Accenture Global Solutions Limited

International Business Machines Corporation

Urvesh Bhowan

Medb Corcoran

Laura O'Malley

Pedro Sacristan

Daniel John McCloskey

Seamus Ronan McAteer

Abhilash Alexander Miranda

Keith Cortis

Bogdan Eugen Sacaleanu

Edward Burgin

Laura Alvarez

Shinichiro Shuda

Robert Willems

Kristine Marie Renker

Kris Timmermans

Kimberly De Maeseneer

Amaury Reychler

Qurrat Ul Ain

Anthony McCoy

Tara Lynn O'Gara

James Robert Priestas

Theresa M Gaffney

Meghan Hildebrand Fotopoulos

Navdeep Sharma

Thomas Doane Perry

Aoife Whelan

Gino Andre Di Paolo

Robert Drysdale

Gavin Kearney

Jivan Virdee

A system, method and computer program product provides improved performance in machine learning, decision making and similar processes. In one example method, a plurality of individual subsets of features of a dataset comprising multiple features are received. The subsets may be provided by applying one or more feature selection methods to the dataset. Each subset is represented as a graph based on a predefined graph template. The example method merges the graphs of the plurality of individual subsets by overlaying the graphs on each other to form a merged feature graph. The merged feature graph may be used for identifying a single subset of features for use in machine learning, decision making and similar processes.

Merging feature subsets using graphical representation

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

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

A device obtains a machine readable document, and extracts plain text information from the machine readable document. The device extracts visual structural information from the machine readable document, and processes the plain text information with an artificial intelligence model to generate artificial intelligence-generated annotations. The device aligns the artificial intelligence-generated annotations and the visual structural information to generate a structured document, and provides the structured document for display.

Generating a structured document based on a machine readable document and artificial intelligence-generated annotations

A device may obtain information included in a corpus of documents relating to an organization. The device may identify a set of values indicating personal information for one or more individuals by using a set of natural language processing (NLP) techniques to analyze the information included in the corpus. The device may determine a set of relationships between one or more values, of the set of values indicating the personal information using one or more additional NLP techniques and/or one or more rules. The device may generate a set of user profiles for the one or more individuals based on the set of relationships between the one or more values indicating the personal information. The device may perform one or more actions associated with using the set of user profiles to service a request for information.

Automated secure identification of personal information

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

Expanding the corpus of a question answering system. The question answering system is adapted to generate a candidate answer to a query. The candidate answer may be incorporated into the corpus of the question answering system if the candidate answer meets or exceeds a predetermined requirement. By expanding the corpus with material that has been determined to be useful for answering a query, for example, the corpus may be expanded automatically in an accurate and efficient manner.

Automatic corpus expansion using question answering techniques

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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Bray, Ireland

Urvesh Bhowan