Accenture Global Solutions Limited

Ashish Jain

Chung-Sheng Li

Guanglei Xiong

Sukryool Kang

Benjamin Nathan Grosof

Emmanuel Munguia Tapia

Colin Connors

Prakash Ghatage

Nirav Sampat

Kumar Viswanathan

Neeru Narang

Mohammad Jawad Ghorbani

Rahul Kotnala

Kameshkumar Lakshminarayanan

Suvendu Kumar Mahapatra

Srikanth Narayanan

Rekha Mani

Aravind Krishnan

Examples of artificial intelligence-based reasoning explanation are described. In an example implementation, a knowledge model having a plurality of ontologies and a plurality of inferencing rules is generated. Once the knowledge model is generated, based on a real-world problem, a knowledge model from amongst various knowledge models is selected to be used for resolving a real-world problem. The data procured from the real-world problem is clustered and classified into an ontology of the determined knowledge model. Inferencing rules to be used for deconstructing the real-world problem are identified, and a machine reasoning is generated to provide a hypothesis for the problem and an explanation to accompany the hypothesis.

Explainable artificial intelligence

In an example, an ontology analyzer may generate an ontology, based on a claim adjudication request. The claim adjudication request may be processed, based on the ontology to provide an ontology based inference. A rule based analyzer may identify a predefined rule corresponding to the claim adjudication request and process the request, based on the predefined rule. A conflict resolver may resolve a conflict which may occur between the ontology based inference and the rule based inference. When a conflict is detected, a predefined criteria may be selected for resolving the conflict, the predefined criteria comprising rules to select one of the ontology based inference and the rule based inference to maximize a probability of accurately processing the claim adjudication request in case of a conflict.

Ontology and rule based adjudication

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

Data extraction and automatic validation from digitized documents in non-editable formats is disclosed. Paper documents are digitized or converted into formats suitable for storage on computers or other digital devices. The digitized documents are classified into one of a plurality of document types and based on the document type, document processing rules are selected for analyzing the digitized documents to enable data extraction and automatic validation. The positions and values of the data fields in the digitized documents are obtained using machine learning techniques. The data field values are automatically validated and assigned confidence scores. Data fields with low confidence scores are flagged for manual review.

Extracting searchable information from a digitized document

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Chennai, India

Ashish Jain