Accenture Global Solutions Limited

Sunjeet Gupta

Shridhar D Rajgopalan

Rahul Mantri

Ajay Sharma

Parikshit Maniar

Vidya Rajagopal

Marin Grace

Amritendu Majumdar

Sanjid Lalitkumar Deshmukh

Sudhir Kudva Karkale

Manish Madanmohan Malaviya

Amesh Jayendra Karekar

Sandip Guha

Inderjit Singh

Vinay Aditya Mantha

Matthew D LeClaire

Prateek Shrivastav

Jeff Ivany

Ashvin Gondalia

Meghashyam Vishwanath Sawant

Kulshrestha Ashwani

Gade Sanjay Gaurav

Asheesh Gupta

Sumit Alawat

This disclosure is directed generally to an automatic intelligent electronic data processing system, platform, and method for computerized multi-facet data pattern recognition and ranking, and particularly to intelligently personalizing recommendation of data items for consumption by a particular entity based on past data consumption history of the entity and/or other entities via machine recognition of intra and/or inter-entity data item selection correlations. Such personalized recommendation may be based on a multi-facet ranking of the data items by integrating various intra-entity and inter-entity correlations and patterns in data item consumption into a quantifiable entity-specific ranking score for each data item that may potentially be selected for consumption by a particular entity.

Intelligent data ranking system based on multi-facet intra and inter-data correlation and data pattern recognition

A device may generate, based on receiving feeder content, a structured format of the feeder content. The device may generate, based on the structured format of the feeder content, one or more semantic mappings for the feeder content. The device may generate, based on the one or more semantic mappings for the feeder content, an electronic storyboard of the feeder content. The device may generate an extended reality rendered content feed based on the electronic storyboard of the feeder content. The device may provide the extended reality rendered content feed to an extended reality device.

Representing an immersive content feed using extended reality based on relevancy

Implementations directed to providing a computer-implemented method for automating analytical model building for code snippets, the method comprising receiving software instructions indicating functionality of a software component, analyzing the software instructions through natural language processing to identify tags to characterize the software instructions and the functionality, performing a search, based on the identified tags, of relevant Internet sites to identify code snippets that address the functionality, scoring one or more of the identified code snippets based on a respective relevance to the functionality, based on the scoring of the one or more of the identified code snippets, determining that at least one of the code snippets has a score that meets a relevance threshold, and providing the at least one of the code snippets based on the determination that the at least one of the code snippets has the score that meets the relevance threshold.

Intellectual automated security, performance and code generation framework

An ingestion code generation architecture facilities making large and complex datasets available for processing by parallel processing clusters. The architecture generates a set of data ingestion interfaces through which the operator specifies characteristics of their dataset. After receiving the specifications, the architecture automatically samples the dataset, analyzes its structure, and generates program code to ingest the dataset. The architecture solves the technical challenges of making complex and extensive datasets readily available to the parallel processing cluster so that the cluster may successfully perform its specialized processing over the dataset.

Dataset ingestion architecture for parallel processing clusters

Growing community of inventors

Pune, India

Sunjeet Gupta