Transformer Model for Grocery Product Description Generation
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Solution Overview
Problem
Conventional methods for generating product descriptions for grocery items are inefficient and costly, as they rely on human copywriters who must manually research and write descriptions, which can be time-consuming and prone to errors.
Innovation Solution
A processor-implemented method and system that utilize a transformer-based deep learning model to generate descriptive copies for grocery products by processing data on product attributes and allergens, creating a vocabulary model, and assigning weights to training data to produce accurate and context-aware descriptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human copywriters are used to write product descriptions, then the descriptions can be tailored to specific products with accurate details, but the process becomes time-consuming and costly
Solution Approach 1:
The patent replaces the mechanical process of human copywriting with an automated AI-based system. The system uses transformer models and natural language processing to generate product descriptions automatically, eliminating the need for manual human intervention while maintaining description quality and accuracy.
Solution Approach 2:
The system creates synthetic training data by copying and transforming existing product information. It generates artificial product descriptions and attributes from available data, which are then used to train the AI model, enabling the system to replicate human copywriting capabilities without actual human involvement.
2Adaptability or versatility
If human copywriters manually research and write descriptions, then the descriptions can be customized for each product, but manual effort and time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing product data into structured formats before generation. It creates training datasets and models in advance, so that when actual product descriptions are needed, the system can quickly generate customized descriptions without time-consuming manual research and writing processes.
Solution Approach 2:
The patent replaces manual customization processes with automated AI generation. The transformer model takes product input data and automatically generates customized descriptions tailored to each product's specific attributes, eliminating the need for manual research and writing while maintaining full customization capability.
3Reliability
If human copywriters write product descriptions, then grammatical errors and spellings can be reviewed and corrected, but manual effort increases
Solution Approach 1:
The system replaces manual proofreading and error correction with automated AI-based generation. The transformer model inherently produces grammatically correct and spell-checked descriptions as part of its generation process, eliminating the need for separate manual review and correction steps while maintaining high description quality.
Solution Approach 2:
The AI system performs self-service by automatically generating and validating its own output. The model incorporates built-in mechanisms for ensuring grammatical correctness and spelling accuracy during generation, without requiring external human review or correction, thereby simplifying the overall process while maintaining reliability.
4Measurement precision
If traditional copywriting approaches are used, then product-specific details can be accurately captured, but the process is slow and expensive
Solution Approach 1:
The patent replaces slow manual copywriting with high-speed automated AI generation. The transformer model processes product data and generates accurate, detailed descriptions instantly, enabling the system to produce large volumes of high-quality descriptions without the time and cost constraints of human writers.
Solution Approach 2:
The system uses copying techniques to create synthetic training data that replicates accurate product information patterns. By copying and transforming existing accurate product data into training datasets, the AI model learns to generate precise descriptions efficiently, achieving both high accuracy and high productivity.
Data Source
AI summary
E-commerce industry is currently expanding rapidly, worldwide. A process of generating product copy for grocery items, which is very challenging as food items, do not have features in common, unlike fashion products. A data associated with one or more grocery products is received as an input. The data is processed to obtain one or more sorted similar grocery products. One or more relevant attributes and allergen information associated with the one or more sorted similar grocery products are extracted. A vocabulary model is created based the one or more relevant attributes and the allergen information associated with the one or more sorted similar grocery products. The vocabulary model is validated based on one or more assigned weights on training data. The one or more descriptive copies associated with grocery products are generated by mapping the validated vocabulary model with the training data.