Automatic Product Description Generation with Dual Feature Extraction
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Solution Overview
Problem
Multi-modal models struggle with generating accurate and relevant product descriptions due to training difficulties, lack of labeled data, and reliance on word frequency, leading to inconsistent and often inaccurate descriptions that require human review.
Innovation Solution
A dual feature extraction model system using a public and non-public training dataset to generate features, combined with a description generating model that boosts probabilities of similar words and adjusts based on user interactions, ensuring accuracy and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multi-modal models are used to generate product descriptions automatically, then productivity is improved, but manufacturing precision deteriorates due to inaccuracy and lack of relevance
Solution Approach 1:
The patent introduces an intermediary system consisting of multiple specialized models (feature extraction model, candidate generation model, re-ranking model) that act as mediators between the input image and the final product description. This intermediary architecture enables automatic generation while improving accuracy through multiple processing stages, each focused on specific aspects like feature extraction, candidate generation, and quality filtering.
Solution Approach 2:
The description generation process is segmented into multiple independent stages: feature extraction from images, candidate description generation, quality assessment, and re-ranking. Each stage is handled by specialized models trained on specific tasks, allowing the system to maintain high productivity while improving manufacturing precision through divided responsibilities.
2Ease of operation
If multi-modal models rely on word frequency for generation, then ease of operation is improved, but manufacturing precision deteriorates due to generic and irrelevant descriptions
Solution Approach 1:
The system applies local quality by training different models for different aspects of description generation: the feature extraction model focuses on image-specific features, the candidate generation model on relevant word selection, and the re-ranking model on quality optimization. This localized specialization ensures each component excels at its specific task, improving overall precision without sacrificing operational simplicity.
3Manufacturing precision
If labeled training data is used to train multi-modal models, then manufacturing precision is improved, but loss of substance increases due to data scarcity
Solution Approach 1:
The patent uses copying by leveraging pre-trained models and publicly available datasets as substitutes for scarce labeled product data. The system copies knowledge from general-purpose image recognition models and adapts it to product description tasks, reducing dependency on large amounts of proprietary labeled training data while maintaining manufacturing precision.
4Productivity
If automatic training is implemented for multi-modal models, then productivity is improved, but manufacturing precision deteriorates due to inability to ensure accuracy without human review
Solution Approach 1:
The system implements feedback mechanisms where the re-ranking model uses quality metrics and user interaction data to continuously improve description accuracy. Automatic training is enhanced through feedback loops that incorporate performance measurements and adjust model parameters accordingly, maintaining productivity while improving manufacturing precision without requiring constant human intervention.
Data Source
AI summary
Systems, devices, and techniques are disclosed for automatic product description generation. A first set of features including labels including words may be generated from an image using a first feature extraction model. A second set of features including labels including words may be generated from the image using a second feature extraction model. A text description of a product depicted in the image may be generated by inputting the image and metadata for the image to a description generating model. The text description may include words. Each of the words may be generated by assigning probabilities to candidate words, boosting the assigned probabilities of candidate words that are similar to words of labels of the first set of features or words of labels of the second set of features, and selecting one of the candidate words based on the assigned probabilities after the boosting as a word of the text description.