Probabilistic Query Scoring for Navigation Parsing Accuracy
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Solution Overview
Problem
Navigation systems face challenges in accurately parsing natural language queries to determine the location and objective portions, leading to inefficient resource usage due to inaccurate parses, especially in large point-of-interest databases.
Innovation Solution
A probabilistic approach using a point-of-interest data structure to identify the correct split between location and objective portions of a query by assigning scores based on past usage, thereby improving accuracy and reducing resource wastage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional parsing methods are used to determine location and objective portions of queries, then the system can process natural language inputs, but the accuracy of parsing deteriorates leading to incorrect identification of location and objective
Solution Approach 1:
The patent changes the approach from rule-based parsing to probabilistic scoring by transforming the problem parameters. Instead of using fixed parsing rules, the system assigns probability scores to different tokens being location or objective based on their characteristics and context, fundamentally changing how the parsing decision is made to improve accuracy
Solution Approach 2:
The patent introduces an intermediary scoring mechanism between the raw query input and the final location/objective identification. The probabilistic scores act as an intermediate layer that evaluates multiple possible interpretations before determining the correct parsing, allowing the system to handle ambiguity effectively
2Measurement precision
If the system processes all possible parsing variations to ensure accuracy, then parsing precision improves, but resource consumption increases due to unnecessary processing
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing probability scores for tokens during an offline training phase. This preliminary processing allows the online query parsing to use these pre-computed scores directly, avoiding the need to re-analyze all possible parsing variations for each query and significantly reducing real-time resource consumption
Solution Approach 2:
The system applies partial action by only processing the most likely parsing interpretations based on probability scores rather than exhaustively analyzing all possible parses. The scoring mechanism naturally prioritizes the most probable interpretations, allowing the system to achieve high accuracy with reduced processing effort
3Reliability
If the system uses a comprehensive point-of-interest database to improve query resolution, then the quality of navigation information improves, but the complexity of data processing increases
Solution Approach 1:
The patent segments the complex query processing task into distinct components: tokenization, scoring, and selection. By dividing the processing into these manageable segments, the system can handle large point-of-interest databases efficiently without being overwhelmed by complexity, as each segment handles a specific aspect of the processing
Data Source
AI summary
A device can include one or more processors to: receive a query; determine a plurality of candidate samples based on the query, a candidate sample, of the plurality of candidate samples, having a location portion of zero or more tokens and an objective portion of zero or more tokens; determine a score for the candidate sample based on a data structure, the score being determined based on at least one of: a location value, associated with the location portion, that is identified using the data structure, or an objective value, associated with the location portion, that is identified using the data structure; and perform an action with regard to the query based on the score.