Accenture Global Solutions Limited

Shridhar D Rajgopalan

Parikshit Maniar

Sunjeet Gupta

Marin Grace Mercylawrence

Dhanashree Dalal

Rahul Mantri

Ashish Kolhe

Ajay Sharma

Pankaj Shrikant Nikumb

Amritendu Majumdar

Badrinath Bahadur

Joel Samuel Kore

Prateek Shrivastav

Meghashyam Vishwanath Sawant

Kulshrestha Ashwani

Gade Sanjay Gaurav

Sumit Alawat

Sandip Guha

Sanjid Lalitkumar Deshmukh

Vinay Aditya Mantha

Asheesh Gupta

Manish Madanmohan Malaviya

Amesh Jayendra Karekar

Inderjit Singh

Sudhir Kudva Karkale

Ashvin Gondalia

A device may receive user input data identifying a canvas, API documents, or tagged assets for an application to be generated, a requirements document for the application, and asset data identifying reusable assets and components for the application. The device may process the user input data, the requirements document, and the asset data, with a first model, to extract entity data and intent classification data. The device may parse the API documents to generate structured data identifying API endpoints, a request API model, and a response API model. The device may process the structured data to generate an API layer. The device may process the canvas to identify UI objects and to map the UI objects to UI elements. The device may generate code for the application based on the asset data, the entity data, the intent classification data, the API layer, and the UI elements.

Utilizing natural language understanding and machine learning to generate an application

A component management platform includes a component library that stores software components to be incorporated into applications, such as components that have been optimized for efficient performance, reliability, and/or other aspects. The platform also provides a workspace that is accessed to create an application that includes component(s) stored in the component library. In some instances, a developer modifies component when incorporating the component into their application. The platform enables the developer to upload the modified version of the component, which is stored in the component library and published, through the platform, to enable others to incorporate the modified version of the component into their application projects. In some implementations, the modified version is tested for backward compatibility with previous versions of the component, and to verify that the modified version does not include any bugs.

Component management platform

A system and method for intelligently and automatically generating deployable code for target platforms and frameworks based on images or other graphical inputs is disclosed. The system and method leverage artificial intelligence to automatically identify and classify elements of a design as feature patterns. Identification is performed using convolutional neural networks, while classification is done using a Softmax classifier. The intelligent system can then automatically generate code for target platforms and frameworks that reproduce the feature patterns. Target platforms may include web platforms, mobile platforms (such as mobile phones), wearable platforms (such as smart watches), and extended reality platforms (which includes augmented reality (AR), virtual reality (VR), and/or combinations of AR/VR).

System and method for generating unified experiences on digital platforms

A device may generate, based on receiving feeder content, a structured format of the feeder content. The device may generate, based on the structured format of the feeder content, one or more semantic mappings for the feeder content. The device may generate, based on the one or more semantic mappings for the feeder content, an electronic storyboard of the feeder content. The device may generate an extended reality rendered content feed based on the electronic storyboard of the feeder content. The device may provide the extended reality rendered content feed to an extended reality device.

Representing an immersive content feed using extended reality based on relevancy

Implementations directed to providing a computer-implemented method for automating analytical model building for code snippets, the method comprising receiving software instructions indicating functionality of a software component, analyzing the software instructions through natural language processing to identify tags to characterize the software instructions and the functionality, performing a search, based on the identified tags, of relevant Internet sites to identify code snippets that address the functionality, scoring one or more of the identified code snippets based on a respective relevance to the functionality, based on the scoring of the one or more of the identified code snippets, determining that at least one of the code snippets has a score that meets a relevance threshold, and providing the at least one of the code snippets based on the determination that the at least one of the code snippets has the score that meets the relevance threshold.

Intellectual automated security, performance and code generation framework

An ingestion code generation architecture facilities making large and complex datasets available for processing by parallel processing clusters. The architecture generates a set of data ingestion interfaces through which the operator specifies characteristics of their dataset. After receiving the specifications, the architecture automatically samples the dataset, analyzes its structure, and generates program code to ingest the dataset. The architecture solves the technical challenges of making complex and extensive datasets readily available to the parallel processing cluster so that the cluster may successfully perform its specialized processing over the dataset.

Dataset ingestion architecture for parallel processing clusters

Growing community of inventors

Bengaluru, India

Shridhar D Rajgopalan