Probabilistic Query State Modification for Dialogue Intent
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Solution Overview
Problem
Current query processing techniques struggle to accurately identify user intent and query domains, leading to unspecialized results and inefficient interaction in query dialogues, especially when handling natural-language inputs and complex query state modifications.
Innovation Solution
The implementation of a query processing system that uses a Bayesian classifier to evaluate query state modifications, infer query domains and intents, and navigate query states, enabling more conversational and accurate interactions by calculating probabilities for query term additions, substitutions, and intent changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a generalized keyword-based query processing approach is used, then the system can handle a broad range of queries, but it fails to accurately identify specialized query domains and user intent
Solution Approach 1:
The system applies different processing approaches based on the identified query domain. Instead of using a uniform keyword-matching approach for all queries, the system first identifies the specific domain (e.g., flight booking, weather, news) and then applies domain-specific interpretation rules and contextual understanding, thereby achieving both versatility across domains and precision within each domain
Solution Approach 2:
The patent introduces an intermediary classification layer between the user's natural language query and the final search execution. This intermediary system analyzes the query to identify domain-specific indicators and intent, then routes the query through appropriate domain-specific processing paths, improving both domain identification and intent recognition accuracy
2Productivity
If the system processes each query independently without maintaining query state, then the processing is simpler and faster, but it cannot support conversational interactions or incremental query refinement
Solution Approach 1:
The system implements a dynamic query state that evolves through the dialogue. Instead of treating each query as static and independent, the system maintains a living state that is continuously updated based on user feedback and contextual information, enabling the query to adapt and refine itself across multiple interaction turns while maintaining efficient processing
Solution Approach 2:
The system performs preliminary processing of each query to identify potential state modifications and update the query state in advance of full execution. By pre-processing queries to extract domain indicators and intent signals, the system prepares the contextual framework ahead of time, enabling faster subsequent processing and more natural conversational flow
3Ease of manufacture
If the system uses traditional query modification techniques, then it can handle basic keyword additions and removals, but it struggles with natural-language inputs and complex query state transitions
Solution Approach 1:
The system changes the fundamental parameters of query processing by transitioning from exact keyword matching to probabilistic domain and intent classification. By using statistical models to evaluate the likelihood of different domains and intents based on query features, the system can naturally handle diverse natural-language inputs while maintaining a relatively simple implementation framework
Solution Approach 2:
The patent replaces the mechanical, rule-based query modification system with a probabilistic classification approach. Instead of relying on predefined rules for handling each type of query modification, the system uses statistical models that can generalize to handle diverse natural-language inputs and complex state transitions, achieving greater versatility without proportionally increasing implementation complexity
Data Source
AI summary
A device may facilitate a query dialog involving queries that successively modify a query state. However, fulfilling such queries in the context of possible query domains, query intents, and contextual meanings of query terms may be difficult. Presented herein are techniques for modifying a query state in view of a query by utilizing a set of query state modifications, each representing a modification of the query state possibly intended by the user while formulating the query (e.g., adding, substituting, or removing query terms; changing the query domain or query intent; and navigating within a hierarchy of saved query states). Upon receiving a query, an embodiment may calculate the probability of the query connoting each query state modification (e.g., using a Bayesian classifier), and parsing the query according to a query state modification having a high probability (e.g., mapping respective query terms to query slots within the current query intent).