18383279. SYSTEM AND METHOD FOR CLASSIFYING TASK simplified abstract (3M INNOVATIVE PROPERTIES COMPANY)

SYSTEM AND METHOD FOR CLASSIFYING TASK

Organization Name

3M INNOVATIVE PROPERTIES COMPANY

Inventor(s)

John W. Henderson of St. Paul MN (US)

Sophia S. Liu of Denver CO (US)

Andrew W. Long of Woodbury MN (US)

Jordan J.W. Craig of White Bear Lake MN (US)

SYSTEM AND METHOD FOR CLASSIFYING TASK - A simplified explanation of the abstract

This abstract first appeared for US patent application 18383279 titled 'SYSTEM AND METHOD FOR CLASSIFYING TASK

The patent application describes a method of classifying a task using images and audio signals.

• Obtain a plurality of images and an audio signal for a predetermined period of time.
• Classify the images using a trained machine learning model to generate class probabilities and labels.
• Classify the audio signal using another trained machine learning model to generate class probabilities and labels.
• Determine the task based on a merging algorithm that combines the class probabilities and labels from the images and audio signal.

Potential Applications: - Task classification in various industries such as security, healthcare, and manufacturing. - Automated monitoring and analysis of activities based on visual and audio data.

Problems Solved: - Efficient and accurate task classification based on multiple sources of data. - Reduction of human error in manual task classification processes.

Benefits: - Improved task classification accuracy. - Time-saving automation of task classification processes. - Enhanced monitoring and analysis capabilities.

Commercial Applications: Title: Automated Task Classification System This technology can be used in industries such as security, healthcare, and manufacturing for automated monitoring and analysis of tasks based on visual and audio data. The system can improve efficiency and accuracy in task classification processes, leading to enhanced productivity and decision-making.

Questions about the technology: 1. How does the merging algorithm combine the class probabilities and labels from the images and audio signal? - The merging algorithm uses a mathematical process to combine the probabilities and labels from the image and audio classifications to determine the final task classification. 2. What are the potential limitations of using machine learning models for task classification? - Some potential limitations include the need for large amounts of training data and potential biases in the models that could affect the accuracy of task classification results.

Original Abstract Submitted

A method of classifying a task includes obtaining a plurality of images via an image capturing device and an audio signal via an audio sensor for a predetermined period of time. The method further includes classifying, via a first trained machine learning model, the plurality of images to generate a list of first class probabilities and a list of first class labels. The method further includes classifying, via a second trained machine learning model, the audio signal to generate a list of second class probabilities and a list of second class labels. The method further includes determining, via a merging algorithm, a list of third class probabilities and a list of third class labels based on the lists of first and second class probabilities. The method further includes determining the task corresponding to the predetermined period of time based on the list of third class probabilities.