Personalized Gaming Experience through AI-driven Controller Configuration

Publication ID: 24-11857868_0005_PTD

Published: October 28, 2025

Category:Future Evolutions & Paradigm Shifts

Legal Citation

pr1or.art Inc., “Personalized Gaming Experience through AI-driven Controller Configuration,” Published Technical Disclosure No. 24-11857868_0005_PTD, Published October 28, 2025, available at https://archive.pr1or.art/24-11857868_0005_PTD

This technical disclosure describes improvements that would be readily apparent to a Person Having Ordinary Skill In The Art (PHOSITA) when considered in combination with the foundational architecture disclosed in U.S. Patent No. 11,857,868.

Summary of the Inventive Concept

A next-generation controller configuration system leveraging AI, machine learning, and cloud-based platforms to provide personalized gaming experiences tailored to individual users' preferences, playstyles, and gaming histories.

Background and Problem Solved

The original automated controller configuration recommendation system, while innovative, has limitations in terms of user data reliance and static recommendations. The present inventive concept addresses these limitations by introducing AI-driven, real-time, and dynamic controller configuration adaptation, enabling a more immersive and effective gaming experience.

Detailed Description of the Inventive Concept

The system comprises a neural network trained on a vast dataset of user profiles and corresponding controller settings, predicting optimal controller settings for new users based on their profiles and gaming histories. Real-time controller configuration adaptation is achieved through monitoring user behavior and performance metrics during gameplay, dynamically adjusting controller settings to optimize the user's experience and improve their skills. A cloud-based controller configuration platform enables users to share and discover new controller configurations, while a machine learning model simulates user behavior to generate virtual controller configurations compatible with multiple gaming platforms and devices. Reinforcement learning is utilized to optimize controller settings, achieving specific gameplay goals and improving the user's experience and performance.

Novelty and Inventive Step

The new claims introduce a paradigm shift in controller configuration technology by incorporating AI, machine learning, and cloud-based platforms, enabling real-time adaptation, virtual controller generation, and reinforcement learning-based optimization. These innovations overcome the limitations of the original patent, providing a more personalized, dynamic, and effective gaming experience.

Alternative Embodiments and Variations

Alternative embodiments may include the integration of biometric sensors, augmented reality, or haptic feedback to further enhance the gaming experience. Variations may involve adapting the system for non-gaming applications, such as accessibility tools or professional simulation software.

Potential Commercial Applications and Market

The inventive concept has vast commercial potential in the gaming industry, with potential applications in esports, gaming hardware, and cloud gaming services. The technology may also be applied to other industries, such as simulation, education, and healthcare, where personalized user experiences are crucial.

CPC Classifications

Section	Class	Group
A	A63	A63F13/23
A	A63	A63F13/22
A	A63	A63F13/42
G	G06	G06N20/00

Section 103 Obviousness Analysis (PHOSITA)

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Field of Art

Interactive software systems, human-computer interaction, gaming technology, and machine learning-based adaptive interfaces, with expertise in controller configuration, user experience optimization, and AI-driven personalization techniques

Person of Ordinary Skill (PHOSITA) Profile

A skilled professional with advanced degrees in computer science, software engineering, or human-computer interaction, possessing expertise in machine learning, neural networks, user behavior modeling, and adaptive interface design, with practical experience in gaming technology and software configuration systems

Obviousness Rationale

A person of ordinary skill would recognize that the PTD's AI-driven controller configuration represents a predictable extension of the source patent's user-centric configuration recommendation system. The fundamental concept of dynamically adjusting controller settings based on user characteristics is already established in the source patent, with the PTD merely introducing more sophisticated machine learning techniques to achieve similar optimization goals. The technical progression from static to adaptive, AI-powered configuration is a natural evolutionary step that would be obvious to a skilled practitioner in the field.

Obvious Combinations & Variations

Source Patent Element

Identifying user characteristics and preferences for controller configuration

PTD Variation

Neural network-based prediction of optimal controller settings using comprehensive user profiles and gaming history

Obviousness Reasoning

Applying machine learning to existing user profiling techniques represents a known approach for enhancing predictive capabilities, with predictable results in personalization technology

Source Patent Element

Gathering user performance data through telemetry

PTD Variation

Real-time controller configuration adaptation by monitoring user behavior and performance metrics during gameplay

Obviousness Reasoning

Extending telemetry-based performance tracking to dynamic, continuous configuration adjustment is a logical and predictable enhancement of existing monitoring techniques

Source Patent Element

Comparing user profiles to provide suggested adjustments

PTD Variation

Cloud-based platform enabling user profile sharing, configuration discovery, and collaborative optimization

Obviousness Reasoning

Leveraging cloud technologies to expand profile-based configuration recommendations is a straightforward technological progression with finite, predictable implementation strategies

Source Patent Element

Controller configuration for different software types and user interactions

PTD Variation

Machine learning model generating virtual controller configurations compatible with multiple platforms and devices

Obviousness Reasoning

Generalizing controller configuration techniques across platforms is a natural design choice driven by interoperability requirements and known software abstraction principles

Source Patent Element

User-specific controller setting recommendations

PTD Variation

Reinforcement learning approach for optimizing controller settings to achieve specific gameplay goals

Obviousness Reasoning

Applying reinforcement learning to configuration optimization represents a known technique for improving adaptive systems, with predictable outcomes in performance enhancement

35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11857868 and the published technical disclosure, a person having ordinary skill in the art would find the claimed AI-driven, adaptive controller configuration techniques to be obvious variations of existing user-centric configuration recommendation systems. The incremental technological advancements, including neural network prediction, real-time adaptation, and reinforcement learning optimization, represent predictable extensions of prior art that would be readily conceived by a skilled practitioner in interactive software and machine learning technologies.

Original Patent Information

Patent Number	US 11,857,868
Title	Automated controller configuration recommendation system
Assignee(s)	Electronic Arts Inc.