Personalized Computer Vision Models Through No-Code ROI Setup
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Non-technical users face barriers in accessing and deploying Computer Vision (CV) models due to technical proficiency requirements and lack of industry-specific solutions, hindering their ability to comprehend and visualize model applications.
Innovation Solution
A no-code system and method for personalizing CV models using mouse interactions and intuitive interfaces, allowing users to select use cases, define regions of interest, and customize models without coding, integrating seamlessly with business tools and supporting cloud deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing CV model platforms are used, then model execution capability is provided, but technical proficiency requirements create barriers for non-technical users
Solution Approach 1:
The patent introduces an intermediary layer between the complex CV model infrastructure and non-technical users. This intermediary provides a simplified interface that translates user-friendly actions (like uploading images or selecting templates) into model configuration and execution commands, thereby masking the underlying technical complexity while maintaining full functionality.
Solution Approach 2:
The system enables non-technical users to independently configure and execute CV models through intuitive interfaces without requiring programming knowledge. Users can upload their own images, select from pre-defined templates, and customize model behavior through visual parameters, allowing them to self-serve the complex task of model deployment.
2Adaptability or versatility
If general-purpose CV models are deployed, then broad applicability is achieved, but lack of industry-specific tailoring hinders effective application in domains like retail or warehouse management
Solution Approach 1:
The patent implements pre-configured industry-specific templates that contain pre-defined model parameters, feature sets, and processing pipelines tailored for specific domains like retail or warehouse management. Users can select these templates and immediately benefit from industry-optimized configurations without having to manually configure complex parameters from scratch.
Solution Approach 2:
The system allows users to apply different configuration qualities to different parts of the model based on their specific needs. Within a single deployment, users can enable or disable specific features, adjust parameter sensitivity, or customize processing pipelines for particular use cases while maintaining the overall model structure, thereby achieving localized optimization without complete customization.
3Adaptability or versatility
If comprehensive model configuration options are provided, then model personalization capability is enhanced, but user interface complexity increases
Solution Approach 1:
The patent segments the model configuration interface into distinct, manageable sections such as image upload, template selection, parameter adjustment, and output configuration. Each segment presents only the relevant controls and options for that specific configuration stage, preventing users from being overwhelmed by the complete set of available parameters all at once.
Solution Approach 2:
The user interface dynamically adapts its complexity based on user selections and progression through the configuration workflow. As users make selections in earlier stages, subsequent interface elements are customized to show only relevant options, and advanced parameters are revealed or hidden based on the user's apparent expertise level or the specific template chosen.
Data Source
AI summary
A system for personalizing a computer vision (CV) model based on use cases is provided. The method includes (i) segmenting media content received from a user device into a sequence of individual frames, (ii) extracting a first frame, (iii) providing a plurality of preference segments to the user device to enable the user to select a preference, (iv) generating an event by enabling the user to interact with the first frame using a mouse, (v) configuring the CV model automatically, (vi) converting the one or more mouse clicks performed on the first frame into coordinates, (vi) converting the input into a compatible format by processing the input of the user with every click of ‘y’ or ‘q’ by the user on the keyboard, (vii) personalizing the CV model based on use cases by crystalizing the input of the CV model to perform interpretation/prediction, upon receiving confirmation from the user.