Information processing device, information processing method, and information processing program

Abstract

An information processing device (100) according to the present application has: an acquisition unit (132) for acquiring history information of parking positions at which a user parked a vehicle; a calculation unit (133) for, on the basis of a predetermined parameter in a first extraction region extracted in accordance with the distribution situation of the parking positions, calculating a degree of regional prosperity that is the degree of prosperity in the first extraction region; and a specification unit (135) for specifying, on the basis of the first extraction region and the degree of regional prosperity, a second extraction region that is a living area of the user.

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] Conventionally, a technique has been proposed in which a user's living area is identified and content corresponding to the user's habits in the identified living area is selected. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2007-264764 Summary of the Invention [Problem to be solved by the invention]

[0004] For example, the above-mentioned conventional technology statistically determines the user's usual range of activity from the user's movement history, and identifies this range of activity as the user's living area.

[0005] However, in reality, a user's range of movement tends to be influenced by the regional characteristics of the surrounding area (for example, the range of movement tends to be narrower in urban areas where services are abundant, while the range of movement tends to be wider in rural areas where services tend to be lacking), and it can be said that there is room for improvement in terms of accurately identifying a user's living area with the above-mentioned conventional technologies, which simply use movement history.

[0006] The present invention has been made in view of the above, and has an object to provide, for example, an information processing device, an information processing method, and an information processing program that can accurately identify a user's living area. [Means for solving the problem]

[0007] The information processing device described in claim 1 is characterized by having an acquisition unit that acquires historical information on parking locations where users have parked their vehicles, a calculation unit that calculates a regional prosperity degree, which is the prosperity degree in a first extraction area, based on predetermined parameters in the first extraction area extracted according to the distribution of the parking locations, and an identification unit that identifies a second extraction area, which is the user's living area, based on the first extraction area and the regional prosperity degree.

[0008] The information processing method described in claim 12 is an information processing method executed by an information processing device, and is characterized by including an acquisition step of acquiring historical information on parking locations where users have parked their vehicles, a calculation step of calculating a regional prosperity degree, which is the prosperity degree in a first extraction area, based on predetermined parameters in the first extraction area extracted according to the distribution of the parking locations, and an identification step of identifying a second extraction area, which is the user's living area, based on the first extraction area and the regional prosperity degree.

[0009] The information processing program described in claim 13 is characterized in that it causes an information processing device to execute an acquisition step of acquiring historical information on parking locations where users have parked their vehicles, a calculation step of calculating a regional prosperity degree, which is the prosperity degree in a first extraction area, based on predetermined parameters in the first extraction area extracted according to the distribution of the parking locations, and an identification step of identifying a second extraction area, which is the user's living area, based on the first extraction area and the regional prosperity degree. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram illustrating an example of an information processing system according to an embodiment. [Figure 2] FIG. 2 is an explanatory diagram illustrating an overall image of information processing according to the embodiment. [Figure 3] FIG. 3 is a diagram illustrating an example of service optimization logic according to the embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of the configuration of the information processing device according to the embodiment. [Figure 5] FIG. 5 is a flowchart showing a collection procedure for collecting history information. [Figure 6] FIG. 6 is a flowchart showing the procedure of the identification process for identifying a living area. [Figure 7] FIG. 7 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device 100. DETAILED DESCRIPTION OF THE INVENTION

[0011] An example of a form for implementing an information processing device, an information processing method, and an information processing program (hereinafter referred to as an "embodiment") will be described in detail below with reference to the drawings. Note that the information processing device, the information processing method, and the information processing program are not limited to this embodiment. Furthermore, the same components in the following embodiments will be assigned the same reference numerals, and duplicated descriptions will be omitted.

[0012] [Embodiment] 1. Introduction For example, for users living in urban areas, the surrounding areas often have a wide range of services (for example, shopping, transportation, public services, welfare, entertainment, etc.), and as they can get everything they need nearby, their range of daily activities tends to be relatively narrow. On the other hand, since the level of service availability is lower in rural areas compared to urban areas, users living in rural areas tend to have a wider range of daily activities than users living in urban areas.

[0013] The present invention was developed with the aim of focusing on the fact that a user's range of activity tends to depend on the availability of services in the surrounding area, i.e., the prosperity of the area (hereinafter sometimes referred to as "regional prosperity"), and by applying this to the user's range of activity, it becomes possible to identify the user's living area with greater accuracy.

[0014] Here, a living area refers to the range in which a user travels on a daily basis, and can be determined from historical information such as where the user arrives and how frequently. However, as in the above-mentioned conventional technology, simply using such historical information does not necessarily enable the user's living area to be identified with high accuracy. Therefore, the present invention proposes logic for determining a user's living area based on the user's arrival history and the regional prosperity level of the area where the user is frequently located (for example, the area where the user's home or workplace is located).

[0015] A specific example of the life sphere identification logic according to the present invention will be described in detail below. Note that the life sphere identification logic according to the embodiment makes it possible to further optimize the services provided to users (e.g., advertisements, route guidance, risk notifications, search results, etc.). Specific examples of such service optimization logic will also be described in detail below.

[0016] [2. System Configuration] First, the configuration of an information processing system according to an embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing an example of an information processing system according to an embodiment. Fig. 1 shows an information processing system 1 as an example of an information processing system according to an embodiment. Information processing according to an embodiment (life zone identification logic and service optimization logic) may be realized in the information processing system 1.

[0017] 1, the information processing system 1 may include an external device 30 and an information processing device 100. The external device 30 and the information processing device 100 are connected to each other via a network N so as to be able to communicate with each other via a wired or wireless connection. The information processing system 1 shown in FIG. 1 may include any number of external devices 30 and any number of information processing devices 100.

[0018] The information processing device 100 is a device that performs information processing according to the embodiment. Specifically, the information processing device 100 acquires historical information on parking locations where users have parked their vehicles, and calculates a regional prosperity degree for a first extraction area based on predetermined parameters for the first extraction area extracted according to the distribution of parking locations indicated in the historical information. Then, the information processing device 100 identifies a second extraction area that is the user's living area based on the first extraction area and the regional prosperity degree.

[0019] 1, the information processing device 100 may be mounted on a vehicle VEx. That is, 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.

[0020] For example, the information processing device 100 may be configured with a navigation device and a recording device. As one example, the information processing device 100 may be a composite device in which a navigation device and a recording device that are independent from each other are communicably connected. As another example, the information processing device 100 may be a single device that has a navigation function and a recording function.

[0021] The information processing device 100 may also include various sensors. For example, the information processing device 100 may include various sensors such as a camera, an acceleration sensor, a gyro sensor, a GPS sensor, and an air pressure sensor. For this reason, the information processing device 100 may also have a function of providing dialogue and information to assist the user in driving based on sensor information acquired by the various sensors.

[0022] 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 a safe driving system.

[0023] Furthermore, by installing a predetermined application into a portable terminal device (for example, a smartphone, a tablet terminal, a notebook PC, a desktop PC, a PDA, or the like) that a user uses on a daily basis, the user can make the portable terminal device operate in the same manner as the information processing device 100. In other words, the user can use his or her own portable terminal device as a vehicle-mounted device. For this reason, the portable terminal device possessed by the user can also be considered as the information processing device 100 according to the embodiment.

[0024] In addition, in this embodiment, a user is a person that may include concepts such as a driver who drives the vehicle VEx or an owner of the vehicle VEx.

[0025] Next, the external device 30 may be any device that realizes the information processing according to the embodiment by cooperating with the information processing device 100. For example, the external device 30 may be a content providing device that provides content candidates (e.g., advertising content or tourist information) according to the driving of the vehicle VEx, or a map information providing device that provides an electronic map. As another example, the external device 30 may be a device that provides timetable information about transportation such as trains and buses, or a device that provides weather reports.

[0026] Here, if the information processing device 100 is an edge computer that performs edge processing near a user, 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.

[0027] 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 a processing result by the external device 30 and provide various services based on the acquired processing result.

[0028] [3. Overview of Information Processing] From here, an overview of information processing according to the embodiment will be described with reference to Fig. 2. Fig. 2 is an explanatory diagram illustrating an overview of information processing according to the embodiment. Specifically, Fig. 2 focuses on the living area identification logic and describes its processing procedure.

[0029] In addition, the example of Figure 2 shows an example in which user U1, an example of a user, is using a content provision service (hereinafter referred to as "service SA") corresponding to the information processing of the embodiment, and the living area of ​​user U1 is identified based on historical information of the parking location where user U1 parked vehicle VE1 (an example of vehicle VEx).

[0030] FIG. 2 also shows a scene in which the living area of ​​user U1 is identified from FIG. 2(a) to FIG. 2(c) as the living area identification logic progresses.

[0031] In addition, Figure 2 shows an example in which the living area identification logic is executed at a specific time when sufficient historical information has been accumulated to identify the living area (for example, one week after user U1 began using service SA).

[0032] First, step S1 in Fig. 2(a) will be described. The information processing device 100 extracts a main stay area AR1, which is the main area where the user U1 mainly stays in daily life, based on the distribution of parking locations indicated in the parking location history information. For example, the information processing device 100 may extract, as the main stay area AR1, the area with the highest density distribution of parking locations among the areas into which the map information indicating parking locations is divided. The main stay area AR1 can be considered, for example, as the home area where the user U1's home is located, and is an area corresponding to the first extracted area.

[0033] The information processing device 100 can also extract multiple main stay areas AR1, for example. In this case, the area with the highest density distribution of parking positions may be regarded as the home area, and the area with the next highest density distribution may be regarded as the workplace area.

[0034] Next, the information processing device 100 calculates the regional prosperity level PR for the primary stay area AR1. For example, the information processing device 100 may calculate the regional prosperity level PR for the primary stay area AR1 based on the number of predetermined facilities (e.g., shops, public facilities, tourist facilities, medical institutions, landmarks, etc.) present in the primary stay area AR1. As an example, the information processing device 100 may calculate the regional prosperity level PR for the primary stay area AR1 based on the number of facilities per unit area in the primary stay area AR1. For example, the information processing device 100 may calculate a higher value of the regional prosperity level PR so that the greater the number of facilities per unit area, the more prosperous the primary stay area AR1 is (the more extensive the variety of services is).

[0035] Next, step S2 in Fig. 2(b) will be described. The information processing device 100 executes a predetermined clustering algorithm on the parking positions included in the parking position history information. In this embodiment, the information processing device 100 executes DBSCAN clustering on the parking positions included in the parking position history information.

[0036] For example, the information processing device 100 can perform clustering under a set condition that if there are "N2" or more parking positions within a radius of "N1 km," the group is grown (a cluster is generated). Such a set condition may be set in advance for the information processing device 100, or the information processing device 100 may dynamically change the set condition as appropriate depending on the distribution status of the parking positions.

[0037] Furthermore, DBSCAN uses "min_distance" as a parameter that adjusts whether or not to grow into the same group depending on the distance between parking positions, and the information processing device 100 corrects this "min_distance" by the regional prosperity index PR. For example, the information processing device 100 corrects "min_distance" by multiplying it by the regional prosperity index PR as a weighting value, and performs DBSCAN clustering using the corrected "min_distance."

[0038] As described above, in DBSCAN, groups are generated based on the result of determining whether or not 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. The example in FIG. 2(b) shows an example in which the main stay area AR1 is corrected (processed) so as to expand its size (area and shape) as a result of DBSCAN clustering and convex hull processing.

[0039] Next, step S3 in Fig. 2(c) will be described. The information processing device 100 specifies a second extracted area, which is a polygon area after the main stay area AR1 (first extracted area) is controlled in step S2, as the living area AR2 of the user U1.

[0040] When the living area AR2 is identified in this way, the information processing device 100 executes a service control process so that a service corresponding to the living area AR2 is provided to the user U1.

[0041] Up to this point, an overview of the information processing according to the embodiment has been described using Figure 2. For example, when there is insufficient parking location history information (for example, in the initial state when user U1 has just started using service SA), the information processing device 100 may identify the main stay area AR1 as essentially the living area AR2 (main stay area AR1 = living area AR2). For example, the information processing device 100 may extract a circular area having a specific radius as the main stay area AR1 based on the distribution density.

[0042] However, the information processing device 100 may repeat the living area identification logic over time, and by accumulating history information during this time, it becomes possible to appropriately control the size of the main stay area AR1, as shown in Fig. 2(b). As a result, the information processing device 100 becomes able to identify a living area AR2 of an optimal size (main stay area AR1<living area AR2), as shown in Fig. 2(c).

[0043] As history information is further accumulated over time, the information processing device 100 may narrow the size of the living area AR2 more than the example in FIG. 2(c) or may further widen it.

[0044] Furthermore, the DBSCAN clustering shown in FIG. 2 corresponds to a process of removing, as noise when identifying a living area, parking locations that are estimated not to be user U1's living area (e.g., locations significantly far from the main stay area AR1) when generating groups (clusters). For example, in FIG. 2(a), such noisy parking locations are indicated by three black circles, while FIG. 2(b) shows an example in which the noisy parking locations have been removed as a result of DBSCAN clustering. Furthermore, by removing the noisy parking locations, as shown in FIG. 2(c), the information processing device 100 can identify a living area AR2 that is closer in size to the original living area considered by user U1.

[0045] [4. Service control according to living area] Next, an example of service optimization logic in which the information processing device 100 controls the services to be provided to the user U1 according to the living area AR2 will be described with reference to Fig. 3. Fig. 3 is a diagram showing an example of the service optimization logic according to the embodiment. Fig. 3 uses the initial state in which the user U1 has just started using the service SA as a comparative example, and explains an example of the optimization logic, showing how the living area AR2 is appropriately identified over time, and as a result, how the content to be provided to the user U1 is optimized.

[0046] First, FIG. 3(a) will be described. FIG. 3(a) shows an example of content provision in an initial state when user U1 has just started using service SA. As shown in FIG. 3(a), in the initial state, the information processing device 100 merely extracts a circular area around the home of user U1 as a main stay area AR1, and regards this main stay area AR1 as user U1's living area AR2 at this point. In this state, as shown in FIG. 3(a), the living area AR2 does not include the travel route RT1 connecting user U1's home and workplace. In other words, in the example of FIG. 3(a), the information processing device 100 is unable to recognize the travel route RT1 as the user U1's daily travel range. In other words, the information processing device 100 is unable to recognize the travel route RT1, which should originally be the living area for user U1, as a living area.

[0047] In such a case, as shown in FIG. 3(a), the information processing device 100 may provide tourist information that would be recognized by user U1, whose living area is along travel route RT1, as tourist information content. For example, as shown in FIG. 3(a), when user U1 is traveling along travel route RT1 and approaches City K, the information processing device 100 may provide tourist information content such as "Kurazukuri townscapes, famous for their streetscapes, are nearby." However, it is clear that user U1 is aware of such information, and it may be considered useless information for user U1. For example, if such tourist information content is presented every time user U1 commutes to work, user U1 may find it annoying.

[0048] However, as explained above, the information processing device 100 can more appropriately identify the living area AR2 as history information accumulates over time. For example, one week after the user U1 starts using the service SA, the information processing device 100 can expand the main stay area AR1 to an area that includes the travel route RT1, as shown in Figure 3(b), and can identify the living area AR2 that is closer to the original living area that the user U1 considers to be.

[0049] In the example of FIG. 3(b), the information processing device 100 can determine that the tourist information content initially provided is useless to user U1. As a result, even if user U1 approaches City K while traveling along travel route RT1, the information processing device 100 controls the information processing device 100 so that the tourist information content is not provided, as shown in FIG. 3(b). This also enables the information processing device 100 to optimize the provision of content so that user U1 does not feel bothered by the content provided.

[0050] 5. Configuration of Information Processing Device From here, the information processing device 100 according to the embodiment will be described with reference to Fig. 4. Fig. 4 is a diagram showing an example of the configuration of the information processing device 100 according to the embodiment. As shown in Fig. 4, the information processing device 100 has a communication unit 110, a storage unit 120, and a control unit 130.

[0051] (Regarding the communication unit 110) The communication unit 110 is realized by, for example, a network interface card (NIC), etc. The communication unit 110 is connected to the network N by wire or wirelessly, and transmits and receives information to and from the in-vehicle device 10, for example.

[0052] (Regarding the storage unit 120) The storage unit 120 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk, an optical disk, etc. The storage unit 120 may include a history information database 121 and a living area information database 122.

[0053] (Regarding History Information Database 121) The history information database 121 stores history information of parking locations where a user parked the vehicle VEx. For example, the history information database 121 may store a pair of a parking location where a user parked the vehicle VEx and the parking time, which is the time at which the parking was performed, as one record, in association with a history ID. Note that the information processing device 100 may have a GPS receiver, and the parking location may be defined by longitude and latitude information derived from the GPS.

[0054] (About the Living Area Information Database 122) The living area information database 122 stores information indicating the living area identified by the living area identification logic. For example, the living area information database 122 may store, as one record, a combination of the date and time when the living area identification logic was executed and information indicating the living area AR2 identified at that time, in association with the living area ID.

[0055] (Regarding the control unit 130) The control unit 130 is realized by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like executing various programs (for example, the information processing program according to the embodiment) stored in a storage device inside the information processing device 100 using RAM as a work area. The control unit 130 is also realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0056] As shown in Fig. 4, the control unit 130 has a detection unit 131, an acquisition unit 132, a calculation unit 133, an analysis unit 134, an identification unit 135, and a service control unit 136, and realizes or executes the functions and actions of information processing described below. Note that the internal configuration of the control unit 130 is not limited to the configuration shown in Fig. 4, and may have other configurations as long as they perform the information processing described below. Furthermore, the connection relationship between the processing units included in the control unit 130 is not limited to the connection relationship shown in Fig. 4, and may be other connection relationships.

[0057] (Regarding the detection unit 131) The detection unit 131 determines whether the user has parked the vehicle VEx, and if it is determined that the user has parked the vehicle VEx, it detects the parking location where the user has parked the vehicle VEx. For example, when the vehicle VEx is stopped, the detection unit 131 may measure the stopping period, and if it recognizes that the vehicle VEx has been stopped for a predetermined period or longer, it may determine that the user has stopped the vehicle VEx. In this case, the detection unit 131 may detect the location where the vehicle VEx is parked as the parking location.

[0058] 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.

[0059] (Regarding the acquisition unit 132) The acquisition unit 132 acquires history information about parking positions where the user parked the vehicle VEx. For example, when the acquisition unit 132 determines that it is time to execute the living area determination logic, the acquisition unit 132 may acquire history information from the history information database 121.

[0060] (Regarding the calculation unit 133) The calculation unit 133 calculates a regional prosperity degree, which is the degree of prosperity in a predetermined area. For example, the calculation unit 133 acquires a distribution status of parking locations based on historical information about parking locations. Then, based on the acquired distribution status, the calculation unit 133 extracts a main stay area AR1, which is a primary area where users of the vehicle VEx mainly stay in their daily lives. For example, the calculation unit 133 may extract, as the main stay area AR1, an area with the highest density distribution of parking locations from among the areas into which the map information indicating the distribution status is divided.

[0061] In this state, the calculation unit 133 calculates the regional prosperity degree 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 a predetermined parameter per unit area in the main stay area AR1 as the regional prosperity degree PR.

[0062] The specified parameters here refer to either the number of specified facilities (e.g., shops, public facilities, tourist facilities, medical institutions, landmarks, etc.) present in the main stay area AR1, the number of public transportation facilities (e.g., stations, bus stops, etc.) present in the main stay area AR1, or feature information indicating the geographical features of the main stay area AR1.

[0063] In addition, the calculation unit 133 may calculate the regional prosperity level PR using the number of specified facilities present in the main stay area AR1 or the number of public transportation facilities present in the main stay area AR1 as a specified parameter, and correct the calculated regional prosperity level PR using characteristic information.

[0064] (Regarding the analysis unit 134) The analysis unit 134 clusters the parking locations indicated by the parking location history information based on the regional prosperity level 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 a set condition that if there are "N2" or more parking locations within a radius of "N1 km," the group is grown.

[0065] In addition, in DBSCAN clustering, the analysis unit 134 uses "min_distance" as a parameter to adjust whether or not to grow into the same group depending on the distance between parking locations, but "min_distance" may be corrected by the regional prosperity level PR and clustering may be performed again using the corrected "min_distance".

[0066] (Regarding the identification unit 135) The 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 level PR. For example, the identification unit 135 performs convex hull processing on the group to which the parking locations included in the main stay area AR1 belong, among the groups of parking locations generated as a result of clustering by the analysis unit 134. The identification unit 135 then identifies the second extraction area, which is a polygon area generated by the convex hull processing, as the user's living area AR2. Note that this processing corresponds to processing of controlling the size of the main stay area AR1 to make its size closer to the original living area considered by the user, and identifying the second extraction area, which is the closer area, as the living area AR2.

[0067] Furthermore, the identification unit 135 may register information indicating the living area AR2 in the living area information database.

[0068] (Regarding the service control unit 136) The service control unit 136 controls the services to be provided to the user of the vehicle VEx according to the living area AR2 (second extracted area) identified by the identification unit 135. For example, the service control unit 136 controls the services to be provided to the user of the vehicle VEx according to service optimization logic based on the living area AR2 identified by the identification unit 135.

[0069] As described with reference to FIG. 3 , the service control unit 136 may perform control so that tourist information content corresponding to the living area AR2 is not provided to the user. On the other hand, the service control unit 136 may perform control so that advertising content related to a predetermined facility (e.g., a store) present in the living area AR2 is provided to the user. For example, the service control unit 136 may extract advertising content for a store present in the living area AR2 from the advertising content acquired from the external device 30, and perform control so that this advertising content is output from the information processing device 100. Note that the advertising content may be image information, and in this case, may be output from a display screen of the information processing device 100. On the other hand, the advertising content may be audio information, and in this case, may be output from a speaker of the information processing device 100.

[0070] For example, in a situation where the user's living area is not accurately identified, advertising content for an area outside the user's daily range of activity may be provided, and such advertising content may not be highly appealing to the user. On the other hand, according to the service optimization logic of the embodiment, advertising content corresponding to a living area AR2, which is closer in size to the user's original living area, is provided, thereby increasing the appeal of the advertising content.

[0071] The service control unit 136 can also control the provision of route guidance based on the destination set by the user and the living area AR2. For example, the service control unit 136 controls the route provided as route guidance to the destination based on whether the destination set by the user is located in the living area AR2. To give a more specific example, if both the user's departure point and destination are within the living area AR2, the service control unit 136 searches the living area AR2 for roads that are routinely used by local residents in the living area AR2. The service control unit 136 then controls the provision of a route from the departure point to the destination that includes the searched road as, for example, a shortcut route (or a detour route). For a user whose daily activities are within the living area AR2, such a shortcut route may be highly likely to be traversed without getting lost, potentially saving time. In other words, the information processing device 100 can increase the variety of route selections in route guidance by providing route guidance based on such possibilities.

[0072] On the other hand, for example, if both the user's departure point and destination are not included within the living area AR2, the service control unit 136 may not provide shortcut routes that are in line with the local residents in the living area AR2, but may instead provide route guidance consisting of representative routes that are common to all users.

[0073] Here, it is assumed that the navigation system corresponding to the information processing device 100 includes a notification control system that notifies the user in advance of the presence of a predetermined dangerous area (e.g., a construction site, etc.). In this case, the service control unit 136 may control to issue a notification based on whether or not the predetermined dangerous area is included in the living area AR2. For example, the service control unit 136 detects whether or not the predetermined dangerous area exists on the route currently being guided to the user within the living area AR2. Here, for example, even if the predetermined dangerous area is detected, the service control unit 136 may presume that the user is already aware of the existence of the predetermined dangerous area and control not to notify the user of the existence of the dangerous area. In this way, the information processing device 100 can control not to notify the user of information that is useless to the user, thereby reducing situations in which the user feels annoyed.

[0074] The service control unit 136 may detect whether or not a predetermined dangerous area exists on the route currently being guided to the user, even on a route outside the living area AR2. For example, if the service control unit 136 detects a predetermined dangerous area, it may infer that the user is unaware of the existence of the predetermined dangerous area, and may perform control so as to notify the user of the existence of the dangerous area.

[0075] Furthermore, when a search request is input by a user, the service control unit 136 may control the search results corresponding to the search request depending on whether the location where the search request was input is within the living area AR2. For example, suppose that while driving within the living area AR2, the user operates the information processing device 100 to request a search for "pasta restaurants." In this case, the service control unit 136 searches for "pasta restaurants" that exist within the living area AR2. The service control unit 136 then performs control so that information about the searched "pasta restaurants" is provided with priority given to the popularity of the "pasta restaurants" over the user's current location. For example, the service control unit 136 may perform control so that information about more popular "pasta restaurants" is provided with priority among the searched "pasta restaurants."

[0076] Meanwhile, for example, suppose that while driving outside the living area AR2, the user operates the information processing device 100 to request a search for "pasta restaurants." In this case, the service control unit 136 searches for "pasta restaurants" that are located within a predetermined range corresponding to the user's current location. The service control unit 136 may then perform control so that, of the "pasta restaurants" that have been searched for, information on "pasta restaurants" that are closer to the user's current location is provided with priority.

[0077] [6. Processing Procedure] Next, the information processing procedure realized by the information processing method according to the embodiment will be described with reference to Figures 5 and 6. The information processing procedure according to the embodiment can be divided into a collection step of collecting parking location history information and a living area identification step using the collected history information. Therefore, the collection step will be described in Figure 5, and the living area identification step will be described in Figure 6.

[0078] [6-1. Processing Procedure (1)] First, the procedure of information processing performed in the collection step of collecting history information will be described with reference to Fig. 5. Fig. 5 is a flowchart showing the collection procedure of collecting history information.

[0079] First, when the vehicle VEx is stopped, the detection unit 131 measures the stopping period to determine whether the vehicle has been stopped for a predetermined period or more (step S501). If the detection unit 131 determines that the stopping period is less than the predetermined period (in other words, if it determines that the vehicle VEx has started to move before the predetermined period has elapsed) (step S501; No), the detection unit 131 waits until it can determine that the vehicle has been stopped for a predetermined period or more.

[0080] On the other hand, if it is determined that the vehicle has been stopped for a predetermined period of time or longer (step S501; Yes), the detection unit 131 determines that the user has parked the vehicle VEx (step S502).

[0081] Next, the detection unit 131 detects the position where the vehicle VEx is parked as the parking position, and stores the position information indicating the parking position as parking position history information in the history information database 121 (step S503).

[0082] [6-2. Processing Procedure (2)] Next, the procedure of information processing performed in the living area identifying step will be described with reference to Fig. 6. Fig. 6 is a flowchart showing the identifying process procedure for identifying a living area.

[0083] First, the acquisition unit 132 determines whether it is time to acquire the parking position history information (step S601). If it is not time to acquire the parking position history information (step S601; No), the acquisition unit 132 waits until it can determine that it is time to acquire the parking position history information.

[0084] On the other hand, when the acquisition unit 132 determines that it is time to acquire the parking position history information (step S601; Yes), the acquisition unit 132 acquires the parking position history information (step S602). For example, the acquisition unit 132 acquires the history information from the history information database 121.

[0085] Next, the calculation unit 133 extracts a main stay area AR1 based on the distribution of parking positions indicated by the parking position history information (step S603). For example, the calculation unit 133 may extract, as the main stay area AR1, an area with the highest density distribution of parking positions from among the areas into which the map information indicating the distribution is divided.

[0086] Then, the calculation unit 133 calculates the regional prosperity degree PR of the main stay area AR1 based on the number of predetermined facilities present in the main stay area AR1 (first extracted area) (step S604). For example, the calculation unit 133 may calculate the number of predetermined facilities per unit area in the main stay area AR1 as the regional prosperity degree PR.

[0087] Next, the analysis unit 134 clusters the parking locations indicated by the parking location history information based on the regional prosperity level PR (for example, performs DBSCAN clustering), and detects the group to which the parking locations included in the main stay area AR1 belong among the groups of parking locations generated as a result of the clustering (step S605).

[0088] The identification unit 135 performs convex hull processing on the group detected in step S605 (step S606). As an example, the identification unit 135 may perform convex hull processing on the parking positions belonging to the detected group.

[0089] As another example, the identification unit 135 may perform convex hull processing using link information corresponding to parking positions belonging to the detected group. For example, the identification unit 135 detects road information corresponding to the driving route of the vehicle VEx to reach the parking position based on the parking positions belonging to the detected group and the history of the driving positions of the vehicle VEx. For example, the identification unit 135 detects, as the road information, link information indicating the link shape of the road corresponding to the driving route of the vehicle VEx. Note that the identification unit 135 may also use predetermined map information to detect the link information. Then, the identification unit 135 performs convex hull processing on the detected road information (link shape). The identification unit 135 may also perform convex hull processing using information on the driving link and the number of times of driving.

[0090] Then, the identifying unit 135 identifies the polygon area (second extracted area) that is the convex hull generated by the convex hull processing as the living area AR2 (step S607).

[0091] Furthermore, the service control unit 136 may perform control so that a service appropriate for the living area AR2 is provided (step S608).

[0092] [7. Modifications] The information processing device 100 according to the above embodiment may be implemented in various different forms other than the above embodiment, so other embodiments of the information processing device 100 will be described below.

[0093] For example, when executing the living area identification logic in a state where sufficient historical information has not been accumulated to identify a living area (for example, in an initial state when a user has just started using the service SA), the information processing device 100 may extract a specific area corresponding to the user as the main stay area AR1. For example, if the user has registered a location where they frequently stay (for example, the location of their home), the information processing device 100 may extract a circular area centered on this location as the main stay area AR1. As an example, the information processing device 100 can extract a circular area having a radius that corresponds to the area of ​​an average living area as the main stay area AR1.

[0094] For example, if the information processing device 100 simply performs DBSCAN clustering in an initial state where sufficient historical information has not been accumulated, many parking positions may be removed as noise by DBSCAN clustering, and clusters may not be generated. However, in an initial state where sufficient historical information has not been accumulated, the information processing device 100 can appropriately identify the living area AR2 even if historical information is insufficient by setting the main stay area AR1 using the above method.

[0095] If the information processing device 100 determines that sufficient history information has been accumulated, it may delete the main stay area AR1 set in the initial state and perform DBSCAN clustering with appropriate parameters. Then, the information processing device 100 may identify the living area AR2 by enclosing the parking positions deemed to be clusters with a convex hull through convex hull processing of the parking positions.

[0096] Furthermore, multiple clusters may be generated as a result of DBSCAN clustering. In such cases, the information processing device 100 may identify multiple living areas AR2 in descending order of the number of parking locations included in the cluster, such as a first living area AR2, a second living area AR2, a third living area AR2, ..., an nth living area AR2, etc.

[0097] Furthermore, by identifying multiple living areas AR2 according to the number of parking locations in this way, the information processing device 100 can change the evaluation for each living area AR2. For example, the information processing device 100 may change the evaluation (importance) between a first living area AR2 with the largest number of parking locations (for example, an area around one's home) and a second living area AR2 with the next largest number of parking locations (for example, an area around one's workplace).

[0098] Furthermore, the information processing device 100 may control the provision of services according to the evaluation by changing the evaluation as described above. For example, the information processing device 100 may control the provision of a reminder based on a schedule when the user is in the first living area AR2. By receiving such a reminder while the user is in the first living area AR2, which is the area around the user's home, the user can use their local knowledge to recover from any forgotten tasks.

[0099] On the other hand, when the user is located in the second living area AR2, the information processing device 100 may not notify the user of the reminder or may reduce the number of times the reminder is notified.

[0100] [8. Removal of restrictions] In the above embodiment, an example was shown in which the information processing device 100 determines the main stay area AR1 and the regional prosperity level PR based on the historical information of the parking location where the user parked the vehicle VEx, and identifies the living area AR2 by clustering processing and convex hull processing using this information.

[0101] However, the information processing device 100 may also use historical information on parking locations where the user parked the vehicle VEx on roads other than public roads (off-road) to determine the main stay area AR1 and the regional prosperity level PR, and use this information to identify the living area AR2.

[0102] In addition, the information processing device 100 may also use historical information of locations where the user is estimated to have temporarily stopped the vehicle VEx (for example, locations where the speed of the vehicle VEx became zero) to determine the main stay area AR1 and the regional prosperity level PR, and use this information to identify the living area AR2.

[0103] [9. Hardware Configuration] The information processing device 100 according to the embodiment described above is realized, for example, by a computer 1000 configured as shown in Fig. 7. Fig. 7 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, a RAM 1200, a ROM 1300, an HDD 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F) 1700.

[0104] The CPU 1100 operates and controls each unit based on programs stored in the ROM 1300 or the HDD 1400. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 starts up, programs that depend on the hardware of the computer 1000, and the like.

[0105] The HDD 1400 stores programs executed by the CPU 1100, data used by such programs, etc. The communication interface 1500 receives data from other devices via a predetermined communication network and sends it to the CPU 1100, and transmits data generated by the CPU 1100 to other devices via the predetermined communication network.

[0106] The CPU 1100 controls output devices such as a display and a printer, and input devices such as a keyboard and a mouse, via the input / output interface 1600. The CPU 1100 acquires data from the input devices via the input / output interface 1600. The CPU 1100 also outputs generated data to the output devices via the input / output interface 1600.

[0107] Media interface 1700 reads a program or data stored in recording medium 1800 and provides it to CPU 1100 via RAM 1200. CPU 1100 loads the program or data from recording medium 1800 onto RAM 1200 via media interface 1700 and executes the loaded program. Recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or a PD (Phase Change Rewritable Disc), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

[0108] For example, when the computer 1000 functions as the information processing device 100 according to the embodiment, the CPU 1100 of the computer 1000 executes programs loaded onto the RAM 1200 to realize the functions of the control unit 130. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800, but as another example, the CPU 1100 may obtain these programs from another device via a predetermined communication network.

[0109] [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 using known methods. In addition, the information including the processing procedures, specific names, various data, and parameters shown in the above documents and drawings can be changed as desired unless otherwise specified. For example, the various information shown in each drawing is not limited to the information shown in the drawings.

[0110] Furthermore, the components of each device shown in the figure are conceptual functional components and do not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

[0111] Furthermore, the above-described embodiments can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0112] Although some of the embodiments of the present application have been described in detail above with reference to the drawings, these are merely examples, and the present invention can be implemented in other forms that include the embodiments described in the Disclosure of the Invention section and that have undergone various modifications and improvements based on the knowledge of those skilled in the art. [Explanation of symbols]

[0113] 1. Information Processing Systems 30 External device 100 Information processing device 120 Storage section 121 Historical Information Database 122 Living Area Information Database 130 control section 131 Detection unit 132 Acquisition Department 133 Calculation Unit 134 Analysis Department 135 Specific part 136 Service Control Section

Claims

1. an acquisition unit that acquires historical information about parking locations where users have parked their vehicles; a calculation unit that calculates, as a regional prosperity degree, a predetermined parameter that indicates the degree of service enrichment in a first extracted area that is a main area where users mainly stay in their daily lives, extracted according to the distribution situation of the parking positions; and an identification unit that identifies an area formed by a group of a plurality of parking positions identified so as to surround the first extracted area from a group of parking positions generated based on each parking position included in the history information and the regional prosperity degree, as a second extracted area that is a living area of ​​the user; An information processing device comprising:

2. The calculation unit calculates a predetermined parameter per unit area in the first extracted region as the regional prosperity degree.

2. The information processing apparatus according to claim 1, wherein:

3. The predetermined parameter is any one of the number of predetermined facilities present in the first extraction area, the number of public transportation facilities present in the first extraction area, and feature information indicating geographical features in the first extraction area.

3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

4. An analysis unit that clusters the parking positions indicated by the history information based on the regional prosperity degree, The identification unit controls the first extraction area by performing convex hull processing on a group to which the parking positions included in the first extraction area belong, among the groups of the parking positions generated as a result of the clustering by the analysis unit, and identifies a second extraction area, which is the area after the control, as the living area of ​​the user.

4. The information processing device according to claim 1, wherein the information processing device is a computer.

5. The analysis unit adjusts a predetermined parameter used for clustering based on the regional prosperity degree when clustering the parking locations.

5. The information processing apparatus according to claim 4,

6. The information processing device further includes a service control unit that controls a service to be provided to the user in accordance with the second extracted area identified by the identification unit.

6. The information processing device according to claim 1, wherein:

7. The service control unit controls so that advertising content related to a predetermined facility present in the second extraction area is provided to the user.

7. The information processing apparatus according to claim 6,

8. The service control unit controls so that route guidance is provided according to the destination set by the user and the second extracted area.

8. The information processing device according to claim 6, wherein:

9. The service control unit controls a route provided as route guidance to a destination set by the user depending on whether the destination is in the second extraction area.

8. The information processing apparatus according to claim 7,

10. The service control unit controls a notification control system that notifies the user of the presence of a predetermined dangerous area in advance during route guidance, so as to make a notification depending on whether the predetermined dangerous area is included in the second extracted area.

10. The information processing device according to claim 7, wherein:

11. When a search request is input by the user, the service control unit controls a search result corresponding to the search request depending on whether the location where the search request is input is within the living area.

11. The information processing device according to claim 7, wherein:

12. An information processing method executed by an information processing device, an acquisition step of acquiring historical information on parking locations where users have parked their vehicles; a calculation step of calculating, as a regional prosperity degree, a predetermined parameter indicating the degree of service provision in a first extracted area, which is a main area where users mainly stay in their daily lives, extracted according to the distribution situation of the parking positions; a step of identifying an area formed by a group of a plurality of parking positions identified so as to surround the first extracted area from a group of parking positions generated based on each parking position included in the history information and the regional prosperity degree, as a second extracted area that is a living area of ​​the user; An information processing method comprising:

13. an acquisition step for acquiring historical information on parking locations where a user has parked a vehicle; a calculation step of calculating a predetermined parameter indicating the degree of service availability in a first extracted area, which is a main area where users mainly stay in their daily lives, extracted according to the distribution situation of the parking positions, as a regional prosperity degree; a step of identifying an area formed by a group of a plurality of parking positions identified so as to surround the first extracted area from a group of parking positions generated based on each parking position included in the history information and the regional prosperity degree, as a second extracted area that is a living area of ​​the user; An information processing program for causing an information processing device to execute the above.

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