SUGGESTING EXECUTABLE ACTIONS IN RESPONSE TO DETECTING EVENTS

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SUGGESTING EXECUTABLE ACTIONS IN RESPONSE TO DETECTING EVENTS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240267453 titled 'SUGGESTING EXECUTABLE ACTIONS IN RESPONSE TO DETECTING EVENTS

Simplified Explanation: The patent application describes systems and processes for suggesting user actions through an electronic device based on predefined events detected in the user's day.

The suggested actions are provided in response to detecting specific events encoded in signals generated by the electronic device.
Machine learning is used to train a model to associate actions with these events, allowing for the provision of suggested actions based on contextual conditions.

Key Features and Innovation:

Detection of predefined events in the user's day through electronic signals.
Training of a model using machine learning to associate actions with detected events.
Provision of suggested user actions based on contextual conditions and previous interactions with the device.

Potential Applications: This technology could be applied in various fields such as personal productivity tools, smart home devices, and healthcare monitoring systems.

Problems Solved: This technology addresses the need for personalized and contextually relevant suggestions for user actions based on their daily activities.

Benefits:

Enhanced user experience through personalized suggestions.
Improved efficiency and productivity by providing timely and relevant actions.
Potential for automation of routine tasks based on user behavior.

Commercial Applications: This technology could have commercial applications in the development of smart devices, personal assistant applications, and data analytics platforms for user behavior analysis.

Prior Art: Prior research in the fields of machine learning, user behavior analysis, and smart device technology may provide insights into similar approaches to suggesting user actions based on detected events.

Frequently Updated Research: Researchers may be exploring advancements in machine learning algorithms for more accurate association of user actions with detected events, as well as improvements in contextual understanding of user behavior.

Questions about the Technology: 1. How does the machine learning model differentiate between different types of events and suggest appropriate actions? 2. What are the privacy implications of monitoring and analyzing electronic signals to detect user behavior patterns?

Original Abstract Submitted

systems and processes for providing, via an electronic device, suggested user actions. the suggested actions are provided in response to detecting an occurrence of a predefined event occurring in the user's day. the occurrence of the anchor is encoded in signals generated by the electronic device. the occurrence of the anchor is detectable via monitoring and analysis of electronic signals. based on the user's previous interactions with the device, the occurrence of the anchor is indicative of user behavior and/or action taken in response to the anchor. machine learning (ml) is employed to train an anchor model to associate actions taken in response to anchor occurrences. the trained anchor model is employed to detect anchors and provide suggested actions in response to the detected anchor occurrence. the suggested action is based on a type of anchor occurrence and contextual conditions of the anchor occurrences.