Mobile Device Call Automation Using Context Data
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Solution Overview
Problem
Users of mobile computing devices may be unavailable to answer calls due to activities like driving or meetings, necessitating a method to improve safety and automation of user interface operations.
Innovation Solution
A mobile computing device determines the unavailability of a user by accessing calendar, location, and social media data, and automatically provides information to the calling user, such as the user's location or estimated time of arrival, using machine learning to infer informational needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the user manually answers calls, then communication quality is maintained, but safety is compromised when the user is driving or engaged in other activities
Solution Approach 1:
The mobile computing device automatically determines user availability by monitoring calendar data, location data, and application usage without requiring user input. The device autonomously decides whether to answer calls, provide information to callers, or defer communication, enabling safety-critical operations during driving or meetings while maintaining communication functionality.
Solution Approach 2:
The system pre-determines user availability status by continuously monitoring calendar appointments, location context, and active applications before calls occur. This preliminary assessment enables automatic call handling decisions that prioritize safety without requiring real-time user intervention during critical moments.
2Reliability
If the mobile device automatically handles calls when the user is unavailable, then safety is improved, but the complexity of the device increases
Solution Approach 1:
The mobile computing device leverages existing multi-functional components (calendar application, GPS location services, accelerometer, microphone) already present in modern smartphones. By integrating call handling automation with these universal functions, the system avoids adding dedicated hardware complexity while achieving safety improvements through software coordination of existing capabilities.
Solution Approach 2:
The system introduces an intelligent software intermediary layer that coordinates between existing device components (calendar, location, sensors) and call handling functions. This mediator processes data from multiple sources to determine user availability and automatically manages call responses, reducing the need for complex dedicated automation hardware.
3Measurement precision
If the device accesses multiple data sources to determine user availability, then accuracy of availability detection is improved, but the loss of information privacy increases
Solution Approach 1:
The system processes availability determination locally on the mobile computing device rather than transmitting raw data to external servers. Calendar data, location information, and sensor readings are analyzed in-place to generate availability status, minimizing external data transmission and preserving user privacy while maintaining detection accuracy through local multi-source integration.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving, at a mobile computing device that is associated with a called user, a call from a calling computing device that is associated with a calling user; in response to receiving the call, determining, by the mobile computing device, that data associated with the called user indicates that the called user will not respond to the call; in response to determining that the called user will not respond to the call, inferring, by the mobile computing device, an informational need of the calling user; and automatically providing, from the mobile computing device to the calling computing device, information associated with the called user and that satisfies the inferred informational need of the calling user.