Natural Language Search Service Using Probabilistic Grammar Parsing
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Solution Overview
Problem
Existing content search and discovery systems are inefficient in providing relevant results to users, leading to unnecessary network resource usage and extended browsing sessions, as they fail to effectively utilize natural language voice input and personalize search outcomes.
Innovation Solution
A natural language-based content search and discovery service that uses a multi-interpretative framework with probabilistic grammar parsing, parts of speech identification, and query object types to generate unified natural language understanding queries, merging interpretations based on probability values and combining them with previous search results to improve relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword-based search systems are used, then system simplicity is maintained, but search result relevance and user satisfaction deteriorate
Solution Approach 1:
The patent replaces traditional keyword-matching mechanical search systems with a natural language processing system that uses probabilistic grammar parsing and multi-interpretative frameworks. This substitution enables the system to understand user intent, context, and semantics, dramatically improving search result relevance while accepting increased system complexity through advanced NLP components.
Solution Approach 2:
The patent transforms the search parameter from simple keywords to comprehensive natural language queries with multiple interpretation dimensions. By changing the input parameter from rigid keyword matching to flexible probabilistic language understanding, the system achieves higher relevance in search results while managing complexity through structured parsing frameworks.
2Adaptability or versatility
If comprehensive content collections are maintained, then content availability is improved, but network resource usage and processing time increase
Solution Approach 1:
The patent extracts and processes only the essential interpretative elements from natural language queries using probabilistic grammar parsing. By extracting key intent, entities, and constraints from user input, the system can search comprehensive content collections efficiently without processing entire datasets, reducing network resource usage while maintaining content availability.
Solution Approach 2:
The patent performs preliminary probabilistic parsing and interpretation of user queries before executing full content searches. This preliminary action pre-processes and structures the search intent, enabling more efficient querying of comprehensive content collections and reducing the computational resources needed during actual search execution.
3Measurement precision
If multiple search interpretations are generated, then search result accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent segments the natural language query processing into distinct probabilistic grammar parsing stages, each handling specific linguistic aspects. This segmentation allows multiple interpretations to be generated systematically through separate processing modules, improving accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent generates multiple partial interpretations of the query through probabilistic parsing, then combines these partial results to form comprehensive search outcomes. By performing partial actions on different query aspects and aggregating results, the system achieves higher accuracy without requiring complete reprocessing of the entire query multiple times.
Data Source
AI summary
A method, a device, and a non-transitory storage medium are described, which provide a natural language-based content search and discovery service. The natural language-based content search and discovery service may use query object types as a basis for interpreting a vocalized search query from a user. The natural language-based content search and discovery service may use a multi-interpretative procedure that includes use of a probabilistic grammar parser, parts of speech, and query object type identification that are configured for a media domain. The natural language-based content search and discovery service may merge different interpretations of the search query based on probability values associated with each interpretation.