Virtual Character Development Using Machine Learning

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Solution Overview

Problem

Traditional simulation applications lack dynamic character development, as they are typically designed to direct users towards predetermined goals with outcomes that are not responsive to user input, limiting the user's ability to create and evolve virtual characters autonomously.

Innovation Solution

A system utilizing machine learning and artificial intelligence, where a server and client device collaborate to allow users to create and interact with virtual characters, enabling autonomic growth and learning through user input processing, using algorithms like neural networks for character recognition and voice recognition, allowing for dynamic development and interaction.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If traditional simulation applications use predetermined outcomes and direct users toward fixed goals, then the simulation structure is simple and easy to implement, but the character development lacks dynamics and responsiveness to user input

Engineering Contradiction:

Improvecharacter development adaptabilityVSAvoidsimulation system complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The virtual character performs self-learning and self-development through machine learning algorithms. The character autonomously processes user inputs, updates its own information state, and generates responses without requiring predetermined scripting for each interaction scenario. This self-service mechanism enables dynamic character development while maintaining system simplicity.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system changes the internal parameters of the virtual character through machine learning by updating information associated with the character based on extracted input data. These parameter changes enable the character to adapt its behavior and responses dynamically, transforming from a static predetermined system to a dynamic learning system.

Inventive Principle:

Principle #35Parameter changes

2Extent of automation

If the simulation uses predetermined outcomes to guide users, then the implementation is straightforward, but the user's ability to create and evolve virtual characters autonomously is limited

Engineering Contradiction:

Improvecharacter evolution automationVSAvoiduser interaction complexity

Core Design Contradiction:

Extent of automationVSEase of operation

Solution Approach 1:

The patent replaces traditional mechanical scripting systems with machine learning algorithms. Instead of using predetermined outcome tables and fixed decision trees, the system uses neural networks and information processing to automatically determine character responses and development trajectories, enabling autonomous character evolution.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system implements continuous feedback loops where user inputs are processed, information is extracted and stored in the virtual character's knowledge base, and subsequent responses are generated based on this accumulated information. This feedback mechanism enables the character to evolve autonomously based on interaction history while keeping user interaction simple.

Inventive Principle:

Principle #23Feedback

3Adaptability or versatility

If machine learning algorithms are implemented to enable dynamic character development, then the character can learn and adapt autonomously, but the computational processing requirements increase

Engineering Contradiction:

Improvecharacter learning capabilityVSAvoidcomputational energy consumption

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The system applies partial machine learning processing by extracting only relevant information from user inputs and updating only necessary portions of the virtual character's information state. This selective processing approach enables character learning capabilities while reducing overall computational energy consumption compared to processing all possible input scenarios.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS10617961B2Online learning simulator using machine learning

Publication Date: 2020.04.14 INTERLAKE RESEARCH LLC

AI summary

Technologies for executing a virtual character development application using machine learning are described herein. In typical simulation applications, a user may be enabled to create a virtual character and navigate a virtual world. A typical simulation application may accept inputs from the user, determine actions initiated by the user based on the inputs, determine subsequent outcomes of the user initiated actions, and mold the simulation according to the outcomes. However most outcomes may be predetermined and predictable by design. In contrast, some embodiments may include a server configured to execute a virtual character development application in conjunction with one or more client devices. A user may utilize a client device to create and develop a virtual character within the application. The user may be enabled to provide inputs to the virtual character development application, and the artificial component may process the input and extract information associated with the virtual character.