NLP Text Extraction for Service Report Automation
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Solution Overview
Problem
Organizations face difficulties in converting service reports into actionable objects in a computer system, as existing automation technologies, such as Robotic Process Automation, are unable to consider customer-specific contexts and require technical know-how for infrastructure and solutions, making manual processing necessary.
Innovation Solution
A natural language processing (NLP) machine learning and rules-based text extraction approach is used to convert textual service recommendations into customer-tailored actions, analyzing unstructured documents, extracting actionable insights, and executing or supporting semi-automated actions based on customer-specific contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If Robotic Process Automation is used to automate service report processing, then automation extent is improved, but adaptability to customer-specific contexts deteriorates
Solution Approach 1:
The patent introduces NLP models and machine learning algorithms as intermediaries between the service report text and the automation system. These intermediaries analyze the unstructured text, extract customer-specific context, and transform it into structured data that can guide automated actions, thereby bridging the gap between generic automation and context-specific adaptability.
Solution Approach 2:
The system dynamically changes parameters such as action selection, configuration settings, and execution commands based on the extracted customer-specific context from service reports. By adjusting these parameters according to the analyzed text, the system achieves both automation and adaptability to different customer environments.
2Adaptability or versatility
If manual processing of service reports is used, then adaptability to customer-specific contexts is improved, but productivity deteriorates
Solution Approach 1:
The system enables self-service by automatically analyzing service reports, extracting actionable insights, and executing remediation actions without requiring manual human intervention. The NLP and machine learning components allow the system to process and understand service reports autonomously, significantly improving productivity while maintaining adaptability.
Solution Approach 2:
The patent replaces manual mechanical processing of service reports with automated NLP and machine learning systems. This substitution transforms the processing from human-driven to algorithm-driven, achieving both high productivity and context-aware adaptability through intelligent automation.
3Ease of operation
If generic recommendations are applied to service issues, then ease of operation is improved, but manufacturing precision of actionable scripts deteriorates
Solution Approach 1:
The system applies local quality by customizing the actionable scripts and remediation actions according to the specific customer context extracted from each service report. Instead of applying uniform generic recommendations, the NLP analysis enables the system to tailor the precision and content of each action to the local requirements of the customer's environment, achieving both ease of operation and high precision.
Data Source
AI summary
In an example embodiment, a natural language processing (NLP) machine learning and rules-based text extraction and analysis approach is used to convert a textual service recommendation document into customer-tailored actions considering the specific context-based executable script. This creates end-to-end automation in implementing suggestions provided in textual documents. Actions mentioned in the document can be processed automatically whenever possible, or at least transformed into a semi-automated action with system support. The solution can be configured to automate end-to-end converting of documents into personalized technical scripts and implementing these scripts at the customer-side.