Dynamic Machine Learning Model Orchestration for Semantic Query Resolution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current solutions lack mechanisms to exploit symbolic representation and leverage the combination of multiple machine learning models to extract semantic information from multimodal content to answer user queries effectively, relying on manual or static indexing of human-annotated data.
Innovation Solution
A computer system and method that automatically maps and combines machine learning models to answer queries by parsing keywords, contextualizing queries, selecting relevant models, and sorting them for dynamic execution on multimodal data, allowing output from one model to serve as input for another, thereby extracting and presenting semantic information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual or static indexing of human-annotated data is used, then data organization is simple, but semantic information extraction accuracy deteriorates
Solution Approach 1:
The system dynamically selects and combines multiple machine learning models based on query characteristics and data types, rather than using a static indexing approach. This allows the system to adapt the extraction methodology to each specific query, improving semantic information extraction accuracy while maintaining manageable complexity through automated model selection.
Solution Approach 2:
The system employs a composite approach by integrating multiple different machine learning models (e.g., OCR, object detection, NLP models) to process multimodal content. This composite methodology combines the strengths of different models to achieve superior semantic extraction accuracy compared to any single model or manual indexing approach.
2Measurement precision
If multiple machine learning models are combined, then semantic information extraction capability is improved, but system complexity increases
Solution Approach 1:
The system segments the complex task of semantic extraction by dividing it into multiple specialized machine learning models, each handling specific aspects (text recognition, object detection, semantic understanding). This segmentation allows each model to focus on its strength while the orchestration layer manages the overall complexity through systematic model selection and combination based on query requirements.
3Measurement precision
If dynamic model selection and combination is implemented, then query answer accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-establishing the repository of machine learning models with their capabilities documented, and by implementing an orchestration layer that can quickly select appropriate models based on query analysis. This preliminary preparation enables faster dynamic model selection at query time, reducing the processing time overhead while maintaining high accuracy through appropriate model combination.
Data Source
AI summary
Automatically mapping and combining the application of machine learning models to answer queries according to semantic specification. A query is parsed to extract keywords from the query and to contextualize the query. Based on the keywords, machine learning models are selected that process concepts associated with the keywords. The machine learning models are sorted according to the contextualization of the query. The machine learning models are run on multimodal data according to a sorted order, where data resulting from an output of one of the machine learning models is used as input to another one of the machine learning models. A query result is output based on a result from running the machine learning models.