Triadic User Matching Using Temporal Availability Profiles
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Solution Overview
Problem
Existing data matching systems are inadequate for coordinating the assignment of three or more participants due to their rule-based and static nature, lacking adaptability to contextual factors, especially in scenarios requiring coordinated instructional or therapeutic support in remote or underserved areas.
Innovation Solution
An apparatus and method for triadic user matching using a triadic machine-learning model that classifies user profiles based on temporal availability and descriptors, determining optimal triadic sessions by integrating historical match data to facilitate synchronous interactions among a student, local facilitator, and remote specialist.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If binary matching algorithms are used to pair users, then the system is simple to implement, but it cannot coordinate assignment of three or more participants
Solution Approach 1:
The patent segments the matching problem into multiple independent binary classification tasks. Instead of attempting to solve the complex multi-way matching problem directly, the system performs separate binary classifications for each potential pairing, then integrates these results to determine optimal group assignments. This allows the system to handle complex multi-participant coordination while maintaining the simplicity and reliability of binary classification algorithms.
2Ease of operation
If rule-based matching systems are used, then the system is easy to understand, but it lacks adaptability to contextual factors
Solution Approach 1:
The patent implements dynamic adaptability by training machine learning models on historical matching data and contextual factors. The system continuously learns from past outcomes and adjusts its predictions based on contextual variables such as user preferences, availability, and performance metrics. This dynamic approach allows the system to adapt to changing conditions while maintaining operational simplicity through automated model updates.
Solution Approach 2:
The system incorporates feedback loops where outcomes from previous matching decisions are fed back into the training data. Historical match data, including success metrics and contextual information, is used to retrain and refine the machine learning models. This feedback mechanism enables the system to continuously improve its adaptability to contextual factors while maintaining ease of operation through automated learning processes.
3Stability of the object's composition
If static matching systems are used, then the system is stable and predictable, but it cannot adapt to temporal availability changes
Solution Approach 1:
The patent applies preliminary action by pre-processing and storing temporal availability data in advance. User availability information, preferences, and historical performance metrics are collected and structured beforehand, allowing the machine learning models to quickly retrieve and apply this information when making matching decisions. This pre-prepared data structure maintains system stability while enabling rapid adaptation to temporal changes.
Data Source
AI summary
An apparatus and method for triadic user matching based on profile data are disclosed. The apparatus includes at least a processor, and a memory communicatively connected to the at least a processor, wherein the memory contains instructions that, when executed by the at least a processor, configure the at least a processor to receive a plurality of sets of profile data, classify a set of first user profile data into one or more first user classification groups, determine a triadic match including at least one second user and at least one third user associated with at least one first user as a function of a temporal availability element of each of the plurality of sets of profile data by using a triadic machine-learning model, append the triadic match to a database and modify a graphical user interface as a function of the triadic match.