Cognitive Insight Platform Role-Based Workflow for Data Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing cognitive models face challenges in effectively training using issue resolution information due to inaccuracies and inefficiencies in processing and preparing data for training, which can lead to incorrect or incomplete question and answer pairs in the data corpus.
Innovation Solution
A cognitive insight platform implements a role-based workflow to receive, assign, and approve issue resolution information, generating question/answer pairs and creating a data corpus for training, ensuring accurate and efficient processing by controlling user access and expertise-based modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If issue resolution information is processed without role-based workflow, then processing speed is faster, but data accuracy and reliability deteriorate
Solution Approach 1:
The patent segments the data processing workflow into distinct roles (data collector, data processor, quality reviewer) with specific responsibilities. Each role performs specialized tasks in a sequential pipeline, ensuring that data accuracy is maintained through multiple layers of review while keeping the overall process structured and efficient.
Solution Approach 2:
The patent implements preliminary actions by requiring data collection and initial processing to be completed and reviewed before finalization. Quality reviewers verify data accuracy in advance before the data is used for cognitive model training, preventing errors from propagating to the final model.
2Reliability
If multiple users with different roles are involved in data processing, then data quality improves, but system complexity increases
Solution Approach 1:
The patent creates a universal role-based framework that can handle multiple types of users (collectors, processors, reviewers) through a common system architecture. Each user type performs specialized functions within the same platform, reducing overall system complexity while maintaining data quality through role-specific responsibilities.
3Manufacturing precision
If issue resolution information is processed without domain hierarchy assignment, then processing is simpler, but training effectiveness deteriorates
Solution Approach 1:
The patent segments issue resolution information into hierarchical domains and sub-domains, organizing data by topic areas. This segmentation allows cognitive models to be trained on domain-specific data more effectively, improving training precision while maintaining manageable processing complexity through structured organization.
Data Source
AI summary
A device may receive issue resolution information, associated with a cognitive model, including an item of issue resolution information that describes an issue and a resolution to the issue. The device may assign the item of issue resolution information to a domain hierarchy, where the assigning is associated with a first user. The device may generate a question and an answer corresponding to the item of issue resolution information, where the generating of the question and the answer is associated with a second user. The device may approve the question and the answer, where the approving is associated with a third user. The device may generate a question/answer (QA) pair for the question and the answer. The device may create a data corpus including the QA pair, and provide the data corpus to cause the cognitive model to be trained based on the data corpus.