How AI Learns to Control Game Characters Based on Their Surroundings
A system that allows digital characters to automatically perform actions by matching their current environment to previously learned experiences stored in a database.
Original patent title: “Artificially intelligent systems, devices, and methods for learning and/or using an avatar's circumstances for autonomous avatar operation”
A system that allows digital characters to automatically perform actions by matching their current environment to previously learned experiences stored in a database. Granted to Individual in 2020 with 23 claims and 26 forward citations, and it is expected to expire in 2036.
Coverage
What does this patent actually cover?
This patent describes a method for teaching a digital character, or avatar, to act on its own by recognizing patterns in its environment. The system maintains a knowledgebase that links specific environmental objects to sets of instructions or actions. When the avatar encounters a new situation, the system compares the current objects in the scene to the stored patterns. If a match is found, the system triggers the corresponding action, allowing the avatar to navigate or interact with the game world without manual player input.
The gap
What does this patent NOT cover?
- Does not cover manual control of avatars by human players.
- Does not cover basic scripted AI behaviors that are hard-coded rather than learned via pattern matching.
- Does not cover the underlying physics engines used to render the game objects themselves.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The system uses a correlation-based knowledgebase that treats environmental objects as data points, allowing the AI to transfer learned behaviors from one avatar or application to another if the environmental context matches.
The Patent Drawing
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Autonomous NPCs in open-world role-playing games
Automated testing bots for game quality assurance
AI-driven character training in game development environments
Why it matters
The bigger picture
As games become more complex, manual programming for every possible NPC (non-player character) behavior is inefficient. This approach moves toward autonomous agents that can adapt to different game states, which is a core challenge in modern game development and simulation software.
Filed
December 19, 2016
Granted
March 31, 2020
Market context
Who's building on this
Companies in this space
Major game engine developers like Unity Technologies and Epic Games are actively researching autonomous agent behavior. Additionally, companies specializing in AI-driven NPC development are building on these concepts to create more reactive virtual worlds.
Market impact
This patent contributes to the broader shift toward generative and adaptive AI in gaming. By automating character behavior, it helps developers reduce the time spent on manual scripting, potentially lowering production costs for large-scale virtual environments.
Claim 1 — Plain English
What this patent covers
This patent describes a method for teaching a digital character, or avatar, to act on its own by recognizing patterns in its environment. The system maintains a knowledgebase that links specific environmental objects to sets of instructions or actions. When the avatar encounters a new situation, the system compares the current objects in the scene to the stored patterns. If a match is found, the system triggers the corresponding action, allowing the avatar to navigate or interact with the game world without manual player input.
The clever bit
The system uses a correlation-based knowledgebase that treats environmental objects as data points, allowing the AI to transfer learned behaviors from one avatar or application to another if the environmental context matches.
What it does not cover
- Does not cover manual control of avatars by human players.
- Does not cover basic scripted AI behaviors that are hard-coded rather than learned via pattern matching.
- Does not cover the underlying physics engines used to render the game objects themselves.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
PatentBrief Score
Impact Score
Moderate
Citation count
29/40
Moderately cited
Claim breadth
15/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
10/20
Granted 5–10 years ago
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$234K – $749K
Midpoint $468K · 10.5 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
Claim text not yet imported for this patent
The original legal language
Original claims
23 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Cosic, J. (2020). How AI Learns to Control Game Characters Based on Their Surroundings (U.S. Patent No. 10,607,134). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/10607134/artificially-intelligent-systems-devices-and-methods-for-learning-andor-using-an-avatars-circumstances-for-autonomous-avatar-operation
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
Frequently Asked Questions
What does How AI Learns to Control Game Characters Based on Their Surroundings cover?
A system that allows digital characters to automatically perform actions by matching their current environment to previously learned experiences stored in a database.
Who owns patent US 10607134?
Individual owns this patent, granted in 2020.
When does this patent expire?
This patent is expected to expire on December 19, 2036, when the invention enters the public domain.
What is patent US 10607134 cited by?
This patent has been cited by 26 later patents that build on its ideas.
What problem does this patent solve?
As games become more complex, manual programming for every possible NPC (non-player character) behavior is inefficient. This approach moves toward autonomous agents that can adapt to different game states, which is a core challenge in modern game development and simulation software.
What does this patent NOT cover?
Does not cover manual control of avatars by human players.
Same assignee
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