IP Library Granted Patent US 12,337,239
Granted Patent B1
US 12,337,239 · App. 18/763,410 · Granted Jun 24, 2025

System and method for self-learning, artificial intelligence character system for entertainment applications

Inventors: Luis Javier Ibanez (Ave Maria, FL); Nelson Johan Vega (Royal Palm Beach, FL)
Assignee: Synchroverse Gaming LLC
A63F13/67A63F13/537
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,337,239
App. No.
18/763,410
Granted
Jun 24, 2025
Kind
B1
Abstract

A system and method where a player's chosen virtual character, either user personalized or pre-made, is able to infinitely learn from all the interactions that it has either with the player, other characters, non-player characters (NPCs), or game match experiences/results, whereby virtual character is then able to communicate with the player in a natural language voice and physical interaction in a separate environment, whereby the personality of the character virtual character is constantly shaped by the backstory and experiences it has in the applications it has been connected to or has been in communication with.

Claims (25)

1. A method for managing a digital asset on one or more platforms, the digital asset comprising a plurality of characteristics including at least one alterable characteristic, the digital asset comprising a virtual character, the method comprising:

receiving a prompt from user input from a user;

analyzing the prompt for past experiences, behavior, current environment, current application, and mood of the virtual character;

storing the prompt for pre-processing of the virtual character;

sending the prompt and an animation list to an external API;

determining character audio response, visemes, and timing for the virtual character;

transmitting the audio response, the visemes, and the timing through the virtual character to the user;

sending the virtual character to a first media source including the at least one alterable characteristic, wherein performance of the first media source is affected by the at least one alterable characteristic;

receiving data from the first media source for altering the at least one alterable characteristic based on an interaction in the first media source;

altering, the at least one alterable characteristic in the digital asset based on the received data;

sending the digital asset data including the at least one altered characteristic to a second media source independent of the first media source, wherein performance of the second media source is affected by the at least one altered characteristic; and

sending the digital asset data to an interactive platform enabling a user to access the digital asset independent of the first media source or the second media source.

2. The method of claim 1 , wherein the first media source is a video game and the second media source is a non-interactive movie or puzzle.

3. The method of claim 1 , presenting the virtual character in augmented reality view wherein the virtual character is displayed in a physical world and can simulate walking around and interacting with the physical world and interacting verbally with the user.

4. The method of claim 1 , further comprising: altering a texture or geometry of the virtual character to generate a look and appearance in response to a theme of aesthetic of the first media source or the second media source.

5. The method of claim 1 , further comprising: storing information learned from another character or NPC in the first media source or the second media source and utilizing it for the pre-processing.

6. The method of claim 4 , further comprising: storing game statistical information from the first media source or the second media source and utilizing it for the pre-processing.

7. The method of claim 1 , further comprising: providing one or more specified dialogue and emotion trees to provide one or more responses through the interactive platform.

8. The method of claim 1 , further comprising: training the one or more responses based on previous interactions in the interactive platform, the first media source, and the second media source including dialogue, emotions and intensity.

9. The method of claim 1 , further comprising: training the one or more responses based on previous interactions in the interactive platform with one or more second virtual characters and one or more second users.

10. The method of claim 1 further comprising, receiving requests and identification information from the first media source and the second media source and the interactive platform;

sending the digital asset data to the first media source and the second media source and the interactive platform; and

and receiving data from the first media source and the second media source and the interactive platform for altering one or more alterable characteristics.

11. The method of claim 10 , further comprising presenting a questionnaire subsystem in the interactive platform to determine one or more attributes of the digital asset and designed to ascertain a background, a relationship, or a personality trait.

12. The method of claim 10 , further comprising: providing interaction system wherein the digital asset has one or more specified dialogue and emotion trees to provide responses through an interactive platform user interface on a user computing device.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 11, 2024
From: IBANEZ, LUIS JAVIER; VEGA, NELSON JOHAN
To: SYNCHROVERSE GAMING LLC
Reel/Frame 067967/0744 →
Continuity (1)
Provisional Application 63524772 · Jul 3, 2023
References Cited (27)
US 7025675B2 · Fogel · 2006 [cited by examiner]
US 7789758B2 · Wright · 2010 [cited by applicant]
US 7814041B2 · Caporale et al. · 2010 [cited by applicant]
US 9895612B2 · Pacey et al. · 2018 [cited by applicant]
US 10112113B2 · Krishnamurthy · 2018 [cited by applicant]
US 10379719B2 · Scapel · 2019 [cited by examiner]
US 10424318B2 · Levy-Rosenthal · 2019 [cited by applicant]
US 10864443B2 · Eatadali et al. · 2020 [cited by applicant]
US 10926173B2 · Shah et al. · 2021 [cited by applicant]
US 10926179B2 · de Plater et al. · 2021 [cited by applicant]
US 11461952B1 · Bosnak et al. · 2022 [cited by applicant]
US 20020082077A1 · Johnson · 2002 [cited by examiner]
US 20040029625A1 · Annunziata · 2004 [cited by applicant]
US 20040053690A1 · Fogel · 2004 [cited by examiner]
US 20100240458A1 · Gaiba · 2010 [cited by examiner]
US 20110294574A1 · Yamada · 2011 [cited by examiner]
US 20130079142A1 · Kruglick · 2013 [cited by examiner]
US 20150126286A1 · Guo · 2015 [cited by applicant]
US 20190081848A1 · Zou · 2019 [cited by examiner]
US 20190095775A1 · Lemberksy et al. · 2019 [cited by applicant]
US 20200001185A1 · Eatedali · 2020 [cited by examiner]
US 20200051460A1 · Bedor · 2020 [cited by examiner]
US 20200384362A1 · Shah et al. · 2020 [cited by applicant]
US 20210390366A1 · Furman et al. · 2021 [cited by applicant]
US 20240102981A1 · Lemos · 2024 [cited by examiner]
“Integrate customizable avatars into your game or app in minutes,” https://readyplayer.me/ [Date accessed: Apr. 2, 2023]. [cited by applicant]
“Metasoul,” https://emoshape.com/metasoul-3/ [Date accessed: Apr. 2, 2023]. [cited by applicant]