Accenture Global Solutions Limited

Medb Corcoran

Urvesh Bhowan

Abhilash Alexander Miranda

Pedro Sacristan

Laura O'Malley

Bogdan Eugen Sacaleanu

Edward Burgin

Laura Alvarez

James Robert Priestas

Tara Lynn O'Gara

Theresa M Gaffney

Shinichiro Shuda

Kristine Marie Renker

Kris Timmermans

Kimberly De Maeseneer

Amaury Reychler

Robert Willems

Georgios Krasadakis

Chahrazed Bouhini

Gaurav Kaila

Mohammad Karzand

Victor Oliveira Antonino

Sarat Chandra Gurram

Konstantinos Mammas

Thomas Doane Perry

Travis Bowers

Meghan Hildebrand Fotopoulos

Orlaith Burke

Nóirín Duggan

Yaxuan Yu

Ascanio Afan De Rivera Costaguti

Morgan Commons

Jessica Maria Kearney

Jivan Virdee

An example implementation described herein involves identifying an artificial intelligence module to train a user; selecting, using the artificial intelligence module, a set of tasks from a plurality of tasks to provide to the user; providing the set of tasks to the user; monitoring a performance parameter associated with the user performing the tasks; identifying a machine learning model to determine a level of expertise of the user; determining, using the performance parameter as an input to the machine learning model, whether the level of expertise of the user satisfies an expertise threshold; obtaining a configuration update to the artificial intelligence module from the user, determining that the level of expertise of the user satisfies the expertise threshold; and updating the artificial intelligence module to use the configuration update in association with training one or more users or selecting a subsequent set of tasks from the plurality of tasks based on determining that the level of expertise of the user satisfies the expertise threshold.

Adaptive artificial intelligence for user training and task management

A hierarchical document classification system is disclosed. The system includes a text-based document classifier model for classifying an input electronic document into one of a set of predefined document categories. The system further includes an image-based metadata identification model for classifying electronic documents of a particular document category into a set of metadata categories. The system further includes a fuzzy text matcher for supplementing classification accuracy of the image-based metadata identification model to obtain a metadata category for the input electronic document.

Automatic hierarchical classification and metadata identification of document using machine learning and fuzzy matching

This document describes systems, methods, devices, and other techniques for performing content moderation. In some implementations, a computing device receives input data in relation to an electronic document. The computing device generates, based on the received input data, data representing one or more features of the electronic document and analyzes the generated data representing one or more features of the electronic document to determine one or more reliability scores indicating respective measures of reliability of the electronic document. The reliability scores include one or more of (i) a content reliability score, (ii), an author reliability score, and (iii) a domain reliability score. The computing device indicates, based on one or more of the reliability scores, whether the electronic document is reliable or not.

Content moderation system and indication of reliability of documents

A self-learning system for categorizing log entries may be provided. The system may display a first log entry and receive a categorical identifier for the first log entry. The system may parse the first log entry for predetermined text information and predetermined image information. The predetermined text information may be included in a datafield classifier and the predetermined image information included in a metadata classifier. The system may identify the predetermined text information in the log entry and adjust a first prioritization of respective categorical identifiers included in the datafield classifier. The system may identify the predetermined image information in the first log entry and adjust a second prioritization of the respective categorical identifiers included in the metadata classifier. The system may map a second log entry to the categorical identifier based on adjustment of the first prioritization or adjustment of the second prioritization.

Self-learning log classification system

A system for self-learning archival of electronic data may be provided. A binary classifier may identify a text segment of an electronic dataset in response to text of the electronic dataset being associated with indicators of a word model. A first multiclass classifier may generate a first classification set comprising respective statistical metrics for the datafield that each predefined identifier in a group of predefined identifiers is representative of the datafield. A second multiclass classifier may receive a context of the electronic dataset and generate a second classification set. A combination classifier may apply weight values to the first classification set and the second classification set and form a weighted classification set and select a predefined identifier as being representative of the datafield based on the weighted classification set. The processor may store, in a memory, a data record comprising an association between the predefined identifier and the datafield.

Statistical self learning archival system

An Artificial Intelligence (AI) based document processing system receives a request including one or more documents related to a process to be automatically executed. The information including the fields and an intent required for the process are extracted from one or more of the request and the documents. The required documents and fields are selected based on the intent and a domain model. The required fields are validated using external knowledge and the discrepancies identified therein are resolved. An internal master document is built based on the required fields. The internal master document is employed for the automatic execution of the process.

Artificial intelligence based document processor

An identification and management system for log entries may filter historical data and generate closed log entries as a reference dataset. A dynamic learning engine may perform statistical modelling using the reference dataset to assign predetermined categories to each of a number of open log entries. An automation index may be generated for each of the open log entries. The automation index is indicative of accuracy of the assigned categories. Some of the open log entries may be identified as priority log entries which are representative of a group of the open log entries. The assigned category of the priority log entries may be analyzed for accuracy and the results of the analysis may be used to train the statistical model so that the open log entries may be iteratively assigned and re-assigned a category until the category of each open log entry reaches a desired accuracy.

Identification and management system for log entries

A document processing system receives an electronic document including component documents generated from various sources in different formats. Plain text data can be extracted from the electronic document in addition to formatting and structuring information. The plain text data is segmented into sections and various entities are extracted and linked from the sections. An interactive graphical user interface (GUI) that displays content including the plain text data is formatted according to the styling information and annotated entity relationships are determined from the linked entities. The GUI enables user edits to the annotated entity relationships.

Document processing

A self-learning system for categorizing log entries may be provided. A text classifier may identify a log description of a log entry in response to text of the log description being associated with indicators of a word model. A datafield classifier may generate a datafield metrics including an accuracy of the categorical identifiers representing the datafield. A metafield classifier may generate a context metrics for the context of the log entry, the context metrics including an accuracy categorical identifiers representing the metafields. A combination classifier may form a weighted classification set and select a categorical identifier as being representative of the datafield based on the weighted classification set. A categorical controller may identify new categories based on an analysis of the context metrics of the log entry.

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Medb Corcoran