Guided Collaboration Platform for Faster Domain AI Labeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems lack the ability to enable domain experts and other users to create domain-specific solutions efficiently, as bespoke environments are cost-prohibitive and ground truth labeling is tedious, leading to time-consuming deployment procedures and lack of defined standards.
Innovation Solution
A computer-implemented system and method that utilizes a file ingestion interface, corpus exploration tool, and AI/ML algorithms to identify a representative set of data relevant to a query, allowing subject matter experts to verify and refine responses through a feedback process, building a model to accurately extract responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If bespoke environments are built for domain-specific solutions, then solution accuracy and domain expertise are improved, but cost and complexity increase significantly
Solution Approach 1:
The platform provides a universal environment that can handle multiple domain-specific tasks through configurable templates and tools. Instead of building separate bespoke environments for each domain, a single platform serves multiple domains by allowing users to configure domain-specific parameters, data sources, and analysis templates, thereby reducing overall system complexity while maintaining solution accuracy.
Solution Approach 2:
The system allows domain-specific solutions to be achieved by changing parameters and configurations within a standardized platform framework. Users can adjust parameters such as data sources, analysis templates, and processing rules to adapt the general platform to specific domain requirements, avoiding the need to build entirely new bespoke environments for each domain.
2Measurement precision
If ground truth labeling is performed manually, then data accuracy is improved, but time consumption and labor costs increase
Solution Approach 1:
The system enables semi-automated labeling where the platform itself performs initial labeling using pre-configured templates and algorithms, then allows domain experts to review and refine the results. This self-service approach reduces the time and labor required for manual ground truth labeling while maintaining data accuracy through the verification process.
Solution Approach 2:
The platform performs preliminary labeling actions using automated templates and algorithms before human review. By pre-processing and generating initial labels, the system reduces the amount of time domain experts need to spend on labeling, focusing their effort only on verification and refinement of critical cases.
3Stability of the object's composition
If traditional deployment procedures are used, then system stability is maintained, but deployment time and resource consumption increase
Solution Approach 1:
The platform uses template copying and replication to deploy domain-specific solutions. Pre-configured templates for common domain scenarios can be copied and adapted to new projects, maintaining system stability through proven configurations while dramatically reducing deployment time compared to building solutions from scratch.
Solution Approach 2:
Deployment templates and configurations are prepared in advance through preliminary action. The platform allows users to pre-configure data pipelines, analysis templates, and processing workflows that can be quickly instantiated and deployed, reducing deployment time while maintaining system stability through tested and validated configurations.
4Measurement precision
If domain experts manually analyze all data, then analysis thoroughness is improved, but productivity and efficiency decrease
Solution Approach 1:
The platform segments the data analysis process into distinct stages: automated preliminary analysis using templates, identification of key findings, and expert verification of critical results. This segmentation allows domain experts to focus their thorough analysis on the most important segments rather than manually reviewing all data, thereby maintaining analysis thoroughness while improving overall productivity.
Solution Approach 2:
The platform acts as an intermediary between automated analysis and expert review. It performs initial data processing and analysis using configurable templates, then presents filtered and organized results to domain experts for verification. This intermediary role enables thorough expert analysis of key findings without requiring experts to manually process all raw data, thus improving productivity while maintaining thoroughness.
Data Source
AI summary
The invention relates to computer-implemented systems and methods for analyzing and standardizing various types of input data such as structured data, semi-structured data, unstructured data, and images and voice. An embodiment of the present invention relates to a guided collaboration space for domain experts, data scientists and solution managers to build Artificial Intelligence (AI)/Machine Learning (ML) solutions, deploy solutions at scale as well as manage quality and consistency.