Intelligent Query Suggestion Using Multi-Dimensional Intent Prediction
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Solution Overview
Problem
Current virtual assistant applications suffer from inaccurate intent classification due to limited understanding of user intent, lack of consideration for user-specific attributes, and failure to adapt to evolving business needs, leading to suboptimal user experiences and inefficient query processing.
Innovation Solution
Implementing a computer system that uses advanced machine learning algorithms to predict user intents and provide customized query suggestions by analyzing user input text in real-time, incorporating prior interactions, and employing data augmentation for robustness, with an auto-trainable model pipeline for continuous retraining based on historical interaction data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional intent classification is used based solely on input text, then the system is simple to operate, but intent prediction accuracy deteriorates
Solution Approach 1:
The patent combines multiple information sources (input text, user profile data, historical interactions, contextual information) into a unified intent classification system. This merging of diverse data types enables more accurate intent prediction while managing system complexity through integrated processing architecture.
Solution Approach 2:
The system transitions from analyzing only the text dimension to incorporating multiple additional dimensions including user demographics, historical behavior patterns, contextual data, and interaction history. This multi-dimensional approach significantly improves intent prediction accuracy beyond what text alone can provide.
2Loss of time
If predefined intents are provided without user confirmation, then the response time is reduced, but user experience deteriorates due to inaccurate suggestions
Solution Approach 1:
The system implements feedback mechanisms where user responses to suggested queries are captured and used to refine future intent predictions. This feedback loop allows the system to learn from user corrections and preferences, improving both the accuracy of suggestions and the efficiency of the interaction over time.
Solution Approach 2:
The intent classification system dynamically adjusts its predictions based on real-time user feedback and changing contextual information. Rather than using static predefined intents, the system adapts its suggestions as the conversation progresses, maintaining both speed and accuracy.
3Adaptability or versatility
If the model is trained only on initial data, then the manufacturing process is simpler, but adaptability to evolving business needs deteriorates
Solution Approach 1:
The system implements self-service capabilities through automated retraining pipelines that continuously update the intent classification model using new interaction data. This self-updating mechanism enables the system to adapt to evolving business needs and emerging user patterns without requiring manual intervention for each update.
Solution Approach 2:
The patent establishes continuous model retraining and data augmentation processes that operate ongoing rather than as one-time events. This continuity ensures the system consistently adapts to new business requirements, user behaviors, and contextual changes while maintaining performance through systematic updates.
Data Source
AI summary
Methods and apparatuses are described for automatic intelligent query suggestion for information retrieval applications. A server a) determines candidate intents associated with user input text received from a remote device, including applying a trained intent classification model to the user input text to predict candidate intents. The server b) calculates a likelihood value for each of the candidate intents. The server c) compiles a list of suggested queries based upon the candidate intents and associated likelihood values. The server d) identifies a subset of the list of suggested queries for display on the remote device. Upon detecting an update to the user input text at the remote device, the server repeats steps a) to d) using the updated user input text, or upon detecting a selection of one of the suggested queries at the remote device, the server retrieves content responsive to the selected query.