International Business Machines Corporation

International Business Machines Corporiation

Lior Limonad

Sergey Zeltyn

Nir Mashkif

Alexander Zadorojniy

Segev Eliezer Wasserkrug

Ari Volcoff

Asaf Adi

David Breitgand

Fabiana Fournier

Roy Abitbol

John Maxwell Cohn

Nirmit V Desai

Yishai Abraham Feldman

Ateret Anaby-Tavor

Michael Masin

Marc Ph Stoecklin

Aviad Sela

Guy Amit

Henry Broodney

Yael Dubinsky

Eyal Cohen

Ashraf Haib

Valentin Dashinsky

Moran Gavish

Dean Haber

Ashafaaq Minhas

Jonathan Muehlstein

Omer Arad

Skarbovsky Yudovich Inna

Yuval David

An example system includes a processor to receive a process discovery model generated based on a process event log using a process discovery process. The processor can generate a causal process model based on the process event log. The processor can generate a causal process discrepancy view based on a comparison of the process discovery model and the causal process model with respect to a causal execution pattern.

Causal process discovery and generation of causal process discrepancy views from process event logs

Providing a machine-generated explanation of an outcome of a process execution engine includes inputting into a complex event processing (CEP) engine at least one predetermined event pattern specification and a sequence of events, the sequence of events generated, at least in part, by a process execution engine executing a predetermined process in real time. Using the CEP engine, an enriched event log is generated based on the input. The enriched event log includes the sequence of events and additionally includes one or more situational events corresponding to one or more of the sequence of events. The one or more situational events are derived by the CEP engine based on the at least one predetermined event pattern specification. A hypothesis-oriented, situationally aware explanation for the outcome of the predetermined process is determined using an artificial intelligence (AI) explainability framework with input of the enriched event log.

Situation-aware explainability for artificial intelligence-enabled, process-aware systems using complex event processing

A method of determining a range between two devices, which can for instance be wireless devices, such as handheld devices. The method performs a double-sided two-way ranging protocol at the two devices. This protocol causes the two devices to transmit and receive signals, with a view to determining the range between the two devices, using a two-way ranging methods, where each of the signals is a composite tone generated as a composition of two waveforms that are timewise separated by a gap to form a bipolar waveform. The gap is a zero or low-amplitude signal, contrasting with the two waveforms. The particular pattern in the correlation footprint eases the determination of the time of flight. The method can be used to measure social distancing.

Double-sided two-way ranging protocol based on composite, ultrasonic tones

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

User authentication based on cognitive profiling. First, usage by a person of one or more computerized devices is automatically and continuously tracked. A cognitive profile of the person is automatically and continuously generated based on the tracked usage. Responsive to a request for authenticating an identity of the person, a cognitive question is automatically generated based on the cognitive profile, and is presented to a user professing to be the person. An answer to the cognitive question is received from the user. Finally, the identity of the person is automatically authenticated when the answer to the cognitive question is determined to be correct.

User authentication based on cognitive profiling

A method, apparatus and product for interaction context-based control of output volume level. The method comprising: obtaining a vocal input from a user, wherein the vocal input is part of an interaction between the user and the voice-based interaction agent; determining an interaction context of the interaction between the user and the voice-based interaction agent; determining an output volume level of the voice-based interaction agent based on the interaction context; and providing to the user an output of the voice-based interaction agent, wherein the output comprises a voice-based output having a volume level of the output volume level.

Interaction context-based control of output volume level

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

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 computer-implemented method, computerized apparatus and computer program product for supporting parallel user interaction with physical and modeled system. A computerized digital model representing a real world physical system is obtained. An indication of a physical component of the system being subject to engagement by a user is received. A model component corresponding to the physical component is automatically determined. An interaction of the user with the model component is controlled based on at least one of the model and data obtained through engagement with the physical component.

Parallel user interaction with physical and modeled system

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.

Physical-interactive IDS (P2D)

A method comprising: obtaining a first viewpoint representing a partial depiction of a system, wherein the first viewpoint comprises a property base, an instance base and a class base; obtaining a second viewpoint representing a partial depiction of the system, wherein the second viewpoint comprises a property base, an instance base and a class base, wherein the instance base of the second viewpoint is different than the instance base of the first viewpoint; creating a third viewpoint based on the first and second viewpoints, wherein the third viewpoint representing the system, wherein the third viewpoint comprises a property base, an instance base and a class base, wherein the instance base comprises instances defined by the instance base of the first viewpoint and instances defined by the instance base of the second viewpoint.

Representing a system using viewpoints

Physical interactive IDs (P2D)

Data storage management by determining, for leaf and summary storage spaces of a data storage space hierarchy having at least two leaf storage spaces descending from at least one summary storage space, an invariant leaf attribute value for each leaf attribute type, an invariant summary attribute value for each descending leaf attribute type as a sum of the invariant leaf attribute values of all leaves descending from the summary storage space, and for each leaf, a variable leaf attribute value for each leaf attribute type, and, for each summary storage space, a variable summary attribute value for each descending leaf attribute type, where for each summary storage space, and for each storage space immediately descending from the summary storage space, each variable leaf attribute value of the immediately descending storage space is expressed as a proportion of the variable summary attribute value for the same attribute type.

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Nesher, Israel

Lior Limonad