AI Image Analysis for Cross-Game Mobile Competition
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Solution Overview
Problem
Mobile gaming platforms lack comprehensive features for cross-game competition and community interaction, and often lack strong AI trainers to challenge players effectively, restricting competitions and communities to specific game titles and lacking robust AI support.
Innovation Solution
A method and apparatus using AI-driven machine learning to categorize images of mobile device software, enabling scoring, ranking, and leveling across multiple game titles, facilitating cross-game competitions and communities, and providing AI trainers without requiring API keys or calls, by capturing images, extracting descriptors, and processing them through a trained neural network to determine user scores and levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If games provide competitions and communities through their respective mobile applications, then player interaction and competition are enabled, but competitions and communities are restricted to players of each specific game title
Solution Approach 1:
The patent implements a universal platform that enables multiple game titles to participate in a single competition system. The platform uses image recognition technology to extract game state information from different games, allowing players to compete across game boundaries. This multi-functional approach allows the same platform infrastructure to handle various game types and styles, achieving cross-game compatibility without requiring separate competition systems for each game.
Solution Approach 2:
The patent introduces an intermediary image recognition system that acts as a mediator between different game applications and the competition platform. Instead of direct integration between games and the competition system, the image recognition technology captures screenshots or video frames from games, processes them to extract relevant information, and feeds this data to the competition platform. This intermediary layer simplifies integration complexity by providing a standardized interface that works across different game titles.
2Reliability
If AI trainers are provided in games, then players can practice gaming skills, but the AI trainers are unable to provide a strong challenge
Solution Approach 1:
The patent merges multiple AI models into a unified competition platform. Instead of each game having its own separate AI trainer, the platform consolidates AI capabilities across multiple games. The image recognition system processes game states from different titles, and the competition logic integrates skill assessment across games. This merging allows players to practice skills in one game and apply them in competitions across multiple games, improving AI training effectiveness while sharing the AI infrastructure.
Solution Approach 2:
The AI competition platform serves multiple functions: it acts as a trainer by providing practice competitions, as a evaluator by assessing player skills, and as a connector by linking different games. The same AI infrastructure that enables cross-game competitions also provides the challenging opposition players need for practice. This multi-functionality allows the AI system to provide strong challenges without requiring separate complex AI models for each training function.
3Adaptability or versatility
If image recognition technology is used to determine user scores and levels, then cross-game competitions and communities are enabled, but processing complexity increases
Solution Approach 1:
The patent implements preliminary processing of game images by capturing standardized screenshots or video frames at specific game states. The system pre-processes images by normalizing formats, cropping to relevant areas, and extracting key visual elements before feeding them to the recognition algorithm. This preliminary action reduces the complexity of the main image recognition task by ensuring that input images are already optimized and standardized, making the subsequent processing more efficient and manageable.
Data Source
AI summary
A method and apparatus for categorising images of mobile device software, the method comprising: running mobile device software through a platform installed on a user device; capturing images of the mobile device software during user operation; extracting image descriptors from the captured images; processing the extracted image descriptors to generate input to a model derived from a trained computer neural network; processing output of the model to determine a category for a captured image from which the image descriptors are extracted; extracting one or more features present in the captured image according to the predetermined one or more features to be extracted for the category; and processing the extracted one or more features to determine a score, ranking or level tied to the user against scores, rankings or levels of other users of the same mobile device software in case that the category relates to scoring, ranking or levelling.