Digital Leaflet Cross-Coding Pipeline Using AI
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Solution Overview
Problem
Current methods for cross-coding digital leaflets are resource intensive, time consuming, and often require human intervention, leading to inefficiencies and errors in processing and extracting causal data.
Innovation Solution
A leaflet cross-coding pipeline that employs AI models, specifically combining computer vision (CV) and natural language processing (NLP) techniques, to automate the process of extracting and cross-coding data from digital leaflets, reducing the need for human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to process digital leaflets, then human intervention can ensure accuracy, but the process becomes time-consuming and resource intensive
Solution Approach 1:
The patent introduces an intermediary AI system that acts as a mediator between the digital leaflet and the final cross-coded data. The AI model processes the leaflet content through multiple stages (OCR, NLP, entity recognition) to achieve accurate cross-coding without requiring human intervention, thus resolving the contradiction between accuracy and processing time
Solution Approach 2:
The patent replaces the mechanical human review process with an automated AI-based system. The AI model uses computer vision and natural language processing to perform tasks that were traditionally done manually, significantly reducing processing time while maintaining or improving accuracy through consistent automated evaluation
2Reliability
If more resources are allocated to manual processing, then accuracy can be maintained, but productivity decreases
Solution Approach 1:
The patent implements a self-service system where the AI model autonomously processes digital leaflets without requiring human resources. The system performs self-correction and validation through multiple processing stages, ensuring reliable data extraction while achieving high throughput by eliminating the need for manual review of each leaflet
Solution Approach 2:
The patent changes the operational parameters from manual human processing to automated AI processing. This parameter change transforms the system's capability, enabling it to process numerous leaflets simultaneously while maintaining high reliability through sophisticated algorithms and validation mechanisms
3Measurement precision
If human intervention is used for cross-coding, then errors can be corrected, but the process requires significant human resources
Solution Approach 1:
The patent segments the cross-coding process into distinct automated stages: OCR for text extraction, NLP for content understanding, entity recognition for identifying key elements, and validation for error checking. Each segment handles specific tasks with high precision, eliminating the need for human intervention while maintaining accuracy through specialized automated functions
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed for processing an image using visual and textual information. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to detect regions of interest corresponding to a product promotion of an input digital leaflet, extract textual features from the product promotion by applying an optical character recognition (OCR) algorithm to the product promotion and associating output text data with corresponding ones of the regions of interest, determine a search attribute corresponding to the product promotion, generate a first dataset of candidate products corresponding to the product in the product promotion by comparing the search attribute against a second dataset of products, and select a product from the first dataset of candidate products to associate with the product promotion, the product selected based on a match determination.