Abstract: A method of obtaining high accuracy urination information is proposed. There may be provided the method of obtaining the urination information, wherein sound data is divided into a plurality of windows, segmented target data corresponding to respective windows is obtained from the sound data, segmented classification data classifying urination sections or non-urination sections and segmented urine flow rate data are obtained by using the obtained segmented target data, and urination data is obtained by using the obtained segmented classification data and the segmented urine flow rate data.

Abstract: Provided is a system and method for automated patient interaction. The method includes parsing a patient complaint comprising a plurality of words, determining a subset of patient queries from a plurality of patient queries based on the patient complaint and patient data, communicating the subset of patient queries to a first computing device; receiving, from the first computing device, responses to at least a portion of the subset of patient queries; generating output data based on the subset of patient queries and the responses; communicating the output data to a second computing device; receiving, from the second computing device, a user input corresponding to at least one patient query of the subset of patient queries; and training, based on the user input, at least one machine-learning algorithm configured to output at least one patient query based on at least one of the patient complaint and a subsequent patient complaint.

Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations thereof, for training a conversational recommendation system. An embodiment generates a probabilistic pseudo-user neural network model based on at least one interest probability distribution corresponding to a pseudo-user profile. The embodiment trains, using the pseudo-user neural network model, the conversational recommendation system to learn a recommendation policy, where the conversational recommendation system includes an interest-exploration engine and a prompt-decision engine. The training includes performing an iterative learning process that includes selecting an interest-exploration strategy based on one or more of the following: an interest-exploration policy, an earlier pseudo-user response generated by the pseudo-user neural network model, content data, and pseudo-user interaction history.

Abstract: An artificial intelligence system training method comprises the operations of: receiving an input; determining an operation sequence according to the input; determining whether or not there is a learned training database with respect to the determined operation sequence; when there is no learned training database with respect to the determined operation sequence, executing an operation according to the determined operation sequence; providing execution completion feedback with respect to the execution of the operation; determining whether or not the determined operation sequence is an input related to user training; when the determined operation sequence is determined to be the input related to the user training, tracking a user input and electronic device execution information; and updating domain and state information according to the tracked user input and electronic device execution information.

Abstract: A hub device, a multi-device system including the hub device, and a method of operating the same may include: converting, by the hub device, received voice input into text; identifying, by the hub device, a device capable of performing an operation corresponding to the text; identifying which device stores a function determination model corresponding to the device capable of performing the operation corresponding to the text, from among the hub device, and a plurality of other devices connected to the hub device; and based on the identified device that stores the function determination model being a device that is different from the hub device, transmitting at least part of the text to the identified device.

Abstract: Systems and methods are described herein for providing media guidance. Control circuitry may receive a first voice input and access a database of topics to identify 5 a first topic associated with the first voice input. A user interface may generate a first response to the first voice input, and subsequent to generating the first response, the control circuitry may receive a second voice input. The control circuitry may 10 determine a match between the second voice input and an interruption input such as a period of silence or a keyword or a phrase, such as “Ahh,”, “Umm,”, or “Hmm.” The user interface may generate a second response that is associated with a second topic related to the first 15 topic. By interrupting the conversation and changing the subject from time to time, media guidance systems can appear to be more intelligent and human.

Abstract: The systems and methods of the present disclosure generally relate to a data processing system that can identify and surface alternative requests when presented with ambiguous, unclear, or other requests to which a data processing system may not be able to respond. The data processing system can improve the efficiency of network transmissions to reduce network bandwidth usage and processor utilization by selecting alternative requests that are responsive to the intent of the original request.

Abstract: The present disclosure is generally related to a data processing system to validate vehicular functions in a voice activated computer network environment. The data processing system can improve the efficiency of the network by discarding action data structures and requests that invalid prior to their transmission across the network. The system can invalidate requests by comparing attributes of a vehicular state to attributes of a request state.

Abstract: A voice controlled apparatus for performing a workflow operation is described. The voice controlled apparatus can include a microphone, a speaker, and a processor. In some examples, the voice controlled apparatus can generate, via the speaker, a voice prompt associated with a task of a workflow and identify, via the microphone, a voice response received from a worker. In this regard, the voice prompt and the voice response can be a part of a voice dialogue. Further, the processor of the voice controlled apparatus can identify a performance status associated with the execution of the task, before providing a next voice prompt subsequent to the voice prompt. In this aspect, the performance status can be identified based on analyzing the voice dialogue using a machine learning model. Furthermore, the voice controlled apparatus can generate a message including a suggestion to improve the performance status of the task.

Abstract: A system includes at least one processor to perform natural language processing on text from at least one document and assign the at least one document to at least one subjective wellbeing dimension by comparing the text from the at least one document with a subjective wellbeing dimension filter for each subjective wellbeing dimension, insert the at least one document into at least one bin, each bin associated with a particular subjective wellbeing dimension, and analyze each document in each bin associated with the particular subjective wellbeing dimension to determine a score for each subjective wellbeing dimension and an overall score that is based on each score for each subjective wellbeing dimension.

Abstract: Methods, apparatus, systems, and computer-readable media are provided for isolating at least one device, from multiple devices in an environment, for being responsive to assistant invocations (e.g., spoken assistant invocations). A process for isolating a device can be initialized in response to a single instance of a spoken utterance, of a user, that is detected by multiple devices. One or more of the multiple devices can be caused to query the user regarding identifying a device to be isolated for receiving subsequent commands. The user can identify the device to be isolated by, for example, describing a unique identifier for the device. Unique identifiers can be generated by each device of the multiple devices and/or by a remote server device. The unique identifiers can be presented graphically and/or audibly to the user, and user interface input. Any device that is not identified can become temporarily unresponsive to certain commands, such as spoken invocation commands.

Abstract: Systems and methods that offer significant improvements to current virtual agent (VA) conversational experiences are disclosed. The proposed systems and methods are configured to manage conversations in real-time with human customers while accommodating a dynamic goal. The VA includes a goal-driven module with a reinforcement learning-based dialogue manager. The VA is an interactive tool that utilizes both task-specific rewards and sentiment-based rewards to respond to a dynamic goal. The VA is capable of handling dynamic goals with a significantly high success rate. As the system is trained primarily with a user simulator, it can be readily extended for applications across other domains.

Abstract: A method includes receiving information from each device in multiple devices of one user, where the information includes a device type, determining, according to at least the received information, a device responding to a voice instruction in the multiple devices, and sending a message to a device different from the device responding to a voice instruction in the multiple devices, where the message is used to instruct a voice assistant of the different device not to respond to the voice instruction.

Abstract: Systems and methods are provided for determining importance and urgency of a task based on acoustic features of audio input associated with the task. The determining includes classifying the task into one or more classes associated with importance, urgency, and priority of the task. The classification may use a trained machine learning model of acoustic features and embedding for a neural network. The task classifier uses feature acoustics of either or both the foreground and background audio. The feature acoustics include a pitch, a tone, and a volume over a time duration of the audio input. A combination of the acoustic features determines a class associated with the task. The machine learning model includes a regression model of acoustic features over time and a model with embedding for a neural network.

Abstract: A method including transcribing, into digital tokens, utterances from a conversation between an agent and a person. The method also includes embedding the digital tokens into an utterances tensor including sequences of the digital tokens. The method also includes obtaining a metadata tensor by encoding metadata related to the utterances into the metadata tensor. The method also includes executing a machine learning model which takes, as input, the utterances tensor and the metadata tensor, and which outputs a predicted source article predicted to be related to the utterances. The method also includes generating an interactive link to the predicted source article.

Abstract: A codec allowing for switching between different coding modes is improved by, responsive to a switching instance, performing temporal smoothing and/or blending at a respective transition.

Abstract: Systems, methods and apparatus of predictive maintenance of automotive engines. For example, a vehicle has an engine, at least one microphone directed to the engine to sense audio signals including sound emitting from the engine, an artificial neural network configured to analyze the audio signals to generate a result, and at least one processor configured to generate a suggestion for a maintenance service of the engine based on the result from the artificial neural network analyzing the audio signals.

Abstract: The various implementations described herein include methods and systems for determining device leadership among voice interface devices. In one aspect, a method is performed at a first electronic device of a plurality of electronic devices, each having microphones, a speaker, processors, and memory storing programs for execution by the processors. The first device detects a voice input. It determines a device state and a relevance of the voice input. It identifies a subset of electronic devices from the plurality to which the voice input is relevant. In accordance with a determination that the subset includes the first device, the first device determines a first score of a criterion associated with the voice input and receives second scores of the criterion from other devices in the subset. In accordance with a determination that the first score is higher than the second scores, the first device responds to the detected input.

Abstract: Systems and methods for determining contextual rules are described herein. In some embodiments, an apparatus may identify a context datum and an interaction datum as a function of a user datum. In some embodiments, an apparatus may determine an interaction feature and a reaction datum as a function of an interaction datum. In some embodiments, an apparatus may determine a contextual rule as a function of the context datum, interaction feature, and reaction datum. In some embodiments, an apparatus may display a visual element to a user as a function of a contextual rule.

Abstract: A method can include receiving a string of characters. The method can include determining one or more possible word boundaries for words in the string of characters based at least partially on a segmentation process. The method can also include determining, for each character in the string of characters, an amount of time between entry of each character on an input device. The method can include determining, based at least partially on the amount of time and the one or more possible word boundaries, one or more actual word boundaries for the words in the string of characters. The method can also include outputting one or more determined words in the string of characters based at least partially on the one or more actual word boundaries.