Scene Builder for Intent Clustering
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Solution Overview
Problem
Current Natural Language Processing systems face challenges in understanding abstract levels of representation and common narratives, as they require manual and costly specialist construction, making it difficult to create accurate and affordable systems that understand literal and abstract meanings.
Innovation Solution
A machine learning-based system and method using Natural Language Processing (NLP) and Natural Language Understanding (NLU) solutions to generate organized intent clusters or scenes by extracting intent features, creating groups, identifying clusters based on co-occurring features, and ranking features within clusters to generate proto-scenes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual construction of semantic schemas is performed by specialists, then the accuracy of understanding literal and abstract meanings is improved, but the cost and time required increase significantly
Solution Approach 1:
The system performs self-service by automatically generating semantic schemas through machine learning algorithms that analyze user interactions and intent features, eliminating the need for specialist manual construction while maintaining accuracy through automated pattern recognition and clustering
Solution Approach 2:
The patent replaces the mechanical process of manual schema construction with an automated computational system that uses machine learning models, natural language processing, and clustering algorithms to generate semantic schemas automatically from user interaction data
2Manufacturing precision
If manual construction of semantic schemas is performed by specialists, then the quality of abstract level understanding is improved, but the scalability and affordability deteriorate
Solution Approach 1:
The system achieves scalability through self-service automation where the machine learning model continuously learns from user interactions and automatically generates and updates semantic schemas, enabling the system to scale to handle large volumes of data and diverse scenarios without additional specialist intervention
Solution Approach 2:
The patent applies parameter changes by transforming the approach from fixed manual schemas to dynamic, data-driven schemas where the system adjusts its understanding parameters based on learned patterns from user interactions, enabling both high quality and scalability
3Productivity
If automated machine learning approaches are used to generate intent clusters, then the cost and time consumption are reduced, but the complexity of the system increases
Solution Approach 1:
The patent applies segmentation by breaking down the complex task of semantic schema generation into distinct modular components: intent feature extraction, user interaction analysis, clustering algorithms, and schema generation modules, making the system more manageable and maintainable despite its automated capabilities
Data Source
AI summary
A system and method for creating organized intent clusters or scenes using machine learning algorithms is provided. A method of creating organized intent clusters or scenes comprises extracting intent features related to the plurality of request inputs. The method also includes creating a plurality of groups comprising the extracted intent features. The method includes identifying a cluster based on co-occurring extracted intent features, the co-occurring extracted intent features belonging to a plurality of domains. The method further includes generating a proto-scene based in part by ranking the extracted intent features within the cluster.