Machine Learning Design Policy Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Design thinking is slow, difficult to replicate across teams, and hard to scale, and generates large amounts of handwritten documentation that is challenging to organize and digitize for use by computing algorithms.
Innovation Solution
Systems and methods that train machine learning algorithms to model interactions between users, entities, and environments, predicting optimal sequences of actions to complete tasks and narrating these actions in natural language or graphical form, reducing the need for user intervention and improving documentation efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If design thinking methodology is used to design experiences, then user interaction quality is improved, but process speed and scalability deteriorate
Solution Approach 1:
The system enables self-service by automatically analyzing user interaction data and generating design recommendations without requiring manual design thinking processes. The machine learning model autonomously processes data from multiple sources and produces actionable insights, eliminating the need for human designers to manually analyze each interaction while maintaining high-quality user experience design.
Solution Approach 2:
The patent replaces the mechanical design thinking process with an automated machine learning system. Instead of human designers manually analyzing user interactions and generating design recommendations, the system uses computational algorithms to process data from multiple sources and generate design insights automatically, significantly improving process speed while maintaining design quality.
2Reliability
If design thinking is applied across teams, then design quality is improved, but complexity of replication and scaling worsens
Solution Approach 1:
The system achieves universality by creating a centralized machine learning model that serves multiple teams and projects simultaneously. The model processes data from various sources and generates design recommendations that can be applied across different teams and contexts, eliminating the need for each team to develop and maintain separate design thinking processes while ensuring consistent design quality throughout the organization.
Solution Approach 2:
The patent enables easy replication by capturing design patterns and insights in a digital format that can be copied and applied across teams. The machine learning model stores learned patterns in a structured database that can be readily replicated and deployed to multiple teams, eliminating the complexity of manually replicating design thinking processes while maintaining design quality consistency.
3Loss of information
If handwritten documentation is generated during design learning, then design insights are captured, but organization and digitization difficulty increases
Solution Approach 1:
The system replaces the mechanical process of handwritten documentation with automated digital data capture and processing. The machine learning model directly ingests data from multiple digital sources, automatically structures it, and generates organized design insights in digital format, eliminating the need for manual handwriting and subsequent digitization efforts while ensuring complete information capture.
Solution Approach 2:
The patent introduces an intermediary layer in the form of the machine learning model that automatically processes raw data from multiple sources and transforms it into organized, structured design insights. This intermediary system handles the complexity of data organization and digitization automatically, eliminating the need for manual documentation management while ensuring comprehensive information capture and easy accessibility.
Data Source
AI summary
Systems, methods, and articles of manufacture for learning design policies based on user interactions. One example includes determining a first task for an environment, receiving data from a plurality of data sources, determining a first time step associated with the received data, determining a plurality of candidate actions for the determined first time step, computing a respective probability value of each candidate action achieving the first task at the first time step based on a first machine learning (ML) model, determining that a first candidate action has a greater probability value for achieving the first task at the first time step relative to the remaining plurality of candidate actions, determining that the first candidate action has not been implemented in the environment at the first time step, and generating an indication specifying to implement the first candidate action as part of a policy to achieve the first task.