Product Label Platform with Locked Compliance Data and AI Validation
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Solution Overview
Problem
The process of designing and customizing product labels is time-consuming and resource-intensive due to multiple iterations and compliance with legal and regulatory requirements, often leading to delays and increased costs.
Innovation Solution
A product label platform that supports real-time design and validation, automatically populating regulated and non-regulated data, and includes an AI component for compliance review, allowing users to generate labels efficiently while ensuring adherence to regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple iterations of product labels are generated and revised through back and forth communications, then compliance with legal and regulatory requirements is achieved, but the time to obtain a final version increases
Solution Approach 1:
The system performs preliminary actions by automatically populating regulated data fields with pre-validated information from product databases before the user begins label design. Compliance requirements are pre-loaded and validated in advance, so that when users create labels, the time-consuming compliance checks have already been performed on the underlying product data.
Solution Approach 2:
The system implements continuous feedback mechanisms that automatically validate label content against regulatory requirements in real-time during the design process. The AI component provides immediate feedback on compliance status, allowing users to correct issues before final submission, thereby reducing iterative revisions and accelerating time to final approval.
2Reliability
If multiple iterations of product labels are generated and revised, then compliance with legal and regulatory requirements is achieved, but resource costs increase
Solution Approach 1:
The system enables self-service by automatically performing compliance validation and generating compliant label versions without requiring manual intervention from designers or compliance officers. The AI component autonomously checks regulatory requirements and makes necessary adjustments, reducing the need for repeated manual revisions and associated resource costs.
Solution Approach 2:
The system replaces manual mechanical processes of compliance checking and label revision with automated AI-based validation and generation. The AI component substitutes for human designers and compliance reviewers in performing repetitive validation tasks, significantly reducing the time and resources required to achieve compliance.
3Adaptability or versatility
If regulated data is allowed to be modified by users, then customization flexibility is improved, but compliance with regulations may be compromised
Solution Approach 1:
The system segments the label into distinct regions: regulated fields that are locked and protected from modification, and non-regulated fields that are open to user customization. This segmentation allows users to freely customize marketing and descriptive content while ensuring that regulated information remains unchanged and compliant with legal requirements.
Solution Approach 2:
The system applies different quality constraints to different parts of the label. Regulated sections maintain fixed, validated content with no modification allowed, while non-regulated sections provide full customization flexibility. This local differentiation of constraints enables both compliance assurance and design adaptability within the same label.
Data Source
AI summary
A method includes displaying multiple products. The method also includes selecting a product from the multiple products in response to obtaining an input. The method also includes displaying a label associated with the product. The label includes at least a first section and a second section. The method further includes automatically populating first data into the first section and second data into the second section. The first data is associated with the product. In response to obtaining a first user input directed to the first data, the method includes restricting modification of the first data. In response to obtaining a second user input directed to the second data, the method includes updating the second data in view of the second user input to generate a revised label.