AI Entity Grouping for Connections Networking Recommendations
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Solution Overview
Problem
Conventional gaming platforms struggle with complex pattern recognition and data structure modeling, requiring manual feature engineering and explicit coding, which is time-consuming and resource-intensive, especially when dealing with high-dimensional data or large variable interactions.
Innovation Solution
An AI system utilizing a dual-model architecture with Large Language Models (LLMs) for generative and critical evaluation processes to enhance gaming services, such as entity grouping, recommendation, and scoring, improving accuracy and efficiency in gaming applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional gaming platforms use manual feature engineering and explicit coding for pattern recognition, then the system structure is simple and easy to understand, but the time consumption and resource intensity increase significantly
Solution Approach 1:
The patent replaces manual feature engineering and explicit coding (mechanical/systematic approach) with AI-based automated pattern recognition and data structure modeling. The system uses machine learning models to automatically learn features from raw data, eliminating the need for manual feature extraction and reducing both time consumption and resource intensity while maintaining system effectiveness.
Solution Approach 2:
The AI system performs self-learning and self-optimization through automated training on gaming data. The models automatically adjust their parameters and structures based on the data they process, eliminating the need for continuous manual intervention and expertise for feature engineering, thereby reducing time consumption and resource requirements.
2Measurement precision
If AI techniques are applied to gaming platforms for pattern recognition and data modeling, then accuracy and efficiency improve, but the device complexity and computational requirements increase
Solution Approach 1:
The patent divides the complex AI processing into distinct modular components: data collection modules, training modules, pattern recognition modules, and execution modules. Each module handles specific tasks independently, allowing the system to achieve high prediction accuracy while managing computational complexity through structured organization and specialized processing for each function.
3Extent of automation
If automated AI models are used for entity grouping and recommendation, then the need for manual updates and expertise is reduced, but the extent of automation and system complexity increase
Solution Approach 1:
The patent implements universal AI models that can perform multiple functions including entity grouping, recommendation, scoring, and pattern recognition across different gaming contexts. These multi-functional models reduce the need for separate specialized systems for each task, thereby increasing automation while managing overall system complexity through consolidated architecture.
Data Source
AI summary
Artificial intelligence techniques for connections networking are described. In one embodiment, for example, a method comprises receiving a natural language query to group a set of entities by a first machine learning model trained on a public dataset, generating a set of entity groups by the first machine learning model based on the natural language query and the set of entities, generating a set of connections entity groups based on the set of entity groups by a second machine learning model trained on a private dataset, selecting a member identifier (ID) representing a member of a connections networking system associated with an entity from a connections entity group of the set of connections entity groups, and sending a recommendation for a networking service to an electronic device based on the member ID. Other embodiments are described and claimed.