Accenture Global Solutions Limited

Neeru Narang

Guanglei Xiong

Chung-Sheng Li

Sukryool Kang

Emmanuel Munguia Tapia

Colin Connors

Anwitha Paruchuri

Benjamin Nathan Grosof

Tsunghan Wu

Madhura Shivaram

Ashish Jain

Jingyun Fan

Kayhan Moharreri

Ullas Balan Nambiar

Kyle Patrick Johnson

Mingzhu Lu

Ditty Mathew

Golnaz Ghasemiesfeh

Omar Razi

Dennis P Delaney

Armando Ferreira Gomes

Angela Yang Sanford

David Alfonso Guerra

Thyagarajan Delli

Michael C Petersen

A document processing system processes documents including typewritten and/or handwritten data by converting them to document images for entity extraction. A received document is initially processed to generate a deep document data structured and for classification as one of a structured or an unstructured document. If the document is classified as a structured document, it is processed for entity extraction based on a matching template and image alignment of the document image with the matching template. If the document is classified as an unstructured document, entities are extracted by obtaining nodes and providing the nodes to a self-supervised masked visual language model.

Deep document processing with self-supervised learning

The validity of engineering drawings is automatically determined based on compliance of the specifications of the engineering drawings with automatically generated rules. A document package including images of the engineering drawings and related documents is received. Rules codifying the requirements to be fulfilled by the engineering drawings are automatically generated from the related documents. Data such as specifications of the various parts of the entities in the engineering drawings are automatically extracted. The extracted data is analyzed to determine compliance with the rules to validate the engineering drawings.

Intelligent engineering data digitization

The proposed systems and methods describe an autonomous asset detection system that leverages artificial intelligence (AI) models for three-dimensional asset identification and damage detection, asset damage classification, automatic in-field asset tag readings, and real-time asset management. In some embodiments, a deep learning-based system receives a set of aerial images of one or more assets and automatically identifies each asset in the image(s) using rotational coordinates. In some embodiments, an image annotation tool labels the images either manually or automatically. The system then detects whether the asset is damaged and, if so, determine the type of damage, and further captures and stores asset tag information for the target asset. The collected and processed data is then provided to end-users via a comprehensive user interface platform for managing the assets in real-time. The platform can also be configured to generate insights on damage and repair, facilitating an expedited and thoughtful response to asset maintenance.

System and method for identifying structural asset features and damage

Examples of medical concierge are provided. In an example, an claim may be received. The claim may include data relating to service provided, by a provider, to multiple patients. The claim may be parsed to determine the provider, the multiple patients and the service provided. Additional information may then be fetched. The additional information may include one of a number of claims filed in the past, status of each claim, number of appeals filed, status of the appeals, and complaints registered by the provider. Thereafter, the claim and the additional information may be analyzed and a category may be determined for the provider. The category may be determined based on a behaviour model that may be computed based on the claim and the additional information. The category may be indicative of an issue in behaviour of the provider.

Medical concierge

Examples of a character recognition system are provided. In an example, the system may receive an object detection requirement pertaining to a video clip. The system may identify a visual media feature map from visual media data to process the object detection requirement. The system may implement an artificial intelligence component to segment the visual media feature map into a plurality of regions, and identify a plurality of image proposals therein. The system may implement a first cognitive learning operation to allocate a human face identity for a human face and an object name for an object present in the video clip. The system may determine a face identity model for the human face present in the plurality of image proposals and generate a tagged face identity model. The system may implement a second cognitive learning operation to assemble the plurality of frames with an appurtenant tagged face identity model.

Character recognition

A system for determining a response to a query includes a receiver to receive a query along with a plurality of potential responses to the query. A detector detects a topic and a type of the query based on information extracted from text and structure. Further, a selector selects at least one of a plurality of techniques for processing the query and the plurality of potential responses, based on the topic and the type of the query. An obtainer obtains an answer by execution of each of the selected techniques for processing the query and the plurality of potential responses along with an associated confidence score. A determinator determines one of obtained answers as a correct response to the query, based on a comparison between confidence scores associated with the answers.

Determination of a response to a query

A classifier receives policy data corresponding to a new policy. Further, the classifier processes the policy data to classify the policy data into an obligation class and an informational class. An information extractor then extracts metadata from the policy data that is classified into the obligation class. Subsequently, a data translator determines if there is an incremental change in the policy data based on a comparison of the policy data with policy data corresponding to existing policies. On determining the incremental change in the policy data, the data translator translates the policy data that is classified into the obligation class into a rule based on the metadata. A rules engine then receives the rule from the data translator for claims adjudication.

Data translation

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San Jose, CA, United States of America

Neeru Narang