Collaboration Server Group Intent Aggregation
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Solution Overview
Problem
Current portable computing devices do not provide tools or infrastructure for groups to achieve real-time collaborative intelligence, where individual users can contribute their intent to a collective consciousness and express a group-wise will effectively.
Innovation Solution
A method and system that allow users on multiple computing devices to interact with a collaboration server, receiving and processing real-time user intents to determine a group intent, updating a graphical object's location, and calculating user performance values based on alignment with the group intent, enabling real-time collaborative control and cohesiveness scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If portable computing devices provide individual user functionality, then device portability and personal use are improved, but group collaborative intelligence capabilities deteriorate
Solution Approach 1:
The patent combines individual device functionality with group collaborative capabilities by merging personal computing functions with real-time group intelligence features. Multiple portable devices are interconnected through a collaboration server, allowing individual users to contribute their device's computational resources and data to collective group tasks while maintaining personal device autonomy and usability.
Solution Approach 2:
The collaboration server provides universal functionality that serves both individual and group needs. It acts as a multi-functional platform that handles personal device communications, group intent aggregation, real-time collaborative processing, and individual performance tracking, making the system adaptable to various collaborative scenarios while preserving individual device capabilities.
2Productivity
If real-time user intent data is collected and processed from multiple users, then group intent determination is improved, but system complexity and computational requirements deteriorate
Solution Approach 1:
The collaboration server segments the complex task of group intent determination into manageable components: individual user intent extraction, intent vector generation, similarity computation, and final group intent aggregation. This segmentation allows parallel processing of multiple user inputs simultaneously, reducing computational complexity while maintaining real-time processing capability.
Solution Approach 2:
The patent introduces intermediary elements including intent vectors and similarity metrics that mediate between raw user input data and final group intent determination. These intermediaries simplify the computational process by transforming complex user intents into standardized representations that can be efficiently compared and aggregated, reducing the overall system complexity.
3Measurement precision
If user performance values are calculated based on alignment with group intent, then individual user contribution assessment is improved, but real-time processing requirements and computational overhead deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-calculating intent vectors and maintaining running similarity metrics as users contribute their intents. This preliminary processing prepares the data structures in advance, so that when performance evaluation is needed, the calculations are already partially complete, significantly reducing the time required for final performance value determination while maintaining precise measurement of user contributions.
Data Source
AI summary
Systems and methods for real-time collaborative computing and collective intelligence are disclosed. A collaborative application runs on a collaborative server connected to a plurality of computing devices. Collaborative sessions are run wherein a group of independent users, networked over the internet, collaboratively answer questions in real-time, thereby harnessing their collective intelligence. A user performance value is determined for each of a plurality of independent users in the group based on each user's participation as compared to other users in the collaborative group. A group cohesiveness score is determined that quantifies the group's overall collaborative effectiveness. In some embodiments, the group cohesiveness score and user performance values are used to adjust weighting factors that affect the relative impact of each of the plurality of users in the collaborative group. In some embodiments, adjusting the weighting factors is performed with the objective of increasing the effectiveness of the resulting collective intelligence.