Abstract: Methods for calculating nutrient content information. In one embodiment, the methods comprise: receiving a recipe having a list of ingredients and quantities, for each of the ingredients a corresponding record is found within a database of known records, the records are associated to quantities and nutritional values. The units of measurement of the recipe ingredients and the identified record are compared. When the units are the same, no conversion is performed. When the units are different, the units of the known record are converted using a conversion factor derived from a relationship between the differing units of measurement. In one variant, the conversion factor may be identified from a table of conversion factors relating various units of measurement to one another. Finally, the converted or the known nutritional values are multiplied by a ratio of the quantity of the ingredient in the recipe to the quantity of the known record.

Abstract: An information processing apparatus is connected to a voice processing server that analyzes text data transmitted from a voice input/output apparatus that converts an instruction by an utterance of a user to the text data and outputs the text data, and outputs an instruction obtained by analysis and utterance language information indicating a language of the utterance, and the information processing apparatus includes: a communicator that communicates with the voice processing server; a destination searcher that determines on the basis of the utterance language information whether to include a space character in a target of the search, and searches for a name indicated in a search character string from a destination list on the basis of a result of the determination; and a hardware processor that performs control to transmit a search result of a destination by the destination searcher to the voice processing server via the communicator.

Abstract: Providing a condition-specific data collection includes displaying, via a user interface of a user device and based on a computing application being accessed, one or more selectable parameters, and receiving, based on a user of the user device providing one or more user inputs, a selection of the one or more selectable parameters, the selection indicating a physical location about which a condition-specific data collection sequence is to be provided. Further, the condition-specific data collection sequence associated with the physical location is provided, where the condition-specific data collection sequence comprises a series of user interface input controls that are displayed, via the user interface, to facilitate obtaining condition-specific data related to a condition at a physical location.

Abstract: A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

Abstract: A system for identifying industrial ergonomics risk root-causes and providing risk control actions, comprising: a computing device configured to obtain textual information describing a series of tasks of a job and forces being exerted during the series of tasks; and a computing server system configured to receive and process the textual information to generate a set of textual entry to correspond to a unique identifier of the job, identify nouns and verbs in the set of textual entry via natural language processing techniques, perform dependency parsing and part-of-speech tagging to associate each identified verb in the set of textual entry with a root noun in order to identify action-object pairs and unpaired actions, determine ergonomic risk root-causes based at least upon the action-object pairs and the unpaired actions in the set of textual entry, and provide ergonomic risk control recommendations to mitigate the ergonomic risk root-causes.

Abstract: A home assistant device captures voice signal expressed by users in the home and extracts vocal features from these captured voice recordings. The device collects data about the current context in the home and requests from an aggregator a background model that is best adapted to the current context. This background model is obtained and locally used by the home assistant device to perform the speaker recognition. Home assistant devices from a plurality of homes contribute to the establishment of a database of background models by aggregating vocal features, clustering them according to the context and computing background models for the different contexts. These background models are then collected, clustered according to their contexts and aggregated by an aggregator in the database. Any home assistant device can then request from the aggregator the background model that fits best its current context, thus improving the speaker recognition.

Abstract: Natural language processing (NLP) methodologies and mechanisms are provided that include a named entity recognition (NER) computer model augmented to operate on an entity pattern embedding input feature in addition to other embedding input features. The mechanisms tokenize natural language content (NLC) to generate tokens and process a selected token in accordance with a predetermined entity pattern embedding technique to generate an entity pattern embedding input feature for the selected token. The entity pattern embedding input feature specifies a pattern of characters present in the selected token. The mechanisms process the NLC to generate the other embedding input features in accordance with other embedding techniques, and process, by the NER computer model, the other embedding input features and the entity pattern embedding input feature for the selected token to generate a predicted tag for the selected token. The predicted tag specifies a named entity type classification for the selected token.

Abstract: The disclosure relates to systems and methods of automatically generating unique content including natural language text based on a corpus of previously generated response documents and discrete requirements defined in a requirements specification. The system may use generative stitching that includes multi-layer processes that execute to influence the generation of unique content including natural language text through an artificial intelligence (AI) language transformer model trained to output the content based on previously written material that is semantically relevant to the discrete requirements and is weighted against labeled attributes. The labeled attributes may determine the influence asserted against the language transformer, thereby generating unique on-target content that may be combined to create a computer-generated response document.

Abstract: A method, apparatus, device, and storage medium for linking an entity, relates to the technical fields of knowledge graph and deep learning are provided. The method may include: acquiring a target text; determining at least one entity mention included in the target text and a candidate entity corresponding to each entity mention; determining an embedding vector of each candidate entity based on the each candidate entity and a preset entity embedding vector determination model; determining context semantic information of the target text based on the target text and each embedding vector; determining type information of the at least one entity mention; and determining an entity linking result of the at least one entity mention, based on the each embedding vector, the context semantic information, and each type information.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating representation of acoustic sequences. One of the methods includes: receiving an acoustic sequence, the acoustic sequence comprising a respective acoustic feature representation at each of a plurality of time steps; processing the acoustic feature representation at an initial time step using an acoustic modeling neural network; for each subsequent time step of the plurality of time steps: receiving an output generated by the acoustic modeling neural network for a preceding time step, generating a modified input from the output generated by the acoustic modeling neural network for the preceding time step and the acoustic representation for the time step, and processing the modified input using the acoustic modeling neural network to generate an output for the time step; and generating a phoneme representation for the utterance from the outputs for each of the time steps.

Abstract: Methods and systems for using natural language processing and machine-learning algorithms to process vehicle-service data to generate metadata regarding the vehicle-service data are described herein. A processor can discover vehicle-service data that can be clustered together based on the vehicle-service data having common characteristics. The clustered vehicle-service data can be classified (e.g., categorized) into any one of a plurality of categories. One of the categories can be for clustered vehicle-service data that is tip-worthy (e.g., determined to include data worthy of generating vehicle-service content (e.g., a repair hint). Another category can track instances of vehicle-service data that are considered to be common to an instance of vehicle-service data classified into the tip-worthy category. The vehicle-service data can be collected from repair orders from a plurality of repair shops. The vehicle-service content generated by the systems can be provided to those or other repair shops.

Abstract: Systems and methods transmit, to a user device across a network, digital communication(s) thereby facilitating displaying the digital communication(s) via a user interface of the user device. Based on a user of the user device providing user input(s) in response to the digital communication(s) response data related to physical location(s) are received and data processing is performed thereon to determine whether additional data collection sequence(s) should be provided.

Abstract: An appropriate vector of any phrase can be generated. A lattice construction unit 212 constructs a lattice structure formed by links binding adjacent word or phrase candidates based on a morphological analysis result and a dependency analysis result of input text. A first learning unit 213 performs learning of a neural network A for estimating nearby word or phrase candidates from word or phrase candidates based on the lattice structure. A vector generation unit 214 acquires a vector of each of the word or phrase candidates from the neural network A and sets the vector as learning data. A second learning unit performs learning of a neural network B for vectorizing the word or phrase candidates based on the learning data.

Abstract: A method, an apparatus, a device and a storage medium for learning a knowledge representation are provided. The method can include: sampling a sub-graph of a knowledge graph from a knowledge base; serializing the sub-graph of the knowledge graph to obtain a serialized text; and reading using a pre-trained language model the serialized text in an order in the sub-graph of the knowledge graph, to perform learning to obtain a knowledge representation of each word in the serialized text. The knowledge representation learning in this embodiment is performed for entity and relationship representation learning in the knowledge base.

Abstract: To present task information to a user, which is more easily recalled by the user. Provided is an information processing apparatus including a task management unit that registers a task to be executed by a user on the basis of a detected utterance, in which the task management unit registers the task and a context acquired along with the utterance and reminding the user of content of the task in association with each other. Furthermore, provided is an information processing method including registering, by a processor, a task to be executed by a user on the basis of a detected utterance, the registering further including registering the task and a context acquired along with the utterance and reminding the user of content of the task in association with each other.

Abstract: Systems and methods may be used to provide transcription and translation services. A method may include initializing a plurality of user devices with respective language output selections in a translation group by receiving a shared identifier from the plurality of user devices and transcribing the audio stream to transcribed text. The method may include translating the transcribed text to one or more of the respective language output selections when an original language of the transcribed text differs from the one or more of the respective language output selections. The method may include sending, a user device in the translation group, the transcribed text including translated text in a language corresponding to the respective language output selection for the user device. In an example, the method may include customizing the transcription or the translation, such as to a particular topic, location, user, or the like.

Abstract: A method includes receiving a dataset that includes a plurality of input texts. Each input text from the plurality of texts is associated with a content category from a plurality of content categories based on a comparison between that input text and an intended meaning that is common for each comparison. For each model in a plurality of models, and for each content category from the plurality of content categories, that model is executed on each input text from the plurality of input texts to generate an average similarity/dissimilarity score for that content category. At least one model from the plurality of models is selected, based on the average similarity score for each content category from the plurality of content categories for each model in the plurality of models, to determine whether an input text is similar/dissimilar to the intended meaning.

Abstract: In a system and method for audio analysis in a cloud-based computerized an authentication (RTA) manager micro-service may send an audio packet to a voice processor micro-service. The voice processor may extract features of the audio. The RTA manager may obtain the extracted features from the voice processor; calculate, based on the extracted features, a quality grade of the audio packet, and send the extracted features to an at least one voice biometrics engine if the quality grade is above a threshold. Each of the at least one voice biometrics engines may be configured to generate a voiceprint of the audio packet, based on the extracted features of the audio packet and to perform at least one of: authenticate a speaker, detect fraudsters, and enrich a previously stored voiceprint of the speaker with the voiceprint of the audio packet.

Abstract: A method for processing a sematic description of a text entity is proposed. The method includes: acquiring a plurality of target texts containing a main entity, and extracting related entities describing the main entity from each target text; acquiring a sub-relation vector of a pair of the main entity and each related entity in each target text; calculating a similarity distance of the main entity between different target texts based on the sub-relation vector; and determining a semantic similarity of the main entity descripted in different target texts based on the similarity distance.

Abstract: The present disclosure relates to different techniques of controlling an agricultural system, as for example a controlled agricultural system, an agricultural light fixture and a method for agricultural management. Furthermore, the disclosure relates to an agricultural system, which comprises a plurality of processing lines for growing plants of a given plant type, wherein a first processing line in the plurality of processing lines is configured to move a first plurality of plants through the agricultural system along a route; and apply a first growth condition to the first plurality of plants to satisfy a first active agent parameter for the first plurality of plants.