Abstract: Described herein are exemplary devices, apparatuses, systems, methods, and non-transitory storage media for providing an application framework. The application framework can provide various machine-learning models to perform a variety of analysis tasks to analyze enterprise data such as communications between one or more employees of an organization and one or more clients of the organization and provide intelligence and insights for a user in the organization. The insights and intelligence can include a recommendation or an observation related to a client or customer of the organization. The recommendation or observation can be provided, for example, in a communication platform, a chatbot, or a variety of other interfaces. Advantageously, to perform an analysis task, the application framework automatically provides to the machine-learning model(s) information in accordance with the enterprise's data sharing and access control requirements to prevent inappropriate access and use of sensitive information.

Abstract: A multi-modal pre-training model acquisition method, an electronic device and a storage medium, which relate to the fields of deep learning and natural language processing, are disclosed. The method may include: determining, for each image-text pair as training data, to-be-processed fine-grained semantic word in the text; masking the to-be-processed fine-grained semantic words; and training the multi-modal pre-training model using the training data with the fine-grained semantic words masked.

Abstract: The present disclosure relates to a home appliance and an operating method thereof. The home appliance according to the present disclosure includes: a user input interface; a storage configured to store a database of a response history, and a controller configured to: in response to receiving an input requesting to perform a previous operation through the user input interface, verify whether a first operation, corresponding to the request for performing the previous operation, is present in the operation history; in response to there being the first operation, verify a type of a first command, mapped to the first operation, among commands included in the database; in response to the type of the first command being a first type, determine the first operation to be the previous operation; and in response to the type of the first command being a second type, generate a second operation corresponding to the first command, and determines the second operation to be the previous operation.

Abstract: A first data that identifies a first device is used to identify within a data store a one or more capabilities of the first device. When a voice input having one or more keywords that have been pre-associated within the data store to an operational problem associated with at least the first device is received, a system causes a state of at least the first device at a time at which the voice input is received to be identified, causes the one or more keywords, the one or more capabilities of the first device, and the state of at least the first device to be used to identify within the data store a set of instructions for interacting with at least the first device to solve the operational problem, and causes the set of instructions to be presented to a user.

Abstract: Techniques for improving the power consumption of a device without impacting or with minimal impact to operations of the device are described. In an example, the device includes a processor. The device receives, while the processor is operating in a first power mode, first input data corresponding to first audio detected by a microphone. Based at least in part on the first input data, the device detects a sound event or ambient noise. Based at least in part on a detection of the ambient noise only, the device causes processor to operate in a second power mode in which the processor consumes less power than in the first power mode.

Abstract: Systems and methods for generating training data are described herein. Pieces of metadata captured by a plurality of networked sensor systems can be captured, where each piece of metadata is associated with a specific set of sensor data captured by one of the plurality of networked sensor systems and includes a set of characteristics for the specific set of captured sensor data. A probabilistic model can be generated based on the received metadata and simulations can be performed based upon a training corpus by generating multiple scenarios, and, for each scenario, a scenario specific version of a particular annotated sample is generated by performing a simulation using the particular annotated sample. The scenario specific versions of annotated samples from the training corpus can be stored as a training data set on the at least one network device.

Abstract: A Wireless Interface Device and System (WIDS) is used as a bridge device for transmitting, translating, transporting and/or transforming a first emergency aid seeking signal to a second emergency aid seeking signal recognizable by a smart voice commendable device (e.g., AMAZON® ECHO®). The first emergency aid seeking signal can be generated from a Personal Emergency Response Systems (PERS), a fall sensor (e.g., a senior fall sensor), home security monitoring device, or any other emergency aid requesting devices.

Abstract: A method of providing a conversational agent for interacting with a user may include declaratively defining a task model of a task using a task modelling language, storing the task model in a computer-readable storage medium, generating a natural language grammar based on the task model, storing the natural language grammar in the computer-readable storage medium, receiving a user input from the user, interpreting the user input with a processor based on the task model and the natural language grammar, generating an agent response to the user input with the processor based on the task model, and communicating the agent response to the user.

Abstract: Disclosed are an electronic device, a system, and a controlling method thereof. The controlling method includes: receiving an input utterance, determining whether domain information and intent information are able to be extracted by analyzing the input utterance, based on at least one of the domain information and the intent information not being extracted, broadcasting a signal requesting previous utterance related information to one or more external devices connected to a same network as the electronic device, receiving the previous utterance related information from the at least one external device, extracting the domain information and the intent information based on the received previous utterance related information and the input utterance, and obtaining and outputting a response result based on the extracted domain information and intent information.

Abstract: A method of dispensing a beverage from a beverage dispenser includes: detecting a user in proximity to the beverage dispenser; prompting the user to provide a first input, wherein the first input is audible; retrieving a user profile for the user based on the first input; receiving a second input from the user, wherein the second input comprises information about a beverage selection of the user, and wherein the second input is provided in a different manner than the first input; and dispensing the beverage.

Abstract: According to an embodiment, an electronic device may include: a display, a communication module comprising communication circuitry, a memory; and a processor operatively connected to the display, the communication module, and the memory.

Abstract: An audio processing method applied to a first terminal is described, and includes: in response to receiving of audio data input by a user at the first terminal, and determination that a voice change function is turned on, determining change parameters; and based on the change parameters, performing change processing on the audio data.

Abstract: A method includes, while displaying a first set of text content via a display device, determining an engagement value that characterizes a level of user engagement with respect to the first set of text content. The method includes, in accordance with a determination that the engagement value satisfies a threshold, replacing the first set of text content with a second set of text content via the display device. The first set of text content is different from the second set of text content. The method includes in accordance with a determination that the engagement value does not satisfy the threshold, maintaining display of the first set of text content via the display device.

Abstract: A device, such as Network Microphone Device or a playback device, receives an indication of a track change associated with a playback queue output by a media playback system. In response, an input detection window is opened for a given time period. During the given time period the device is arranged to receive an input sound data stream representing sound detected by a microphone. The input sound data stream is analyzed for a plurality of command keywords and/or a wake-word for a Voice Assistant Service (VAS) and, based on the analysis, it is determined that the input sound data stream includes voice input data comprising a command keyword or a wake-word for a VAS. In response, the device takes appropriate action such as causing the media playback system to perform a command corresponding to the command keyword or sending at least part of the input sound data stream to the VAS.

Abstract: Provided are a server for providing a response message, based on a voice input of a user, and an operation method of the server. Provided are a server that recognizes health state information of a user, based on a voice input from the user, analyzes pre-stored health data, generates a response message, based on the analyzed health data, and outputs the generated response message, and an operation method of the server. Provided are a server that recognizes event information of a user from a voice input from the user, generates a response message, based on information about the type and frequency of a recognized event, and provides the generated response message, and an operation method of the server.

Abstract: An electronic device for predicting an intention of a user is configured to provide a question to the user and predict at least one first intention of the user based on context information associated with the user. The device is also configured to determine a question based on the at least one first intention of the user. The device is further configured to provide the question to the user. The device is additionally configured to receive a response to the question from the user. The device is also configured to predict at least one second intention of the user based on the at least one first intention of the user and the response to the question from the user.

Abstract: Devices and techniques are generally described for data-to-text generation. In various examples, a first machine learned model may receive first data including a structured representation of linguistic data. In various examples, the first machine learned model may generate first output data comprising a first natural language representation of the first data. In at least some examples, a second machine learning model may determine second data indicating that the first natural language representation is a semantically accurate representation of the first data. In some examples, the first output data may be selected for output based at least in part on the second data.

Abstract: A method includes receiving a spoken utterance that includes a plurality of words, and generating, using a neural network-based utterance classifier comprising a stack of multiple Long-Short Term Memory (LSTM) layers, a respective textual representation for each word of the of the plurality of words of the spoken utterance. The neural network-based utterance classifier trained on negative training examples of spoken utterances not directed toward an automated assistant server. The method further including determining, using the respective textual representation generated for each word of the plurality of words of the spoken utterance, that the spoken utterance is one of directed toward the automated assistant server or not directed toward the automated assistant server, and when the spoken utterance is directed toward the automated assistant server, generating instructions that cause the automated assistant server to generate a response to the spoken utterance.

Abstract: A system comprising a transponder having a user interface to receive commands from a user and to operate a virtual assistant. A micro-computer in an automobile, which is responsive to the transponder, and wherein the transponder and the micro-computer configured to have conversations. These include conversations regarding the automobile, and between a user of the transponder and the virtual assistant utilized by the transponder. The conversations are recorded and recallable at a later time, wherein the virtual assistant responds to future commands or questions from the user based on prior commands or questions from the user.

Abstract: Determining whether, upon cessation of a second automated assistant session that interrupted and supplanted a prior first automated assistant session: (1) to automatically resume the prior first automated assistant session, or (2) to transition to an alternative automated assistant state in which the prior first session is not automatically resumed. Implementations further relate to selectively causing, based on the determining and upon cessation of the second automated assistant session, either the automatic resumption of the prior first automated assistant session that was interrupted, or the transition to the state in which the first session is not automatically resumed.