# US 9,519,881 B2 · Estimating journey destination based on popularity factors > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 9,519,881 B2 - Document ID: US9519881B2 - Status: Granted - Kind: B2 - Application number: 14/059,407 - Filed: 2013-10-21 - Published: 2015-04-23 - Granted: 2016-12-13 - Earliest claimed: 2013-10-21 - Assignees: Toyota Jidosha Kabushiki Kaisha - Applicants: Toyota Jidosha Kabushiki Kaisha - Inventors: David Frank Russell Weir; Roger Melen - Web page: https://patentgrep.com/patent/US9519881B2 ## CPC - G06Q 10/08355: {Routing methods} - G01C 21/3476: {using point of interest [POI] information, e.g. a route passing visible POIs} - 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 - 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) ## Abstract The disclosure includes technology for estimating journey destinations based on crow-sourced popularity factors. The technology includes an example system including a processor and a memory storing instructions that when executed cause the system to: receive location data; determine a current route associated with a user based on the location data; determine one or more crowd-sourced popularity factors; estimate one or more destination estimations along the current route based on the one or more crowd-sourced popularity factors; and suggest the one or more destination estimations to the user. ## Claims ### Claim 1 A computer-implemented networked method comprising: receiving location data from one or more of a geolocation determination device and a computing device configured to provide a location-related information signal; determining a current route associated with a user based on the location data; determining a set of potential destinations; determining one or more crowd-sourced popularity factors; clustering one or more popular destinations from the potential destinations of the set into two or more different destination regions along the current route based on the one or more crowd-sourced popularity factors; determining that the user is traveling toward a first destination region from among the two or more destination regions; estimating one or more refined destinations within the first destination region based on the one or more crowd-sourced popularity factors; and suggesting the one or more refined destinations to the user. ### Claim 2 (depends on 1) The method of claim 1, wherein determining the one or more crowd-sourced popularity factors comprises: receiving data describing the set of potential destinations; and determining one or more of an overall popularity for each potential destination, a variable popularity for each potential destination, a destination saturation level for each potential destination, and a schedule popularity for each potential destination based on schedule data associated with the user. ### Claim 3 (depends on 2) The method of claim 2, wherein the overall popularity for a particular potential destination included in the set of potential destinations is determined based at least in part on a number of total visits to the particular potential destination in comparison to other potential destinations included in the set. ### Claim 4 (depends on 2) The method of claim 2, wherein the variable popularity for a particular potential destination in the set of potential destinations is determined based at least in part on one or more of a time of day, a day of week, a weather condition, a date, and a schedule of events. ### Claim 5 (depends on 2) The method of claim 2, wherein the destination saturation level for a particular potential destination in the set of potential destinations is determined based at least in part on a comparison between a total number of people possibly to be accommodated at the particular potential destination and an actual number of people at the particular potential destination. ### Claim 6 (depends on 2) The method of claim 2, further comprising: determining a popularity score for each potential destination in the set of potential destinations based at least in part on one or more of the overall popularity, the variable popularity, the destination saturation level, and the schedule popularity, wherein clustering the one or more popular destinations from the potential destinations of the set in to the two or more different destinations regions along the current route is based on the popularity score corresponding to each of the one or more popular destinations of each of the two or more different destination regions. ### Claim 7 A computer program product comprising a non-transitory computer usable medium including a computer readable program, wherein the computer readable program when executed on a computer causes the computer to: receive location data from one or more of a geolocation determination device and a computing device configured to provide a location-related information signal; determine a set of potential destinations; determine a current route associated with a user based on the location data; determine one or more crowd-sourced popularity factors; cluster one or more popular destinations from the potential destinations of the set into two or more different destination regions along the current route based on the one or more crowd-sourced popularity factors; and determine that the user is traveling toward a first destination region from among the two or more destination regions; estimate one or more refined destinations within the first destination region based on the one or more crowd-sourced popularity factors; and suggest the one or more refined destinations to the user. ### Claim 8 (depends on 7) The computer program product of claim 7, wherein determining the one or more crowd-sourced popularity factors comprises: receiving data describing the set of potential destinations; and determining one or more of an overall popularity for each potential destination, a variable popularity for each potential destination, a destination saturation level for each potential destination, and a schedule popularity for each potential destination based on schedule data associated with the user. ### Claim 9 (depends on 8) The computer program product of claim 8, wherein the overall popularity for a particular potential destination included in the set of potential destinations is determined based on a number of total visits to the particular potential destination in comparison to other potential destinations included in the set. ### Claim 10 (depends on 8) The computer program product of claim 8, wherein the variable popularity for a particular potential destination in the set of potential destinations is determined based at least in part on one or more of a time of day, a day of week, a weather condition, a date, and a schedule of events. ### Claim 11 (depends on 8) The computer program product of claim 8, wherein the destination saturation level for a particular potential destination in the set of potential destinations is determined based at least in part on a comparison between a total number of people possibly to be accommodated at the particular potential destination and an actual number of people at the particular potential destination. ### Claim 12 (depends on 8) The computer program product of claim 8, further comprising: determining a popularity score for each potential destination in the set of potential destinations based at least in part on one or more of the overall popularity, the variable popularity, the destination saturation level, and the schedule popularity, wherein clustering the one or more popular destinations from the potential destinations of the set in to the two or more different destinations regions along the current route is based on the popularity score corresponding to each of the one or more popular destinations of each of the two or more different destination regions. ### Claim 13 A system comprising: a processor; and a memory storing instructions that, when executed, cause the system to: receive location data from one or more of a geolocation determination device and a computing device configured to provide a location-related information signal; determine a set of potential destinations; determine a current route associated with a user based on the location data; determine one or more crowd-sourced popularity factors; cluster one or more popular destinations from the potential destinations of the set into two or more different destination regions along the current route based on the one or more crowd-sourced popularity factors; determine that the user is traveling toward a first destination region from among the two or more destination regions; estimate one or more refined destinations within the first destination region based on the one or more crowd-sourced popularity factors; and suggest the one or more refined destinations to the user. ### Claim 14 (depends on 13) The system of claim 13, wherein the instructions when executed cause the system to determine the one or more crowd-sourced popularity factors by: receiving data describing the set of potential destinations; and determining one or more of an overall popularity for each potential destination, a variable popularity for each potential destination, a destination saturation level for each potential destination, and a schedule popularity for each potential destination based on schedule data associated with the user. ### Claim 15 (depends on 14) The system of claim 14, wherein the overall popularity for a particular potential destination included in the set of potential destinations is determined based on a number of total visits to the particular potential destination in comparison to other potential destinations included in the set. ### Claim 16 (depends on 14) The system of claim 14, wherein the variable popularity for a particular potential destination in the set of potential destinations is determined based at least in part on one or more of a time of day, a day of week, a weather condition, a date, and a schedule of events. ### Claim 17 (depends on 14) The system of claim 14, wherein the destination saturation level for a particular potential destination in the set of potential destinations is determined based at least in part on a comparison between a total number of people possibly to be accommodated at the particular potential destination and an actual number of people at the particular potential destination. ### Claim 18 (depends on 14) The system of claim 14, wherein the instructions when executed cause the system also: determining a popularity score for each potential destination in the set of potential destinations based at least in part on one or more of the overall popularity, the variable popularity, the destination saturation level, and the schedule popularity, wherein clustering the one or more popular destinations from the potential destinations of the set in to the two or more different destinations regions along the current route is based on the popularity score corresponding to each of the one or more popular destinations of each of the two or more different destination regions.