Core Logic Inc.

Cognizant Technology Solutions India Pvt. Ltd.

Peeta Basa Pati

Salaka Sivananda

Vinaya Sathyanarayana

Harish Kongara

Ravi Veera

Srinivas Nagamalla

Ejiribidi Rajeshekhar Rao

Rajarajan T R

In accordance with an aspect of the present invention, a system and method for automated processing of electronic documents is provided. The said system comprising a precursor module configured to receive an electronic document and convert into a textual equivalent; a data ascertainment module configured to identify the textual snippets, more particularly, the demarcated sections, corresponding to the at least one structured section and logically separate the demarcated sections the remaining textual equivalent; and, a pass-receiving module configured to receive said logically separated demarcated sections and the remaining textual equivalent and capture business relevant data committed therein, wherein said pass-receiving module captures the business relevant data from the demarcated sections by locating at least one master trigger and at least one proximate co-trigger in the demarcated sections and geometrically coupling said located triggers and co-triggers into at least one potential zone and extracting the business relevant data committed within these potential zones.

System and method for automated processing of electronic documents

A data extraction system for receiving and scanning documents to generate ordered input for storage in a database employs a non-linear statistical model for a data extraction sequence having a plurality of transformations. Each transformation transitions an extracted data value in various forms from a raw data image to a computed data value. For each transformation, a confidence model learns a confidence component for the particular transformation. The learned confidence components, generated from a control set of documents having known values, are employed in a production mode with actual raw data. The confidence component corresponds to a likelihood of transformation accuracy, and the confidence model aggregates the confidence components to compute a confidence for the extracted data value. A database stores the extracted data value labeled with the computed confidence attribute for subsequent use by an application employing the extracted data.

Data extraction confidence attribute with transformations

Techniques disclosed herein include systems and methods for data validation and correction. Such systems and methods can reduce costs, improve productivity, improve scalability, improve data quality, improve accuracy, and enhance data security. A data manager can execute such data validation and correction. The data manager identifies one or more anomalies from a given data set using both contextual information and validation rules, and then automatically corrects any identified anomalies or missing information. Identification of anomalies includes generating similar data elements, and correlating against contextual information and validation rules.

Automatic data validation and correction

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Bengaluru, India

Peeta Basa Pati