International Business Machines Corporation

University College Dublin

Yassine Lassoued

Julien Monteil

Martin Mevissen

Giovanni Russo

Rodrigo Hernan Ordonez-Hurtado

Robert Shorten

Claudio Gambella

Jonathan P Epperlein

Wynita M Griggs

Jakub Marecek

Sergiy Zhuk

Lea Deleris

Anton Dekusar

Elizabeth Daly

Yufang Hou

Charles Arthur Jochim

Stephane Deparis

Sergio Cabrero Barros

Vinod A Valecha

Bei Chen

Jeffrey L Rogers

Radu Marinescu

Emanuele Ragnoli

Killian Levacher

Bo Wen

Joao H Bettencourt-Silva

Andrea Simonetto

Nigel Hinds

Carlos A Alzate Perez

Francesca Bonin

Kostas Katrinis

Ulrike Fischer

Wynita May Griggs

Jonathan Peter Epperlein

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

Embodiments for establishing trust in commerce negotiations are provided. Information associated with negotiation actions of each of a plurality of entities is stored in a shared ledger. A trustworthiness score is calculated for at least one of the plurality of entities based on the information associated with the negotiation actions of the at least one of the plurality of entities. The calculated trustworthiness score is provided to at least one other of the plurality of entities.

Methods and systems for establishing trust in commerce negotiations

Distributing and rebalancing crewless vehicles (CV) by receiving a CV demand-availability gap, identifying candidate CV to relocate to close the CV demand-availability gap, identifying a candidate transport to relocate the CV to close the CV demand-availability gap, generating a candidate CV relocation plan, optimizing an overall CV relocation plan, and executing the overall CV relocation plan.

Rebalancing autonomous vehicles according to last-mile delivery demand

Embodiments for providing intelligent transportation service management in a transportation system by a processor. Transportation service requests may be assigned amongst multiple transportation service providers according to one or more transportation service request distribution models and various parameters and preferences for each user. The transportation service request distribution models may protect information relation to each of the transportation service providers and suggests a selected order for distributing the plurality of transportation service requests.

Intelligent on-demand management of ride sharing in a transportation system

Generating parking area statistics for identifying parking areas within a geographic region. Navigation support for reaching a top-ranked parking area according to collected parking area data, user preferences, parking feedback, and real-time traffic conditions. Selection of a top-ranked parking area and monitoring progress during a parking event improve future identification and ranking of parking areas.

Identifying available parking areas

Embodiments for intelligent monitoring of a health state of a user by a processor. A health state of a user may be learned while engaged in one or more activities associated with a computing device. One or more mitigating actions may be identified and recommended to implement by the user to minimize one or more possible negative impacts upon the health state of the user while engaged in the one or more activities associated with the computing device.

Intelligent monitoring of a health state of a user engaged in operation of a computing device

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

Embodiments for implementing an intelligent health recommendation service by a processor. A health state of a user may be learned according to user behavior, physical conditions of the user, activities of daily living (ADL) and associated context of daily living (CDL) of the user, or a combination thereof. One or more applications, one or more application components, one or more internet of things (IoT) devices, or a combination thereof may be identified and recommended to minimize one or more possible negative impacts upon the health state of the user.

Intelligent health recommendation service

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

In an approach to predicting physiological and behavioral states utilizing models representing relationships between driver health states and vehicle dynamics data, one or more computer processors capture one or more vehicle motion parameters. The one or more computer processors to capture one or more physiological parameters; identify contextual data associated with the one or more captured vehicle motion parameters and the one or more captured physiological parameters; predict one or more driving behavior parameters by utilizing one or more physical models fed with the one or more vehicle motion parameters and the identified contextual data; predict one or more driver health parameters by utilizing a model trained with the one or more captured physiological parameters and the identified contextual data; generate a risk assessment based on the one or more predicted driving behavior parameters and the one or more predicted driver health parameters.

Mitigating risk behaviors

Embodiments for planning vehicular driving actions in the presence of non-recurrent events by a processor. One or more dynamics of non-recurrent events in a transport network may be learned according to one or more contextual factors. One or more vehicle-specific factors may be learned in relation to a historical journey and current journey of a vehicle. One or more action responses associated with the one or more non-recurrent events and the one or more vehicle-specific factors may be generated.

Vehicular driving actions in the presence of non-recurrent events

Embodiments for relationship graph expansion and extraction from a collection of unstructured text data by a processor. A query relating to one or more concepts may be received. The query may be expanded according to a logical reasoning operation and a domain ontology having a set of logical rules. A relationship graph between one or more concepts from a plurality of unstructured text data may be extracted based on an expanded query according to a domain ontology and the set of logical rules.

Logic-based relationship graph expansion and extraction

Various embodiments are provided for planning computational unit (e.g., virtual machine (VM)) migration based on vehicle mobility prediction in a computing environment by a processor. A computational unit handover operation may be dynamically planned associated with a vehicle using one or more predicted routes according to global positioning satellite ('GPS') data, contextual data, historical data, a maximum latency requirement for the vehicle, one or more load balancing requirements for an edge computing server.

Planning vehicle computational unit migration based on mobility prediction

Embodiments for co-reference resolution and entity linking from unstructured text data by a processor. Semantic co-references and mentions of one or more entities may be resolved occurring in unstructured text data by linking the one or more entities using a domain knowledge ontology.

Co-reference resolution and entity linking

Embodiments for utilizing vehicles to investigate events are provided. Spatiotemporal information is received from each of a plurality of vehicles. Each of the plurality of vehicles includes an onboard sensor. Information associated with an event is received. At least some of the plurality of vehicles are selected based on the information associated with the event and the spatiotemporal information from each of a plurality of vehicles. A request is caused to be transmitted to each of the selected at least some of the plurality of vehicles for data detected by the respective onboard sensor.

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Dublin, Ireland

Yassine Lassoued