AI Mining of Collaboration Network Signals for Process Optimization
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Solution Overview
Problem
Existing collaboration networks in enterprises generate vast amounts of data from user interactions, but there is a lack of effective methods to optimize processes based on these interactions using AI-driven recommendations.
Innovation Solution
A system that clusters user signals from collaboration networks, extracts key data, generates optimization prompts, and uses a machine learning model to provide process automation recommendations, seamlessly adapting across platforms and integrating with various applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vast amounts of collaboration network data are collected and processed, then process optimization recommendations can be generated, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex data processing task into distinct functional modules: a collaboration network data receiver that collects raw interaction data, a data processor that analyzes and extracts patterns, and a recommendation generator that produces optimization suggestions. This modular segmentation reduces system complexity by organizing the data flow into manageable, independent components that can be developed and maintained separately.
Solution Approach 2:
The patent introduces an intermediary processing layer between the collaboration network data sources and the recommendation output. This intermediary component processes and structures the raw interaction data into meaningful patterns before feeding them to the recommendation engine, thereby simplifying the overall system architecture and reducing the complexity burden of handling vast amounts of raw data directly.
2Extent of automation
If AI-driven recommendations are implemented, then process automation capability improves, but implementation complexity increases
Solution Approach 1:
The system employs self-service mechanisms where the AI model automatically learns from collaboration network interaction patterns and generates process optimization recommendations without requiring manual configuration or complex implementation setup. The model adapts and improves autonomously by processing incoming data and adjusting its recommendations, thereby reducing implementation complexity while maintaining high automation capability.
Solution Approach 2:
The patent utilizes parameter changes in the AI model to simplify implementation. By adjusting model parameters and training data characteristics rather than changing the fundamental system architecture, the system achieves improved process automation with reduced implementation complexity. This allows flexible adaptation to different collaboration networks without requiring complex re-engineering.
Data Source
AI summary
The described technology provides a method including receiving a plurality of user signals from one or more collaboration networks, wherein the plurality of signals are clustered with reference to a relevant user node, extracting a plurality of key data relating to the relevant user node, generating an optimization prompt using the key data, inputting the optimization prompt into a machine learning model, and using the machine learning model, generating an optimization recommendation in response to receiving the optimization prompt, and modifying a process using the process automation recommendation.