Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for dynamically controlling requestor device queues by monitoring and utilizing the throughput rate of matched provider devices and requestor devices. In some embodiments, the disclosed systems determine throughput rate of matched provider devices and requestor devices in real-time and/or predicts throughput rate utilizing historical features of a particular location. The disclosed systems can generate and provide queue request notifications to requestor devices based on a throughput rate at the location. Specifically, the disclosed systems can monitor a current queue status over time, compare the queue status to a queue threshold, and dynamically generate queue request notifications that reflects throughput-based queue modifiers as the current queue status approaches the queue threshold.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for dynamically controlling requestor device queues by monitoring and utilizing the throughput rate of matched provider devices and requestor devices. In some embodiments, the disclosed systems determine throughput rate of matched provider devices and requestor devices in real-time and/or predicts throughput rate utilizing historical features of a particular location. The disclosed systems can generate and provide queue request notifications to requestor devices based on a throughput rate at the location. Specifically, the disclosed systems can monitor a current queue status over time, compare the queue status to a queue threshold, and dynamically generate queue request notifications that reflects throughput-based queue modifiers as the current queue status approaches the queue threshold.

Abstract: Methods involving interpreting signals from a brain-machine interface (BMI) are described, as well as methods involving adjusting an implanted or wearable BMI device. The method includes receiving neural signals from a brain of a subject into a BMI decoder. The method includes determining an activity change of the subject based on a sensor. The method includes routing the neural signals from a first model to a second model in the BMI decoder based on the determined activity change. The method includes translating, using the second model in the BMI decoder, the neural signals into a command. The method includes sending the command to a controller.

Abstract: The present disclosure relates to systems, non-transitory computer readable media, and methods that intelligently provide dynamic transportation modes and corresponding user interfaces to client devices within multi-pickup-location area geofences to satisfy dynamic limitations corresponding to certain alternate location options. For instance, the disclosed systems can provide a transportation option corresponding to a first pickup location for display via the requestor device. Based on determining that the requestor device is associated with a location within a geofence of a multi-pickup-location area, the disclosed systems can provide a selectable mode option for initiating an alternate location transportation mode that includes modifying the pickup location for a transportation request to a second pickup location.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for dynamically controlling requestor device queues by monitoring and utilizing the throughput rate of matched provider devices and requestor devices. In some embodiments, the disclosed systems determine throughput rate of matched provider devices and requestor devices in real-time and/or predicts throughput rate utilizing historical features of a particular location. The disclosed systems can generate and provide queue request notifications to requestor devices based on a throughput rate at the location. Specifically, the disclosed systems can monitor a current queue status over time, compare the queue status to a queue threshold, and dynamically generate queue request notifications that reflects throughput-based queue modifiers as the current queue status approaches the queue threshold.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for intelligently pre-dispatching candidate provider devices to a geographic area by utilizing a pre-dispatch model accounting for forecasted transportation requests. For example, the disclosed systems can determine how many candidate provider devices to pre-dispatch to a geographic area based on a variety of inputs, including a projected requestor device queue, a projected provider device queue, and/or a queue capacity. In particular, the disclosed systems can utilize a look-ahead model and a look-behind model to compare a queue capacity with projected provider device queues corresponding to estimated times of arrival for one or more candidate provider devices. If the projected provider device queues comport with the queue capacity according to both the look-ahead model and the look-behind model, the disclosed systems can proceed with pre-dispatching the candidate provider device to the geographic area.

Abstract: Systems and methods for using virtual keyboards for high dimensional controllers in accordance with embodiments of the invention are illustrated. One embodiment includes a virtual keyboard system including a processor, and a memory, including a virtual keyboard application, where the virtual keyboard application directs the processor to display a plurality of 3D keys in a virtual environment, where each 3D key represents at least one symbol via a display device, display a cursor in the virtual environment, where the cursor is movable in at least three dimensions via the display device, obtain a user input data from an input device, move the cursor to a 3D key in the plurality of 3D keys based on the user input data, and record the at least one symbol represented by the 3D key.

Abstract: A brain machine interface (BMI) for improving a performance of a subject is provided. The BMI has two decoders that act in real-time and in parallel to each other. The first decoder is for intention execution of a subject's intention. The second decoder is for error detection in a closed-loop error fashion with the first detector and to improve the performance of the first detector. Embodiments of this invention may enable an entirely new way to substantially increase the performance and robustness, user experience, and ultimately the clinical viability of BMI systems.

Abstract: Systems and methods for using virtual keyboards for high dimensional controllers in accordance with embodiments of the invention are illustrated. One embodiment includes a virtual keyboard system including a processor, and a memory, including a virtual keyboard application, where the virtual keyboard application directs the processor to display a plurality of 3D keys in a virtual environment, where each 3D key represents at least one symbol via a display device, display a cursor in the virtual environment, where the cursor is movable in at least three dimensions via the display device, obtain a user input data from an input device, move the cursor to a 3D key in the plurality of 3D keys based on the user input data, and record the at least one symbol represented by the 3D key.

Abstract: A brain machine interface (BMI) for improving a performance of a subject is provided. The BMI has two decoders that act in real-time and in parallel to each other. The first decoder is for intention execution of a subject's intention. The second decoder is for error detection in a closed-loop error fashion with the first detector and to improve the performance of the first detector. Embodiments of this invention may enable an entirely new way to substantially increase the performance and robustness, user experience, and ultimately the clinical viability of BMI systems.