Predictive Mobile Event Automation via Geospatial Monitoring
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Solution Overview
Problem
Users face challenges in efficiently managing recurring tasks on mobile devices due to factors like fatigue and lack of time, leading to suboptimal adjustments in settings such as temperature and communication status, which are often manually controlled.
Innovation Solution
A method and system that monitor a user's geospatial location and interactions with mobile devices, predicting future events and enabling automatic actions on connected devices, such as temperature control or lighting, based on analyzed behavioral patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual control of settings such as temperature and communication status is performed, then the user can adjust settings according to personal preference, but the user experiences fatigue, stress, and lack of time leading to suboptimal adjustments
Solution Approach 1:
The system performs preliminary actions by monitoring user behavior patterns and geospatial location data in advance, predicting future events before they occur. This allows the system to automatically execute actions (such as adjusting temperature or communication status) before the user would need to manually control them, eliminating the time loss and fatigue associated with manual adjustment while maintaining optimal setting quality.
2Productivity
If automatic prediction and action performance is implemented, then manual intervention is reduced and efficiency is improved, but the system complexity increases due to monitoring and prediction mechanisms
Solution Approach 1:
The patent introduces a server as an intermediary component that handles the complex monitoring, data analysis, and prediction functions. This externalizes the computational complexity from the mobile device itself, allowing the device to maintain simplicity while still benefiting from advanced predictive capabilities. The server acts as a mediator between the user's behavior data and the automated actions, managing the system complexity externally.
3Measurement precision
If the system monitors geospatial location and user interactions continuously, then prediction accuracy is improved, but energy consumption and data processing requirements increase
Solution Approach 1:
The system applies partial monitoring by selectively tracking specific behavioral patterns and geospatial locations that are most relevant to prediction accuracy, rather than continuously monitoring all possible data points. This approach achieves sufficient prediction accuracy while reducing the energy consumption and data processing requirements associated with exhaustive continuous monitoring.
Data Source
AI summary
A method includes monitoring a geospatial location of a user of a mobile device having a processor communicatively coupled to a memory through the mobile device, date stamping and time stamping the geospatial location of the user through the mobile device, and monitoring, through a server having another processor communicatively coupled to another memory and/or the mobile device, an interaction of the user with the mobile device and/or a device communicatively coupled to the server based on the geospatial location of the user. The method also includes predicting, through the server and/or the mobile device, an event related to the mobile device and/or the device based on the monitoring of the interaction of the user therewith, and enabling, through the server and/or the mobile device, automatic performance of an action on the mobile device and/or the device on behalf of the user in accordance with the prediction of the event.