User Availability Derivation from Context and Responses
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Solution Overview
Problem
Current methods for determining user availability in telecommunications systems are inadequate as they either burden the user with manual status updates or rely on coarse presence information, failing to accurately differentiate between different communication scenarios and contexts.
Innovation Solution
A system that derives user availability by analyzing past responses to communication requests under various contexts, using a service node to monitor and learn user behavior, providing an accurate approximation of availability for timely communication without requiring explicit user action.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If presence information is used to indicate user availability, then some level of information about user availability is provided, but the information is coarse and not accurate enough to differentiate between different communication scenarios
Solution Approach 1:
The patent segments availability indication into multiple levels: basic presence information (online/offline), detailed context information (calendar status, location, device state), and communication-specific availability (voice call availability, messaging availability). This segmentation allows the system to provide appropriately detailed information without overwhelming complexity.
Solution Approach 2:
The patent adds temporal dimension by analyzing historical communication patterns and response times, and contextual dimension by incorporating calendar events, location data, and device state. This multi-dimensional approach transforms coarse presence information into refined availability indicators without proportionally increasing system complexity.
2Measurement precision
If users manually set availability status, then accurate user availability information can be provided, but users must remember to set and update their status
Solution Approach 1:
The system automatically collects context information from multiple sources (calendar applications, location services, device state sensors) and uses machine learning algorithms to infer user availability without requiring manual input. The system serves itself by gathering data and making intelligence decisions about availability status.
Solution Approach 2:
The system incorporates feedback loops where user responses to communication requests (answering calls, responding to messages) are analyzed to continuously refine availability predictions. This feedback mechanism improves accuracy over time without requiring users to manually update their status.
3Reliability
If personal agent call screening and routing is used, then user availability can be determined, but no direct feedback is provided to the requestor about the user's availability
Solution Approach 1:
The patent introduces an availability service as an intermediary between the communication requestor and the target user. This service aggregates information from multiple sources (presence information, context data, historical patterns) and provides comprehensive availability feedback to requestors, including predicted response times and recommended communication channels.
Solution Approach 2:
The availability service performs multiple functions: it determines user availability, predicts response times, recommends optimal communication channels, and provides context information about user state. This multi-functional approach ensures reliable availability determination while providing rich feedback information to requestors.
4Adaptability or versatility
If a single availability status message is used, then user availability can be indicated, but it cannot distinguish between different communication types (e.g., phone calls vs. email)
Solution Approach 1:
The patent applies local quality by providing different availability indicators for different communication channels. A user may show high availability for messaging but low availability for voice calls, based on their current context (e.g., in a meeting, driving, watching TV). This channel-specific availability information is derived from analyzing user responses to different types of communication requests.
Solution Approach 2:
The system dynamically adjusts availability indicators based on real-time context changes and communication type. Availability status is not static but evolves as user context changes (calendar events start/end, location changes, device state changes), providing precise and adaptable availability information for each communication scenario.
Data Source
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AI summary
Disclosed herein are methods, systems, and computer readable media for deriving user availability from user context and user responses to communications requests. According to an aspect, a method includes monitoring communications requests. Each of the communications requests may include a request for conducting a communication session between a requesting user and a requested user. The method may also include monitoring user responses to the communications requests. Further, the method may include determining user contexts for the requested user corresponding to each of the communications requests communicated to the requested user. The method may include determining an indication of user availability for the requested user based on the user responses and the corresponding user contexts. The method may also include providing the indication of user availability to one or more authorized users.