Ranking Communication Paths via Task Data Graph
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Solution Overview
Problem
Users face difficulties in determining the best communication method to contact other users across various social networking and productivity applications for specific tasks, as they often have multiple accounts with different usage frequencies and varying communication types, making it challenging to select the appropriate platform for reaching contacts effectively.
Innovation Solution
A system that generates a graph combining social networking and productivity application data to identify and rank paths for communication based on user relationships and task data, recommending the most suitable communication type for connecting with specific contacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users maintain multiple social networking accounts across different platforms, then the versatility of communication channels increases, but the complexity of selecting the appropriate communication method worsens
Solution Approach 1:
The patent segments the complex communication selection problem into distinct components: (1) gathering communication history data from multiple sources, (2) analyzing task-type associations, ( (3) generating ranked path recommendations. This segmentation transforms the overwhelming whole into manageable discrete steps that systematically resolve the selection complexity while preserving multi-platform versatility
Solution Approach 2:
The patent introduces an intermediary system (the graph engine and task data structure) that mediates between the user's multiple communication accounts and the decision-making process. This intermediary automatically analyzes communication patterns, identifies task associations, and presents ranked recommendations, eliminating the need for users to manually evaluate each communication option across platforms
2Measurement precision
If users manually track communication effectiveness across multiple platforms, then the precision of communication selection improves, but the time and effort required worsens
Solution Approach 1:
The patent performs preliminary action by automatically gathering and analyzing communication data from multiple platforms in advance, building comprehensive task data structures that capture communication effectiveness metrics. This preliminary automated analysis eliminates the need for users to manually track communications in real-time, as the system has already processed and organized the data when recommendations are needed
Solution Approach 2:
The system implements self-service by automatically monitoring its own communication data across platforms, generating task associations, and computing effectiveness metrics without user intervention. The system serves itself by gathering data from integrated accounts, analyzing patterns, and producing recommendations autonomously, freeing users from manual tracking efforts
3Measurement precision
If comprehensive communication data is collected from multiple sources, then the accuracy of contact recommendations improves, but the quantity of data to process worsens
Solution Approach 1:
The patent extracts only the essential and relevant features from comprehensive communication data, focusing on task-type associations and communication effectiveness metrics while filtering out redundant information. The graph engine extracts specific patterns (task-contact-communication type relationships) from the raw data, converting vast amounts of unstructured communication logs into condensed, actionable insights that maintain recommendation accuracy without requiring processing of all raw data
Data Source
AI summary
Data from social networking applications and other applications that can be used to communicate are combined for a user to generate a graph of the various relationships that the user has with other users in the social networking applications and other applications. In addition, the behaviors of each user with respect to communicating through the various social networking applications and other applications are monitored to generate task data that describes user preferences for communicating using each social networking application or other application for different tasks. At a later time, when a user is looking to connect with another user for an indicated task such as networking, the graph can be used to recommend paths to other users in the various social networking applications and other applications, and the generated task data can be used to rank the recommended paths based on the indicated task.