Hybrid Swarm Intelligence System for Collaborative Decision Making
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Solution Overview
Problem
Current technologies lack the ability to enable real-time group-wise experiences that facilitate collaborative intelligence among electronically networked individuals, failing to provide tools for groups to collectively contribute their will and express a unified intent effectively, and do not incorporate machine learning to augment human input.
Innovation Solution
A hybrid intelligence system that allows human participants and machine agents to converge on an answer through a central server communicating with computing devices, using a software-controlled pointer in a simulated environment, with user input and machine agent values determining the pointer's motion to select answers based on elapsed time and relative location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a system enables real-time group-wise collaborative intelligence, then the ability to express unified intent is improved, but the system complexity increases
Solution Approach 1:
The patent introduces a central server as an intermediary that coordinates communication between multiple computing devices. The server manages the pointer position, receives input values from users, and determines the selected answer based on the pointer's final position. This intermediary architecture enables collaborative intelligence while centralizing the complexity management, allowing users to interact simply through their devices without directly managing the complex coordination logic.
Solution Approach 2:
The system employs a universal pointer mechanism that can be controlled by multiple users through different computing devices, serving as a common interface for collective decision-making. The pointer functions as a multi-functional element that aggregates individual user inputs into a unified group decision, enabling the system to handle various collaborative tasks (selecting from answer choices, expressing group intent) through a single coordinated interface.
2Measurement precision
If machine learning is incorporated to augment human input, then the accuracy of predictions is improved, but the device complexity increases
Solution Approach 1:
The patent combines human input values with machine learning-generated values in a hybrid swarm intelligence system. The central server receives input values from multiple computing devices and integrates them with values generated by machine learning models. This merging of human and machine intelligence allows the system to leverage both human judgment and algorithmic pattern recognition, improving prediction accuracy while distributing the complexity across different components rather than concentrating it in a single device.
Data Source
AI summary
Systems and methods for real-time collaborative computing and collective intelligence are disclosed. A hybrid swarm intelligence system includes a central collaboration server, a plurality of computing devices in communication with the central server, and an agent application in communication with the central server. In response to information sent from the central server during a group collaboration session, user input is sent to the central server via the computing devices, and machine input is given to the server via the agent application, which determines input based on rules, additional data, and/or machine learning techniques. The central server uses the user input and the machine input to repeatedly provide feedback to the agent application and users during the group collaboration session.