18745654. AUTOMATED INPUT-DATA MONITORING TO DYNAMICALLY ADAPT MACHINE-LEARNING TECHNIQUES simplified abstract (Apple Inc.)

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 18745654 titled 'AUTOMATED INPUT-DATA MONITORING TO DYNAMICALLY ADAPT MACHINE-LEARNING TECHNIQUES

The abstract of this patent application describes systems and methods for updating a machine-learning model when a recently collected input data set is significantly different from the training data set used to train the model. The update may involve adjusting the weights of the model and/or retraining it.

• The innovation involves triggering updates to a machine-learning model based on detecting significant differences between input data distributions.
• The update process may include morphing weights and retraining the model to adapt to the new data distribution.
• The system can identify to which distribution individual data elements belong, allowing for precise updates to the model.
• This approach ensures that the machine-learning model remains accurate and effective in real-world applications.
• By continuously updating the model, it can better handle changes in data patterns and improve its performance over time.

Potential Applications: - This technology can be applied in various industries such as finance, healthcare, and e-commerce to enhance the accuracy of predictive models. - It can be used in autonomous vehicles to adapt to changing road conditions and environments. - Companies can utilize this innovation to improve customer recommendation systems and optimize marketing strategies.

Problems Solved: - Addresses the challenge of maintaining the accuracy of machine-learning models as data distributions change over time. - Solves the problem of outdated models that may not perform well on new data sets. - Provides a solution for ensuring the continuous improvement of machine-learning algorithms in dynamic environments.

Benefits: - Improved accuracy and performance of machine-learning models. - Enhanced adaptability to changing data patterns. - Increased efficiency in updating models without the need for manual intervention.

Commercial Applications: Title: "Dynamic Machine-Learning Model Updating Technology for Enhanced Predictive Accuracy" This technology can be commercially used in industries such as finance, healthcare, and autonomous vehicles to improve predictive modeling and decision-making processes. It can also benefit companies seeking to optimize their data-driven strategies and enhance customer experiences.

Questions about Dynamic Machine-Learning Model Updating Technology: 1. How does this technology compare to traditional methods of updating machine-learning models? This technology differs from traditional methods by automatically triggering updates based on detected differences in data distributions, ensuring the model remains accurate over time.

2. What are the potential implications of using this technology in industries with rapidly changing data patterns? Implementing this technology in industries with dynamic data patterns can lead to more accurate predictions and better decision-making processes, ultimately improving operational efficiency and customer satisfaction.

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.