Abstract: A dialog system and intelligent personal assistant capable of semantic-understanding mapping between user intents and machine services includes an interface to receive a voice or text request from a user. Semantics of the request including at least one of entities, intent, or context are extracted from the user's request. A sequence of action features is selected based on the extracted semantics. A sequence of application programming interfaces (APIs) corresponding to the sequence of action features is then executed to generate a result. An action sequence optimizer may optimize the sequence of action features based on user configuration. The examples provide a technical solution to model a richer semantic-understanding mapping between user intents and available APIs, that will be greatly improving user experience in the spoken dialogue system, as core of personal assistant.

Abstract: Systems, computer program products, and methods are described herein for mitigating data loss in an edge computing environment using machine learning and distributed ledger techniques. The present invention is configured to receive an indication that one or more edge computing nodes is processing one or more portions of a transaction; retrieve dark data associated with each of the one or more edge computing nodes; initiate a machine learning algorithm on the dark data retrieved from each of the one or more edge computing nodes; capture, using the machine learning algorithm, representation information for each of the one or more edge computing nodes from their respective dark data; generate a ledger record for the representation information for each of the one or more edge computing nodes; and register the ledger record for the representation information for each of the one or more edge computing nodes on a first distributed ledger.

Abstract: Systems and methods for similarity search are described. Embodiments identify a document and a query corresponding to a matching phrase in the document, encode the query and a candidate phrase, score the candidate phrase using at least one learning-based score and at least one surface form score, wherein the at least one learning based score is based on the encoding, and the at least one surface form score is based on a surface form of the query and a surface form of the candidate phrase, and select the matching phrase based on the scoring.

Abstract: An online system generates a set of genomic representations, each including multiple genes, in which each gene represents users assigned to a control or test group for performing a test. A metric is identified based on a treatment associated with the test group and a score for each representation is computed based on a difference between two values, in which each value is based on the metric associated with users assigned to the test or control group. A propagation process is executed by identifying representations having at least a threshold score, propagating genes included in the representations to an additional set of representations through recombination and/or mutation, and computing the score for each additional representation. The propagation process is repeated for each additional set of representations until stopping criteria are met and a representation is selected based on scores associated with one or more representations.

Abstract: Techniques and embodiments are described herein for detecting and mitigating fraudulent activity within user experience (UX) test applications. In some embodiments, a system applies a set of rules and/or machine learning (ML) models to each respondent of an online survey or UX test. Different ML models may be trained to learn domain-specific patterns indicative of fraudulent activity. The system may then select the ML models based on attributes of the UX test and/or respondent. The selected rules and/or ML models may generate a probabilistic score representing a likelihood that the respondent is currently engaging in or will engage in fraudulent activity with respect to a UX test. If the score exceeds a threshold, then the system may take action to mitigate the fraudulent activity, such as triggering the removal of the user from an accepted respondent pool, halting further engagement between the respondent and the UX test, and generating alerts.

Abstract: Systems and methods are disclosed for managing a generative artificial intelligence (AI) model to improve performance. Managing the generative AI model includes using a second generative AI model to generate outputs from similar inputs and comparing the outputs of the generative AI models to determine their similarity. The second generative AI model is trained using fresher training data but may not be manually supervised and adjusted to the level of the generative AI model being managed. As such, an output of the second generative AI model is compared to an output of the managed generative AI model by a classification model to determine a relevance of the output from the managed generative AI model. An output of the classification model is used to perform various suitable policies to optimize the performance of the managed generative AI model, such as providing alternate outputs, preventing providing the output, or retraining the model.

Abstract: Aspects of the disclosure relate to computing platforms that implement data driven testing through automation using continuous machine learning combined with continuous integration for development testing. A computing platform may extract production data from user interaction with an application, perform a masking operation on the production data to create modified production data, convert the modified production data into a format suitable for machine learning, and generate one or more independent scenarios using machine learning. The computing platform may automatically trigger execution of test scripts associated with the one or more independent scenarios using the modified production data and receive results of the executed test scripts indicating whether a version of the application performs as expected.

Abstract: Automating conversion of drawings to indoor maps and plans. One example is a computer-implemented method of machine learning, the method comprising: receiving, by a device, a first set of updates to a first indoor map, the first indoor map previously created by a production machine-learning system having a production map accuracy; training, by a device, a supporting machine-learning system using the first set of updates to the first indoor map; and then applying, by a device, test data to the supporting machine-learning system, the applying results in first-evaluation indoor map with a first-evaluation map accuracy; and when the first-evaluation map accuracy is within a predetermined window above the production map accuracy refraining, by a device, from updating the production machine-learning system based on the first set of updates.

Abstract: Systems and methods for retrieval of augmented parameters for an artificial intelligence (AI)-based character are provided. An example method includes receiving, from a user via a user interface, at least one keyword describing the AI-based character; retrieving, from at least one data source and based on the at least one keyword, the augmented parameters describing the AI-based character; and generating, based on the augmented parameters, an AI-based character model corresponding to the AI-based character. The at least one data source includes a database configured to store records associated with the AI-based character, an online search service, and a set of clusters associated with a type of a feature of the AI-based character and at least one hidden prompt corresponding to the type of the feature. The type of the feature includes one of the following: a voice, a dialog style, an emotional state, an age, and temperament.

Abstract: The present disclosure provides systems and methods that perform machine-learned natural language processing. A computing system can include a machine-learned natural language processing model that includes an encoder model trained to receive a natural language text body and output a knowledge graph and a programmer model trained to receive a natural language question and output a program. The computing system can include a computer-readable medium storing instructions that, when executed, cause the processor to perform operations. The operations can include obtaining the natural language text body, inputting the natural language text body into the encoder model, receiving, as an output of the encoder model, the knowledge graph, obtaining the natural language question, inputting the natural language question into the programmer model, receiving the program as an output of the programmer model, and executing the program on the knowledge graph to produce an answer to the natural language question.

Abstract: A method is provided for operating a multi-channel messaging system. The method may provide automated conversation across multiple communication channels associated with a user by: selecting a first communication channel from the multiple communication channels, wherein the first communication channel is hosted by a first communication server; receiving, via an existing user interface of the first communication channel, a first user input via as part of a conversation with a chatbot, wherein the chatbot comprises a communication data structure comprising a plurality of communication paths; selecting a communication path based on the first user input; and generating a first feedback in response to the first user input according to the selected communication path.

Abstract: Disclosed is a dynamic network risk predicting method based on a graph neural network, which comprises the following steps: collecting network data firstly and constructing a network data sequence graph; extracting the time sequence of the network data sequence graph and obtaining a time sequence feature graph; extracting network attribute features, network structure features and network change features of the dynamic network; obtaining the representation vector of the network sequence graph by learning the representation vector; constructing the anomaly detection model and carrying out the dynamic network risk prediction.

Abstract: Provided is a cluster node recommendation system. A method of controlling the cluster node recommendation system includes: inputting user selection information from a user, the user selection information including at least one of a cloud vendor, an Information Technology (IT) resource size, and a free resource size; checking resource requirements of a designated application; outputting a node configuration by inputting the input user selection information and the checked resource requirements of the application to an artificial intelligence module; verifying validity by arranging a container in which the application is executed, in the output node configuration; and providing a final node configuration in which validity verification is made, to the user.

Abstract: Methods and systems are provided for sequence-to-sequence conversion from unstructured search queries to structured database queries, so that lay persons may retrieve information from relational databases without specialized knowledge of database query languages. An encoder module and a decoder module of a learning model are trained to convert an unstructured search query to an intermediate feature vector by computing co-attention and self-attention based on a context string and a database schema, encoding the database schema in the context string by application of self-attention between the context string containing tokens of the database schema with learned structural attention heads which relate the token to logic of the database. Training is performed using labeled training datasets which include structured database queries which are normalized by parsing into a semantic representation thereof, followed by linearization.

Abstract: The present invention may have a computer that monitors conversation between a user and a first virtual assistant from a plurality of virtual assistants, for a confidence score value being below a confidence threshold value, where the confidence score value is associated with each answer of the first virtual assistant. When the computer determines the confidence score value is below the confidence threshold value, the computer generates a dialog flow for answering by a second virtual assistant, where the dialog flow is related to an answer associated with the confidence score value being below the confidence threshold value. Then, based on determining the second virtual assistant having a second confidence score value that is higher than the confidence score value, the computer transfers a knowledge base data of a topic associated with the dialog flow from the first virtual assistant to the second virtual assistant.

Abstract: Method and system are provided for uncertainty quantification analysis with efficient two dimensional (2D) random fields. The method may be used in a modeling system. The method may include providing a covariance matrix of a set of parameters with a covariance function describing an occurring real world phenomenon; and generating a random field based on the covariance matrix including a controlled rank reduction to produce a reduced-rank random field dataset provided as a data structure with data compression. Generating the random field applies rank reduction of a block circulant with circulant blocks (BCCB) representation of a covariance matrix obtained from the covariance matrix using existing symmetry of eigenvalues to eliminate redundant computations. The method may include outputting a reduced-rank random field dataset for use in a user-provided model of the modeling system for uncertainty quantification analysis and simulation of the real world phenomenon.

Abstract: A system and method for managing medication orders, including changes to those orders, is described. A server receives medication orders from a pharmacy and communicates those orders to an infusion pump. The infusion pump communicates status messages to the server and the server compares the status messages to the medication orders to determine if the there were any changes to the order, or if a medication was administered absent a medication order. The server assigns a new order identification number to the status message if needed. The system may also reconcile original medication orders with information associated with a new order identification number and stores the information in the eMAR of a patient.

Abstract: A fault identification may be triggered by a component of a power generation system (PGS), such as a hardware component, a controller of a hardware component, a device of the PGS, a computer connected to the PGS, a computer configured to monitor the PGS, and/or the like. The fault identification may be the result of a failure of a component of the PGS, a future failure of a component of the PGS, a routine maintenance of the PGS, and/or the like. The fault is converted to a notification on a user interface using a mapping of faults, root-causes, notification rules, and/or the like. The conversion may use one or more lookup tables and/or formulas for determining the impact of the fault on the PGS, and/or the like.

Abstract: An example computing platform is configured to (i) cause a client device to display an interface for deploying a new data science environment, where the interface presents (a) a list of data science applications and (b) a set of user-defined configuration parameters, (ii) receive, from the client device, data indicating (a) a user selection of a given data science application from the list and (b) a user selection of one or more user-defined configuration parameters from the set, (iii) based on the user selection of the given data science application, determine a deployment template for use in deploying the new data science environment, the deployment template specifying (a) an executable environment package and (b) a set of predefined configuration parameters, and (iv) use the given executable environment package, the set of predefined configuration parameters, and the one or more user-defined configuration parameters to deploy the new data science environment.

Abstract: A system for responding to external requests received at an internal bot network is provided. The system may include an internal bot network including a plurality of internal bots. The plurality of internal bots may interact with a plurality of external bots. The system may include a translation layer/barrier. Each bot included in the plurality of internal bots and the plurality of external bots may be resident on one or more hardware processors. Each external bot may communicate using its own unique set of external specifications. Each internal bot may communicate using a universal set of internal specifications and is prevented from communicating using a set of external specifications. The translation layer/barrier may intercept requests and responses between the plurality of internal bots and the plurality of external bots. The translation layer/barrier reformats intercepted requests and responses to correspond to the set of specifications specific to the receiving bot.

Abstract: Systems and methods for labeling data are disclosed. An example method may be performed by one or more processors of a labeling system and include retrieving labeled data, identifying characteristics predictive of labels that would be entered for unlabeled data items having the respective characteristics based on the labeled data, training an analysis model to predict labels that would be entered for unlabeled data items, generating, for unlabeled data items, using the trained analysis model, a prediction of a label that will be entered for the respective unlabeled data item if the respective unlabeled data item is presented for labeling, selecting, based on the generated predictions, a subset of unlabeled data items to be presented for labeling, receiving labels for the subset of unlabeled data items, determining that a completion criteria associated with the trained analysis model is met, and generating labels for remaining unlabeled data items.

Abstract: The described technology is generally directed towards a machine learning development hub, and corresponding methods and computer readable media. The machine learning development hub can comprise a machine learning development platform complete with various tools for various stages of machine learning development. The machine learning development hub can furthermore comprise translation functions to translate received inputs into inputs to other machine learning development platforms. The machine learning development hub can collect credentials for the other machine learning development platforms and can connect to the other machine learning development platforms via their respective interfaces, in order to supply inputs and instructions thereto. The machine learning development hub can encrypt its communications to other machine learning development platforms to secure its interactions.

Abstract: An approach is provided in which the approach generates a parallel chart based on multiple records that includes a set of variable values corresponding to a set of variables. To generate the parallel chart, the approach arranges the set of variables on the parallel chart in a variable order based on at least one variable arrangement rule. The approach arranges an initial variable value order for each one of the set of variables, and computes a lucidity score based on the variable order and the initial variable value order of each of the set of variables. The lucidity score is a measurement of the clarity of the parallel chart. The approach adjusts the variable value order of at least one of the set of variables to increase the lucidity score and optimizes the clarity of the parallel chart based on the adjusted variable value order.

Abstract: An apparatus, computer-readable medium, and computer-implemented method for efficiently classifying a data object, including representing the data object as a data object vector in a vector space, each dimension of the data object vector corresponding to a different feature of the data object, determining a distance between the data object vector and centroids of data domain clusters in the vector space, each data domain cluster comprising data domain vectors representing data domains, sorting the data domain clusters according to their respective distances to the data object vector, and iteratively applying data domain classifiers corresponding to data domains represented in a closest data domain cluster in the sorted data domain clusters to the data object.

Abstract: Techniques for tuning parameters for machine learning models are described. Different values for a parameter are tested to determine the value that results in an optimized model. A parameter value may be selected for testing using a search algorithm based on how the model performs with respect to other values for the parameter. Different values may be tested until a stopping criterion (such as time for testing, number of trials, amount of enhancement in performance, etc.) is met. In some embodiments, the techniques may be used to determine parameter values for natural language processing models.

Abstract: An electronic device and method are provided. The method includes identifying a speech section of a user and a speech section of a neighbor in a received audio signal, identifying a user utterance in the speech section of the user and a neighbor answer to the user utterance in the speech section of the neighbor, obtaining preference information associated with the user utterance, giving a first reliability to the neighbor answer and a second reliability to an agent answer of an artificial intelligence agent generated in response to the user utterance, based on the preference information, not responding to the user utterance when the second reliability is lower than the first reliability, and outputting the agent answer when the second reliability is equal to or higher than the first reliability.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: creating a log data specific lexicon based on log data samples, each word in the log data specific lexicon corresponding to a weighted sentiment score with a binary polarity. A log message is obtained, and a sentiment value of the log message is assigned based on respective weighted sentiment scores of words appearing in the log message. The log message is classified for a class indicating an issue the log message addresses. An alert type for the log message is determined based on the sentiment value, the class, and a priority of the log message, and a system alert including a set of key performance indicators according to the alert type is produced to a user.

Abstract: A method of automatically providing personalized learning activities to users of an online learning platform is described. A quest is built based on an input of at least a first user's educational objective. The quest includes a primary quest that starts with a start node, ends with the end node, and includes a set of intermediate nodes, wherein each node of the primary quest must be traversed by the first user in order to complete the quest. The quest includes at least one secondary quest that starts with a node of the primary quest as its start node and includes a second set of intermediate nodes. As the user selects a node of the quest, educational activities related to its respective concept are presented to the user. The user's progress in the quest is tracked.

Abstract: Broadly speaking, the present techniques relate to a method and apparatus for performing action recognition, and in particular to a computer-implemented method for performing action recognition on resource-constrained or lightweight devices such as smartphones. The ML model may be adjusted to achieve required accuracy and efficiency levels, while also taking into account the computational capability of the apparatus that is being used to implement the ML model. One way is to adjust the number of channels assigned to the first set of channels, i.e. the full temporal resolution channels. Another way is to adjust the point in the ML model where the temporal pooling layer or layers are applied.

Abstract: A system, method, and computer-readable medium are disclosed for cognitive information processing. The cognitive information processing includes receiving data from a plurality of data sources; processing the data from the plurality of data sources to provide cognitively processed insights via an augmented intelligence system, the augmented intelligence system and an information processing system providing a cognitive computing function; performing a learning operation to iteratively improve the cognitively processed insights over time; and, providing the cognitively processed insights to a destination, the destination comprising a cognitive application, the cognitive application enabling a user to interact with the cognitive insights.

Abstract: An example method for processing wind field data includes generating wind field base data using the preliminary data and one or more empirical equations based on climatology. The wind field base data includes multiple data sets each associated with a different time-point in a first set of time-points. The method also includes performing spatial interpolation and temporal interpolation over the wind field base data to generate a sequence of two-dimensional wind field representations each associated with a different time-point in a second set of time-points, and visualizing the sequence of two-dimensional wind field representations.

Abstract: The invention relates to a method and system that implements an assessment tool that assesses regulations. The system comprises: a server interface; a data store; and an assessment engine comprising a computer processor, coupled to the server interface and the data store, programmed to: identify a regulation; divide the regulation into a plurality of rules; convert each rule into a question, test and one or more conditions; present, via a user interface, a first question to a user; receive, via the user interface, a response to the first question; store, in the data store, the response; present, via the user interface, a subsequent question responsive to the response; store, in the data store, the subsequent question and corresponding response; and dynamically generate an audit trail of each question and answer combination.

Abstract: Methods for building and training a network of chatbots are provided. Methods may perform data analysis on a first chatbot in the network. The first chatbot may include a first domain of intents. The data analysis may identify and rank the intents in the first domain. The first domain may represent answers to a first domain of user queries. Methods may select a predetermined number of highest-ranking intents from the first domain based on the data analysis. Methods may input the selected intents into a second chatbot in the network. The second chatbot may include a second domain of intents. The second domain may represent answers to a second domain of user queries. Methods may input a hook into the second chatbot. The hook may include a trigger word and may correspond to the first domain. When the hook is triggered, the second chatbot may invoke the first chatbot.

Abstract: Systems and methods are described herein for updating search results based on a user's comment or a conversation among users using a media guidance application. A set of search results may be presented to a user or users. Comments or a conversation about the search results may be analyzed by the media guidance application. Selected comments by a user determined to have a greater weight may be used to update the search results.

Abstract: A response activity pattern may be ascertained from a set of computing devices obtained during a monitoring mode of operation. The monitoring mode of operation can be initiated when a user command is determined to be a new or unknown command. The response activity pattern may be used to generate a response profile indicating an activity to carry out using one or more user devices to perform a task associated with the user command. When an indication of a previously unknown user command to perform a task is received, the generated response profile can be used to perform the designed task by carrying out the activity using the one or more user device. In this way, the activity that should be carried out to perform the task can be learned based on the monitored user activity related to the one or more user devices.

Abstract: Dynamic state-space modeling within a special purpose hardware platform to determine non-conversion risks for each trial user and churn risks for each active subscriber having exhibited a sequence of behaviors. The state-space model may be operable to determine a loss risk for each of a provider's active trial users and/or subscribers.

Abstract: In an illustrative embodiment, methods and systems for automatically labeling unstructured data include accessing unstructured data representing data entry and analyzing the unstructured data by applying natural language processing to a text component of the unstructured data to obtain a set of term counts of words and/or phrases identified in the text component. Analyzing may include applying at least one clustering algorithm to the set of term counts to determine a term cluster, identifying a preexisting term cluster most closely matching the term cluster, and applying, to the unstructured data, a predefined label corresponding to the preexisting term cluster. The unstructured data may be analyzed to obtain formatting counts of formatting elements, and a formatting cluster may be determined and applied to match to a preexisting formatting cluster, thus deriving a predefined label corresponding to the preexisting formatting cluster.

Abstract: A method for providing an evidence service to facilitate evidence analytic and controls assessment for an enterprise toolchain is disclosed. The method includes compiling raw data via an adapter, the raw data including an event that relates to a software development life cycle; retrieving, from the adapter, the compiled raw data; identifying, by using a model, information that relates to the event from the raw data; extracting the identified information; generating, by using the model, a structured data set based on the extracted information; and determining a controls measurement based on the structured data set.

Abstract: A computer-implemented method that includes obtaining a plurality of values each corresponding to one of a plurality of variables. The plurality of variables include variables of interest. The method includes obtaining a prediction for the values from a model, determining metric(s) for each of the variables of interest, and determining one or more of the variables of interest to be one or more influential variables based on the metric(s) determined for each of the variables of interest. The variables include one or more non-influential variables that is/are different from the influential variable(s). The influential variable(s) has/have a greater influence on the prediction than the non-influential variable(s).

Abstract: The method disclosed herein includes determining a user interaction pattern with respect to a digital marker, the user interaction pattern based at least in part on user interaction with a capacitive sensor of a computing device, determining a geometric characteristic of the digital marker, and generating a predicted digital marking position of the digital marker on a digital medium of the computing device based at least in part on the determined geometric characteristic of the digital marker and the determined user interaction pattern.

Abstract: A method for determining an answer to a question of a product includes: receiving a question for a target product; obtaining a knowledge graph constructed in advance for the target product, in which, the knowledge graph includes entities and a relationship among the entities, and the entities at least include one question for the target product and a corresponding answer; and determining an answer corresponding to the question based on the relationship among the entities included in the knowledge graph.

Abstract: In a computer-implemented method for facilitating creation of a reusable application, during development of the reusable application, a plurality of artifacts associated with source code of the reusable application is received, wherein an artifact of the plurality of artifacts includes information related to the development of the reusable application. The plurality of artifacts is maintained as metadata for the source code of the reusable application. An application module including the source code of the reusable application and the metadata is generated, wherein the application module is configured for use by a developer for facilitating creation of a new version of the reusable application, and wherein the metadata is accessible by the developer during the creation of the new version of the reusable application for accessing the information related to the development of the reusable application.

Abstract: Systems and methods are provided for the control of an industrial asset, such as a power generating asset. Accordingly, a cyber-attack model predicts a plurality of operational impacts on the industrial asset resulting from a plurality of potential cyber-attacks. The cyber-attack model also predicts a corresponding plurality of potential mitigation responses. In operation, a cyber-attack impacting at least one component of the industrial asset is detected via the cyber-attack neutralization module and a protected operational impact of the cyber-attack is identified based on the cyber-attack model. The cyber-attack neutralization module selects at least one mitigation response of the plurality of mitigation responses based on the predicted operational impact and an operating state of the industrial asset is altered based on the selected mitigation response.

Abstract: Methods, apparatus and systems related to automated evaluation of acting performance are disclosed. In one example aspect, a system for an automated evaluation of performance activities includes a user interface configured to allow a user to create a customized automated evaluation service. The user can select a subset of machine learning engines from one or more available machine learning engines to create such service. The user interface is further configured to receive a set of input data of a performance performed by a performer. The system includes the one or more available machine learning engines each configured to generate a sub-score in a specific area using the set of input data received from the user interface. The system also includes an aggregator configured to produce an output indicating a quality of the performance.

Abstract: A face authentication apparatus includes a face image acquisition unit, a collation unit, a time measurement unit, and a threshold change unit. The face image acquisition unit acquires a face image of an authentication target. The collation unit performs face authentication on the face image of the authentication target on the basis of a threshold. The time measurement unit measures the elapsed time from the time of starting operation of the face authentication apparatus. The threshold change unit restricts changing of the threshold to a value larger than a value determined depending on the measured elapsed time.

Abstract: A method includes, through a server, determining a context of interaction between a user of a data processing device communicatively coupled to the server through a computer network and the server, fetching a set of queries from a database associated with the server in accordance with determining the context of interaction, and loading the set of queries one by one on a first media device configured to render the set of queries in an audio, a video and/or a gesture format. The method also includes, through the server, receiving a response to a query of the set of queries from the user via the first media device and/or the data processing device, and refining the set of queries based on the response received to the query from the user in accordance with an Artificial Intelligence (AI) and/or a Machine Learning (ML) engine executing on the server.

Abstract: Embodiments of the present disclosure present devices, methods, and computer readable medium for techniques for creating machine learning models. Application developers can select a machine learning template from a plurality of templates appropriate for the type of data used in their application. Templates can include multiple templates for classification of images, text, sound, motion, and tabular data. A graphical user interface allows for intuitive selection of training data, validation data, and integration of the trained model into the application. The techniques further display a numerical score for both the training accuracy and validation accuracy using the test data. The application provides a live mode that allows for execution of the machine learning model on a mobile device to allow for testing the model from data from one or more of the sensors (i.e., camera or microphone) on the mobile device.

Abstract: The present invention provides systems and methods for exchanging health data. A health data exchange platform includes health data from a plurality of data sources. The health data exchange platform preferably utilizes blockchain technology. The health data exchange platform includes both buyers of health data and suppliers of health data. The health data exchange platform includes a phenotype network system including at least one remote server, a user profile database, a report database, a permissions database, a geographic incident database, and a medical community database.

Abstract: Mechanisms are provided for determining values to associate with medical conditions of a patient. A patient assessment is received that comprises a natural language question and a corresponding answer, about a patient, provided in response to the question. Cognitive natural language processing is performed on the patient assessment to extract features from the natural language question and corresponding answer. The extracted features are evaluated within a context of a pre-existing electronic medical record of the patient. A value for a medical condition of the patient is determined based on results of the evaluation and stored in the electronic medical record for the patient.

Abstract: An online system that allows users to interact with it using expressions in natural language form includes an intent inference module allowing it to infer an intent represented by a user expression. The intent inference module has a set of possible intents, along with a small set of example natural language expressions known to represent that intent. When a user interacts with the system using a natural language expression for which the intent is not already known, the intent inference module applies a natural language inference model to compute scores indicating whether the user expression textually entails the various example natural language expressions. Based on the scores, the intent inference module determines an intent that is most applicable for the expression. If an intent cannot be determined with sufficient confidence, the intent inference module may further attempt to determine whether the various example natural language expressions textually entail the user expression.