18604444. Integrated Development Environments for Generating Machine Learning Models simplified abstract (Google LLC)
Contents
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 18604444 titled 'Integrated Development Environments for Generating Machine Learning Models
Simplified Explanation:
The patent application describes a method where a user interacts with a GUI on a device, providing input text and ground-truth output text for training a machine learning (ML) model. The ML model is adjusted based on the training samples received from the device. Test input text is then used to generate test output text using the adjusted ML model, which is displayed back to the user within the GUI.
Key Features and Innovation:
- User interaction with GUI prompts for training ML model.
- Training samples provided by user for adjusting ML model.
- Test input text used to generate test output text.
- ML model adjusted based on training samples for improved accuracy.
- Display of test output text within the GUI for user feedback.
Potential Applications: This technology can be applied in various fields such as natural language processing, chatbots, virtual assistants, and automated customer service systems.
Problems Solved: This technology addresses the need for efficient training and adjustment of ML models based on user-provided data, improving the accuracy and performance of such models.
Benefits:
- Enhanced user interaction with GUI prompts.
- Improved accuracy of ML models through user-provided training samples.
- Real-time feedback on test output text within the GUI.
Commercial Applications: The technology can be utilized in customer service applications, chatbot development, language translation services, and other AI-driven systems for enhanced user experience and efficiency.
Questions about the Technology: 1. How does the method ensure the security and privacy of user-provided data during training? 2. What measures are in place to prevent bias in the ML model based on user-provided training samples?
Frequently Updated Research: Stay updated on advancements in natural language processing, machine learning algorithms, and user interaction design to enhance the performance and usability of the technology.
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.
