Visual Feedback Data Analysis Pipeline Modification
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Solution Overview
Problem
Existing machine-learning models for identifying features in images are complex and difficult to modify, leading to a time-consuming and expensive trial-and-error process for addressing errors or outliers, which constrains their performance and user experience.
Innovation Solution
A computer system that dynamically modifies a data-analysis pipeline using visual performance feedback, allowing users to interactively update the pipeline by providing feedback on images or videos, which can include modifying or adding operations based on user input, such as changing or bifurcating data-analysis operations, and presenting visual performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-learning models are made more complex to improve feature identification accuracy, then measurement precision is improved, but device complexity increases and ease of operation deteriorates
Solution Approach 1:
The patent segments the complex machine-learning model into multiple simpler component models arranged in a pipeline architecture. Each component model performs a specific function (e.g., object detection, feature extraction, classification), allowing the system to achieve high overall accuracy while maintaining individual component simplicity and ease of modification.
Solution Approach 2:
The patent implements dynamic modification capabilities where the data-analysis pipeline can be adjusted in real-time based on performance feedback. Users can add, remove, or modify component models without retraining the entire system, enabling adaptive optimization while keeping individual components manageable in complexity.
2Measurement precision
If machine-learning models are made more complex to improve feature identification accuracy, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
By dividing the system into independent modular components, the patent enables users to modify specific parts of the pipeline without affecting the entire system. This segmentation makes the complex model easier to operate and modify compared to a monolithic approach.
Solution Approach 2:
The patent incorporates performance feedback mechanisms that monitor model output and provide information about accuracy and errors. This feedback enables users to identify which components need modification and makes the modification process more intuitive, improving ease of operation despite the overall system complexity.
3Reliability
If trial-and-error process is used to modify machine-learning models to address errors, then reliability is improved, but loss of time increases and productivity deteriorates
Solution Approach 1:
The patent performs preliminary actions by pre-training multiple component models on different aspects of feature identification before deployment. During operation, these pre-trained components can be quickly combined and adjusted without requiring time-consuming retraining, significantly reducing modification time while maintaining reliability.
Solution Approach 2:
The system continuously monitors performance and provides feedback about errors and outliers. This feedback enables targeted modifications to specific pipeline components rather than random trial-and-error, accelerating the improvement process while ensuring reliable performance through systematic optimization.
4Reliability
If trial-and-error process is used to modify machine-learning models to address errors, then reliability is improved, but productivity deteriorates
Solution Approach 1:
The modular pipeline architecture enables parallel development of different component models, increasing development productivity. Multiple components can be trained, tested, and optimized independently and simultaneously, then integrated to form a reliable complete system, avoiding the sequential bottleneck of traditional trial-and-error approaches.
Solution Approach 2:
Automated performance monitoring and feedback mechanisms identify issues systematically, enabling rapid iteration and improvement. This reduces the time and resources needed for manual trial-and-error testing while maintaining high reliability through continuous optimization based on measured performance metrics.
Data Source
AI summary
After analyzing images or videos, a computer system may display or present visual performance feedback with an interactive visual representation of a data-analysis pipeline, where the visual representation includes separate and coupled data-analysis operations in a set of data-analysis operations that includes the one or more machine-learning models. Moreover, in response to a user-interface command the specifies a given data-analysis operation, the computer system may display or present a group of images or videos and associated performance information for the given data-analysis operation, where a given image or video corresponds to an instance of the given data-analysis operation. Furthermore, when the computer system receives user feedback about one at least one of the images or videos in the group of images or videos, the computer system performs a remedial action based at least in part on the user feedback. For example, the computer system may dynamically modify the data-analysis pipeline.