International Business Machines Corporation

University College Dublin

Nationaluniversity of Ireland, Maynooth

Robert Shorten

Sergiy Zhuk

Randall Lee Cogill

Rudi Verago

Giovanni Russo

Tigran Tchrakian

Martin Mevissen

Martin M Rufli

Zubair Nabi

Chungmok Lee

Fabian Wirth

Jakub Marecek

Yassine Lassoued

Julien Monteil

Jonathan P Epperlein

Olivier Gallay

Rodrigo Hernan Ordonez-Hurtado

Wynita M Griggs

Claudio Gambella

Joe Naoum-Sawaya

Paulito Pedregosa Palmes

Fabian R Wirth

Freddy Lecue

Douglas Leith

Anton Dekusar

Jia Yuan Yu

Bissan Ghaddar

Julia O'Connell

Eleni Pratsini

Barry Cardiff

Pietro Ferraro

Paul Dermot Cuffe

Wynita May Griggs

Jonathan Peter Epperlein

Embodiments for identifying stochastic models representing the individual decision makers in a computing environment by a processor. One or more non-deterministic (stochastic, probabilistic) models may be identified according to a sequence of outcomes from decisions of each of a plurality of decision makers.

Identification of non-deterministic models of multiple decision makers

Provided are an apparatus and system for directing power flow between multiple devices, the apparatus comprising: one or more inlet ports for connection to one or more devices; a plurality of outlet ports configured for supplying electrical power; and a computing device configured to route power from the one or more inlet ports to the outlet ports. The system comprises a plurality of the apparatus connected to each other.

Apparatus for directing power flow between multiple devices

Embodiments for implementing intelligent risk detection and mitigation in a transport network by a processor. Data gathered from a plurality of data sources relating to an entity and a selected region of interest may be analyzed. Behavior of an entity, in relation to a risk event, may be learned and interpreted based on a plurality of identified contextual factors, geographical data, current data, historical data, a learned risk event model, or a combination thereof. One or more mitigation actions may be performed to mitigate risk of occurrence or a possible negative impact of the risk event caused at least in part by the behavior of the entity.

Detecting and mitigating risk in a transport network

In an approach for determining the illumination necessary along a vehicle's planned trajectory, a processor receives information relevant to the trajectory of a vehicle. A processor estimates the position of the vehicle at the particular time on the trajectory of the vehicle, using the data from the sensors. A processor estimates an illumination of the trajectory of the vehicle, using the model of the illumination of the road network and the data from the sensors, wherein a threshold is employed. A processor determines the threshold is exceeded. A processor changes a setting of the at least one headlight on the vehicle. A processor determines the threshold is exceeded. A processor alerts the user of the vehicle when adjustments to the at least one headlight that are necessary to illuminate the trajectory of the vehicle exceed physical limitations of the at least one headlight.

Adaptive headlights for the trajectory of a vehicle

A system and method for cognitive journey monitoring are presented. Embodiments comprise journey prediction, parsing of data sources, risk assessment and mitigation, and natural-language user interaction by a cognitive processor. Data is gathered from a plurality of data sources and analyzed in the context of one or more of the user's intention(s). A dialogue with the user, in natural language, aims to provide and select one or more suggestions relating to the one or more user intention(s) such that the risk(s) relating to the one or more user's intention(s) is reduced. During the dialogue, cognitive reasoning may be performed, wherein the cognitive reasoning includes the ability to justify each suggestion and the ability to infer information from the interaction such as, for example, data obtained in a dialogue may inform subsequent inferences. The embodiments may use speech synthesis and speech recognition in an interactive spoken dialogue.

Cognitive journey companion system

A method for forecasting time delays added to a scheduled start time and a scheduled end time of a task includes generating a stochastic model of the task and resources affecting the task, the stochastic model includes a reactionary delay component that is a function of previous task end times and a root cause delay component that is an independent random process at a specific time. The method further includes: calculating a probability distribution of time delays added to the scheduled start time as a combination of the reactionary delay component and the root cause delay component using the stochastic model to provide a probability distribution of start times; and calculating a probability distribution of time delays added to the scheduled end time as a combination of the reactionary delay component and the root cause delay component using the stochastic model to provide a probability distribution of end times.

Disruption forecasting in complex schedules

Embodiments for testing embedded systems and their applications in an Internet of Things (IoT) environment by a processor, denoted as a Hardware-in-the-Loop as a Service (HiLaaS). In a simulated environment, one or more simulated entities and one or more real entities in a networked system may be tested in real-time according to received control parameters, for a price. The price is estimated by the system, based on other parameters, and offered to the user to accept or reject. Alternatively, the user may specify the price, the system estimates control parameters, and the user can accept or reject the control parameters. One or more properties of the one or more entities, the network system, or combination thereof may be estimated based on the testing of the one or more simulated entities, when the price and control parameters are accepted.

Testing embedded systems and application using hardware-in-the-loop as a service (HILAAS)

Embodiments for improving travel mobility as a service (MaaS) by one or more processors. A selected mode of transportation may be matched with a selected route to generate a travel package according to a multi-objective model based on a route profile for a plurality of routes, a user profile of the one or more users, an environmental profile, and a collaboration of monitored data relating to preferences of a mode of transportation and routes of the one or more users, wherein the travel package includes at least the matching selected mode of transportation, the selected route, and one or more travel suggestions.

Travel mobility as a service (MaaS)

A system and method for learning and predicting personalized risk associated with a journey for a user are presented. Contextual data may be gathered and analyzed from a plurality of data sources relating to a journey of a user. A risk associated with the journey may be learned for the user according to the contextual data. One or more risk models may be generated according to the learned risks. One or more potential risks associated with a subsequent journey may be predicted using the one or more risk models.

Assessing personalized risk for a user on a journey

Charging of a mobile device in an ad hoc parked vehicle charging system. A plurality of mobile device users and associated mobile devices are registered in an ad hoc parked vehicle charging system with a charging profile. A plurality of charging vehicles are also registered in the system with a charge supply profile. A mobile device user and associated mobile device are authenticated as a registered user in the system. A plurality of the registered charging vehicles parked within a preset distance range of the authenticated user and associated mobile device are located and one of the parked registered charging vehicles is selected based on an energy allocation criteria. A charging connection is established between the authenticated mobile device and the selected parked vehicle and charging of the mobile device is automatically controlled according to the user's charging profile, the vehicle's charge supply profile and the energy allocation criteria.

System for charging mobile device using an ad-hoc infrastructure with energy harvesting capabilities

A system and method for cognitive risk mitigation are presented. Embodiments comprise journey prediction, parsing of data sources, risk assessment and mitigation, and natural-language user interaction by a cognitive processor. Data is gathered from a plurality of data sources and analyzed in the context of one or more of the user's intention(s). A dialogue with the user, in natural language, aims to provide and select one or more suggestions relating to the one or more user intention(s) such that the risk(s) relating to the one or more user's intention(s) is reduced. During the dialogue, cognitive reasoning may be performed, wherein the cognitive reasoning includes the ability to justify each suggestion and the ability to infer information from the interaction such as, for example, data obtained in a dialogue may inform subsequent inferences. The embodiments may use speech synthesis and speech recognition in an interactive spoken dialogue.

Aspects of the present invention include methods, systems and computer program products. The method includes a detecting, by a processor, a presence of a user at a location. The method further includes detecting, by the processor, a presence of one or more sensors at the location, where the one or more sensors are communicatively coupled together and to the processor. The method further includes determining, by the processor, that the user can be sensed by the one or more sensors at the location. The method further includes notifying, by the processor, the user that a selectable personalized service is available to the user at the location. The method further includes providing, by the processor, the selectable personalized service to the user at the location, where the selectable personalized service notifies the user as to an existence of any anomalies with respect to the user.

Personalized location-based services for users

Embodiments for driver confidence evaluation while operating a vehicle by a processor. Driver confidence, based upon one or more psychophysical parameters of a driver, is combined and correlated with a plurality of environmental factors and route information. A confidence in vehicular operational capabilities of the driver may be detected according to the correlation.

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Robert Shorten