Feature Type Spectrum Technique for Design Fixation
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Solution Overview
Problem
Design fixation occurs when creating novel solutions, as individuals tend to focus on features of existing solutions, leading to the overlook of useful features that are lacking in those solutions.
Innovation Solution
A system that uses sensors to generate sample set data representing objects, and a computer system analyzes this data to determine the frequencies of features, employing machine learning to identify obscure features and alleviate design fixation by providing a panoramic view of possible feature types and their frequencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If designers focus on features of existing solutions, then design efficiency is improved, but design fixation occurs and novel features are overlooked
Solution Approach 1:
The system inverts the traditional design approach by not starting from existing solutions, but rather from a comprehensive analysis of feature frequencies across diverse objects. By presenting obscure (low-frequency) features first, the system reverses the conventional wisdom-based design process and enables discovery of novel features that would otherwise be overlooked.
Solution Approach 2:
The system introduces a new dimension to design by analyzing features across multiple object categories and representing them in a frequency spectrum. This dimensional transformation from traditional single-object analysis to multi-object feature frequency analysis reveals patterns and obscure features that are not apparent in conventional design processes.
2Measurement precision
If comprehensive feature analysis is performed across multiple objects, then identification of obscure features is improved, but system complexity increases
Solution Approach 1:
The system uses sensors to generate sample set data representing physical objects, creating digital copies that can be analyzed without handling the physical objects. This copying approach enables comprehensive feature analysis while reducing the complexity of physical manipulation and observation.
Solution Approach 2:
The system replaces manual feature analysis with automated sensor-based data collection and computer-based frequency analysis. This substitution of mechanical/human analysis with automated systems reduces system complexity while improving measurement precision of feature frequencies.
Data Source
AI summary
Sensors are used to generate sample set data representing objects in a sample set. A computer system analyzes the sample set data to determine the frequencies with which features in a feature set are observed in the objects in the sample set. An example of such output is a bar chart representing the frequency of observation of features in the feature set in a particular object. The feature output may be used to identify one or more obscure (i.e., low frequency) features in the particular object. Machine learning may be used to learn associations between sample set data and features in the feature set.