Bank of America Corporation

Madhusudhanan Krishnamoorthy

Vaasudevan Sundaram

Samrat Bhasin

Harikrishnan Rajeev

Shilpi Choudhari

Kapil Juneja

Ruchi Mathur

Manmohan Singh

Vineet Kumar Upadhyay

Karishma Prithviraj Pamnani

Yudhit Mehta

Ganesh Balasubramanian

Arun Sriraman

Shantanu Singh

Prince Noel Pradeep Santhappa Durai

Muthu Krishnan Subramanian Rajalakshmi

Shadab Bubere

Srinath M R

Amit Ohri

Nithya C

Vinothkumar Babu

Nagar P Varadarajan

Ravindran Padmanaban

Neeraj Kumar Choudhary

Sivasankari Abirami Karthikeyan

Ayesha Farha AmeerHamza

Abhiraam Venkatesan

MadhuMathi Rajesh

Thenamudhan Arumugasamy

Lingaraj Sabat

Tamilselvi Elango

Anbarasan Murthy

Pawan Kumar Jha

Paramita De

Kumar Amit Dwary

Nityashree Pannerselvam

Prashant Anna Bidkar

Meenakshi MeenakshiSundaram

Sreekanth Raghuveer Maringanti

Karthik Rajan Venkataraman Palani

Aruna Jonna

Rahul Uniyal

Oliver Sampson

Bhaswati Mitra

Prasanna Soppimath

Dimple Kapadia

Sheirly Stephen Soans

Anjani Mishra

Ganesan Vijayan

Sreeram Raghavan

Meenakshi Meenakshishisundaram

Rajasekhar Reddy Patlolla

Giddaiah Kummari

Kannan Govindan

Urmila Shetty

Raghavendran Sukumaran

Surjeet Singh

Jayavijay Sarathy

Manikandan Madhu

Sarvesh Neelakanta Subramani

Hitesh Chopra

Rani Kuncham

S Ushma Kaleshwari

Gurubaran Vt

Preethi Dhayalan

Charulatha Krishnakumar

Karthikeyan Janakiraman

Ramya Gangathara Rao

Ayesha Farha Ameer Hamza

A machine learning training device having a clustering device configured to group machine learning training requests into a consolidated training block. The clustering device includes an isomorphism engine and an energy estimator configured to construct the consolidated training block based upon an amount of energy that will be saved by training the machine learning training requests together, thereby reducing an overall energy consumption by machine learning processes.

Machine learning training device

An apparatus includes a memory and a processor. The memory stores a machine learning algorithm configured to classify telemetry data into a set of categories. The processor implements a communication synchronization scheme to receive a first set of telemetry data associated with a first user and a second set of telemetry data associated with a second user. The processor applies the machine learning algorithm to each of the first and second sets of telemetry data, to classify the data. The processor transmits, to a server, training data that includes at least the classified data or a set of parameters derived from the classified data. The server uses the training data to refine a reinforcement learning algorithm that is configured to generate a recommendation of computational resources to provision to a new user.

Synchronized data collection for use in hosted virtual desktop slicing

The present invention generally relates to the field of automated and flexible information extraction and protection for graphical data. In particular, the invention provides a unique platform for analyzing, classifying, extracting, and processing information from images using deep learning image detection models. Embodiments of the inventions are configured to provide an end to end automated solution for intelligently hiding or obscuring private data from graphical displays via the use of embedded steganographic image data techniques.

Systems and methods for intelligent steganographic protection

Systems, computer program products, and methods are described herein for decentralized edge computing enablement in robotic process automation. The present invention is configured to receive an indication that a hosted virtual desktop (HVD) has received a first set of instructions for execution from a controller hosted virtual desktop (CHVD); electronically receive, from the HVD, an indication that the first set of instructions have been executed by the HVD; predict, using the edge computing enablement engine, a second task to be executed by the HVD; determine, using the quantum database search algorithm, a location of the second task in the knowledge repository; retrieve a second set of instructions associated with the second task from the location of the second task in the knowledge repository; and receive, from the HVD, an indication that the second set of instructions have been executed by the HVD.

System for decentralized edge computing enablement in robotic process automation

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 code repository stores application code. A code management determines, based at least in part on requested features selected in a graphical user interface, code requirements that include attributes of application code needed to achieve the requested features. The code management system determines, based at least in part on the determined code requirements and the metadata for each entry of application code stored in the code repository, one or more candidate application code entries from the code repository. The code management system presents the candidate application code entries for user selection in the graphical user interface. After receipt of a user selection of a selected application code, the selected application code is provided to a computing device associated with the user.

Reusable code management for improved deployment of application code

Systems, computer program products, and methods are described herein for dynamic allocation of navigation tools based on learned user interaction. The present invention is configured to generate a training dataset based on at least the information associated with the interaction of the user with the one or more GUI grids, information associated with the one or more interactions of the one or more peers with the one or more GUI grids, information associated with the user, and information associated with the one or more peers; initiate one or more machine learning algorithms on the training dataset; receive, via the user computing device, a user selection of an unseen navigation tool for placement on the GUI; and classify the unseen navigation tool using the first set of parameters to predict a placement of the unseen navigation tool in at least one of one or more GUI grids associated with the GUI.

System for dynamic allocation of navigation tools based on learned user interaction

Aspects of the disclosure relate to using synthetic DNA stranding and mutant nucleotide processes to conduct enterprise market volatility predictions. In some embodiments, a computing platform may initiate a set of instructions associated with performing an action on a synthetic DNA market data set associated with a plurality of lines of business across an enterprise organization. Thereafter, the computing platform may convert the set of instructions to a mutant nucleotide sequence, and insert the mutant nucleotide sequence into the synthetic DNA market data set. The computing platform may extract, using the mutant nucleotide sequence, target information from the synthetic DNA market data set, and validate the target information to detect one or more anomalies. The computing platform may remove the one or more data anomalies, and subsequently output a validated synthetic DNA market data set to a synthetic DNA client server.

Enterprise market volatility prediction through synthetic DNA and mutant nucleotides

Aspects of the disclosure relate to using synthetic DNA stranding and mutant nucleotide processes to conduct enterprise market volatility predictions. In some embodiments, a computing platform may receive market data from a plurality of lines of business across an enterprise, wherein the market data is received in a raw, uncompressed format. Thereafter, the computing platform may assimilate and preprocess the market data to output vectored market data. The computing platform may perform a synthetic DNA stranding process on the vectored market data to create one or more strands of synthetic DNA market data, and output the one or more strands of synthetic DNA market data to a synthetic DNA client server, wherein the one or more stands of synthetic DNA market data is configured for input in a market volatility prediction model.

Enterprise market volatility predictions through synthetic DNA and mutant nucleotides

Aspects of the disclosure relate to using synthetic DNA stranding and mutant nucleotide processes to conduct enterprise market volatility predictions. In some embodiments, a computing platform may receive raw market data from a plurality of lines of business of an enterprise organization. Thereafter, the computing platform may preprocess the raw market data to obtain enterprise level market data, execute synthetic DNA stranding of the enterprise level market data to obtain synthetic DNA stranded market data, run the synthetic DNA stranded market data through one or more market volatility models, and compile results from the market volatility models on the synthetic DNA stranded market data. The computing platform may transmit results from the market volatility models on the synthetic DNA stranded market data. The transmitted results may be configured to display a market application interface that includes market volatility forecasting parameters based on results of the market volatility models.

Systems, computer program products, and methods are described herein for generating an execution sequence using learning reinforcement. The present invention is configured to electronically receive one or more requirement modules from one or more computing devices associated with one or more resource development teams; store the one or more requirement modules in a resource development pipeline, wherein the one or more requirement modules are in a first order of execution; initiate a reinforcement learning algorithm on the one or more requirement modules; determine, using the reinforcement learning algorithm, a final order of execution for the one or more requirement modules; initiate a reordering of the one or more requirement modules from the first order of execution to a final order of execution; and initiate an execution of the one or more requirement modules in the final order of execution.

System for generating an execution sequence using learning reinforcement

A code converter uses machine learning to determine conflicts and redundancies in software code. Generally, the code converter uses machine learning to convert software code into vectors that represent the code. These vectors may then be compared with other vectors to determine similarities between code. The similarities may be used to detect conflicts and/or redundancies created during the development process (e.g., when a developer attempts to change the code).

Software code vectorization converter

Growing community of inventors

Chennai, India

Madhusudhanan Krishnamoorthy