Abstract: A data storage device and method for host-determined proactive block clearance are provided. In one embodiment, a data storage device is provided comprising a memory and a controller. The controller is configured to receive a command from a host that specifies a parameter of an upcoming burst mode; and in response to receiving the command, proactively perform a garbage collection operation according to the parameter to create available storage space in the memory to store data from the host during the upcoming burst mode. Other embodiments are possible, and each of the embodiments can be used alone or together in combination.

Abstract: Protecting configuration data in a clustered container system may include, in some embodiments, protecting an ETCD data store in a Kubernetes cluster. A data storage management system addresses the unique needs of protecting an ETCD data store of a target Kubernetes cluster, as well as protecting non-ETCD data payloads. The illustrative data storage management system defines ETCD as a unique kind of workload. ETCD protection is integrated within the data storage management system, which automatically creates data structures and resources within the system for, and provides special-purpose features to protect, ETCD contents and associated security certificates. One of the special-purpose features deploys a temporary data transfer agent within the target Kubernetes cluster to safeguard an ETCD snapshot and transmit its contents, along with the security certificates, to a backup infrastructure that operates outside of the target Kubernetes cluster.

Abstract: A data storage device includes a host interface for coupling the data storage device to a host system. The data storage device also includes a device memory for storing or providing data in response to commands receive from the host system. The data storage device also includes a controller comprising a storage subsystem and a security subsystem. The storage subsystem is configured to receive a host command from the host interface, and process the host command for the device memory. The security subsystem includes a device recovery circuit configured to monitor the storage subsystem for an exception state, and reinitialize pending operations for the storage subsystem after the exception state. Methods and systems are also disclosed.

Abstract: A system that detects the location of a user gaze at a display and in response to the duration of the gaze exceeding a threshold, auto-playing content on the display. The system may also determine gaze event data associating the gaze event with the source of the content the user is gazing at. Other information may also be associated with the gaze event such as user ID, time/duration data, or the like. Various actions can be taken in response to the gaze event such as auto-playing of content, outputting a visual indication of the detected gaze, interpreting detected speech using the gaze event data, data aggregation, etc.

Abstract: Techniques for recommending a skill experience to a user after a user-system dialog session has ended are described. Upon a dialog session ending, the system uses a first machine learning model to determine potential intents to recommend to a user. The system then uses a second machine learning model to determine a particular skill and intent to recommend. The system then prompts the user to accept the recommended skill and intent. If the user accepts, the system calls the recommended skill to execute. As part of calling the skill, the system sends to the skill at least one entity provided in a natural language user input of the ended dialog session. This enables the skill to skip welcome prompts, and initiate processing to output a response based on the intent and the at least one entity of the ended dialog session.

Abstract: A natural language system may be configured to act as a participant in a conversation between two users. The system may determine when a user expression such as speech, a gesture, or the like is directed from one user to the other. The system may processing input data related the expression (such as audio data, input data, language processing result data, conversation context data, etc.) to determine if the system should interject a response to the user-to-user expression. If so, the system may process the input data to determine a response and output it. The system may track that response as part of the data related to the ongoing conversation.

Abstract: Systems, methods, and computer-readable media are disclosed for dynamic voice search transitioning. Example methods may include receiving, by a computer system in communication with a display, a first incoming voice data indication, initiating a first user interface theme for presentation at a display, wherein the first user interface theme is a default user interface theme, and receiving first voice data. Example methods may include sending the first voice data to a remote server for processing, receiving an indication from the remote server to initiate a second user interface theme, and initiating the second user interface theme for presentation at the display.

Abstract: Techniques for recommending a skill experience to a user after a user-system dialog session has ended are described. Upon a dialog session ending, the system uses a first machine learning model to determine potential intents to recommend to a user. The system then uses a second machine learning model to determine a particular skill and intent to recommend. The system then prompts the user to accept the recommended skill and intent. If the user accepts, the system calls the recommended skill to execute. As part of calling the skill, the system sends to the skill at least one entity provided in a natural language user input of the ended dialog session. This enables the skill to skip welcome prompts, and initiate processing to output a response based on the intent and the at least one entity of the ended dialog session.

Abstract: Devices and techniques are generally described for learning personalized concepts for natural language processing. In various examples, a first natural language input may be received. In some examples, a determination may be made that the first natural language input comprises non-actionable slot data. A dialog session may be initiated with the user. In some examples, first slot data that is indicated by the user during the dialog session may be determined. In various examples, data representing the first slot data may be stored in a database in association with the first natural language input.

Abstract: A data storage device and method for progressive fading for video surveillance systems are provided. In one embodiment, a data storage device is provided comprising a memory and a controller. The controller is configured to store a plurality of digital video frames in the memory over time; and create free space in the memory by deleting some of the plurality of digital video frames across a plurality of subsets of digital video frames, wherein fewer digital video frames are deleted from a subset stored more recently in time than from a subset stored less recently in time. Other embodiments are provided.

Abstract: A data storage device includes interfaces for coupling the data storage device to a host system and a server system. The data storage device also includes a device memory for storing or providing data. The data storage device also includes a controller comprising a storage subsystem and a security subsystem. The storage subsystem is configured to receive a host command, receive a probe request for an exception state, generate and transmit, to the security subsystem, a report for the exception state. In response to receiving an update from the security subsystem, the storage subsystem restarts at least a portion of the controller. The security subsystem is configured to monitor and transmit a probe request to the storage subsystem, transmit the report to the server system, receive the update from the server system, and transmit the update to the storage subsystem. Methods and systems are also disclosed.

Abstract: A data storage device includes a host interface for coupling the data storage device to a host system. The data storage device also includes a device memory for storing or providing data in response to commands receive from the host system. The data storage device also includes a controller comprising a storage subsystem and a security subsystem. The storage subsystem is configured to receive a host command from the host interface, and process the host command for the device memory. The security subsystem includes a device recovery circuit configured to monitor the storage subsystem for an exception state, and reinitialize pending operations for the storage subsystem after the exception state. Methods and systems are also disclosed.

Abstract: Systems, methods, and computer-readable media are disclosed for dynamic voice search transitioning. Example methods may include receiving, by a computer system in communication with a display, a first incoming voice data indication, initiating a first user interface theme for presentation at a display, wherein the first user interface theme is a default user interface theme, and receiving first voice data. Example methods may include sending the first voice data to a remote server for processing, receiving an indication from the remote server to initiate a second user interface theme, and initiating the second user interface theme for presentation at the display.

Abstract: Techniques for enrolling a user in a system's user recognition functionality without requiring the user speak particular speech are described. The system may determine characteristics unique to a user input. The system may generate an implicit voice profile from user inputs having similar characteristics. After an implicit voice profile is generated, the system may receive a user input having speech characteristics similar to that of the implicit voice profile. The system may ask the user if the user wants the system to associate the implicit voice profile with a particular user identifier. If the user responds affirmatively, the system may request an identifier of a user profile (e.g., a user name). In response to receiving the user's name, the system may identify a user profile associated with the name and associate the implicit voice profile with the user profile, thereby converting the implicit voice profile into an explicit voice profile.

Abstract: Described are techniques for predicting when data associated with a user input is likely to be selected for deletion. The system may use a trained model to assist with such predictions. The trained model can be configured based on deletions associated with a user profile. An example process can including receiving user input data corresponding to the user profile, and processing the user input data to determine a user command. Based on characteristic data of the user command, the trained model can be used to determine that data corresponding to the user command is likely to be selected for deletion. The trained model can be iteratively updated based on additional user commands, including previously received user commands to delete user input data.

Abstract: Described are techniques for predicting when data associated with a user input is likely to be selected for deletion. The system may use a trained model to assist with such predictions. The trained model can be configured based on deletions associated with a user profile. An example process can including receiving user input data corresponding to the user profile, and processing the user input data to determine a user command. Based on characteristic data of the user command, the trained model can be used to determine that data corresponding to the user command is likely to be selected for deletion. The trained model can be iteratively updated based on additional user commands, including previously received user commands to delete user input data.

Abstract: Techniques for ensuring content output to a user conforms to a quality of the user's speech, even when a speechlet or skill ignores the speech's quality, are described. When a system receives speech, the system determines an indicator of the speech's quality (e.g., whispered, shouted, fast, slow, etc.) and persists the indicator in memory. When the system receives output content from a speechlet or skill, the system checks whether the output content is in conformity with the speech quality indicator. If the content conforms to the speech quality indicator, the system may cause the content to be output to the user without further manipulation. But, if the content does not conform to the speech quality indicator, the system may manipulate the content to render it in conformity with the speech quality indicator and output the manipulated content to the user.

Abstract: The disclosed technology relates to intent driven network management. A system is configured to maintain an inventory store comprising records for a set of network entities in a network, wherein each network entity in the set of network entities is associated with a record in the inventory store. The system receives a user intent statement comprising an action and a flow filter representing network data flows on which the action is to be applied and queries, based on the flow filter, the inventory store to identify a plurality of network entities in the set of network entities to which the user intent statement applies. The system generates a plurality of network policies that implement the user intent statement based on the plurality of network entities and the action and enforces the plurality network policies.

Abstract: A data storage device and method for host-determined proactive block clearance are provided. In one embodiment, a data storage device is provided comprising a memory and a controller. The controller is configured to receive a command from a host that specifies a parameter of an upcoming burst mode; and in response to receiving the command, proactively perform a garbage collection operation according to the parameter to create available storage space in the memory to store data from the host during the upcoming burst mode. Other embodiments are possible, and each of the embodiments can be used alone or together in combination.

Abstract: Techniques for recommending a skill experience to a user after a user-system dialog session has ended are described. Upon a dialog session ending, the system uses a first machine learning model to determine potential intents to recommend to a user. The system then uses a second machine learning model to determine a particular skill and intent to recommend. The system then prompts the user to accept the recommended skill and intent. If the user accepts, the system calls the recommended skill to execute. As part of calling the skill, the system sends to the skill at least one entity provided in a natural language user input of the ended dialog session. This enables the skill to skip welcome prompts, and initiate processing to output a response based on the intent and the at least one entity of the ended dialog session.