# US 10,482,559 B2 · Personalizing ride experience based on contextual ride usage data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,482,559 B2 - Document ID: US10482559B2 - Status: Granted - Kind: B2 - Application number: 15/349,648 - Filed: 2016-11-11 - Published: 2018-05-17 - Granted: 2019-11-19 - Earliest claimed: 2016-11-11 - Assignees: Uatc, LLC - Applicants: Uatc, LLC - Inventors: Nemanja Djuric; Vladan Radosavljevic - Web page: https://patentgrep.com/patent/US10482559B2 ## CPC - G06Q 50/40: Business processes related to the transportation industry (shipping G06Q10/083) - G06Q 10/063112: {Skill-based matching of a person or a group to a task} - 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 ## Abstract A transport system can manage an on-demand transportation service to connect available vehicles with users, and can compile ride history data for each user. The ride history data can indicate the contextual usage of the on-demand transportation service by the user. Based on the ride history data, the transport system can determine demographic and personal interest information of the respective user. The transport system may then personalize one or more ride characteristics of any ride requested by the user based on the demographic and personal interest information determined from the ride history of the user. ## Claims ### Claim 1 A transport system comprising: a communications interface; one or more processors; and one or more memory resources storing instructions that, when executed by the one or more processors, cause the one or more processors to: compile ride history data for each respective user of an on-demand transportation service, the ride history data comprising contextual usage of the on-demand transportation service by the respective user; based on the ride history data, determine demographic and personal interest information of the respective user; receive, via the communications interface over one or more networks, a pick-up request from the respective user; transmit, using the communications interface, a transport invitation to an available vehicle to provide a current ride for the respective user to service the pick-up request; personalize one or more ride characteristics of the current ride based on the demographic and personal interest information of the respective user, the personalization of one or more ride characteristics comprises transmitting personalized content to the available vehicle for display on a display screen of the available vehicle; receive, via the communications interface over one or more networks and after a current ride begins, information indicative of an amount of the personalized content displayed during the current ride; and provide a discount for the current ride to an account associated with the respective user based, at least in part, on the amount of the personalized content displayed during the current ride. ### Claim 2 (depends on 1) The transport system of claim 1, wherein the executed instructions further cause the one or more processors to: provide a selectable feature on the at least one of a display screen of the available vehicle or a display screen of the user device, the selectable feature enabling the respective user to select from (i) viewing the personalized content to discount the ride, or (ii) provide the ride with no personalized content. ### Claim 3 (depends on 1) The transport system of claim 1, wherein the information indicative of the amount of the personalized content displayed during the current ride includes information indicative of at least one of a consumption of or an interaction with the personalized content during the current ride; wherein the executed instruction cause the one or more processors to further discount the ride based on the monitored level of consumption of or interaction with the personalized content. ### Claim 4 (depends on 3) The transport system of claim 3, wherein the information indicative of the amount of the personalized content displayed during the current ride is based at least in part on one or more of perception data or interaction data associated with the respective user. ### Claim 5 (depends on 4) The transport system of claim 4, wherein the perception data is generated from a content monitor. ### Claim 6 (depends on 1) The transport system of claim 1, wherein the executed instructions further cause the one or more processors to: identify a pick-up location and a destination from the pick-up request; wherein the executed instructions cause the one or more processors to personalize the one or more ride characteristics by personalizing a route between the pick-up location and the destination. ### Claim 7 (depends on 1) The transport system of claim 1, wherein the ride history data indicates drop-off locations of the respective user, and wherein the executed instructions cause the one or more processors determine the demographic and personal interest information of the respective user based, at least in part, on the drop-off locations. ### Claim 8 (depends on 1) The transport system of claim 1, wherein the instructions comprise a machine learning model that, when executed, causes the one or more processors to determine the demographic information of the respective user based, at least in part, on the contextual usage of the on-demand transport service by the respective user. ### Claim 9 (depends on 8) The transport system of claim 8, wherein the demographic information comprises at least an estimated age and sex of the respective user. ### Claim 10 (depends on 8) The transport system of claim 8, wherein the pick-up request is received from a user device executing a rider application specific to the on-demand transportation service managed by the transport system, and wherein the executed machine learning model further causes the one or more processors to: monitor accelerometer data received over the one or more networks from the user device; and based on the accelerometer data, construct a gait profile corresponding to walking characteristics of the respective user; wherein the executed machine learning model causes the one or more processors to further determine the demographic information of the respective user based on the constructed gait profile. ### Claim 11 (depends on 8) The transport system of claim 8, wherein the executed machine learning model further causes the one or more processors to: analyze the ride history data for the users of the on-demand transportation service in order to cluster the users of the on-demand transportation service based on at least one of common demographics or common personal interests; and for each specified cluster, establish a set of parameters that provides each user in the specified cluster with ride experience characteristics specific to the specified cluster. ### Claim 12 (depends on 11) The transport system of claim 11, wherein the set of parameters specify content to be displayed to users in the specified cluster. ### Claim 13 (depends on 11) The transport system of claim 11, wherein the set of parameters specify one or more vehicle types to be selected for the users in the specified cluster. ### Claim 14 (depends on 13) The transport system of claim 13, wherein the one or more vehicle types comprise at least one of a standard vehicle, a luxury vehicle, a high capacity vehicle, a sport utility vehicle, a specific vehicle brand, or a self-driving vehicle. ### Claim 15 (depends on 11) The transport system of claim 11, wherein the set of parameters specify one or more ride service types to be selected for the users in the specified cluster. ### Claim 16 (depends on 15) The transport system of claim 15, wherein the one or more ride service types comprise one or more of a carpooling service, a standard ride-sharing service, a professional driver service, a black car service, a luxury ride service, a high capacity vehicle service, or a self-driving vehicle service. ### Claim 17 (depends on 11) The transport system of claim 11, wherein the executed machine learning model further causes the one or more processors to: classify the respective user into one or more clusters; wherein the executed machine learning model causes the one or more processors to personalize the one or more ride characteristics of the ride based on the classification of the respective user in each of the one or more clusters. ### Claim 18 (depends on 11) The transport system of claim 11, wherein the executed machine learning model clusters the users, based on at least one of common demographics or common personal interests, by representing each of the users as a vector comprising a series of values indicative of each cluster in which the user is classified. ### Claim 19 A computer-implemented method of personalizing ride experience, the method being performed by one or more processors and comprising: compiling ride history data for each respective user of an on-demand transportation service, the ride history data comprising contextual usage of the on-demand transportation service by the respective user; based on the ride history data, determining demographic and personal interest information of the respective user; receiving, via the communications interface over one or more networks, a pick-up request from the respective user; transmitting, using the communications interface, a transport invitation to an available vehicle to provide a current ride for the respective user to service the pick-up request; personalizing one or more ride characteristics of the current ride based on the demographic and personal interest information of the respective user, the personalization of one or more ride characteristics comprises transmitting personalized content to the available vehicle for display on a display screen of the available vehicle; receiving, via the communications interface over one or more networks and after a current ride begins, information indicative of an amount of the personalized content displayed during the current ride; and providing a discount for the current ride to an account associated with the respective user based, at least in part, on the amount of the personalized content displayed during the current ride. ### Claim 20 A non-transitory computer readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to: compile ride history data for each respective user of an on-demand transportation service, the ride history data comprising contextual usage of the on-demand transportation service by the respective user; based on the ride history data, determine demographic and personal interest information of the respective user; receive, via the communications interface over one or more networks, a pick-up request from the respective user; transmit, using the communications interface, a transport invitation to an available vehicle to provide a current ride for the respective user to service the pick-up request; personalize one or more ride characteristics of the current ride based on the demographic and personal interest information of the respective user, the personalization of one or more ride characteristics comprises transmitting personalized content to the available vehicle for display on a display screen of the available vehicle; receive, via the communications interface over one or more networks and after a current ride begins, information indicative of an amount of the personalized content displayed during the current ride; and provide a discount for the current ride to an account associated with the respective user based, at least in part, on the amount of the personalized content displayed during the current ride.