Google llc (20240311100). Integrated Development Environments for Generating Machine Learning Models simplified abstract
Integrated Development Environments for Generating Machine Learning Models
Organization Name
Inventor(s)
Andy Coenen of Mountain View CA (US)
Ludovic Peran of Mountain View CA (US)
Michael Terry of Mountain View CA (US)
Aaron Donsbach of Mountain View CA (US)
Integrated Development Environments for Generating Machine Learning Models - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240311100 titled 'Integrated Development Environments for Generating Machine Learning Models
The method described in the abstract involves receiving a request indication for a GUI interaction from a user on a user device, providing a response to display a structured prompt within the GUI, capturing user input for training samples, adjusting an ML model based on the training samples, and generating test output text using the adjusted ML model.
- Receiving a request indication for a GUI interaction from a user on a user device
- Providing a response to display a structured prompt within the GUI
- Capturing user input for training samples with corresponding text input fields
- Adjusting the ML model based on the training samples received from the device
- Generating test output text using the adjusted ML model for display within the GUI
Potential Applications: - This technology can be applied in various industries such as customer service, healthcare, and education for improving user interactions and responses. - It can be used in chatbots, virtual assistants, and automated systems to enhance communication and accuracy.
Problems Solved: - Streamlining the process of capturing user input for training ML models. - Improving the efficiency and accuracy of ML models through user interaction.
Benefits: - Enhanced user experience with structured prompts and input fields. - Improved performance of ML models with adjusted training samples. - Increased accuracy of test output text generated by the ML model.
Commercial Applications: Title: Enhanced User Interaction Technology for ML Models This technology can be utilized in customer service chatbots, virtual assistants in healthcare settings, and educational platforms for personalized learning experiences. The market implications include improved user engagement, higher efficiency in automated systems, and enhanced data processing capabilities.
Questions about Enhanced User Interaction Technology for ML Models: 1. How does this technology improve the accuracy of ML models through user interaction? 2. What are the potential applications of this technology in the healthcare industry?
Frequently Updated Research: Stay updated on advancements in natural language processing and machine learning algorithms to enhance the performance of ML models in user interaction technologies.
Original Abstract Submitted
a method includes receiving a request indication indicating a gui interaction by a user on a user device, and in response, providing to the device a response configured to cause the device to display, within a gui, a structured prompt including a plurality of user input fields, each user input field representing a corresponding training sample and including a first corresponding text input field for capturing input text to be provided to an ml model, and a second corresponding text input field for capturing ground-truth output text. the method also includes receiving, from the device, the training samples, and, in response, adjusting the ml model using the training samples. the method further includes receiving, from the device, test input text, generating, using the adjusted ml model, test output text based on the test input text, and providing, to the user device, the test output text for display within the gui.
