Weighted Finite-State Automata for Compact Dialog Flow Inference
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Solution Overview
Problem
Existing dialog inference techniques suffer from overgeneration, leading to infinite loops and inclusion of non-occurring dialog sequences, resulting in large and inefficient dialog flow models that require significant resources and cause user delays.
Innovation Solution
A dialog modeling system that utilizes non-deterministic finite state automata (NFSA) to represent dialog flows, generates a deterministic finite state automaton (DFSA) that exactly represents included flows, and employs a top-K carving algorithm to prune low-weight paths, focusing on the most likely dialog sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing dialog inference techniques are used to model all possible dialog sequences, then the dialog flow model becomes comprehensive and covers all potential user interactions, but the model size increases significantly leading to large resource requirements and user delays
Solution Approach 1:
The patent extracts and removes low-probability or non-occurring dialog sequences from the dialog flow model using statistical analysis of actual user interactions. By identifying and eliminating these unnecessary paths, the model size is reduced while retaining coverage of the most important dialog flows that actually occur in practice.
Solution Approach 2:
The patent changes the parameter of dialog sequence probability by using statistical data from actual user interactions to weight different dialog paths. This allows the system to prioritize high-probability sequences and eliminate low-probability ones, transforming the model from a comprehensive but bloated structure to a streamlined version that focuses on realistic dialog flows.
2Adaptability or versatility
If existing dialog inference techniques are used to include all possible dialog sequences, then the model is comprehensive, but infinite loops are generated causing user delays
Solution Approach 1:
The patent identifies and extracts infinite loops from the dialog flow model by analyzing the graph structure of dialog sequences. Using statistical data from actual user interactions, the system detects cycles that cannot be reached or are extremely unlikely to occur, and removes them to prevent user delays while maintaining coverage of valid dialog paths.
Solution Approach 2:
Instead of including all possible dialog sequences (excessive action), the patent applies partial action by selectively including only the most probable dialog sequences based on statistical analysis. This partial inclusion strategy avoids infinite loops and unnecessary complexity while still covering the essential dialog flows that users actually experience.
3Adaptability or versatility
If non-deterministic finite state automata are used to represent dialog flows, then all possible paths are captured, but the cognitive load and processing complexity increase
Solution Approach 1:
The patent extracts and removes low-probability paths from the non-deterministic finite state automata by analyzing actual user interaction data. This extraction process simplifies the automata structure by eliminating unnecessary states and transitions, reducing cognitive load and processing complexity while preserving the essential dialog flow representation.
Solution Approach 2:
The patent changes the complexity parameter of the finite state automata by using statistical probability data to prune the state space. By transforming the automata from a comprehensive non-deterministic structure to a simplified version weighted by actual usage frequency, the system reduces processing complexity while maintaining accurate dialog flow representation.
Data Source
AI summary
In some implementations, a system may receive non-deterministic finite state automata (NFSA) to represent a set of dialog flows associated with a human-machine interface. The system may generate a deterministic finite state automaton (DFSA) that includes a minimum set of states that represents all dialog flows included in the set of dialog flows represented in the NFSA and does not represent any dialog flows that are not included in the set of dialog flows represented in the NFSA. The system may traverse the DFSA to identify a set of K paths that have a highest total weight based on a weight assigned to each transition in the DFSA. The system may prune the DFSA to remove any states and any transitions that do not belong to the set of K paths. The system may generate an output related to one or more subsets of the set of K paths.