Intent Language Model for Enterprise Data Intent Discovery
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Solution Overview
Problem
Current data analysis and processing systems are unable to interpret domain-specific intent from enterprise data in real-time and fail to adapt quickly to different domains or contexts within disparate enterprise areas or industries, making it challenging to derive meaningful insights from streaming data.
Innovation Solution
The development of an adaptable system and method that utilizes an Intent Language Model (ILM) to translate domain-specific data into defined abstractions, enabling the discovery and correlation of intent from enterprise data across various domains without the need for customization, and continuously improves its accuracy through continuous data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional data analysis systems are used to process enterprise data, then basic data processing can be performed, but the system cannot interpret domain-specific intent and cannot adapt quickly to different domains
Solution Approach 1:
The patent implements a universal intent discovery system that can process multiple domain-specific enterprise data types (healthcare, automotive, industrial equipment, etc.) through a single platform. The system uses domain-agnostic NLU models that can be configured to understand different domains without requiring separate systems, thereby achieving multi-functionality and adaptability across diverse enterprise contexts.
Solution Approach 2:
The system employs dynamic intent models that can adapt and learn from incoming enterprise data in real-time. The NLU capabilities are not static but continuously evolve through machine learning, allowing the system to quickly adjust to new domains and contexts as data patterns emerge, providing dynamic adaptability rather than fixed domain expertise.
2Measurement precision
If real-time intent discovery from streaming data is implemented, then actionable insights can be obtained, but processing speed and real-time performance may be compromised
Solution Approach 1:
The system performs preliminary processing of enterprise data by pre-segmenting and pre-processing data streams before full intent analysis. NLU models are pre-trained on domain-specific languages and patterns, so when real-time data arrives, the system can quickly apply these pre-established understanding frameworks rather than building understanding from scratch, maintaining both speed and accuracy.
Solution Approach 2:
The intent discovery process is segmented into multiple stages: data segmentation, feature extraction, intent classification, and action determination. This segmented approach allows parallel processing of different data aspects simultaneously, improving real-time performance while maintaining comprehensive intent analysis accuracy through multi-stage verification.
3Measurement precision
If domain-specific customization is performed for each enterprise area, then accurate intent interpretation can be achieved, but system complexity and implementation time increase
Solution Approach 1:
The patent creates a universal intent discovery platform that handles multiple domains (healthcare, automotive, industrial equipment, etc.) through a single system architecture. The NLU models are designed to be domain-agnostic at the core level but can be configured with domain-specific vocabularies and patterns, eliminating the need for separate customized systems for each enterprise area while maintaining accurate intent interpretation.
Solution Approach 2:
The system achieves domain-specific accuracy through parameter configuration rather than structural customization. By changing parameters such as domain vocabularies, entity types, and relationship patterns in the NLU models, the same system can accurately interpret intent across different domains without requiring structural modifications or reimplementation.
4Loss of information
If comprehensive enterprise data is processed to discover intent, then valuable insights can be obtained, but data processing time and computational resources increase
Solution Approach 1:
The system extracts only the essential intent-relevant features from comprehensive enterprise data using NLU techniques. Instead of processing and analyzing every detail of the data, the system identifies and extracts key intent-bearing elements (such as problem descriptions, diagnostic indicators, action requests) and focuses computational resources on interpreting these extracted features, thereby maintaining information completeness while reducing processing time.
Solution Approach 2:
The intent discovery system performs partial processing on large volumes of enterprise data by focusing computational effort on the most intent-relevant portions of the data stream. The system applies sophisticated NLU analysis selectively to data segments that show intent indicators, while using lighter processing for routine data, achieving comprehensive insight discovery without processing every byte at full computational intensity.
Data Source
AI summary
Systems are disclosed to improve data-driven decision-making in an enterprise by discovering intent that is applicable to an enterprise domain.