AI Collaboration System Using Vector Embeddings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for determining collaboration between employees in organizations are inefficient and time-consuming, especially in large organizations, leading to potential resource mismanagement and errors.
Innovation Solution
An AI-based collaboration system that uses a pre-trained machine learning model to create a hierarchical tree of employee nodes, generate vector embeddings, and determine the degree of collaboration based on collaboration parameters such as skillset, role, and satisfaction, facilitating efficient resource allocation and performance evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to track and manage employee collaboration, then flexibility and adaptability are maintained, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical tracking methods with an automated AI-based system that uses machine learning models to analyze collaboration data. The system automatically processes employee interaction data, generates vector embeddings, and determines collaboration degrees without human intervention, thereby substituting manual mechanical processes with intelligent automated systems.
Solution Approach 2:
The system enables self-service by automatically tracking and analyzing employee collaboration patterns without requiring manual input from employees or managers. The AI model autonomously processes data from various sources, generates embeddings, and produces collaboration assessments, allowing the system to serve itself in managing collaboration tracking.
2Reliability
If manual resource management is used for collaboration, then human judgment and flexibility are preserved, but mistakes and inefficiencies increase
Solution Approach 1:
The patent replaces manual resource management with an AI-based automated system that uses machine learning models to objectively assess collaboration. The system processes collaboration data through vector embedding generation and mathematical operations to determine collaboration degrees, eliminating human errors while maintaining high efficiency in resource allocation and management.
3Loss of information
If comprehensive collaboration tracking is implemented, then complete information is obtained, but system complexity increases
Solution Approach 1:
The patent introduces vector embeddings as an intermediary representation that simplifies the complex collaboration data. The machine learning model transforms raw collaboration information into compact vector representations, which serve as intermediaries between the complex original data and the final collaboration degree determination, thereby reducing system complexity while preserving information completeness.
Solution Approach 2:
The system changes parameters by transforming complex collaboration data into vector embeddings with specific dimensional representations. This parameter transformation allows the system to handle comprehensive collaboration information in a standardized, manageable format that reduces complexity while maintaining the completeness of the underlying information.
Data Source
AI summary
A method and system for determining collaboration between employees is disclosed. In some embodiments, the method includes receiving a plurality of collaboration parameters associated with a set of employees. The method further includes creating a plurality of employee nodes associated with the set of employees in a hierarchical tree, based on the plurality of collaboration parameters and a first pre-trained machine learning model. The method further includes generating a plurality of vector embeddings associated with the plurality of employee nodes, based on the first pre-trained machine learning model. The method further includes determining a degree of collaboration between at least two employees from the set of employees based on one or more vector embeddings from the generated plurality of embeddings.