Abstract: Embodiments are provided for detecting overlapping topics in a messaging system. In an example system, a plurality of trigger phrases is received, where each trigger phrase is configured to trigger a bot that receives the trigger phrase to select a corresponding topic for conversation. For each trigger phrase, a vector representation is generated. Measures of similarity are generated based at least on the vector representations, where each measure of similarity represents a degree of similarity between a respective pair of vector representations. A topic overlap is detected based on a pair of vector representations having a measure of similarity above a similarity threshold, where the topic overlap indicates two trigger phrases that are overlapping. The topic overlap is provided to an authoring tool that comprises one or more interactive elements to enable a user to change at least one of the two trigger phrases that are overlapping.

Abstract: Systems and methods are provided for dynamically adjusting a website of an entity using information that has been received, stored, gathered, and/or otherwise obtained about what people want to find on the entity's website. A website may be dynamically adjusted using trending information. Dynamically adjusting may comprise generating and presenting links on the website to webpages in order of volume over a defined time threshold, or by variance over a standard volume over a longer established time frame. Dynamically adjusting the website may comprise providing an element on a website for what is most urgent to customers. A unit associated with a website may be monitored, and when the unit volume or activity starts to spike beyond normal expectations, then information about the unit is proactively and/or preemptively offered on the website, or a webpage of the website, without ever having a conversation with a user.

Abstract: A body and hand association method includes: an image to be detected of which an image content includes a body and a hand is acquired; a body bounding box of the body and a hand bounding box of the hand are determined in the image to be detected; an association probability between the body bounding box and the hand bounding box is determined; a circumscribed box of the body bounding box and the hand bounding box is determined; and a degree of association between the body and hand in the circumscribed box is determined based on a hand key point in the circumscribed box and the association probability.

Abstract: A method, a device, and a non-transitory storage medium are described, which provide a recommendation system with performance management service. The service may include selecting and ranking content that relate to a user query and a problem associated with an application and/or a device. The service may include invoking a remedial procedure based on user feedback data. The service may include similarity and classification models based on successful recommendation records.

Abstract: Methods of and systems to provide performance measurement are provided utilizing an architecture configured to efficiently merge and monitor different types of performance data. Connectors are provided to receive and translate different types of performance data from different sources. The performance data is translated into and stored in a common data model format. In some embodiments, key attributes are defined for each of the performance data sources that uniquely characterizes each relevant performance data so that is can be parsed into separate processing streams to increase system performance. The key attributes also act as cues to organize the performance data as it is being merged so that it can be accessed without requiring a specific source data linkage. Using model listeners, determinations can be quickly made regarding when performance data is changed to reduce calculations necessary to determine measure values. Some embodiments merge different types of performance data in real-time.

Abstract: The Multidimensional Machine Learning Data and User Interface Segment Tagging Engine Apparatuses, Methods and Systems (“MLUI”) transforms ambient condition data, sales data, user interface selections, cognitive intelligence question input inputs via MLUI components into project projections, campaigns, user interface visualizations, cognitive intelligence question output outputs. A cognitive intelligence (CI) datapoint identifier cache datastructure is generated, the CI datapoint identifier cache datastructure configured to comprise a category identifier and an entity segment identifier. A CI datapoint value cache datastructure is generated, the CI datapoint value cache datastructure configured to comprise a set of module datastructures, each module datastructure corresponding to a module identifier associated with the category identifier, each module datastructure comprising a set of metric datastructures, each metric datastructure corresponding to a set of calculated metrics.

Abstract: Systems for using cognitive automation techniques to detect unauthorized events are provided. In some examples, a request for a product or service (e.g., event processing) may be received. The request may be received electronically, via a telephone communication, in person, or the like. The request may be analyzed (e.g., using a cognitive automation model) to determine whether it is potentially unauthorized. If so, one or more requests for additional information may be generated or identified. For instance, requests for authentication information, responses to questions or a series of questions, or the like, may be generated. In some examples, the additional information requested may be identified using the cognitive automation model. Responses to the request for additional information may be analyzed (e.g., using the cognitive automation model) to determine whether the request for the product or service is unauthorized. If so, processing the request may be prevented.

Abstract: Anomaly detection and self-healing for robotic process automation (RPA) via artificial intelligence (AI)/machine learning (ML) is disclosed. RPA robots that utilize AI/ML models and computer vision (CV) may interpret and/or interact with most encountered graphical elements via normal learned interactions. However, such RPA robots may occasionally encounter new, unhandled anomalies where graphical elements cannot be identified and/or normal interactions will not work. Such anomalies may be processed by an anomaly handler. The RPA robots may have self-healing functionality that seeks to automatically find information that addresses anomalies.

Abstract: Aspects of systems and methods for providing contextual and event driven insights are provided. The system monitors information about the users and their conversations. Upon receiving a natural language request for information for a topic, the system utilizes a model to extract one or more topics from the request. The system utilizes the topic to query a resource for candidate users with knowledge about the topic. The system then queries a resource to identify candidate content items associated with the topic and the candidate users. Thereafter, the system refines the candidate users and the candidate content items to identify relevant users and content items that are meaningful to the user.

Abstract: From a quantum program a first mutant is generated using a processor and a memory, where the first mutant is a randomly-generated transformation of the quantum program. A quality score, a correctness distance, and a probability of acceptance corresponding to the first mutant are computed. An acceptance corresponding to the first mutant is determined according to the probability of acceptance. Upon determining that an acceptance of the first mutant corresponding to the probability of acceptance exceeds an acceptance threshold, the quantum program is replaced with the first mutant. Upon determining that the quality score exceeds a storage threshold and that the correctness distance is zero, the first mutant is stored. These actions are iterated until reaching an iteration limit.

Abstract: An intelligent system, such as an autonomous robot agent, includes systems and methods to learn various aspects about a task in response to instructions received from a human instructor, to apply the instructed knowledge immediately during task performance following the instruction, and to instruct other intelligent systems about the knowledge for performing the task. The learning is accomplished free of training the intelligent system. The instructions from the human instructor may be provided in a natural language format and may include deictic references. The instructions may be received while the intelligent system is online, and may be provided to the intelligent system in one shot, e.g., in a single encounter or transaction with the human instructor.

Abstract: State-of-the-art techniques hardly attempt to address controlled resource access problem in context of Basic Emergent Users (BEUs). Embodiments of the present disclosure provide a method and system for Proof of Work (POW) based protection of resources. The method includes using the POW for work done by BEUs in physical world and mapping it to digital world to generate crypto currency in terms of credit score, wherein an end user is eligible or authorized to use a resource of an entity to get a desired service if accumulated credit score is above a credit threshold. Gaining points to improve the credit score is challenging as it is based on percentage of compliance achieved by the BEU through actual work in accordance with a compliance protocol. Further, the method includes authenticating the authorized user based on a set of questions with increasing difficulty, derived based on a culture graph.

Abstract: In some examples, a user, either a customer or potential customer of a business, engages in conversations with a virtual assistant (VA) provided by the business. The virtual assistant (VA) is further supported by one or more human assistants (HA), if needed. In embodiments, to facilitate seamless transitions between a VA and a HA, when needed, an intelligent decision maker (IDM) is provided. The IDM receives a user question and a proposed answer to the question from a VA, evaluates the proposed answer in the context of the conversation, and determines if the proposed answer requires further review by an HA. In response to a determination that the proposed answer requires further review, the IDM sends the proposed answer to an HA, and, in response to an indication by the HA, takes further action in the conversation.

Abstract: A computerized method is provided for automatically determining answers to a plurality of questions. The method includes automatically discovering a plurality of questions by processing historical data related to prior customer interactions. The automatically discovering includes applying a linguistic analytical model on the data related to historical customer interactions to detect the plurality of questions, vectoring the plurality of questions to generate mathematical representations of the questions, and grouping the plurality of questions into one or more clusters in accordance with similarities of the questions as measured based on their mathematical representations. The method also includes identifying the questions that do not have an existing answer. The method further includes determining at least one probable answer to each of the representative questions using a content mining technique that mines pertinent data from one or more identified content sources.

Abstract: A data analysis apparatus executes: a selection process selecting a first feature variable group that is a trivial feature variable group contributing to prediction and a second feature data group other than the first feature variable group from a set of feature variables; an operation process operating a first regularization coefficient related to a first weight parameter group corresponding to the first feature variable group in a manner that the loss function is larger, and operating a second regularization coefficient related to a second weight parameter group corresponding to the second feature variable group in a manner that the loss function is smaller, among a set of weight parameters configuring a prediction model, in a loss function related to a difference between a prediction result output in a case of inputting the set of feature variables to the prediction model and ground truth data corresponding to the feature variables.

Abstract: Embodiments described herein are directed to improving machine learning (ML) model-based techniques for automatically labeling data items based on identifying and resolving labels that are problematic. An ML model may be trained to predict labels for any given data item. The ML model may be validated to determine a confusion metric with respect to each distinct pair of labels predicted by the ML model. Each confusion metric indicates how a particular label is being mistaken for another particular label. The confusion metrics are analyzed to determine whether any of the ML model-generated labels are problematic (e.g., a label conflicts with another label, a label that is rarely predicted, a label that is incorrectly predicted, etc.). Steps for resolving the problematic labels are implemented, and the ML model is retrained based on the resolution steps. By doing so, the ML model generates a more accurate label for a data item.

Abstract: An online connection server is configured to more accurately predict connections for a viewing member of an online connection network. The online connection server may implement a machine-learning model that uses prior interactions by the viewing member to determine those connections that are likely to lead to more substantial interactions with the viewing member. The machine-learning model may be implemented using a reinforcement learning technique, such as a Deep Q network. The online connection server may further implement a state representation module that generates a state from a graph-based embedding of the viewing member profile, where the state is used to train the machine-learning model and determine an optimal candidate to recommend as a connection for the viewing member.

Abstract: A method, computer program product, and computer system identifying, by a computing device, an intent relating to a query associated with an object. A plurality of informational items associated with the object may be identified. At least a first informational item of the plurality of informational items associated with the object for which a first portion of information is already known may be identified. At least a second informational item of the plurality of informational items associated with the object for which a second portion of information is unknown may be identified. A question may be generated to determine the second portion of information based upon, at least in part, the second portion of information being unknown. An answer may be received for the question generated to determine the second portion of information.

Abstract: A system and a method of displaying a predictive answer in a conversation window, are described. The method includes transmitting, to a device executing an application, a first message to cause the device to display a conversation window within the application, wherein the conversation window has a character entry field. The method includes generating, by one or more processors, a predictive answer based on a Uniform Resource Locator (URL) associated with the conversation window or a URL shape of the URL, wherein the predictive answer includes a predicted inquiry of a user. The method includes transmitting, to the device, a second message to cause the device to display the predictive answer in the conversation window.

Abstract: Techniques for iterative model training and deployment for automated learning systems are described. A method of iterative model training and deployment for automated learning systems comprises generating training data based on inference data, provided by a first version of a model hosted at an endpoint of a machine learning service, and feedback data, received from a client application, using an identifier associated with the inference data and the feedback data, generating a second version of the model using the training data, and deploying the model to the endpoint of the machine learning service.

Abstract: Methods, systems, and computer-readable media for interactive workflow generation for machine learning lifecycle management are disclosed. A machine learning management system determines one or more prompts associated with use of a machine learning model. Input representing one or more responses to the one or more prompts is received. The one or more responses are provided via a user interface. The machine learning management system determines one or more workflows associated with the machine learning model. The workflow(s) are determined based at least in part on the one or more responses. The workflow(s) comprise a plurality of tasks associated with use of the machine learning model at a plurality of stages of a lifecycle of the model. One or more computing resources are determined, and at least a portion of the workflow(s) is performed using the one or more computing resources.

Abstract: A problem is inputted into an operation unit. A computation unit searches for a ground state of an Ising model. A management unit converts the problem inputted from the operation unit to the Ising model, inputs the Ising model produced by conversion and initial operating conditions into the computation unit, and has the computation unit search for the ground state using overall operating conditions produced by changing the initial operating conditions based on a result of the computation unit searching for the ground state using the initial operating conditions.

Abstract: The specification discloses a question answer matching method, system and computer storage medium. The method comprises: transforming the user query and one of one or more suggested answers corresponding to the user query by using a pre-trained word vector to obtain vector representations of the user query and the one of one or more suggested answers corresponding to the user query; performing a convolutional operation on the vector representations of the user query and the one of one or more suggested answers, respectively, to extract features; and mapping convolution results of the vector representations of the user query and the vector expression of the one of one or more suggested answers into a sample annotating space, to obtain a matching result of the user query.

Abstract: A computerized neural network training and testing environment trains and validates a neural network to produce outputs corresponding to a digital signal processing (DSP) algorithm. After validation and testing, the neural network is replicable in any processing environment, independent of device architecture. Such devices may also include a neural network having a reversed process flow to produce a digital signal corresponding to an inverse of the DSP algorithm.

Abstract: In particular embodiments, an Orphaned Data Action System is configured to analyze one or more data systems (e.g., data assets), identify one or more pieces of personal data that are one or more pieces of personal data that are not associated with one or more privacy campaigns of the particular organization, and notify one or more individuals of the particular organization of the one or more pieces of personal data that are one or more pieces of personal data that are not associated with one or more privacy campaigns of the particular organization.

Abstract: The present disclosure describes transaction-enabling systems and methods. A system can include a facility having a core task and a controller. The controller may include a facility description circuit to interpret historical facility parameter values and corresponding outcome values. A facility prediction circuit operates an adaptive learning system to train a facility resource allocation circuit in response to the historical facility parameter values and corresponding outcome values. The facility description circuit further interprets a plurality of present state facility parameter values and the trained facility resource allocation circuit adjusts facility resource values in response.

Abstract: A computerized personal assistant includes a natural language user interface, a natural language processing machine, an identity machine, and a knowledge-base updating machine. The knowledge-base updating machine is configured to update a user-centric artificial intelligence knowledge base associated with the particular user to include a new or updated user-centric fact based on the computer-readable representation of the user input, wherein the knowledge-base updating machine updates the user-centric artificial intelligence knowledge base via an update protocol useable by a plurality of different computer services.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an artificial neural network architecture based on a synaptic connectivity graph. According to one aspect, there is provided a method comprising: obtaining a synaptic resolution image of at least a portion of a brain of a biological organism; processing the image to identify: (i) a plurality of neurons in the brain, and (ii) a plurality of synaptic connections between pairs of neurons in the brain; generating data defining a graph representing synaptic connectivity between the neurons in the brain; determining an artificial neural network architecture corresponding to the graph representing the synaptic connectivity between the neurons in the brain; and processing a network input using an artificial neural network having the artificial neural network architecture to generate a network output.

Abstract: Embodiments of the present systems and methods may provide an intelligent systems framework for analysis of user-generated content from various capture points to determine user intent. For example, a method may be implemented in a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the method may comprise receiving, at the computer system, data relating to a plurality of aspects of at least one person, including data from at least one of physical or physiological sensors and communicatively connected devices, extracting, at the computer system, from the received data, features relevant to events relating to at least one person, extracting, at the computer system, at least one intent of at least one event relating to at least one person, and performing, at the computer system, an action based on the extracted at least one intent.

Abstract: Systems, methods and computer-readable media are provided for facilitating clinical decision support and managing patient population health by health-related entities including caregivers, health care administrators, insurance providers, and patients.

Abstract: A processing system including at least one processor may obtain a first set of performance records of a database system, train a machine learning model in accordance with the first set of performance records, where the machine learning model that is trained in accordance with the first set of performance records is configured to predict a latency of a query transaction for a designated time period, present a user interface with a plurality of settings of the database system that are user-adjustable, where the plurality of settings is associated with at least a portion of the first set of performance records, calculate a first predicted latency of a query transaction at the designated time period via the machine learning model in accordance with a set of values of the plurality of settings, and present the first predicted latency via the user interface.

Abstract: Disclosed are systems and methods for determining an execution location for a utility component. In an exemplary aspect, the systems and methods comprise receiving, at a client device, a request to execute the utility component that performs an action on a target component; determining the execution location of the utility component; determining whether the execution location is on the client device or on a remote application server; and responsive to determining the execution location is on the remote application server, transmitting the request to be executed within a remote session between the client device and the remote application server.

Abstract: In some examples, a system for implementing a protocol for data transmission can include a processor to transmit a public key or an encrypted name to a ledger of a source computing device and receive a scoring code, training data, and data definitions from the source computing device. The processor can also transmit predicted labels encrypted with the public key to the source computing device, the predicted labels based on the training data and the data definitions, and receive labeled test data from the source computing device. The processor can also transmit decrypted predicted labels to the source computing device, receive a list of evaluation results from a set of remote computing devices, transmit scoring results based on the list of evaluation results to the set of remote computing devices, and execute an instruction based on a techniques corresponding to a highest score from the scoring results.

Abstract: A stackable filesystem architecture that curtails data theft and ensures file integrity protection. In this architecture, processes are grouped into ranked filesystem views, or “security domains.” Preferably, an order theory algorithm is utilized to determine a proper domain in which an application is run. In particular, a root domain provides a single view of the filesystem enabling transparent filesystem operations. Each security domain transparently creates multiple levels of stacking to protect the base filesystem, and to monitor file accesses without incurring significant performance overhead. By combining its layered architecture with view separation via security domains, the filesystem maintains data integrity and confidentiality.

Abstract: Physical assets in a network are graphically modeled and controlled using a monitoring and control interface. Prior to committing configuration changes to the physical assets, the draft configuration changes are analyzed to determine if they violate any logical configuration change rules. The logical configuration rules can be model-based according to target physical asset(s) for the draft configuration changes. A logical configuration change rule violation can be graphically displayed on the monitoring and control interface. The configuration changes can be stored to debug the physical assets and to roll back the configuration of the physical assets to a prior state.

Abstract: A method includes receiving data comprising a plurality of requests and a plurality of responses to the requests. The requests and the responses are associated with a virtual assistant programmed to address the plurality of requests. In the method, a machine learning (ML) classifier is used to partition the requests into a plurality of partitions corresponding to a plurality of request types. An interface for a user is generated to display a subset of the requests corresponding to at least one partition of the plurality of partitions and to display a response corresponding to the subset of the plurality of requests, wherein the response is based on one or more of the plurality of responses. The interface is configured to permit editing of the response by the user. The method also includes processing the response edited by the user, and transmitting the edited response to the virtual assistant.

Abstract: The disclosure presents processes and methods for determining an adjusted drag friction factor, where the adjusting utilizes a hole cleaning function. In some aspects, the drag friction factor utilizes viscous drag. In some aspects, the drag friction factor utilizes viscous torque. In some aspects, the drag friction factor can be utilized to determine one or more decomposed friction factors. The decomposed friction factors or the adjusted drag friction factor can be utilized in a friction processor to improve the efficiency of borehole operations. The hole cleaning function can utilize various parameters, for example, a cuttings density, a cuttings load, a cuttings shape, a cuttings size, a deviation, a drill pipe rotation rate, a drill pipe size, a flow regime, a hole size, a mud density, a mud rheology, a mud velocity, a pipe eccentricity, and other parameters. A system is disclosed that is capable of implementing the processes and methods.

Abstract: The present disclosure describes a system, method, and computer program for converting a natural language update instruction to a structured update database statement. In response to receiving a natural language query for a database, an NLU model is applied to the query to identify an intent and entities associated with the query. If the intent is to update a data object, the system evaluates the entities to identify update fields and update values. Update fields are matched to update values based on update parameters, operand type of the update value, and location of the update fields and values. For each update field and value pair, an update context is calculated to determine whether the update value is absolute or relative to an existing field value. An update plan is created with the update field and value pairs and corresponding update contexts, and a database update statement is generated from the update plan.

Abstract: A method described herein includes receiving a digital image, wherein the digital image includes a first element that corresponds to a first domain and a second element that corresponds to a second domain. The method also includes automatically assigning a label to the first element in the digital image based at least in part upon a computed probability that the label corresponds to the first element, wherein the probability is computed through utilization of a first model that is configured to infer labels for elements in the first domain and a second model that is configured to infer labels for elements in the second domain. The first model receives data that identifies learned relationships between elements in the first domain and elements in the second domain, and the probability is computed by the first model based at least in part upon the learned relationships.

Abstract: A device may receive, from a source system, data in a first format, data in a second format, or a combination of data in the first format and data in the second format. The device may identify a data format type, a hierarchy depth, levels, and objects at each of the levels for the data and may generate a template based on the data format type, the hierarchy depth, the levels, and the objects at each of the levels. The device may generate a query, for the data in the first format, based on the template and may execute the query, on the data in the first format, to generate query results associated with a target system. The device may process the query results, with the template, to generate output data in a third format associated with the target system and may perform actions based on the output data.

Abstract: A method may include a processing system having at least one processor for receiving a first machine learning model, the first machine learning model in a first format associated with a first development environment, adapting the first machine learning model to a containerized environment, validating the first machine learning model according to at least one validation criterion associated with a repository, and publishing the first machine learning model to the repository.

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: A machine learning model is trained. A feature importance metric is determined for each machine learning feature of a plurality of machine learning features of the machine learning model. Based on the feature importance metrics, one or more machine learning features of the plurality of machine learning features of the machine learning model are managed.

Abstract: Users of an endpoint remediation system can be assigned to different roles, from which they can request exceptions, approve exceptions, and/or enable remediation on endpoint devices. The compliance scanning and enforcing process can be automated, while allowing entities to request and/or approve certain exceptions. Therefore, security compliance for customers can be actively managed to provide visibility to the endpoint device compliance state at any time.

Abstract: A method for creating an assessment within a multi-disciplined learning tool regarding a topic includes deriving a first set of knowledge test-points for a first learning object regarding the topic based on a first set of knowledge bullet-points, an illustrative asset, and a first descriptive asset of the first learning object. The method further includes deriving a second set of knowledge test-points for a second learning object regarding the topic based on a second set of knowledge bullet-points, the illustrative asset, and a second descriptive asset of the second learning object. The method further includes generating a knowledge assessment asset based on the first and second knowledge test-points.

Abstract: Embodiments of the present invention are directed to facilitating data preprocessing for machine learning. In accordance with aspects of the present disclosure, a training set of data is accessed. A preprocessing query specifying a set of preprocessing parameter values that indicate a manner in which to preprocess the training set of data is received. Based on the preprocessing query, a preprocessing operation is performed to preprocess the training set of data in accordance with the set of preprocessing parameter values to obtain a set of preprocessed data. The set of preprocessed data can be provided for presentation as a preview. Based on an acceptance of the set of preprocessed data, the set of preprocessed data is used to train a machine learning model that can be subsequently used to predict data.

Abstract: Systems and methods are provided for receiving a request for data associated with a particular functionality of an application, identifying a first attribute for which data is to be generated to fulfill the request, and determining that the first attribute corresponds to data to be generated by a first machine learning model. The systems and methods further providing for executing a view or procedure to generate data for input to the first machine learning model, inputting the generated data into the first machine learning model, and receiving output from the first machine learning model. The output is provided in response to the request for data associated with the particular functionality of the application.

Abstract: Users of an endpoint remediation system can be assigned to different roles, from which they can request exceptions, approve exceptions, and/or enable remediation on endpoint devices. The compliance scanning and enforcing process can be automated, while allowing entities to request and/or approve certain exceptions. Therefore, security compliance for customers can be actively managed to provide visibility to the endpoint device compliance state at any time.

Abstract: A recommendation system includes: an input operation mode recognition unit configured to recognize a mode of an input operation from a user to an input device; a user mood estimation unit configured to estimate a mood of the user, based on the mode of the input operation; a proposal activity information acquisition unit configured to access an activity database, to extract a proposal activity suitable for the mood of the user, and to acquire information on the proposal activity; and an output control unit configured to allow an output device to output the information on the proposal activity, the output device being used by the user.

Abstract: An information processing apparatus includes a processor configured to cause a first region in which a sorted set, which is any of sets into which plural kinds of data have been sorted according to kind, is displayed and a second region in which data that is unsuitable for the sorted set is displayed to be displayed on an identical screen, receive selection of data displayed in the second region, and sort the received data into the sorted set displayed in the first region.