Accenture Global Solutions Limited

Pradeep Senapati

Koushik M Vijayaraghavan

Rajendra Tanniru Prasad

Bhaskar Ghosh

Aditi Kulkarni

Mohan Sekhar

Rajesh Nagarajan

Purnima Jagannathan

Priya Athreyee

Koustuv Jana

Kamakshi Girish

Lakshmi Narasimhan

Luke Higgins

Jeffson Felix Dsouza

Senthil Jeyachandran

Balaji Venkateswaran

Parul Jagtap

Sangeetha Jayaram

Badrinath Parameswar

Ranjith Tharayil

Somen Roy

Arulmozhi Dharmar

Dibyendu Chattopadhyay

Nivedita Shah

Gaurav Sood

Niyaz Shaffi

Ramanathan Venkataraman

Manas Mishra

Syed Mohammed Yusuf

Gaurav Goenka

Vaijayanthi Ramaswamy

Amaresh Swain

Mahesh Zurale

A system may receive enterprise information associated with a client enterprise. The system may select, using an industry analysis model, a set of queries associated with obtaining status information that is associated with a technology profile of the client enterprise. The system may generate client data that is associated with the enterprise information and the status information. The system may convert, using a matrix factorization technique, the client data associated with the client enterprise to a client matrix. The system may convert, using the matrix factorization technique, reference data associated with reference enterprises to a reference matrix. The system may determine, based on a comparison of the client matrix and the reference matrix, a set of scores associated with technology metrics of the technology profile. The system may perform an action associated with the client enterprise based on the set of scores.

Enterprise management system using artificial intelligence and machine learning for technology analysis and integration

A device may receive requirement data identifying a requirement for modification of software code, and may process the requirement data, with a machine learning model, to identify entities and intents in the software code and to generate a query. The device may process the query, with a code locator model, to encode text of the query into high-dimensional vectors and to identify a semantic similarity between the high-dimensional vectors and code text. The device may process the query, the semantic similarity, and the code text, with a code developer model, to generate metadata, and may utilize the metadata to identify an identifier associated with the software code. The device may determine, utilizing the identifier, a portion of the software code, and may modify the portion of the software code based on the query to generate modified software code. The device may perform actions based on the modified software code.

Utilizing machine learning models for automated software code modification

A device may obtain ticket data relating to a set of tickets, and process the ticket data to generate a ticket analysis model that is a clustering based natural language analysis model of natural language text associated with tickets of the set of tickets. The device may classify the set of tickets using the ticket analysis model, may determine an automation plan for at least one class of ticket determined based on classifying the set of tickets, and may implement the automation plan to configure an automatic ticket resolution or ticket generation mitigation procedure for the at least one class of ticket. The device may receive a ticket after configuring the automatic ticket resolution or ticket generation mitigation procedure, may classify, using the ticket analysis model, the ticket into the at least one class of ticket, and may automatically implement a response action for the ticket based on classifying the ticket and using the automatic ticket resolution or ticket generation mitigation procedure.

Automation plan generation and ticket classification for automated ticket resolution

A device may obtain process data relating to a set of processes. The device may process the process data to generate a process analysis model. The device may classify, using the process analysis model, a particular process into a particular class of a set of classes of the process analysis model. The device may automatically assess the particular process based on the particular class, wherein a set of assessment parameters for assessing the particular process is selected based on the particular class, and wherein an assessment score is assigned to the particular process based on values for the set of assessment parameters. The device may determine, based on the assessment score and the particular class, an automation recommendation for the particular process. The device may automatically complete the particular process using a particular tool based on determining the automation recommendation.

Automated process analysis and automation implementation

Growing community of inventors

Bengaluru, India

Pradeep Senapati