Accenture Global Solutions Limited

Akanksha Juneja

Anshul Solanki

Chung-Sheng Li

Jorjeta Gueorguieva Jetcheva

Madhura Shivaram

Guanglei Xiong

Emmanuel Munguia Tapia

Swati Tata

Jingyun Fan

Mohammad Jawad Ghorbani

Kayhan Moharreri

Pratip Samanta

Sawani Bade

Anurag Tripathi

Kyle Patrick Johnson

Rinki Arya

Bibudh Lahiri

Shruti Chhabra

Sonam Gupta

Golnaz Ghasemiesfeh

Praneeth Medhatithi Shishtla

Rajeev Sinha

Samatha Kottha

Himani Bhatt

Debayan Chakraborty

Priyanka Chowdhary

Kedar Bhat

Karthick Selvamuthukumaran

Manoj Shroff

Srinivasan Ponpathirkoottam Raghavan

Sreerekha T V

Rishi Vig

Amit Nandi

Jill K Goldstein

Soubhagya Ranjan Mohapatra

Ashok Kumar Shivarajan

A hybrid rule-based Artificial Intelligence (AI) document processing system processes a non-editable document with at least one invoice to accurately extract data from tables in the invoices. The non-editable document is preprocessed for conversion into a markup format and pages including the invoice are identified. The invoice is processed via a document process by parsing the pages in different directions to generate a first set of predictions and via a block process wherein logical information blocks from the invoice are processed to generate a second set of predictions. The missing entries from a selected table are identified by applying rules to the first set of predictions and the second set of predictions. Any discrepancy between the missing entry values between the first and second set of predictions are resolved and the resulting data is exported to downstream systems for further uses.

Document processing using hybrid rule-based artificial intelligence (AI) mechanisms

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

A system for categorizing words into clusters includes a receiver to receive a set of sentences formed by a plurality of words. The set of sentences is indicative of interaction of a user with a virtual assistant. A categorizer categorizes the plurality of words into a first set of clusters by using a first clustering technique, and categorizes the plurality of words into a second set of clusters by using a second clustering technique. A detector detects words that appear in similar clusters after categorization by the first clustering technique and the second clustering technique. Similarity of clusters is based on a nature of words forming the clusters. A generator generates a confidence score for each of the plurality of words based on the detection. The confidence score of a word is indicative of accuracy of the categorization of the word.

Word clustering and categorization

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Dwarka, India

Akanksha Juneja