Dynamic Phasing Scores for Virtual Instance Engagement
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Solution Overview
Problem
Existing virtual environments face challenges in optimizing user engagement and load balancing, as users are often statically assigned to instances without consideration for their individual preferences or behaviors, leading to suboptimal interaction and experience.
Innovation Solution
A method and system that utilize a trained machine learning model to generate phasing scores for users based on their profiles, dynamically selecting instances where users are more likely to engage with other entities, thereby enhancing interaction and optimizing multiple metrics such as engagement and server usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If users are statically assigned to instances without consideration for their individual preferences or behaviors, then the system implementation is simple, but user engagement is suboptimal
Solution Approach 1:
The patent implements dynamic phasing by transitioning from static instance assignment to dynamic user profiling and scoring. The system continuously updates user profiles based on behavior data and dynamically calculates phasing scores to determine optimal instance placement, allowing the system to adapt to changing user preferences and behaviors in real-time
Solution Approach 2:
The patent changes the parameter of instance assignment from fixed/static to variable/dynamic by introducing user profiles with multiple attributes and phasing scores. The system evaluates multiple parameters including user behavior patterns, preferences, and engagement metrics to dynamically adjust which instance a user is phased into, rather than using a static assignment method
2Productivity
If dynamic phasing with machine learning models is implemented to optimize user engagement, then user engagement increases, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models with historical user behavior data before deployment. User profiles are pre-populated with baseline attributes and the phasing system is pre-configured with scoring algorithms, enabling rapid real-time decision-making without complex runtime computations
Solution Approach 2:
The patent introduces an intermediary layer consisting of user profiles and phasing score calculations that mediate between raw user behavior data and instance assignment decisions. This intermediary structure simplifies the overall system architecture by decoupling data collection from decision-making, allowing the machine learning models to process information in manageable stages
3Productivity
If users are dynamically phased based on machine learning scores, then user engagement is optimized, but computational resources increase
Solution Approach 1:
The patent implements partial action by calculating phasing scores selectively rather than re-evaluating all users continuously. The system updates user profiles and recalculates scores only when significant behavior changes occur or at scheduled intervals, reducing unnecessary computational overhead while maintaining engagement optimization
Solution Approach 2:
The patent creates a universal user profile structure and phasing scoring system that serves multiple functions simultaneously: it optimizes user engagement, enables load balancing across instances, and provides insights into user behavior patterns. This multi-functionality reduces the need for separate systems and computations for each objective
Data Source
AI summary
Techniques for selective phasing using machine learning are provided. A phasing event for a user in an interactive virtual environment is identified, and a plurality of instances to which the user can be phased is identified. For each respective instance of the plurality of instances, a respective phasing score is generated by processing a first user profile of the first user using a trained machine learning model, where each respective phasing score indicates a respective predicted engagement of the first user with respect to one or more respective entities that are present within the respective instance. An instance, of the plurality of instances, is selected based on the respective phasing score, and the user is phased into the instance.