International Business Machines Corporation

International Business Machines Corporiation

Sergey Zeltyn

Alexander Zadorojniy

Segev Eliezer Wasserkrug

Lior Limonad

Nir Mashkif

Asaf Adi

Ari Volcoff

Yishai Abraham Feldman

David Breitgand

Avi Yaeli

Michael Masin

Eliezer Segev Wasserkrug

Evgeny Shindin

Oded Dubovsky

Murray Robert Cantor

Ateret Anaby-Tavor

Mike A Marin

Amit Fisher

Dagan Gilat

Ashraf Haib

Sateesh S Kannegala

Alexey Tsitkin

Moran Gavish

Disclosed herein are methods and systems for generating automatically transactional dialog flows for a virtual assistant based on a set of predefined (labeled) transactional flows defining user interaction paths to accomplish one or more tasks. The virtual assistant is represented by a tree structure comprising a plurality of dialog nodes where each transactional flow comprises a respective subset of the nodes descending from a parent node to one or more completion nodes. New transactional flows may be generated automatically based on similarity with the predefined transactional flows, by selecting a candidate node and one of the predefined transactional flows comprising a completion node most similar to the candidate completion node and further selecting a candidate parent node most similar to the parent node of the selected predefined transactional flow. The new transactional flows may be then generated to comprise all descendant nodes of the candidate parent node.

Automatic flow generation for virtual assistants

An example system includes a processor that can receive conversation logs of a dialogue system to be analyzed. The processor can train a predictive machine learning model using a training set of the conversation logs on a selected feature to obtain feature values with associated importance values. The processor can select a number of feature values using a significance score calculated based on the associated importance values. The processor can generate an interactive user interface including the selected number of feature values.

Extracting and selecting feature values from conversation logs of dialogue systems using predictive machine learning models

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

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

Constructing a MARS prediction model using predictor variables at a first point in time within a time horizon, including directly-controllable variables of first physical characteristics of a system and that are associated with adjustable operational control settings for directly controlling the first physical characteristic, and including controllable variables of second physical characteristics that are affected by the first physical characteristics, recursively using the prediction model to define an optimization problem for later point in time within the time horizon, transforming the optimization problem into a MILP problem, and solving the MILP problem using an optimization engine to determine, for any given one of the directly-controllable variables and corresponding to at least one of the points in time, for adjusting, using the optimized value, the adjustable operational control setting corresponding to the given directly-controllable variable and thereby control the physical characteristic associated with the directly-controllable variable.

Control system with optimized periodic adjustments of system control settings using MARS-based MILP optimization

A computer implemented method of detecting complex user activities, comprising using processor(s) in each of a plurality of consecutive time intervals for: obtaining sensory data from wearable inertial sensor(s) worn by a user, computing an action score for continuous physical action(s) performed by the user, the continuous physical action(s) extending over multiple time intervals are indicated by repetitive motion pattern(s) identified by analyzing the sensory data, computing a gesture score for brief gesture(s) performed by the user, the brief gesture(s) bounded in a single basic time interval is identified by analyzing the sensory data, aggregating the action and gesture scores to produce an interval activity score of predefined activity(s) for a current time interval, adding the interval activity score to a cumulative activity score accumulated during a predefined number of preceding time intervals and identifying the predefined activity(s) when the cumulative activity score exceeds a predefined threshold.

Detecting complex user activities using ensemble machine learning over inertial sensors data

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

A computer-implemented method, computerized apparatus and computer program product for activity recognition using adaptive window size segmentation of sensor data stream. A data stream generated by one or more sensors is obtained. A frequency analysis of the data in a first segment of the data stream is performed. A size of a second segment is determined based on the frequency analysis. Activity recognition is performed for the second segment by extracting one or more features of the data therein and applying a machine learning process on the extracted features to obtain a classification of the data into an activity class.

Adaptive window size segmentation for activity recognition

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 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)

Embodiments of the present invention may provide the capability to identify a specific object being interacted with that may be cheaply and easily included in mass-produced objects. In an embodiment, a computer-implemented method for object identification may comprise receiving a signal produced by a physical interaction with an object to be identified, the signal produced by an identification structure coupled to the object during physical interaction with the object, processing the signal to form digital data identifying the object, and accessing a database using the digital data to retrieve additional information identifying or describing properties of the object identified.

Growing community of inventors

Haifa, Israel

Sergey Zeltyn