Query Performance Predictor for Voice Search Error Mitigation
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Solution Overview
Problem
The processing of free-form audio inputs into queries often results in errors, leading to nonsensical or irrelevant search results, inefficient use of resources, and a lack of relevant top search results when presented via an audio interface, due to inaccuracies in speech recognition and parsing.
Innovation Solution
Implementing a query performance predictor and a query rewriting module that assesses the likelihood of search results relevance and generates alternative queries based on user input and metadata, without reprocessing the audio input, to improve search result accuracy and resource efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If free-form audio inputs are processed directly into queries without additional validation, then the system responds quickly to user input, but the search results become inaccurate, ambiguous, or irrelevant due to speech recognition and parsing errors
Solution Approach 1:
The patent applies preliminary action by performing query validation and performance prediction before executing the search query. The system predicts query performance and validates parsed queries against expected patterns, identifying and correcting potential errors before they affect search results, thereby improving reliability without requiring complex post-processing
Solution Approach 2:
The patent implements feedback mechanisms where the system evaluates the quality of parsed queries using performance predictors and validation rules. When queries are identified as potentially inaccurate or ambiguous, the system provides feedback by generating alternative query interpretations or requesting clarification, creating a closed-loop system that continuously improves search accuracy
2Reliability
If speech recognition and parsing are performed on audio input, then the system can understand user intent, but errors in recognition lead to nonsensical or irrelevant search results
Solution Approach 1:
The patent applies copying by creating multiple alternative interpretations of the parsed query when the original query is identified as potentially erroneous. Instead of relying on a single speech recognition output, the system generates alternative query versions that maintain the original user intent while correcting potential recognition errors, allowing selection of the most accurate interpretation
3Productivity
If the system processes and executes all user queries without filtering, then resource utilization is maximized, but many resources are wasted on queries that will not return relevant results
Solution Approach 1:
The patent applies preliminary action by executing query performance prediction and validation before actual search execution. The system assesses whether a parsed query is likely to return relevant results and filters out queries predicted to be ineffective, preventing waste of computational resources and time on queries that would not produce useful outcomes
Data Source
AI summary
Systems and methods are described for mitigating errors introduced during processing of user input such as voice input. A query may be derived from processed user input. A performance predictor analyzes the query and uses historical data to predict whether the query will return relevant results if executed. If the query's predicted performance is below a threshold, a query rewriter may identify potential alternatives to the query from a library of “known good” queries. Different analyzers may be applied to identify different sets of alternatives, and machine learning models may be applied to rank the outputs of the analyzers. The best-matching alternatives from each analyzer may then be provided as inputs to a further machine learning model, which assesses the probability that each of the identified alternatives reflects the intent of the user. A most likely alternative may then be selected to execute in place of the original query.