Automated User Research Protocol Generation System
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Solution Overview
Problem
Existing methods for user research, such as interviews and surveys, rely heavily on subject matter experts to generate questions, leading to inconsistent quality and excessive data collection, which is inefficient and resource-intensive, with no standard approach for determining data saturation.
Innovation Solution
A processor-implemented method and system for generating a user research protocol that collects input on research objectives, domains, user research methods, phases, and touchpoints to create questions, investigative suggestions, measurement keywords, and question formats, dynamically updating the protocol based on response saturation levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subject matter experts manually generate questions for user research, then the questions can be tailored to research objectives, but the quality of questions becomes inconsistent and depends on expert skills and interpretations
Solution Approach 1:
The system enables automated generation of research questions and protocols without relying on manual expert intervention. The computer-implemented system autonomously generates questions, selects touchpoints, and creates user research protocols based on input parameters, eliminating the inconsistency inherent in manual expert-based approaches while maintaining alignment with research objectives.
2Loss of information
If comprehensive data is collected from all users, then complete information is obtained, but excessive data causes storage inconvenience and requires more time and resources for assessment
Solution Approach 1:
The system extracts and collects only the specific data elements needed to achieve research objectives and determine saturation. By systematically selecting questions and touchpoints aligned with research goals, the system collects minimal sufficient data rather than comprehensive data, eliminating unnecessary storage burden and assessment time while preserving all information needed for valid conclusions.
Solution Approach 2:
The system implements continuous monitoring of data saturation levels during the research process. When saturation is detected—indicating that additional data would not provide new insights—the system automatically terminates data collection, providing real-time feedback that prevents excessive data accumulation and optimizes resource utilization.
3Measurement precision
If manual review is performed to determine adequate data provision, then data sufficiency can be assessed, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The system replaces the manual mechanical review process with an automated computer-implemented system that objectively assesses data saturation based on predefined criteria and research objectives. This substitution eliminates the cumbersome nature of manual review while maintaining precise assessment of data adequacy through systematic analysis of collected responses against research goals.
4Loss of information
If data collection continues until manual determination of adequacy, then complete information is gathered, but unnecessary data collection increases resource consumption
Solution Approach 1:
The system continuously monitors data saturation levels during collection and provides real-time feedback on whether research objectives are being met. When saturation is detected—indicating that additional data would not contribute new insights—the system automatically terminates collection, preventing waste of resources on redundant data gathering while ensuring all necessary information is captured.
Data Source
AI summary
Manually framing questions for research and evaluation has the disadvantage that quality and effectiveness of questions of the questions depend on knowledge and expertise of subject matter expert who is framing the questions. The disclosure herein generally relates to data processing, and, more particularly, to a method and system for generating protocol for data extraction from one or more users. The system identifies sub-objectives corresponding to an identified research objective, and then generates questions to get response that matches the objective and sub-objectives. Further, a sequence is decided for the questions, and accordingly recommendations are generated. The system also assesses whether response obtained from users has reached saturation, and accordingly generates trigger to terminate data collection.