US 12,271,711 B2
Interoperable composite data units for use in distributed computing execution environments
David McDonald, Auckland (NZ)
Assigned to Futureverse IP Limited, Auckland (NZ)
Filed by Futureverse IP Limited, Auckland (NZ)
Filed on May 23, 2024, as Appl. No. 18/673,122.
Application 18/673,122 is a continuation of application No. 18/612,121, filed on Mar. 21, 2024, granted, now 12,039,300.
Application 18/612,121 is a continuation of application No. 18/369,728, filed on Sep. 18, 2023.
Application 18/369,728 is a continuation of application No. 17/353,898, filed on Jun. 22, 2021, granted, now 11,797,274, issued on Oct. 24, 2023.
Prior Publication US 2024/0394023 A1, Nov. 28, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 8/35 (2018.01); A63F 13/77 (2014.01); G06F 8/41 (2018.01); G06N 20/00 (2019.01); H04L 9/00 (2022.01); H04L 9/32 (2006.01)
CPC G06F 8/35 (2013.01) [A63F 13/77 (2014.09); G06F 8/441 (2013.01); G06N 20/00 (2019.01); H04L 9/3213 (2013.01); H04L 9/50 (2022.05)] 27 Claims
OG exemplary drawing
 
1. Non-transitory computer readable media for providing a character entity for an execution environment of a computing system, the computer readable media including software modules recorded thereon, the modules comprising:
a model module including an Artificial Intelligence (AI) learning model having an artificial neural network that, when executed within the execution environment, implements the character entity, wherein the character entity is one of a Player Entity or a Non-Player Character (NPC);
an interface definition module including a pointer to an interface definition associated with the Al learning model, that is used by the execution environment to communicate with the Al learning model;
an input value matrix that contains a set of values that are mapped to input variables of the Al learning model to thereby have an effect on activity of the character entity within the execution environment; and
an activity training module to be updated by a machine learning model trainer to thereby train the Al learning model based on activity of the character entity in the execution environment.