Mphasis gets US patent for AI Infrastructure and App Management solution

Mphasis was awarded a US patent for its Artificial Intelligence (AI) driven application and infrastructure management solution.

The US patent number 10,726038 focuses on AI systems for tracking, managing, and analyzing data from unstructured data sources, notes the announcement.

Mphasis is one of the leading providers of IT services, which is having its specialization in digital and cloud solutions. It has deep expertise in Financial Services, Insurance Sector, and Manufacturing industry

Srikumar Ramanathan, Senior Vice President, Global Head – Solutions, Mphasis, said, "The indispensable technology ecosystems of today have made it crucial for enterprises to stay ahead in terms of their IT investments and frameworks. The solution harnesses the power of AI-driven predictive analysis to improve application and infrastructure efficiency and enables enterprises to automate decision-making for a healthy technological environment".

Dr. Jai Ganesh, Senior Vice President, Head – Mphasis NEXT Labs, said, "Technology applications and infrastructure have become all pervasive and the Mphasis solution empowers decision-makers to identify dependencies between components and predict system anomalies. The benefits include early warning of system anomalies, optimization of enterprise application and infrastructure landscape and elimination of system downtime".

The newly issued patent is for its DeepInsights and InfraGraf solutions. These solutions help organizations to optimize their technology investment via in-depth data analysis.

The solution helps in predicting failures in infrastructure and software. Additionally, the AI method enables preventive maintenance measures.

The solutions are supported by Machine Learning, graph theory-based algorithms. These AI algorithms perform complex system analysis and predict the stand-alone as well as chain incidents and events which lead to failure in technology infrastructure.

It offers an early warning system and near to real-time device failures prediction with the help of network evolution and pattern recognition.

The complex systems-based modeling solves problems arising from the indirect and direct factors affecting infrastructures and enables the automation of repeatable tasks with respect to resolution and monitoring.