Microsoft Technology Licensing, LLC

Adam Hakim

Guy David Amir

Aviv Slobodkin

Nadav Linenberg

Yael Livne

Nadav Shlomo Ben-Amram

Yoel Yehezkel Einhoren

Etai Zajonts

Anatoly Tsvetov

Roy-Gan Maiberger

Methods, systems, apparatuses, and computer program products are provided herein for determining the handedness of input provided by a user via a touch interface. For instance, for each touch-based input detected, a score indicating a probability whether the touch-based input was inputted by a particular hand of the user is generated. A classification for the touch-based input is then generated based on a drift diffusion model-based technique in which inter-dependencies between a series of touch-based input are approximated. The determined classifications are used to determine the handedness of the user.

Diffusion-based handedness classification for touch-based input

Examples are disclosed herein relating to simulating force-sensing functionality for a touch interface using a machine-learning model that is trained based at least on training data generated by a training touch interface including a plurality of force sensors. In one example, a computing system includes a touch interface configured to output a touch heatmap based at least on touch input detected by a plurality of touch sensors of the touch interface. The computing system is configured to execute a machine-learning model that is configured to receive the touch heatmap, output a force estimation of the touch input based at least on analyzing the touch heatmap, and execute a computing operation based at least on the force estimation. The machine-learning model is trained based at least on training data generated by a training touch interface including a plurality of force sensors.

Touch input force estimation using machine learning

Examples are disclosed that relate to improving speed and accuracy of touch input classification. In one example, a touch detection device includes an array of antennas configured to measure touch input and output a touch matrix of pixels having touch values corresponding to the touch input measured at each antenna of the array of antennas. The touch detection device further includes a neural network having an input layer including a plurality of nodes. Each node is configured to receive a touch value corresponding to a different pixel of the touch matrix. The neural network is configured to output classified touch data corresponding to the measured touch input based at least on the touch matrix.

Neural network-based touch input classification

Methods, systems, apparatuses, and computer program products are provided herein for determining the handedness of input provided by a user via a touch interface. For instance, for each touch-based input detected, a score indicating a probability whether the touch-based input was inputted by a particular hand of the user is generated. A classification for the touch-based input is then generated based on a drift diffusion model-based technique in which inter-dependencies between a series of touch-based input are approximated. The determined classifications are aggregated and compared to threshold(s) to determine the handedness of the user. For example, if the aggregated classifications meet a first threshold, then a determination is made that touch-based input provided by a user was inputted by the user's left hand. If the aggregated classifications meet a second threshold, then a determination is made that touch-based input provided by the user was inputted by the user's right hand.

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Tel Aviv, Israel

Adam Hakim