Digital Catalog Data Standardization for Attribute-Rich 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 updates, and difficulty in categorizing emerging product 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 natural language processing, incorporating a hierarchy of categories and attribute types, and providing structured unstructured review data, enabling enhanced APIs to operate on highly granular product information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional non-standardized data formats are used in online shopping catalogs, then implementation simplicity is maintained, but data quality and API functionality deteriorate due to spelling errors, inconsistent updates, and difficulty in categorizing product attributes
Solution Approach 1:
The patent transforms product data by changing parameters from non-standardized formats to standardized formats with defined schemas. This includes converting free-text product descriptions into structured data with standardized attribute types (e.g., brand, category, specifications), thereby improving data quality and reliability while enabling consistent API operations across the e-commerce platform
Solution Approach 2:
The patent segments product information into distinct standardized components including product identifiers, category hierarchies, attribute types, and review data. This segmentation allows each data element to be independently validated, stored, and processed, resolving the contradiction by organizing complex data into manageable standardized units that improve reliability without overwhelming system complexity
2Quantity of substance
If non-standardized review data is incorporated into the catalog, then data quantity increases, but data usability and API performance deteriorate due to unstructured format and difficulty in processing
Solution Approach 1:
The patent introduces an intermediary data standardization layer that processes unstructured review data before it enters the catalog system. This intermediary layer parses free-text reviews, extracts meaningful attributes (e.g., sentiment, product features mentioned, ratings), and transforms them into structured formats that can be efficiently processed by APIs, thereby maintaining data quantity while dramatically improving ease of operation
Solution Approach 2:
The patent applies parameter changes by converting unstructured review text into structured parameters including review ratings, sentiment scores, extracted product attributes, and temporal metadata. This transformation enables the system to maintain large volumes of review data while making it easily queryable and processable through standardized API calls
3Loss of information
If detailed product attributes are implemented in the catalog, then product information quality improves, but system complexity increases due to the need to manage and process numerous attribute types
Solution Approach 1:
The patent implements a universal standardized attribute schema that can accommodate multiple product types and categories through a consistent data structure. This multi-functional framework allows the same standardized attributes (e.g., brand, specifications, pricing) to be applied across diverse product categories, thereby maintaining complete product information while reducing the complexity of managing different data formats for different products
Solution Approach 2:
The patent resolves the contradiction by adding a hierarchical dimension to attribute management, organizing attributes into category-specific schemas and sub-attributes. This dimensional organization allows detailed product information to be captured through a structured hierarchy where general attributes apply broadly and specific attributes apply to particular categories, thereby maintaining information completeness while simplifying management through hierarchical abstraction
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.