Client-Side Embeddings for Player Engagement Monitoring and Optimization
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Solution Overview
Problem
Video game companies face challenges in keeping players engaged due to data processing complexities and high costs associated with real-time data analysis using machine learning models, which are often too complex and require significant engineering efforts.
Innovation Solution
A distributed machine learning infrastructure that processes raw data locally on gaming devices and sends processable data to a remote server, reducing server load and enhancing data security, while optimizing embeddings with contextual and time-related information for improved player engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are run on servers to analyze player data, then player engagement can be optimized, but processing costs and engineering complexity increase significantly
Solution Approach 1:
The patent segments the data processing system into client-side embedding generation components and server-side ML inference components. The client device performs local processing to generate embeddings from raw game data, while the server performs inference using pre-trained models. This segmentation reduces server engineering complexity while maintaining engagement optimization capabilities.
Solution Approach 2:
The patent implements preliminary action by pre-training ML models offline and pre-processing raw game data into embeddings on the client side before transmission. This allows the server to receive ready-to-use embeddings that require minimal processing, reducing runtime engineering complexity while preserving the ability to optimize player engagement through predictive analytics.
2Measurement precision
If more player data is tracked and processed in real-time, then player engagement can be better monitored, but server processing load and costs increase
Solution Approach 1:
The patent extracts the computationally intensive embedding generation process from the server and relocates it to the client device. This extraction allows the server to receive pre-processed embeddings rather than raw data, significantly reducing server processing power requirements while maintaining high measurement precision for player engagement monitoring through comprehensive data tracking.
Solution Approach 2:
The patent implements partial action by having the client device perform only the specific task of generating embeddings from raw game data, while the server performs only the inference task. This division allows comprehensive data collection (excessive action for monitoring precision) without requiring the server to handle all processing steps, thus reducing server power consumption.
3Loss of information
If data is centralized on servers for analysis, then ML models can access comprehensive player information, but data security risks and breach vulnerabilities increase
Solution Approach 1:
The patent segments data storage and processing across client and server environments. Sensitive raw player data remains localized on the client device where it is transformed into embeddings, while only the compressed embedding representations are transmitted to and stored on the server. This segmentation reduces data breach risk by minimizing the attack surface on servers while preserving complete player information through distributed storage.
Solution Approach 2:
The patent introduces embeddings as an intermediary representation between raw player data and server storage. Instead of transmitting and storing sensitive raw data, the system uses embeddings as a compressed, less sensitive intermediary form that retains the essential information needed for ML analysis while reducing data security risks associated with centralized server storage.
Data Source
AI summary
Described are various embodiments of system and method for monitoring and optimizing player engagement. In some embodiments, the computer-implemented method comprises generating, on a server, a storage layer in the form of a graph drawn according to a schema description of objects and relationships in a virtual game environment. The server produces, from the received schema and learning system objectives, one or more instructions. The instructions are transmitted to and applied by a gaming device configured to execute a designated interactive software program, to produce from raw data generated one or more embeddings. The embeddings are stored in the graph, and retrieved to perform one or more data analysis tasks on the designated embeddings by one or more machine learning algorithms. The embeddings can be augmented or optimized into contextualized preferences embeddings or contextualized timeline embeddings, to allow better contextual learning and predictive outputs.