International business machines corporation (20240289622). AI MODEL RECOMMENDATION BASED ON SYSTEM TASK ANALYSIS AND INTERACTION DATA simplified abstract
Contents
AI MODEL RECOMMENDATION BASED ON SYSTEM TASK ANALYSIS AND INTERACTION DATA
Organization Name
international business machines corporation
Inventor(s)
Vagner Figueredo De Santana of São Paulo (BR)
Larissa Monteiro Da Fonseca Galeno of Niterói (BR)
Emilio Ashton Vital Brazil of Rio de Janeiro (BR)
Renato Fontoura De Gusmao Cerqueira of Rio de Janeiro (BR)
AI MODEL RECOMMENDATION BASED ON SYSTEM TASK ANALYSIS AND INTERACTION DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240289622 titled 'AI MODEL RECOMMENDATION BASED ON SYSTEM TASK ANALYSIS AND INTERACTION DATA
The abstract describes a computer-implemented method that involves generating an interaction usage graph and an interaction embedding model based on user interactions with an analytical system user interface. The method then determines the similarity of a portion of the interaction embedding model corresponding to a specific analytical task with a machine learning model from a set of models, and outputs an indication of the particular machine learning model to the user interface.
- Generate an interaction usage graph based on user interactions with an analytical system user interface
- Create an interaction embedding model in a vector space from the interaction usage graph
- Determine the similarity of a portion of the interaction embedding model related to a specific analytical task with a machine learning model
- Output an indication of the particular machine learning model to the user interface
Potential Applications: - Enhancing user experience in analytical systems - Improving machine learning model selection for specific tasks
Problems Solved: - Streamlining the process of selecting machine learning models for analytical tasks - Enhancing the efficiency of user interactions with analytical systems
Benefits: - Increased accuracy in selecting machine learning models - Improved user satisfaction with analytical system interfaces
Commercial Applications: Title: Enhanced Machine Learning Model Selection for Analytical Systems This technology could be utilized in various industries such as finance, healthcare, and marketing to optimize machine learning model selection for specific analytical tasks, leading to improved decision-making processes and outcomes.
Prior Art: Readers interested in exploring prior art related to this technology may consider researching advancements in machine learning model selection algorithms and user interface design for analytical systems.
Frequently Updated Research: Stay updated on the latest developments in machine learning model selection algorithms and user interface design for analytical systems to ensure the continued relevance and effectiveness of this technology.
Questions about Machine Learning Model Selection for Analytical Systems: 1. How does this technology improve the efficiency of machine learning model selection for analytical tasks? 2. What are the potential implications of using this method in various industries such as finance and healthcare?
Original Abstract Submitted
in a first aspect of the invention, there is a computer-implemented method including: generate, by a processor set, an interaction usage graph based on user interaction data on user interactions with an analytical system user interface; generate, by the processor set, an interaction embedding model in a vector space based on the interaction usage graph; determine, by the processor set and based on the interaction embedding model in the vector space, a similarity of a portion of the interaction embedding model that corresponds to a particular analytical task among the user interactions with the analytical system user interface with a particular machine learning model from a set of one or more machine learning models; and output, by the processor set, to the analytical system user interface, an indication of the particular machine learning model.
