Automated Natural Language Data Classification System
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Solution Overview
Problem
Conventional classification techniques for aggregated user feedbacks, particularly those in natural language format, are inconsistent and resource-intensive, leading to ineffective identification of issues, escalation opportunities, and financial impacts in service platforms.
Innovation Solution
A method utilizing artificial intelligence to automatically classify and tag natural language data in real-time, involving data conversion, metadata determination, action initiation, and documentation generation, to enhance the management of user requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional classification techniques are used for aggregated user feedbacks, then resource consumption is high and consistency is poor, but implementing automated AI-based classification increases system complexity
Solution Approach 1:
The patent introduces an intermediary AI service platform that sits between the data sources (social media, support channels) and the classification system. This intermediary automatically extracts, structures, and prepares natural language data before classification, reducing the complexity burden on the core classification system while improving processing efficiency and consistency.
Solution Approach 2:
The system implements self-service through automated data extraction and structuring capabilities that operate without manual intervention. The AI models automatically process incoming natural language data, extract relevant features, and prepare structured outputs for classification, enabling the system to serve itself and reducing operational complexity.
2Measurement precision
If manual processing of natural language feedback is used, then classification consistency is poor, but automated processing increases resource intensity
Solution Approach 1:
The patent segments the classification process into distinct modular stages: data extraction, structuring, feature identification, and classification. Each stage is handled by specialized AI models that process specific aspects of the data independently. This segmentation improves classification consistency by ensuring each step is optimized for its specific task while managing resource intensity through targeted processing.
Solution Approach 2:
The system performs preliminary actions by automatically extracting and structuring data before the main classification task. AI models pre-process natural language feedback to identify key features, entities, and relationships in advance, creating structured representations that make the subsequent classification more consistent and resource-efficient by reducing the complexity of the main processing task.
3Reliability
If conventional techniques process unstructured natural language data, then issue identification is inconsistent, but structuring the data increases processing time
Solution Approach 1:
The patent applies preliminary action by automatically structuring unstructured natural language data before classification. AI models extract key information, identify entities, relationships, and sentiments, and organize this data into structured formats in advance. This preliminary structuring improves issue identification reliability while minimizing processing time loss by performing extraction and structuring operations concurrently with initial data intake.
Data Source
AI summary
A method for automatically classifying and tagging natural language data is disclosed. The method includes receiving, via an application programming interface, requests from various sources, each of the requests including raw data in a natural language format; converting the raw data into structured data sets; determining, by using a model, a metadata output for each of the requests based on the corresponding structured data sets; appending the metadata output to the corresponding requests; determining an action for each of the requests based on the corresponding metadata output; and automatically initiating the action.