JP5491061B2 - User management method and information processing system - Google Patents

Description

  The present invention relates to a user management method and an information processing system that contribute to grasping a user's lifestyle.

  Conventionally, various methods for providing a service that matches a user are known. For example, Patent Document 1 below describes a system that provides advertisement information based on a user's lifestyle and behavior pattern. This system estimates the user's interest based on historical information including search keywords entered in the past, website access status, and referenced product information, and delivers advertisement information based on the estimation result To decide. Moreover, estimating a user's lifestyle based on the questionnaire result to a user is also performed.

JP 2008-97545 A

  In order to estimate the user's lifestyle, the behavior pattern of the person may be grasped. However, when using the questionnaire results, such as a decrease in reliability due to subjective evaluation, the burden on the user who answers the questionnaire, etc. Challenges arise. Moreover, it can be said that the method described in Patent Document 1 requires a user to input a keyword and access to a site, which places a burden on the user.

  The present invention has been made to solve the above-described problem, and provides a user management method and an information processing system that can objectively obtain the behavior tendency of a person without imposing a burden on the user. Objective.

The user management method of the present invention is a user management method executed by the information processing system, the position acquisition step for acquiring position information indicating the position of the measured mobile device, and the position information acquired in the position acquisition step From the area estimation step for estimating the area where the user of the mobile device stayed, the area storage step for storing the area estimated in the area estimation step in the first storage means as the stay area information, and the first storage means based on the history of the read stay area information, the user calculates the stay times for each area to stay, anda activity estimation step of estimating the tendency of behavior of the user based on the stay times, activity estimation step Calculates the average movement distance of the user based on the position information acquired in the position acquisition step, By calculating based on the active level is an index indicating which degree behavioral stay times and the average distance traveled, including active calculating step of estimating the tendency of behavior, characterized in that.

In addition, the information processing system of the present invention includes a position acquisition unit that acquires position information indicating the position of the measured mobile device, and an area where the user of the mobile device stayed based on the position information acquired by the position acquisition unit. Area estimation means for estimating the area, first storage means for storing the area estimated by the area estimation means as stay area information, and the area where the user stayed based on the history of stay area information read from the first storage means Behavior estimation means for calculating the number of stays for each time and estimating the tendency of the user's behavior based on the number of stays , the behavior estimation means based on the location information acquired by the location acquisition means. By calculating the average travel distance and calculating the degree of activity that is an index indicating how active the user is based on the number of stays and the average travel distance, Comprising an active calculation means for estimating a trend.

According to such a user management method and information processing system, first, the area where the user stayed is estimated based on the position information (positioning result) of the mobile device and stored as stay area information. Subsequently, the number of stays for each stay area is calculated based on the history of stay area information, and the user's behavior tendency is estimated based on the number of stays. In this way, since the tendency of the user's behavior is estimated by performing a predetermined calculation based on the position information of the mobile device, it is possible to objectively obtain the tendency of the person's behavior without imposing a burden on the user. it can. In addition, not only the number of stays but also the average moving distance of the user is calculated, and the degree of activity is calculated based on these pieces of information. This makes it possible to objectively know how active the user is.

  In the user management method of the present invention, the lifestyle corresponding to the behavior tendency estimated in the behavior estimation step is extracted from the second storage means that stores the behavior tendency of the user and the user's lifestyle in association with each other, It is preferable to further include a lifestyle estimation step of estimating the extracted lifestyle as the user's lifestyle.

  In this case, since the lifestyle corresponding to the estimated behavior tendency is extracted and this is estimated as the user's lifestyle, the lifestyle of the person can be objectively obtained without imposing a burden on the user.

  In the user management method of the present invention, a route estimation step for estimating a user's travel route based on the location information acquired in the location acquisition step, and the travel route estimated in the route estimation step as route information in the third storage means A path storing step for storing, and in the activity degree calculating step, it is preferable to calculate the average moving distance based on the history of the path information read from the third storage means.

  In this case, since the average moving distance is calculated based on the history of the moving route estimated based on the position information, the accuracy of the distance calculation is increased, and as a result, the degree of activity can be calculated more accurately.

  In the user management method of the present invention, the behavior estimation step calculates the number of times the user passes for each movement route based on the position information acquired in the position acquisition step, and indicates how new the user is challenging. It is preferable to include an aggressiveness degree calculating step of estimating an action tendency by calculating an aggressiveness degree as an index based on the number of stays and the number of passages.

  In this case, not only the number of stays but also the number of passages for each movement route is calculated, and the aggressiveness is calculated based on these pieces of information. Thereby, it is possible to objectively know how much the user is trying to do new things.

  In the user management method of the present invention, a route estimation step for estimating a user's travel route based on the location information acquired in the location acquisition step, and the travel route estimated in the route estimation step as route information in the third storage means A path storing step for storing, and in the aggressiveness calculating step, the number of passages is preferably calculated based on the history of the path information read from the third storage means.

  In this case, since the number of passes for each route is calculated based on the history of the travel route estimated based on the position information, the accuracy of calculation of the number of passes is increased, and as a result, the aggressiveness can be calculated more accurately.

  In the user management method of the present invention, setting information related to a user's target lifestyle is read from a predetermined storage means, and the target lifestyle is reached based on the behavior tendency estimated in the behavior estimation step and the setting information. It is preferable to further include an achievement degree calculating step for calculating the degree.

  In this case, the degree of achievement is calculated based on the estimated behavior tendency and the setting information related to the target lifestyle. This makes it possible to objectively obtain how close the user's behavior is to the target lifestyle.

  According to such a user management method and information processing system, a tendency of the user's behavior is estimated by performing a predetermined calculation based on the location information of the mobile device, so that the person's behavior can be estimated without imposing a burden on the user. Objective behavioral trends can be obtained.

It is a figure which shows the function structure of the moving apparatus which concerns on embodiment. It is a figure which shows the hardware constitutions of the moving apparatus shown in FIG. It is a figure which shows the area | region shown by position information. It is a figure which shows the example of a user's stay or movement. It is a figure which shows the example of the stay area information memorize | stored in the stay area storage part of FIG. It is a figure which shows the determination method of a movement path | route. It is a figure which shows the determination method of a movement path | route. It is a figure which shows the determination method of a movement path | route. It is a figure which shows the example of the route information memorize | stored in the movement path | route storage part of FIG. It is a figure which shows the example of the definition information which the lifestyle estimation part shown in FIG. 1 memorize | stores. It is a figure which shows the example of the lifestyle information memorize | stored in the lifestyle storage part shown in FIG. It is a figure which shows the example of the target value information which the reach | attainment calculation part shown in FIG. 1 memorize | stores. It is a flowchart which shows the estimation process of a stay area and a movement route performed by the estimation system shown in FIG. It is a flowchart which shows the lifestyle estimation process performed by the estimation system shown in FIG. It is a figure which shows the function structure of the estimation system which concerns on a modification.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. In this embodiment, the information processing system according to the present invention is applied to a mobile device. In the description of the drawings, the same or equivalent elements are denoted by the same reference numerals, and redundant description is omitted.

  First, the function and configuration of the mobile device 1 according to the embodiment will be described with reference to FIGS. Examples of the mobile device 1 include, but are not limited to, a mobile phone, a personal digital assistant (PDA), a personal computer, and a portable game machine. In addition, the mobile device 1 may not have a communication function with other communication terminals.

  As shown in FIG. 1, the mobile device 1 includes, as functional components, a positioning unit (position acquisition unit) 11, a stay determination unit 12, an area estimation unit (area estimation unit) 13, and a stay area storage unit (first storage unit). 14, movement route estimation unit 15, movement route storage unit (third storage unit) 16, factor estimation unit (behavior estimation unit) 17, lifestyle estimation unit (second storage unit) 18, lifestyle storage unit 19, reach degree A calculation unit 20 and a display unit 21 are provided.

  As shown in FIG. 2, the mobile device 1 includes a CPU 101 that executes an operating system, application programs, and the like, a main storage unit 102 that includes a ROM and a RAM, an auxiliary storage unit 103 that includes a memory, The communication control unit 104 performs data communication, the monitor unit 105 includes a liquid crystal monitor, and the operation unit 106 includes input keys. Each function of the mobile device 1 reads predetermined software on the CPU 101 and the main storage unit 102, operates the communication control unit 104 under the control of the CPU 101, and controls the main storage unit 102 and the auxiliary storage unit 103. This is realized by reading and writing data. As described above, the mobile device 1 may not include a portion corresponding to the communication control unit 104.

  Returning to FIG. 1, the positioning unit 11 is a part that measures the current position of the mobile device 1 and generates position information indicating the position. Specifically, the positioning unit 11 performs positioning using the global positioning system (GPS) and base station information, and generates position information including measurement time, latitude / longitude, and positioning error. The positioning unit 11 outputs the generated position information to the stay determination unit 12. The positioning unit 11 executes such a series of processes at predetermined time intervals.

  The positioning unit 11 may generate relative position information obtained by positioning using a wireless LAN, an IC tag (Radio Frequency IDentification: RFID), Bluetooth (Bluetooth (registered trademark)), or the like.

  The stay determination unit 12 is a part that determines whether or not the user of the mobile device 1 is staying in an area of a certain range.

  The stay determination unit 12 accumulates the position information input from the positioning unit 11. Subsequently, the stay determination unit 12 determines whether or not there are portions that overlap each other continuously in time among the plurality of regions indicated by the recently acquired predetermined amount of position information. Here, the area indicated by the position information is a rectangular area A having a latitude and longitude p as the center and one side being twice the positioning error e as shown in FIG. Hereinafter, a portion where the areas indicated by the position information overlap with each other is referred to as an “AND area”.

  For example, when the regions A1 to A7 at the times t1 to t7 are spatially arranged as shown in FIG. 4, the stay determination unit 12 sets the AND regions B where the regions A3 to A5 overlap in the time zones t3 to t5. It is determined that the user is staying, and it is determined that the user is moving in other time zones t1 to t3 and t5 to t7. In FIG. 4, the movement of the user is schematically indicated by an arrow Y.

  Subsequently, the stay determination unit 12 outputs a plurality of pieces of position information corresponding to the areas constituting the AND area to the area estimation unit 13. In the example of FIG. 4, the stay determination unit 12 outputs three pieces of position information corresponding to the areas A <b> 3 to A <b> 5 to the area estimation unit 13. In addition, the stay determination unit 12 outputs a plurality of pieces of position information corresponding to the time zone in which it is determined that the user is moving to the movement route estimation unit 15. In the example of FIG. 4, the stay determination unit 12 outputs a set of three position information corresponding to the areas A1 to A3 and a set of three position information corresponding to the areas A5 to A7 to the movement route estimation unit 15. To do.

  In addition, the timing which the stay determination part 12 performs the said process is not limited. For example, the stay determination unit 12 may start the above process in response to the input of position information, or may start the above process at a predetermined time interval or at a predetermined time.

  The area estimation unit 13 is a part that estimates the area where the user of the mobile device 1 stayed based on the position information input from the stay determination unit 12. Specifically, the area estimation unit 13 determines the area indicated by the smallest positioning error among the input pieces of position information, that is, position information constituting the AND area, as the stay area. Moreover, the area estimation part 13 calculates a stay time based on the input positional information. Then, the area estimation unit 13 outputs stay area information including the user ID, the position information corresponding to the stay area, the stay start time, the stay end time, and the stay time to the stay area storage unit 14.

  In the example of FIG. 4, since the positioning error of the region A4 is the smallest among the regions A3 to A5, the area estimation unit 13 sets the region A4 indicated by the position information at time t4 as the stay area. Moreover, the area estimation part 13 calculates the time from stay start time t3 to stay end time t5 as stay time.

  The stay area storage unit 14 is a part that stores the stay area information input from the area estimation unit 13. An example of information stored in the stay area storage unit 14 is shown in FIG. In the example of FIG. 5, three stay area information is stored for the user indicated by the user ID “u001”.

  The movement route estimation unit 15 is a part that estimates the movement route of the user of the mobile device 1 based on the position information input from the stay determination unit 12. Specifically, the movement path estimation unit 15 calculates a minimum bounding rectangle (MBR) that includes two regions indicated by two pieces of position information that are temporally continuous. Subsequently, if the calculated minimum rectangle does not overlap with the other minimum rectangles, the movement path estimation unit 15 uses the calculated minimum rectangle as the movement path, and if the minimum rectangles spatially overlap, A range composed of rectangles is defined as a user's movement route.

The process of the movement path estimation unit 15 when position information (latitude / longitude) p1 to p3 of a certain user that is continuous in time is input will be described with reference to FIG. In this case, the movement route estimation unit 15 calculates the minimum rectangle MBR _{12 including the} area A1 indicated by the position information p1 and the area A2 indicated by the position information p2, and the area A3 indicated by the area A2 and the position information p3. The minimum rectangle MBR _{23 including} Then, the movement route estimation unit 15 sets a range including two minimum rectangles MBR ₁₂ and MBR ₂₃ as the user's movement route R ₁₃ .

  Subsequently, the movement route estimation unit 15 determines whether or not a movement route similar to the estimated movement route (hereinafter referred to as “similar route”) already exists in the movement route storage unit 16. Hereinafter, the travel route estimated this time is referred to as “estimated route”, and the travel route already stored in the travel route storage unit 16 is referred to as “stored route”. The movement route estimation unit 15 checks whether or not there is a stored route in which the regions constituting the estimated route continuously overlap a predetermined number of times or more, and if so, sets the stored route as a similar route. . That is, if the estimated route and the stored route are continuously overlapped within a certain range, the moving route estimation unit 15 determines that both are similar.

  If there is no similar route, the moving route estimation unit 15 outputs route information indicating the estimated route to the moving route storage unit 16. The route information includes a user ID, a departure area indicating the position of the user (latitude and longitude and positioning error) at the start of movement, an arrival area indicating the position of the user (latitude and longitude and positioning error) at the end of movement, It includes a series of position information indicating a movement route from the departure area to the arrival area, a movement start time, and a movement end time expressed based on the position information. The movement start time is represented by the stay end time in the departure area, and the movement end time is represented by the stay start time in the arrival area.

  On the other hand, when there is a similar route, the moving route estimation unit 15 updates the route information regarding the route using the route information of the estimated route, and outputs the updated route information to the moving route storage unit 16. For this purpose, the movement route estimation unit 15 executes the following processing for each piece of position information (latitude and longitude) pi constituting the estimated route.

  That is, the movement route estimation unit 15 first specifies position information (latitude and longitude) pn that is closest to the position information pi from among the position information that constitutes a similar route immediately after reading or during update. Subsequently, the movement route estimation unit 15 determines whether or not the area indicated by the position information pi and the area indicated by the position information pn are spatially overlapped.

  When it is determined that both areas overlap, the movement path estimation unit 15 determines which of the positioning information pi and pn has a smaller positioning error. At this time, if the positioning error of the position information pi is smaller, the moving route estimation unit 15 deletes the position information pn from the position information series of the similar route, and adds the position information pn to the route information. On the other hand, when the positioning error of the position information pn is smaller, the movement route estimation unit 15 does not replace the position information.

An example of such replacement processing will be described with reference to FIG. Similar areas are represented by R ₁₃ in FIG. 6, when the position information p4 that indicates the area A4 of and constitutes the estimated path exists as shown in FIG. 7, the movement path estimator 15 closest to the location p4 The position information p2 is specified. The region A2, A4 are overlapped, and since the direction of the positioning error of the area A4 is small, the movement path estimator 15 replaces the positional information p4 and the position information p2 in the position information sequence similar route R _13. As a result, the similar route R ₁₃ is updated to a moving route R ₁₃ ′ composed of the minimum rectangle MBR ₁₄ including the regions A1 and A4 and the minimum rectangle MBR ₄₃ including the regions A4 and A3.

On the other hand, when the two areas do not overlap spatially, the movement route estimation unit 15 inserts the position information pi into the position information series of the similar route. First, the movement route estimation unit 15 extracts the position information pf and pr arranged before and after the position information pn. Here, a point represented by the position information pf is referred to as a front vicinity point, and a point represented by the position information pr is referred to as a rear vicinity point. Subsequently, the movement route estimation unit 15 calculates a distance L _nf between the points indicated by the position information pf and pn and a distance L _nr between the points indicated by the position information pn and pr. Further, the movement route estimation unit 15 calculates a distance L _if between the points indicated by the position information pf and pi and a distance L _ir between the points indicated by the position information pi and pr. Subsequently, the movement path estimation unit 15 compares the value related to the front neighbor point (| L _if −L _nf |) with the value related to the rear neighbor point (| L _ir −L _nr |), and determines the value in the position information series. The position information pi is inserted on the side closer to the neighboring point with the smaller.

An example of such insertion processing will be described with reference to FIG. When the position information p5 indicating the area A5 constituting the estimated route exists as shown in FIG. 8 with respect to the route R ₁₃ ′ shown in FIG. 7, the moving route estimation unit 15 is closest to the position information p5. The position information p4 is specified. Subsequently, the movement path estimation unit 15 specifies the front neighboring point indicated by the position information p1 and the rear neighboring point indicated by the position information p3, and the value (| L ₅₁ −L ₄₁ |) and the value (| L ₅₃ − L ₄₃ |) is calculated. In the example of FIG. 8, since | L ₅₁ −L ₄₁ | <| L ₅₃ −L ₄₃ |, the position information p5 is between two pieces of position information p1 and p4 constituting the position information series of the similar route R ₁₃ ′. Inserted. As a result, the similar path R ₁₃ ′ is composed of the minimum rectangle MBR ₁₅ including the areas A1 and A5, the minimum rectangle MBR ₅₄ including the areas A5 and A4, and the minimum rectangle MBR ₄₃ including the areas A4 and A3. Updated to travel route R _{13 ″} .

  The movement route storage unit 16 is a part that stores the route information input from the movement route estimation unit 15. Thereby, route information is registered or updated. For example, the movement route storage unit 16 stores route information as shown in FIG.

  The factor estimation unit 17 is a part that estimates the tendency of user behavior. The factor estimating unit 17 includes an active degree calculating unit 17a and an aggressive degree calculating unit 17b.

The activity degree calculation unit 17a is a part that calculates an activity degree that is an index indicating how active the user of the mobile device 1 is. That is, the degree of activity is a kind of index indicating the tendency of user behavior. Specifically, the activity level calculation unit 17a calculates the activity level Ac using the following equation (1).

  The average stay frequency and the average moving distance are calculated by the activity level calculation unit 17a. The degree-of-activity calculation unit 17a reads out stay area information that is information within a predetermined period in the past and whose stay time is a predetermined value or more from the stay area storage unit 14. Subsequently, the activity level calculation unit 17a calculates the average number of stay areas per day and sets this as the average number of stays. In addition, the activity degree calculation unit 17a calculates a linear distance between the read stay areas as a movement distance, and calculates an average movement distance per day based on the movement distance. In order to calculate the average moving distance more accurately, the activity degree calculation unit 17a reads out the route information corresponding to the acquired stay area information from the moving route storage unit 16, and calculates the moving distance based on the route information. May be.

  On the other hand, α is predetermined in the range from 0 to 1. Further, the maximum average stay frequency and the maximum average moving distance are also determined in advance. The degree-of-activity calculation unit 17a stores these values in a predetermined memory in advance and reads them as necessary.

The aggressiveness degree calculation unit 17b is a part that calculates an aggressiveness degree that is an index indicating how new the user is trying. Therefore, the aggressiveness is also a kind of index indicating the tendency of the user's behavior. First, the aggressiveness degree calculation part 17b reads the stay area information in the past predetermined period from the stay area storage part 14, and calculates the stay frequency in the period for each stay area i. Moreover, the aggressiveness degree calculation part 17b reads the route information in the same period from the movement path | route storage part 16, and calculates the frequency | count of having passed in the said period about each movement route j. Subsequently, the aggressiveness degree calculation unit 17b calculates the aggressiveness degree Ag using the following formula (2).

  N is the number of stay areas i that have been read, and M is the number of travel routes j that have been read. β is predetermined in the range from 0 to 1. The aggressiveness degree calculation unit 17b stores the value β in a predetermined memory in advance and reads it as necessary.

  As described above, the factor estimating unit 17 estimates the tendency of the user's behavior based on two kinds of numerical values, ie, the active degree and the aggressive degree, and outputs these numerical values to the lifestyle estimating unit 18.

  The timing at which the factor estimating unit 17 executes the above process is not limited. For example, the factor estimation unit 17 may start the above process at a predetermined time interval or at a predetermined time. In addition, the factor estimation unit 17 may start the above process when an input of an estimation request is received from the user.

  The lifestyle estimation unit 18 is a part that estimates the user's lifestyle based on the degree of activity and aggressiveness input from the factor estimation unit 17. Here, the lifestyle is an index representing the user's lifestyle, the personality or values of the person.

  For this reason, the lifestyle estimation unit 18 stores in advance definition information indicating the relationship between the lifestyle and the active degree and the aggressive degree. For example, the lifestyle estimation unit 18 stores four pieces of definition information as shown in FIG. And the lifestyle estimation part 18 compares the input parameter | index with this definition information, and estimates a user's present lifestyle. For example, if the input activity and aggressiveness are 0.8 and 0.2, respectively, and the definition information is as shown in FIG. Estimated to be "behavior". The lifestyle estimation unit 18 outputs information indicating the estimated lifestyle and the input degree of activity and aggressiveness to the achievement level calculation unit 20 and the lifestyle storage unit 19.

  The lifestyle storage unit 19 is a part that stores information (lifestyle information) related to the user's lifestyle. The lifestyle information includes a user ID for identifying the user, an estimated degree of activity, aggressiveness, and lifestyle, a target lifestyle set in advance by the user, and an achievement level. The degree of achievement is a value indicating how much the estimated current lifestyle matches a preset target lifestyle. An example of lifestyle information is shown in FIG.

  The lifestyle storage unit 19 stores the lifestyle, the activity level, and the aggressiveness level input from the lifestyle estimation unit 18 in association with the user ID. Further, the lifestyle storage unit 19 stores the degree of achievement input from the degree of achievement calculator 20 described later in association with the user ID. Through these processes, lifestyle information is registered or updated.

  The achievement level calculation unit 20 is a part that calculates the achievement level based on the activity level and the aggressiveness level input from the lifestyle estimation unit 18.

  For this purpose, the achievement level calculation unit 20 stores target value information indicating the target values of the activity level and the aggressive level in each lifestyle. This target value information is setting information related to the target lifestyle. In FIG. 12, for example, a target value “1” is set for both the active degree and the aggressive degree for the lifestyle of “a new favorite behavior group”.

  The target value shown in FIG. 12 is set in relation to the threshold value (0.5) indicated by the definition information in FIG. That is, “1” is set for a portion set as “0.5 or more” in FIG. 10, and “0” is set for a portion set as “less than 0.5” in FIG. Is set. Apart from the examples of FIGS. 10 and 12, when the definition information defines “threshold value TH1 or more and less than threshold value TH2”, an average value of TH1 and TH2 is set in the corresponding portion of the target value information.

The degree of achievement calculation unit 20 calculates the degree of achievement by substituting the input value and the target value of the active degree and the aggressive degree into the following formula (3), and outputs the result to the lifestyle storage unit 19. In Equation (3), respectively an active level and aggressive degree inputted _LFV 1 _(u001), LFV 2 expressed by (u001), respectively active level and aggressive of the target value _{LFV 1 (r001), LFV 2} (r001 ). Moreover, M and N are the number of types of indices indicating the user's behavior tendency. In the present embodiment, since the degree of activity and the degree of aggressiveness are used as the index, both M and N are 2.

Assuming the data shown in FIGS. 11 and 12, for the user identified by the user ID “u001”, LFV ₁ (u001) = 0.8, LFV ₂ (u001) = 0.2, LFV ₁ (r001) = Since 0, LFV ₂ (r001) = 1, the degree of achievement is calculated as follows.
Reachability = 1-√ {(0.8-0) ² + (0.2-1) ² }
/ √ [{max (0,1-0)} ² + {max (1,1-1)} ² ]
= 1-√ (1.28) / √ (1 + 1)
= 0.2

  The display unit 21 is a part that displays an estimation result of the activity level, the aggressiveness level, and the lifestyle, and the calculated achievement level on the monitor. When the process of the achievement level calculation unit 20 is completed, the display unit 21 reads and displays the lifestyle information stored in the lifestyle storage unit.

  Next, the operation of the mobile device 1 shown in FIG. 1 will be described with reference to FIGS. 13 and 14, and the user management method according to the present embodiment will be described.

  First, a process for estimating a stay area and a travel route will be described with reference to FIG. First, the positioning unit 11 acquires position information by measuring the current position (step S11, position acquisition step). Subsequently, the stay determination unit 12 determines whether the user has stayed or moved in an area of a certain range based on the accumulated position information (step S12).

  When it is determined that the user has stayed in an area (step S12; stay), the area estimation unit 13 estimates a stay area based on one or more pieces of position information (step S13, area estimation step). And the stay area storage part 14 memorize | stores the estimation result as stay area information (step S14, area storage step).

  When it is determined that the user has moved (step S12; movement), the movement route estimation unit 15 estimates a movement route based on one or more pieces of position information (step S15, route estimation step), and moves. The route storage unit 16 stores the estimation result as route information (step S16, route storage step). At this time, the movement route estimation unit 15 determines whether or not a route similar to the estimated route is already stored in the movement route storage unit 16, and if there is a similar route, the route information is obtained as described above. A process of replacing or adding position information to be configured is executed.

  Through such a series of processes, information related to the user's stay area and travel route is accumulated.

  Next, a process for estimating a lifestyle will be described with reference to FIG. First, the factor estimation unit 17 estimates a user's behavior tendency. Specifically, the activity level calculation unit 17a calculates the above formula (1) based on the stay area information, and calculates the activity level (step S21, action estimation step). Moreover, the aggressiveness degree calculation part 17b calculates the said Formula (2) based on stay area information and route information, and calculates an aggressiveness degree (step S22, action estimation step).

  Subsequently, the lifestyle estimation unit 18 estimates the user's lifestyle based on the calculated degree of activity and aggressiveness and the definition information stored in advance (step S23, lifestyle estimation step). Subsequently, the attainment degree calculation unit 20 calculates the attainment degree based on the active degree, the aggressive degree, and the target value information (step S24).

  And the display part 21 displays the active degree, aggressive degree, lifestyle, and achievement degree which were calculated or estimated in this way on a monitor (step S25). Thereby, the user of the mobile device 1 can confirm the lifestyle estimation result and the degree of achievement related to the user.

  As described above, according to the present embodiment, first, the area where the user stayed is estimated based on the position information (positioning result) of the mobile device 1 and stored as stay area information. Subsequently, the number of stays for each stay area is calculated based on the history of stay area information, and the user's behavior tendency is estimated based on the number of stays. Thus, since the tendency of the user's behavior is estimated by performing a predetermined calculation based on the position information of the mobile device 1, the tendency of the behavior of the person can be objectively obtained without imposing a burden on the user. Can do.

  In the present embodiment, a lifestyle corresponding to the estimated behavior tendency is extracted, and this is estimated as the user's lifestyle. Therefore, the lifestyle of the person can be objectively obtained without imposing a burden on the user. be able to.

  In this embodiment, not only the number of stays but also the average moving distance of the user is calculated, and the degree of activity is calculated based on these pieces of information. This makes it possible to objectively know how active the user is. In addition, when the average moving distance is calculated based on the history of the moving route, the accuracy of the distance calculation is increased, and as a result, the degree of activity can be calculated with higher accuracy.

  In the present embodiment, not only the number of stays but also the number of passes for each movement route is calculated, and the aggressiveness is calculated based on these pieces of information. Thereby, it is possible to objectively know how much the user is trying to do new things. At this time, since the number of times of passage is calculated based on the history of the movement route, the accuracy of calculation of the number of times of passage increases, and as a result, the aggressiveness can be calculated with higher accuracy.

  In the present embodiment, the degree of achievement is calculated based on the estimated behavior tendency and the target value information regarding the target lifestyle. This makes it possible to objectively obtain how close the user's behavior is to the target lifestyle.

  The present invention has been described in detail based on the embodiments. However, the present invention is not limited to the above embodiment. The present invention can be modified in various ways as described below without departing from the scope of the invention.

  In the above embodiment, the information processing system according to the present invention is applied to the mobile device 1, but the present invention may be realized by a configuration of a client server type system. For example, as shown in FIG. 15, the present invention may be applied to an estimation system 2 including a mobile device 3 and an estimation server 4. In the estimation system 2, most of the functions of the mobile device 1 described above are installed in the estimation server 4, and the mobile device 3 transmits and receives data between the positioning unit 11, the display unit 21, and the estimation server 4. Only. In this case, since the function executed by the mobile device 3 is greatly reduced, the power consumption of the mobile device 3 can be suppressed.

  In the above embodiment, the degree of activity is calculated based on the average number of stays and the average moving distance, but the degree of activity may be calculated based only on the average moving distance. For example, the activity calculating unit 17a may be 1 if the moving distance of the day is 100 km or more, 0.8 if the moving distance is 80 km or more and less than 100 km, 0.6 if it is 60 km or more and less than 80 km, 0.6, 40 km or more and less than 60 km. For example, the degree of activity may be determined as 0.4, 20 km or more and less than 40 km, 0.2, or less than 20 km, 0.

  In the above-described embodiment, the active degree and the aggressive degree are used as the indices indicating the user's behavior tendency, but only one of these indices may be used.

  In the said embodiment, although the stay determination part 12 specified the user's stay area, you may abbreviate | omit such a process. In this case, the mobile device 1 stores the received location information as it is in a predetermined storage means, and the area estimation unit 13 may determine the stay area based on the history of location information.

DESCRIPTION OF SYMBOLS 1 ... Mobile device (information processing system), 2 ... Estimation system (information processing system), 3 ... Mobile device, 4 ... Estimation server, 11 ... Positioning part (position acquisition means), 12 ... Stay determination part, 13 ... Area estimation Part (area estimation means), 14 ... stay area storage part (first storage means), 15 ... movement route estimation part, 16 ... movement route storage part (third storage means), 17 ... factor estimation part (behavior estimation means) , 17a ... activity calculation unit, 17b ... aggressive degree calculation unit, 18 ... lifestyle estimation unit (second storage means), 19 ... lifestyle storage unit, 20 ... reach level calculation unit, 21 ... display unit, 22 ... communication Department.

Claims (7)

A user management method executed by an information processing system,
A position acquisition step for acquiring position information indicating the position of the measured mobile device;
Based on the position information acquired in the position acquisition step, an area estimation step for estimating the area where the user of the mobile device stayed;
An area storage step of storing the area estimated in the area estimation step in the first storage means as stay area information;
A behavior estimation step of calculating the number of stays for each area where the user has stayed based on the history of the stay area information read from the first storage means, and estimating a tendency of the user's behavior based on the number of stays When,
Including
The behavior estimation step includes:
The average movement distance of the user is calculated based on the position information acquired in the position acquisition step, and the degree of activity that is an index indicating how active the user is is based on the number of stays and the average movement distance. by calculating Te, comprising an active degree calculating step of estimating the tendency of the action,
The user management method characterized by the above-mentioned. A lifestyle corresponding to the behavior tendency estimated in the behavior estimation step is extracted from second storage means for storing the behavior tendency of the user and the user's lifestyle in association with each other, and the extracted lifestyle is The user management method according to claim 1, further comprising a lifestyle estimation step of estimating a user lifestyle. A route estimation step of estimating the movement route of the user based on the position information acquired in the position acquisition step;
A route storage step for storing the travel route estimated in the route estimation step in the third storage means as route information;
Further including
In the activity level calculating step, the average moving distance is calculated based on a history of the route information read from the third storage unit.
The user management method according to claim 1 or 2 , wherein The behavior estimation step includes:
Based on the position information acquired in the position acquisition step, the number of passes for each movement route of the user is calculated, and the degree of aggressiveness, which is an index indicating how new the user is challenging, is calculated as the number of stays and the number of stays. An aggressive degree calculation step of estimating the tendency of the behavior by calculating based on the number of passages;
The user management method as described in any one of Claims 1-3 characterized by the above-mentioned. A route estimation step of estimating the movement route of the user based on the position information acquired in the position acquisition step;
A route storage step for storing the travel route estimated in the route estimation step in the third storage means as route information;
Further including
In the aggressiveness calculating step, the number of passages is calculated based on the history of the route information read from the third storage unit.
The user management method according to claim 4 , wherein: Reachability calculation that reads setting information related to a user's target lifestyle from a predetermined storage means, and calculates a reach to the target lifestyle based on the behavior tendency estimated in the behavior estimation step and the setting information user management method according to any one of claims 1 to 5, further comprising a step. Position acquisition means for acquiring position information indicating the position of the measured mobile device;
Area estimation means for estimating the area where the user of the mobile device stayed based on the position information acquired by the position acquisition means;
First storage means for storing the area estimated by the area estimation means as stay area information;
Behavior estimating means for calculating the number of stays for each area where the user stayed based on the history of the stay area information read from the first storage means, and estimating the tendency of the user's behavior based on the number of stays When,
With
The behavior estimation means is
The average moving distance of the user is calculated based on the position information acquired by the position acquisition means, and the degree of activity that is an index indicating how active the user is is based on the number of stays and the average moving distance. An activity degree calculating means for estimating the behavior tendency by calculating
Information processing system.

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