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: 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: 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: 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: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) is configured such that a SIM card inserted in the UE, which is invalid to camp on a network, may be allowed by the UE to camp on the network based on a preconfigured policy. The policy, for example, may require a valid SIM card to be inserted into a second SIM card slot on the UE. In some policies, the valid SIM card may also be required to camp on a network with full service in order to allow the invalid SIM card to camp on a network. In some policies, the valid SIM card may also be required to camp on a specific type of network, for example a standalone non-public network (SNPN) or a 3GPP network.

Abstract: A speech interface device is configured to receive response data from a remote speech processing system for responding to user speech. This response data may be enhanced with information such as remote NLU data. The response data from the remote speech processing system may be compared to local NLU data to improve a speech processing model on the device. Thus, the device may perform supervised on-device learning based on the remote NLU data. The device may determine differences between the updated speech processing model and an original speech processing model received from the remote system and may send data indicating these differences to the remote system. The remote system may aggregate data received from a plurality of devices and may generate an improved speech processing model.

Abstract: Features are disclosed for detecting words in audio using contextual information in addition to automatic speech recognition results. A detection model can be generated and used to determine whether a particular word, such as a keyword or “wake word,” has been uttered. The detection model can operate on features derived from an audio signal, contextual information associated with generation of the audio signal, and the like. In some embodiments, the detection model can be customized for particular users or groups of users based usage patterns associated with the users.

Abstract: The disclosed technology relates to a network agent for reporting to a network policy system. A network agent includes an agent enforcer and an agent controller. The agent enforcer is configured to implementing network policies on the system, access data associated with the implementation of the network policies on the system, and transmit, via an interprocess communication, the data to the agent controller. The agent controller is configured to generate a report including the data and transmit the report to a network policy system.

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: 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: Systems, methods, and devices for computer-generating responses and sending responses to communications when the recipient of the communication is unavailable are disclosed. An individual may send a message (either audio or text) to a recipient. The recipient may be unavailable to contemporaneously respond to the message (e.g., the recipient may be performing an action that makes is difficult or impractical for the recipient to contemporaneously respond to the audio message). When the recipient is unavailable, a response to the message is generated and sent without receiving an instruction from the recipient to do so. The response may be sent to the message originating individual, and content of the response may thereafter be sent to the recipient to receive feedback regarding the correctness of the response. Alternatively, the response content may first be sent to the recipient to receive the feedback, and thereafter the response may be sent to the message originating individual.

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: 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: The disclosed technology relates to a network agent for reporting to a network policy system. A network agent includes an agent enforcer and an agent controller. The agent enforcer is configured to implementing network policies on the system, access data associated with the implementation of the network policies on the system, and transmit, via an interprocess communication, the data to the agent controller. The agent controller is configured to generate a report including the data and transmit the report to a network policy system.

Abstract: Systems, methods, and devices for computer-generating responses and sending responses to communications when the recipient of the communication is unavailable are disclosed. An individual may send a message (either audio or text) to a recipient. The recipient may be unavailable to contemporaneously respond to the message (e.g., the recipient may be performing an action that makes is difficult or impractical for the recipient to contemporaneously respond to the audio message). When the recipient is unavailable, a response to the message is generated and sent without receiving an instruction from the recipient to do so. The response may be sent to the message originating individual, and content of the response may thereafter be sent to the recipient to receive feedback regarding the correctness of the response. Alternatively, the response content may first be sent to the recipient to receive the feedback, and thereafter the response may be sent to the message originating individual.

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: A system can operate a speech-controlled device in a mode where the speech-controlled device determines that an utterance is directed at the speech-controlled device using image data showing the user speaking the utterance. If the user is directing the user's gaze at the speech-controlled device while speaking, the system may determine the utterance is system directed and thus may perform further speech processing based on the utterance. If the user's gaze is directed elsewhere, the system may determine the utterance is not system directed (for example directed at another user) and thus the system may not perform further speech processing based on the utterance and may take other actions, for example discarding audio data of the utterance.

Abstract: Techniques for limiting natural language processing performed on input data are described. A system receives input data from a device. The input data corresponds to a command to be executed by the system. The system determines applications likely configured to execute the command. The system performs named entity recognition and intent classification with respect to only the applications likely configured to execute the command.

Abstract: The disclosed technology relates to a network agent for generating platform specific network policies. A network agent is configured to receive a platform independent network policy from a network policy system, determine implementation characteristics of the network entity, generate platform specific policies from the platform independent network policy based on the implementation characteristics of the network entity, and implement the platform specific policies on the network entity.

Abstract: A speech interface device is configured to receive response data from a remote speech processing system for responding to user speech. This response data may be enhanced with information such as remote NLU data. The response data from the remote speech processing system may be compared to local NLU data to improve a speech processing model on the device. Thus, the device may perform supervised on-device learning based on the remote NLU data. The device may determine differences between the updated speech processing model and an original speech processing model received from the remote system and may send data indicating these differences to the remote system. The remote system may aggregate data received from a plurality of devices and may generate an improved speech processing model.