BENGALURU: Mphasis (BSE: 526299; NSE: MPHASIS), an Information Technology solutions provider specializing in cloud and cognitive services, today announced that it has been awarded a U.S. patent for its revolutionary deep learning-based framework, Autocode.AI, which applies Artificial Intelligence (AI) to automatically generate code and streamline the software development process. The newly issued patent – U.S. Patent No. 10,824,401 provides a solution for the automated creation of graphical user interface applications.
Autocode.AI is a deep learning-based solution, which enables users to quickly transition from whiteboards to code in hours and rapidly prototype applications through hyper-personalized designs and code. The code creation process in software development is manual, time, cost & effort-intensive and it involves converting a large number of wireframes and screenshots created by designers into computer code. Revision and user feedback cycles are slow and wait periods long between prototypes. Many of the tasks are repetitive and prime for automation and disruption. Autocode.AI drastically reduces the time taken for software prototyping and development time through the automation of repetitive and standard code blocks. It reduces the cost of software development and maintenance.
“In these emerging and ever-changing scenarios, it has become imperative for enterprises to adopt more efficient and future-ready frameworks that streamline and simplifies their processes. With Autocode.AI we aimed at automating code generation for coherent turnaround and comprehensive development. This patent strengthens the foothold of our commitment to constantly innovate and harness technologies such as AI to assist our customers in mobilizing technology for higher benefits,” said Nitin Rakesh, Chief Executive Officer, and Executive Director, Mphasis
Mphasis was also recently awarded a U.S. patent for its AI system for tracking, managing, and analyzing data from unstructured data sources. The patented system enables enterprises to draw actionable insights in real-time from enterprise data sources such as emails, call center transcripts, insurance policy documents, broker submissions, bank statements, customer complaints, etc.
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2014 The Global Indian New Network (TGINN)