Team Classification via Concurrency Data Analysis
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Solution Overview
Problem
Group-based communication systems fail to accurately determine the specific use case for teams, leading to suboptimal provision of features and messages, as existing methods rely on incomplete or inaccurate classification techniques, such as email address domains, which can misclassify teams and fail to tailor experiences effectively.
Innovation Solution
The system employs machine learning techniques to classify teams based on concurrency data and other team-specific features, using machine learning models to predict classifiers and tailor user experiences, messaging, and feature offerings, optimizing for work, educational, or social teams by analyzing user interactions and behavior patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses traditional classification methods (e.g., email address domains) to categorize teams, then the implementation is simple and quick, but the classification accuracy is low and teams are frequently misclassified
Solution Approach 1:
The patent replaces traditional rule-based classification mechanisms (checking email domains, team names) with machine learning models that automatically learn classification patterns from concurrency data and team features, significantly improving accuracy while managing complexity through automated training pipelines
Solution Approach 2:
The system transforms classification from static rules to dynamic parameter-based decisions by using machine learning models that process multiple features (concurrency patterns, team size, activity levels) to determine team type, allowing accurate classification without simple domain checks
2Ease of operation
If the system provides generic features and messages to all teams, then the system implementation is straightforward, but user experience and feature relevance are suboptimal
Solution Approach 1:
The patent implements local quality by tailoring features, messages, and recommendations to each team's specific classification and behavior patterns, such as providing different onboarding sequences for work teams versus social teams, rather than applying uniform treatment to all users
Solution Approach 2:
The system dynamically adapts feature recommendations and messaging based on real-time concurrency data and team evolution, allowing the system to adjust to changing team needs and classifications as teams grow and evolve over time
3Measurement precision
If the system collects and analyzes extensive concurrency data and team features for classification, then classification accuracy improves, but data processing complexity and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and storing concurrency data as teams interact with the system, preparing features in advance for classification rather than computing everything in real-time, which reduces computational complexity during inference
Solution Approach 2:
The system uses copying by creating simplified representations of team behavior patterns through machine learning models that capture essential classification features without requiring full analysis of all raw data, reducing processing complexity while maintaining accuracy
Data Source
AI summary
Methods, systems, apparatuses, devices, and computer program products are described. A group-based communication system may use machine learning techniques to classify teams of the system, determine discount messaging for teams of the system, or both. The group-based communication system may receive concurrency data for a team of users and may input the concurrency data (e.g., with one or more other features associated with the team) into a machine learning model to generate a classifier for the team. The classifier may indicate whether the team is a work team, an educational team, or a social team. Based on the classifier for the team, the system may send a message to at least one user of the team (e.g., an administrative user). In some examples, the system may use another machine learning model to generate a discount message for sending to the at least one user based on the team classifier.