Questionnaire Pre-population via Semantic Knowledge Base
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Solution Overview
Problem
Companies face inefficiencies and high costs in completing complex, custom questionnaires for due diligence with vendors, requiring timely and accurate responses to secure business engagements.
Innovation Solution
A computer-implemented system using a semantic-based machine learning model to pre-populate questionnaire responses by associating new questions with similar past questions, allowing for efficient and accurate management of compliance-related questionnaires through a knowledge base that updates based on user confirmations and edits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual completion of complex questionnaires is used, then accuracy of responses can be maintained, but time and resources required increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-populating questionnaire fields with data from existing knowledge base records before the user submits the questionnaire. This advance preparation significantly reduces the time needed to complete questionnaires while maintaining accuracy, as the preliminary data population is followed by user review and confirmation steps.
Solution Approach 2:
The system creates copies of relevant information from existing knowledge base records and populates them into the questionnaire fields. This copying mechanism allows rapid reproduction of accurate information without manual re-entry, directly addressing the time consumption issue while preserving data accuracy through the copying process.
2Reliability
If comprehensive due diligence questionnaires are used, then reliability of vendor assessment improves, but complexity of the process increases
Solution Approach 1:
The system enables self-service by automatically retrieving and populating questionnaire responses using existing knowledge base records. This automation reduces the manual complexity of managing comprehensive due diligence questionnaires while maintaining reliability, as the system autonomously handles data gathering and population tasks.
Solution Approach 2:
The system provides multi-functionality by handling multiple questionnaire types and formats through a single unified platform. It can manage various due diligence questionnaires, retrieve data from different knowledge base records, and adapt to different vendor assessment requirements, thereby reducing overall process complexity while maintaining comprehensive assessment reliability.
3Reliability
If repeated questionnaire exchanges are conducted, then completeness of due diligence improves, but resource consumption increases
Solution Approach 1:
The system performs preliminary actions by pre-generating complete questionnaire responses using existing knowledge base records before actual questionnaire exchanges occur. This preliminary preparation ensures due diligence completeness is achieved in fewer exchange iterations, thereby reducing the cumulative computing resources consumed across multiple repeated exchanges.
Solution Approach 2:
The system implements feedback mechanisms where completed questionnaires and their responses are stored in the knowledge base and used to automatically populate future questionnaires. This feedback loop ensures continuous improvement and completeness of due diligence while reducing resource consumption by leveraging previously gathered information rather than repeating the same data collection processes.
Data Source
AI summary
An apparatus, computer-implemented method, and a system are disclosed that maintain a knowledge base to pre-populate prospective database inputs, by: receiving one or more database input forms containing database input fields; associating the database input fields to previous database input fields stored in a repository based on a recognition process on corresponding respective word strings and previous word strings corresponding to the previous database input fields; retrieving prior inputs to the previous database input fields based on the associating; formatting the retrieved one or more prior inputs; updating the received one or more database input forms; transmitting the updated database input forms; receiving at least one of a confirmation and an edit; finalizing the updated one or more database input forms; and transmitting the finalized database input forms to one or more source computing apparatuses associated with the one or more database input forms.