Hybrid Computer-Human Data Analysis for Pattern Detection
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Solution Overview
Problem
Traditional data analysis is limited by human experts' inability to deduce key hypotheses, accuracy issues due to human error, and the inability to handle large or complex data sets, leading to missed opportunities and inaccurate insights.
Innovation Solution
A combined computer-human approach that leverages automated data analysis with untrained humans providing feedback to enhance pattern detection and hypothesis formation, using structured crowdsourcing to validate insights and reduce human error, while automating the analysis of large data sets to identify actionable patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human experts conduct traditional data analysis, then domain knowledge and hypothesis formulation are applied, but human error and bias lead to inaccurate or missed insights
Solution Approach 1:
The patent introduces an automated data analysis system as an intermediary between the data and the human expert. The system automatically performs data cleaning, exploration, hypothesis generation, and validation, eliminating human error from these critical steps while preserving human domain knowledge for interpreting results and making decisions.
Solution Approach 2:
The patent replaces the mechanical process of manual data analysis with an automated computational system. Instead of humans manually examining data and performing statistical tests, the system automatically executes comprehensive analyses, generating hypotheses and validating them through systematic statistical methods, thereby eliminating human error and bias.
2Adaptability or versatility
If analysts manually review data subsets to form hypotheses, then domain expertise is applied, but the scope of hypothesis detection is limited by time and resources
Solution Approach 1:
The patent applies partial action by having the automated system perform comprehensive data exploration and generate multiple hypotheses automatically, then prioritize and test the most promising ones. This allows the system to examine far more hypotheses than a human could manually, while still focusing computational resources on the most likely candidates based on automated pattern recognition.
Solution Approach 2:
The patent changes the parameter of hypothesis generation from manual to automated, enabling the system to evaluate thousands of potential hypotheses simultaneously through automated data exploration. The system systematically varies parameters and conditions to generate hypotheses that would be impractical for human analysts to conceive within reasonable timeframes.
3Productivity
If automated analysis is used to handle large data sets, then processing capacity is improved, but the ability to detect complex patterns and form meaningful hypotheses is reduced
Solution Approach 1:
The patent merges automated data processing capabilities with human domain expertise in a hybrid system. The automated system handles large-scale data processing, cleaning, and initial pattern recognition, then presents findings and generated hypotheses to human experts who apply their domain knowledge to validate and interpret the results, ensuring neither automation nor human insight is lost.
Solution Approach 2:
The patent implements feedback loops where the automated system continuously refines its analysis based on results from previous iterations and human expert input. The system generates hypotheses, tests them, receives feedback from both statistical results and human domain knowledge, then uses this feedback to generate improved hypotheses in subsequent iterations, progressively improving pattern detection capability.
Data Source
AI summary
A combined computer/human approach is used to detect actionable insights in large data sets. Automated computer analysis used to identify patterns (e.g., possibly meaningful patterns or subsets within the data). These are presented to humans for feedback, where the humans may have little to no training in the statistical methods used to detect actionable insights. Feedback from the humans is used to improve the pattern detection and facilitate the detection of actionable insights.