Abstract: Systems and methods are disclosed for processing medical claims to determine vector embeddings for predictive analytics and fraud detection. The method includes receiving claim data for a medical claim, the claim data comprising a plurality of medical codes. A plurality of embedding vectors is determined based on the plurality of medical codes, each embedding vector determined based on a corresponding medical code. A multi-layered matrix is determined based on the plurality of embedding vectors determined for the plurality of medical codes, wherein the multi-layered matrix is representative of the medical claim. One or more actions such as predictive analytics and fraud detection are performed based on the multi-layered matrix.

Abstract: Systems, methods, and computer program products for identifying a fraudulent device. A device analytics engine receives device data from a computing device, the device data including parameters associated with the computing device. The device analytics engine selects a set of rules in a plurality of rules that indicate at least one parameter in the plurality of parameters in the device data for determining a device identifier. The set of rules are evaluated in an order until the device identifier is determined from the at least one parameter indicated in the set of rules, the device data, and previously stored data from multiple computing devices. A score is generated for the computing device using one or more of the device identifier, device data, a set of rules, and previously receive device data that corresponds to the device identifier. A computing device is identified as a fraudulent computing device based on the score.

Abstract: A wireless user device wirelessly receives an indication of selected resource blocks in a frequency channel that are selected for radio signal measurement and unselected resource blocks in the frequency channel that are unselected for the radio signal measurement. The wireless user device wirelessly measures a radio signal metric for the selected resource blocks in the radio channel and does not measure the radio signal metric for the unselected resource blocks in the radio channel. The wireless user device wirelessly transfers the radio signal metric for the selected resource blocks in the radio channel. The wireless user device wirelessly receives a schedule for the selected resource blocks in the radio channel. The wireless user device wirelessly receives data over the selected resource blocks in the radio channel based on the schedule.

Abstract: Food holding system, liner thereof, and associated methods. A food tray receiver liner is configured to be installed in a food tray receiver. The liner is configured to collect food that may fall into the food tray receiver. Desirably, the liner obstructs food from passing between the liner and side walls of the food tray receiver.

Abstract: Food holding system, liner thereof, and associated methods. A food tray receiver liner is configured to be installed in a food tray receiver. The liner is configured to collect food that may fall into the food tray receiver. Desirably, the liner obstructs food from passing between the liner and side walls of the food tray receiver.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using simulated local demonstration data for robotic demonstration learning. One of the methods includes receiving perceptual data of a workcell of a robot to be configured to execute a task according to a skill template, wherein the skill template specifies one or more subtasks required to perform the skill, wherein at least one of the subtasks is a demonstration subtask that relies on learning visual characteristics of the workcell. A virtual model is generated of a portion of the workcell. A training system generates simulated local demonstration data from the virtual model of the portion of the workcell and tunes a base control policy for the demonstration subtask using the simulated local demonstration data generated from the virtual model of the portion of the workcell.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing skill templates for robotic demonstration learning. One of the methods includes receiving from the user device by a skill template distribution system, a selection of an available skill template. The skill template distribution system provides a skill template, wherein the skill template comprises information representing a state machine of one or more tasks, and wherein the skill template specifies which of the one or more tasks are demonstration subtasks requiring local demonstration data. The skill template distribution system trains a machine learning model for the demonstration subtask using a local demonstration data to generate learned parameter values.

Abstract: Methods and wearable devices for optimizing power consumption using sensor-based position and use determinations are described here. One example method is performed at a device that includes a first sensor configured to operate with a first power consumption rate and a second sensor configured to operate with a second power consumption rate. The method includes, while a component associated with the second sensor is in an inactive state, receiving first sensor data, and determining whether the first sensor data indicates movement of the device. The method also includes, when movement of the device is indicated, operating the second sensor in an active state. The method further includes, after activating the second sensor, when second sensor data from the second sensor indicates that the device has been placed on a user’s body, continuing to operate the second sensor in the active state.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a demonstration device for robotic demonstration learning. One of the methods includes generating, by a demonstration device for a robot, a representation of a sequence of states input by a user of the demonstration device. The representation is provided by the demonstration device to a robot execution system. The representation of the sequence of actions is translated into a plurality of robot commands corresponding to the representation of the sequence of states input by the user on the demonstration device. The plurality of robot commands corresponding to the sequence of actions input by the user on the demonstration device are executed. Demonstration data is generated from one or more sensor streams of the robot while executing the plurality of robot commands corresponding to the sequence of actions input by the user on the demonstration device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for computing interpolated robot control parameters. One of the methods includes receiving, by a real-time bridge from a control agent for a robot, a non-real-time command for the robot, wherein the non-real-time command specifies a trajectory to be attained by a component of the robot and a target value for a control parameter, wherein the control parameter controls how a real-time controller will cause the robot to react to one or more external stimuli encountered during a control cycle of the real-time controller. The real-time bridge provides the one or more real-time commands translated from the non-real-time command and interpolated control parameter information to the real-time controller, thereby causing the robot to effectuate the trajectory of the non-real-time command according to the interpolated control parameter information.

Abstract: Disclosed herein are methods, systems, and media for pressure sensing for physiological measurements. In some embodiments, a method comprises: obtaining sensor data from one or more sensors disposed in or on a wrist-worn device worn on a wrist of a user. The method may involve determining, based on the sensor data, a measure of a contact pressure of a bottom portion of the wrist-worn device on the wrist of the user. The method may involve based on the measure of the contact pressure, providing one or more instructions to the user to adjust the wrist-worn device to bring about a change in the contact pressure toward a target contact pressure or a target contact pressure range. The method may involve initiating a physiological measurement of the user using other sensors disposed in or on the wrist-worn device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for computing interpolated robot control parameters. One of the methods includes receiving, by a real-time bridge from a control agent for a robot, a non-real-time command for the robot, wherein the non-real-time command specifies a trajectory to be attained by a component of the robot and a target value for a control parameter, wherein the control parameter controls how a real-time controller will cause the robot to react to one or more external stimuli encountered during a control cycle of the real-time controller. The real-time bridge provides the one or more real-time commands translated from the non-real-time command and interpolated control parameter information to the real-time controller, thereby causing the robot to effectuate the trajectory of the non-real-time command according to the interpolated control parameter information.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing skill templates for robotic demonstration learning. One of the methods includes receiving, from the user device by a skill template distribution system, a selection of an available skill template. The skill template distribution system provides a skill template, wherein the skill template comprises information representing a state machine of one or more tasks, and wherein the skill template specifies which of the one or more tasks are demonstration subtasks requiring local demonstration data. The skill template distribution system trains a machine learning model for the demonstration subtask using a local demonstration data to generate learned parameter values.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributed robotic demonstration learning. One of the methods includes receiving a skill template to be trained to cause a robot to perform a particular skill having a plurality of subtasks. One or more demonstration subtasks defined by the skill template are identified, wherein each demonstration subtask is an action to be refined using local demonstration data. On online execution system uploads sets of local demonstration data to a cloud-based training system. The cloud-based training system generates respective trained model parameters for each set of local demonstration data. The skill template is executed on the robot using the trained model parameters generated by the cloud-based training system.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a demonstration device for robotic demonstration learning. One of the methods includes generating, by a demonstration device for a robot, a representation of a sequence of states input by a user of the demonstration device. The representation is provided by the demonstration device to a robot execution system. The representation of the sequence of actions is translated into a plurality of robot commands corresponding to the representation of the sequence of states input by the user on the demonstration device. The plurality of robot commands corresponding to the sequence of actions input by the user on the demonstration device are executed. Demonstration data is generated from one or more sensor streams of the robot while executing the plurality of robot commands corresponding to the sequence of actions input by the user on the demonstration device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using learnable robotic control plans. One of the methods comprises obtaining a learnable robotic control plan comprising data defining a state machine that includes a plurality of states and a plurality of transitions between states, wherein: one or more states are learnable states, and each learnable state comprises data defining (i) one or more learnable parameters of the learnable state and (ii) a machine learning procedure for automatically learning a respective value for each learnable parameter of the learnable state; and processing the learnable robotic control plan to generate a specific robotic control plan, comprising: obtaining data characterizing a robotic execution environment; and for each learnable state, executing, using the obtained data, the respective machine learning procedures defined by the learnable state to generate a respective value for each learnable parameter of the learnable state.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for integrating sensor streams for robotic demonstration learning. One of the methods includes selecting, by a learning system for a robot, a base update rate for combining multiple sensor streams into a task state representation. The learning system repeatedly generates the task state representation at the base update rate, including combining, during each time period defined by the update rate, the task state representation from most recently updated sensor data processed by the plurality of neural networks. The learning system repeatedly uses the task state representations to generate commands for the robot at the base update rate.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for providing user feedback for robotic demonstration learning. One of the methods includes initiating a local demonstration learning process to collect respective local demonstration data for each of one or more demonstration subtasks defined by a skill template to be executed by a robot. Local demonstration data is repeatedly collected for each of the one or more demonstration subtasks of the skill template while a user manipulates a robot to perform each of the one or more demonstration subtasks defined by the skill template. A respective progress value for each of the one or more demonstration subtasks defined by the skill template is maintained. A user interface presentation is generated that presents a suggested demonstration to be performed by the user based on a respective progress value for each demonstration subtask.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a demonstration device for robotic demonstration learning. One of the methods includes generating, by a demonstration device for a robot, a representation of a sequence of states input by a user of the demonstration device. The representation is provided by the demonstration device to a robot execution system. The representation of the sequence of actions is translated into a plurality of robot commands corresponding to the representation of the sequence of states input by the user on the demonstration device. The plurality of robot commands corresponding to the sequence of actions input by the user on the demonstration device are executed. Demonstration data is generated from one or more sensor streams of the robot while executing the plurality of robot commands corresponding to the sequence of actions input by the user on the demonstration device.