Generative AI Attribute Extraction for Fast, Adaptive Item Data
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Solution Overview
Problem
Current network platforms face inefficiencies in attribute extraction for item elements due to reliance on user input, which often results in omitted or incorrect attribute values, and existing machine learning models require significant resources and are slow to adapt to changing data.
Innovation Solution
A system utilizing generative AI models, such as large language models (LLMs), operates in an unsupervised process to extract attributes from item element data through a single prompt configuration, combining outputs from multiple LLMs to generate a final attribute set efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specific machine learning models are used for attribute extraction, then extraction accuracy can be improved, but processing speed and adaptability deteriorate due to increased training and deployment resources
Solution Approach 1:
The patent uses multiple smaller, less computationally intensive machine learning models instead of a single large complex model. These smaller models can be quickly trained and deployed, providing accurate attribute extraction without the heavy resource requirements of larger models, thus maintaining productivity while achieving good extraction accuracy.
Solution Approach 2:
The patent divides the attribute extraction task into multiple parallel processing streams, each handled by a separate machine learning model specialized in extracting specific types of attributes. This segmentation allows multiple extractions to occur simultaneously, improving overall processing speed while maintaining accuracy for each attribute type.
2Measurement precision
If specific machine learning models are used for attribute extraction, then extraction accuracy can be improved, but adaptability to changing data deteriorates due to increased training resources
Solution Approach 1:
The patent implements a dynamic model selection and training system where machine learning models can be quickly retrained or replaced based on changes in item element data. The system monitors data changes and automatically triggers retraining of relevant models, enabling rapid adaptation to new product types, attributes, or data formats without requiring complete system retraining.
Solution Approach 2:
The patent allows for dynamic adjustment of model parameters and extraction criteria based on changing data characteristics. When new item element types are introduced, the system can modify extraction parameters and retrain models with updated parameters, enabling flexible adaptation while maintaining extraction accuracy for both new and existing attributes.
3Ease of operation
If user entry is used for attribute values, then data input flexibility is maintained, but attribute extraction accuracy deteriorates due to omitted or incorrect values
Solution Approach 1:
The patent implements an automated attribute extraction system where machine learning models automatically extract attribute values from item element data without requiring manual user input. The system processes data such as product descriptions, titles, and specifications autonomously, identifying and extracting relevant attributes with high accuracy while maintaining the flexibility to handle diverse data formats and structures.
Data Source
AI summary
Systems and methods for attribute extraction using generative models are disclosed. An attribute extraction request identifying item element data is received and at least one generative prompt is generated based on the attribute extraction request and the item element data. At least one generative model is configured based on the at least one generative prompt to extract a value of one or more attributes identified in the attribute extraction request and the value of the one or more attributes is extracted by the at least one generative model. A final attribute set including at least a portion of the value of the one or more attributes identified in the attribute extraction request is generated and an attribute-based automated process is implemented based on at least one attribute value in the final attribute set.