Deep Machine Learning to Perform Touch Motion Prediction

A computer-implemented method to predict motion of user input objects, the method comprising:

• obtaining, by one or more computing devices, a first set of motion data associated with a user input object, the first set of motion data descriptive of a location of the user input object over time;
• inputting, by the one or more computing devices, the first set of motion data into a recurrent neural network of a machine-learned motion prediction model;
• receiving, by the one or more computing devices as an output of the motion prediction model, a motion prediction vector that describes one or more predicted future locations of the user input object respectively for one or more future times;
• and
• rendering, by the one or more computing devices, a display screen that has been modified according to the one or more predicted future locations of the user input object.

A computing device that predicts motion of user input objects, the mobile computing device comprising:

• at least one processor;
• a machine-learned motion prediction model that comprises a recurrent neural network, wherein the motion prediction model is trained to receive motion data indicative of motion of a user input object and, in response to receipt of the motion data, output predicted future locations of the user input object;
• and
• at least one tangible, non-transitory computer-readable medium that stores instructions that, when executed by the at least one processor, cause the at least one processor to:
• obtain a first set of motion data associated with the user input object, the first set of motion data descriptive of a location of the user input object over time;
• input the first set of motion data into the recurrent neural network of the motion prediction model;
• receive, as an output of the motion prediction model, a motion prediction vector that describes the one or more predicted future locations of the user input object respectively for one or more future times;
• and
• perform one or more actions associated with the one or more predicted future locations described by the motion prediction vector.

A computing system, comprising:

• a user computing device, the user computing device comprising:
• at least one processor;
• and
• at least one non-transitory computer-readable medium that stores:
• a machine-learned motion prediction model configured to receive motion data indicative of motion of a user input object and, in response to receipt of the motion data, output predicted future locations of the user input object, the motion prediction model trained by backpropagation of a loss function through the motion prediction model, the loss function comprising an error ratio that describes a first sum of one or more lengths respectively of one or more error vectors divided by a second sum of one or more lengths respectively of one or more ground truth vectors;
• and
• instructions that, when executed by the at least one processor, cause the user computing device to use the machine-learned motion prediction model to obtain the predicted future locations of the user input object.