International Business Machines Corporation

International Business Machines Corporiation

Segev Eliezer Wasserkrug

Sergey Zeltyn

Alexander Zadorojniy

Lior Limonad

Michael Masin

Nir Mashkif

Amit Fisher

Daniel Rose

Dagan Gilat

Eyal Cohen

Erez L M Bilgory

Dany Moshkovich

Opher Etzion

Michael Katz

Yossi Shiloach

Vladimir Lipets

Alexey Tsitkin

Gal A Shachor

Yishai Abraham Feldman

Murray Robert Cantor

Pnina A Vortman

Asaf Adi

Ofer Biran

Evgeny Shindin

Mike A Marin

Aviad Sela

Ofer Shir

Ariel Landau

Dolev Dotan

Natalia Razinkov

Tirtsa Hochberg

Sateesh S Kannegala

Gil Rapaport

Michael Massin

Sarel Aiber

Michael Sambur

Eitan Israeli

Lena Granovsky

Method, apparatus and product for utilizing stochastic controller to provide user-controlled notification rate of wearable-based events. The method comprises obtaining events issued by a module based on analysis of multiple sensor readings of one or more sensors of a wearable device. The method further comprises determining by a stochastic controller whether to provide an alert to a user based on the events and based on a user preference, wherein the user preference is indicative of a desired notification rate of the user, wherein the stochastic controller comprises a stochastic model of an environment. Based on such determination, alerts are outputted to the user.

Utilizing stochastic controller to provide user-controlled notification rate of wearable-based events

A system for generating a statistical model for fault diagnosis comprising at least one hardware processor, adapted to: extract a plurality of structured values, each associated with at least one of a plurality of semantic entities of a semantic model or at least one of a plurality of semantic relationships of the semantic model, from structured historical information organized in an identified structure and related to at least some of a plurality of historical events, the semantic model represents an ontology of an identified diagnosis domain, each of the plurality of semantic entities relates to at least one of a plurality of domain entities existing in the identified diagnosis domain, and each of the plurality of semantic relationships connects two of the plurality of semantic entities and represents a parent-child relationship therebetween; extract a plurality of unstructured values, each associated with at least one of the plurality of semantic entities.

System and method for semantics based probabilistic fault diagnosis

A computer implemented method of routing a verbal input to one of a plurality of handlers, comprising using one or more processors adapted to execute a code, the code is adapted for receiving a verbal input from a user, applying a plurality of verbal content identifiers to the verbal input, each of the verbal content identifiers is adapted to evaluate an association of the verbal input with a respective one of a plurality of handlers by computing a match confidence value for one or more features, such as an intent expressed by the user and/or an entity indicated by the user, extracted from the verbal input and routing the verbal input to a selected one of the handlers based on the matching confidence value computed by the plurality of verbal content identifiers. The selected handler is adapted to initiate one or more actions in response to the verbal input.

Creating modular conversations using implicit routing

Embodiments of the present invention may provide the capability to detect complex events while providing improved detection and performance. In an embodiment of the present invention, a method for detecting an event may comprise receiving data representing measurement or detection of physical parameters, conditions, or actions, quantizing the received data and selecting a number of samples from the quantized data, generating a hidden Markov model representing events to be detected using initial model values based on ideal conditions, wherein a desired output is defined as a sequence of states, and wherein a number of states of the hidden Markov model is less than or equal to the number of samples of the quantized data, adjusting the quantized data and the initial model values to improve accuracy of the model, determining a state sequence of the hidden Markov model, and outputting an indication of a detected event.

Method for complex events detection using hidden markov models

According to some embodiments of the present invention there is provided a method for determining a control action in a control system using a Markov decision process. The method comprises an action of receiving two or more predefined transition probability values of a Markov decision process (MDP) of a control system, where each of the predefined transition probability values is associated with a transition between a first state and a second state, both from two or more system states, resulting from execution of one or more control actions of the control system. The method comprises an action of computing one or more new transition probability values by an analysis of the predefined transition probability values, the system states and the control actions. The method comprises an action of determining one or more recommended control actions for the respective system state based on the new transition probability value.

Interpolation of transition probability values in Markov decision processes

According to some embodiments of the present invention there is provided a method for determining a control action in a control system using a Markov decision process. The method comprises an action of receiving measured transition probability values of a Markov decision process (MDP) and receiving simulated transition probability values generated by performing a control system simulation. New transition probability values are computed by calculating a measured data count of some of the sensor measurements and a simulated data count of some of the simulated transition data. New transition probability values are computed from a weighted average between the measured transition probability values and the simulated transition probability values using the measured data count and the simulated data count. A new control action is determined based on the one or more new transition probability value.

Hybrid estimation of transition probability values in markov decision processes

A computer-implemented method, computerized apparatus and computer program product, the method comprising: obtaining data measured by one or more sensors; segmenting the data into a plurality of sliding windows; extracting one or more features from each of the plurality of sliding windows; analyzing, by a machine learning process, the extracted features to determine, for each sliding window, an activity detection in the sliding window; and determining an activity detection result in the data to be positive responsive to activity detection by the machine learning process in at least a number M of sliding windows out of a number N of consecutive sliding windows, wherein M&#x3e;1.

Optimizing performance of event detection by sensor data analytics

A method comprising obtaining a scheduling problem comprising: a set of agents and a set of tasks to be performed by the set of agents, wherein solving the scheduling problem using an automated solver is not feasible using available predetermined resources. The method comprises automatically generating a plurality of alternative scheduling problems, wherein a solution to each such alternative scheduling problem defines a solution to the scheduling problem and determining a solution to the scheduling problem by applying the automated solver to solve, while using the available predetermined resources, an alternative problem of the plurality of alternative scheduling problems to determine a solution to the alternative problem and by mapping the solution to the alternative problem to the scheduling problem, whereby determining the solution.

Automatic solution to a scheduling problem

A computer implemented method, a computerized system and a computer program product for anomaly classification. The computer implemented method comprises obtaining a data set, wherein the data set comprises a plurality of data points. The method further comprises filtering the data set based on an absolute distance criterion and performing anomaly classification on a test data point of the data set, wherein the anomaly classification is based on a relative density criterion. The method further comprises outputting an outcome of the anomaly classification.

Hybrid method for anomaly classification

A method for wastewater treatment that comprises receiving influent readings from sensors located along influent stream(s) of a wastewater treatment unit, effluent readings from sensors located along effluent stream(s) of the wastewater treatment unit, a feedback flow variable calculated according to a state of a feedback flow channel between an effluent output and an influent input, analyzing the influent readings and the effluent readings to extract an influent flow variable, a total nitrogen at effluent variable and a total phosphorus at effluent variable, and calculating control instructions to control the wastewater treatment unit by assigning a combination of a cost variable reflecting a treatment cost for treating the influent stream(s), a time period, the influent flow variable, the total nitrogen at effluent variable, the total phosphorus at effluent variable, and the feedback flow variable in a state space of the wastewater treatment unit.

Concise state model for operational optimization of wastewater treatment unit(s)

A computer program product comprising a non-transitory computer readable storage medium retaining program instructions configured to cause a processor to perform actions, which program instructions implement: a framework for creating a model of an NP-hard problem, the model comprising at least one entity selected from the group comprising: an objective, a variable, an equation and a constraint, wherein the framework provides methods for automatically transforming the model, comprising: one or more methods for manipulating or changing a status of the entity of the model, the methods comprising a method for imposing or ignoring the constraint; and one or more methods related to operations to be applied to the entity of the model.

Reusable modeling for solving problems

A method for determining a variable near-optimal policy for a problem formulated as Markov Decision Process, the problem comprising at least one limited action entry, the limited action entry being an entry of an action of a finite set of actions limited in the number of times its value may be changed, the method comprising using at least one hardware processor for: receiving data elements with respect to the problem, the data elements comprising: (a) a finite set of states, (b) the finite set of actions, (c) a transition probabilities matrix determining transition probabilities between states of the finite set of states, once actions of the set of actions are performed; (d) an immediate cost function, wherein the value of the immediate cost function is determined for a pair of a state of the finite set of states and an action of the finite set of actions, and (e) a discount factor; updating one or more data elements of the received data elements relating to the at least one limited action entry, wherein the one or more data elements are selected from the group consisting of: the transition probabilities matrix, the immediate cost function and the discount factor, and wherein the updating is triggered by a change of a value of a limited action entry of the at least one limited action entry; and following the updating of the one or more data elements, calculating a current near-optimal policy for the problem based on the updated one or more data elements.

Determining variable optimal policy for an MDP paradigm

A computerized method for scalable management optimization of pressurized water distribution networks, comprises receiving a network model representing a pressurized water distribution physical network having water flow variables. The network model is simulated by solving non-linear mathematical equations representing the behavior of the water flow variables. One or more result sets are fed to the non-linear mathematical equations. The non-linear mathematical equations are linearized. Network model optimization requirements are received from a user. The linearized mathematical equations are optimized according to the received network model optimization requirements. A local search starting from the at least one optimized solution is performed using the non-linearized mathematical equations, thereby generating a solution.

Pressurized water distribution network management

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Haifa, Israel

Segev Eliezer Wasserkrug