Reachability Engine Aggregating Status Data for Communication Prediction
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Solution Overview
Problem
Existing systems for determining reachability in computer and telecommunications networks rely on limited presence information, which indicates device availability but not user availability, and do not accurately predict the likelihood of successful communication attempts.
Innovation Solution
A reachability engine within a reachability server that calculates the probability of a user being available for communication by aggregating direct, indirect, and historical status data, including presence states, routing rules, calendar events, and communication patterns, to provide a comprehensive assessment of reachability and suggest optimal communication modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If presence information is used to determine user availability, then device availability can be indicated, but user availability cannot be accurately predicted
Solution Approach 1:
The patent combines multiple data sources including direct status data (presence information), indirect status data (calendar events, routing rules), and historical status data (communication patterns) to create a comprehensive reachability prediction. This merging of diverse information sources resolves the contradiction by maintaining device availability indication while adding user availability prediction capability through aggregated analysis.
Solution Approach 2:
The patent introduces a reachability engine as an intermediary component that processes and analyzes multiple status data sources. This intermediary transforms raw presence information and auxiliary data into predicted reachability probabilities, enabling accurate user availability prediction without losing the fundamental device availability information from presence data.
2Measurement precision
If multiple status data sources are aggregated to improve reachability prediction, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the reachability prediction system into distinct functional modules: a reachability engine that receives and processes multiple status data sources, and a separate component that provides suggestions based on predictions. This segmentation manages complexity by organizing the data aggregation process into manageable, specialized components rather than a monolithic system.
Solution Approach 2:
The reachability engine serves multiple functions: it aggregates direct status data, indirect status data, and historical status data; analyzes communication patterns; and generates reachability predictions. This multi-functionality reduces overall system complexity by consolidating diverse operations into a single versatile component rather than requiring separate systems for each function.
Data Source
AI summary
A reachability engine can determine a reachability for a specified party in response to a request for reachability. The reachability can characterize a probability that the specified party will answer a request for communication using a specified mode of communication at a given time. The reachability can be based on status data that characterizes an aggregate of at least two of direct status data, indirect status data and historical status data for the specified party.