Intelligent IoT Design Structure Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems for designing and selecting structures, such as homes or buildings, lack cognitive and interactive solutions that cater to multiple user preferences, leading to inefficiencies in time and cost during the design phase, as they primarily provide fixed two-dimensional or three-dimensional images without considering contextual factors like user profiles, environmental conditions, and user satisfaction levels.
Innovation Solution
A computing processor-based method for intelligent design structure selection in an IoT environment that learns user behavior patterns and satisfaction levels to suggest cognitive design solutions, incorporating machine learning and augmented reality to dynamically design or modify structures based on user profiles, preferences, and contextual factors, including environmental and safety considerations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed two-dimensional or three-dimensional images are provided for structure design, then the system complexity is reduced, but the adaptability to user preferences and contextual factors deteriorates
Solution Approach 1:
The patent implements dynamic adaptability by enabling the system to learn and evolve user behavior patterns over time. The system transitions from static fixed images to dynamic personalized recommendations by continuously analyzing user interactions, satisfaction levels, and contextual factors to adapt structure design suggestions to individual user preferences.
Solution Approach 2:
The system changes multiple parameters simultaneously including user profile attributes, behavioral patterns, satisfaction levels, and contextual factors. By monitoring and adjusting these parameters dynamically, the system transforms from providing generic fixed images to delivering personalized structure design solutions tailored to each user's specific preferences and needs.
2Adaptability or versatility
If cognitive solutions considering multiple user preferences are implemented, then the adaptability improves, but the device complexity increases
Solution Approach 1:
The patent segments the complex cognitive system into distinct functional modules: user profile analysis component, behavior pattern learning component, satisfaction level evaluation component, and structure design suggestion component. This segmentation allows the system to handle multiple user preferences through specialized subsystems, reducing overall system complexity while maintaining high adaptability.
Solution Approach 2:
The system implements a universal platform that handles multiple functions including user authentication, behavior tracking, satisfaction measurement, and structure design recommendation. This multi-functional approach consolidates various cognitive capabilities into a single integrated system, managing complexity through universality rather than requiring separate systems for each function.
3Manufacturing precision
If iterative design processes are used, then the manufacturing precision improves, but the loss of time increases
Solution Approach 1:
The system performs preliminary actions by pre-analyzing user profiles, learning behavior patterns, and evaluating satisfaction factors before the actual structure design selection. This advance preparation enables the system to provide highly precise design recommendations immediately, eliminating the need for time-consuming iterative design processes while maintaining high manufacturing precision.
Solution Approach 2:
The patent implements continuous feedback loops where user satisfaction levels are monitored and fed back into the system to refine behavior patterns and improve future recommendations. This feedback mechanism allows the system to achieve high design selection precision through learning and adaptation rather than through iterative trial-and-error processes, significantly reducing the time required.
4Adaptability or versatility
If contextual factors like environmental conditions are considered, then the adaptability improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The system introduces intermediary components that translate complex contextual factors into measurable data. User satisfaction levels and behavior patterns serve as intermediaries between difficult-to-measure contextual factors like environmental conditions and the structure design recommendations. These intermediaries make the invisible visible by converting subjective experiences into quantifiable metrics that can be analyzed and acted upon.
Data Source
AI summary
Embodiments for intelligent design structure selection in an Internet of Things (IoT) computing environment by a processor. Levels of satisfaction and behavior patterns of one or more users having similar user profiles influencing the behavior patterns may be learned and evaluated. One or more design structure solutions may be cognitively suggested according to the levels of satisfaction and the behavior patterns.