WO2011102541A1 - Dispositif d'extraction de caractéristiques de comportement, système d'extraction de caractéristiques de comportement, procédé d'extraction de caractéristiques de comportement et programme d'extraction de caractéristiques de comportement - Google Patents

Dispositif d'extraction de caractéristiques de comportement, système d'extraction de caractéristiques de comportement, procédé d'extraction de caractéristiques de comportement et programme d'extraction de caractéristiques de comportement Download PDF

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Publication number
WO2011102541A1
WO2011102541A1 PCT/JP2011/054063 JP2011054063W WO2011102541A1 WO 2011102541 A1 WO2011102541 A1 WO 2011102541A1 JP 2011054063 W JP2011054063 W JP 2011054063W WO 2011102541 A1 WO2011102541 A1 WO 2011102541A1
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Prior art keywords
point
stay
residence
days
staying
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PCT/JP2011/054063
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English (en)
Japanese (ja)
Inventor
岳夫 大野
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日本電気株式会社
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Priority to JP2012500698A priority Critical patent/JPWO2011102541A1/ja
Publication of WO2011102541A1 publication Critical patent/WO2011102541A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/335—Filtering based on additional data, e.g. user or group profiles
    • G06F16/337—Profile generation, learning or modification

Definitions

  • the present invention relates to a behavior feature extraction device, a behavior feature extraction system, a behavior feature extraction method, and a behavior feature extraction program that extract feature information of user behavior.
  • Patent Literature 1 discloses a situation estimation device that defines a situation transition model of a behavioral state (sleeping, working, going out, etc.) and estimating a user's current situation from position information and time information. Yes.
  • Patent Literature 2 Patent Literature 3
  • Patent Literature 4 disclose a method of automatically extracting such behavior feature information of the user instead of the user inputting it.
  • the behavior history analysis apparatus of Patent Literature 2 estimates a user's private-related point and business-related point based on a staying time zone, a noise level, and an illuminance level at the staying place of the user.
  • the characteristic extraction device of Patent Document 3 analyzes the visit history data of a moving body for each place according to the characteristic extraction rule, and extracts a frequently visited place or a home place.
  • the destination prediction apparatus of Patent Document 4 accumulates the movement history of a moving body and determines the home based on the frequency of arrival and the staying time.
  • Patent Document 5 discloses a navigation device that accumulates arrival and departure histories of vehicles and detects changes in home positions based on changes in the accumulation frequency of arrival and departure histories for home positions.
  • Patent Document 6 discloses a destination prediction device that acquires boarding / alighting data of a vehicle and detects a permanent place based on variations in boarding time.
  • the behavior history analysis device of Patent Document 2 described above classifies whether the staying place is a private related point or a business related point based on the illuminance level and the noise level, and further, the staying time exists in a specific time zone. Depending on whether or not the classification result is corrected.
  • the behavior history analysis apparatus of Patent Document 2 in order to classify whether the staying place is a private-related point or a business-related point, information on illuminance and noise other than position information, and a user's working hours There was a problem of needing information about.
  • Patent Document 3 and Patent Document 4 described above a method for extracting a home position based on a history of position information of a moving object is disclosed, but a method for extracting a work position is not disclosed.
  • One object of the present invention is to extract a home feature and a work location that characterize a user's behavior from a location information history without using environmental information such as illuminance and noise and information about the user's working hours.
  • a behavior feature extraction system a behavior feature extraction method, and a behavior feature extraction program.
  • the behavior feature extraction apparatus stores behavior point information that includes each residence point of a plurality of residences of a user, a residence start date and time at the residence point, and a residence end date and time at the residence point. And a staying day for each of the plurality of staying points based on the staying point information of the means and the behavior type storage means, and among the plurality of staying points, the staying point having the most staying days is set at home. It is extracted as a stay point, and is provided with behavior feature extraction means for extracting the stay point having the second most stay days among the plurality of stay points as a workplace stay point.
  • the behavior feature extraction system includes a terminal having a position information acquisition unit that acquires a positioning point indicating a position of a user together with a positioning date and time, and outputs position information including the positioning point and the positioning date and time, Based on the position information, the behavior of extracting the residence point information including each residence point of the plurality of residences of the user, the residence start date and time at the residence point, and the residence end date and time at the residence point, and storing them in the behavior type storage unit
  • a behavior type extraction device having a type extraction means; and the stay point information is acquired from the behavior type extraction device, and based on the stay point information, a staying day is calculated for each of the plurality of stay points, Among the stay points, the stay point with the most stay days is extracted as a home stay point, and the stay point with the second stay days is the second place among the plurality of stay points.
  • the behavior feature extraction method includes a plurality of residence points including a residence point of each of a plurality of residences of the user, a residence start date and time at the residence point, and a residence end date and time at the residence point.
  • the residence days are calculated for each of the residence points, and the residence point with the largest residence days is extracted as the home residence point among the plurality of residence points, and the residence days is 2 among the plurality of residence points.
  • the second most frequent residence point is extracted as a workplace residence point.
  • the computer-readable recording medium stores, in a computer, residence point information including each residence point of a plurality of residences of a user, a residence start date and time at the residence point, and a residence end date and time at the residence point.
  • the stay days are calculated, and among the plurality of stay points, the stay point having the most stay days is extracted as a home stay point, and among the plurality of stay points,
  • An action feature extraction program for executing a process of extracting the stay point having the second most stay days as a workplace stay point is stored.
  • the effect of the present invention is that the home position and the work position characterizing the user's behavior can be extracted from the position information history without using environmental information such as illuminance and noise and information on the user's working hours.
  • behavior type information (residence point information) and a living behavior model in the embodiment of the present invention will be described.
  • the terminal 100 that moves together with the user periodically acquires position information.
  • the behavior type extraction device 200 extracts the stay point information as the user's behavior type information based on the acquired position information.
  • the staying point information is information regarding a staying point (a staying place) where the user has visited and stayed, and includes a staying point identifier and a staying date and time.
  • the user's life behavior model is defined as follows. It is considered that a user who has a general social life stays in a specific place for a certain time or more almost every day for sleeping.
  • the stay point with the most stay days indicates the user's home. Further, it is considered that the user stays at a specific place several times a week for a certain time or more for work, school work or the like. There are various time forms such as day shift, night shift, shift work, full time, part time, short-term part-time job, etc., in the embodiment of the present invention. Estimate the staying point with many days as the workplace where the user commute. In addition, when a user is a student, it is good also as a school where a user goes to school at a staying point with the longest staying day after home.
  • the number of staying days is not counted in units of one day in the calendar, but is counted as one day until 24 hours have elapsed from the time when the staying started in the place for the first time in one day. For example, if you start staying at 9 pm and end your stay the next day at 8 am, the staying end time (8 am the next day) is within 24 hours from the staying start time (9 pm). One day. Also, if you go home at 9:00 pm and leave the house the next day at 10 pm, the staying end time (10 pm the next day) is over 24 hours from the staying start time (9 pm), so the staying days are 2 It will be a day. Moreover, even if it stays at the same staying point many times during 24 hours, the staying days are 1 day.
  • FIG. 2 is a block diagram showing a configuration of the behavior feature extraction system according to the first embodiment of the present invention.
  • the behavior feature extraction system includes a terminal 100, a behavior type extraction device 200, a behavior feature extraction device 300, and a behavior feature reference device 400.
  • the terminal 100 and the behavior type extraction device 200, the behavior type extraction device 200 and the behavior feature extraction device 300, and the behavior feature extraction device 300 and the behavior feature reference device 400 are connected by a network (not shown) and can communicate with each other.
  • the terminal 100 is an information terminal that can move with the user.
  • the terminal 100 is a mobile phone terminal.
  • the terminal 100 may be an information terminal such as a PDA (Personal Data Assistant), a personal computer, or a car navigation system terminal, instead of a mobile phone terminal.
  • a plurality of terminals 100 may be provided.
  • the terminal 100 acquires the position information of the terminal 100 and outputs it as position information data 111.
  • the terminal 100 includes a position information acquisition unit 101.
  • the position information acquisition unit 101 acquires position information of the terminal 100 by GPS (Global Positioning System).
  • the terminal 100 includes an antenna and receives radio waves transmitted from a GPS satellite (not shown).
  • the position information acquisition unit 101 calculates the position (positioning point) of the terminal 100 based on the radio wave received from the satellite. Further, the position information acquisition unit 101 calculates a positioning point and simultaneously acquires a positioning time. The positioning point calculated by the position information acquisition unit 101 is the position of the terminal 100. The position information acquisition unit 101 acquires the positioning point from the information on the installation position of the reader attached to the reader of an RFID (Radio Frequency IDentification) installed in a specific place (store, etc.), for example, instead of the GPS. May be. Further, the position information acquisition unit 101 may acquire a positioning point by estimating the moving distance of the terminal 100 using an acceleration sensor or a geomagnetic sensor.
  • the position information acquisition unit 101 may acquire other information related to the positioning point, such as positioning accuracy, at the same time as calculating the positioning point, and may include it in the position information.
  • the position information acquisition unit 101 periodically calculates a positioning point, and transmits position information including the positioning point, positioning time, and positioning accuracy information to the behavior type extraction device 200 as the position information data 111.
  • the time interval at which position information is acquired by the position information acquisition unit 101 may be set in advance in the position information acquisition unit 101 by the administrator, or may be set in the position information acquisition unit 101 by the user.
  • the behavior type extraction device 200 extracts the behavior type information of the terminal 100 based on the position information and outputs it as behavior type data 211.
  • the behavior type extraction apparatus 200 includes a behavior type extraction unit 201 and a behavior type storage unit 202.
  • the behavior type extraction unit 201 receives the position information data 111 from the terminal 100 and extracts the stay point information as the behavior type information of the terminal 100 based on the received position information data 111.
  • the behavior type extraction unit 201 stores the extracted stay point information as behavior type data 211 in the behavior type storage unit 202 for each terminal 100.
  • the behavior type extraction unit 201 transmits the behavior type data 211 to the behavior feature extraction device 300.
  • FIG. 6 is a diagram showing an example of staying point information in the first embodiment of the present invention.
  • the stay point information includes a stay data identifier for identifying each stay, stay point identifiers P1 to P4 for identifying a stay point in each stay, a stay start date and time, and a stay end date and time.
  • the stay point information may include information indicating the position of the stay point.
  • the behavior feature extraction device 300 extracts behavior feature information of the terminal 100 based on the behavior type information and outputs it as behavior feature data 311.
  • the behavior feature extraction apparatus 300 includes a behavior type reference unit 301, a behavior feature extraction unit 302, and a behavior feature storage unit 303.
  • the behavior type reference unit 301 receives the behavior type data 211 from the behavior type extraction device 200.
  • the behavior feature extraction unit 302 Based on the behavior type data 211, the behavior feature extraction unit 302 extracts a home residence point that is a residence point that is estimated as a home location and a workplace residence point that is a residence point that is estimated as a workplace location. Information and workplace stay point information are generated.
  • the behavior feature extraction unit 302 calculates the residence days of each residence point based on the behavior type data 211, the residence point with the most residence days is the home residence point, and the residence point with the second residence days is the second. It will be the staying point in the workplace.
  • the behavior feature extraction unit 302 stores behavior feature data 311 including the generated home residence point information and workplace residence point information in the behavior feature storage unit 303 for each terminal 100.
  • the behavior feature extraction device 300 transmits behavior feature data 311 to the behavior feature reference device 400.
  • FIG. 9 is a diagram showing an example of the behavior feature data 311 in the first embodiment of the present invention.
  • the behavior characteristic data 311 includes home stay point information and workplace stay point information.
  • Home residence point information includes the residence point identifier of the residence point extracted as the home residence point.
  • the workplace residence point information includes the residence point identifier of the residence point extracted as the workplace residence point.
  • the home stay point information may include information indicating the stay date and time at the home stay point and the position of the home stay point.
  • the workplace residence point information may include information indicating the residence date and time and the location of the workplace residence point at the workplace residence point.
  • the behavior feature reference device 400 is a server on which an application that uses the behavior feature information of the user operates.
  • the application may be any application as long as the behavior feature information is used.
  • the application may be an application that provides an advertisement distribution service or the like based on user behavior characteristic information.
  • the application of the behavior feature reference device 400 performs a predetermined process based on the behavior type information received from the behavior feature extraction device 300.
  • the terminal 100, the behavior type extraction device 200, the behavior feature extraction device 300, and the behavior feature reference device 400 may each be a computer that operates according to a program.
  • the terminal 100, the behavior type extraction device 200, the behavior feature extraction device 300, and the behavior feature reference device 400 include a storage unit, a processing unit, an input / output unit, and a communication unit (not shown), which are electrically connected by a common bus.
  • the storage unit includes a ROM (Read Only Memory), a RAM (Random Access Memory), a flash memory, and the like, and stores programs and data for realizing the functions of each device.
  • the processing unit is configured by a CPU (Central Processing Unit), and executes the function of each device by reading the program in the storage unit and performing processing.
  • the input / output unit includes an LCD (Liquid Crystal Display), a keyboard, a mouse, a speaker, and the like, and is an input / output interface with an administrator of each device.
  • the communication unit performs communication with other devices by performing wireless communication or wired communication.
  • the terminal 100, the behavior type extraction device 200, the behavior feature extraction device 300, and the behavior feature reference device 400 are realized.
  • the behavior feature extraction device 300 is different from the terminal 100, the behavior type extraction device 200, and the behavior feature reference device 400.
  • the behavior feature extraction device 300 may constitute one or more of the terminal 100, the behavior type extraction device 200, and the behavior feature reference device 400, and one device.
  • the behavior type extraction device 200 and the behavior feature extraction device 300 may constitute one device.
  • each component of the behavior feature extraction apparatus 300 may be disposed in a physically different place and connected via a network. That is, the configuration of the behavior feature extraction system illustrated in FIG. 2 is merely an example, and each of the terminal 100, the behavior type extraction device 200, the behavior feature extraction device 300, and the behavior feature reference device 400 includes any component. It can be changed flexibly.
  • the operation of the behavior feature extraction system in the first embodiment of the present invention will be described. First, the operation of the terminal 100 in the first embodiment of the present invention will be described.
  • FIG. 3 is a flowchart showing position information acquisition processing of the terminal 100 in the first embodiment of the present invention.
  • the position information acquisition unit 101 of the terminal 100 receives radio waves from the satellite and periodically calculates positioning points (step S101).
  • the position information acquisition unit 101 acquires the positioning accuracy information and the positioning time at the same time when calculating the positioning point (step S102).
  • the position information acquisition unit 101 transmits position information data 111 including a positioning point, positioning accuracy information, and positioning time to the behavior type extraction device 200 (step S103).
  • mold extraction apparatus 200 in 1st embodiment of this invention is demonstrated.
  • FIG. 4 is a flowchart showing the behavior type extraction process of the behavior type extraction device 200 according to the first embodiment of the present invention.
  • the behavior type extraction unit 201 of the behavior type extraction device 200 receives the position information data 111 from the terminal 100 (step S201).
  • the behavior type extraction unit 201 extracts the stay point information from the position information data 111 transmitted from the terminal 100 (step S202).
  • the behavior type extraction unit 201 stores the extracted stay point information in the behavior type storage unit 202 as behavior type data 211 (step S203).
  • the behavior type extraction device 200 transmits the behavior type data 211 to the behavior feature extraction device 300 (step S204).
  • FIG. 5 is a flowchart showing the behavior feature extraction process of the behavior feature extraction apparatus 300 according to the first embodiment of the present invention.
  • the behavior type reference unit 301 of the behavior feature extraction device 300 receives the behavior type data 211 from the behavior type extraction device 200 (step S301).
  • the behavior type reference unit 301 receives the stay point information as illustrated in FIG. 6 as the behavior type data 211.
  • the behavior feature extraction unit 302 calculates the stay days of each stay point included in the stay point information received by the behavior type reference unit 301 (step S302).
  • FIG. 7 is a diagram showing an example of a method for calculating the staying days in the first embodiment of the present invention.
  • FIG. 8 is a figure which shows the example of the calculation result of the staying days in 1st embodiment of this invention.
  • the behavior feature extraction unit 302 calculates the stay days as shown in FIG.
  • the behavior feature extraction unit 302 obtains the calculation result of the stay days as shown in FIG. Based on the stay days calculated for each stay point, the behavior feature extraction unit 302 extracts the stay point with the most stay days as a home stay point, and generates home stay point information (step S303). In addition, the behavior feature extraction unit 302 extracts a stay point having the second most stay days as a workplace stay point and generates workplace stay point information (step S304). The behavior feature extraction unit 302 stores home residence point information and workplace residence point information in the behavior feature storage unit 303 (step S305).
  • the behavior feature extraction unit 302 extracts the stay point of the stay point identifier P1 having the most stay days as the stay point at home based on the calculation result of the stay days in FIG. Further, the behavior feature extraction unit 302 extracts the stay point of the stay point identifier P2 having the second most stay days as the workplace stay point.
  • the behavior feature extraction unit 302 generates home residence point information and workplace residence point information as illustrated in FIG. 9 and stores them in the behavior feature storage unit 303.
  • the behavior feature extraction unit 302 transmits the behavior feature data 311 to the behavior feature reference device 400 (step S306).
  • the behavior feature extraction device 300 may periodically execute the processing from step S301 to step S305 at predetermined time intervals, or obtain behavior feature data 311 received from the behavior feature reference device 400. It may be executed in response to a request. Thereafter, the behavior feature information extracted by the behavior feature extraction unit 302 is used by an application on the behavior feature reference device 400. Thus, the operation of the first embodiment of the present invention is completed. Next, a characteristic configuration of the first embodiment of the present invention will be described.
  • FIG. 1 is a block diagram showing a characteristic configuration of the first embodiment of the present invention. Referring to FIG. 1, the behavior feature extraction apparatus 300 includes a behavior type storage unit 202 and a behavior feature extraction unit 302.
  • the behavior type storage unit 202 stores stay point information including each stay point of the plurality of stays of the user, a stay start date and time at the stay point, and a stay end date and time at the stay point.
  • the behavior feature extraction unit 302 calculates the residence days for each of the plurality of residence points based on the residence point information in the behavior type storage unit 202, and selects the residence point with the largest residence days among the plurality of residence points.
  • the residence point is extracted as the home residence point, and the residence point having the second largest residence day among the plurality of residence points is extracted as the workplace residence point.
  • the home position and the work position can be extracted from the position information history without using environmental information such as illuminance and noise and information on the user's working hours.
  • the behavior feature extraction unit 302 calculates the stay days for each of the plurality of stay points based on the stay point information, and selects the stay point with the largest stay days among the plurality of stay points. This is because the residence point is extracted as a home residence point, and the residence point having the second largest number of residence days among the plurality of residence points is extracted as a workplace residence point.
  • the home position and the work position can be extracted regardless of the time form in which the user works.
  • the behavior feature extraction unit 302 calculates the residence days based on the presence or absence of residence in units of 24 hours with reference to the residence start date and time of the first residence in one day at each residence point, This is because the home position and the workplace position are extracted based on the stay days.
  • the behavior feature extraction unit 302 calculates the residence days based on the presence or absence of residence in units of 24 hours with reference to the residence start date and time of the first residence in one day at each residence point, This is because the home position and the workplace position are extracted based on the stay days.
  • FIG. 10 is a flowchart showing the behavior feature extraction process of the behavior feature extraction apparatus 300 according to the second embodiment of the present invention.
  • the behavior type reference unit 301 of the behavior feature extraction device 300 receives the behavior type data 211 from the behavior type extraction device 200 (step S401).
  • FIG. 11 is a diagram illustrating an example of staying point information according to the second embodiment of the present invention.
  • the behavior type reference unit 301 receives the stay point information as illustrated in FIG. 11 as the behavior type data 211.
  • the behavior feature extraction unit 302 calculates the stay days of each stay point included in the stay point information received by the behavior type reference unit 301 (step S402).
  • FIG. 12 is a diagram illustrating an example of a calculation result of the staying days in the second embodiment of the present invention.
  • the behavior feature extraction unit 302 obtains the calculation result of the stay days as shown in FIG. 12 for the stay points of the stay point identifiers P1 to P4 included in the stay point information of FIG.
  • the behavior feature extraction unit 302 determines whether or not there are a plurality of stay points having the most stay days based on the calculated stay days of each stay point (step S403).
  • the behavior feature extraction unit 302 like the first embodiment of the present invention (steps S303 and S304), each staying point.
  • the home stay point and the workplace stay point are extracted (steps S404 and S405).
  • the behavior feature extraction unit 302 calculates the total stay time from the stay point information for each stay point with the most stay days (step S406). ).
  • the behavior feature extraction unit 302 calculates the total residence time from the residence start date and time and the residence end date and time of the residence point information of FIG. 11 for the residence point identifiers P1 and P2 of the residence point with the most residence days in FIG. The calculation result of the total residence time as shown in FIG. 13 is obtained. Based on the calculated total residence time of each residence point, the behavior feature extraction unit 302 extracts the residence point with the longest total residence time as the home residence point and generates home residence point information (step S407).
  • the behavior feature extraction unit 302 extracts a stay point having the second longest stay time as a workplace stay point, and generates workplace stay point information (step S408).
  • FIG. 14 is a diagram showing an example of the behavior feature data 311 in the second embodiment of the present invention.
  • the behavior feature extraction unit 302 extracts the stay point of the stay point identifier P1 having the longest total stay time as the home stay point based on the calculation result of the total stay time in FIG. Further, the behavior feature extraction unit 302 extracts the stay point of the stay point identifier P2 having the second longest stay time as the workplace stay point. Then, the behavior feature extraction unit 302 generates home residence point information and workplace residence point information as shown in FIG.
  • the behavior feature extraction unit 302 stores the home residence point information and the workplace residence point information in the behavior feature storage unit 303 (step S409).
  • the behavior feature extraction unit 302 transmits the behavior feature data 311 to the behavior feature reference device 400 in response to the acquisition request for the behavior feature data 311 received from the behavior feature reference device 400 (step S410).
  • the home stay point and the workplace stay point can be extracted even when there are a plurality of stay points with the longest stay days. The reason is that when there are a plurality of stay points having the most stay days, the behavior feature extraction unit 302 extracts the home stay point and the workplace stay point based on the total stay time at each stay point.
  • the night stay days are used. It differs from the first embodiment of the present invention in that the home residence point is extracted. For many users, when positioning is started from Monday, the stay days at the home stay point and the work stay point are the same until the first weekend comes. Here, if you work overtime for a long time at work, or if you work for a long time at a place other than your home or work, the residence time at the workplace will become longer or the residence time at your home will become shorter.
  • FIG. 15 is a flowchart showing behavior feature extraction processing of the behavior feature extraction apparatus 300 according to the third embodiment of the present invention.
  • the behavior type reference unit 301 of the behavior feature extraction device 300 receives the behavior type data 211 from the behavior type extraction device 200 (step S501).
  • FIG. 16 is a diagram showing an example of staying point information according to the third embodiment of the present invention.
  • the behavior type reference unit 301 receives the stay point information as illustrated in FIG. 16 as the behavior type data 211.
  • the behavior feature extraction unit 302 calculates the stay days of each stay point included in the stay point information received by the behavior type reference unit 301 (step S502).
  • FIG. 17 is a diagram illustrating an example of a method for calculating the staying days in the third embodiment of the present invention.
  • FIG. 18 is a figure which shows the example of the calculation result of the staying days in 3rd embodiment of this invention.
  • the behavior feature extraction unit 302 calculates the stay days as shown in FIG. 17 for the stay points of the stay point identifiers P1 to P3 included in the stay point information of FIG. As a result, the behavior feature extraction unit 302 obtains the calculation result of the stay days as shown in FIG. Next, the behavior feature extraction unit 302 determines that the number of days of positioning of all staying point information to be extracted in step S502 (the number of days from the first staying start time to the last staying end time included in the staying point information) is a predetermined number of days. It is determined whether it is below (step S503).
  • the behavior feature extraction unit 302 calculates the number of staying days at night at each staying point (step S506).
  • the number of staying days at night is the number of staying days calculated for staying including a predetermined time at night.
  • FIG. 19 is a diagram illustrating an example of a calculation result of the number of days staying at night in the third embodiment of the present invention.
  • the number of days of positioning is 7 days (predetermined number of days) or less, and the number of days of staying at night is calculated
  • the number of days of positioning in the staying point information in FIG. 16 is 3 days.
  • the number of staying days at night is calculated for the staying points of the staying point identifiers P1 to P3 included in the information.
  • the behavior feature extraction unit 302 calculates the staying days at night as shown in FIG.
  • the behavior feature extraction unit 302 obtains the calculation result of the staying days at night as shown in FIG.
  • the behavior feature extraction unit 302 Based on the calculated number of staying days at each staying point, the behavior feature extraction unit 302 extracts the staying point having the largest number of staying days at night as a home staying point, and generates home staying point information (step S507). In addition, the behavior feature extraction unit 302 extracts the stay point with the most stay days as the work place stay point based on the calculated stay days of each stay point, and generates work place stay point information. (Step S508).
  • FIG. 20 is a diagram illustrating an example of the behavior feature data 311 according to the third embodiment of this invention. For example, the behavior feature extraction unit 302 extracts the stay point of the stay point identifier P1 having the largest number of stay days at night as the stay point at home based on the calculation result of the stay days at night in FIG.
  • the behavior feature extraction unit 302 extracts the stay point of the stay point identifier P2 having the most stay days as a work stay point, excluding the stay point at home, based on the calculation result of the stay days in FIG.
  • the behavior feature extraction unit 302 stores the home stay point information and the workplace stay point information in the behavior feature storage unit 303 (step S509).
  • the behavior feature extraction unit 302 transmits the behavior feature data 311 to the behavior feature reference device 400 in response to the acquisition request for the behavior feature data 311 received from the behavior feature reference device 400 (step S510).
  • the third embodiment of the present invention there are a plurality of residence points with the most residence days, and even when the total residence time at the workplace residence point is longer than the total residence time at the home residence point, And workplace residence points can be extracted.
  • the behavior feature extraction unit 302 extracts the home stay point based on the night stay days calculated for the stay including the predetermined time at night. Because. (Fourth embodiment)
  • the stay days are calculated for stay points staying for a predetermined time or more per day.
  • the number of days staying at the nearest station or transfer station of the home or work place where the user stays for a short time every day or every working day can be close to the stay time at the home stay point or work stay point. High nature. Therefore, in the fourth embodiment of the present invention, in order to prevent these short-time stay points from being extracted as homes or workplaces, the stay points staying for a predetermined time or more per day are targeted. Calculate the number of days.
  • the configuration of the fourth embodiment of the present invention is the same as the configuration of the first embodiment of the present invention. Next, the operation of the behavior feature extraction apparatus 300 according to the fourth embodiment of the present invention will be described. FIG.
  • FIG. 21 is a flowchart showing a behavior feature extraction process of the behavior feature extraction apparatus 300 according to the fourth embodiment of the present invention.
  • the behavior type reference unit 301 of the behavior feature extraction device 300 receives the behavior type data 211 from the behavior type extraction device 200 (step S601).
  • FIG. 22 is a diagram illustrating an example of staying point information according to the fourth embodiment of the present invention.
  • the behavior type reference unit 301 receives the stay point information as illustrated in FIG. 22 as the behavior type data 211.
  • the behavior feature extraction unit 302 extracts a stay point that stays for a predetermined time or more per day from the stay point information received by the behavior type reference unit 301 (step S602).
  • the behavior feature extraction unit 302 when extracting a stay point that has stayed for 2 hours (predetermined time) per day, extracts the stay points of the stay point identifiers P1 and P2 from the stay point information of FIG. To do. Similar to the first embodiment of the present invention (steps S302 to S306), the behavior feature extraction unit 302 uses the staying points extracted in step S602 as a target, based on the staying days of each staying point. The stay point and the workplace stay point are extracted, stored in the behavior feature storage unit 303, and transmitted to the behavior feature reference device 400 (steps S603 to S607).
  • FIG. 23 is a diagram illustrating an example of the calculation result of the stay days in the fourth embodiment of the present invention.
  • the behavior feature extraction unit 302 calculates the stay days for the stay points of the stay point identifiers P1 and P2, and obtains the stay day calculation result as shown in FIG.
  • FIG. 24 is a diagram showing an example of the behavior feature data 311 in the fourth embodiment of the present invention.
  • the behavior feature extraction unit 302 extracts the stay point of the stay point identifier P1 with the most stay days as the home stay point based on the calculation result of the stay days in FIG. Further, the behavior feature extraction unit 302 extracts the stay point of the stay point identifier P2 having the second most stay days as the workplace stay point.
  • the behavior feature extraction unit 302 generates home stay point information and workplace stay point information as shown in FIG. Thus, the operation of the fourth embodiment of the present invention is completed.
  • the residence days of each residence point are targeted for residence points that stay for a predetermined time or more per day, as in the first embodiment of the present invention.
  • the home residence point and the workplace residence point were calculated, and the residence days that stayed for a predetermined time or more per day are the same as in the second embodiment of the present invention.
  • the total stay time at each stay point may be calculated to extract the home stay point and the workplace stay point.
  • the staying points staying for a predetermined time or more per day when the number of positioning days in the staying point information is equal to or less than the predetermined number of days, the number of staying days at night The home residence point may be extracted based on.
  • the operation of the fourth embodiment of the present invention is completed.
  • the fourth embodiment of the present invention it is possible to extract the home residence point and the workplace residence point even when there is a residence point other than the home or workplace that stays every day or every working day.
  • the reason is that the behavior feature extraction unit 302 calculates the stay days for the stay points where the stay time per day is equal to or longer than a predetermined time.
  • the present invention in the extraction of home stay points and workplace stay points by the behavior feature extraction device 300, the present invention is characterized in that the stay days are calculated for stay point information for a predetermined period. This is different from the first embodiment.
  • the staying point at the relocation destination cannot be promptly extracted as the home staying point or the work staying point because the staying days at the relocation destination of the home or work are few immediately after the relocation. Therefore, in the fifth embodiment of the present invention, the staying days are calculated for the staying point information for a predetermined period.
  • the configuration of the fifth embodiment of the present invention is the same as the configuration of the first embodiment of the present invention. Next, the operation of the behavior feature extraction apparatus 300 according to the fifth embodiment of the present invention will be described. FIG.
  • FIG. 25 is a flowchart showing a behavior feature extraction process of the behavior feature extraction apparatus 300 according to the fifth embodiment of the present invention.
  • the behavior type reference unit 301 of the behavior feature extraction device 300 receives the behavior type data 211 from the behavior type extraction device 200 (step S701).
  • FIG. 26 is a diagram illustrating an example of staying point information according to the fifth embodiment of the present invention.
  • the behavior type reference unit 301 receives the stay point information as illustrated in FIG. 26 as the behavior type data 211 at the current date and time “2010/02/10 17:30”.
  • the behavior feature extraction unit 302 extracts the residence point information for a predetermined period (fixed period) from the current date and time from the residence point information received by the behavior type reference unit 301 (step S702).
  • the behavior feature extraction unit 302 may request and receive the stay point information for a predetermined period from the current date and time to the behavior type extraction device 200. For example, when the stay point information from the current date and time to 7 days before (predetermined period) is extracted, the behavior feature extraction unit 302 uses the stay start time “2010/02/03 19: The staying point information after “00” is extracted. Similar to the first embodiment of the present invention (steps S302 to S306), the behavior feature extraction unit 302 targets the staying point information extracted in step S702, based on the staying days of each staying point. The home residence point and the workplace residence point are extracted, stored in the behavior feature storage unit 303, and transmitted to the behavior feature reference device 400 (steps S703 to S707).
  • FIG. 27 is a diagram illustrating an example of the calculation result of the staying days in the fifth embodiment of the present invention.
  • the behavior feature extraction unit 302 obtains the calculation result of the stay days as shown in FIG. 27 for the stay point information after the stay start time “2010/02/03 19:00”.
  • FIG. 28 is a diagram showing an example of behavior feature data 311 in the fifth embodiment of the present invention.
  • the behavior feature extraction unit 302 extracts the stay point of the stay point identifier P1 having the most stay days as the stay point at home based on the calculation result of the stay days in FIG.
  • the behavior feature extraction unit 302 extracts the stay point of the stay point identifier P6 having the second most stay days as the workplace stay point.
  • the behavior feature extraction unit 302 generates home residence point information and workplace residence point information as shown in FIG.
  • the residence days at each residence point are calculated for residence point information for a predetermined period, as in the first embodiment of the present invention, Points and workplace stay points are extracted, but when there are a plurality of stay points with the most stay days, as in the second embodiment of the present invention, for stay point information for a predetermined period,
  • the total residence time at the residence point may be calculated to extract the home residence point and the workplace residence point.
  • the home stay point is based on the stay days at night. May be extracted.
  • the operation of the fifth embodiment of the present invention is completed.
  • the behavior feature extraction unit 302 extracts the staying point and the staying point in the workplace using the staying point information on staying performed for a predetermined period from the current date and time.
  • the stay at the home stay point or the work stay point previously extracted is not performed for a predetermined period.
  • the home stay point and the workplace stay point are extracted for the stay point information after the date and time when the stay is not performed.
  • the configuration of the sixth embodiment of the present invention is the same as the configuration of the first embodiment of the present invention.
  • FIG. 33 and FIG. 35 are diagrams showing examples of behavior feature data 311 in the sixth embodiment of the present invention.
  • the behavior feature data 311 includes stay point analysis start point information, home stay point information, and workplace stay point information.
  • the stay point analysis start point information includes a stay data identifier indicating the start point of the stay point information used when extracting the home stay point and the workplace stay point.
  • FIG. 29 is a flowchart showing a behavior feature extraction process of the behavior feature extraction apparatus 300 according to the sixth embodiment of the present invention.
  • the behavior type reference unit 301 of the behavior feature extraction device 300 receives the behavior type data 211 from the behavior type extraction device 200 (step S801).
  • the behavior feature extraction unit 302 refers to the behavior feature data 311 stored in the behavior feature storage unit 303 and the received stay point information, and extracts the previous behavior feature from the current date and time for a predetermined period (a certain period).
  • the behavior feature extraction unit 302 adds the residence point of the home residence point information included in the behavior feature data 311 stored in the behavior feature storage unit 303 to the residence point information for a predetermined period (fixed period) from the current date and time. It is determined whether the identifier and the stay point identifier of the workplace stay point information exist.
  • the behavior feature extraction unit 302 proceeds to the processing after step S804.
  • the behavior feature extraction unit 302 stores the stay point analysis start point information. Is updated with the staying data identifier of the staying performed after the last staying at the home staying point or workplace staying point where the staying is not performed, and stored in the behavior feature storage unit 303 (step S803).
  • the behavior feature extraction unit 302 extracts the stay point information after the stay data identifier indicated by the stay point analysis start point information from the stay point information received by the behavior type reference unit 301 (step S804).
  • the behavior feature extraction unit 302 targets the stay point information extracted in step S803 based on the stay days of each stay point.
  • Home residence points and workplace residence points are extracted (steps S805 to S807).
  • FIG. 30 is a diagram illustrating an example of staying point information according to the sixth embodiment of the present invention.
  • 32 and 34 are diagrams illustrating examples of calculation results of the staying days in the sixth embodiment of the present invention.
  • the behavior type reference unit 301 receives the stay point information of the stay data identifiers 1 to 14 in FIG. 30 as the behavior type data 211 at the current date and time “2010/02/09 17:30”.
  • the behavior feature extraction unit 302 extracts home residence points and workplace residence points based on the residence point information (retention data identifiers 1 to 12).
  • Action feature data 311 in FIG. 31 is stored.
  • the current date “2010/02/09” is determined. 17:30 ”to 5 days ago, because staying at the home stay point (stay point identifier P1) and workplace stay point (stay point identifier P2) extracted last time is performed (step S802 / Yes).
  • the behavior feature extraction unit 302 extracts the stay point information of the stay data identifiers 1 to 14 from the stay point information of FIG.
  • the behavior feature extraction unit 302 calculates the stay days for the stay point information of the stay data identifiers 1 to 14 as shown in FIG. 32, and sets the stay point of the stay point identifier P1 with the most stay days as the home stay point, 2
  • the stay point of the stay point identifier P2 with the second most stay days is extracted as the workplace stay point.
  • the behavior feature extraction unit 302 generates home stay point information and workplace stay point information as shown in FIG.
  • the behavior type reference unit 301 receives the stay point information of the stay data identifiers 1 to 16 in FIG. 30 as the behavior type data 211 at the current date and time “2010/02/10 17:30”.
  • the feature extraction unit 302 updates the stay point analysis start point information with the stay data identifier 11 of the stay performed after the last stay (stay data identifier 10) at the work stay point (stay point identifier P2).
  • the behavior feature extraction unit 302 extracts the stay point information of the stay data identifiers 11 to 16 from the stay point information of FIG.
  • the behavior feature extraction unit 302 calculates the stay days as shown in FIG.
  • the behavior feature extraction unit 302 generates home stay point information and workplace stay point information as shown in FIG.
  • the behavior feature extraction unit 302 stores the home residence point information and the workplace residence point information in the behavior feature storage unit 303 (step S808).
  • the behavior feature extraction unit 302 transmits the behavior feature data 311 to the behavior feature reference device 400 (step S809).
  • the moved home residence point or the workplace residence point can be quickly extracted.
  • the stay point analysis start point information is displayed after the last stay at the work stay point.
  • To update the home stay point and the workplace stay point using the stay point information after the stay indicated by the stay data identifier included in the stay point analysis start point information. It is. Further, according to the sixth embodiment of the present invention, it is possible to extract the home stay point and the work place stay point more accurately than in the fifth embodiment of the present invention.
  • the behavior feature extraction device 300 does not update the stay point analysis start point information when staying at the home stay point or the work stay point previously extracted is performed for a predetermined period. Or, unless the workplace residence point is moved, the residence residence point and the workplace residence point can be extracted using residence point information for as long a period as possible. (Seventh embodiment) Next, a seventh embodiment of the present invention will be described. In the seventh embodiment of the present invention, in the extraction of the home residence point and the workplace residence point by the behavior feature extraction device 300, the home residence point and the workplace residence point extracted when the residence point analysis start point information is updated.
  • the configuration of the seventh embodiment of the present invention is the same as the configuration of the first embodiment of the present invention.
  • 38, 40, 42, 44, and 46 are diagrams showing examples of behavior feature data 311 in the seventh embodiment of the present invention.
  • the behavior characteristic data 311 includes stay point analysis start point information, home stay point information, and workplace stay point information, pre-update stay point analysis start point information, pre-update home stay point information, and pre-update workplace stay point information. Including.
  • FIG. 36 is a flowchart showing behavior feature extraction processing of the behavior feature extraction apparatus 300 according to the seventh embodiment of the present invention.
  • the behavior type reference unit 301 of the behavior feature extraction device 300 receives the behavior type data 211 from the behavior type extraction device 200 (step S901).
  • the behavior feature extraction unit 302 refers to the behavior feature data 311 stored in the behavior feature storage unit 303 and the received stay point information, and extracts the previous behavior feature from the current date and time for a predetermined period (a certain period). It is determined whether or not staying at the home staying point and workplace staying point extracted in the process has been performed (step S902).
  • the behavior feature extraction unit 302 adds the residence point of the home residence point information included in the behavior feature data 311 stored in the behavior feature storage unit 303 to the residence point information for a predetermined period (fixed period) from the current date and time. It is determined whether the identifier and the stay point identifier of the workplace stay point information exist.
  • step S902 / Yes When staying at the home stay point and workplace stay point extracted last time has been performed for a predetermined period from the current date and time (step S902 / Yes), the same as steps S804 to S808 of the sixth embodiment of the present invention
  • the home stay point and the workplace stay point are extracted and stored for the stay point information after the stay data identifier indicated by the stay point analysis start point information (steps S903 to S907).
  • the behavior feature extraction unit 302 stores the stay point analysis start point information if the stay at the home stay point or the work place stay point previously extracted has not been performed for a predetermined period from the current date and time (step S902 / No), the behavior feature extraction unit 302 stores the stay point analysis start point information.
  • step S908 Is updated with the staying data identifier of staying performed after the last staying at the home staying point or workplace staying point where the staying is not performed, and stored in the behavior feature storage unit 303 (step S908). Then, similarly to steps S804 to S808 of the sixth embodiment of the present invention, the home stay point and the workplace stay point are extracted for the stay point information after the stay data identifier indicated by the stay point analysis start point information. And save (steps S908 to S913).
  • the behavior feature extraction unit 302 refers to the behavior feature data 311, and the extracted residence point identifier of the home residence point and the residence point identifier of the workplace residence point are obtained as follows. It is determined whether or not the stay point identifiers of the stay points coincide with each other (step S914).
  • the behavior feature extraction unit 302 Is the stay point analysis start point information, home stay point information, and workplace stay point information in the previous feature extraction process (before updating the stay point analysis start point information).
  • the stay point information and the pre-update workplace stay point information are set and stored in the behavior feature storage unit 303 (step S915).
  • the residence point identifier of the extracted home residence point and the residence point identifier of the workplace residence point match the residence point identifier of the home residence point before update and the residence point identifier of the workplace residence point before update (residence of home residence point)
  • the point identifier matches the stay point identifier of the pre-update workplace stay point and the stay point identifier of the work place stay point matches the stay point identifier of the pre-update workplace stay point) step S914 / Yes
  • behavior feature extraction The unit 302 refers to the behavior feature data 311 and sets the stay data identifier of the stay point analysis start point information before update in the stay point analysis start point information (step S916).
  • the behavior feature extraction unit 302 initializes the pre-update stay point analysis start point information, the pre-update home stay point information, and the pre-update workplace stay point information (sets null), and saves it in the behavior feature storage unit 303 (step) S917). In response to the acquisition request for the behavior feature data 311 received from the behavior feature reference device 400, the behavior feature extraction unit 302 transmits the behavior feature data 311 to the behavior feature reference device 400 (step S918).
  • FIG. 37 is a diagram illustrating an example of staying point information according to the seventh embodiment of the present invention.
  • FIG. 39, FIG. 41, FIG. 43, and FIG. 45 are diagrams showing examples of calculation results of stay days in the seventh embodiment of the present invention.
  • the behavior type reference unit 301 receives the stay point information of the stay data identifiers 1 to 14 in FIG. 37 as the behavior type data 211 at the current date and time “2010/02/09 17:30”.
  • the behavior feature extraction unit 302 extracts home residence points and workplace residence points based on the residence point information (retention data identifiers 1 to 12).
  • the behavior characteristic data 311 of FIG. 38 is stored.
  • the current date “2010/02/09” is determined.
  • the behavior feature extraction unit 302 extracts the stay point information of the stay data identifiers 1 to 14 from the stay point information of FIG.
  • the behavior feature extraction unit 302 calculates the stay days as shown in FIG. 39 for the stay point information of the stay data identifiers 1 to 14, and sets the stay point of the stay point identifier P1 with the most stay days as the home stay point, 2
  • the stay point of the stay point identifier P2 with the second most stay days is extracted as the workplace stay point.
  • the behavior feature extraction unit 302 generates home stay point information and workplace stay point information as shown in FIG.
  • the behavior type reference unit 301 receives the stay point information of the stay data identifiers 1 to 16 in FIG. 37 as the behavior type data 211 at the current date and time “2010/02/10 17:30”.
  • the stay at the current workplace stay point (stay point identifier P2) is not performed between the current date and time “2010/02/10 17:30” and 5 days ago (step S902 / No).
  • the feature extraction unit 302 updates the stay point analysis start point information with the stay data identifier 11 of the stay performed after the last stay (stay data identifier 10) at the work stay point (stay point identifier P2).
  • the behavior feature extraction unit 302 extracts the stay point information of the stay data identifiers 11 to 16 from the stay point information of FIG.
  • the behavior feature extraction unit 302 calculates the stay days for the stay point information of the stay data identifiers 11 to 16 as shown in FIG. 41, and sets the stay point of the stay point identifier P1 with the most stay days as the home stay point, 2
  • the stay point of the stay point identifier P7 having the second stay day is extracted as the workplace stay point.
  • the behavior feature extraction unit 302 generates home stay point information and workplace stay point information as shown in FIG.
  • the stay point identifier P7 of the extracted workplace stay point does not match the stay point identifier (null) of the pre-update workplace stay point (step S914 / No)
  • the behavior feature extraction unit 302 performs the previous stay point analysis. As shown in FIG.
  • the staying data identifier 1 of the starting point information, the staying point identifier P1 of the home staying point information, and the staying point identifier P2 of the working staying point information are set as shown in FIG. Set to stay point information and pre-update workplace stay point information.
  • the behavior type reference unit 301 receives the stay point information of the stay data identifiers 1 to 26 in FIG. 37 as the behavior type data 211 at the current date “2010/02/17 17:30”.
  • the feature extraction unit 302 updates the stay point analysis start point information with the stay data identifier 21 of the stay performed after the last stay (stay data identifier 20) at the work stay point (stay point identifier P7).
  • the behavior feature extraction unit 302 extracts the stay point information of the stay data identifiers 21 to 26 from the stay point information of FIG.
  • the behavior feature extraction unit 302 calculates the stay days as shown in FIG.
  • the behavior feature extraction unit 302 generates home stay point information and workplace stay point information as shown in FIG.
  • the residence point identifier P1 of the extracted home residence point coincides with the residence point identifier P1 of the home residence point before update
  • the residence point identifier P2 of the extracted workplace residence point is the residence of the workplace residence point before update.
  • the behavior feature extraction unit 302 sets the stay data identifier 1 of the stay point analysis start point information before update in the stay point analysis start point information as shown in FIG. To do. Then, the behavior feature extraction unit 302 initializes the pre-update stay point analysis start point information, the pre-update home stay point information, and the pre-update workplace stay point information as shown in FIG. Further, the behavior type reference unit 301 receives the stay point information of the stay data identifiers 1 to 30 in FIG. 37 as the behavior type data 211 at the current date and time “2010/02/19 17:30”.
  • the behavior feature extraction unit 302 extracts the stay point information of the stay data identifiers 1 to 30 from the stay point information of FIG.
  • the behavior feature extraction unit 302 calculates the stay days for the stay point information of the stay data identifiers 1 to 30 as shown in FIG. 45, and sets the stay point of the stay point identifier P1 with the most stay days as the home stay point, 2
  • the stay point of the stay point identifier P2 with the second most stay days is extracted as the workplace stay point.
  • the behavior feature extraction unit 302 generates home stay point information and workplace stay point information as shown in FIG.
  • the behavior feature extraction unit 302 transmits the behavior feature data 311 to the behavior feature reference device 400 (step S918).
  • the operation of the seventh embodiment of the present invention is completed.
  • the reason is that the home residence point and workplace residence point extracted when the residence point analysis start point information is updated are the same as the home residence point and workplace residence point extracted before the update of the previous residence point analysis start point information.
  • the behavior feature extraction device 300 returns the stay point analysis start point information to the pre-update stay point analysis start point information used before the update of the previous stay point analysis start point information. Thereby, it is prevented that the period of the residence point information used for extracting the home residence point and the workplace residence point due to the temporary home residence point or the movement of the workplace residence point is shortened.
  • the behavior type extraction device 200 extracts the behavior type information based on the position information acquired by the terminal 100, and the behavior feature extraction device 300 extracts the behavior feature information based on the behavior type information. It is assumed that However, the behavior feature extraction device 300 may extract behavior feature information based on the behavior type information manually input to the behavior type extraction device 200. Moreover, you may combine some structures and operation
  • behavior type storage means for storing stay point information including a stay point, a stay start date and time at the stay point, and a stay end date and time at the stay point; Based on the stay point information of the behavior type storage means, the stay days are calculated for each of the plurality of stay points, and among the plurality of stay points, the stay point having the most stay days is set as a home stay point.
  • Action feature extraction means for extracting and extracting, as a workplace residence point, the residence point having the second largest residence day among the plurality of residence points;
  • a behavior feature extraction apparatus comprising: (Appendix 2) The behavior feature extraction unit extracts the home residence point and the workplace residence point using the residence point information on the residence performed during a predetermined period until the current date and time (Appendix 1). Feature extraction device.
  • the residence days are calculated for each of the plurality of residence points, Among the plurality of staying points, the staying point having the most staying days is extracted as a home staying point, and among the plurality of staying points, the staying point having the second most staying days is extracted as a workplace staying point.
  • Do Behavior feature extraction method (Appendix 4) In the calculation of the stay days, the stay days based on the presence / absence of the stay every 24 hours based on the stay start date and time of the first stay in each of the plurality of stay points.
  • the stay point information further includes a stay data identifier for each of the plurality of stays, In calculating the stay days, When the stay at the home residence point or the workplace residence point extracted last time is not performed for a predetermined period until the current date and time, the residence point analysis start point information is the last of the home residence point or the workplace residence point.
  • the stay days are calculated for the stay point information after the stay indicated by the stay data identifier included in the stay point analysis start point information.
  • the behavior feature extraction method according to any one of (Appendix 3) to (Appendix 7).
  • the residence point analysis start point information before the update Set starting point analysis start point information (Supplementary note 8)
  • the residence days are calculated for each of the plurality of residence points, Among the plurality of staying points, the staying point having the most staying days is extracted as a home staying point, and among the plurality of staying points, the staying point having the second most staying days is extracted as a workplace staying point.
  • a computer-readable recording medium storing an action feature extraction program for executing processing.
  • a computer-readable recording medium storing the behavior feature extraction program according to (11).
  • Appendix 13 In extraction of the home stay point and the workplace stay point, among the plurality of stay points, when there are a plurality of stay points having the most stay days, each of the stay points having the most stay days Calculating the total residence time, and extracting the residence point having the longest total residence time as the home residence point among the residence points having the largest residence days, and out of the residence points having the largest residence days. , And the second residence point having the second total residence time is extracted as the workplace residence point.
  • a computer-readable recording medium storing the behavior feature extraction program according to (Appendix 11) or (Appendix 12).
  • the stay point information further includes a stay data identifier for each of the plurality of stays, In calculating the stay days, When the stay at the home residence point or the workplace residence point extracted last time is not performed for a predetermined period until the current date and time, the residence point analysis start point information is the last of the home residence point or the workplace residence point.
  • the stay days are calculated for the stay point information after the stay indicated by the stay data identifier included in the stay point analysis start point information.
  • a computer-readable recording medium storing the behavior feature extraction program according to any one of (Appendix 11) to (Appendix 15).
  • the staying days are calculated using the staying point information on the staying performed during a predetermined period until the current date and time.
  • the present invention can be applied not only to information distribution using user behavior characteristic information, but also to user probe data creation in a trade area survey and traffic volume survey.

Abstract

Selon l'invention, un lieu de domicile et un lieu de travail, qui caractérisent le comportement d'un utilisateur, sont extraits d'un historique d'informations de localisation. Un dispositif d'extraction de caractéristiques de comportement (300) comprend une unité de stockage de type de comportement (202) et une unité d'extraction de caractéristiques de comportement (302). L'unité de stockage de type de comportement (202) stocke des informations de lieu de résidence contenant les lieux de résidence auxquels un utilisateur a résidé, les dates de début de résidence auxdits lieux de résidence, et les dates de fin de résidence auxdits lieux de résidence. L'unité d'extraction de caractéristiques de comportement (302), sur la base de ces informations de lieu de résidence présentes dans l'unité de stockage de type de comportement (202), calcule le nombre de jours de résidence au niveau de chacun des lieux de résidence, extrait desdits lieux de résidence le lieu de résidence ayant le plus grand nombre de jours de résidence à titre de lieu de résidence de domicile, et extrait desdits lieux de résidence le lieu de résidence ayant le deuxième plus grand nombre de jours de résidence à titre de lieu de résidence de travail.
PCT/JP2011/054063 2010-02-19 2011-02-17 Dispositif d'extraction de caractéristiques de comportement, système d'extraction de caractéristiques de comportement, procédé d'extraction de caractéristiques de comportement et programme d'extraction de caractéristiques de comportement WO2011102541A1 (fr)

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