Predicate Model for Dynamic Game Guidance
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Solution Overview
Problem
Online games struggle to provide users with effective guidance to progress, as they lack the ability to predict user directions and offer hints based on current location and user attributes, leading to a potentially unsatisfactory gaming experience due to multiple possible paths and lack of personalized recommendations.
Innovation Solution
A system that uses a predicate model generated from machine learning algorithms, combining in-game GPS and telemetry data from prior players' sessions to provide a dynamically updated predicate list of activities, filtered by user attributes, to help users achieve game objectives efficiently, whether by shortest or longest paths, and including activities in the vicinity of their current location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the online game provides multiple possible directions for progression, then the game becomes more engaging and versatile, but the user may get lost and unable to progress satisfactorily
Solution Approach 1:
The system performs preliminary actions by pre-computing activity graphs and predicate models from telemetry data before the user needs them. The activity graph is built in advance showing all possible progression paths, and the predicate model is pre-trained on prior player data, enabling quick recommendation generation when the user needs guidance
Solution Approach 2:
The patent introduces an intermediary guidance system that acts as a mediator between the game world and the user. The activity graph and predicate model serve as intermediaries that process the complex game state and translate it into simplified, personalized activity recommendations, helping users navigate without restricting their freedom
2Adaptability or versatility
If the online game cannot predict user direction, then the game maintains flexibility, but the game cannot provide personalized hints to help users progress
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring user attributes, current location, and game state, then using this feedback to dynamically update activity recommendations. The predicate model learns from telemetry data and adapts its predictions based on user behavior patterns, providing increasingly personalized guidance while maintaining game flexibility
Solution Approach 2:
The system performs preliminary analysis of user attributes and game state to pre-calculate relevant activities and progression paths before the user actually needs guidance. The activity graph is pre-computed showing all possible paths, and the predicate model is pre-trained on historical data, enabling rapid personalized recommendation generation
3Productivity
If the system provides comprehensive activity recommendations, then user progression is improved, but the system complexity increases due to map traversal and predicate model generation
Solution Approach 1:
The patent segments the complex guidance system into distinct modular components: the activity graph module that identifies possible activities, the predicate model module that predicts user intent, and the recommendation generation module that combines them. This segmentation allows each component to be developed, optimized, and maintained independently, reducing overall system complexity while maintaining comprehensive functionality
Solution Approach 2:
The system performs preliminary computations by pre-building the activity graph and pre-training the predicate model on telemetry data. These pre-computed structures are then reused during gameplay, avoiding the need to recalculate everything from scratch and reducing real-time computational complexity
Data Source
AI summary
Methods and systems for providing assistance for an online game executed by a cloud gaming system includes receiving a game objective of a user playing the online game. A current location of the user in the online game is identified. A map of vector points defined for the online game is traversed in a backward direction from a vector point corresponding to location of the game objective to the current location, to identify vector points corresponding to activities performed by a plurality of prior players to achieve the game objective for the online game. A predicate list of the activities identified from the traversing is generated for presenting to the user. The predicate list specifies a sequence in which the activities need to be performed to achieve the game objective. The activities presented in the predicate list are dynamically adjusted based on changes to the current location of the user detected in the online game.