Abstract: Machine classifiers in accordance with embodiments of the invention capture long-term temporal dependencies in the dialogue data better than the existing recurrent neural network-based architectures. Additionally, machine classifiers may model the joint distribution of the context and response as opposed to the conditional distribution of the response given the context as employed in sequence-to-sequence frameworks. Further, input data may be bidirectionally encoded using both forward and backward separators. The forward and backward representations of the input data may be used to train the machine classifiers using a single generative model and/or shared parameters between the encoder and decoder of the machine classifier. During inference, the backward model may be used to reevaluate previously generated output sequences and the forward model may be used to generate an output sequence based on the previously generated output sequences.

Abstract: A user authentication device includes: a collection part collecting information of a user; a generation part generating a question for the user on the basis of the information of the user collected by the collection part and a skill model of the user; a presentation part presenting the question for the user generated by the generation part to the user; a reception part receiving, from the user, a response to the question presented by the presentation part; and a determination part determining authentication of the user on the basis of the response received by the reception part.

Abstract: The prediction system accesses a flowchart of questions relating to surgical cases and receives, for each of set of surgical case identifiers, surgical case information and an actual surgical case length. The prediction system trains a machine learning model to predict surgical case lengths using the surgical case information and prunes the flowchart by removing questions associated with a uniform set of answers. The prediction system receives, from a client device, a request to reserve an operating room for a surgical case, and transmits, for display via a user interface of the client device, questions from the flowchart. The prediction system receives a feature vector of answers to the transmitted questions from the client device and inputs a type surgical case and the feature vector to the machine learning model, which outputs a predicted surgical case length. The prediction system reserves an operating room for the predicted surgical case length.

Abstract: Described herein are exemplary devices, apparatuses, systems, methods, and non-transitory storage media for providing an application framework. The application framework can provide various machine-learning models to perform a variety of analysis tasks to analyze enterprise data such as communications between one or more employees of an organization and one or more clients of the organization and provide intelligence and insights for a user in the organization. The insights and intelligence can include a recommendation or an observation related to a client or customer of the organization. The recommendation or observation can be provided, for example, in a communication platform, a chatbot, or a variety of other interfaces. Advantageously, to perform an analysis task, the application framework automatically provides to the machine-learning model(s) information in accordance with the enterprise's data sharing and access control requirements to prevent inappropriate access and use of sensitive information.

Abstract: One aspect of the present disclosure relates to a method of sentiment analysis based on ambiguity analysis, which includes analyzing information with the sentiment analysis models and the ambiguity analysis models. Another aspect of the present disclosure relates to a method of training the sentiment analysis models and ambiguity analysis models, which includes acquiring information, constructing lexicons, conducting sentiment analysis and ambiguity analysis with said lexicons, acquiring corpus, and training models, etc. Meanwhile, another aspect of the present disclosure relates to a system of sentiment analysis, which includes input, and output modules, acquisition modules, processing modules and database.

Abstract: Technology is disclosed herein for the integration of spreadsheet environments with an LLM service. In an implementation, an application receives a natural language input from a user associated with visualization of data hosted by the application. The application generates a prompt for a large language model (LLM) service based on the user input and the visualization and submits the prompt to the LLM service. The application receives a reply to the prompt from the LLM service and modifies the visualization based on the reply from the LLM service. In an implementation, the data includes spreadsheet data and the visualization includes a chart. In some implementations, to modify the visualization, the application generates source code based on the reply from the LLM service when the classification of the natural language input is a command.

Abstract: In one embodiment, a method is provided. The method includes obtaining sensor data indicative of a set of objects detected within an environment. The method also includes determining a set of positions of the set of objects and a set of properties of the set of objects based on the sensor data. The method further includes generating a state graph based on the sensor data. The state graph represents the set of objects and the set of positions of the set of objects. The state graph includes a set of object nodes to represent the set of objects and a set of property nodes to represent the set of properties of the set of objects. The state graph is provided to a graph enhancement module that updates the state graph with additional data to generate an enhanced state graph.

Abstract: A method implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media. The method can include generating training data for an intent classification machine learning model by: (a) determining, via a text-to-text machine learning model, one or more respective paraphrases for each sample phrase of training phrases; (b) generating, via a label generating machine learning model, labeled data based on unlabeled live logs by: (i) determining live-log samples from the unlabeled live logs based at least in part on: a respective timestamp of each live log of the unlabeled live logs, or random sampling; and (ii) generating, via the label generating machine learning model, the labeled data based on the live-log samples and one or more labeling functions; and (c) adding the one or more respective paraphrases for the each sample phrase of the training phrases and the labeled data to the training data.

Abstract: Systems and methods for providing interactions of an Artificial Intelligence (AI) character model with users are provided. An example method includes receiving a message from a user of a client-side computing device; retrieving, from a graph, information concerning relationships between the AI character model and the user; generating, based on the message and the information concerning relationships, an action associated with AI character model; and causing the AI character model to perform the action in a virtual environment provided to the user via the client-side computing device. The client-side computing device may be in communication with a computing platform. The graph may include a first node associated with the AI character model, a second node associated with the user, and an edge between the first node and the second node. The edge may be associated with the information concerning relationships between the AI character model and the user.

Abstract: Methods, systems, and computer-readable media are disclosed herein for improved state identification and prediction in computerized queries. In an aspects, a neural network model is trained via word embedding, using a plurality of workflows having a plurality of steps as input training data. The model may be searched using a string to locate and identify semantic matches as potential results, where the potential results correspond to a specific step and/or a particular workflow. Markov chaining may also be performed, using the potential results, in order to predict one or more additional results, where the additional results correspond to a specific succeeding step within a particular workflow, in some aspects. The results and predicted steps may be displayed.

Abstract: Methods and systems for efficient caching and retrieval of responses in conversation service applications includes a server that captures an utterance and converts the utterance into an utterance index key. The server searches a first response cache to determine whether the utterance index key matches a response index key. When there is a match, the server transmits a response that matches the utterance index key to a client device. When there is not a match, the server converts the utterance into an utterance embedding and searches a second response cache to identify a response embedding. The server captures a fuzzy response index key associated with the closest matching response embedding and searches the first response cache to identify a response index key that matches the fuzzy response index key.

Abstract: Certain aspects of the disclosure are directed to context aggregation in a data communications network. According to a specific example, user-data communications between a client-specific endpoint device and the other participating endpoint device during a first time period can be retrieved from a plurality of interconnected data communications systems. The client station can be configured and arranged to interface with a data communications server providing data communications services on a subscription basis. Context information for each respective user-data communication between the client station and the participating station during the first time period can be aggregated, such that subsequent user-data communications received from the participating station and intended for the client entity, can be routed based on the aggregated context information.

Abstract: A data processing system includes a queue manager receiving data processing requests and determining a queue depth representing the number of pending requests. A load supervisor assigns a service level to each request based on the queue depth when the request is at the head of the queue. The system offers two service levels, with the second level requiring fewer computing resources than the first. This dynamic management system optimizes resource allocation by adjusting service levels based on the workload, ensuring efficient processing of data requests.

Abstract: This disclosure describes example implementations for generating context-dependent embedding vectors of words in the multi-dimensional embedding space based on a generic and non-domain-specific pretrained word embeddings. Such an implementation requires no domain specific training corpus and is capable of generating context-dependent embedding vectors of multi-semantic words using a few contextual texts. Such an implementation thus provides an efficient way to generate a library of multiple domain specific embedding vectors for multi-semantic words without any domain-specific training process. Other example embodiments further apply the principles of the context-dependent word embedding generation to a text-to-image application.

Abstract: An example method of language detection includes: identifying a document comprising a plurality of words in one or more natural languages; for each word of at least a subset of words of the document: generating a plurality of sets of tokens representing the word, wherein each set of tokens of the plurality of sets of tokens represents the word using a corresponding plurality of tokens defined for a corresponding natural language of a set of natural languages, and identifying, based on the plurality of sets of tokens, a primary natural language associated with the word; associating each natural language of the set of natural languages with a corresponding word count indicating a number of words of the subset of words for which the natural language has been identified as the primary natural language; identifying, among the set of natural languages, a natural language associated with a maximum word count; and associating the identified natural language with the document.

Abstract: An artificial intelligence (AI) search system can build or include one or more knowledge graphs and databases of indexes. The knowledge graphs can encode information and relationships from a variety of domains. The databases of indexes can store pointers to public and/or private data and raw content in those domains. An example of a domain includes fields requiring or handling geospatial data. The AI search system can receive a user query and process the query with AI models, including language models and knowledge graphs to identify data matching the intention and context of the user query. The AI search system can present relevant results in an assortment of user interfaces.

Abstract: Techniques are described herein for a method of obtaining a token based on a conversation in real time. The method further includes predicting, using a large language model (LLM) and the token, a next token. The method further includes predicting, using a classifier and the next token, a completion of a user turn. The method further includes triggering a next turn of the conversation in real time using the completion of the user turn.

Abstract: A computer-implemented method, computer program product, and/or computing system for determining unexpected data includes: running for a given claim a machine learning term prediction model with first and second term types to predict third type terms in a specific domain that uses a machine learning term embedding model that has learned the correlation between term types in the specific domain; determining cohesiveness between the predicted third type terms and the actual third type terms from the given claim; determining a value score between the predicted third type terms and the actual third type; and running a machine learning propensity model using the value score determined for the given claim and context data for the given claim to determine if the given claim is unexpected.

Abstract: A computer-implemented method for processing a logic rule in a graph database. The method includes obtaining a graph database comprising at least one graph, each graph of the database being represented in one or more adjacency matrices (R-Matrix), each adjacency matrix representing a group of tuples of the graph comprising a same predicate, obtaining the logic rule concluding to a head predicate, generating a virtual adjacency matrix comprising one of the one or more adjacency matrices (R-Matrix) and an entailed data matrix (E-Matrix), the virtual adjacency matrix representing the head predicate, the entailed data matrix representing a group of tuples that are computed by applying the logic rule, and receiving a query by the database using the head predicate.

Abstract: A large language model predicts a topic summarization of a long document given a set of segments from the long document that pertain to a topic of interest. The set of segments from the long document are selected by searching for similar segments from other documents that have been labeled to indicate whether or not the segment pertains to a topic of interest. The search is based on an embedding of a segment from the long document closely matching embeddings of the labeled segments. Each segment of the long document is scored based on the labels of the closest-matching similar segments. The segments from the long document are ranked by their respective score and the highest-scored segments are included in a prompt to the large language model for the model to generate a topic summarization of the long document.

Abstract: Certain examples described herein provide a system for classification of unstructured text data relating to a legal query. The system has a session interface to receive session data relating to the legal query, a text interface to receive unstructured text data from a user, a text pre-processor to apply one or more text pre-processing functions to the unstructured text data to output a structured numeric representation of the unstructured text data, at least one machine learning classifier to map the structured numeric representation of the unstructured text data to one or more classes within a defined set of classes, and a classifier optimizer to process the session data to generate configuration data for the at least one machine learning classifier, the configuration data indicating a subset of the defined set of classes that are valid given the session data.

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for segmenting a transcript into paragraphs. In an example embodiment, a transcript is segmented to start a new paragraph whenever there is a change in speaker and/or a long pause in speech. If any remaining paragraphs are longer than a designated length or duration (e.g., 50 or 100 words), each of those paragraphs is segmented using dynamic programming to minimize a cost function that penalizes candidate paragraphs based on divergence from a target paragraph length and/or that rewards candidate paragraphs that group semantically similar sentences. As such, the transcript is visualized, segmented at the identified paragraphs.

Abstract: There is provided an information processing apparatus that includes a preprocessing unit that determines whether or not to permit provision of a service based on emotion recognition on the basis of a predetermined criterion, an emotion estimation processing unit that performs the emotion recognition for a user by using an artificial intelligence function, and a service providing processing unit that provides a service based on an emotion recognition result by the emotion estimation processing unit, in which, when the preprocessing unit determines to permit the provision of the service, the emotion estimation processing unit performs the emotion recognition or the service providing processing unit provides the service.

Abstract: A method of optimizing full text search results for multiple languages includes importing a document from a first organization including one or more first organization users, wherein the one or more first organization users are associated with a first organization location; determining a first organization language based on the first organization location; analyzing the imported document with a natural language processing (NLP) service to determine a primary document language; and indexing a determined document language to the imported document based at least in part on the first organization language and the primary document language, wherein indexing the determined document language to the imported document causes the document to be searched using a document search tool in the determined document language.

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: In an embodiment, the disclosed technologies are capable of receiving, by a digital model, data representing a first text sequence in a first language; using the digital model, modifying the first text sequence to result in creating and digitally storing a second text sequence in the first language; and outputting, by the digital model, the second text sequence in the first language. The modifying may include any one or more of: deleting text from the first text sequence, adding text to the first text sequence, modifying text of the first text sequence, reordering text of the first text sequence, adding a digital markup to the first text sequence. The digital model may have been fine-tuned, after having been machine-learned, using a subset of values of model parameters associated with an encoding layer or an embedding layer or both the encoding layer and the embedding layer.

Abstract: A natural language understanding (NLU) framework includes a lookup source framework, which enables a lookup source system to be defined having one or more lookup sources. The lookup source system can operate in a number of different manners to facilitate repository-aware inference of user utterances, for example, by facilitating vocabulary injection during compilation of an utterance meaning model and/or an understanding model. Additionally, the lookup source system can be leveraged to cleanse client-specific training data of sensitive values to generate generic training data that can be used to train the NLU framework of other clients. The lookup sources can be compiled in a synchronous or asynchronous manner, which enables lookup sources to be compiled in an on-demand basis from test source data. Additionally, understanding models that reference lookup sources can be periodically recompiled while leveraging the latest versions of the lookup sources for vocabulary injection.

Abstract: A conversational agent is provided for maintaining or improving the wellbeing of a user presenting with mental health needs by the delivery of a care protocol. The conversational agent comprises: a plurality of sub-dialogue units, each configured to deliver an element of the care protocol, and an orchestrator configured to present the sub-dialogue units to the user, sequentially, wherein each sub-dialogue unit and the orchestrator comprises: a natural language understanding module configured to receive an input and/or reply and determine at least one intent and, where present, at least one slot within the input and/or reply; a dialogue planning module configured to determine an output based, at least in part, on the at least one intent and/or slot associated within the input and/or reply, and a natural language generation module configured to provide the output to the user.

Abstract: A system for answering queries using one or more families of large language models (h-LLMs) including a processor, a communication device, and storage with software that, when executed by the processor, is operable to receive a user prompt, generate derived prompts from the user prompt, generate prompt embeddings from the derived prompts, transmit the plurality of prompt embeddings to a vector database, the vector database comprising a database of knowledge documents, each knowledge document comprised by the database of knowledge documents having one or more embeddings associated therewith, receive knowledge documents relevant to the prompt embeddings, generate context-aware prompts, transmit the context-aware prompts to the one or more h-LLMs, receive h-LLM results, process the h-LLM results to produce processed h-LLM results, each having a score, identify one or more preferred results responsive to the scores, and transmit the one or more preferred results to a user via the user interface.

Abstract: Provided are systems and methods for automated language testing for use in continuous integration and continuous delivery software development. In one example, the method may include identifying language tests in a plurality of different natural languages to be performed for a software application, executing the language tests and validating the language tests based on results of executed scripts, automatically assigning the language tests among a plurality of language testers based on execution of a machine learning model which pairs together a language test with a language tester based on test attributes and tester attributes, and transmitting notifications to computing devices of the plurality of language testers with information about the automatically assigned language tests.

Abstract: Systems and methods for deep technology innovation management by cross-pollinating innovations dataset are disclosed. A system extracts context-based keyword from an innovation dataset by transforming the innovation dataset to a vector. Further, the system searches semantically relevant keywords for the extracted context-based keyword, by extracting an entity and a key phrase from the extracted a context-based keyword. Furthermore, system clusters the vector, by identifying frequent keywords in the semantically relevant keywords to obtain cluster centroids of the frequent keywords. Thereafter, the system determines weighted keywords in each cluster using the obtained cluster centroids, and classifies the weighted keywords to identify emerging innovation trends relevant to the innovation in the innovation dataset. The system forms cohorts of innovators to explore the reuse of innovations, assets, code, and build focused monetization model.

Abstract: Embodiments relate to metadata-based generic parsing of an incoming data object to transform it for different purposes, such as conversion into a format more suitable for a specific storage type. The received data object comprises members organized according to a structure, each member containing data. The object's structure and the member's type are defined by the associated metadata. The metadata is referenced by parsing logic to generate a schema representing the structure of the data object in a different format (such as an ER table schema). The schema is then stored. When data is received, the metadata is referenced to parse the corresponding object's data accordingly and for ingestion into the generated schema and storage.

Abstract: An apparatus and method for a machine learning engine for domain generalization which trains a vision transformer neural network using a training dataset including at least two domains for diagnosis of a medical condition. Image patches and class tokens are processed through a sequence of feature extraction transformer blocks to obtain a predicted class token. In parallel, intermediate class tokens are extracted as outputs of each of the feature extraction transformer blocks, where each transformer block is a sub-model. One sub-model is randomly sampled from the sub-models to obtain a sampled intermediate class token. The intermediate class token is used to make a sub-model prediction. The vision transformer neural network is optimized based on a difference between the predicted class token and the sub-model prediction. Inferencing is performed for a target medical image in a target domain that is different from the at least two domains.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a generative neural network to convert conditioning text inputs to audio outputs. The generative neural network includes an alignment neural network that is configured to receive a generative input that includes the conditioning text input and to process the generative input to generate an aligned conditioning sequence that comprises a respective feature representation at each of a plurality of first time steps and that is temporally aligned with the audio output.

Abstract: A video game system provides dialog responses based on a natural language model (NLM). The NLM is a language model that receives a language input, such as a dialog selection, audio recording, or natural language text input provided by a user of the video game system. In response to the language input, and based on a corpus of natural language candidate lines, the NLM identifies one or more potential responses. The video game system selects a final response from the identified potential responses and provides the selected response to the user via, for example, one or more display frames or via an audio output.

Abstract: Systems and methods are disclosed for automatically extracting keys and corresponding values in any type of source document. Extracting desired words from the tokens in any type of document is based on a uniform approach to represent the source document. This uniform representation encodes features of the desired tokens along with the neighborhood information so that values associated with a given key can be extracted. The disclosed technique learns the representation of tokens independent of source document type and the learned representation is then used to determine relationships between multiple tokens. The neighborhood information and position information are used to determine various relationships between keys and values.

Abstract: Systems, methods, devices, and non-transitory, computer-readable storage media are disclosed for matching a service requester with a service provider via a taxonomy based directed graph. The method includes: receiving a keyword associated with a service; accessing a directed graph including a root node and nodes connected by edges, each node having a title; identifying a second node of the directed graph for each of service providers, each second node having a title matching a skill of a respective service provider; determining a distance between the first node and each second node along the directed graph; and ranking the service providers based at least in part on the distance between the first node and each second node. Systems, methods, devices, and non-transitory, computer-readable storage media are further disclosed for determining and storing a quality score for the revised linguistic content.

Abstract: Systems and processes for operating an intelligent automated assistant are provided. An example method includes, at an electronic device having one or more processors and memory, receiving an utterance including a user request, determining a natural language representation of the user request, determining a first software process associated with the natural language representation, determining whether the natural language representation can be executed by a task flow of the first software process, and in accordance with a determination that the natural language representation cannot be executed by the task flow of the first software process: determining a set of transformation instructions, determining a revised natural language representation using the set of transformation instructions, and providing the revised natural language representation to a second software process.

Abstract: This disclosure improves computer functionality by enabling various hierarchies of chatbot application programs operative based on data structures containing unstructured texts. Therefore, such hierarchies enable some chatbot application programs to manage other chatbot application programs, which improves virtual assistance, reduces programming efforts, customizes output by user types, and enhances process management.

Abstract: A method of training a model, a method of determining a word vector, a device, a medium, and a product are provided, which may be applied to fields of natural language processing, information processing, etc. The method includes: acquiring a first word vector set corresponding to a first word set; and generating a reduced-dimensional word vector for each word vector in the first word vector set based on a word embedding model, generating, for other word vector in the first word vector set, a first probability distribution in the first word vector set based on the reduced-dimensional word vector, and adjusting a parameter of the word embedding model so as to minimize a difference between the first probability distribution and a second probability distribution for the other word vector determined by a number of word vector in the first word vector set.

Abstract: A model training method, a model training platform, an electronic device and a storage medium are provided, which can be used in the field of artificial intelligence, particularly the fields of natural language processing and deep learning. The model training method includes: receiving an input; determining, based on the input, a user-oriented prefabricated function; determining, based on the input, a model training function; determining, based on the input, a pre-trained model; determining, based on the input, a network structure associated with the pre-trained model so as to support use of the pre-trained model; training, based on the input, the model by using the prefabricated function, the model training function, and the pre-trained model; and providing an output associated with a trained model.

Abstract: A hybrid Hidden Markov Model (HMM) and Machine Learning (ML) systems and apparatus for classification in the case of data instances with imbalanced class distribution, including a Hidden Markov Model for generating a log-likelihood score for each data instance. Implementations of the hybrid system and method detect fraudulent activity and classifies documents with accuracy that surpasses conventional classifiers. In one implementation, Hidden Markov Model (HMM) for generating a log-likelihood score based on an attribute value vector for a set of keyword features characterizing a Web page. In one implementation, the HMM generates a log-likelihood score based on an attribute value vector for page layout characterizing a document image. Resulting attribute value vectors are ranked and divided into bins grouped by log-likelihood scores within equal ranges. Various machine learning models are trained using the balanced vectors obtained by accumulating from all the bins of vectors.

Abstract: In a data lake, a control data object is defined. The control object defines the processes and relationships of processes associated with a data set in the data lake. The control has states that are tied to and adapt in response to state changes of the associated data set. A control can have a control type. The system automatically carries forward enabled processes from one data set version to the next data set version. The system uses the control definition to execute processes, such as compaction or data quality scans, on data sets in the data lake.

Abstract: Implementations relate to managing multimedia content that is obtained by large language model(s) (LLM(s)) and/or generated by other generative model(s). Processor(s) of a system can: receive natural language (NL) based input that requests multimedia content, generate a response that is responsive to the NL based input, and cause the response to be rendered. In some implementations, and in generating the response, the processor(s) can process, using a LLM, LLM input to generate LLM output, and determine, based on the LLM output, at least multimedia content to be included in the response. Further, the processor(s) can evaluate the multimedia content to determine whether it should be included in the response. In response to determining that the multimedia content should not be included in the response, the processor(s) can cause the response, including alternative multimedia content or other textual content, to be rendered.

Abstract: A method of training a ranking model, and an electronic device, which relate to technical fields of natural language processing and intelligent search. The method includes: in training the ranking model, firstly acquiring a plurality of first sample pairs and respective label information; for each first sample pair, inputting a first search text, a first title text of a first candidate text, and a first target summary corresponding to the first candidate text into an initial language model to obtain a second relevance score corresponding to the each first sample pair; then using the first target summary to replace the first candidate text to participate in the training of the ranking model, and updating at least one network parameter of the initial language model according to the label information and the second relevance score corresponding to each first sample pair.

Abstract: A computer-implemented system includes identifying a target hierarchical taxonomy comprising a plurality of distinct hierarchical taxonomy categories; extracting a plurality of distinct taxonomy tokens from the plurality of distinct hierarchical taxonomy categories; computing a taxonomy vector corpus based on the plurality of distinct taxonomy tokens; computing a plurality of distinct taxonomy clusters based on an input of the taxonomy vector corpus; constructing a hierarchical taxonomy classifier based on the plurality of distinct taxonomy clusters; converting a volume of unlabeled structured datasets to a plurality of distinct corpora of taxonomy-labeled structured datasets based on the hierarchical taxonomy classifier; and outputting at least one corpus of taxonomy-labeled structured datasets of the plurality of distinct corpora of taxonomy-labeled structured datasets based on an input of a data classification query.

Abstract: A schema and cell value aware Named Entity Recognition (NER) model is used to perform natural language queries. Natural language queries may be received via an interface of a natural language query processing system. A fuzzy search may be performed that allows non-exact matches for column names or cell values of data sets potentially used to answer the natural language query. An NER model that adds a type embedding for an exact match of a column name or cell found in the fuzzy search that corresponds to a span of one or more words may be applied as part of generating the entity prediction for the natural language query. One or more queries to at least one of the data sets may be performed to return a result to the natural language query using the entity prediction generated by the NER machine learning model.

Abstract: Provided are a text duplicate checking method, an electronic device and a computer-readable storage medium. The method includes storing a fingerprint set and a corresponding text ID in a byte data manner to obtain a fingerprint library; acquiring a target text and creating a target fingerprint; obtaining a comparison fingerprint set from map memories according to the target fingerprint, and calculating a similarity between the target fingerprint and each comparison fingerprint in the comparison fingerprint set separately; and based on a determination result that a number of 1s in binary values of one similarity is less than or equal to a preset value, querying a text ID corresponding to the one similarity, to implement duplicate checking of the target text.

Abstract: A method of extracting and displaying postural measurements from patient data includes retrieving, by a processor of a computing device, the patient data from memory. The patient data includes a geometric mesh representation of a patient, including a plurality of data points corresponding to spatial coordinates of a plurality of vertices in three dimensions. The method also includes determining, by the processor, a reference geometry along the geometric mesh representation in a fixed position with respect to the spatial coordinates; determining, by the processor, a landmark corresponding to one of skeletal or soft tissue anatomy for the patient; and determining, by the processor, a postural deviation of a body portion of the patient by comparing the reference geometry and the landmark. The method further includes displaying, by a display of the computing device, a graphical user interface indicating a characteristic related to the postural deviation.

Abstract: To provide enhanced search capabilities in a process control system, a knowledge repository is generated that includes both contextual data and time series data. The contextual data organizes process plant-related data according to semantic relations between the process plant-related data and the process plant entities. When a user submits a process plant search query related to process plant entities within a process plant, search results are obtained by identifying a data set from the knowledge repository. The contextual data categorizes process parameters so that users can search for a particular process parameter category. Users can tag previous searches to execute them once again at a later time.