Semi-Structured Data Categorization via Hybrid ML Algorithms
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Solution Overview
Problem
The inconsistency in user-entered data across different formats, languages, and structures hinders effective search and categorization, leading to missed relevant information, especially in semi-structured data like recipes where misspellings and synonyms are common.
Innovation Solution
A computer system with data interpretative algorithms, including hybrid Maximum Entropy and LDA models, and term frequency-inverse document frequency, is used to categorize and organize semi-structured data, enabling accurate search and retrieval by selecting and analyzing subsets of data entries based on topical fields and cosine similarity analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is collected from multiple users in different formats and languages, then the quantity and diversity of data increases, but the consistency and reliability of data decreases
Solution Approach 1:
The patent introduces an intermediary processing layer between data collection and storage that automatically standardizes semi-structured data. This intermediary system uses natural language processing and machine learning models to normalize different data formats, resolve synonyms, and correct misspellings, thereby maintaining data consistency while accepting diverse user inputs in various formats and languages.
Solution Approach 2:
The system dynamically adjusts data processing parameters based on the input data characteristics. It uses adaptive algorithms that change the level of standardization applied to different data types, selecting appropriate normalization strategies for each data entry to maintain consistency across the entire dataset while preserving the diversity of original inputs.
2Adaptability or versatility
If data is stored in semi-structured format to maintain flexibility, then the adaptability of data storage increases, but the precision of data retrieval decreases
Solution Approach 1:
The patent applies preliminary processing actions to semi-structured data during the ingestion phase. It pre-categorizes, tags, and structures data before storage using machine learning models that predict the most appropriate classification. This preliminary structuring enables precise retrieval operations later while the system maintains the underlying flexibility of semi-structured storage formats.
3Ease of operation
If traditional search methods are used on unstructured data, then the simplicity of search operation is maintained, but the completeness of search results decreases
Solution Approach 1:
The patent replaces traditional mechanical keyword-matching search methods with intelligent information retrieval systems that use natural language processing and semantic analysis. These systems substitute simple text comparison with advanced algorithms that understand context, synonyms, and relationships between terms, thereby maintaining user-friendly search operations while dramatically improving the completeness of retrieved results.
Data Source
AI summary
A computer system interconnected to a community of users having a data processor input module programmed to receive communications from said users including one or more inputs regarding food recipes and store said inputs in accessible memory. A data processor determining module programmed to access stored data and to apply a data interpretative algorithm to said data to unify and organize disparate data inputs into a cohesive database relating to recipes. Also, a search entry module connected to the recipe database to permit access to the database to support a search algorithm applied to the database.