Accenture Global Solutions Limited

Ravi Shankar Nori

Mathangi Sandilya

Srikanth G Rao

Anand Vijendra

Manu Khanna

Vinay Avinash Dorle

Santosh Kumar Soni

Paresh Vinay Takawale

Siva Rama Sarma Theerthala

Devang Shantilal Shah

Mukul Dilip Patidar

Garima Mittal

Ravishankar Krishnan

Dayapatra Nevatia

Shrikant Sarda

Sagnik Mazumder

Sumeet Pushpam

Abhinav Shukla

Jigarkumar Ramanlal Pandya

Nitin Vilas Tonapi

A device may identify standard parameters and real-time parameters associated with content of a content type, and may process the content type, the standard parameters, and the real-time parameters, with a parameter unification model, to generate derived parameters for the content. The device may process the derived parameters and the content type, with a multi-level linear regression machine learning model, to calculate a content score for the content, and may process the derived parameters and the content score, with a linear regression machine learning model, to calculate a quantity of f-NFTs to generate for the content and a divestment ratio. The device may create a unique reference to the content, and may create an NFT for the content based on the unique reference. The device may generate the quantity of f-NFTs for the content based on the NFT, and may provide the quantity of f-NFTs to a content exchange.

Determining an optimum quantity of fractional non-fungible tokens to generate for content and a content exchange

Examples of prospect recommendation including a prospect recommendation system are provided. The system may receive a prospect assessment query. The system may receive and sort prospect data, sales attribute data, and product data by applying an artificial intelligence component. The system may determine a prospect matrix by correlating the prospect data with the product data. The system may determine a prospect profile by collating historical prospect data and the product data. The system may determine a prospect assessment matrix by correlating the prospect matrix and the prospect profile. The system may determine a decisional pathway based on a comparison between the prospect profile and the prospect assessment matrix. The decisional pathway may include a plurality of assessment interpretations. The system may determine a prospect recommendation index based on the decisional pathway. The prospect recommendation index may facilitate the generation of a prospect assessment result for resolution of the prospect assessment query.

Prospect recommendation

A security monitoring platform may use an unsupervised machine learning technique to cluster historical data related to user access rights associated with multiple cloud applications based on various features that relate to user permissions and attributes within the multiple cloud applications. The security monitoring platform may use a supervised machine learning technique to train an access rights data model based on the clustered historical data and perform one or more actions that relate to current access rights assigned to at least one user within one or more of the multiple cloud applications based on a score representing a probability that an access level assigned to the at least one user within the one or more of the multiple cloud applications is correct. The security monitoring platform may apply a reinforcement learning technique to update the access rights data model based on feedback related to the one or more actions.

Security monitoring platform for managing access rights associated with cloud applications

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for accessing a database comprising multiple datasets. Each dataset includes data derived from a respective application. A machine-learning engine determines an analytical rule using at least one dataset of the multiple datasets. The analytical rule is determined by processing input data obtained from the at least one dataset derived from the respective application. A structured dataset is generated based on the determined analytical rule. The structured dataset is generated in response to using the determined analytical rule to analyze data from each dataset of the multiple datasets derived from the respective application. One or more data sequences that represent information flow of a transaction process are determined based on the structured dataset.

Automated structuring of unstructured data

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Bengaluru, India

Ravi Shankar Nori