US 12,762,897 B1Granted

System to model motion with predictive AI models

OwnerChengjie Huanginventor

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Abstract

The present invention relates to a system for training artificial intelligence models to generate human-like motion decisions in interactive environments. The system incorporates multiple neural network architectures to model fast action movement decisions and less active movement flows in sports, as well as AI networks that direct how to switch between different movement states. Ordinary differential equation networks model hidden state changes over time, allowing sparse sampling and sparse inferences. This system uses fast transformer networks to forecast into the future and outpace real time, allowing additional time for complex calculations of intelligent decisions. Future forecasts allow networks to anticipate opponent movements furthering its intelligence and capabilities. This technology has applications in sports simulation, animation, and automated systems requiring human-like motion intelligence.

Claims10

Dependent claims

A system of Artificial Intelligence (AI) Motion Neural Networks (MNNs) to generate movement decisions based on past, current, and expected future movements, comprising:

at least one localized or server computerized device containing at least one software application, wherein said at least one localized or server computerized device includes one or more processors and a memory;
said at least one localized or server computerized device in communication via a network with one or more input, output, and display devices;
said memory in communication with the one or more processors and data storage devices, the memory storing instructions that, when executed by the one or more processors, are configured to cause the software application to:
i. process historical video and sensor data of real and simulated games into training data of body parts of actors in 3-dimensional space defined by location coordinates, velocity, and acceleration vectors over time;
ii. partition said training data into periods of relatively high activity and relatively low activity, with relatively high activity defined by data values greater than a certain velocity, greater than a certain acceleration, less than a certain distance between actors, or any combination of said factors, and with relatively low activity defined by data values less than a certain velocity, less than a certain acceleration, greater than a certain distance between actors, or any combination of said factors;
iii. train neural networks on said relatively high activity data and neural networks on said relatively low activity data, said trained neural networks structured and parameterized such that given a real or simulated game or motion system, said neural networks receive coordinates, velocity, and acceleration vectors of an actor being controlled by said neural networks and other actors in contest against or partnership with said controlled actor and output movement directions including a new motion state of coordinates and vectors to direct movements of said controlled actor;
iv. minimize the average difference between said movement directions generated by said neural networks and real movement directions in said training data by iteratively updating said neural networks parameters with training algorithms;
and
v. select between said neural networks trained on said relatively high activity data and said neural networks trained on said relatively low activity data depending on movement directions generated by said neural networks trained on said relatively high activity data and said neural networks trained on said relatively low activity data, game clock, distance between actors, hazard head function probability, an input of high activity data from other actors, or any combination of said factors.

6 more elements

A system of Artificial Intelligence (AI) Motion Neural Networks (MNN) to generate movement decisions based on past, current, and expected future movements, comprising:

at least one localized or server computerized device containing at least one software application, wherein said at least one localized or server computerized device includes one or more processors and a memory;
said at least one localized or server computerized device in communication via a network with one or more input, output, and display devices;
said memory in communication with the one or more processors and data storage devices, the memory storing instructions that, when executed by the one or more processors, are configured to cause the software application to:
i. process historical video and sensor data of real and simulated games into training data of body parts of actors in 3-dimensional space defined by location coordinates, velocity, and acceleration vectors over time;
ii. partition said training data into periods of relatively high activity and relatively low activity, with relatively high activity defined by data values greater than a certain velocity, greater than a certain acceleration, less than a certain distance between actors, or any combination of said factors, and with relatively low activity defined by data values less than a certain velocity, less than a certain acceleration, greater than a certain distance between actors, or any combination of said factors;
iii. train an ordinary differential equation (ODE) network on said relatively high activity data, said ODE network configured to take an input sequence of movement data, time, and a hidden state and to model changes to said hidden state over time as differential equations;
iv. train a transformer decoder network on said relatively low activity data, said transformer decoder network configured to apply an attention mechanism to previous states of coordinate locations and velocity vectors to predict a next state of movement coordinates and velocity vectors;
v. minimize an average difference between movement directions generated by said ODE network and said transformer decoder network and real movement directions in said training data by iteratively updating parameters of said ODE network and said transformer decoder network with training algorithms;
and
vi. select between said ODE network and said transformer decoder network depending on movement states generated by said ODE network or said transformer decoder network, game clock, distance between actors, hazard head function probability, an input of high activity data from other actors, or any combination of said factors.

7 more elements

Dependencies are inferred from the claim text.