# US 2013/0158854 A1 · Navigation System > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2013/0158854 A1 - Document ID: US20130158854A1 - Status: Published application - Kind: A1 - Application number: 13/328,756 - Filed: 2011-12-16 - Published: 2013-06-20 - Earliest claimed: 2011-12-16 - Assignees: Toyota Infotechnology Center Co., Ltd.; Toyota Jidosha Kabushiki Kaisha - Inventors: David Frank Russell Weir; Kentaro Oguchi; Roger Melen - Web page: https://patentgrep.com/patent/US20130158854A1 ## CPC - G01C 21/3617: {using user history, behaviour, conditions or preferences, e.g. predicted or inferred from previous use or current movement} - G08G 1/0112: {from the vehicle, e.g. floating car data [FCD]} - G08G 1/0129: {for creating historical data or processing based on historical data} - G08G 1/096827: {where the route is computed onboard} - G08G 1/096838: {where the user preferences are taken into account or the user selects one route out of a plurality} - H04W 4/024: Guidance services - H04W 4/029: Location-based management or tracking services - H04W 4/21: for social networking applications - 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) - 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}) - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) ## Abstract A system and method for estimating journey destinations is disclosed. The system comprises a driving history module, a frequency module, a duration module, a direction module, a metric module and a quality module. The driving history module retrieves a set of learning parameters including driver history data describing one or more past journeys. The frequency module analyzes the learning parameters to determine a candidate set including data describing frequent start locations and frequent end locations for one or more potential journeys to one or more destinations. The duration module estimates journey duration data for the one or more potential journeys. The direction module estimates direction data describing one or more directions for the one or more potential journeys. The metric module determines one or more metrics for the one or more potential journeys. The quality module determines one or more quality scores for the one or more potential journeys. ## Claims ### Claim 1 A method for a navigation system comprising: retrieving, with one or more processors, a set of learning parameters including driver history data describing one or more past journeys; analyzing, with the one or more processors, the set of learning parameters to determine a candidate set including data describing frequent start locations and frequent end locations for one or more potential journeys to one or more destinations; measuring, with the one or more processors, a current time of day; estimating, with the one or more processors, based at least in part on the current time of day, a set of journey duration data for the one or more potential journeys described by the candidate set; segmenting, with the one or more processors, the journey duration data into non-uniform segments; estimating, with the one or more processors, direction data describing one or more directions for the one or more potential journeys to the one or more destinations; determining, with the one or more processors, one or more metrics for the one or more potential journeys using a metric estimation network that indicates one or more associations between the one or more destinations and one or more journey variables; determining, with the one or more processors, one or more quality scores for the one or more potential journeys based at least in part on the one or more metrics; and storing, with the one or more processors, the journey duration data in a memory accessible by one or more system modules; and deleting, with the one or more processors, destination data describing the one or more destinations and the one or more journey variables associated with the one or more destinations based on a trigger event triggering data deletion. ### Claim 2 (depends on 1) The method of claim 1, wherein the set of learning parameters include converted driver history data that describes the one or more past journeys. ### Claim 3 (depends on 1) The method of claim 1, wherein the set of learning parameters are arranged in a learning table. ### Claim 4 (depends on 1) The method of claim 1, wherein the candidate set describes three potential journeys. ### Claim 5 (depends on 1) The method of claim 1, wherein different quality scores are associated with different potential journeys. ### Claim 6 (depends on 1) The method of claim 1 further comprising ordering the one or more potential journeys in the candidate set based at least in part on the one or more quality scores. ### Claim 7 (depends on 1) The method of claim 1 further comprising outputting the candidate set and the one or more quality scores to a learning system. ### Claim 8 A computer program product comprising a non-transitory computer readable medium encoding instructions that, in response to execution by a computing device, cause the computing device to perform operations comprising: retrieving a set of learning parameters including driver history data describing one or more past journeys; analyzing the set of learning parameters to determine a candidate set including data describing frequent start locations and frequent end locations for one or more potential journeys to one or more destinations; measuring a current time of day; estimating, based at least in part on the current time of day, a set of journey duration data for the one or more potential journeys described by the candidate set; segmenting the journey duration data into non-uniform segments; estimating direction data describing one or more directions for the one or more potential journeys to the one or more destinations; determining one or more metrics for the one or more potential journeys using a metric estimation network that indicates one or more associations between the one or more destinations and one or more journey variables; determining one or more quality scores for the one or more potential journeys based at least in part on the one or more metrics; storing the journey duration data in a memory accessible by one or more system modules; and deleting destination data describing the one or more destinations and the one or more journey variables associated with the one or more destinations based on a trigger event triggering data deletion. ### Claim 9 (depends on 8) The computer program product of claim 8, wherein the set of learning parameters include converted driver history data that describes the one or more past journeys. ### Claim 10 (depends on 8) The computer program product of claim 8, wherein the set of learning parameters are arranged in a learning table. ### Claim 11 (depends on 8) The computer program product of claim 8, wherein the candidate set describes three potential journeys. ### Claim 12 (depends on 8) The computer program product of claim 8, wherein different quality scores are associated with different potential journeys. ### Claim 13 (depends on 8) The computer program product of claim 8, wherein the instructions cause the computing device to perform operations further comprising ordering the one or more potential journeys in the candidate set based at least in part on the one or more quality scores. ### Claim 14 (depends on 8) The computer program product of claim 8, wherein the instructions cause the computing device to perform operations further comprising outputting the candidate set and the one or more quality scores to a learning system. ### Claim 15 A navigation system comprising: a non-transitory computer-readable medium storing computer-executable code, the computer-readable medium comprising: a driving history module retrieving a set of learning parameters including driver history data describing one or more past journeys; a frequency module communicatively coupled to the driving history module, the frequency module analyzing the set of learning parameters to determine a candidate set including data describing frequent start locations and frequent end locations for one or more potential journeys to one or more destinations; a timestamp generator measuring a current time of day; a duration module communicatively coupled to the frequency module and the timestamp generator, the duration module estimating, based at least in part on the current time of day, a set of journey duration data for the one or more potential journeys described by the candidate set, the duration module storing the journey duration data in a memory accessible by one or more system modules; a conversion module communicatively coupled to the duration module, the conversion module segmenting the journey duration data into non-uniform segments; a direction module communicatively coupled to the frequency module, the direction module estimating direction data describing one or more directions for the one or more potential journeys to the one or more destinations; a metric module communicatively coupled to the frequency module, the duration module, the conversion module and the direction module, the metric module determining one or more metrics for the one or more potential journeys using a metric estimation network that indicates one or more associations between the one or more destinations and one or more journey variables; and a quality module communicatively coupled to the metric module, the quality module determining one or more quality scores for the one or more potential journeys based at least in part on the one or more metrics; a forgetting module communicatively coupled to the direction module and the metric module, the forgetting module deleting destination data describing the one or more destinations and the one or more journey variables associated with the one or more destinations based on a trigger event triggering data deletion. ### Claim 16 (depends on 15) The system of claim 15, wherein the set of learning parameters include converted driver history data that describes the one or more past journeys. ### Claim 17 (depends on 15) The system of claim 15, wherein the set of learning parameters are arranged in a learning table. ### Claim 18 (depends on 15) The system of claim 15, wherein the candidate set describes three potential journeys. ### Claim 19 (depends on 15) The system of claim 15, wherein different quality scores are associated with different potential journeys. ### Claim 20 (depends on 15) The system of claim 15, wherein the quality module is further configured to order the one or more potential journeys in the candidate set based at least in part on the one or more quality scores. ### Claim 21 (depends on 15) The system of claim 15 further comprising: an output module communicatively coupled to the quality module, the output module outputting the candidate set and the one or more quality scores to a learning system. ### Claim 22 (depends on 1) The method of claim 1, wherein the one or more journey variables include one or more of the current time of day, the journey duration data and the direction data. ### Claim 23 (depends on 1) The method of claim 1, wherein each of the one or more associations indicates an occurring probability of one of the one or more destinations given one of the one or more journey variables. ### Claim 24 A method for a navigation system comprising: retrieving, with one or more processors, a set of learning parameters including driver history data describing one or more past journeys; analyzing, with the one or more processors, the set of learning parameters to determine a candidate set including data describing frequent start locations and frequent end locations for one or more potential journeys to one or more destinations; measuring, with the one or more processors, a current time of day; estimating with the one or more processors, based at least in part on the current time of day, a set of journey duration data for the one or more potential journeys described by the candidate set; segmenting, with the one or more processors, the journey duration data into non-uniform segments; estimating, with the one or more processors, direction data describing one or more directions for the one or more potential journeys to the one or more destinations; determining, with the one or more processors, one or more metrics for the one or more potential journeys based at least in part on the non-uniformly segmented journey duration data; determining, with the one or more processors, one or more quality scores for the one or more potential journeys based at least in part on the one or more metrics; storing, with the one or more processors, the journey duration data in a memory accessible by one or more system modules; and deleting, with the one or more processors, destination data describing the one or more destinations and the one or more journey variables associated with the one or more destinations based on a trigger event triggering data deletion.