Unified Trie Filtering for Real-Time Bot Flow Visualization
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Solution Overview
Problem
Existing Trie data structures for bot flow visualization in contact centers face scalability issues due to computational intensity when filtering and querying capabilities are required, leading to significant processing overhead and operational costs.
Innovation Solution
A scalable solution using a single 'big picture' Trie data structure for each organization, integrating with bot flows and event attributes, reduces processing operations and maintains reasonable latency, allowing for diverse and customizable functionalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate Trie structures are created for each query event to enable filtering and querying capabilities, then measurement precision and adaptability improve, but device complexity and processing overhead increase significantly
Solution Approach 1:
The patent implements a single universal Trie data structure that can handle multiple bot flows and various filtering/querying operations simultaneously. Instead of creating separate Trie structures for different query types, one Trie structure is designed to accommodate diverse querying needs through parameter-based filtering, reducing overall system complexity while maintaining full functionality.
Solution Approach 2:
The patent merges multiple filtering and querying capabilities into a single Trie data structure. By combining what would traditionally require separate data structures into one unified structure, the system reduces processing overhead and complexity while maintaining the ability to perform various filtering and querying operations efficiently.
2Adaptability or versatility
If multiple Trie structures are maintained for different bot flows and events, then adaptability improves, but loss of energy and processing costs increase
Solution Approach 1:
The single Trie data structure is designed to be universally applicable across multiple bot flows and event types. It maintains adaptability and versatility by accommodating different filtering and querying requirements without requiring separate structures, thereby reducing the energy and processing costs associated with maintaining multiple Trie instances.
Solution Approach 2:
The patent utilizes parameter changes to achieve adaptability within a single Trie structure. By modifying filtering parameters and query conditions rather than creating separate data structures, the system maintains customizable functionalities while significantly reducing processing and operational costs.
3Measurement precision
If comprehensive filtering and querying is implemented on the complete Trie data structure, then measurement precision improves, but speed and productivity decrease due to computational intensity
Solution Approach 1:
The patent extracts and applies filtering criteria directly to the single Trie data structure without requiring complete traversal or processing of the entire structure. By taking out only the relevant portions that match the filtering parameters, the system maintains high filtering accuracy while significantly improving processing speed and reducing computational intensity.
4Device complexity
If a single Trie data structure is used for all bot flows, then device complexity reduces, but adaptability for specific filtering queries may be limited
Solution Approach 1:
The single Trie data structure is designed with universal capabilities to handle diverse filtering and querying operations. It maintains adaptability and flexibility by supporting various filtering criteria and query types within the same structure, eliminating the need for multiple specialized Trie structures while preserving full functionality.
Solution Approach 2:
The patent implements dynamic filtering capabilities within the single Trie structure, allowing the system to adapt to different querying requirements in real-time. The dynamic nature of the filtering parameters enables the structure to be flexible and versatile without requiring multiple static Trie structures.
Data Source
AI summary
A method for filtering a Trie data structure for real-time bot flow visualization according to an embodiment includes receiving data associated with the Trie data structure, wherein the Trie data structure includes data for a plurality of contact center bot flows of an organization, and wherein each bot flow of the plurality of contact center bot flows includes at least one event, receiving a user selection associated with filtering the Trie data structure for modified visualization of the Trie data structure, performing a search of the Trie data structure to identify events of the Trie data structure to be deleted, deleting the identified events to generate an updated Trie data structure, updating event attributes of remaining events of the updated Trie data structure in response to deleting the identified events, and providing the updated Trie data structure for real-time bot flow visualization.