Hierarchical Expert Tree for SIP Group Communication
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Solution Overview
Problem
Current group communication systems rely on 'flat lists' that provide equal connectivity to all users, leading to inefficient network usage and overhead, especially when dealing with a large number of expert users, as they do not effectively optimize communication for specific tasks or dynamically adjust based on user needs.
Innovation Solution
A hierarchical expert tree system that directs queries to the most relevant experts, allowing for dynamic formation and adjustment of communication paths based on user-specified criteria, optimizing network usage and reducing overhead by propagating messages only as needed, and enabling scalable communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a flat list structure is used for group communication, then all users receive equal connectivity, but network overhead increases and scalability decreases
Solution Approach 1:
The patent segments the flat group communication into a hierarchical tree structure with multiple levels. Instead of all users being at the same level (flat list), users are organized into parent-child relationships where root nodes represent group leaders and child nodes represent members. This segmentation reduces the communication overhead by allowing targeted queries to specific subgroups rather than broadcasting to all users equally.
Solution Approach 2:
The patent introduces a hierarchical dimension to the previously flat communication structure. By adding the vertical level dimension (root, intermediate, leaf nodes) to the horizontal user dimension, the system creates a two-dimensional communication space. This allows queries to be routed efficiently through specific paths in the hierarchy rather than requiring all-users connectivity, reducing network overhead while maintaining accessibility.
2Ease of manufacture
If a flat list structure is used for group communication, then implementation is simple, but scalability is limited
Solution Approach 1:
The hierarchical tree structure segments the user base into manageable subgroups at different levels. This segmentation allows the system to scale by adding more levels or more users at existing levels without requiring complete reconfiguration. Each subtree can be managed independently, making the system more adaptable to growing user bases while maintaining implementation simplicity through modular design.
Solution Approach 2:
The patent implements dynamic tree construction where the hierarchical structure can be automatically generated and adjusted based on user profiles, query types, and communication needs. This dynamic adaptation allows the system to scale efficiently by creating appropriate hierarchical levels and relationships on-demand, rather than requiring a fixed, pre-defined structure that limits scalability.
3Reliability
If queries are directed to all users in a flat list, then no expert is missed, but communication efficiency decreases
Solution Approach 1:
The patent applies local quality by directing queries to specific regions of the hierarchical tree based on the query type and user profiles. Instead of uniformly broadcasting to all users, the system identifies relevant subgroups or individual experts within the hierarchy and routes queries locally to those specific nodes. This maintains reliable expert coverage by targeting the right people while significantly improving communication efficiency by avoiding unnecessary broadcasts to unrelated users.
Solution Approach 2:
The hierarchical tree structure acts as an intermediary between the query source and the expert users. Root nodes and intermediate nodes serve as mediators that receive queries, process them according to user profiles and query characteristics, and route them to the appropriate child nodes or leaf nodes representing expert users. This intermediary layer ensures no relevant expert is missed while improving efficiency by preventing direct queries to all users.
4Loss of energy
If a hierarchical expert tree is implemented, then network overhead is reduced, but system complexity increases
Solution Approach 1:
The patent implements self-service by enabling the hierarchical tree structure to be automatically constructed and maintained based on user profiles and query characteristics. The system autonomously determines the appropriate hierarchical relationships, routes queries through the tree, and adjusts the structure as needed without requiring manual configuration or complex external management. This automation reduces network overhead while managing system complexity through intelligent self-organization.
Data Source
AI summary
A method, apparatus, and electronic device for hierarchical communications are disclosed. A connection interface 1260 may receive a query. A processor 1210 may select an initial expert from the hierarchical expert tree based upon the query and direct the query towards the initial expert. A session initiation protocol server 418 may generate a hierarchical expert tree from an expert pool.