Accenture Global Solutions Limited

Hari Kumar Karnati

Anil Kv Kumar

Nirav Sampat

Vinu Varghese

Saran Prasad

Balaji Janarthanam

Abhishek Patni

A system for solution architecture prediction may identify a previous solution from a data source and may create a historical solution evaluation matrix by mapping a plurality of concern categories with the previous solution. The system may identify a plurality of solution components preponderant to deriving a solution associated with the solution architecture prediction and create a potential solution evaluation matrix therefrom. The system may evaluate the historical solution evaluation matrix and the potential solution evaluation matrix to determine a credibility score for each solution comprised therein. Based on the evaluation, a solution prediction data may be generated including a previous solution, a potential solution, and the associated credibility score. A service solution may be selected from the solution prediction data to resolve the solution prediction requirement.

Solution architecture prediction

A device may receive input data identifying a technical architecture diagram, a design document, an interface specification document, and technical architecture icons, and may process the input data identifying the technical architecture diagram, with a model, to determine hierarchical objects from the technical architecture diagram. The device may perform OCR and NLP of the hierarchical objects to determine blocks of data, and may compare the blocks of data and the input data identifying the design document to identify functionalities of applications. The device may compare the blocks of data and the input data identifying the interface specification document to identify attributes, and may compare the blocks of data and the input data identifying the technical architecture icons to identify icons. The device may consolidate the blocks of data, the functionalities, the attributes, and the icons into a final document, and may perform actions based on the final document.

Utilizing deep learning and natural language processing to convert a technical architecture diagram into an interactive technical architecture diagram

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Bengaluru, India

Hari Kumar Karnati