Digital Catalog Standardization for Granular Product APIs
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Solution Overview
Problem
Traditional online shopping catalogs are non-standardized, leading to limited product information and hindered user experiences due to spelling errors, inconsistent data, and difficulty in updating with emerging attributes, which limits the functionality of application programming interfaces (APIs).
Innovation Solution
A method and system for standardizing data by transforming it into a common format using machine learning and artificial intelligence, incorporating a hierarchy of defined categories with numerous attribute types, anonymized profiles, and structured unstructured review data, enabling advanced APIs to operate efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional non-standardized catalog data is used, then implementation is simpler and requires less processing, but product information quality deteriorates due to spelling errors, inconsistent data, and limited attributes
Solution Approach 1:
The patent applies preliminary action by implementing data standardization and enrichment processes before the catalog data is used by APIs. A service receives non-standardized data, converts it to a standardized format with consistent spelling, proper categorization, and enriched attributes, and stores it in a database. This pre-processing ensures high data quality is available when needed without adding complexity to the API operations themselves.
2Adaptability or versatility
If traditional non-standardized catalog data is used, then data processing is faster and requires fewer resources, but API functionality deteriorates due to inability to support advanced search and filtering operations
Solution Approach 1:
The system performs preliminary data standardization and attribute enrichment before API operations. The service converts non-standardized data into a standardized format with consistent categorization, spelling, and enriched attributes, storing this processed data in a database. This allows APIs to execute advanced search and filtering operations efficiently without real-time processing delays.
Solution Approach 2:
The patent introduces an intermediary service that sits between the raw data source and the APIs. This service acts as a mediator that standardizes and enriches catalog data, customer information, and review data before making it available to APIs. The intermediary handles the complex data processing tasks, allowing APIs to focus on providing versatile functionality without being burdened by data quality issues.
3Loss of information
If detailed standardized data with numerous attribute types is implemented, then product information granularity improves, but data storage and processing complexity increases
Solution Approach 1:
The patent applies segmentation by organizing detailed product information into a hierarchical category structure with defined attribute types. Each category has specific attributes relevant to that category, allowing the system to store comprehensive product information in a structured manner. This segmentation makes the complex data manageable and enables APIs to efficiently query specific attributes without handling the entire data set.
4Manufacturing precision
If manual data standardization is performed, then data quality improves, but processing time and resource consumption increase significantly
Solution Approach 1:
The patent implements self-service by enabling the system to automatically standardize and enrich catalog data without requiring manual intervention. The service receives non-standardized data, automatically converts it to a standardized format, enriches it with additional attributes, and stores it in the database. This automated process maintains high data consistency while preserving processing efficiency by eliminating manual data standardization tasks.
Data Source
AI summary
Techniques for standardizing a catalog of data and for using the standardized data to implement various APIs are disclosed. Non-standardized data is received. This data includes information describing items, customer information, and unstructured review data. The non-standardized data is converted to a standardized format, resulting in the generation of standardized data. The standardized data includes a hierarchy of defined categories. Each category is associated with a set of attribute types. The standardized data also includes anonymized profiles. The unstructured review data is also provided structure. A data model is generated based on the standardized data. Various APIs can then use the data model to perform operations.