Dynamic Data Structure Generation for Multi-User Problem Resolution
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Solution Overview
Problem
Existing systems are not adapted to handle complex problems in a multi-user context, where multiple users with different interests need to solve issues involving trade-offs, as they lack versatility and efficiency in managing multiple-user interactions and computing resources.
Innovation Solution
A multi-user complex problems resolution system with interconnected devices that dynamically generate a customized data structure based on input patterns from multiple users, allowing for efficient problem-solving by segregating variables into sub-groups and enforcing rules to optimize user interactions and reduce computing power requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed data structure is used for problem-solving, then the system is simpler to implement, but it cannot adapt to different user inputs and problem types in a multi-user context
Solution Approach 1:
The patent implements dynamic data structures that automatically adapt their organization and properties based on user inputs and problem characteristics. The system transitions from static to dynamic structure management, allowing the data model to evolve during problem-solving sessions to accommodate different user needs and problem types.
Solution Approach 2:
The system changes parameters of the data structure dynamically based on detected input patterns from multiple users. This includes modifying data organization, variable groupings, and structural properties according to the specific problem context and user interactions, rather than using a fixed predetermined structure.
2Loss of energy
If all user inputs are processed equally without pattern detection, then no input is lost, but the computing power and memory requirements increase significantly
Solution Approach 1:
The system performs preliminary pattern detection and analysis on user inputs before full processing. By identifying patterns early in the input sequence, the system can anticipate required computations and optimize resource allocation, avoiding unnecessary processing of redundant or predictable input variations.
Solution Approach 2:
The system uses feedback from detected input patterns to dynamically adjust processing strategies. When patterns are recognized, the system modifies its processing approach to reduce computational overhead while maintaining complete information processing, creating a closed-loop optimization system.
3Ease of operation
If multiple users can simultaneously modify all variables, then collaboration is maximized, but conflicts and processing complexity increase
Solution Approach 1:
The patent segments variables into different groups based on detected input patterns and user roles. This segmentation allows multiple users to work on different variable groups simultaneously with reduced conflicts, while maintaining overall system coherence. The system automatically divides the problem space into manageable segments for parallel user engagement.
Solution Approach 2:
Different users are granted different levels of access and modification rights to different variable groups based on their roles and the detected problem context. This local quality approach allows maximum collaboration freedom in appropriate areas while maintaining control and reducing conflicts in critical areas.
4Productivity
If the system provides detailed customization options for data structures, then user control is maximized, but the ease of use and speed of problem-solving decrease
Solution Approach 1:
The system automatically generates and configures customized data structures based on detected input patterns without requiring manual user configuration. The system serves itself by autonomously adapting the data model to the problem context, eliminating the need for users to make detailed customization decisions while still providing tailored solutions.
Solution Approach 2:
The system performs preliminary customization of data structures based on initial pattern detection before users need to interact with the detailed configuration. This preliminary action prepares the optimal data structure in advance, allowing users to focus on problem-solving rather than configuration.
Data Source
AI summary
A multi-user complex problems resolution system, in various instances an urban design complex problem resolution system, comprising a plurality of interconnected user devices, each device embedding a model data structure comprising a representation of a physical system, the system being provided with at least one module connected to the devices, the module carrying out the steps of receiving data and metadata modifying variables, from all the devices; detecting an input pattern based on the data and metadata received from all the devices; dynamically generating a customized data structure based on the input pattern; updating the model data structure of the devices with the customized data structure. Also, a corresponding computer-implemented method.