Abstract: A system for retrieving patient information using large language models including a computing device configured to receive a natural language query as a function of a user input, input the natural language query into a large language model communicatively connected to the least a processor, receive a computer language query comprising a plurality of nodes from the large language model, map the plurality of nodes to one or more entries in a patient database, receive a database response from the patient database as a function of the mapping, generate a final database query as a function of the database response. query the patient database using the final database query, receive a user response as a function of the final database query, and transmit the user response to a graphical user interface as a function of the final database query.

Abstract: An electronic apparatus includes an interface configured to connect with an external apparatus, and a processor. The processor is configured to, in response to a first user speech received by the electronic apparatus including a trigger word, identify a first noise level corresponding to the first user speech received by the electronic apparatus. The processor is configured to identify a first recognition apparatus among a plurality of recognition apparatuses having a highest first noise level corresponding to the first user speech. The plurality of recognition apparatuses identify the first user speech as the trigger word and include the electronic apparatus and the external apparatus. The processor is configured to perform a control operation to implement a function corresponding to a second user speech in response to identifying a second recognition apparatus as having a highest second noise level corresponding to the second user speech among the plurality of recognition apparatuses.

Abstract: A device receives document information associated with a document, and receives a request to identify insights in the document information. The device performs, based on the request, natural language processing on the document information to identify words, phrases, and sentences in the document information, and utilizes a first machine learning model with the words, the phrases, and the sentences to identify information indicating abstract insights, concrete insights, and non-insights in the document. The device utilizes a second machine learning model to match the abstract insights with particular concrete insights that are different than the concrete insights, and utilizes a third machine learning model to determine particular insights based on the non-insights. The device generates an insight document that includes the concrete insights, the abstract insights matched with the particular concrete insights, and the particular insights determined based on the non-insights.

Abstract: A meeting managing method, a server and a meeting managing system, and relates to the technical field of computers. The method according to the present application includes receiving data of a plurality of participants sent by a first client; binding data of each participant in the plurality of participants with a corresponding electronic-table-card identifier, wherein electronic table cards corresponding to each of the electronic-table-card identifiers correspond to microphones one to one; receiving the voice data sent by the target microphone; according to a target-electronic-table-card identifier corresponding to the target microphone, determining data of the target participant; and according to the voice data and the data of the target participant, generating a meeting document.

Abstract: According to an embodiment, an electronic apparatus includes a plurality of service engines corresponding to a plurality of speech recognition services, respectively; a communication module connected via wireless communication to a plurality of service servers for providing the plurality of speech recognition services, respectively; a mike configured to receive speech of a user; and a service controller configured to store at least one wakeup word corresponding to each of the plurality of speech recognition services in advance, determine a speech recognition service corresponding to a wakeup word received by the mike from among the plurality of speech recognition services based on the plurality of wakeup words stored in advance, send a user command received by the mike to a service engine corresponding to the determined speech recognition service among the plurality of service engines.

Abstract: According to various embodiments, provided is a control operation comprising: receiving a first utterance from a device; determining a specific category corresponding to the received first utterance from among a plurality of categories; determining a first domain for processing of the first utterance from among a plurality of domains related to the determined specific category; acquiring information enabling an operation corresponding to the first utterance to be performed based on the determined first domain; receiving a second utterance from the device; identifying whether processing of the second utterance is possible in the determined first domain; based on identifying that the processing of the second user utterance based on the first domain is not possible, checking a second domain among the plurality of domains, and identifying whether processing of the second utterance based on the second domain is possible; and based on identifying that the processing of the second utterance based on the second domai

Abstract: A system and method for interacting with a voice-assisted member interface hosted by a provider backend server of a provider using a voice enabled-apparatus hosted by an apparatus vendor separate and distinct from the provider, the voice-enabled apparatus including a microphone unit, a speaker and a processor coupled to the microphone unit and the speaker, the processor configured to cause the voice-enabled apparatus to perform one or more functions in response to audio signals received at the microphone unit.

Abstract: Systems and methods for pre-training a dialogue model with semantic information include: generating a dialogue-level abstract meaning representation (AMR) graph for an input dialogue associated with a speaker, learning core semantic units of the input dialogue based on nodes of the dialogue-level AMR graph, learning semantic relations between words of a sentence of the input dialogue based on edges of the dialogue-level AMR graph, learning an overall agreement of the input dialogue and the dialogue-level AMR graph, and training the dialogue model based on the learned core semantic units, semantic relations between words, and overall agreement.

Abstract: An audio encoder for encoding audio input data has: a preprocessor for preprocessing the audio input data to obtain audio data to be coded; a coder processor for coding the audio data to be coded; and a controller for controlling the coder processor so that, depending on a first signal characteristic of a first frame of the audio data to be coded, a number of audio data items of the audio data to be coded by the coder processor for the first frame is reduced compared to a second signal characteristic of a second frame, and a first number of information units used for coding the reduced number of audio data items for the first frame is stronger enhanced compared to a second number of information units for the second frame.

Abstract: An audio encoder for encoding audio input data has: a preprocessor for preprocessing the audio input data to obtain audio data to be coded; a coder processor for coding the audio data to be coded; and a controller for controlling the coder processor so that, depending on a first signal characteristic of a first frame of the audio data to be coded, a number of audio data items of the audio data to be coded by the coder processor for the first frame is reduced compared to a second signal characteristic of a second frame, and a first number of information units used for coding the reduced number of audio data items for the first frame is stronger enhanced compared to a second number of information units for the second frame.

Abstract: A system and a method for creation of notes for items of interest mentioned in audio content is provided. The system detects a playback of audio content on a media device. The audio content includes a talk by a host or a conversation between the host and one or more persons. The system analyzes a portion of the audio content using a machine learning model and determines one or more items of interest that are mentioned in the talk or the conversation, based on the analysis. The system extracts information associated with the determined one or more items of interest from a web-based data source and controls the media device to display a user interface that includes the extracted information.

Abstract: Implementations relate to determining multilingual content to render at an interface in response to a user submitted query. Those implementations further relate to determining a first language response and a second language response to a query that is submitted to an automated assistant. Some of those implementations relate to determining multilingual content that includes a response to the query in both the first and second languages. Other implementations relate to determining multilingual content that includes a query suggestion in the first language and a query suggestion in a second language. Some of those implementations relate to pre-fetching results for the query suggestions prior to rendering the multilingual content.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for managing dialogs. In one aspect, a method includes receiving a request associated with a task from a user device; submitting the request to each of a plurality of distinct data providers; receiving a plurality of suggested dialog responses from two or more of the data providers; scoring the one or more suggested dialog responses based on one or more scoring factors; determining a particular dialog response to provide to the user based on the scoring; and providing the determined dialog response to the user device.

Abstract: An apparatus comprises at least one processing device configured to receive a query to determine associations between named entities and aspect terms for a document, to generate, utilizing a first machine learning model, a first set of encodings classifying words of the document as being aspect or non-aspect terms, to generate, utilizing a second machine learning model, a second set of encodings classifying associations of the words, and to determine, for a given aspect term, attention weights for a given subset of the words surrounding the given aspect term. The processing device is also configured to generate, utilizing a third machine learning model, predictions of association between the given aspect term and named entities recognized in the given subset of the words, and to provide a response to the query comprising at least one of the predicted associations.

Abstract: Computer generated speech can be generated for cross-lingual natural language textual data streams by utilizing a universal phoneme set. In a variety of implementations, the natural language textual data stream includes a primary language portion in a primary language and a secondary language portion that is not in the primary language. Phonemes corresponding to the secondary language portion can be determined from a set of phonemes in a universal data set. These phonemes can be mapped back to a set of phonemes for the primary language. Audio data can be generated for these phonemes to pronounce the secondary language portion of the natural language textual data stream utilizing phonemes associated with the primary language.

Abstract: An apparatus for synthesizing speech according to an embodiment is a computing apparatus that includes one or more processors and a memory storing one or more programs executed by the one or more processors. The apparatus for synthesizing speech includes a pre-processing module that marks a preset classification symbol on each of unit texts input; and a speech synthesis module that receives each unit text marked with the classification symbol and synthesizes speech uttering the unit text based on the input unit text.

Abstract: Methods and systems are disclosed herein for improvements relating to compressed automatic speech recognition (ASR) systems. The ASR system may comprise a compressed acoustic engine and an adaptive decoder. The adaptive decoder may be dynamically compiled based on characteristics of the compressed acoustic engine and a current state of the application device. In some embodiments, a dynamic command list is used to manage context-specific commands. Two or more commands recognized by the adaptive decoder may be confusable due to compression of the ASR system. Alternate commands may be determined that are semantically equivalent but phonetically different than the confusable commands to reduce classification error of the adaptive decoder. An alternate command may replace one or more of the confusable commands in the adaptive decoder. In some embodiments, a user interface is displayed to a user of the ASR system to select the alternate command for replacement in the decoder.

Abstract: A cognitive agent system provides a centralized capability for users to configure and deploy cognitive agents across multiple heterogeneous vendor platforms. The cognitive agent system provides a design environment that allows users to define skills, as well as a new conversation construct that supports more complex interactions with users. The cognitive agent system also includes a deployment environment that allows users to register users and cognitive agents, deploy skills and conversations, and monitor the activity of cognitive agents across multiple vendor platforms. These users may use the cognitive agent system to define skills and conversations once and then deploy the skills and conversations to multiple service endpoints across different vendor platforms. In addition, the cognitive agent system allows user to directly manage cognitive agents that are not specific to any particular vendor.

Abstract: Systems, methods, and computer-readable media for automatically altering information within an electronic document based on an externally detected occurrence are disclosed. The systems and methods may involve accessing an electronic word processing document; displaying an interface presenting at least one tool for enabling an author of the electronic word processing document to define an electronic rule triggered by an external network-based occurrence; receiving, in association with the electronic rule, a conditional instruction to edit the electronic word processing document in response to the network-based occurrence; detecting the external network-based occurrence; and in response to the detection of the external network-based occurrence, implementing the conditional instruction and thereby automatically edit the electronic word processing document.

Abstract: Systems and processes for intelligent device identification are provided. In one example process, audio input may be sampled with a microphone at each of two or more of the plurality of electronic devices. A first electronic device of the plurality of electronic devices for determining a task associated with sampled audio input may be identified. The process may determine the task based on the sampled audio input with the first electronic device and identify identifying a second electronic device of the plurality of electronic devices for performing the task. The task be performed with the second electronic device. The second electronic device is not the first electronic device in some examples.