Accenture Global Solutions Limited

Pratip Samanta

Madhura Shivaram

Swati Tata

Chung-Sheng Li

Guanglei Xiong

Golnaz Ghasemiesfeh

Priyanka Chowdhary

Jorjeta Gueorguieva Jetcheva

Emmanuel Munguia Tapia

Deepak Kumar

Abhishek Gunjan

Jingyun Fan

Mohammad Jawad Ghorbani

Kayhan Moharreri

Sumeet Sawarkar

Kyle Patrick Johnson

Anshul Solanki

Akanksha Juneja

Himani Shukla

Nagendra Kumar M R

Rajeev Sinha

Srikrishna Raamadhurai

Giulio Cattozzo

Amit Sharma

Lisa Blackwood

Manoj Shroff

Rishi Vig

Ankit Chouksey

Arnest Tony Lewis

Manash Jyoti Konwar

Jill K Goldstein

Keshav Bohra

Nagendra Kumar Karamala

Examples of a content alignment system are provided. The system may receive a content record and a content creation requirement. The system may implement an artificial intelligence component to sort the content record into a plurality of objects and for identifying an object boundary for each of the plurality of objects. The system may identify a plurality of images and implement a first cognitive learning operation to identify an image boundary for each of the plurality of images. The system may identify a plurality of exhibits and implement a second cognitive learning operation to identify a data pattern associated with each of the plurality of exhibits. The system may implement a third cognitive learning operation for determining a content creation model by evaluating the plurality of objects, the plurality of images, and the plurality of exhibits. The system may generate a content creation output to resolve the content creation requirement.

Content alignment

An Artificial Intelligence (AI)-based data processing system employs a trained AI model for extracting features of products from various product classes and building a product ontology from the features. The product ontology is used to respond to user queries with product recommendations and customizations. Training data for the generation of the AI model for feature extraction is initially accessed and verified to determine of the training data meets a data density requirement. If the training data does not meet the data density requirement, data from one of a historic source or external sources is added to the training data. One of the plurality of AI models is selected for training based on the degree of overlap and the inter-class distance between the datasets of the various product classes within the training data.

Artificial intelligence (AI) based data processing

Examples of employee concierge are provided. In an example, an issue may be determined for an employee. The issue may be determined based on a query shared by the employee or upon occurrence of an unusual event. The unusual event may be indicative of a deviation in behaviour and routine of the employee. A session may be initiated and the issue may be parsed to determine a context. A bot may be selected from multiple bots for the issue where each bot includes information relating to a solution to address the issue. Data associated with the issue may be collected from a central database and other bots. The data may then be analyzed to determine a solution. The solution comprises a response to the query and a suggestion to mitigate the unusual event.

Employee concierge

Examples of analyzing documents are defined. In an example, a request to analyze a document may be received. A knowledge model corresponding to a guideline associated with the document may be obtained. The knowledge model may include at least one of a hypothetical question and a logical flow to determine an inference to the hypothetical question. The hypothetical question relates to an element of the guideline. Based on the knowledge model, data from the document may be extracted for analysis using an artificial intelligence (AI) component. The Ai component may be configured to extract and analyze data, based on the knowledge model. Based on the analysis, a report indicating whether the document falls within a purview of the guideline may be generated.

Document analyzer

A narrative response generator receives a user data query specifying variables and data sources from which to extract information desired by a user. The narrative response presents the information desired by the user in a non-textual format such as graphs and a textual format such as one or more paraphrases that are automatically generated by a sentence struct model. The sentence struct model generates context free grammar (CFG) which provides templates for generating word sequences that contain natural language words and placeholders. The placeholders are replaced with values obtained from the user data query for generating grammatically-accurate, complete paraphrases. The narrative response may additionally include information extracted from external data sources.

Automated generation of narrative responses to data queries

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