Gameplay Roulette: ML-Based Content Recommendation System
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Solution Overview
Problem
Users face inefficiency in finding new interactive content titles that match their preferences due to the vast selection and difficulty in discerning contributing factors to an enjoyable gameplay experience, with existing systems lacking in personalized recommendations and analysis of user enjoyment patterns.
Innovation Solution
A method and system for selecting new interactive content titles based on user profiles that include preferences and historic data, using a machine learning model to identify common attributes and randomize elements for personalized recommendations, integrating with a network environment to provide tailored suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually browse through all available interactive media titles to find new content, then they can potentially discover titles of interest, but the process becomes extremely time-consuming and inefficient due to the vast selection
Solution Approach 1:
The system automatically analyzes user gameplay data and interactions to generate personalized content recommendations without requiring manual user input or active searching. The system serves itself by processing user behavior data to identify patterns and preferences, then autonomously curating and presenting relevant content titles to the user.
Solution Approach 2:
The patent replaces the mechanical manual browsing process with an automated computational system that uses machine learning algorithms to analyze user data and generate recommendations. Instead of users physically scrolling and evaluating content, the system computationally processes gameplay data and automatically presents curated recommendations.
2Ease of operation
If users focus on actual gameplay and succeeding within specific gameplay sessions, then they can enjoy the gaming experience, but they cannot identify or quantify aspects that contribute to their enjoyment
Solution Approach 1:
The system continuously collects and analyzes user gameplay data, then uses this feedback to refine and improve content recommendations. By processing user interactions and gameplay patterns, the system identifies what aspects contribute to user enjoyment and uses this feedback loop to increasingly accurately predict and recommend content that aligns with user preferences.
Solution Approach 2:
The patent introduces an intermediary analytical system that bridges the gap between user gameplay experiences and content selection. This intermediary system processes raw gameplay data, identifies patterns and contributing factors to enjoyment, and translates this analysis into actionable recommendations, allowing users to benefit from analyzed enjoyment factors without needing to perform the analysis themselves.
3Measurement precision
If game developers analyze gameplay data to understand user enjoyment patterns, then they can identify factors contributing to enjoyment, but they lack access to aggregate data across different platforms, systems, and titles
Solution Approach 1:
The system is designed to process and analyze gameplay data across multiple platforms, systems, and game titles through a universal analytical framework. The patent creates a multi-functional system that can handle diverse data sources and formats, enabling aggregate analysis that transcends individual platform limitations and provides comprehensive insights into user enjoyment patterns.
Solution Approach 2:
The patent merges data from multiple sources including gameplay data, user interactions, and platform-specific information into a unified analytical system. By combining these diverse data streams, the system creates aggregate insights that are more comprehensive than what any single platform could achieve alone, enabling developers to understand cross-platform enjoyment patterns.
4Loss of information
If users rely on marketing materials or review publications to evaluate game titles, then they can gather information about available content, but they cannot fully capture or glean the actual gameplay experience
Solution Approach 1:
The system performs preliminary analysis of user preferences and gameplay patterns before presenting content recommendations. By pre-processing user data and identifying preferences in advance, the system can recommend content that is likely to match user experiences, eliminating the need for users to manually evaluate marketing materials and reviews.
Solution Approach 2:
The patent transforms the evaluation parameters from static marketing materials to dynamic, personalized recommendations based on actual user gameplay data. Instead of relying on fixed descriptions and reviews, the system changes the recommendation parameters to reflect real user preferences and experiences, providing a more accurate assessment of content suitability.
Data Source
AI summary
A method and system for selecting new interactive content titles for users is disclosed. A plurality of user profiles is stored in memory. Each user profile including one or more preferences and historic gameplay data associated with a user associated with the user profile. A request to select a new interactive content title for the user over a communication network from a client device associated with the user profile is received. At least one of a plurality of available interactive content titles that are not associated with the historic gameplay data is selected. The at least one interactive content titles is selected from a subset of the available interactive content titles that correspond to one or more of the preferences in the stored user profile of the user.