Natural Language Analytics Platform for Intent Detection
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Solution Overview
Problem
Current data analytics platforms lack the ability to effectively analyze natural language interactions, failing to capture implicit customer intent and contextual information, which limits their ability to provide personalized insights and optimize natural language-based applications like virtual assistants.
Innovation Solution
A natural language data analytics platform with a software API that utilizes machine learning and a natural language interaction query language (NLIQL) to process and analyze NLI data from various sources, enabling implicit personalization and optimization of NLI applications by identifying key information and improving user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If metric-based information is used for data analysis, then analysis operations are simple and straightforward, but the ability to understand implicit customer intent and contextual information is lost
Solution Approach 1:
The platform segments NLI data into distinct analytical components including intent detection, sentiment analysis, and contextual understanding. This segmentation allows the system to process different aspects of natural language separately, capturing implicit customer intent while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces natural language processing algorithms as intermediary layers between raw NLI data and analytical insights. These intermediaries translate unstructured natural language into structured data representations, enabling the system to understand implicit intent without requiring direct complex analysis of raw text.
2Measurement precision
If comprehensive NLI data is collected and analyzed, then personalized insights and user understanding improve, but data processing time and computational resources increase
Solution Approach 1:
The platform performs preliminary processing of NLI data by pre-processing and structuring natural language inputs before detailed analysis. This preliminary action includes tokenization, part-of-speech tagging, and initial intent classification, which reduces the computational burden of subsequent analysis and accelerates overall processing while maintaining detection accuracy.
Solution Approach 2:
The system dynamically adjusts its analysis depth and processing intensity based on the complexity of incoming NLI data. For straightforward queries, the system applies lighter processing, while more complex interactions receive deeper analysis. This dynamic approach optimizes processing time while ensuring adequate accuracy for each specific case.
3Productivity
If automated NLI analysis is implemented, then operational efficiency improves, but the ability to handle complex contextual nuances decreases
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously learns from analyzed NLI data to improve its contextual understanding. Automated analysis results are fed back into the model training process, allowing the system to refine its ability to handle nuanced contexts while maintaining high operational efficiency through automated processing.
Solution Approach 2:
The analytics platform combines multiple analysis techniques and algorithms into a composite analytical system. This includes integrating rule-based approaches with machine learning models, and combining different NLP techniques, to create a robust system that maintains high efficiency while accurately handling complex contextual nuances through the synergistic effect of multiple methods.
Data Source
AI summary
A system for natural language analytics, stored and operating on a network-connected computing device, comprising a natural language application data importer, further comprising a natural language application data importer, a natural language application data augmenter that enriches the data and an analytics component which provides a means of querying structured as well as unstructured data and which also contains a method for providing adaptive natural language analytics.