Token Weighting Model for Search Query Precision
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Solution Overview
Problem
Search engines often provide suboptimal results due to inability to distinguish between important and unimportant tokens in search queries, leading to irrelevant items and exclusion of relevant items.
Innovation Solution
A system utilizing a weight determination model within a machine learning architecture to assign token weights predicting the probability of matching metadata, allowing for segregation of unimportant tokens and improved search result identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search engines treat all tokens equally in identifying search results, then the search engine operation is simple, but the search result precision deteriorates
Solution Approach 1:
The patent segments the search query into individual tokens and assigns different importance weights to each token using machine learning models. This segmentation allows the system to differentiate between important and unimportant tokens, improving search result precision without requiring complete redesign of the search engine architecture.
Solution Approach 2:
The patent changes the parameter of token importance by introducing weight values derived from machine learning models. Instead of treating all tokens equally (uniform parameter), the system dynamically adjusts token weights based on contextual relevance, user behavior patterns, and semantic analysis, thereby improving search precision.
2Measurement precision
If search engines include all tokens from search queries, then the search engine operation is simple, but the search result relevancy deteriorates
Solution Approach 1:
The patent extracts and removes unimportant or extraneous tokens from the search query using machine learning-based importance assessment. By taking out these irrelevant tokens (such as stop words, filler words, or contextually insignificant terms), the system improves search result relevancy while reducing the processing burden on the search engine.
Solution Approach 2:
The patent performs preliminary token filtering and weighting before the main search execution. Machine learning models pre-assess token importance and prepare a refined set of meaningful tokens, which then feed into the search engine. This preliminary action reduces the complexity of token processing during the actual search operation.
3Ease of operation
If search engines use traditional token processing, then the system complexity is low, but the user experience deteriorates due to irrelevant items
Solution Approach 1:
The patent implements self-service mechanisms where the machine learning models automatically learn from user interactions and search patterns to improve token weighting. The system serves itself by continuously refining its understanding of token importance based on feedback from actual search outcomes, thereby improving user experience without requiring manual intervention or system reconfiguration.
Solution Approach 2:
The patent incorporates feedback loops where search result performance (user clicks, dwell time, conversion) feeds back into the machine learning models. This feedback mechanism allows the system to continuously optimize token weighting and improve search relevancy, enhancing user experience while justifying the increased system complexity through measurable performance gains.
Data Source
AI summary
A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions, that when executed on the one or more processors, cause the one or more processors to execute operations: determining, using a weight determination model of a machine learning architecture, (a) token weights for tokens included in a search query and (b) extraneous tokens from among the tokens based on at least the token weights; comparing the token weights for the tokens to a probability threshold; segregating, based on at least the token weights, the extraneous tokens from a remainder of the tokens; after segregating, identifying first tokens of the remainder of the tokens that satisfy the probability threshold; and identifying, based on the first tokens of the remainder of the tokens that satisfy the probability threshold, search results for the search query. Other embodiments are described.