Synthetic Computing Operators for Scalable Human Collaboration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing computing systems struggle to efficiently and scalably address complex challenges that require multifactorial human collaboration, such as designing a vehicle or creating music, due to arcane operational interfaces and the complexity of recruiting and managing human teams, leading to high costs and low success rates.
Innovation Solution
A synthetic engagement system that utilizes a computing system with operatively coupled resources and a user interface to engage a human operator, allowing selection and interaction with synthetic operators informed by convolutional neural networks, to collaboratively address complex challenges through a predetermined process configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human teams are used to address complex challenges, then creative problem-solving and multifactorial analysis capabilities are improved, but system scalability and operational efficiency deteriorate due to high recruitment costs and management complexity
Solution Approach 1:
The patent creates synthetic operators that replicate the problem-solving capabilities of human experts through AI models trained on domain-specific knowledge. These synthetic operators can perform complex analysis, generate designs, and execute tasks without the recruitment and management overhead of human teams, directly addressing the scalability issue while maintaining creative problem-solving capabilities.
Solution Approach 2:
The patent replaces the mechanical process of hiring, training, and managing human teams with an automated AI-based system. The synthetic operators are deployed through software interfaces that can be scaled indefinitely without additional operational complexity, substituting human resource management with computational processes that are inherently scalable and cost-effective.
2Productivity
If conventional computing interfaces are used, then basic computational tasks are completed, but ease of operation deteriorates due to arcane interfaces requiring specialized knowledge
Solution Approach 1:
The patent introduces synthetic operators as intermediary entities between users and complex computational systems. These operators provide natural language interfaces and automated workflows that mediate between simple user inputs and complex computational tasks, eliminating the need for users to directly interact with arcane interfaces while maintaining full computational capability.
Solution Approach 2:
The patent transforms the interface parameters from technical command-line syntax to natural language and visual interactions. By changing the mode of communication from specialized commands to intuitive interfaces, the system maintains computational productivity while dramatically improving ease of operation for users without specialized knowledge.
3Reliability
If human experts are recruited for specialized tasks, then domain expertise and skill requirements are met, but device complexity and cost increase due to recruitment and management requirements
Solution Approach 1:
The patent creates synthetic operators that copy the domain expertise of human experts through training on extensive domain-specific datasets. These synthetic operators possess the necessary knowledge and skills to perform specialized tasks independently, eliminating the need to recruit and manage human experts while maintaining the required level of domain expertise and reliability.
Solution Approach 2:
The synthetic operators are designed to be self-sufficient, autonomously performing complex tasks without requiring human management or supervision. They self-manage their operations, make decisions based on trained models, and execute tasks independently, thereby eliminating the management complexity associated with human teams while maintaining specialized expertise.
Data Source
AI summary
One embodiment is directed to a synthetic engagement system for process-based problem solving, comprising: a computing system comprising one or more operatively coupled computing resources; and a user interface operated by the computing system and configured to engage a human operator in accordance with a predetermined process configuration toward an established requirement based at least in part upon one or more specific facts; wherein the user interface is configured to allow the human operator to select and interactively engage one or more synthetic operators operated by the computing system to proceed through the predetermined process configuration, and to return result to the human operator selected to at least partially satisfy the established requirement; and wherein each of the one or more synthetic operators is informed by a convolutional neural network informed at least in part by historical actions of a particular actual human operator and a synthetic operator background configuration.