Apple inc. (20240338612). AUTOMATED INPUT-DATA MONITORING TO DYNAMICALLY ADAPT MACHINE-LEARNING TECHNIQUES simplified abstract

AUTOMATED INPUT-DATA MONITORING TO DYNAMICALLY ADAPT MACHINE-LEARNING TECHNIQUES

Organization Name

apple inc.

Inventor(s)

Moises Goldszmidt of Palo Alto CA (US)

Anatoly D. Adamov of Palo Alto CA (US)

Juan C. Garcia of San Francisco CA (US)

Julia R. Reisler of Kirkland WA (US)

Timothy S. Paek of Mercer Island WA (US)

Vishwas Kulkarni of Redmond WA (US)

Yu-Chung Hsiao of Millbrae CA (US)

Pavan Chitta of Cupertino CA (US)

AUTOMATED INPUT-DATA MONITORING TO DYNAMICALLY ADAPT MACHINE-LEARNING TECHNIQUES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240338612 titled 'AUTOMATED INPUT-DATA MONITORING TO DYNAMICALLY ADAPT MACHINE-LEARNING TECHNIQUES

Simplified Explanation: The patent application describes a system and method for updating a machine-learning model when a recently collected input data set is significantly different from the training input data set used to train the model.

Key Features and Innovation:

• Triggers an update to the machine-learning model based on detecting differences in data distributions.
• Classifier identifies to which distribution individual data elements belong.
• Update may include morphing weights used by the model and/or retraining the model.

Potential Applications: This technology can be applied in various fields such as finance, healthcare, marketing, and more where machine-learning models are used for decision-making based on changing data distributions.

Problems Solved: This technology addresses the challenge of maintaining the accuracy and relevance of machine-learning models when faced with evolving data distributions.

Benefits:

• Ensures machine-learning models stay up-to-date with changing data patterns.
• Improves the performance and reliability of machine-learning models in dynamic environments.

Commercial Applications: The technology can be utilized in industries such as financial services for fraud detection, healthcare for patient diagnosis, and e-commerce for personalized recommendations, enhancing decision-making processes and overall efficiency.

Prior Art: Readers interested in prior art related to this technology can explore research papers, patents, and industry publications on machine-learning model updates based on changing data distributions.

Frequently Updated Research: Researchers in the field of machine learning and artificial intelligence are constantly exploring new methods and techniques to improve the adaptability and accuracy of machine-learning models in response to changing data distributions.

Questions about Machine Learning Model Updates: 1. How does this technology compare to traditional methods of updating machine-learning models? 2. What are the potential challenges in implementing this system across different industries?

Original Abstract Submitted

systems and methods are disclosed for triggering an update to a machine-learning model upon detecting that a distribution of particular (e.g., recently collected) input data set is sufficiently different from a distribution training input data set used to train the model. the distributions may be determined to be sufficiently different when a classifier can identify to which distribution individual data elements belong (e.g., to at least a predetermined degree). an update to the machine-learning model can include morphing weights used by the model and/or retraining the model.