Intent-Based Reminder System Using WLAN Geofence Traversal
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Solution Overview
Problem
Existing reminder systems for computing devices are inadequate for users who need to remember tasks related to entering or departing specific locations, as they either rely on time-based or location-based reminders, which may not be suitable for situations where the user's intent to leave or arrive is not tied to a specific time or location.
Innovation Solution
An intent-based reminder system that uses wireless network identifiers and signal strengths to track a user's path and infer their intent to depart or enter a location, allowing for the setup of geofences along a path to trigger reminders when the user is leaving or arriving, thereby providing a more accurate and efficient reminder mechanism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If time-based or location-based reminders are used, then users can receive reminders at specific times or places, but these reminders are not suitable for situations where the user's intent to leave or arrive is not tied to a specific time or location
Solution Approach 1:
The system changes the detection parameter from fixed time/location thresholds to dynamic path-based geofence traversal patterns. By monitoring sequential geofence breaches along a trained path, the system adapts to various departure/arrival scenarios without requiring specific time or location constraints, thus improving both versatility and reliability
Solution Approach 2:
The system dynamically adjusts reminder triggering based on real-time path traversal detection. Instead of static time/location-based triggers, the system continuously monitors geofence breaches and determines intent based on the sequence and pattern of traversals, making the reminder system adaptable to different user behaviors while maintaining accuracy
2Measurement precision
If the intent-based reminder system processes wireless network identifiers and signal strengths continuously, then the system can accurately infer user intent, but this increases processing requirements and battery consumption
Solution Approach 1:
The system performs preliminary path training during which geofences are established and wireless network characteristics are captured in advance. This pre-processing allows the system to use stored geofence identifiers and signal strength thresholds during actual intent detection, reducing real-time processing requirements and battery consumption while maintaining detection accuracy
Solution Approach 2:
The system segments the continuous monitoring task into discrete geofence-based detection units. By dividing the monitoring space into distinct geofences with pre-defined characteristics, the system only needs to compare current location against stored geofence profiles rather than continuously analyzing all wireless signals, thus reducing processing load while maintaining precision
Data Source
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AI summary
Intent-based reminders are provided. A user (102) is enabled to initiate a reminder request based on an intent to enter or leave a given location. In a geofence training process, a plurality of geofences (122) are created for plotting a path (120) and subsequently tracking the user's traversal of the path for inferring the user's intent to depart or enter the location. A signal strength of a WLAN (126) is recorded at each geofence. As the user traverses the path, a determination is made as to whether a predetermined percentage of the geofences is triggered in a sequential order by comparing the signal strength of the WLAN against the recorded WLAN signal strengths at the geofences. In some examples, signal strengths of neighboring WLANs are recorded and used to filter out false triggers. When a determination is made that the user's intent is to depart or enter the location, a reminder (106) is provided.