Abstract: A method or system is capable of detecting operator behavior (“OB”) utilizing a virtuous cycle containing sensors, machine learning center (“MLC”), and cloud based network (“CBN”). In one aspect, the process monitors operator body language captured by interior sensors and captures surrounding information observed by exterior sensors onboard a vehicle as the vehicle is in motion. After selectively recording the captured data in accordance with an OB model generated by MLC, an abnormal OB (“AOB”) is detected in accordance with vehicular status signals received by the OB model. Upon rewinding recorded operator body language and the surrounding information leading up to detection of AOB, labeled data associated with AOB is generated. The labeled data is subsequently uploaded to CBN for facilitating OB model training at MLC via a virtuous cycle.

Abstract: A method or system is capable of detecting operator behavior (“OB”) utilizing a virtuous cycle containing sensors, machine learning center (“MLC”), and cloud based network (“CBN”). In one aspect, the process monitors operator body language captured by interior sensors and captures surrounding information observed by exterior sensors onboard a vehicle as the vehicle is in motion. After selectively recording the captured data in accordance with an OB model generated by MLC, an abnormal OB (“AOB”) is detected in accordance with vehicular status signals received by the OB model. Upon rewinding recorded operator body language and the surrounding information leading up to detection of AOB, labeled data associated with AOB is generated. The labeled data is subsequently uploaded to CBN for facilitating OB model training at MLC via a virtuous cycle.

Abstract: Techniques for block storage using a hybrid memory device are described. In at least some embodiments, a hybrid memory device includes a volatile memory portion, such as dynamic random access memory (DRAM). The hybrid memory device further includes non-volatile memory portion, such as flash memory. In at least some embodiments, the hybrid memory device can be embodied as a non-volatile dual in-line memory module, or NVDIMM. Techniques discussed herein employ various functionalities to enable the hybrid memory device to be exposed to various entities as an available block storage device.

Abstract: A method or system capable of managing automobile parking space (“APS”) using containerized sensors, machine learning center, and cloud based network is disclosed. A process, in one aspect, monitors the surrounding information observed by a set of onboard sensors of a vehicle as the vehicle is in motion. After selectively recording the surrounding information in accordance with instructions from a containerized APS model which is received from a machine learning center, an APS and APS surrounding information are detected when the vehicle is in a parked condition. Upon rewinding recorded surrounding information leading up to the detection of APS, labeled data associated with APS is generated based on APS and the recorded surrounding information. The process subsequently uploads the labeled data to the cloud based network for facilitating APS model training at the machine learning center via a virtuous cycle.

Abstract: A method or system is capable of detecting operator behavior (“OB”) utilizing a virtuous cycle containing sensors, machine learning center (“MLC”), and cloud based network (“CBN”). In one aspect, the process monitors operator body language captured by interior sensors and captures surrounding information observed by exterior sensors onboard a vehicle as the vehicle is in motion. After selectively recording the captured data in accordance with an OB model generated by MLC, an abnormal OB (“AOB”) is detected in accordance with vehicular status signals received by the OB model. Upon rewinding recorded operator body language and the surrounding information leading up to detection of AOB, labeled data associated with AOB is generated. The labeled data is subsequently uploaded to CBN for facilitating OB model training at MLC via a virtuous cycle.

Abstract: A method or system is capable of detecting operator behavior (“OB”) utilizing a virtuous cycle containing sensors, machine learning center (“MLC”), and cloud based network (“CBN”). In one aspect, the process monitors operator body language captured by interior sensors and captures surrounding information observed by exterior sensors onboard a vehicle as the vehicle is in motion. After selectively recording the captured data in accordance with an OB model generated by MLC, an abnormal OB (“AOB”) is detected in accordance with vehicular status signals received by the OB model. Upon rewinding recorded operator body language and the surrounding information leading up to detection of AOB, labeled data associated with AOB is generated. The labeled data is subsequently uploaded to CBN for facilitating OB model training at MLC via a virtuous cycle.

Abstract: Techniques for block storage using a hybrid memory device are described. In at least some embodiments, a hybrid memory device includes a volatile memory portion, such as dynamic random access memory (DRAM). The hybrid memory device further includes non-volatile memory portion, such as flash memory. In at least some embodiments, the hybrid memory device can be embodied as a non-volatile dual in-line memory module, or NVDIMM. Techniques discussed herein employ various functionalities to enable the hybrid memory device to be exposed to various entities as an available block storage device.

Abstract: Techniques for block storage using a hybrid memory device are described. In at least some embodiments, a hybrid memory device includes a volatile memory portion, such as dynamic random access memory (DRAM). The hybrid memory device further includes non-volatile memory portion, such as flash memory. In at least some embodiments, the hybrid memory device can be embodied as a non-volatile dual in-line memory module, or NVDIMM. Techniques discussed herein employ various functionalities to enable the hybrid memory device to be exposed to various entities as an available block storage device.

Abstract: Techniques for block storage using a hybrid memory device are described. In at least some embodiments, a hybrid memory device includes a volatile memory portion, such as dynamic random access memory (DRAM). The hybrid memory device further includes non-volatile memory portion, such as flash memory. In at least some embodiments, the hybrid memory device can be embodied as a non-volatile dual in-line memory module, or NVDIMM. Techniques discussed herein employ various functionalities to enable the hybrid memory device to be exposed to various entities as an available block storage device.

Abstract: Techniques for block storage using a hybrid memory device are described. In at least some embodiments, a hybrid memory device includes a volatile memory portion, such as dynamic random access memory (DRAM). The hybrid memory device further includes non-volatile memory portion, such as flash memory. In at least some embodiments, the hybrid memory device can be embodied as a non-volatile dual in-line memory module, or NVDIMM. Techniques discussed herein employ various functionalities to enable the hybrid memory device to be exposed to various entities as an available block storage device.

Abstract: Embodiments of the present disclosure provide for a method and system for storing content based on one or more attributes associated with the content. Specifically, the present disclosure provides for determining storage capabilities of one or more storage devices associated with a computing device and also determining a retention policy associated with the content. In certain embodiments, the retention policy is identified by the one or more attributes. When the retention policy and the capabilities of the one or more storage devices have been determined, the content is written to the one or more storage devices based on the retention policy.

Abstract: A method or system is capable of detecting operator behavior (“OB”) utilizing a virtuous cycle containing sensors, machine learning center (“MLC”), and cloud based network (“CBN”). In one aspect, the process monitors operator body language captured by interior sensors and captures surrounding information observed by exterior sensors onboard a vehicle as the vehicle is in motion. After selectively recording the captured data in accordance with an OB model generated by MLC, an abnormal OB (“AOB”) is detected in accordance with vehicular status signals received by the OB model. Upon rewinding recorded operator body language and the surrounding information leading up to detection of AOB, labeled data associated with AOB is generated. The labeled data is subsequently uploaded to CBN for facilitating OB model training at MLC via a virtuous cycle.

Abstract: A method or system capable of managing automobile parking space (“APS”) using containerized sensors, machine learning center, and cloud based network is disclosed. A process, in one aspect, monitors the surrounding information observed by a set of onboard sensors of a vehicle as the vehicle is in motion. After selectively recording the surrounding information in accordance with instructions from a containerized APS model which is received from a machine learning center, an APS and APS surrounding information are detected when the vehicle is in a parked condition. Upon rewinding recorded surrounding information leading up to the detection of APS, labeled data associated with APS is generated based on APS and the recorded surrounding information. The process subsequently uploads the labeled data to the cloud based network for facilitating APS model training at the machine learning center via a virtuous cycle.

Abstract: Techniques for block storage using a hybrid memory device are described. In at least some embodiments, a hybrid memory device includes a volatile memory portion, such as dynamic random access memory (DRAM). The hybrid memory device further includes non-volatile memory portion, such as flash memory. In at least some embodiments, the hybrid memory device can be embodied as a non-volatile dual in-line memory module, or NVDIMM. Techniques discussed herein employ various functionalities to enable the hybrid memory device to be exposed to various entities as an available block storage device.

Abstract: A technique and associated mechanism is described for registering event metadata at a first site, transferring the event metadata to a second site using a portable module, and processing the event metadata at the second site. A user can register the event metadata at the first site in the course of consuming broadcast content. Namely, when the user encounters an interesting portion of the broadcast content, the user activates an input mechanism, resulting in the storage of event metadata associated with the interesting portion on the portable module. The second site can upload the event metadata from the portable module and, in response, provide content associated with the event metadata, including recommended content associated with the event metadata.

Abstract: Techniques for block storage using a hybrid memory device are described. In at least some embodiments, a hybrid memory device includes a volatile memory portion, such as dynamic random access memory (DRAM). The hybrid memory device further includes non-volatile memory portion, such as flash memory. In at least some embodiments, the hybrid memory device can be embodied as a non-volatile dual in-line memory module, or NVDIMM. Techniques discussed herein employ various functionalities to enable the hybrid memory device to be exposed to various entities as an available block storage device.

Abstract: Methods and devices are provided for adapting an I/O pattern, with respect to a processing device using a non-volatile block storage device based on feedback from the non-volatile block storage device. The feedback may include information indicating a status of the non-volatile block storage device. In response to receiving the feedback, a storage subsystem, included in an operating system executing on processing device, may change a behavior with respect to the non-volatile block storage device in order to avoid, or reduce, a negative impact to the non-volatile block storage device or to enhance an aspect of the non-volatile block storage device. The feedback may include performance information and/or operating environmental information of the non-volatile block storage device. When the non-volatile block storage device is not capable of providing the feedback, the processing device may request information about the non-volatile block storage device from a database service.

Abstract: Processes for identifying and recovering a lost transient storage device are provided. In some processes, information regarding the owner of the device is obtained. The device ownership information may be stored on a remote service with which the device is registered and/or may be stored on the device itself. In one process, the remote service provides the device with customized device-executable code when the device is registered. The device may also contain information regarding trusted systems. The process includes obtaining status information indicating whether a device is lost when the device is connected to a host system. In some processes, the status is determined by a remote service. In other processes, the status is determined by the device. If the device is lost then a device recovery plan is executed. Portions of a device recovery plan may be executed on the remote service, the host system, and/or the device.

Abstract: Methods and devices are provided for adapting an I/O pattern, with respect to a processing device using a non-volatile block storage device based on feedback from the non-volatile block storage device. The feedback may include information indicating a status of the non-volatile block storage device. In response to receiving the feedback, a storage subsystem, included in an operating system executing on processing device, may change a behavior with respect to the non-volatile block storage device in order to avoid, or reduce, a negative impact to the non-volatile block storage device or to enhance an aspect of the non-volatile block storage device. The feedback may include performance information and/or operating environmental information of the non-volatile block storage device. When the non-volatile block storage device is not capable of providing the feedback, the processing device may request information about the non-volatile block storage device from a database service.

Abstract: Methods and devices are provided for adapting an I/O pattern, with respect to a processing device using a non-volatile block storage device based on feedback from the non-volatile block storage device. The feedback may include information indicating a status of the non-volatile block storage device. In response to receiving the feedback, a storage subsystem, included in an operating system executing on processing device, may change a behavior with respect to the non-volatile block storage device in order to avoid, or reduce, a negative impact to the non-volatile block storage device or to enhance an aspect of the non-volatile block storage device. The feedback may include performance information and/or operating environmental information of the non-volatile block storage device. When the non-volatile block storage device is not capable of providing the feedback, the processing device may request information about the non-volatile block storage device from a database service.