Search Query Parsing Using Pre-stored Templates

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Solution Overview

Problem

Current search engine technologies are computationally intensive, error-prone, and user-confusing when parsing and annotating search queries, often leading to irrelevant results due to the lack of effective term or phrase classification.

Innovation Solution

A computer-implemented method and system that uses pre-stored query templates to parse and semantically annotate search queries by identifying sequences of character substrings and associating them with categories, improving the accuracy and relevance of search results.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If search engines use dictionary or database comparisons to identify term types, then term classification is achieved, but a large number of false positives are introduced

Engineering Contradiction:

Improveterm classification accuracyVSAvoidfalse positive rate

Core Design Contradiction:

Measurement precisionVSReliability

Solution Approach 1:

The system performs preliminary actions by storing multiple possible meanings and contexts for each search term in advance. When a user submits a query, the system retrieves pre-stored contextual information and user behavior data to determine the most likely intended meaning, rather than relying on simple dictionary matching that produces false positives.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system incorporates feedback mechanisms by analyzing user click-through behavior, search history, and interaction patterns to continuously refine and update term classifications. This feedback loop allows the system to learn from actual user intent and improve classification accuracy over time, reducing false positives.

Inventive Principle:

Principle #23Feedback

2Adaptability or versatility

If search engines separate queries into terms and search all combinations, then comprehensive search coverage is achieved, but computational intensity increases

Engineering Contradiction:

Improvesearch coverageVSAvoidcomputational resources

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The system segments the search process by first classifying individual terms into contextual categories (such as person, location, organization, product) before generating search combinations. This segmentation allows the system to intelligently prune unnecessary search combinations based on term relationships, significantly reducing computational requirements while maintaining comprehensive coverage.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Instead of exhaustively searching all possible term combinations, the system performs partial action by focusing computational resources on the most likely relevant combinations based on term classification and user behavior patterns. This approach achieves sufficient search coverage without the excessive computational cost of complete enumeration.

Inventive Principle:

Principle #16Partial or excessive action

3Measurement precision

If search engines require users to select fields or codes for query terms, then precise search control is achieved, but user confusion and restriction increase

Engineering Contradiction:

Improvesearch control precisionVSAvoiduser interface simplicity

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system implements self-service by automatically classifying search terms and inferring user intent without requiring explicit user input for field selection. The system analyzes the query context, user history, and behavioral patterns to autonomously determine the most appropriate search categories, thereby maintaining precision while preserving interface simplicity.

Inventive Principle:

Principle #25Self-service

4Ease of operation

If search engines provide natural language query fields, then user convenience is improved, but term classification accuracy decreases

Engineering Contradiction:

Improvequery input convenienceVSAvoidterm classification accuracy

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The system introduces an intermediary layer between natural language input and search execution. This intermediary component performs sophisticated term classification by analyzing contextual cues, user behavior patterns, and semantic relationships within the natural language query, thereby maintaining both user convenience and classification accuracy that would otherwise be mutually exclusive.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS9317608B2Systems and methods for parsing search queries

Publication Date: 2016.04.19 VERIZON PATENT & LICENSING INC

AI summary

Computer-implemented systems and methods are provided for parsing search queries. In accordance with some embodiments, search records including character strings are retrieved, and search query templates are generated comprising sequences of categories corresponding to character substrings of the character strings. Also, in accordance with some embodiments, search queries are parsed into character substrings and matched with a search query template. The search query template may then be used to associate categories with the character substrings. A search engine may use the categories to focus or otherwise refine a search based on parsed search query.