Collaborative Generative Design via Persona-Specific Interfaces
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Solution Overview
Problem
Current generative design software is not configured for multi-user collaboration, having complex interfaces navigable only by experienced designers, and lacks the ability to balance diverse stakeholder inputs, resulting in incomplete or infeasible design solutions.
Innovation Solution
A distributed computing system with a shared design model that uses pre-defined selectable block items associated with specific personas, allowing multiple stakeholders to contribute to a collaborative generative design process, generating candidate solutions that reflect diverse design goals and constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If generative design software uses a complex interface with rigid input parameters, then design precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system segments the design interface into multiple persona-specific interfaces, each with simplified parameters tailored to specific stakeholder roles. This allows non-expert users to interact with only the parameters relevant to their domain, maintaining design precision while improving ease of operation.
Solution Approach 2:
The system introduces an intermediary layer that translates simplified persona-specific inputs into the complex parameter set required by the generative design engine. This intermediary handles the complexity internally while presenting a simplified interface to users, resolving the contradiction between precision and ease of use.
2Device complexity
If generative design software is designed for single-user operation, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The system implements a universal backend architecture that can handle both single-user and multi-user operations. The same core generative design engine serves multiple personas with different capabilities and goals, allowing the system to adapt to various collaboration scenarios without requiring separate systems for each use case.
Solution Approach 2:
The system adds the dimension of multiple users with different personas to the traditionally single-user design process. By organizing users around persona-specific capabilities and goals rather than just individual users, the system enables collaboration while maintaining a relatively simple underlying architecture.
3Reliability
If design software accepts inputs from multiple stakeholders with different domain knowledge, then design completeness is improved, but device complexity increases
Solution Approach 1:
The system segments the stakeholder input process into distinct persona-specific interfaces, each tailored to specific domain knowledge areas. This segmentation allows multiple stakeholders to contribute their expertise through simplified, role-appropriate interfaces without requiring the system to handle all possible input types uniformly, thus managing complexity while improving completeness.
4Manufacturing precision
If generative design software requires deep understanding of design framework, then manufacturing precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system applies local quality by providing different interface complexities for different user roles. Each persona receives an interface with parameters and controls appropriate to their specific domain knowledge and goals, rather than requiring all users to master the complete complex interface. This ensures design quality is maintained through specialized interfaces while improving ease of use for each user type.
Data Source
AI summary
In various embodiments, a computer-implemented method for generating a multi-objective model shared between multiple participants comprises receiving a first input associated with a first selectable component in a set of pre-defined selectable components associated with a shared design model, where the first selectable component is a first component type that is associated with a first persona in a plurality of personas, and each persona includes a distinct set of design goals that the shared design model represents in a multi-objective design problem, in response to the first input, modifying the shared design model to generate a modified shared design model that includes the first selectable component, and generating, based on the modified shared design model, a set of candidate design solutions that satisfy the distinct sets of design goals, where each candidate design solution includes additional selectable components of a second component type.