Bot Automation Using Contextual AI for Data Validation
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Solution Overview
Problem
Current process automation techniques are limited by the need for human intervention, leading to errors and security risks, and are not capable of end-to-end data processing without human intervention, resulting in unreliable and insecure data processing outcomes.
Innovation Solution
A system and method for bot process automation using a knowledge base in contextual artificial intelligence (AI), which processes images using optical character recognition (OCR) and contextual AI based on neural networks and natural language processing (NLP) to generate outputs that validate and build interdependencies among image fields, enabling end-to-end data processing without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human intervention is used in data processing, then data input and validation can be performed, but errors and security risks increase
Solution Approach 1:
The system enables self-service automation where bots perform end-to-end data processing tasks independently without human intervention. The bots automatically extract data from images using OCR, validate extracted data against business rules, and complete workflows autonomously, eliminating human error and security risks associated with manual data handling
Solution Approach 2:
The patent replaces manual mechanical data processing with automated digital systems. OCR technology converts images to machine-readable text, AI models validate data automatically, and bot orchestration systems manage workflows digitally, substituting human mechanical operations with reliable automated processes
2Productivity
If end-to-end data processing is automated without human intervention, then productivity increases, but system complexity increases
Solution Approach 1:
The automated system is segmented into distinct functional modules: OCR engines for image-to-text conversion, AI validation models for data verification, bot orchestration layers for workflow management, and knowledge bases for contextual understanding. Each module handles specific tasks independently, making the complex system manageable and maintainable while achieving high productivity
Solution Approach 2:
The patent introduces intermediary components such as knowledge bases that store business rules and contextual information, and orchestration layers that coordinate between different automation modules. These intermediaries simplify the overall system architecture by providing standardized interfaces and mediation between complex components
3Reliability
If users manually input data, then data security can be maintained, but processing time and costs increase
Solution Approach 1:
The system replaces manual data input with automated OCR technology that extracts text from images and converts it to machine-readable format. AI validation models then verify the extracted data against business rules, maintaining security through automated verification while eliminating the time-consuming manual input process
Solution Approach 2:
The automated system performs self-service data extraction and validation without requiring user intervention. Bots autonomously process images, extract relevant information, validate data integrity, and complete workflows, significantly reducing processing time while maintaining security through programmatic control
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides reliable, secure, and efficient end-to-end data processing, reducing human effort and costs while improving the quality of service and user experience through automated data validation and interdependency building.
Implementation Method 1
process said image using optical character recognition (OCR) to generate a first output
Implementation Method 2
said contextual AI being based on a combination of a neural network, one or more knowledge graphs, and natural language processing (NLP)
Data Source
AI summary
A system and method for bot processing automation are provided. The system includes a processor. The system also includes a memory comprising a set of instructions, which when executed by the processor cause the processor to: receive, from a repository, the images from VDI are converted to user defined text input (CSV, XLSX, etc.,) and uses the contextual AI that can contribute to the accuracy improvement and can help to build the context for the domain knowledge base. We use Named Entity Recognition (NER) technique in Natural Language Processing (NLP) for tagging the fields and associate with knowledge graphs. With this information, the contextual AI can act like a wrapper to the BOT and validate the front end rejections.