Accenture Global Solutions Limited

Swati Tata

Madhura Shivaram

Pratip Samanta

Chung-Sheng Li

Guanglei Xiong

Deepak Kumar

Guruprasad Dasappa

Kavita V V Ganeshan

Golnaz Ghasemiesfeh

Omar Razi

Himani Shukla

Anjani Kumari

Priyanka Chowdhary

Krishna Kummamuru

Jorjeta Gueorguieva Jetcheva

Emmanuel Munguia Tapia

Chinnappa Guggilla

Abhishek Gunjan

Jingyun Fan

Mohammad Jawad Ghorbani

Soujanya Soni

Kayhan Moharreri

Ullas Balan Nambiar

Sumeet Sawarkar

Chetan Narasimha Yadati

Ditty Mathew

Kyle Patrick Johnson

Akanksha Juneja

Anshul Solanki

Nagendra Kumar M R

Sivasubramanian Arumugam Jalajam

Venkatesan Kamalakannan

Srinivasan Krishnan Rajagopalan

Rajeev Sinha

Srikrishna Raamadhurai

Ilesha Malhotra

Deepak Dalwani

Venkata Ramakrishnap

Giulio Cattozzo

Mudita Shah

Lisa Blackwood

Jill K Goldstein

Ankit Chouksey

Arnest Tony Lewis

Manash Jyoti Konwar

Keshav Bohra

Nagendra Kumar Karamala

Amit Sharma

Kamlesh Narayan Chaudhari

Divyayan Dey

Harbhajan Singh

Achal Gambhir

Prakhar Gupta

Ranjan Sarmah

Madhur Bhasini Chaini

Thyagarajan Delli

Manoj Shroff

Rishi Vig

Abhishek Singh

Vivek Chenna

Srivasan Sridharan

Shama Firoz

Jandhyala Sree Kalyani

Venkatesh Nagilla

Methods, systems, and computer-readable storage media for classification and normalization of transactional data. Description for each name included in the transactional data is extracted. Words and a frequency of words in the description with respect to each of features pre-defined for the transactional data are identified. Based on weights of the words and features and feature encoding rules, the transactional data is classified and names from the transactional data are identified for normalization. Further, one or more of various types (type 1, 2, 3, and 4) of determinations are performed to determine whether the names identified to be normalized match with any of raw names present in a global table of a database. In response to positive results of any of the determinations, normalized names are extracted from the global table for the names. The global table includes a mapping of the raw names and associated normalized names.

Computer-implemented method, computer program product and computer system for managing transactional data

Systems and methods for generating an output representation are disclosed. A system may include a processor including a representation generator. The representation generator may receive an input data comprising an input content and an instruction. The representation generator may include a parsing engine to parse the input data to obtain parsed information. The representation generator include a mapping engine to map the parsed information with a pre-stored base template pertaining to a pre-defined module, to obtain a mapped template. The representation generator may generate, through a machine learning (ML) model, based on the mapped template, an output representation in a pre-defined format. The output representation may correspond to the expected representation of the input content.

Generation and optimization of output representation

A document processing system processes a document image to identify document image regions including floating images, structured data units, and unstructured floating text. A first masked image is generated by deleting any floating images from the document image and a second masked image is generated by deleting any structured data units from the first masked image. The structured data units and the unstructured floating text are thus identified serially one after another. Textual data is extracted from the structured data units and the unstructured floating text by processing the corresponding document image regions via optical character recognition (OCR). Entities are extracted from the textual data using natural language processing (NLP) techniques.

Entity extraction via document image processing

A system for providing automated and domain specific contextual processing for context based verification may classify a plurality of extracted parameters from a set of digitized training document to assign a document similarity score with respect to a set of reference documents. The system may automatically detect a domain for the set of digitized training documents based on the document similarity score. The system may load a domain based neural model for the detected domain to generate a plurality of pre-defined contextual parameters. The system may receive a set of input documents and perform a contextual processing of the received set of documents based on the pre-defined contextual parameters to obtain an output in form of a plurality of filtered snippets, each bearing a corresponding rank. The context based verification may be performed based on the plurality of filtered snippets and the corresponding rank.

Automated contextual processing of unstructured data

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

An AI-based data processing system analyzes a received information request to generate an interactive visualization including data responsive to the information request. The information request is processed to obtain the primary entity and one or more informational items related to the primary entity. Auxiliary entities and informational items related to the primary entity are identified and searches are executed on a knowledge base and the internet. The results from the searches are analyzed to obtain knowledge nuggets which are included into a selected one of a visualization template to generate the interactive visualization. If it is determined via user interactions with the interactive visualization that an informational gap exists between the information request and the data in the interactive visualization, the interactive visualization can be updated to address the informational gap.

Artificial intelligence based-document processing

Implementations are directed to providing an electronic document, and include receiving text content including a plurality of segments, the text content being received from data sources, determining a set of topics to be included in the electronic document, for each topic in the set of topics, providing a set of contextual words associated with a respective topic, contextual words being determined from a lexical database, each contextual word having a respective frequency, determining a score for each segment and topic pair, the score indicating a relevance of a respective topic to a respective segment, each score being determined based on respective contextual words of the respective topic and frequencies of the respective contextual words, for each topic, providing, by the one or more processors, a summary including at least one segment based on respective score, and providing, to a user device, the electronic document including one or more summaries.

Electronic document generation using data from disparate sources

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Bengaluru, India

Swati Tata