Abstract: A method is provided for prioritizing rule creation for computer-assisted customer care. When a question from a user of a device is received by a customer care analytics engine for which no rule is automatically fired, the question and a related device profile of the device are added to an unfired questions list. The analytics engine parses the question to match terms in other questions in the list. The question is also added to an appropriate category in the list based on the device profile. A prioritization algorithm is used to rank the question among other questions within the list or the category, ranking the question more highly according to the frequency of those terms in the category or the list. According to its rank-wise order, the question is directed for creation of a rule to permit automatic handling of questions having the same or similar terms in the future.

Abstract: A neuromorphic processing device has a device input, for receiving an input data signal, and an assemblage of neuron circuits. Each neuron circuit comprises a resistive memory cell which is arranged to store a neuron state, indicated by cell resistance, and to receive neuron input signals for programming cell resistance to vary the neuron state, and a neuron output circuit for supplying a neuron output signal in response to cell resistance traversing a threshold. The device includes an input signal generator, connected to the device input and the assemblage of neuron circuits, for generating neuron input signals for the assemblage in dependence on the input data signal. The device further includes a device output circuit, connected to neuron output circuits of the assemblage, for producing a device output signal dependent on neuron output signals of the assemblage, whereby the processing device exploits stochasticity of resistive memory cells of the assemblage.

Abstract: Systems and methods are provided for performing predictive assignments pertaining to genetic information. One embodiment is a system that includes a genetic prediction server. The genetic prediction server includes an interface that acquires records that each indicate one or more genetic variants determined to exist within an individual, and a controller. The controller selects one or more machine learning models that utilize the genetic variants as input, and loads the machine learning models. For each individual in the records: the controller predictively assigns at least one characteristic to that individual by operating the machine learning models based on at least one genetic variant indicated in the records for that individual. The controller also generates a report indicating at least one predictively assigned characteristic for at least one individual, and transmits a command via the interface for presenting the report at a display.

Abstract: An “Engagement Predictor” provides various techniques for predicting whether things and concepts (i.e., “nuggets”) in content will be engaging or interesting to a user in arbitrary content being consumed by the user. More specifically, the Engagement Predictor provides a notion of interestingness, i.e., an interestingness score, of a nugget on a page that is grounded in observable behavior during content consumption. This interestingness score is determined by evaluating arbitrary documents using a learned transition model. Training of the transition model combines web browsing log data and latent semantic features in training data (i.e., source and destination documents) automatically derived by a Joint Topic Transition (JTT) Model. The interestingness scores are then used for highlighting one or more nuggets, inserting one or more hyperlinks relating to one or more nuggets, importing content relating to one or more nuggets, predicting user clicks, etc.

Abstract: A method, system and computer-usable medium for performing cognitive computing operations comprising receiving streams of data from a plurality of data sources; processing the streams of data from the plurality of data sources, the processing the streams of data from the plurality of data sources performing data enriching for incorporation into a cognitive graph; defining a travel-related cognitive persona within the cognitive graph, the travel-related cognitive persona corresponding to an archetype user model, the travel-related cognitive persona comprising a set of nodes in the cognitive graph, links among the set of nodes being weighted to provide a weighted cognitive graph; associating a user with the travel-related cognitive persona; and, performing a cognitive computing operation based upon the travel-related cognitive persona associated with the user.

Abstract: Systems, methods, and media for generating sanitized data, sanitizing anomaly detection models, and generating anomaly detection models are provided. In some embodiments, methods for sanitizing anomaly detection models are provided. The methods including: receiving at least one abnormal anomaly detection model from at least one remote location; comparing at least one of the at least one abnormal anomaly detection model to a local normal detection model to produce a common set of features common to both the at least one abnormal anomaly detection model and the local normal detection model; and generating a sanitized normal anomaly detection model by removing the common set of features from the local normal detection model.

Abstract: A complex event processing system comprises one or more rule engines configured to receive information from a source system via a message broker. Multiple rule engines may be used in parallel, with the same/different rules deployed. According to an embodiment, a rule engine may include a manager component, a proxy component, a reasoner component, and a working memory. The manager and proxy serve as interfaces with the message broker to allow asynchronous communication with a provider storing state information. The reasoner is configured to execute rules based upon occurrence of events in the source system. Embodiments may be particularly suited to implementing a gamification platform including a business entity provider, with an existing business source system (e.g. CRM, ERP).

Abstract: Methods to provide selective supporting evidence processing by applying a first machine learning (ML) model to a first candidate answer to generate a first confidence score that does not consider supporting evidence for the first candidate answer, determining, from a second ML model, an expected contribution of processing supporting evidence for the first candidate answer, and upon determining that the expected contribution does not exceed a specified threshold, skipping supporting evidence processing for the first candidate answer.

Abstract: In a mobile device, the text entered by users is analyzed to determine a set of responses commonly entered by users into text applications such as SMS applications in response to received messages. This set of responses is used to provide suggested responses to a user for a currently received message in a soft input panel based on the text of the currently received message. The suggested responses are provided before any characters are provided by the user. After the user provides one or more characters, the suggested responses in the soft input panel are updated. The number of suggested responses displayed to the user in the soft input panel is limited to a total confidence value to reduce user distraction and to allow for easier selection. An undo feature for inadvertent selections of suggested responses is also provided.

Abstract: Systems and computer program products to provide selective supporting evidence processing by applying a first machine learning (ML) model to a first candidate answer to generate a first confidence score that does not consider supporting evidence for the first candidate answer, determining, from a second ML model, an expected contribution of processing supporting evidence for the first candidate answer, and upon determining that the expected contribution does not exceed a specified threshold, skipping supporting evidence processing for the first candidate answer.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a factorization model to learning features of model inputs of a trained model such that the factorization model is predictive of outcome for which the machine learned model is trained.

Abstract: A monitor-mine-manage cycle is described, for example, for managing a data center, a manufacturing process, an engineering process or other processes. In various example, the following steps are performed as a continuous automated loop: receiving raw events from an observed system; monitoring the raw events and transforming them into complex events; mining the complex events and reasoning on results; making a set of proposed actions based on the mining; and managing the observed system by applying one or more of the proposed actions to the system. In various examples, the continuous automated loop proceeds while raw events are continuously received from the observed system and monitored. In some examples an application programming interface is described comprising programming statements which allow a user to implement a monitor-mine-manage loop.

Abstract: A method of digitizing a well log includes identifying automatically with a computational circuitry a legend and at least one graph of the well log; determining based on a machine learning a parameter of the at least one graph indicated in the legend; and digitizing the at least one graph based on the parameter.

Abstract: A method of digitizing a well log includes identifying automatically with a computational circuitry a legend and at least one graph of the well log; determining based on a machine learning a parameter of the at least one graph indicated in the legend; and digitizing the at least one graph based on the parameter.

Abstract: A method of digitizing a well log includes identifying automatically with a computational circuitry a legend and at least one graph of the well log; determining based on a machine learning a parameter of the at least one graph indicated in the legend; and digitizing the at least one graph based on the parameter.

Abstract: A method of digitizing a well log includes identifying automatically with a computational circuitry a legend and at least one graph of the well log; determining based on a machine learning a parameter of the at least one graph indicated in the legend; and digitizing the at least one graph based on the parameter.

Abstract: A method, system and computer-usable medium for performing cognitive computing operations comprising receiving streams of data from a plurality of data sources; processing the streams of data from the plurality of data sources, the processing the streams of data from the plurality of data sources performing data enriching for incorporation into a cognitive graph; defining a travel-related cognitive persona within the cognitive graph, the travel-related cognitive persona corresponding to an archetype user model, the travel-related cognitive persona comprising a set of nodes in the cognitive graph; associating a user with the travel-related cognitive persona; defining a travel-related cognitive profile within the cognitive graph, the travel-related cognitive profile comprising an instance of the travel-related cognitive persona that references personal data associated with the user; associating the user with the travel-related cognitive profile; and, performing a cognitive computing operation based upon the tra

Abstract: Machines can be controlled using advanced control systems. Such control systems may use an automated version of singular spectrum analysis to control a machine. For example, a control system can perform singular spectrum analysis on a time series by: generating a trajectory matrix from the time series, performing singular value decomposition on the trajectory matrix to determine elementary matrices and corresponding eigenvalues, and automatically categorizing the elementary matrices into groups. The elementary matrices can be automatically categorized into the groups by: generating a matrix of w-correlation values based on the eigenvalues, categorizing the w-correlation values into a predefined number of w-correlation sets, and forming the groups based on the predefined number of w-correlation sets. The control system can then determine component time-series based on the groups, and generate a predictive forecast using the component time-series.

Abstract: Computer systems, machine-implemented methods, and stored instructions are provided for minimizing an approximate global error in an artificial neural network that is configured to predict model outputs based at least in part on one or more model inputs. A model manager stores the artificial neural network model. The model manager may then minimize an approximate global error in the artificial neural network model at least in part by causing evaluation of a mixed integer linear program that determines weights between artificial neurons in the artificial neural network model. The mixed integer linear program accounts for piecewise linear activation functions for artificial neurons in the artificial neural network model. The mixed integer linear program comprises a functional expression of a difference between actual data and modeled data, and a set of one or more constraints that reference variables in the functional expression.

Abstract: A method, system and computer-usable medium for performing cognitive computing operations comprising receiving streams of data from a plurality of data sources; processing the streams of data from the plurality of data sources, the processing the streams of data from the plurality of data sources performing data enriching for incorporation into a cognitive graph; defining a cognitive persona within the cognitive graph, the cognitive persona corresponding to an archetype user model, the cognitive persona comprising a set of nodes in the cognitive graph; associating a user with the cognitive persona; defining a cognitive profile within the cognitive graph, the cognitive profile comprising an instance of the cognitive persona that references personal data associated with the user; associating the user with the cognitive profile; and, performing a cognitive computing operation based upon the cognitive profile associated with the user.