retrieved from each of the one or more edge computing nodes; capture, using the machine learning algorithm, representation information for each of the one or more edge computing nodes from their respective dark data; generate a ledger record for the representation information for each of the one or more edge computing nodes; and register the ledger record for the representation information for each of the one or more edge computing nodes on a first distributed ledger.

System for mitigating data loss in an edge computing environment using machine learning and distributed ledger techniques

A system for data processing using machine learning processing and distributed architecture is described. Specifically, proprietary data transformation rules to be applied for the data processing may be stored at edge computing devices, while the bulk of data processing may be performed at a central computing node that houses the databases. A subset of a data set, in the database, may be sent from the central computing node to the edge computing node. The edge computing node may generate a second data set based on applying data transformation rules to the subset of the data set. The central computing node may determine, using a machine learning (ML) algorithm and based on the subset of the data set and the second data set, the data transformation rules, which may then be applied to the rest of the data set.

Rule-based data transformation using edge computing architecture

A data manager receives a first data layout from a data source and classifies the first data layout as corresponding to a first data domain. The data manager compares the first data layout with one or more existing data layouts that are already classified as corresponding to the first data domain and detects that at least one attribute and corresponding dataset of the first data layout matches with a second attribute and corresponding second dataset from an existing data layout. Data manager generates a second data layout corresponding to the first data layout, wherein the second data layout is a copy of the first data layout with the at least one attribute and the corresponding dataset from the first data layout replaced by the second attribute and the second dataset from the first existing data layout. Data manager loads a data warehouse from the second data layout.

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Tamil Nadu, India

Raja Arumugam Maharaja