Abstract: A method includes detecting whether there is a very short pitch lag in a speech or audio signal that is shorter than a conventional minimum pitch limitation using a combination of time domain and frequency domain pitch detection techniques. The pitch detection techniques include using pitch correlations in a time domain and detecting a lack of low frequency energy in the speech or audio signal in a frequency domain. The detected very short pitch lag is coded using a pitch range from a predetermined minimum very short pitch limitation that is smaller than the conventional minimum pitch limitation.

Abstract: This disclosure describes, in part, context-driven device arbitration techniques to select a speech interface device from multiple speech interface devices to provide a response to a command included in a speech utterance of a user. In some examples, the context-driven arbitration techniques may include executing multiple pipeline instances to analyze audio signals and device metadata received from each of the multiple speech interface devices which detected the speech utterance. A remote speech processing service may execute the multiple pipeline instances and analyze the audio signals and/or metadata, at various stages of the pipeline instances, to determine which speech interface device is to respond to the speech utterance.

Abstract: Methods and systems for processing of voice input to identify intents and mapped standard terminologies are provided. Using natural language processing, an intent of a voice input is identified. The intent is utilized to identify a standard terminology that maps to the intent. The standard terminology is utilized to identify information relevant to the standard terminology in a patient's electronic health record.

Abstract: An intelligence-driven virtual assistant for automated documentation of new ideas is provided. During a brainstorming session, one or more user participants may discuss and identify one or more ideas. Such ideas may be tracked, catalogued, analyzed, developed, and further expanded upon through use of an intelligence-driven virtual assistant. Such virtual assistant may capture user input data embodying one or more new ideas and intelligently process the same in accordance with creativity tool workflows. Such workflows may further stimulate creativity for capturing ideas, while continuing to document, analyze, and identify further aspects to develop and expand.

Abstract: In some examples, a software agent executing on a server receives a communication comprising a first utterance from a customer and predicts, using an intent classifier, a first intent of the first utterance. Based on determining that the first intent is order-related, the software agent predicts, using a dish classifier, a cart delta vector based at least in part on the first utterance and modifies a cart associated with the customer based on the cart delta vector. The software agent predicts, using a dialog model, a first dialog response based at least in part on the first utterance and provides the first dialog response to the customer using a text-to-speech converter.

Abstract: As noted above, example techniques relate to toggling a cloud-based VAS between enabled and disabled modes. An example implementation involves a NMD detecting that the housing is in a first orientation and enabling a first mode. Enabling the first mode includes disabling voice input processing via a cloud-based VAS and enabling local voice input processing. In the first mode, the NMD captures sound data associated with a first voice input and detects, via a local natural language unit, that the first voice input comprises sound data matching one or more keywords. The NMD determines an intent of the first voice input and performs a first command according to the determined intent. The NMD may detect that the housing is in a second orientation and enables the second mode. Enabling the second mode includes enabling voice input processing via the cloud-based VAS.

Abstract: Techniques for decreasing (or eliminating) the possibility of a skill performing an action that is not responsive to a corresponding user input are described. A system may train one or more machine learning models with respect to user inputs, which resulted in incorrect actions being performed by skills, and corresponding user inputs, which resulted in the correct action being performed. The system may use the trained machine learning model(s) to rewrite user inputs that, if not rewritten, may result in incorrect actions being performed. The system may implement the trained machine learning model(s) with respect to ASR output text data to determine if the ASR output text data corresponds (or substantially corresponds) to previous ASR output text data that resulted in an incorrect action being performed.

Abstract: Technologies are provided for determining deficiencies in narrative textual data that may impact decision-making in a decisional context. A candidate text document and a reference corpus of text may be utilized to generate one or more topic models and document-term matrices, and then to determine a corresponding statistical perplexity and probabilistic coherence. Statistical determinations of a degree to which the candidate deviates from the reference normative corpus are determined, in terms of the statistical perplexity and probabilistic coherence of the candidate as compared to the reference. If the difference is statistically significant, a message may be reported to user, such as the author or an auditor of the candidate text document, so that the user has the opportunity to amend the candidate document so as to improve its adequacy for the decisional purposes in the context at hand.

Abstract: An example computer-implemented method embodying the disclosed technology comprises digitally storing a plurality of digital objects comprising first type digital objects and second type digital objects, each digital object comprising an electronic digital representation of natural language text, and certain first type digital objects each being associated with exactly one second type digital object; programmatically generating a corpus of digital documents from the plurality of digital objects; programmatically embedding the electronic digital representation of natural language text of each first type digital object not associated with one of the second type digital objects, thereby generating a second set of vectors; programmatically determining, for each of the second set of vectors, a corresponding set of nearest neighbor vectors from the first set of vectors; programmatically generating recommendation data indicating potential new associations between first type digital objects and second type digital ob

Abstract: Disclosed herein are various embodiments for pronoun-based natural language processing. An embodiment operates by receiving a plurality of text-based sentences each comprising a plurality of words, and each text-based sentence including a pronoun. A plurality of candidate nouns are identified amongst the plurality of words. A trigger word is identified from the plurality of words, wherein the trigger word is associated with both the pronoun and one of the plurality of candidate nouns. A score for each of the candidate nouns is received based on a relationship with the trigger word. The candidate noun with a highest score is selected as being associated with the pronoun.

Abstract: A computing system includes a processor, and a storage device holding instructions executable by the processor. The instructions are executable to receive a source code through an application programming interface (“API”) exposed to a graphical user interface (“GUI”). The GUI is configured to enable an author to define a proposed model with a pedagogical programming language, the proposed model including an input, one or more concept nodes, and an output. The GUI is further configured to enable the author to provide a program annotation indicating an execution behavior for the source code, to generate an assembly code from the source code with a compiler of an artificial intelligence (“AI”) engine configured to work with the GUI; and to build an executable, trained AI model including a neural-network layout having one or more layers derived from the assembly code.

Abstract: An indication associated with an AI mode enabled by an AI system is received from a user with an affirmative opt-in status. A first task request associated with the user is identified. One or more instructions associated with the first task request are contextually recognized. The first task request is completed based on the contextually recognized one or more instructions.

Abstract: Example methods, apparatus and articles of manufacture to determine semantic information for audio are disclosed. Example apparatus disclosed herein are to process an audio signal obtained by a media device to determine values of a plurality of features that are characteristic of the audio signal, compare the values of the plurality of features to a first template having corresponding first ranges of the plurality of features to determine a first score, the first template associated with first semantic information, compare the values of the plurality of features to a second template having corresponding second ranges of the plurality of features to determine a second score, the second template associated with second semantic information, and associate the audio signal with at least one of the first semantic information or the second semantic information based on the first score and the second score.

Abstract: A system for determining intent in a voice signal receives a first voice signal that indicates to perform a task. The system sends a first response that comprises a hyperlink associated with a particular webpage used to perform the task. The system receives a second voice signal that indicates whether to access the hyperlink. The system determines intent of the second voice signal by comparing keywords of the second voice signal with keywords of the first response. The system activates the hyperlink in response to determining that the keywords of the second voice signal correspond to the keywords of the first response.

Abstract: Implementations set forth herein relate to an automated assistant that can operate as an interface between a user and a separate application to search application content of the separate application. The automated assistant can interact with existing search filter features of another application and can also adapt in circumstances when certain filter parameters are not directly controllable at a search interface of the application. For instance, when a user requests that a search operation be performed using certain terms, those terms may refer to content filters that may not be available at a search interface of the application. However, the automated assistant can generate an assistant input based on those content filters in order to ensure that any resulting search results will be filtered accordingly. The assistant input can then be submitted into a search field of the application and a search operation can be executed.

Abstract: A network validation system is described which may perform operations such as generating, analyzing, verifying, correcting, recommending, and deploying language, symbols, etc., such as domain specific language, configured to allow users to express their intent on the configuration and operation of a network, such as a cloud-based network. The network validation system may provide domain specific language that includes rules, statements, symbols, data, etc., configured to convey the intent of users on the configuration and operation of networks for purposes such as configuring and/or validating communication paths, testing or setting associated network object configurations, and may be employed to report violations in such configurations relative to user intent of the one or more users. The network validation system may also be employed to monitor such domain specific language and generate telemetry signaling, for example, that a rule has or has not been violated, actions a user may take, etc.

Abstract: Audio data, corresponding to an utterance spoken by a person within a detection range of a voice communications device, can include an audio message portion. The audio data can be captured and analyzed to determine the intent to send a message. Based at least in part upon that intent, a remaining portion of the audio data can be analyzed to determine the intended message target or recipient, as well as the portion corresponding to the actual message payload. Once determined, the audio file can be trimmed to the message payload, and the message payload of the audio data can be delivered as an audio message to the target recipient.

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: Methods, devices, and systems for processing audio information are disclosed. An exemplary method includes receiving an audio stream. The audio stream may be monitored by a low power integrated circuit. The audio stream may be digitized by the low power integrated circuit. The digitized audio stream may be stored in a memory, wherein storing the digitized audio stream comprises replacing a prior digitized audio stream stored in the memory with the digitized audio stream. The low power integrated circuit may analyze the stored digitized audio stream for recognition of a keyword. The low power integrated circuit may induce a processor to enter an increased power usage state upon recognition of the keyword within the stored digitized audio stream. The stored digitized audio stream may be transmitted to a server for processing. A response received from the server based on the processed audio stream may be rendered.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for computing numeric representations of words. One of the methods includes obtaining a set of training data, wherein the set of training data comprises sequences of words; training a classifier and an embedding function on the set of training data, wherein training the embedding function comprises obtained trained values of the embedding function parameters; processing each word in the vocabulary using the embedding function in accordance with the trained values of the embedding function parameters to generate a respective numerical representation of each word in the vocabulary in the high-dimensional space; and associating each word in the vocabulary with the respective numeric representation of the word in the high-dimensional space.