Abstract: A system and method are disclosed for identifying a user based on the classification of user movement data. An identity verification system receives a sequence of motion data from a mobile device operated by a target user. From the sequences of motion data, the identity verification system identifies a plurality of identity blocks representing different movements performed by the target user and encodes a set of signature sequences from each identity block into a feature vector. Each feature vector is input to a confidence model to output an identity confidence value for an identity block. An identity confidence value describes a confidence that the movement in the identity block was performed by the target user. The identity confidence value is compared to an operational security threshold and if identity confidence value is above the threshold, the target user is granted access to an operational context.

Abstract: An identity verification system determines a communication channel that transmits signals between the computer system and an authentication device by selecting a data link circuit at the computer system. The identity verification system transmits an authentication request received at the computer system to the authentication device via the communication channel and the authentication device validates the authentication request. In response to the authentication device validating the authentication request, the identity verification system measures a proximity of the authentication device to the computer system based on data transmitted through a proximity channel. If the measured proximity of the authentication device satisfies a threshold proximity, the user verification system grants grant the target user access to the to the computer system.

Abstract: A system and method are disclosed for identifying a user based on the classification of user movement data. An identity verification system receives a sequence of motion data characterizing movements performed by a target user. The sequence of motion data is received as a point cloud of the motion data. The point cloud is input a machine-learned model trained based on manually labeled point clusters of a training set of motion data that each represent a movement. The machine-learned model identifies a movement represented by the point cloud of the motion data and assigns a label describing the movement the point cloud. The system generates a labeled representation of the sequence of motion data comprising the label identifying a portion of the sequence of motion data corresponding to the identified movement.

Abstract: A system and method are disclosed for identifying a user based on the classification of user movement data. An identity verification system receives a sequence of motion data from a mobile device operated by a target user. From the sequences of motion data, the identity verification system identifies a plurality of identity blocks representing different movements performed by the target user and encodes a set of signature sequences from each identity block into a feature vector. Each feature vector is input to a confidence model to output an identity confidence value for an identity block. An identity confidence value describes a confidence that the movement in the identity block was performed by the target user. The identity confidence value is compared to an operational security threshold and if identity confidence value is above the threshold, the target user is granted access to an operational context.

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 remotely-controlled (RC) and/or autonomously operated inspection device, such as a ground vehicle or drone, may capture one or more sets of imaging data indicative of at least a portion of an automotive vehicle, such as all or a portion of the undercarriage. The one or more sets of imaging data may be analyzed based upon data indicative of at least one of vehicle damage or a vehicle defect being shown in the one or more sets of imaging data. Based upon the analyzing of the one or more sets of imaging data, damage to the vehicle or a defect of the vehicle may be identified. The identified damage or defect may be compared to a claimed damage or defect to determine whether the claimed damage or defect occurred.

Abstract: A track system for hobby robotics is provided. The track system includes a first track component having a pair of lateral edges and a connection portion disposed at each lateral edge. The track system also includes a second track component having a pair of lateral edges and a connection portion disposed at each lateral edge. The first track component is hingedly coupled to the second track component at a respective connection portion of each of the first and second track components. An individual track component is also provided.

Abstract: Controllers that allow for adjusting various features of the controller are disclosed. The controller may include may include one or more speakers for outputting sound or audio content. A user may contact one or more of the speakers with a finger(s) to adjust a characteristic of the audio content and/or to adjust audio settings. For example, a user may touch a speaker with his/her finger to change (e.g., increase or decrease) the volume or to mute the sound. In some instances, the speakers may be located on the controller body of the handheld controller for convenient and quick access by the user.

Abstract: A system and method are disclosed for identifying a user based on the classification of user movement data. An identity verification system receives a sequence of motion data characterizing movements performed by a target user. The sequence of motion data is received as a point cloud of the motion data. The point cloud is input a machine-learned model trained based on manually labeled point clusters of a training set of motion data that each represent a movement. The machine-learned model identifies a movement represented by the point cloud of the motion data and assigns a label describing the movement the point cloud. The system generates a labeled representation of the sequence of motion data comprising the label identifying a portion of the sequence of motion data corresponding to the identified movement.

Abstract: An excavation vehicle capable of autonomously actuating an excavation tool or navigating an excavation vehicle to perform an excavation routine within an excavation site is described herein. Sensors mounted to the excavation vehicle and the excavation tool produce signals representative of a position and orientation of the corresponding joint relative on the excavation vehicle relative to the excavation site, a position and orientation of the excavation vehicle relative to the excavation site, and one or more features of the excavation site based on the position of the excavation vehicle within the excavation site. A set of solenoids are configured to couple to corresponding hydraulic valves of the excavation tool to actuate the valve. A controller produces actuating signals to control the joints of the excavation tool to autonomously perform the excavation routine based on the signals produced by the sensors.

Abstract: An excavation vehicle capable of autonomously actuating an excavation tool or navigating an excavation vehicle to perform an excavation routine within an excavation site is described herein. Sensors mounted to the excavation vehicle and the excavation tool produce signals representative of a position and orientation of the corresponding joint relative on the excavation vehicle relative to the excavation site, a position and orientation of the excavation vehicle relative to the excavation site, and one or more features of the excavation site based on the position of the excavation vehicle within the excavation site. A set of solenoids are configured to couple to corresponding hydraulic valves of the excavation tool to actuate the valve. A controller produces actuating signals to control the joints of the excavation tool to autonomously perform the excavation routine based on the signals produced by the sensors.

Abstract: An identity verification system receives a candidate signal transmitted from a computing device located near a secured asset in response to a target user requesting access to the secure asset. The identity verification system compares the candidate signal to a cluster of signals measured during past successful authentications by the computing device to determine a modulation factor and determines a match probability for the target user based on the determined modulation factor. The identity verification system grants the requesting target user access to the secured asset in response to determining that the match probability is greater than the operational security threshold.

Abstract: An identity verification system receives context signals from an authenticating computing device in response to a target user requesting access to a secure asset. The identity verification system identifies candidate locations for the operational context assigned to historical context signals labeled as being measured at a known location and compares the context signal to each historical signal to determine a location of the operational context corresponding to the received context signal. The identity verification system determines a match probability for the target user based on a risk score assigned to the location of the operational received context signal and grants the requesting target user access to the secured asset in response to determining that the match probability is greater than the operational security threshold.

Abstract: A computer-implemented method for determining features of a dataset that are indicative of anomalous behavior of one or more computers in a large group of computers comprises (1) receiving log files including a plurality of entries of data regarding connections between a plurality of computers belonging to an organization and a plurality of websites outside the organization, each entry being associated with the actions of one computer, (2) executing a time series decomposition algorithm on a portion of the features of the data to generate a first list of features, (3) implementing a plurality of traffic dispersion graphs to generate a second list of features, and (4) implementing an autoencoder and a random forest regressor to generate a third list of features.

Abstract: A computing device generates a graphical user interface displaying a three-dimensional representation of the site comprising a position of a vehicle capable of moving material within the site and a target location within the site for the vehicle to move material. The computing device transmits a set of instructions for the vehicle to move a volume of material from the target location. The computing device receives sensor data describing a depth of the target location, a current volume of material moved from the target location, and a position of the vehicle. The graphical user interface displayed on the computing device is modified to display the current depth of the target location, and the position of the vehicle relative to the target location. The computing device modifies the set of instructions based on the received sensor data and provides the modified set of instructions to the vehicle.

Abstract: A computer-implemented method for detecting anomalous behavior of one or more computers in a large group of computers comprises (1) receiving log files including a plurality of entries of data regarding connections between a plurality of computers belonging to an organization and a plurality of websites outside the organization, each entry being associated with the actions of one computer, (2) applying a first plurality of algorithms to determine features of the data which may contribute to anomalous behavior of the computers, and (3) applying a second plurality of algorithms to determine which computers are behaving anomalously based upon the features.

Abstract: In response to a first user input to a graphical user interface, a computing device generates an initial geofence around a target location in a site. The computing device transmits operations for an earth shaping vehicle (ESV) to perform while navigating within the initial geofence. The computing device receives an indication of an obstacle within the initial geofence detected by the ESV. In response to a second user input via the graphical user interface, the computing device generates an updated geofence that includes the target location but excludes the obstacle. The computing device transmits instructions for the ESV to navigate within the updated geofence.

Abstract: A computing device generates a graphical user interface displaying a three-dimensional representation of the site comprising a position of a vehicle capable of moving material within the site and a target location within the site for the vehicle to move material. The computing device transmits a set of instructions for the vehicle to move a volume of material from the target location. The computing device receives sensor data describing a depth of the target location, a current volume of material moved from the target location, and a position of the vehicle. The graphical user interface displayed on the computing device is modified to display the current depth of the target location, and the position of the vehicle relative to the target location. The computing device modifies the set of instructions based on the received sensor data and provides the modified set of instructions to the vehicle.

Abstract: A system and method are disclosed for identifying a user based on the classification of user movement data. An identity verification system receives a sequence of motion data from a mobile device operated by a target user. From the sequences of motion data, the identity verification system identifies a plurality of identity blocks representing different movements performed by the target user and encodes a set of signature sequences from each identity block into a feature vector. Each feature vector is input to a confidence model to output an identity confidence value for an identity block. An identity confidence value describes a confidence that the movement in the identity block was performed by the target user. The identity confidence value is compared to an operational security threshold and if identity confidence value is above the threshold, the target user is granted access to an operational context.

Abstract: Systems and methods for analyzing image data to assess customer opportunities is disclosed. According to certain aspects, a server may access aerial and other image data depicting a set of properties, and may analyze the aerial and other image data to identify the set of properties and determine relevant information associated with the set of properties. The server may further assess which of the set of properties represents a customer opportunity, and may identify a retail terminal that may be configured to facilitate the customer opportunity. The server may generate and transmit a communication to the retail terminal that includes relevant information associated with the customer opportunity.