Pattern Matching Search Engine Query Sub-Phrase Segmentation
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Solution Overview
Problem
Conventional search engines for electronic commerce websites sacrifice precision in search results due to the loss of context information when splitting search queries into individual words, leading to issues like polysemy, where a word or phrase has multiple meanings, and failing to detect the intent behind search queries effectively.
Innovation Solution
Implementing a pattern matching strategy that divides search queries into sub-phrases and matches them against entire field values in a database, rather than individual words, to detect patterns and improve search result relevance by considering the context of the query.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If search queries are split into individual words for processing, then the search engine can process queries more efficiently, but context information is lost leading to reduced precision in search results
Solution Approach 1:
The patent segments the search query into sub-phrases of varying lengths (not just individual words) while maintaining their contextual relationships. This allows the system to process queries efficiently by breaking them down into manageable units while preserving the context within each sub-phrase, thus resolving the contradiction between processing efficiency and search result precision.
Solution Approach 2:
The patent implements a nested structure where sub-phrases are embedded within partitions, and partitions are organized in a hierarchy that preserves contextual information. This nested organization allows the system to maintain context at multiple levels while still enabling efficient processing, thereby improving search result precision without sacrificing productivity.
2Quantity of substance
If the search engine matches individual words against database fields, then it can retrieve more results, but it fails to detect the intent behind search queries leading to reduced relevance
Solution Approach 1:
The patent applies different matching strategies to different parts of the query structure. Sub-phrases are matched against database fields with consideration for their positional and contextual characteristics. This local quality approach ensures that the matching process is tailored to the specific intent expressed in each sub-phrase, improving both the quantity and quality of relevant results returned.
Solution Approach 2:
The patent adds a dimensional layer to the search process by organizing queries into partitions and sub-phrases with associated metrics and weights. This multi-dimensional structure allows the system to evaluate matches not just on keyword presence but on contextual relevance, thereby improving intent detection accuracy while maintaining result quantity.
3Measurement precision
If the search engine uses exact matching for search terms, then it reduces polysemy issues, but it fails to handle cases where brand names or phrases have multiple meanings
Solution Approach 1:
The patent implements a dynamic matching approach where the strictness of matching can be adjusted based on the context and type of query. For terms that may have multiple meanings, the system can adaptively apply different matching criteria, allowing it to resolve polysemy issues while maintaining the ability to handle brand names and phrases with multiple meanings appropriately.
Solution Approach 2:
The patent changes the parameters of the matching process based on the specific query context. By adjusting matching thresholds, weights, and criteria dynamically according to the detected intent and query structure, the system can effectively handle both polysemy resolution and multi-meaning terms, achieving both precision and adaptability.
Data Source
AI summary
A method including the steps of: receiving a search query containing search terms from a user device; retrieving from one or more data storage devices, search configuration information in response to the received search query, wherein the search configuration information comprises a designated field set; generating a plurality of query partitions based on the received search query; generating one or more internal query clauses based on all possible combinations of sub-phrases of the plurality of query partitions and fields from the designated field set; executing a query on a database based on the one or more internal query clauses; generating query results based on the executed query; calculating match metrics for all query results; determining query results having match metrics that satisfy configured match constraints as legitimate matches; and detecting patterns based on corresponding matched terms and fields of the legitimate matches.