Accenture Global Solutions Limited

Sudhir Hanumanthappa

Koushik M Vijayaraghavan

Aditi Kulkarni

Rajesh Nagarajan

Roopalaxmi Manjunath

Rajendra Prasad Tanniru

Purnima Jagannathan

Shankaranand Mallapur

Srikanth Prasad

Jayashri Sridevi

Parul Jagtap

Pareshkumar Ramchandbhai Gelot

Sangeetha Jayaram

Abhijit Avinash Kulkarni

Joydeep Sarkar

Sudha Srinivasan

Nishanth Kumar

A device may receive, in near-real time, unstructured data associated with an application or a system, and may extract textual data from the unstructured data. The device may parse the textual data to generate parsed textual data, and may perform natural language processing on the parsed textual data to generate processed textual data. The device may process the processed textual data, with a clustering model, to identify topical data associated with the processed textual data, and may process the topical data, with a classification model, to group the topical data into categories. The device may generate a knowledge graph based on the categories, and may store the knowledge graph in a data structure. The knowledge graph may enable the device to provide answers to questions associated with the application or the system.

Utilizing artificial intelligence models to manage and extract knowledge for an application or a system

A device may receive project management data associated with development of a software product and may process a first portion of the project management data, with first models, to generate timeliness scores and an overall timeliness score for the software product. The device may process a second portion of the project management data, with second models, to generate quality scores and an overall quality score for the software product and may process a third portion of the project management data, with third models, to generate product readiness scores and an overall product readiness score for the software product. The device may utilize a fourth machine learning model, with the overall timeliness score, the overall quality score, and the overall product readiness score, to generate a success probability for the software product and may perform one or more actions based on the success probability for the software product.

Utilizing a combination of machine learning models to determine a success probability for a software product

Growing community of inventors

Bengaluru, India

Sudhir Hanumanthappa