Scorable Tree Event Dispatch for Dynamic Conversational Context
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Solution Overview
Problem
Current chatbots struggle to effectively handle ambiguous user inputs and changes in conversational context, leading to poor performance in dynamic conversations.
Innovation Solution
A scorable tree mechanism is employed to dispatch incoming events based on scorable functions, which determine a score for each event to identify its relevance to different conversational tracks, allowing for dynamic tracking and diversion within conversations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If rigid conversational tracks are used, then the chatbot maintains structured processing flow, but it performs poorly in responding to changes in conversational direction or context
Solution Approach 1:
The patent implements dynamic conversational tracks that can be modified at runtime based on incoming events. The system allows adding, removing, and modifying conversational tracks during execution, enabling the chatbot to adapt to changing conversational directions and contexts while maintaining structured processing through the event-driven framework.
Solution Approach 2:
The conversational processing is divided into discrete events and event handlers. Each conversational track consists of sequential events that can be independently processed, allowing the system to handle multiple conversational paths simultaneously and switch between them based on event scoring without increasing overall system complexity.
2Adaptability or versatility
If multiple conversational tracks are maintained, then the chatbot can handle diverse user inputs, but the complexity of managing and selecting appropriate tracks increases
Solution Approach 1:
The system uses scorable functions that evaluate incoming events against conversational tracks and generate scores. This feedback mechanism automatically determines event relevance by comparing event characteristics with track requirements, eliminating the need for complex manual determination while handling diverse user inputs across multiple tracks.
Solution Approach 2:
The event scoring system automatically selects appropriate conversational tracks by evaluating events against predefined criteria without requiring external intervention. The scorable functions self-assess event relevance and route events to appropriate tracks, reducing the complexity of track management while maintaining high adaptability.
3Measurement precision
If dynamic event processing with scorable functions is implemented, then the chatbot accurately interprets user intents, but the computational overhead increases
Solution Approach 1:
The system evaluates events against conversational tracks using scorable functions, but only processes events that meet certain scoring thresholds. This partial action approach maintains high user intent interpretation accuracy by focusing computational resources on relevant events while avoiding unnecessary processing of low-scoring events, thereby reducing overall energy consumption.
Data Source
AI summary
Methods, systems, and computer program products for dispatching an incoming event are described. A scorable tree is obtained and the incoming event is submitted to a root node of the scorable tree for processing. The incoming event is augmented with scoring data generated by the root node to create a scoring event and the scoring event is iteratively passed from each parent node of the scorable tree to each corresponding child node of the scorable tree for further processing until each leaf node of each branch of the scorable tree is reached, wherein each child node augments an incoming scoring event with additional scoring data and each leaf node generates an intermediate scoring event. Each intermediate scoring result is iteratively passed from each child node of the scorable tree to each corresponding parent node, where each child node produces a scoring result, the scoring result comprising an identification of a path to one or more selected child nodes and final scoring information based on data received from one or more corresponding child nodes.