Automated Action Rule Selection for Smart Devices
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Solution Overview
Problem
Existing smart device automation systems, such as IFTTT, burden users with the manual specification of action rules, which are not personalized and often redundant, leading to user frustration and inefficiency.
Innovation Solution
The development of a rule selector processing method that determines preferred action rules based on domain information, retrieves activity patterns, and selects candidate action rules using metrics like confidence, specificity, and total action coverage to provide users with a curated set of rules for automation, allowing for interactive selection and modification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually specify action rules in existing automation systems, then the system provides intelligibility and control, but the user burden increases and efficiency decreases
Solution Approach 1:
The system performs self-service by automatically generating action rules from observed user behavior patterns. The rule generator analyzes user actions and context data to autonomously create personalized automation rules without requiring manual user specification, thereby reducing user burden while maintaining high automation efficiency
Solution Approach 2:
The system performs preliminary action by pre-generating candidate action rules based on observed user patterns before the user needs them. The rule generator continuously monitors user behavior and prepares personalized automation rules in advance, making them ready for immediate deployment when conditions are met
2Adaptability or versatility
If existing systems provide generic action rules, then the rules are easy to implement, but they lack personalization and cause user frustration
Solution Approach 1:
The system applies local quality by tailoring action rules to individual user characteristics, behaviors, and preferences. The rule generator analyzes personal user patterns and generates customized rules specific to each user's needs and context, ensuring high adaptability and personalization while managing complexity through focused analysis
3Reliability
If the system presents many candidate action rules to users, then comprehensive coverage is achieved, but redundancy and user annoyance increase
Solution Approach 1:
The system extracts only the most relevant and high-value action rules from the set of generated candidates. The rule selector applies selection criteria to identify and extract top-priority rules that provide the greatest automation benefit with minimal redundancy, presenting a curated subset to users that maintains comprehensive coverage while improving user experience
Data Source
AI summary
A method for action automation includes determining, using an electronic device, an action based on domain information. Activity patterns associated with the action are retrieved. For each activity pattern, a candidate action rule is determined. Each candidate action rule specifies one or more pre-conditions when the action occurs. One or more preferred candidate action rules are determined from multiple candidate action rules for automation of the action.