Amazon technologies, inc. (20240296838). MACHINE LEARNING MODEL UPDATING simplified abstract
Contents
MACHINE LEARNING MODEL UPDATING
Organization Name
Inventor(s)
Anil K. Ramakrishna of Los Angeles CA (US)
Rahul Gupta of Waltham MA (US)
Yuval Merhav of Cambridge MA (US)
Heather Brooke Spetalnick of Scotch Plains NJ (US)
MACHINE LEARNING MODEL UPDATING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240296838 titled 'MACHINE LEARNING MODEL UPDATING
Simplified Explanation: The abstract describes techniques for updating a machine learning model based on input data, data categories, and ML training types.
- The device or system receives input data, determines the data category, and selects an ML training type.
- Using the input data, the selected ML training type is performed to update the ML model.
Key Features and Innovation:
- Utilizes input data to update a machine learning model.
- Selects ML training types based on data categories.
- Improves the performance of the ML model through updates.
Potential Applications:
- Natural language processing systems.
- Gesture recognition devices.
- Predictive analytics platforms.
Problems Solved:
- Outdated machine learning models.
- Inefficient model updating processes.
- Lack of adaptability in ML systems.
Benefits:
- Enhanced accuracy of machine learning models.
- Improved performance in data categorization.
- Increased efficiency in model updating.
Commercial Applications: The technology can be applied in various industries such as healthcare, finance, and e-commerce for optimizing data analysis and decision-making processes.
Prior Art: Readers can explore existing patents related to machine learning model updating techniques to understand the evolution of this technology.
Frequently Updated Research: Stay informed about the latest advancements in machine learning model updating techniques to ensure the implementation of cutting-edge strategies.
Questions about Machine Learning Model Updating: 1. How does the selection of ML training types impact the updating process of the model? 2. What are the potential challenges in implementing these techniques in real-world applications?
Original Abstract Submitted
techniques for updating a machine learning (ml) model are described. a device or system may receive input data corresponding to a natural or non-natural language (e.g., gesture) input. using a first ml model, the device or system may determine the input data corresponds to a data category of a plurality of data categories. based on the data category, the device or system may select a ml training type from among a plurality of ml training types. using the input data, the device or system may perform the selected ml training type with respect to a runtime ml model to generate an updated ml model.
