Docu-Narrative Records With Authenticated Image Chains
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Solution Overview
Problem
Existing technologies lack the ability to produce accurate, reliable, and consistently complete images or time-lapse videos of construction sites, leading to confusion between similar equipment types and inconsistent, unreliable images that fail to meet evidentiary standards due to manual augmentation issues and lack of forensic integrity.
Innovation Solution
The use of software-based tools and deep learning systems to create a Docu-Narrative, a secure and authenticated visual documentary of site events, with a Docu-Vault for image storage and advanced data protection, ensuring probative chain of custody and forensic reliability through processes like EC-F, EC-IC, and EC-OD.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual image augmentation is used to remove obstructions, then image quality may be improved, but the process becomes time-consuming and costly while impacting evidentiary quality
Solution Approach 1:
The patent replaces manual image augmentation with automated deep learning-based image processing systems. The system automatically detects and removes obstructions from images using machine learning models, eliminating the need for manual intervention while maintaining or improving image quality. This substitution of mechanical/manual processes with automated intelligent systems directly resolves the contradiction between image quality improvement and time consumption.
2Manufacturing precision
If manual image augmentation is used to remove obstructions, then image quality may be improved, but the process becomes costly and impacts evidentiary quality
Solution Approach 1:
The patent replaces costly manual image augmentation services with automated deep learning systems. The automated system processes images at scale without requiring expensive human expertise, thereby reducing costs while maintaining image quality. The system can be deployed once and then processes numerous images automatically, providing cost-effectiveness compared to ongoing manual services.
Solution Approach 2:
The system performs self-service by automatically detecting, analyzing, and augmenting images without human intervention. The deep learning models autonomously identify obstructions and perform appropriate image processing tasks, eliminating the need for external manual services and reducing associated costs.
3Productivity
If deep learning systems are used to create Docu-Narrative, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex image processing task into distinct modules: image capture, deep learning-based obstruction detection, automated image augmentation, quality verification, and Docu-Narrative generation. Each module performs a specific function, making the overall complex system manageable through functional segmentation. This modular approach enables high productivity while controlling complexity through clear separation of concerns.
Data Source
AI summary
A method for producing a visual record, referred to as a docu-narrative, of events at a location. The record comprises still and video images that are acquired by appropriately configured hardware. A benchmark focus and resolution are determined for an image acquisition device. Changes to the images are tightly controlled and authenticated, certified, and/or verified such that the docu-narrative includes all versions of the images and provides a chain-of-custody record. Image quality and image accuracy are significant attributes of the visual record, where accuracy is determined based on differences between an image of an object and a ground truth object within the image. Image characteristics can also be altered so that each image appears consistent with a previous and a next image; all such alterations are recorded. A target model detection spec is used to detect specific objects within an image.