NLP Support Record Generation via Entity Mapping
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Solution Overview
Problem
IT support professionals face challenges in efficiently capturing and processing user-generated text to create standardized support records, which hinders effective issue resolution and knowledge management within IT environments.
Innovation Solution
A system utilizing natural language processing (NLP) to analyze support text, identify sentence parts and named entities, and generate support records by mapping these elements to relevant fields, facilitating automatic and accurate record creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual creation of support data records is used, then data accuracy can be ensured through professional judgment, but time consumption and labor effort increase significantly
Solution Approach 1:
The system enables self-service by automatically extracting information from unstructured support text and populating structured support data records without requiring manual intervention. The NLP engine performs entity recognition, classification, and field mapping autonomously, allowing the system to serve itself in converting unstructured text to structured data format.
Solution Approach 2:
The patent replaces the mechanical manual process of data entry and classification with an automated NLP-based system. The mechanical system of manual information extraction is substituted by computational algorithms that perform entity recognition, sentiment analysis, and automatic field population, dramatically reducing time consumption while maintaining accuracy.
2Difficulty of detecting and measuring
If manual processing of support text is used, then complex contextual understanding can be achieved, but processing speed and productivity decrease
Solution Approach 1:
The NLP processing is segmented into distinct modular components: entity recognition module, sentiment analysis module, classification module, and field mapping module. Each segment handles a specific aspect of contextual understanding, allowing parallel processing and improving overall speed while maintaining comprehensive analysis capabilities.
Solution Approach 2:
The patent introduces an intermediary NLP engine that acts as a mediator between raw support text and structured support records. This intermediary performs contextual analysis, entity extraction, and meaning interpretation, bridging the gap between unstructured text and structured data while enabling fast automated processing.
3Stability of the object's composition
If standardized support record formats are enforced, then data consistency is improved, but flexibility in capturing diverse support scenarios is reduced
Solution Approach 1:
The system dynamically adjusts parameters such as entity types, classification categories, and field mappings based on the specific support scenario. The NLP engine recognizes different entity types (hardware, software, service) and adapts the record structure accordingly, allowing standardized formats to accommodate diverse scenarios through parameter variation rather than structural rigidity.
Solution Approach 2:
The support record system is designed with universal fields that can accommodate multiple types of support scenarios. The NLP engine maps various entities and concepts to appropriate fields in a unified record structure, enabling a single standardized format to handle diverse IT support situations including hardware issues, software problems, and service requests.
Data Source
AI summary
A view generator receives support text characterizing a support requirement for available information technology (IT) support, the support text being received in sentence form via a graphical user interface (GUI). A text analyzer performs natural language processing on the support text and thereby identifies at least one sentence part and at least one named entity within the support text. A support record generator relates each of the at least one sentence part and the at least one named entity to a support record type, and generates a support data record for the support requirement, including filling individual fields of the support data record using the at least one sentence part and the at least one named entity.