NLP Entity Extraction for Semi-Structured Device Data
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Solution Overview
Problem
Conventional systems face challenges in extracting meaningful information from semi-structured and unstructured historical data related to electronic device operations, as they struggle to understand and classify text descriptions without pre-defined structures, especially when dealing with large volumes of diverse data formats.
Innovation Solution
A method and system that utilize processors to receive semi-structured datasets, extract unique classes, create n-grams representing relationships, determine entity frequencies, and generate hypotheses for operation solutions based on pre-defined thresholds, effectively addressing operation problems in electronic devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If regular association rules and machine learning algorithms are used to analyze historical operation data, then structured data can be processed, but hidden information in semi-structured and unstructured data cannot be effectively extracted
Solution Approach 1:
The patent replaces conventional machine learning algorithms with Natural Language Processing (NLP) techniques to process semi-structured and unstructured data. Specifically, the system uses NLP to extract entities, relationships, and attributes from text descriptions in service tickets, enabling effective utilization of hidden information that traditional algorithms cannot access.
Solution Approach 2:
The patent transforms semi-structured and unstructured data into structured formats by applying NLP techniques. The system extracts entities, relationships, and attributes from text and converts them into structured data representations, changing the parameter structure from unorganized text to organized data fields that can be systematically analyzed.
2Quantity of substance
If huge volume of semi-structured and unstructured historical data is processed, then more comprehensive analysis is possible, but natural language processing becomes difficult to apply effectively
Solution Approach 1:
The patent segments the large volume of semi-structured and unstructured data into smaller manageable units by extracting individual entities, relationships, and attributes. The system processes service ticket data by identifying and separating distinct information elements (e.g., device types, operations, problems, solutions) which can then be analyzed independently and reassembled into comprehensive insights.
Solution Approach 2:
The patent introduces an intermediary NLP processing layer that mediates between the raw semi-structured data and the analysis engine. This intermediary layer extracts and structures key information from unstructured text, converting it into a format that facilitates efficient processing and analysis of large data volumes without overwhelming the system.
3Measurement precision
If data is imparted with pre-defined structure to enable classification, then meaning can be extracted, but flexibility to handle diverse data formats is reduced
Solution Approach 1:
The patent implements a dynamic data structure approach where the system adapts its classification schema based on the input data characteristics. The NLP engine extracts entities and relationships from diverse data formats and dynamically creates appropriate structured representations, allowing the system to maintain high classification accuracy while accommodating various data formats without requiring rigid pre-defined structures for each format.
Data Source
AI summary
A method and a system are provided for facilitating operation of an electronic device. The method comprises receiving a semi-structured dataset comprising one or more entities, wherein the semi-structured dataset corresponds to at least an indication of an operation problem associated with an electronic device. The method comprises extracting one or more unique classes associated with one or more entities from the semi-structured dataset. The method comprises creating one or more n-grams representative of a relationship between the one or more entities and the one or more unique classes. The method comprises generating a hypothesis associated with the one or more entities based on a first set of entities from the one or more entities using one or more n-grams, wherein the generated hypothesis corresponds to an operation solution to solve the operation problem associated with the electronic device.