Information processing device

Abstract

To accurately identify the user's living area. [Solution] The information processing device according to the present invention includes an acquisition unit that acquires area information indicating the user's living area on a map, an identification unit that identifies features in which part of the site is included within the living area, a site area indicating the extent of the site occupied by the features identified by the identification unit, and an update unit that updates the living area based on the living area.

Description

Technical Field

[0006] , ,

[0007] , ,

[0001] The present invention relates to an information processing apparatus.

Background Art

[0002] Conventionally, a technique has been proposed for identifying the living area of a user and selecting content corresponding to the user's habits in the identified living area.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in the above prior art, it is not always possible to accurately identify the living area of the user.

[0005] For example, the above prior art identifies this action range as the living area by statistically determining the normal action range of the user from the user's movement history. However, with such a statistical method, for example, when the true target object for the user exists within a vast site, it is not always possible to obtain a living area that includes this target object, and it can be said that there is room for improvement in accurately identifying the living area of the user.

[0006] The present invention has been made in view of the above, and proposes, for example, an information processing apparatus capable of accurately identifying the living area of a user.

Means for Solving the Problems

[0007] The information processing device described in claim 1 includes an acquisition unit that acquires area information indicating the user's living area on a map, an identification unit that identifies features in which a part of the site is included within the living area, a site area indicating the extent of the site occupied by the features identified by the identification unit, and an update unit that updates the living area based on the living area. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 shows an example of an information processing system according to an embodiment. [Figure 2] Figure 2 is an explanatory diagram illustrating the overall picture of the process for identifying living areas. [Figure 3] Figure 3 shows an example of the service optimization logic according to the embodiment. [Figure 4] Figure 4 is an explanatory diagram illustrating the overview of the living area renewal process. [Figure 5] Figure 5 shows an example of the configuration of an information processing device according to the embodiment. [Figure 6] Figure 6 is a flowchart showing the procedure for the living area renewal process. [Figure 7] Figure 7 shows an example of a merging method that merges the site area and the living area. [Figure 8] Figure 8 shows another example of a merging method that merges the site area and the living area. [Figure 9] Figure 9 is a hardware configuration diagram showing an example of a computer that implements the functions of the information processing device 100. [Modes for carrying out the invention]

[0009] Below, an example of an embodiment for implementing the information processing device (hereinafter referred to as "Embodiment") will be described in detail with reference to the drawings. Note that the information processing device is not limited to this embodiment. Furthermore, the same parts will be denoted by the same reference numerals in the following embodiments, and redundant explanations will be omitted.

[0010] [Embodiment] [1. Introduction] This document describes an example of a living area identification process (living area identification logic) that the information processing device 100 according to the proposed technology of the present invention can perform as a preprocessing step for living area update processing.

[0011] For example, considering users living in urban areas, the surrounding area often has a wide range of services (e.g., shopping, transportation, public services, welfare, entertainment, etc.), and from the perspective that they can get by close to home, their range of daily activities tends to be relatively narrow. On the other hand, in rural areas compared to urban centers, the level of service availability is lower, so users living in rural areas tend to have a wider range of daily activities than users living in urban areas.

[0012] Therefore, the information processing device 100 can identify the user's living area by applying the fact that the user's range of activity tends to depend on the level of service availability in the surrounding area, i.e., the level of prosperity of the region (hereinafter sometimes referred to as "regional prosperity"), to the user's range of activity.

[0013] Here, "living area" refers to the range of movement a user typically makes, and can be determined from historical information such as where and how often the user arrives. However, as with the conventional technology described above, simply using such historical information does not guarantee that the user's living area can be identified with high accuracy. Therefore, the information processing device 100 has a logic for identifying the user's living area based on the user's arrival history and the level of regional prosperity in areas where the user frequently resides (for example, the area where their home or workplace is located).

[0014] The following sections will provide a detailed explanation of specific examples of lifestyle area identification logic. According to the lifestyle area identification logic of this embodiment, it becomes possible to further optimize the services provided to users (e.g., advertisements, directions, risk notification, search results, etc.). Specific examples of such service optimization logic will also be explained in detail below.

[0015] [2. System Configuration] First, the configuration of the information processing system according to the embodiment will be described using FIG. 1. FIG. 1 is a diagram showing an example of the information processing system according to the embodiment. In FIG. 1, as an example of the information processing system according to the embodiment, an information processing system 1 is shown. In the information processing system 1, not only the above-mentioned living area identification logic and service optimization logic but also living area update processing may be realized.

[0016] As shown in FIG. 1, the information processing system 1 may include an external device 30 and an information processing device 100. Also, the external device 30 and the information processing device 100 are communicably connected by wire or wirelessly via a network N. Further, the information processing system 1 shown in FIG. 1 may include an arbitrary number of external devices 30 and an arbitrary number of information processing devices 100.

[0017] The information processing device 100 is a device that performs information processing according to the embodiment. The information processing device 100 may perform living area identification processing and living area update processing for further improving the accuracy of the living area obtained by the living area identification processing as information processing according to the embodiment. From this, the living area identification processing may be performed as a preprocessing of the living area update processing.

[0018] For example, the information processing device 100 can perform the following processing as living area identification processing. The information processing device 100 acquires history information of parking positions where a user has parked a vehicle, and calculates the regional prosperity degree in a first extraction area based on predetermined parameters in the first extraction area extracted according to the distribution status of the parking positions indicated by the history information. Then, the information processing device 100 identifies a second extraction area that is the living area of the user based on the first extraction area and the regional prosperity degree.

[0019] Also, the information processing device 100 can perform service optimization processing using the living area identified by the living area identification processing or the living area after being updated by the living area update processing.

[0020] As shown in Figure 1, the information processing device 100 may be mounted on the vehicle VEx. In other words, the information processing device 100 may be an in-vehicle device. For example, the information processing device 100 may be a dedicated navigation device built into or mounted on the vehicle VEx.

[0021] For example, the information processing device 100 may consist of a navigation device and a recording device. As one example, the information processing device 100 may be a composite device in which an independent navigation device and a recording device are communicated with each other. As another example, the information processing device 100 may be a single device having a navigation function and a recording function.

[0022] Furthermore, the information processing device 100 may be equipped with various sensors. For example, the information processing device 100 may be equipped with various sensors such as a camera, an accelerometer, a gyroscope, a GPS sensor, and a barometric pressure sensor. In this way, the information processing device 100 may also have a function to provide dialogue and information to support the user's driving based on sensor information acquired by the various sensors.

[0023] Furthermore, the information processing device 100 can use not only the sensors provided in the in-vehicle device 10, but also sensor information detected by sensors provided in the vehicle VEx itself as part of the safe driving system.

[0024] Furthermore, users can operate their everyday portable terminal devices (e.g., smartphones, tablet devices, notebook PCs, desktop PCs, PDAs, etc.) in the same way as the information processing device 100 by installing a predetermined application on these devices. In other words, users can use their own portable terminal devices as in-vehicle devices. For these reasons, a portable terminal device owned by a user can also be understood as the information processing device 100 according to this embodiment.

[0025] Furthermore, in this embodiment, the term "user" may include the driver of the vehicle VEx, or the owner of the vehicle VEx.

[0026] Next, the external device 30 can be any device that works in cooperation with the information processing device 100 to realize the information processing according to the embodiment. For example, the external device 30 may be a content providing device that provides candidate content (e.g., advertising content or tourist information) corresponding to the movement of the vehicle VEx, or it may be a map information providing device that provides an electronic map. As another example, the external device 30 may provide timetable information for transportation such as trains and buses, or it may provide a weather report.

[0027] Here, if the information processing device 100 is an edge computer that performs edge processing near the user, then the external device 30 may be, for example, a cloud computer that performs processing on the cloud side. In other words, the external device 30 may be a server device.

[0028] In the following embodiment, an example is shown in which the information processing according to the embodiment is performed by the information processing device 100, which is an in-vehicle device. However, the information processing according to the embodiment may also be performed by an external device 30 on the cloud side. In this case, the external device 30 may have some or all of the functions of the information processing device 100. Furthermore, when the external device 30 performs the information processing according to the embodiment, the information processing device 100 may be configured to acquire the processing results from the external device 30 and provide various services based on the acquired processing results.

[0029] [3. Overview of the process for identifying living areas] From here, we will use Figure 2 to explain the overall picture of the habitat identification process that is performed as a preprocessing step. Figure 2 is an explanatory diagram that illustrates the overall picture of the habitat identification process.

[0030] Furthermore, in the example shown in Figure 2, assuming that user U1, an example of a user, is using the content provision service (hereinafter referred to as "Service SA") provided by the information processing device 100, the living area of ​​user U1 is identified based on the history information of the parking location where user U1 parked vehicle VE1 (an example of vehicle VEx).

[0031] Furthermore, Figure 2 shows a scenario in which the living area of ​​user U1 is identified as the logic for identifying the living area progresses, from Figure 2(a) to Figure 2(c).

[0032] Figure 2 shows an example where the living area identification logic is executed at a specific point in time when sufficient historical information has been accumulated to identify the living area (for example, one week after user U1 starts using service SA).

[0033] First, step S1 in Figure 2(a) will be explained. The information processing device 100 extracts the main stay area AR1, which is the main area where user U1 primarily stays in their daily life, based on the distribution of parking locations shown in the parking location history information. For example, the information processing device 100 may extract the area with the highest density distribution of parking locations from among the divided areas of the map information showing the parking locations as the main stay area AR1. The main stay area AR1 can be considered, for example, as the home area where user U1's home is located, and is the area corresponding to the first extracted area.

[0034] Furthermore, the information processing device 100 can, for example, extract multiple primary residence areas AR1. In this case, the area with the highest density distribution of parking locations may be considered the home area, and the area with the next highest density distribution may be considered the workplace area.

[0035] Next, the information processing device 100 calculates the regional prosperity PR for the main stay area AR1. For example, the information processing device 100 may calculate the regional prosperity PR for the main stay area AR1 based on the number of predetermined facilities (e.g., shops, public facilities, tourist facilities, medical facilities, landmarks, etc.) present in the main stay area AR1. As an example, the information processing device 100 may calculate the regional prosperity PR for the main stay area AR1 based on the number of facilities per unit area in the main stay area AR1. For example, the information processing device 100 may calculate a higher regional prosperity PR value to indicate that the main stay area AR1 is more prosperous (has a wide range of services) the more facilities there are per unit area.

[0036] Next, step S2 in Figure 2(b) will be explained. The information processing device 100 executes a predetermined clustering algorithm on the parking locations included in the parking location history information. In this embodiment, the information processing device 100 performs DBSCAN clustering on the parking locations included in the parking location history information.

[0037] For example, the information processing device 100 can perform clustering under the condition that if there are N2 or more parking locations within a radius of N1km, it will grow a group (generate a cluster). Such conditions may be pre-set for the information processing device 100, or the information processing device 100 may dynamically change the conditions as appropriate depending on the distribution of parking locations.

[0038] Furthermore, DBSCAN uses a parameter called "min_distance" to adjust whether or not to grow as the same group based on the distance between parking spaces. The information processing device 100 corrects this "min_distance" using the regional prosperity PR. For example, the information processing device 100 corrects it by multiplying "min_distance" by the regional prosperity PR as a weight value, and then performs DBSCAN clustering using the corrected "min_distance".

[0039] Furthermore, as described above, DBSCAN generates groups based on the determination of whether they belong to the same group. Therefore, the information processing device 100 controls the size of the main stay area AR1 by performing convex hull processing on the group to which the parking position corresponding to the main stay area AR1 belongs among the generated groups. In the example in Figure 2(b), an example is shown in which the main stay area AR1 is corrected (processed) to increase its size (area and shape) as a result of DBSCAN clustering and convex hull processing.

[0040] Next, step S3 in Figure 2(c) will be explained. The information processing device 100 identifies the second extraction region, which is a polygon region after the main residence region AR1 (first extraction region) has been controlled in step S2, as the user U1's living area AR2.

[0041] Once the living area AR2 is identified in this way, the information processing device 100 executes service control processing so that services corresponding to the living area AR2 are provided to the user U1.

[0042] So far, we have explained the overall picture of the living area identification process using Figure 2. For example, if there is insufficient parking location history information, the information processing device 100 may identify the primary stay area AR1 as living area AR2 (primary stay area AR1 = living area AR2). For example, the information processing device 100 may extract a circular area with a specific radius as the primary stay area AR1 based on the distribution density.

[0043] However, the information processing device 100 may repeat the living area identification logic over time, and as historical information is accumulated during this time, it becomes possible to appropriately control the size of the main dwelling area AR1, as shown in Figure 2(b). As a result, the information processing device 100 becomes able to identify a living area AR2 of optimal size (main dwelling area AR1 < living area AR2), as shown in Figure 2(c).

[0044] Furthermore, as more historical information is accumulated over time, the information processing device 100 may either narrow or widen the size of the living area AR2 compared to the example in Figure 2(c).

[0045] Furthermore, the DBSCAN clustering shown in Figure 2 is a process that removes parking locations that are estimated not to be within user U1's living area (for example, locations significantly far from the main area of ​​stay AR1) as noise when identifying the living area, in order to generate groups (clusters). For example, in Figure 2(a), such noisy parking locations are shown as black circles (3), while in Figure 2(b), an example is shown where the noisy parking locations have been removed as a result of DBSCAN clustering. In addition, by removing the noisy parking locations, as shown in Figure 2(c), the information processing device 100 becomes able to identify a living area AR2 that is closer in size to the actual living area that user U1 considers to be.

[0046] [4. Service control tailored to the living area] From here, we will explain an example of service optimization logic that controls the services that the information processing device 100 should provide to user U1 according to the living area AR2, using Figure 3. Figure 3 is a diagram showing an example of service optimization logic according to the embodiment. In Figure 3, using the initial state when user U1 has just started using service SA as a comparison example, we will explain an example of optimization logic that shows how the content provided to user U1 is optimized as the living area AR2 is appropriately identified over time.

[0047] First, let's explain Figure 3(a). Figure 3(a) shows an example of content provision in the initial state when user U1 has just started using service SA. As shown in Figure 3(a), in the initial state, the information processing device 100 only extracts the circular area around user U1's home as the main stay area AR1, and considers this main stay area AR1 to be user U1's living area AR2 at this point. In this state, the living area AR2 does not include the travel route RT1 connecting user U1's home and workplace, as shown in Figure 3(a). In other words, in the example in Figure 3(a), the information processing device 100 does not recognize the travel route RT1 as user U1's daily travel range. To put it another way, the information processing device 100 does not recognize the travel route RT1, which should be user U1's living area, as part of the living area.

[0048] In such cases, as shown in Figure 3(a), the information processing device 100 may provide information as tourist information content that a user U1, whose living area is along the travel route RT1, should already be aware of. For example, as shown in Figure 3(a), when user U1 is traveling along the travel route RT1 and approaches City K, the information processing device 100 may provide tourist information content such as, "The famous warehouse district of City K is nearby." However, it is clear that user U1 is already aware of this information, and it can be said that it is useless to user U1. Furthermore, if user U1 is presented with such tourist information content every time they commute, for example, they may find it annoying.

[0049] However, as explained above, the information processing device 100 can more accurately identify the living area AR2 as historical information accumulates over time. For example, one week after user U1 starts using service SA, the information processing device 100 can expand the main area of ​​stay AR1 to include the travel route RT1, as shown in Figure 3(b), and can identify a living area AR2 that is closer in size to the actual living area that user U1 considers to be.

[0050] Furthermore, in the example shown in Figure 3(b), the information processing device 100 can recognize that the tourist information content initially provided is useless to user U1. Therefore, even if user U1 approaches City K while traveling along route RT1, the information processing device 100 controls the system so that tourist information content is not provided, as shown in Figure 3(b). This also allows the information processing device 100 to optimize content provision so that user U1 does not feel bothered by the content.

[0051] [5. Overview of the process of renewing living areas] From here, we will explain the overview of the living area update process, starting from the issues in the living area identification process explained in Figure 2. Figure 4 is an explanatory diagram illustrating the overview of the living area update process. Figure 4 shows the living area AR2 of user U1, which has been identified on the map MP by the living area identification process. The area shown by living area AR2 corresponds to the living area of ​​user U1 that is currently known, and may be defined in the coordinate system corresponding to the map MP.

[0052] In this example, according to Figure 4, the map MP contains two facilities, FA1 and FA2, which are estimated to be frequently visited (used) by user U1.

[0053] Facility FA1 is a structure built within site area SA1, as shown in Figure 4. In other words, site area SA1 is the site area that indicates the extent of the land occupied by facility FA1. For example, if facility FA1 is a store that is the true purpose of use for user U1, then a living area should be identified that includes the true location where facility FA1 exists (a frequently visited destination). However, living area AR2 does not include the true location where facility FA1 exists, and only includes a part of the land indicated by site area SA1.

[0054] As explained in Figure 2, since the lifestyle area identification process uses parking location history information, this portion of the site included in lifestyle area AR2 is highly likely to be a parking lot located within site area SA1.

[0055] The same can be said for facility FA2. As shown in Figure 4, facility FA2 is a structure built within site area SA2. In other words, site area SA2 is the site area that indicates the extent of the site occupied by facility FA2. For example, if facility FA2 is a station platform which is the true purpose of use for user U1, then a living area should be identified that includes the true location where facility FA2 exists (a frequently visited destination). However, living area AR2 does not include the true location where facility FA2 exists, and only includes a part of the site indicated by site area SA2.

[0056] As described above, since the lifestyle area identification process uses parking location history information, this portion of the site included in lifestyle area AR2 is highly likely to be a parking lot located within site area SA2.

[0057] Thus, in the process of identifying living areas, the true location that should be included in the living area (i.e., the location of the feature that is the true purpose of use for user U1) is not included, and only a living area that includes parking locations for getting to the true location is generated, indicating that there is room for improvement in terms of accuracy.

[0058] Therefore, if the feature that is the true purpose of use for the user is located, for example, within a vast site, the information processing device 100 performs a living area update process to identify a living area that includes the true location of this feature as the user's new living area. Specifically, the information processing device 100 updates this living area based on the site area, which is a specific range occupied by the feature that is the true purpose of use for the user, and the living area that has been identified by any method. More specifically, the information processing device 100 updates the living area by merging (integrating) the site area with the living area identified by any method, thereby updating the merged area as a new living area that replaces the previous living area.

[0059] The arbitrary method referred to here may be the living area identification process described in Figure 2, or any other conventional method. For example, the arbitrary method may be one of the methods listed as prior art.

[0060] Furthermore, taking living area AR2 as an example, the service control process shown in Figure 3 may be performed using the updated living area obtained by applying the living area update process to living area AR2.

[0061] [6. Configuration of the Information Processing Device] From here, the information processing device 100 according to the embodiment will be described using Figure 5. Figure 5 is a diagram showing an example of the configuration of the information processing device 100 according to the embodiment. As shown in Figure 5, the information processing device 100 has a communication unit 110, a storage unit 120, and a control unit 130.

[0062] (Regarding Communications Unit 110) The communication unit 110 is implemented, for example, by a NIC (Network Interface Card). The communication unit 110 is connected to the network N by wire or wireless connection and performs information transmission and reception, for example, with the in-vehicle device 10.

[0063] (Regarding memory unit 120) The storage unit 120 is implemented by, for example, semiconductor memory elements such as RAM (Random Access Memory) and flash memory, or storage devices such as hard disks and optical discs. The storage unit 120 may have a history information database 121 and a lifestyle information database 122.

[0064] (Regarding the historical information database 121) The history information database 121 stores historical information of parking locations where users have parked their vehicles VEx. For example, the history information database 121 may store a pair of the parking location where a user parked their vehicle VEx and the parking time as one record, associated with a history ID. The information processing device 100 may have a GPS receiver, and the parking location may be defined by latitude and longitude information derived from GPS.

[0065] (Regarding the Living Area Information Database 122) The living area information database 122 stores information indicating the living area identified by the living area identification process. For example, the living area information database 122 may store a pair of information, namely the date and time the living area identification process was performed and the information indicating the living area AR2 identified at that time, as a single record, associated with the living area ID.

[0066] Furthermore, the living area information database 122 may also store information indicating the living area after it has been updated by applying a living area update process to the living area AR2.

[0067] Furthermore, the information indicating a living area, as used here, may be defined by the location coordinates corresponding to the map MP.

[0068] (Regarding the control unit 130) The control unit 130 is implemented by a CPU (Central Processing Unit) or MPU (Micro Processing Unit), etc., which executes various programs (for example, information processing programs according to the embodiment) stored in the memory device inside the information processing device 100 using RAM as the working area. Alternatively, the control unit 130 can be implemented by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array).

[0069] As shown in Figure 5, the control unit 130 includes a detection unit 131, an acquisition unit 132, a calculation unit 133, an analysis unit 134, a first identification unit 135, a service control unit 136, a second identification unit 137, and an update unit 138, and realizes or executes the information processing functions and operations described below. Note that the internal configuration of the control unit 130 is not limited to the configuration shown in Figure 5, and other configurations are also possible as long as they perform the information processing described later. Also, the connection relationships of the various processing units in the control unit 130 are not limited to the connection relationships shown in Figure 5, and other connection relationships are also possible.

[0070] For example, the calculation unit 133, the analysis unit 134, and the first identification unit 135 are processing units that correspond to the living area identification process. On the other hand, the second identification unit 137 and the update unit 138 are processing units that correspond to the living area update process.

[0071] (Regarding the detection unit 131) The detection unit 131 determines whether the user has parked the vehicle VEx, and if it determines that the user has parked the vehicle VEx, it detects the parking location where the user parked the vehicle VEx. For example, if the vehicle VEx is stopped, the detection unit 131 may measure the stopping period, and if it recognizes that the vehicle has been stopped for a predetermined period or longer, it may determine that the user has parked the vehicle VEx. In this case, the detection unit 131 may also detect the location where the vehicle VEx was parked as the parking location.

[0072] Furthermore, the detection unit 131 registers a pair of location information indicating the parking location and the time when the vehicle VEx was parked as parking location history information in the history information database 121. For example, the detection unit 131 may register a pair of location information indicating the parking location and the time when the vehicle VEx was parked in the history information database 121 in association with a history ID.

[0073] (Regarding acquisition section 132) The acquisition unit 132 acquires historical information of the parking location where the user parked the vehicle VEx. For example, if the acquisition unit 132 determines that it is time for the living area determination logic to be executed, it may acquire historical information from the historical information database 121.

[0074] Furthermore, the acquisition unit 132 may also acquire area information indicating the user's living area in the map MP. For example, the acquisition unit 132 may acquire location coordinates indicating the location of the living area determined by the living area determination logic as area information. For example, the acquisition unit 132 may acquire location coordinates from the living area information database 122 when it determines that it is time to execute the living area update process.

[0075] (Regarding calculation unit 133) The calculation unit 133 calculates the regional prosperity score, which is the degree of prosperity in a predetermined area. For example, the calculation unit 133 obtains the distribution status of parking locations based on the history information of parking locations. Then, based on the obtained distribution status, the calculation unit 133 extracts the main stay area AR1, which is the main area where users of the vehicle VEx mainly stay in their daily lives. For example, the calculation unit 133 may extract the area with the highest density distribution of parking locations among the divided areas of the map information showing the distribution status as the main stay area AR1.

[0076] In this state, the calculation unit 133 calculates the regional prosperity PR in the main stay area AR1 (first extracted area) based on predetermined parameters in the main stay area AR1. For example, the calculation unit 133 may calculate the regional prosperity PR as predetermined parameters per unit area in the main stay area AR1.

[0077] The specified parameters referred to here are one of the following: the number of specified facilities (e.g., shops, public facilities, tourist facilities, medical facilities, landmarks, etc.) located in the main stay area AR1; the number of public transportation facilities (e.g., train stations, bus stops, etc.) located in the main stay area AR1; or feature information indicating the geographical characteristics of the main stay area AR1.

[0078] Furthermore, the calculation unit 133 may calculate the regional prosperity PR using predetermined parameters, such as the number of predetermined facilities in the main stay area AR1 or the number of public transportation systems in the main stay area AR1, and then correct the calculated regional prosperity PR with feature information.

[0079] (Regarding Analysis Section 134) The analysis unit 134 clusters the parking locations indicated by the parking location history information based on the regional prosperity PR. For example, the analysis unit 134 may execute a predetermined clustering algorithm on the parking locations included in the parking location history information. For example, the analysis unit 134 may execute DBSCAN clustering. More specifically, the analysis unit 134 may execute DBSCAN clustering under the setting condition that if there are N2 or more parking locations within a radius of N1km, the group will grow.

[0080] Furthermore, in DBSCAN clustering, the analysis unit 134 uses "min_distance" as a parameter to adjust whether or not to grow as the same group according to the distance between parking locations. However, "min_distance" may be corrected by the regional prosperity PR, and clustering may be performed again using the corrected "min_distance".

[0081] (Regarding Section 135 of the First Specific Section) The first identification unit 135 identifies a second extraction area, which is the user's living area, based on the main stay area AR1 (first extraction area) and the regional prosperity PR. For example, the first identification unit 135 performs a convex hull process on the group of parking locations generated as a result of clustering by the analysis unit 134, to which the parking locations included in the main stay area AR1 belong. The first identification unit 135 then identifies the second extraction area, which is a polygon region generated by the convex hull process, as the user's living area AR2. This process is equivalent to controlling the size of the main stay area AR1 to bring it closer to the user's intended living area, and identifying the second extraction area, which is the area brought closer, as the living area AR2.

[0082] Furthermore, the first specific unit 135 may register information indicating the living area AR2 in the living area information database.

[0083] (Regarding the service control unit 136) The service control unit 136 controls the services to be provided to the vehicle VEx user according to the living area AR2 (second extraction area) identified by the first identification unit 135. For example, the service control unit 136 controls the services to be provided to the vehicle VEx user using a service optimization logic based on the living area AR2 identified by the first identification unit 135.

[0084] As explained in Figure 3, the service control unit 136 may control the provision of tourist information content corresponding to the living area AR2 to the user. On the other hand, the service control unit 136 may control the provision of advertising content related to a predetermined facility (e.g., a store) located in the living area AR2 to the user. For example, the service control unit 136 may extract advertising content for stores located in the living area AR2 from the advertising content acquired from the external device 30, and control the output of this advertising content from the information processing device 100. The advertising content may be image information, in which case it may be output from the display screen of the information processing device 100. On the other hand, the advertising content may also be audio information, in which case it may be output from the speaker of the information processing device 100.

[0085] For example, in situations where a user's living area cannot be accurately identified, advertising content may be provided for areas outside the user's usual range of activity. Such advertising content may not be highly appealing to the user. On the other hand, according to the service optimization logic of this embodiment, advertising content corresponding to a living area AR2 that is closer in size to the user's actual living area will be provided, thereby increasing the appeal of the advertising content.

[0086] (Regarding Section 2, Item 137) The second identification section 137 identifies features whose site is partially included in a living area. For example, the second identification section 137 identifies features whose site is partially included in a living area identified by any method such as the living area identification process described above.

[0087] For example, the second identification unit 137 identifies features that correspond to a user among features whose site is partly located within the living area. For example, the second identification unit 137 may identify objects that correspond to a user among features having a site area greater than or equal to a predetermined value. As an example, the second identification unit 137 may identify features that correspond to a user based on the user's history information regarding features. For example, the second identification unit 137 may identify features that correspond to a user based on the user's visit history to the site or the user's usage history of features.

[0088] (Regarding update section 138) The update unit 138 updates the living area based on the site area, which indicates the extent of the site occupied by the features identified by the second identification unit 137, and the living area, which is identified by any method. Specifically, the update unit 138 updates the area after merging the site area with the living area, thereby updating the merged area as a new living area for users to replace the previous living area.

[0089] For example, the update unit 138 may generate a buffer in the site area that contains the features identified by the second identification unit 137, and then merge the site area after buffer generation with the living area. Alternatively, the update unit 138 may merge the site area with the living area by performing a convex hull process to include the location information of the features identified by the second identification unit 137.

[0090] [7. Processing Procedure] Next, we will explain the procedure for the living area renewal process using Figure 6. Figure 6 is a flowchart showing the procedure for the living area renewal process.

[0091] First, the acquisition unit 132 determines whether it is time to update the living area (step S601). If it is not time to update the living area (step S601; No), the acquisition unit 132 waits until it can determine that it is time to update the living area.

[0092] On the other hand, if the acquisition unit 132 determines that it is time to update the living area (step S601; Yes), it acquires area information indicating the living area of ​​user U1 on the map MP (step S602). For example, the acquisition unit 132 may acquire area information indicating the living area determined by any method.

[0093] For example, the acquisition unit 132 may acquire area information indicating the living area determined by the living area determination logic described in Figure 2. As an example, the acquisition unit 132 may acquire position coordinates defined in a coordinate system corresponding to map MP as area information indicating the living area. In the following, the procedure for updating the living area will be described assuming that the acquisition unit 132 has acquired position coordinates indicating the area of ​​living area AR2 (hereinafter referred to as "living area AR2") in response to the living area determination logic identifying living area AR2 as the living area of ​​user U1.

[0094] First, the second identification unit 137 identifies facilities (land features) whose site is partially included in the living area AR2 (step S603). For example, the second identification unit 137 may identify facilities whose site is partially included in the living area AR2 from among land features having a site area of ​​a predetermined value or more. Alternatively, the second identification unit 137 may identify facilities whose site is partially included in the living area AR2 based on the map MP. Here, using the example in Figure 4, the second identification unit 137 identifies facility FA1 and facility FA2. Furthermore, the area of ​​site area SA1 corresponding to the site occupied by facility FA1, and the area of ​​site area SA2 corresponding to the site occupied by facility FA2, may both be greater than or equal to a predetermined value.

[0095] Next, the second identification unit 137 identifies the facility corresponding to user U1 from among facility FA1 and facility FA2 based on user U1's activity history (step S604). Specifically, the second identification unit 137 identifies the facility that is used on a daily basis (i.e., frequently used) from among facility FA1 and facility FA2 based on user U1's history information. For example, the second identification unit 137 determines whether facility FA1 and facility FA2 have been visited more than a predetermined number of times within a specific period, based on the distribution of parking locations shown in the history information. If the second identification unit 137 has been visited more than a predetermined number of times, it may identify that facility as the facility corresponding to user U1. In the following, the procedure for updating the living area will be described assuming that the second identification unit 137 has identified facility FA1 as the facility corresponding to user U1 from among facility FA1 and facility FA2.

[0096] The update unit 138 merges the site area SA1 occupied by facility FA1 into the living area AR2, thereby updating the merged area as a new living area for user U1, replacing living area AR2 (step S605). By merging the site area SA1 into the living area in this way, the accuracy of the relationship between features and the living area for user U1 can be improved.

[0097] Here, we will explain an example of a merging method using Figure 7. Figure 7 is a diagram illustrating an example of a merging method that merges a site area and a living area. Figure 7 shows a scenario in which the site area SA1 is merged with the living area AR2.

[0098] As shown in Figure 7(a), the update unit 138 first generates a buffer BF for the site area SA1. For example, the update unit 138 generates a buffer BF around the site area SA1 by calculating the area within a predetermined distance from the site area SA1. This buffering process may be performed on the map MP, and the update unit 138 obtains a buffer area AR(BF) that indicates the range of the buffer BF. For this reason, the buffer area AR(BF) may be defined in a coordinate system corresponding to the map MP.

[0099] Next, as shown in Figure 7(b), the update unit 138 merges the living area AR2 and the buffer area AR(BF) to merge the overlapping portions between the living area AR2 and the buffer area AR(BF). As a result, a new living area AR2n is obtained, as shown in Figure 7(b). The update unit 138 then updates the merged area, living area AR2n, as the new living area for user U1, replacing living area AR2.

[0100] Note that the living area AR2n shown in Figure 7(b) has a complex shape as a polygon. Therefore, the update unit 138 may control the shape of the living area AR2n by performing a convex hull operation on the position coordinates indicating the location of the living area AR2n. As a result, as shown in Figure 7(b), a living area AR2n is obtained in which part is replaced with a dotted line.

[0101] Next, we will explain another example of the merging method using Figure 8. Figure 8 is a diagram showing another example of the merging method for merging the site area and the living area. Figure 8 also shows a scenario in which the site area SA1 is merged with the living area AR2.

[0102] As shown in Figure 8(a), the update unit 138 first obtains the true position coordinates PT1 where facility FA1 is located within the site area SA1. Then, the update unit 138 controls the shape of living area AR2 by performing a convex hull operation between the position coordinates indicating the location of living area AR2 and the position coordinates PT1. Through this process, as shown in Figure 8(b), a new living area AR2m is obtained by merging site area SA1 and living area AR2. The update unit 138 then updates living area AR2m, which is the merged area, as the new living area for user U1, replacing living area AR2.

[0103] [8. Restriction lifted] Figures 7 and 8 illustrate the method of merging the site area with the living area, but the merging method is not limited to these examples. For example, the update section 138 may update a rectangular area surrounding the living area AR2n (and similarly the living area AR2m) as a new living area to replace the living area AR2.

[0104] Furthermore, although Figure 7(a) shows an example where a buffer is generated only on the site area SA1 side, the update unit 138 may also generate a buffer in the living area AR2, thereby combining the buffer areas of both sides.

[0105] [9. Hardware Configuration] Furthermore, the information processing device 100 according to the above-described embodiment is realized by a computer 1000 having the configuration shown in Figure 9. Figure 9 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device 100. The computer 1000 has a CPU 1100, RAM 1200, ROM 1300, HDD 1400, communication interface (I / F) 1500, input / output interface (I / F) 1600, and media interface (I / F) 1700.

[0106] The CPU 1100 operates based on programs stored in the ROM 1300 or HDD 1400, controlling various components. The ROM 1300 stores boot programs executed by the CPU 1100 when the computer 1000 starts up, as well as programs that depend on the computer 1000's hardware.

[0107] The HDD1400 stores programs executed by the CPU1100, as well as data used by such programs. The communication interface1500 receives data from other devices via a predetermined communication network and sends it to the CPU1100, and transmits data generated by the CPU1100 to other devices via the predetermined communication network.

[0108] The CPU 1100 controls output devices such as displays and printers, and input devices such as keyboards and mice, via the input / output interface 1600. The CPU 1100 acquires data from input devices via the input / output interface 1600. The CPU 1100 also outputs the generated data to output devices via the input / output interface 1600.

[0109] The media interface 1700 reads a program or data stored in the recording medium 1800 and provides it to the CPU 1100 via the RAM 1200. The CPU 1100 loads the program from the recording medium 1800 onto the RAM 1200 via the media interface 1700 and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or PD (Phase Change Rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

[0110] For example, when the computer 1000 functions as an information processing device 100 according to the embodiment, the CPU 1100 of the computer 1000 realizes the functions of the control unit 130 by executing a program loaded on the RAM 1200. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800, but as another example, these programs may be obtained from other devices via a predetermined communication network.

[0111] [10. Other] Furthermore, among the processes described in each of the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically by known methods. In addition, the processing procedures, specific names, and information including various data and parameters shown in the above document and drawings can be changed at will unless otherwise specified. For example, the various information shown in each figure is not limited to the information shown.

[0112] Furthermore, each component of the illustrated device is a functional concept and does not necessarily have to be physically configured as shown. In other words, the specific forms of distribution and integration of each device are not limited to those shown, and all or part of them can be functionally or physically distributed and integrated in any unit according to various loads and usage conditions. For example, the information processing device may be a server. The server may be configured to acquire (receive) information necessary for identifying the living area from the vehicle's terminal, and to perform the living area identification and update processing on the server.

[0113] Furthermore, the above embodiments can be combined as appropriate, provided that the processing content is not contradictory.

[0114] Although some embodiments of the present invention have been described in detail above with reference to the drawings, these are illustrative examples, and the present invention can be implemented in various other forms with modifications and improvements based on the knowledge of those skilled in the art, starting with the embodiments described in the disclosure section of the invention. [Explanation of Symbols]

[0115] 1. Information Processing System 30 External device 100 Information Processing Devices 120 Storage section 121 History Information Database 122 Living Area Information Database 130 Control Unit 131 Detection unit 132 Acquisition Department 133 Calculation Section 134 Analysis Department 135 First Specific Part 136 Service Control Unit 137 Second Specific Part 138 Update Department

Claims

[Claim 1] An acquisition unit that acquires area information indicating the user's living area on a map, A specific section that identifies a geographical feature whose site is partly included in the aforementioned living area, A site area indicating the extent of the site occupied by the features identified by the specified part, and a living area, based on the living area, a renewal part that renews the living area. An information processing device characterized by having the following features.

Images