# US 10,768,002 B2 · Assessing personalized risk for a user on a journey > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,768,002 B2 - Document ID: US10768002B2 - Status: Granted - Kind: B2 - Application number: 15/794,321 - Filed: 2017-10-26 - Published: 2019-05-02 - Granted: 2020-09-08 - Earliest claimed: 2017-10-26 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Jonathan Epperlein; Yassine Lassoued; Martin Mevissen; Julien Monteil; Giovanni Russo; Robert Shorten - Web page: https://patentgrep.com/patent/US10768002B2 ## CPC - G01C 21/3461: {Preferred or disfavoured areas, e.g. dangerous zones, toll or emission zones, intersections, manoeuvre types or segments such as motorways, toll roads or ferries} - G01C 21/3492: {employing speed data or traffic data, e.g. real-time or historical (traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968)} - G01C 21/3641: {Personalized guidance, e.g. limited guidance on previously travelled routes} - G01C 21/3697: {Output of additional, non-guidance related information, e.g. low fuel level (G01C21/3679 takes precedence)} - G06Q 10/04: Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) - G06Q 10/0635: Risk analysis of enterprise or organisation activities - G06Q 10/0639: Performance analysis of employees; Performance analysis of enterprise or organisation operations - G06Q 50/00: Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism (healthcare informatics G16H) - G06Q 50/14: Travel agencies - G08G 1/0141: {for traffic information dissemination} - G08G 1/096827: {where the route is computed onboard} - G01C 21/3469: {Fuel consumption; Energy use; Emission aspects} - G01C 21/3484: {Personalized, e.g. from learned user behaviour or user-defined profiles} - G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S) - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G08G: TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; arrangement of road signs or traffic signals E01F9/00; radar or analogous systems, sonar systems, lidar systems specially adapted for traffic control G01S13/91, G01S15/88, G01S17/88; {radar or analogous systems, sonar systems, lidar systems specially adapted for anti-collision purposes G01S13/93, G01S15/93, G01S17/93}) ## Abstract 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. ## Claims ### Claim 1 A method for learning and predicting personalized risk associated with a journey for a user by a processor, comprising: analyzing contextual data gathered from a plurality of data sources relating to a journey of a user; learning one or more risks associated with the journey according to the contextual data; generating one or more risk models associated with the one or more risks; wherein generating the one or more risk models includes detecting an anomaly including a deviation between an expected route and an actual route taken by the user, and collecting feedback information from the user regarding the anomaly for determining the one or more risks and a cause of the one or more risks, the feedback information collected by cognitively interacting with the user via a cognitive component such that the user explains the feedback information, including a reasoning for the anomaly, to the cognitive component using natural language dialog; and predicting one or more potential risks associated with a subsequent journey using the one or more risk models; wherein the one or more potential risks are automatically presented to the user via a computing device associated with the processor. ### Claim 2 (depends on 1) The method of claim 1, further including monitoring the journey, the user, one or more alternative users associated with the journey, or combination thereof to collect the contextual data, wherein the contextual data includes traffic data, weather data, road conditions, behavior of the user, a health state of the operator, biometric data of the user, sensor data, learned behavioral data of the user in relation to the journey, learned behavioral data of the one or more alternative users, risks shared from the one or more alternative users, or a combination thereof. ### Claim 3 (depends on 1) The method of claim 1, wherein the detecting of the anomaly includes: comparing current behavior of the user with previously observed behavior of the user on the journey to detect the anomaly; comparing current behavior of the user with behavior of one or more alternative users on the journey to detect the anomaly; or comparing the current behavior of the user with defined, standardized behavior. ### Claim 4 (depends on 1) The method of claim 1, further including matching the anomaly with one or more causes of the anomaly from a list of previously received information relevant to the journey. ### Claim 5 (depends on 1) The method of claim 1, further including learning the one or more risks or the one or more risk models from the user, a plurality of collaborative users on the journey, a machine learning operation, or a combination thereof. ### Claim 6 (depends on 1) The method of claim 1, further including initializing a machine learning mechanism to learn behavior of the user on the journey and the one or more risk models, wherein the plurality of data sources includes at least sensor based devices associated with the user or vehicle, wearable sensors, camera devices, data sources relating to Internet of Things (IoT) computing networks, governmental entities, commercial entities, or combination thereof. ### Claim 7 (depends on 1) The method of claim 1, further including: monitoring the contextual data and each event associated with the journey; and detecting the one or more risks associated with the journey, the one or more potential risks associated with the subsequent journey, or combination thereof according to the contextual data, one or more identified events, one or more risk models, or combination thereof. ### Claim 8 A system for learning and predicting personalized risk associated with a journey for a user, comprising: one or more computers with executable instructions that when executed cause the system to: analyze contextual data gathered from a plurality of data sources relating to a journey of a user; learn one or more risks associated with the journey according to the contextual data; generate one or more risk models associated with the one or more risks; wherein generating the one or more risk models includes detecting an anomaly including a deviation between an expected route and an actual route taken by the user, and collecting feedback information from the user regarding the anomaly for determining the one or more risks and a cause of the one or more risks, the feedback information collected by cognitively interacting with the user via a cognitive component such that the user explains the feedback information, including a reasoning for the anomaly, to the cognitive component using natural language dialog; and predict one or more potential risks associated with a subsequent journey using the one or more risk models; wherein the one or more potential risks are automatically presented to the user via one of the one or more computers associated with the executable instructions. ### Claim 9 (depends on 8) The system of claim 8, wherein the executable instructions further monitor the journey, the user, one or more alternative users associated with the journey, or combination thereof to collect the contextual data, wherein the contextual data includes traffic data, weather data, road conditions, behavior of the user, a health state of the operator, biometric data of the user, sensor data, learned behavioral data of the user in relation to the journey, learned behavioral data of the one or more alternative users, risks shared from the one or more alternative users, or a combination thereof. ### Claim 10 (depends on 8) The system of claim 8, wherein the detecting of the anomaly includes: comparing current behavior of the user with previously observed behavior of the user on the journey to detect the anomaly; comparing current behavior of the user with behavior of one or more alternative users on the journey to detect the anomaly; or comparing the current behavior of the user with defined, standardized behavior. ### Claim 11 (depends on 8) The system of claim 8, wherein the executable instructions further match the anomaly with one or more causes of the anomaly from a list of previously received information relevant to the journey. ### Claim 12 (depends on 8) The system of claim 8, wherein the executable instructions further: learn the one or more risks or the one or more risk models from the user, a plurality of collaborative users on the journey, a machine learning operation, or a combination thereof; initialize the machine learning mechanism to learn behavior of the user on the journey and one or more risk models, wherein the plurality of data sources includes at least sensor based devices associated with the user or vehicle, wearable sensors, camera devices, data sources relating to Internet of Things (IoT) computing networks, governmental entities, commercial entities, or combination thereof; monitor the contextual data and each event associated with the journey; or detect the one or more risks associated with the journey, the one or more potential risks associated with the subsequent journey, or combination thereof according to the contextual data, one or more identified events, one or more risk models, or combination thereof. ### Claim 13 A computer program product for, by a processor, learning and predicting personalized risk associated with a journey for a user, the computer program product comprising a non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising: an executable portion that analyzes contextual data gathered from a plurality of data sources relating to a journey of a user; an executable portion that learns one or more risks associated with the journey according to the contextual data; an executable portion that generates one or more risk models associated with the one or more risks; wherein generating the one or more risk models includes detecting an anomaly including a deviation between an expected route and an actual route taken by the user, and collecting feedback information from the user regarding the anomaly for determining the one or more risks and a cause of the one or more risks, the feedback information collected by cognitively interacting with the user via a cognitive component such that the user explains the feedback information, including a reasoning for the anomaly, to the cognitive component using natural language dialog; and an executable portion that predicts one or more potential risks associated with a subsequent journey using the one or more risk models; wherein the one or more potential risks are automatically presented to the user via a computing device associated with the processor. ### Claim 14 (depends on 13) The computer program product of claim 13, further including an executable portion that monitors the journey, the user, one or more alternative users associated with the journey, or combination thereof to collect the contextual data, wherein the contextual data includes traffic data, weather data, road conditions, behavior of the user, a health state of the operator, biometric data of the user, sensor data, learned behavioral data of the user in relation to the journey, learned behavioral data of the one or more alternative users, risks shared from the one or more alternative users, or a combination thereof. ### Claim 15 (depends on 13) The computer program product of claim 13, wherein the detecting of the anomaly includes: comparing current behavior of the user with previously observed behavior of the user on the journey to detect the anomaly; comparing current behavior of the user with behavior of one or more alternative users on the journey to detect the anomaly; or comparing the current behavior of the user with defined, standardized behavior. ### Claim 16 (depends on 13) The computer program product of claim 13, further including an executable portion that matches the anomaly with one or more causes of the anomaly from a list of previously received information relevant to the journey. ### Claim 17 (depends on 13) The computer program product of claim 13, further including an executable portion that: learns the one or more risk models from the user, a plurality of collaborative users on the journey, a machine learning operation, or a combination thereof; or initializes a machine learning mechanism to learn behavior of the user on the journey and one or more risk models, wherein the plurality of data sources includes at least sensor based devices associated with the user or vehicle, wearable sensors, camera devices, data sources relating to Internet of Things (IoT) computing networks, governmental entities, commercial entities, or combination thereof; monitors the contextual data and each event associated with the journey; or detects the one or more risks associated with the journey, the one or more potential risks associated with the subsequent journey, or combination thereof according to the contextual data, one or more identified events, one or more risk models, or combination thereof.