Abstract: A urine containment and deodorizing device having a funnel portion having a top side, left side, right side, bottom side, left side raised funnel portion, right side raised funnel portion, funnel portion upper side and funnel portion underneath side wherein the top side is wider than the bottom side; a bottom portion having a shaped portion, a narrower tapered portion, a bottom portion upper side, a bottom portion underneath side and at least one opening between the bottom portion upper side and the bottom portion underneath side; and a connecting portion between the narrower tapered portion of the bottom portion and the bottom side of the funnel portion.

Abstract: A trained machine learning model(s) is used to determine scores indicative of probabilities that certain types of user input will be provided to a player's game controller while playing a video game in order to compensate for latency between player action and player perception of video game content relating to the player action. In an example process, sensor data received from a client machine and/or game state data received from a video game is provided as input to a trained machine learning model(s), and a score as output therefrom, the score relating to a probability that a type of user input will be provided to a player's game controller. In this manner, game control data corresponding to the type of user input can be generated based on the score and provided to the video game as input before actual game control data is even received, thereby compensating for latency.

Abstract: Described herein are controllers with sensor-rich controls for enhanced controller functionality. An example control may include a pressure sensor that is configured to detect an amount of a force of a press on a cover of the control based at least in part on a proximity of a metal layer to the pressure sensor. This control may further include a touch sensor for detecting an object contacting the cover of the control. Additional embodiments disclose, among other things, integrated trackpads and D-pads, as well as backlighting features that indicate a functional state of the controller.

Abstract: A urine containment and deodorizing device having a funnel portion having a top side, left side, right side, bottom side, left side raised funnel portion, right side raised funnel portion, funnel portion upper side and funnel portion underneath side wherein the top side is wider than the bottom side; a bottom portion having a shaped portion, a narrower tapered portion, a bottom portion upper side, a bottom portion underneath side and at least one opening between the bottom portion upper side and the bottom portion underneath side; and a connecting portion between the narrower tapered portion of the bottom portion and the bottom side of the funnel portion.

Abstract: Described herein are controllers with sensor-rich controls for enhanced controller functionality. An example control may include a pressure sensor that is configured to detect an amount of a force of a press on a cover of the control based at least in part on a proximity of a metal layer to the pressure sensor. This control may further include a touch sensor for detecting an object contacting the cover of the control. Additional embodiments disclose, among other things, integrated trackpads and D-pads, as well as backlighting features that indicate a functional state of the controller.

Abstract: A handheld controller may include a movable control that is usable for activating and deactivating a motion control feature of the handheld controller based on a detection of, or a failure to detect, a finger contacting a surface of the movable control. For example, a touch sensor associated with the movable control may provide touch sensor data indicative of a proximity of a finger relative to the movable control, and based on this touch sensor data, the motion control feature of the handheld controller can be toggled on and off, depending on whether the finger is contacting the movable control or not. When the motion control feature is activated, motion sensor data from a motion sensor(s) is sent to an application (e.g., a video game) as application input. When the motion control feature is deactivated, the motion sensor data is not used as application input.

Abstract: Logic of a handheld controller can implement sensor fusion algorithms based on force data provided by a force sensing resistor (FSR) in combination with touch data or proximity data provided by a touch sensor or an array of proximity sensors, respectively. An example sensor fusion algorithm can be used to re-calibrate the FSR when an object contacts an associated control, as detected by the touch sensor. Another example sensor fusion algorithm can be used to ignore spurious inputs detected by the FSR when an object is in contact with an adjacent control. Another example sensor fusion algorithm can be used to detect a hand size of a hand grasping a handle of the controller, as detected by the array of proximity sensors, and to adjust the threshold force to register a FSR input event at the FSR according to the hand size.

Abstract: A handheld controller may include a movable control that is usable for activating and deactivating a motion control feature of the handheld controller based on a detection of, or a failure to detect, a finger contacting a surface of the movable control. For example, a touch sensor associated with the movable control may provide touch sensor data indicative of a proximity of a finger relative to the movable control, and based on this touch sensor data, the motion control feature of the handheld controller can be toggled on and off, depending on whether the finger is contacting the movable control or not. When the motion control feature is activated, motion sensor data from a motion sensor(s) is sent to an application (e.g., a video game) as application input. When the motion control feature is deactivated, the motion sensor data is not used as application input.

Abstract: Described herein are controllers with sensor-rich controls for enhanced controller functionality. An example control may include a pressure sensor that is configured to detect an amount of a force of a press on a cover of the control based at least in part on a proximity of a metal layer to the pressure sensor. This control may further include a touch sensor for detecting an object contacting the cover of the control. Additional embodiments disclose, among other things, integrated trackpads and D-pads, as well as backlighting features that indicate a functional state of the controller.

Abstract: Various game controller hardware and user interface configurations are disclosed. Some configurations may comprise two circular trackpads, driven by the player's thumbs, which may be clickable, allowing the entire surface to act as a button. Haptic feedback may be based on dual linear resonant actuators (e.g., by means of strong, weighted electro-magnets attached to each of the dual trackpads), capable of delivering a wide range of force and vibration, allowing control over frequency, amplitude, and direction of movement. The haptics-related components in certain implementations may also play audio waveforms and thereby function as speakers. In the center of the controller according to certain configurations may be another touch-enabled surface, this one backed by a display screen. In some configurations this entire screen itself may also be clickable, like a large single button. A variety of other exemplary implementations and configurations are described.

Abstract: A trained machine learning model(s) is used to determine scores (e.g., trust scores) for user accounts registered with a video game service, and the scores are used to match players together in multiplayer video game settings. For example, sensor data received from client machines can be provided as input to the trained machine learning model(s), and the trained machine learning model(s) generates scores as output, which relate to probabilities that the game control data received from those client machines was generated by handheld devices, as opposed to having been synthesized and/or modified using software. In this manner, subsets of logged-in user accounts executing a video game can be assigned to different matches (e.g., by isolating non-human players from human players) based at least in part on the scores determined for those logged-in user accounts, and the video game is executed in the assigned match for each logged-in user account.

Abstract: A handheld controller may include a movable control that is usable for activating and deactivating a motion control feature of the handheld controller based on a detection of, or a failure to detect, a finger contacting a surface of the movable control. For example, a touch sensor associated with the movable control may provide touch sensor data indicative of a proximity of a finger relative to the movable control, and based on this touch sensor data, the motion control feature of the handheld controller can be toggled on and off, depending on whether the finger is contacting the movable control or not. When the motion control feature is activated, motion sensor data from a motion sensor(s) is sent to an application (e.g., a video game) as application input. When the motion control feature is deactivated, the motion sensor data is not used as application input.

Abstract: A trained machine learning model(s) is used to determine scores indicative of probabilities that certain types of user input will be provided to a player's game controller while playing a video game in order to compensate for latency between player action and player perception of video game content relating to the player action. In an example process, sensor data received from a client machine and/or game state data received from a video game is provided as input to a trained machine learning model(s), and a score as output therefrom, the score relating to a probability that a type of user input will be provided to a player's game controller. In this manner, game control data corresponding to the type of user input can be generated based on the score and provided to the video game as input before actual game control data is even received, thereby compensating for latency.

Abstract: A trained machine learning model(s) is used to determine scores (e.g., trust scores) for user accounts registered with a video game service, and the scores are used to match players together in multiplayer video game settings. For example, sensor data received from client machines can be provided as input to the trained machine learning model(s), and the trained machine learning model(s) generates scores as output, which relate to probabilities that the game control data received from those client machines was generated by handheld devices, as opposed to having been synthesized and/or modified using software. In this manner, subsets of logged-in user accounts executing a video game can be assigned to different matches (e.g., by isolating non-human players from human players) based at least in part on the scores determined for those logged-in user accounts, and the video game is executed in the assigned match for each logged-in user account.

Abstract: Described herein are, among other things, handheld controllers that include one or more touchpads (i.e., touch-sensitive controls) configured to detachably couple to one or more overlays that effectively transform the respective touchpad into a different control. For instance, one example handheld controller may include a circular trackpad that is configured to detect a position of a finger on a top surface of the trackpad. This position may be used to control a cursor or avatar, for scrolling, or for any other type of command. In general, most or all of the top surface may be usable by a user for providing input to the controller and/or a remote system coupled to the controller. As described herein, however, one or more different overlays may detachably couple to a housing of the controller and/or the trackpad itself to effectively change the use of the trackpad.

Abstract: A handheld controller may include a movable control that is usable for activating and deactivating a motion control feature of the handheld controller based on a detection of, or a failure to detect, a finger contacting a surface of the movable control. For example, a touch sensor associated with the movable control may provide touch sensor data indicative of a proximity of a finger relative to the movable control, and based on this touch sensor data, the motion control feature of the handheld controller can be toggled on and off, depending on whether the finger is contacting the movable control or not. When the motion control feature is activated, motion sensor data from a motion sensor(s) is sent to an application (e.g., a video game) as application input. When the motion control feature is deactivated, the motion sensor data is not used as application input.

Abstract: Described herein are, among other things, handheld controllers that include housings having one or more receiver portions for detachably coupling to one or more controls. For example, a housing of one such controller may include, on a front surface of the housing, a receiver that is configured to detachably couple to one or more joysticks, one or more D-pads, one or more track pads, one or more buttons, and/or the like. In some instances, a user may swap a first control for a second control based on a current application (e.g., game title) that the user is playing, based on comfort of the user, and/or for any other reason.

Abstract: A hand-held video game controller includes a controller body having a front and a back, with at least one front control on the front of the controller body. A removable and resilient unitary back shell is removably attached to the back of the controller body. At least one back control button underlies the unitary back shell, and is depressible by flexing of the unitary back shell into contact with that back control button. At least one battery compartment may underlie the unitary back shell, and may be accessible by removal of the removable unitary back shell.

Abstract: Logic of a handheld controller can implement sensor fusion algorithms based on force data provided by a force sensing resistor (FSR) in combination with touch data or proximity data provided by a touch sensor or an array of proximity sensors, respectively. An example sensor fusion algorithm can be used to re-calibrate the FSR when an object contacts an associated control, as detected by the touch sensor. Another example sensor fusion algorithm can be used to ignore spurious inputs detected by the FSR when an object is in contact with an adjacent control. Another example sensor fusion algorithm can be used to detect a hand size of a hand grasping a handle of the controller, as detected by the array of proximity sensors, and to adjust the threshold force to register a FSR input event at the FSR according to the hand size.

Abstract: Logic of a handheld controller can implement sensor fusion algorithms based on force data provided by a force sensing resistor (FSR) in combination with touch data or proximity data provided by a touch sensor or an array of proximity sensors, respectively. An example sensor fusion algorithm can be used to re-calibrate the FSR when an object contacts an associated control, as detected by the touch sensor. Another example sensor fusion algorithm can be used to ignore spurious inputs detected by the FSR when an object is in contact with an adjacent control. Another example sensor fusion algorithm can be used to detect a hand size of a hand grasping a handle of the controller, as detected by the array of proximity sensors, and to adjust the threshold force to register a FSR input event at the FSR according to the hand size.