Objective Journey Mapping for Low-Energy Intent Execution
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Solution Overview
Problem
Computer-simulated environments, such as industrial metaverses, experience high energy consumption and carbon footprints due to extensive user interactions, particularly in scenarios with multiple users and autonomous systems requiring user intervention, leading to reduced sustainability.
Innovation Solution
A method for optimizing interaction steps through objective journey mapping, utilizing intelligent algorithms to analyze user intent, generate probabilistic action options, and determine optimal actions based on past and present data, reducing energy consumption and enhancing sustainability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If autonomous systems require user involvement to address new challenges, then problem-solving capability is improved, but energy consumption increases and sustainability is reduced
Solution Approach 1:
The system pre-generates multiple potential action options and simulates their outcomes before user interaction. This preliminary computational work allows the system to present optimized choices to the user, reducing the need for extensive real-time computation and repeated user interactions, thereby lowering energy consumption while maintaining adaptability.
Solution Approach 2:
The autonomous system independently performs simulation and probability analysis to evaluate action options. By self-generating and pre-evaluating multiple action paths, the system reduces reliance on continuous user intervention, allowing users to make decisions based on pre-analyzed information rather than requiring exhaustive real-time analysis.
2Reliability
If extensive data is used for autonomous training, then system capability is improved, but energy consumption and carbon footprint increase
Solution Approach 1:
The system generates and simulates multiple action options beyond what a single optimal path would require. This partial exploration of alternative paths provides robust training data and decision-making information without requiring exhaustive simulation of all possible scenarios, achieving reliable system capability with reduced computational overhead and lower carbon footprint.
3Adaptability or versatility
If multiple users engage simultaneously in computer-simulated environments, then collaborative capability is improved, but cumulative energy consumption and carbon footprint increase
Solution Approach 1:
The system performs preliminary simulation and probability analysis for multiple users simultaneously, pre-computing action options and their likely outcomes. This advance computation reduces the need for real-time processing during user interactions, allowing multiple users to collaborate effectively while minimizing the cumulative energy consumption and carbon footprint associated with simultaneous engagements.
Data Source
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AI summary
Presented is a system (100) and a method (300) to optimize one or more actions to be performed by a user in a computer simulated environment (108), the method comprising receiving an input comprising an intent to execute the one or more action by the user. The method (300) further comprising analyzing if an optimized one or more actions exist in a database comprising one or more optimized actions relating to the intent. The method (300) further comprising generating a plurality of options relating to one or more actions for the user based on past data and present data and simulating each option of the options relating to one or more actions. The method (300) resulting in a determination of an optimal action to execute the one or more actions defined in the intent input of the user based on the simulated options.