Input Correction System for Search Accuracy
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Solution Overview
Problem
Networked systems face difficulties in accurately interpreting user input due to misspellings, slang, and variations in search terms, leading to inadequate search results and user dissatisfaction.
Innovation Solution
A system that generates and filters string pairs based on character operator differences, using statistical machine translation to correct user input by replacing less frequently used terms with more commonly used alternatives that yield better search results, thereby improving user interaction and satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses officially recognized terms only, then search accuracy is improved, but user input flexibility and satisfaction deteriorate due to misspellings and slang variations
Solution Approach 1:
The patent introduces an intermediary component (input correction system) that mediates between user input and the search system. This intermediary translates various user inputs (misspellings, slang, shortcuts) into officially recognized terms before processing, thereby maintaining both search accuracy and user input flexibility. The system acts as a bridge that preserves the benefits of both approaches.
Solution Approach 2:
The system performs preliminary correction of user input before the search process begins. By pre-processing the input to convert it into recognized terms, the system ensures that the subsequent search operation can proceed with high accuracy while the user retains the freedom to input in their preferred manner. This preliminary transformation resolves the contradiction before it affects search results.
2Ease of operation
If the system accepts various user input variations, then user satisfaction is improved, but search result quality deteriorates due to misspelled and incorrect terms
Solution Approach 1:
The input correction system serves as an intermediary layer that accepts diverse user inputs (improving ease of operation) while translating them into standardized recognized terms (maintaining search result quality). This mediator ensures that user satisfaction is not compromised by input restrictions, nor is search quality degraded by misspellings.
Solution Approach 2:
The system replaces the mechanical approach of exact term matching with a statistical translation approach. Instead of requiring precise input matching, the system uses statistical models to infer the intended meaning of varied user inputs, thereby maintaining search result quality while accepting diverse input forms that improve user satisfaction.
3Measurement precision
If the system implements input correction, then search accuracy is improved, but system complexity increases due to additional processing steps
Solution Approach 1:
The input correction system is designed to be self-training and self-improving. It automatically learns from user corrections and feedback without requiring extensive manual configuration or maintenance. This self-service capability reduces the operational complexity of managing the correction system while maintaining its effectiveness in improving search accuracy.
Solution Approach 2:
The system uses parameter-based statistical translation models that can be adjusted and optimized. By changing parameters in the statistical model rather than restructuring the entire system, the complexity of implementing and maintaining input correction is reduced. The parameter-based approach allows for fine-tuning search accuracy without proportionally increasing system complexity.
4Measurement precision
If the system uses statistical machine translation, then user input correction is improved, but processing time increases due to analysis and filtering operations
Solution Approach 1:
The system applies partial correction by focusing statistical machine translation only on the specific portions of user input that appear to be misspelled or non-standard, rather than processing the entire input string uniformly. This selective approach maintains input correction accuracy for problematic terms while reducing overall processing time by skipping already-correct terms.
Solution Approach 2:
The system incorporates feedback mechanisms where user corrections are immediately applied and used to refine future corrections. This feedback loop allows the system to learn from user interactions and improve correction accuracy over time, reducing the need for extensive statistical analysis in subsequent operations and thereby decreasing processing time while maintaining or improving correction quality.
Data Source
AI summary
In various example embodiments, a system and method for correction of user input are presented. In one embodiment, a method includes receiving a plurality of user strings, selecting one or more string pairs from the plurality of user strings based on a character operator difference between the first string and the second string being below a threshold number, filtering the one or more string pairs to generate a filtered set of strings pairs representing corrections, and correcting user input in a different session by replacing input that matches a first string in a filtered string pair with a second string in the filtered string pair.