Bank of America Corporation

Wells Fargo Bank, N.a.

Suki Ramasamy

Sasidhar Purushothaman

Joseph Benjamin Castinado

Patrick Nicholas Lawrence

Jisoo Lee

William August Stahlhut

John Christopher Checco

Brad Romano

John Dinakar Iruvanti

Lekha Ananthakrishnan

Mahesh Ganesan

Ravikanth Tadepally

Manu Jacob Kurian

William P Jacobson

Sean Michael Gutman

James McCormack

Sudhansu Mohanty

Nithya C

Meenakshi MeenakshiSundaram

Samson Paulraj

Magesh Sripathy

Sireesh Kumar Vasantha

Sudharsan Ramadurai

Mohan Sundaresan

Utkarsh Singhal

Vishnuvardhan Rao Regalla

Sachin Thakral

Deepan Kanagaraj

Prasanna K

Awadhesh Pratap Singh

Balamurali Lakshminarayanan

Raghavendra Veerupakshappa

Mudit Chawla

Nikhil Ram

Kumaraguru Mohan

Sivasundaram Umapathy

Sreenivas Chintada

Vinu Viswanathan

Robert William Childress

Tara R Ross

Gokarna Kc

Donnell Douglas

Kenneth William Schmidt, Jr

Vijay Talupula

Naresh Kumar Koppuravuri

Jaisimha L Gyanmote

Alejandro J Carao

Santanu Sarkar

Christophe M Marin

Sita Maha Lakshmi Pidaparthi

Vipul Chaudhari

Srinivasan Bhaskaran Kasyap

Debendra Sharma

Karrie A Loatman

Sai Sushma Dasam

Satish Kumar Arunachalam Lakshmanan

Bhuvaneswari Govindarajan

Payal Mattoo

Venkatarao Gorantla

Joshua C Wolfe

Swetha Madhalam

Vidhya Ramanathan

Phillip Matt Hancock

Jonathan Wayne Ingram

Subhashri Seshadri

Scott B Desalvo

Balasubramanian Bagavathiappan

Gina L Tammelleo

Radhika Thilakar

Cynthia D Adams

Garima Dhir

Timothy Krak

Kavitha Ganapathi Raman

Satish Reddy Andela

A method may include accessing digital characteristics associated with a user identifier, the digital characteristics including a task identifier assigned to the user identifier; encoding the digital characteristics into components of an input vector, the components of the input vector corresponding to inputs of a machine learning model; inputting the input vector into the machine learning model; executing the machine learning model; subsequent to the executing, accessing an output of the machine learning model, the output corresponding to a cluster identifier associated with a plurality of user identifiers with similar digital characteristics; and automatically transmitting a request to change a resource access right for the user identifier based on access rights associated with the cluster identifier.

Automated machine learning access rights engine

An artificial intelligence (AI) model may be trained to identify errors in mapping between relational databases and objects during code compilation. Multiple AI models may be used, with different models being associated with different programming frameworks, thereby making this technique framework agnostic. Errors in mapping between relational databases and objects may be identified based on comparison between object relational mapping (ORM) metadata (e.g., as identified using an AI natural language processing (NLP) model applied to one or more ORM files) and attributes associated with the relational databases.

Artificial intelligence (AI) framework to identify object-relational mapping issues in real-time

Embodiments of the present invention provide a system for leveraging distributed register technology to securely monitoring, tracking, and recommending utilization of resources. The system is configured for gathering one or more input parameters from one or more entity systems, collecting activity data from one or more third party systems, analyzing the activity data collected from the one or third party systems, generating one or more recommendations based on the one or more input parameters and analyzing the activity data, wherein the one or more recommendations are associated with one or more activities, estimating resource usage for the one or more recommendations, and allocating resources to the one or more recommendations.

Leveraging distributed registers to monitor, track, and recommend utilization of resources

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

Various systems, methods, and computer program products are provided for optimizing database querying. The method includes receiving a search request to perform a search operation associated with a database. The search request includes one or more search values. The method also includes identifying one or more search columns of the database based on at least one of the one or more search values. The identification of the one or more search columns includes comparing the at least one of the one or more search values with a reference matrix. The reference matrix relates to contents of the database. The method further includes updating the search request based on the one or more search columns identified. The method still further includes causing a transmission of the updated search request for performance of the search operation.

System and method for generating optimized data queries to improve hardware efficiency and utilization

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

Systems, methods and apparatus are provided for AI-based generation of data warehouse quality protocols. An attribute classifier may quantify relationships between source data and target data from an enterprise data warehouse. A data quality engine may apply these relationships to identify specific data quality concerns and generate customized data quality metrics.

Smart data quality protocols

Various aspects of this disclosure relate to determining mapping issues in object relational mapping (ORM). An artificial intelligence (AI) model may be trained to identify errors in mapping between relational databases and objects during code compilation. Multiple AI models may be used, with different models being associated with different programming frameworks, thereby making this technique framework agnostic.

Real-time prevention of computerized event manipulation through the use of Artificial Intelligence (AI)-assisted quantum computing. Determine, at an event request time, if the event is being manipulated and/or is part of a manipulation scheme and, if so, prevent the event from being initiated. A classical computing platform analyzes a batch of historical events using AI including machine learning classification algorithms to result in a manipulation indicator for each of the historical events. A quantum computing platform converts each of the historical events in the batch to a qubit sequence based on attributes associated with the events. The qubits are entangled with qubits of a primary qubit sequence and maintained an entangled list. When an event is requested, attributes associated with the pending event are communicated to the quantum computing platform, which transforms/converts the pending event to the primary qubit sequence based on the attributes associated with the pending event.

Systems for real-time event manipulation prevention through artificial intelligence-assisted quantum computing

Methods and systems for using block chain technology to verify transaction data are described herein. A computing platform may receive data about events related to transactions, personal or corporate information, supply chains, and other relevant information about a person or corporate entity. The event information may be received, aggregated, and processed to determine metadata about the person or corporate entity. The metadata may indicate, for example, a trustworthiness of the person or corporate entity for various purposes. Such event information and/or metadata may be stored as transactions in a block chain that may be accessible by counterparties to a potential transaction involving the person or corporate entity. The automated event processing computing platform may further use automated techniques to implement smart transactions between the person/entity and counterparty based on the trust metadata.

Automated event processing computing platform for handling and enriching blockchain data

An apparatus includes a processor. The processor extracts a column from an external source for import into a database configured to store a set of columns including a first and second column. The processor splits the entries of the import column into a set of terms. The processor generates a first, second, and third vector based on the frequency of each term of the set of terms in the first, second, and import columns, respectively. The processor determines a first similarity measure between the first and third vectors and a second similarity measure between the second and third vectors. The first similarity measure is greater than the second. In response, the processor provides an indication to a user that the first column is a mapping candidate for the import column, such that entries of the import column may be stored in the database as additional entries in the first column.

Data mapper tool

Methods, systems, and apparatuses for blockchain-based digital token utilization are described herein. Data corresponding to digital tokens may be stored on a blockchain associated with a decentralized peer-to-peer (P2P) network. Smart contracts for collecting digital tokens in exchange for service provider tokens may be deployed to the blockchain. Smart contracts for distributing digital tokens based on credit tokens may be deployed to the blockchain. Through execution of the smart contracts, digital tokens may be utilized by users in various ways.

Blockchain-based digital token utilization

Systems, methods and apparatus are provided for AI-based generation of data warehouse quality protocols. An attribute classifier may quantify relationships between source data and target data from an enterprise data warehouse. A data quality engine may apply these relationships to identify specific data quality concerns and generate customized data quality metrics. A user interface may enable a user to enter parameters for the classification protocols and corresponding rule-based generation of data quality metrics.

Smart data warehouse protocols

Growing community of inventors

Chennai, India

Suki Ramasamy