Context-Aware Messaging System for Dynamic Caller Information
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Solution Overview
Problem
Current messaging systems, such as email, SMS, and voicemail, lack the ability to convey dynamic, contextual information about a user's availability or activities to specific callers, as they rely on static messages that cannot differentiate between various contacts.
Innovation Solution
A messaging system that includes a network interface, presentation unit, monitoring unit, message generation unit, and output unit, which prompts users to grant access to data sources and set preferences for contacts, allowing the system to generate semantic contextual messages based on collected data when the user is unavailable, such as health metrics, activities, and environment, and send them to callers across the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If static messages (out of office, default text, voice message) are used to respond to callers, then the system is simple and easy to operate, but the caller cannot know what the user is currently doing and private messages cannot be conveyed selectively to specific contacts
Solution Approach 1:
The system pre-collects data from multiple sources (calendar, location, health sensors, social media) and pre-processes it into contextual information before a call occurs. This preliminary action enables the generation of personalized messages without requiring complex real-time processing during the call event.
Solution Approach 2:
The system automatically generates personalized voicemail messages without requiring user intervention. It self-services by collecting data from various sources, processing the information, and creating context-aware messages that reflect the user's current situation, eliminating the need for manual message composition.
2Adaptability or versatility
If the system collects data from multiple data sources to generate personalized messages, then contextual information is improved, but the complexity of data collection and processing increases
Solution Approach 1:
The system employs a multi-functional architecture where a single message generation engine can handle multiple data sources (calendar, location, health data, social media) and generate personalized messages for different contact types. This universal approach allows the system to adapt to various contacts while maintaining a unified processing framework.
Solution Approach 2:
The system segments contacts into different categories (family, friends, colleagues, etc.) and associates specific data sources with each segment. This segmentation allows selective data collection and processing tailored to each contact group, reducing unnecessary complexity while maintaining high adaptability.
3Reliability
If the system generates dynamic contextual messages automatically, then communication effectiveness is improved, but the processing time and computational resources increase
Solution Approach 1:
The system continuously pre-processes and updates contextual information from various data sources in the background before a call occurs. This preliminary action ensures that when a call is received, the message can be generated quickly by retrieving pre-processed information rather than collecting and analyzing raw data in real-time.
Solution Approach 2:
The system uses feedback loops to continuously monitor user activities and update contextual information dynamically. This feedback mechanism ensures message accuracy by constantly comparing actual user state with the generated message content, allowing for rapid adjustments without requiring complete re-processing.
Data Source
AI summary
A method for automatically generating a semantic contextual message is provided. The method includes: prompting a user to grant access to a plurality of data sources of the user and to a plurality of contacts of the user; prompting the user to set a preference for each contact listing which of the data sources are shareable with the corresponding contact; monitoring a network for an incoming communication from a caller to the user, and determining whether the user is available to receive the incoming communication; identifying one of the contacts associated with the incoming communication, collecting data from the data sources listed by the preference of the identified one contact, and generating a semantic contextual message based on the collected data, when it is determined that the user is not available; and outputting the semantic contextual message across the network to the caller.