Abstract: The present invention relates to a system for the control of a medical implant in a mammal body. The system comprises a first and a second part being adapted for communication with each other. In the system the first part is adapted for implantation in the mammal body for the control of and communication with the medical implant, and the second part is adapted to be worn on the outside of the mammal body and adapted to receive control commands from a user and to transmit these commands to the first part.

Abstract: A pharmacy order facilitation method includes receiving a message; training a machine learning model; generating an intent; generating a response message; and transmitting the response message. A computing system includes a processor and a memory storing instructions that, when executed by the one or more processors, cause the computing system to: receive a message; train a machine learning model; generate an intent; generate a response message; and transmit the response message. A computing system includes a mobile device; a server device configured to: train a machine learning model; analyze the inbound message; identify a customer intent; perform an action based on the customer intent; and transmit an outbound message.

Abstract: Embodiments of the present invention provide a speech recognition method and a terminal. The method includes: listening, by a speech wakeup apparatus, to speech information in a surrounding environment; when determining that the speech information obtained by listening matches a speech wakeup model, buffering, by the speech wakeup apparatus, speech information, of first preset duration, obtained by listening, and sending a trigger signal for triggering enabling of a speech recognition apparatus, where the trigger signal is used to instruct the speech recognition apparatus to read and recognize the speech information buffered by the speech wakeup apparatus; and recognizing first speech information buffered by the speech wakeup apparatus and the second speech information obtained by listening, to obtain a recognition result.

Abstract: A method of building a natural language understanding application is provided. The method includes receiving at least one electronic record containing programming code and creating executable code from the programming code. Further, the executable code, when executed by a processor, causes the processor to create a parse and an interpretation of a sequence of input tokens, the programming code includes an interpret-block and the interpret-block includes an interpret-statement. Additionally, the interpret-statement includes a pattern expression and the interpret-statement includes an action statement.

Abstract: The present technology extends to methods, systems, and computer program products for expanding semantic classes via user feedback. Aspects of the technology learn how a set of labels can be expanded from user-generated tags. Text labels applied by human reviewers to digital content can be inspected and compared to one another. When a threshold of human-generated text tags contain similar terminology, the set of labels can be expanded to define a representation of the similar terminology. Similar terminology can include terms that originate from the same base term, are synonyms, are more specific terms related to a general term category, etc. Similar terminology can be consolidated into a defining term that is used to generate a new (more granular) label or a new top level label. Accordingly, new semantic classes can be discovered from user-generated feedback. New semantic classes can provide a more granular representation of content item classification.

Abstract: The present disclosure relates to systems and methods for providing subtitle for a video. The video's audio is transcribed to obtain caption text for the video. A first machine-trained model identifies sentences in the caption text. A second model identifies intra-sentence breaks with in the sentences identified using the first machine-trained model. Based on the identified sentences and intra-sentence breaks, one or more words in the caption text are grouped into a clip caption to be displayed for a corresponding clip of the video.

Abstract: A system and method for voice-based control of sexual stimulation devices. In some configurations, the system and method involve receiving voice data, analyzing the voice data to detect spoken commands, and generating control signals based on the commands. In some configurations, the system and method involve receiving voice data, analyzing the voice data for non-speech vocalizations, detecting voice stress patterns, and generating control signals based on the detected patterns. In some configurations, the analyses of the voice data are performed by machine learning algorithms which may be trained on associations between speech and non-speech vocalizations of a user while the user engages in one or more voice-based training tasks, associating speech and non-speech vocalizations with controls of the sexual stimulation device. In some configurations, machine learning algorithms are used to make the associations. In some configurations, data from other biometric sensors is included in the associations.

Abstract: One or more non-transitory computer readable media contain program instructions that, when executed, cause one or more processors to: receive first raw data including one or more first data points generated by a first object of a plurality of objects associated with one or more buildings; generate first input timeseries according to the one or more data points; access a database of interconnected smart entities, the smart entities including object entities representing each of the plurality of objects and data entities representing stored data, the smart entities being interconnected by relational objects indicating relationships between the smart entities; identify a first object entity representing the first object from a first identifier in the first input timeseries; identify a first data entity from a first relational object indicating a relationship between the first object entity and the first data entity; and store the first input timeseries in the first data entity.

Abstract: A method for decoding an encoded audio bitstream is disclosed. The method includes receiving the encoded audio bitstream and decoding the audio data to generate a decoded lowband audio signal. The method further includes extracting high frequency reconstruction metadata and filtering the decoded lowband audio signal with an analysis filterbank to generate a filtered lowband audio signal. The method also includes extracting a flag indicating whether either spectral translation or harmonic transposition is to be performed on the audio data and regenerating a highband portion of the audio signal using the filtered lowband audio signal and the high frequency reconstruction metadata in accordance with the flag.

Abstract: A frequency band expansion device includes processing circuitry to calculate a weighting coefficient based on a frequency gradient of the input signal; to generate a white noise signal; to generate a first white noise signal by performing filtering on the white noise signal; to generate a second white noise signal by regulating a phase characteristic of the white noise signal; to generate a third white noise signal by performing weighted addition on the first white noise signal and the second white noise signal by using the weighting coefficient; and to generate the output signal by adding together the input signal and a signal corresponding to the third white noise signal, wherein the processing circuitry is configured so that the phase characteristic of the second white noise signal becomes the same as the phase characteristic of the first white noise signal.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for neural network adaptive beamforming for multichannel speech recognition are disclosed. In one aspect, a method includes the actions of receiving a first channel of audio data corresponding to an utterance and a second channel of audio data corresponding to the utterance. The actions further include generating a first set of filter parameters for a first filter based on the first channel of audio data and the second channel of audio data and a second set of filter parameters for a second filter based on the first channel of audio data and the second channel of audio data. The actions further include generating a single combined channel of audio data. The actions further include inputting the audio data to a neural network. The actions further include providing a transcription for the utterance.

Abstract: The present disclosure describes a communication environment having a service provider server that receives an audio command from a display control device within the communication environment. The service provider server can translate this audio command into an electrical command for controlling the display device. The service provider server autonomously performs a specifically tailored search of a catalog of command words and/or phrases for the audio command to translate the audio command to the electrical command. This specifically tailored search can include one or more searching routines having various degrees of complexity. The most simplistic searching routine from among these searching routines represents a textual search to identify one or more command words and/or phrases from the catalog of command words and/or phrases that match the audio command.

Abstract: Techniques are described herein for multi-modal interaction between users, automated assistants, and other computing services. In various implementations, a user may engage with the automated assistant in order to further engage with a third party computing service. In some implementations, the user may advance through dialog state machines associated with third party computing service using both verbal input modalities and input modalities other than verbal modalities, such as visual/tactile modalities.

Abstract: A method includes obtaining a first sequence of reference-sample feature vectors that quantify acoustic features of different respective portions of at least one reference speech sample, which was produced by a subject at a first time while a physiological state of the subject was known, and a second sequence of test-sample feature vectors that quantify the acoustic features of different respective portions of at least one test speech sample, which was produced by the subject at a second time while the physiological state of the subject was unknown. The test-sample feature vectors are mapped to respective ones of the reference-sample feature vectors, under predefined constraints, such that a total distance between the test-sample feature vectors and the respective ones of the reference-sample feature vectors is minimized. In response to the mapping, an output indicating the physiological state of the subject at the second time is generated.

Abstract: A method for determining the answer to a query in a document, including: encoding, by an encoder, the query and the document; generating a query-aware context encodings G by a bidirectional attention system using the encoded query and the encoded document; performing a hierarchical self-attention on the query aware document by a hierarchical self-attention system by applying a word to word attention and a word to sentence attention mechanism resulting in a matrix M; and determining the starting word and the ending word of the answer in the document by a span detector based upon the matrix M.

Abstract: An electronic device is provided. The electronic device according to an embodiment includes a microphone, a communicator comprising communication circuitry, and a processor configured to control the communicator to transmit a control command to an external audio device for reducing an audio output level of the external audio device in response to a trigger signal for starting a voice control mode being received through the microphone and to control the electronic device to operate in the voice control mode.

Abstract: In an approach to controlling voice command execution via boundary creation, one or more computer processors determine one or more devices included in an Internet of Things platform. One or more computer processors receive, from a user, an indication of a boundary around two or more devices of the one or more devices. One or more computer processors create a boundary around the two or more devices of the one or more devices. One or more computer processors receive a voice command from the user associated with the two or more devices of the one or more devices. One or more computer processors transmit the voice command to the two or more devices of the one or more devices within the boundary.

Abstract: System and methods for controlling healthcare devices and systems using voice commands are presented. In some aspects a listening device may receive voice command from a person. The voice command may be translated into human readable or machine readable text via a speech-to-text service. A control component may receive the text and send device-specific instructions to a medical device associated with a patient based on the translated voice command. In response to the instructions, the medical device may take an action on a patient. Some examples of actions taken may include setting an alarm limit on a monitor actively monitoring a patient and adjusting the amount of medication delivered by an infusion pump. Because these devices may be controlled using a voice command, in some cases, no physical or manual interaction is needed with the device. As such, multiple devices may be hands-free controlled from any location.

Abstract: Example techniques involve a control hierarchy for a “smart” home having smart appliances and related devices, such as wireless illumination devices, home-automation devices (e.g., thermostats, door locks, etc.), and audio playback devices, among others. An example home includes various rooms in which smart devices might be located. Under the example control hierarchy described herein and referred to as “home graph,” a name of a room (e.g., “Kitchen”) may represent a smart device (or smart devices) within that room. In other words, from the perspective of a user, the smart devices within a room are that room. This hierarchy permits a user to refer to a smart device within a given room by way of the name of the room when controlling smart devices within the home using a voice user interface (VUI) or graphical user interface (GUI).

Abstract: A method for decoding an encoded audio bitstream is disclosed. The method includes receiving the encoded audio bitstream and decoding the audio data to generate a decoded lowband audio signal. The method further includes extracting high frequency reconstruction metadata and filtering the decoded lowband audio signal with an analysis filterbank to generate a filtered lowband audio signal. The method also includes extracting a flag indicating whether either spectral translation or harmonic transposition is to be performed on the audio data and regenerating a highband portion of the audio signal using the filtered lowband audio signal and the high frequency reconstruction metadata in accordance with the flag.