JP7417564B2 - Information processing device, information processing method, and information processing program - Google Patents

Description

The present invention relates to an information processing device, an information processing method, and an information processing program.

Conventionally, various techniques have been provided for generating information regarding real space (real space) using various information. For example, a technique has been provided that generates information such as polygon data using images captured by a camera (for example, Patent Document 1).

Japanese Patent Application Publication No. 2012-118666

However, the above-mentioned conventional technology has room for improvement. For example, in the above-mentioned conventional technology, in order to generate information such as polygon data, it is necessary to move in a vehicle or the like on which a camera is loaded, and the load for generating information is large. Therefore, it is desired to appropriately generate information using information regarding user behavior.

The present application has been made in view of the above, and aims to provide an information processing device, an information processing method, and an information processing program that appropriately generate information using information regarding user behavior.

The information processing device according to the present application includes an acquisition unit that acquires location information indicating a user's position when performing a predetermined action and first information indicating a map; a determining unit that determines a relevant part that is a part that has relevance to the predetermined action in the map based on the location of the user indicated; A generation unit that generates second information indicating .

According to one aspect of the embodiment, it is possible to appropriately generate information using information regarding user behavior.

FIG. 1 is a diagram illustrating an example of information processing according to an embodiment. FIG. 2 is a diagram illustrating an example of information processing according to the embodiment. FIG. 3 is a diagram illustrating a configuration example of an information processing system according to an embodiment. FIG. 4 is a diagram illustrating a configuration example of an information processing device according to an embodiment. FIG. 5 is a diagram illustrating an example of a user information storage unit according to the embodiment. FIG. 6 is a diagram illustrating an example of a store information storage unit according to the embodiment. FIG. 7 is a diagram illustrating an example of an action history information storage unit according to the embodiment. FIG. 8 is a flowchart illustrating an example of information processing according to the embodiment. FIG. 9 is a flowchart illustrating an example of information processing according to the embodiment. FIG. 10 is a diagram illustrating a concept regarding another method of estimation processing. FIG. 11 is a diagram illustrating an example of another method of estimation processing. FIG. 12 is a diagram illustrating an example of another method of estimation processing. FIG. 13 is a diagram illustrating an example of another method of estimation processing. FIG. 14 is a diagram illustrating an example of another method of estimation processing. FIG. 15 is a hardware configuration diagram showing an example of a computer that implements the functions of the information processing device.

DESCRIPTION OF THE PREFERRED EMBODIMENTS An information processing apparatus, an information processing method, and an embodiment of an information processing program (hereinafter referred to as "embodiments") according to the present application will be described in detail below with reference to the drawings. Note that the information processing apparatus, information processing method, and information processing program according to the present application are not limited to this embodiment. Further, in each of the embodiments below, the same parts are given the same reference numerals, and redundant explanations will be omitted.

(Embodiment)
[1. Information processing〕
First, an example of various information processing will be explained using FIGS. 1 and 2. 1 and 2 are diagrams illustrating an example of information processing according to the embodiment. First, an overview of the processing in FIGS. 1 and 2 will be explained. In FIG. 1, the information processing device 100 identifies users based on a predetermined range (also referred to as a "user range") indicating the user's location (latitude and longitude) and a range occupied by a store (also referred to as a "store range"). Execute estimation processing to estimate stores that have geographical relevance (also referred to as "related stores"). Note that the term "store" here is not limited to a retail store in the narrow sense, but broadly refers to facilities occupying a certain physical area, including various services and public facilities.

In FIG. 1, estimation processing is performed based on a user range indicating the user's position (latitude and longitude) detected by a GPS (Global Positioning System) sensor of a terminal device 10 used by the user, and a store range indicated by store polygon data. This will be explained as an example. Further, in FIG. 1, a case will be described as an example in which a store that the user has entered is estimated as the related store. Note that the information used to estimate the user range is not limited to information (sensor information) detected by a GPS sensor (also simply referred to as "GPS"), for example, Wi-Fi (registered trademark) (Wireless-Fidelity), Bluetooth ( Any information may be used as long as it can be used to estimate the user range, such as information regarding beacons (registered trademark), beacons, etc. Further, the information indicating the store range used in the estimation process is not limited to polygon data, and any information that can be used to estimate the store range may be used.

In addition, in FIG. 2, the information processing device 100 uses location information indicating the user's position when performing a predetermined action (also referred to as "position at the time of action") to perform a predetermined action on the map indicated by the first information. A generation process is executed to determine a portion having a relationship with the second information (also referred to as a “related portion”) and generate second information. In Figure 2, the position when the user made the payment for purchasing the product at the store is used as the user's action position, the relevant part on the map is determined, and the polygon data of the store where the user purchased the product is generated as the second information. The generation process will be explained as an example.

Note that the user's predetermined action is not limited to payment, and may be various actions related to product purchase or payment. Furthermore, the user's predetermined behavior is not limited to the user's purchase-related behavior, but may be any behavior that can generate the desired second information, such as searching or accessing the Wi-Fi of the store. You can. Further, the second information to be generated is not limited to the polygon data of the store, but may be any information as long as it indicates a real object (that is, an object existing in real space). The information processing apparatus 100 may generate not only polygon data but also various vector data. For example, if the actual object for which the second information is to be generated is a parking lot that has some entrances and exits, the information processing device 100 generates a polyline with open portions corresponding to the entrances and exits of the parking lot. Data or the like may be generated as the second information.

[1-1. Information processing system configuration]
Prior to describing FIGS. 1 and 2, the configuration of the information processing system 1 will be described using FIG. 3. FIG. 3 is a diagram illustrating a configuration example of an information processing system according to an embodiment. As shown in FIG. 3, the information processing system 1 includes a terminal device 10, a store device 20, a data providing device 50, and an information processing device 100. The terminal device 10, the store device 20, the data providing device 50, and the information processing device 100 are connected via a predetermined network N so that they can communicate by wire or wirelessly. Note that the information processing system 1 shown in FIG. 3 includes multiple terminal devices 10, multiple terminal devices 10, multiple store devices 20, multiple data providing devices 50, and multiple An information processing device 100 may also be included.

The terminal device 10 is a computer used by a user. The terminal device 10 is a device (portable terminal) that can be carried by a user. The terminal device 10 is realized by, for example, a smartphone, a tablet terminal, a notebook PC (Personal Computer), a mobile phone, a PDA (Personal Digital Assistant), or the like. 1 and 2 show a case where the terminal device 10 is a smartphone. Note that below, the terminal device 10 may be referred to as a user. That is, in the following, the user can also be read as the terminal device 10.

Furthermore, it is assumed that the terminal device 10 has a function such as a GPS sensor, and is capable of detecting the user's position (latitude and longitude) and acquiring information indicating the latitude and longitude. Further, the terminal device 10 may acquire information indicating the user's location using a predetermined wireless communication function such as Wi-Fi or Bluetooth, or a function such as a beacon. Additionally, the terminal device 10 transmits various information such as location to the information processing device 100. For example, the terminal device 10 transmits information indicating the user's behavior (behavior information) including the user's location to the information processing device 100.

The store device 20 is a computer used by the manager of each store. For example, the manager of restaurant A uses the store device 20 to send and receive information to and from the information processing device 100. For example, the store device 20 is realized by a smartphone, a tablet terminal, a notebook PC, a desktop PC, a mobile phone, a PDA, or the like. The store device 20 transmits various information regarding the store to the information processing device 100. The store device 20 transmits information requested by the information processing device 100 to the information processing device 100. The store device 20 transmits store information including various information such as the location of the store to the information processing device 100.

The data providing device 50 is a server that provides various data to the information processing device 100. The data providing device 50 may provide the information processing device 100 with open data, which is data that is permitted to be widely used. Further, the data providing device 50 may provide the information processing device 100 with information (map information) regarding a map displayed by a map display application or the like. For example, the data providing device 50 provides the information processing device 100 with map information including an image showing a two-dimensional map showing structures as lines or areas surrounded by lines. Note that the above is just an example, and the data providing device 50 transmits various information necessary for processing by the information processing device 100 to the information processing device 100.

The information processing device 100 is an estimation device (computer) that performs estimation processing to estimate a user's related stores. Further, the information processing device 100 is a generation device (computer) that generates second information using the user's position at the time of action and the first information. The information processing apparatus 100 may provide a service based on the information estimated by the estimation process. Further, the information processing apparatus 100 may provide a service based on the information estimated in the generation process. For example, the information processing apparatus 100 may perform the estimation process using second information such as polygons estimated in the generation process.

[1-2. Example of estimation process]
An example of information processing performed by the information processing apparatus 100 will now be described using FIG. 1. FIG. 1 is a diagram illustrating an example of estimation processing performed by the information processing apparatus 100 (see FIG. 3). In addition, in FIG. 1, the case where the user who uses the terminal device 10 is the user U1 will be described as an example.

First, the information processing device 100 acquires information indicating a user range from the terminal device 10 used by the user U1. For example, the terminal device 10 transmits sensor information detected by a GPS that detects the position of the user U1 to the information processing device 100. For example, the information processing device 100 acquires information indicating a user range in which the accuracy of GPS for detecting the position of the user U1 is equal to or higher than a predetermined threshold. For example, the information processing device 100 uses information indicating a circular range (also referred to as "circular range information") that indicates a confidence interval with accuracy equal to or higher than a predetermined value (for example, 95%) as information indicating the user range. get. In FIG. 1, the information processing apparatus 100 acquires the circular range BA1 as the user range of the user U1. Note that the information processing device 100 may generate information on the circular range BA1, or may receive information on the circular range BA1 from an external device such as the terminal device 10.

Then, the information processing device 100 converts the circular range information indicating the user range into information indicating the polygon range (also referred to as "polygon range information") (step S1). For example, the information processing device 100 converts a circular range into a polygonal range (“polygonal range”) according to the area of the circular range. For example, the information processing device 100 converts a circular range into a polygonal range having the same area as the circular range. For example, the information processing device 100 converts the circular range into a polygonal range whose center coincides with the circular range. The information processing device 100 generates a polygonal range from a circular range by such conversion processing.

In FIG. 1, the information processing device 100 converts a circular range BA1 into a square polygonal range AA1. Thereby, the information processing device 100 generates a polygonal range AA1 that has the same area as the circular range BA1 and whose centers coincide with each other. For example, the information processing device 100 generates the polygon range AA1 by aligning the polygon range AA1 so that the sides of the polygon range AA1 are along either latitude (latitude lines) or longitude (meridian lines). For example, the information processing apparatus 100 generates the polygon range AA1 so that the vertical sides of the polygon range AA1 are parallel to the meridian lines, and the horizontal sides of the polygon range AA1 are parallel to the latitude lines. Note that the direction of the polygon range AA1 may be set to any direction. Moreover, the square is only an example, and the polygonal range is not limited to the square, but may be a rectangle other than the square, a triangle, or a polygon larger than a pentagon.

Further, the information processing device 100 acquires polygon data of a store (also simply referred to as a "polygon") as information indicating a store range. The information processing device 100 acquires polygons of stores located within a predetermined distance from the user range of the user U1. In FIG. 1, the information processing device 100 acquires a polygon PG1 of Z convenience store, which is a store, as information indicating the store range of Z convenience store. Note that the information processing device 100 may acquire information on the polygon PG1 of the Z convenience store from the storage unit 120 (see FIG. 4), or may receive information on the polygon PG1 of the Z convenience store from an external device such as the data providing device 50. You may.

Then, the information processing device 100 executes an estimation process for estimating a related store that has a geographical relationship with the user based on the polygon range and the store range. The information processing device 100 calculates the area (also referred to as "overlapping area") of a region (overlapping region) where the user range of the user and the store range of Z convenience store overlap (step S2). In FIG. 1, the information processing device 100 calculates the area of an overlapping region OA1 where the polygon range AA1 of the user U1 and the polygon PG1 of the Z convenience store overlap.

For example, the information processing device 100 calculates the area of the overlapping region OA1 based on the positions of a first point PT1, which is one vertex of the polygon range AA1, and a second point PT2, which is one vertex of the polygon PG1. . In FIG. 1, the information processing apparatus 100 has a first point PT1 located within the polygon PG1 among the vertices of the polygon range AA1, and a second point PT2 located within the polygon range AA1 among the vertices of the polygon PG1. The area of the overlapping area OA1 is calculated based on the position of . Note that the above is just an example, and the information processing apparatus 100 may calculate the area of the overlapping area OA1 in any way as long as it can calculate the area of the overlapping area OA1.

Then, the information processing device 100 uses the calculated overlapping area to calculate a score indicating the degree to which the Z convenience store is an affiliated store of the user U1 (step S3). In FIG. 1, the information processing device 100 calculates, as a score, the ratio of the area of the overlapping region OA1 to the area of the polygon range AA1, which is the user range of the user U1. The information processing device 100 calculates the score of the Z convenience store by dividing the area of the overlapping area OA1 by the area of the polygon range AA1, as shown in calculation result SC1 in FIG.

Then, the information processing device 100 estimates related stores using the calculated score (step S4). In FIG. 1, the information processing device 100 uses the calculated score of Z convenience store to estimate whether Z convenience store is an affiliated store of user U1.

For example, when the score of Z convenience store is equal to or greater than the threshold value, the information processing device 100 estimates that Z convenience store is the store (related store) that the user U1 entered. That is, the information processing device 100 determines that the user U1 has entered the Z convenience store when the score of the Z convenience store is equal to or higher than a threshold value (for example, an arbitrary value such as 0.5 or 0.7).

Furthermore, when the score of Z convenience store is less than the threshold value, information processing device 100 estimates that Z convenience store is not the store (related store) that user U1 entered. That is, when the score of the Z convenience store is less than the threshold, the information processing device 100 determines that the user U1 has not entered the Z convenience store.

In FIG. 1, since the score of Z convenience store is less than the threshold value, the information processing device 100 estimates that Z convenience store is not the store (related store) that the user U1 entered. That is, the information processing device 100 determines that the user U1, who is the target user, is outside the Z convenience store, which is the target store, as shown in the estimation result RS1 in FIG.

In this way, the information processing device 100 uses the polygon range that is the user range and the polygon of the store that is the store range to estimate related stores that are stores that have a geographical relationship with the user. , it is possible to appropriately estimate stores that are geographically related to the user. Thereby, the information processing device 100 can appropriately measure a user's visit to the store.

[1-2-1. Lifting of restrictions on estimation processing, etc.]
Note that the above is only an example, and the information processing apparatus 100 may perform the estimation process using various information as appropriate. For example, the information processing device 100 may perform estimation processing using information generated by a geocoding method based on latitude and longitude, such as Geohash, but in this regard, using FIG. 10-14. will be described later.

In the example described above, the information processing device 100 calculates the probability that the user is in the store based on the degree of overlap between the GPS accuracy area as the user range and the polygon of the store. For example, the information processing device 100 calculates the score based on the ratio of the area that overlaps with the polygon of the store in the user range (accuracy area).

Here, the accuracy area of GPS generally becomes wider or narrower. Furthermore, for example, since the degree of coincidence between points inside the GPS accuracy area and points inside the store polygon is checked, the amount of calculation increases. For example, when calculating the area of an overlapping region while keeping the user range circular, the shape of the overlapping region becomes complicated and the cost of calculating the area of the overlapping region is high.

Therefore, the information processing device 100 calculates the area of the overlapping region using a polygonal range obtained by converting the user range from a circle to a polygon, thereby suppressing an increase in the amount of calculation and appropriately determining the area of the overlapping region. It is possible to calculate (approximate) a value.

Note that in the example described above, a case where a store that is likely to be visited by the user is estimated as an associated store is shown, but the associated store is not limited to a store that the user has visited. For example, the information processing device 100 may estimate a store that the user is likely to visit as the related store. For example, the information processing device 100 may estimate stores whose calculated scores fall within a predetermined range as related stores that the user is likely to visit. For example, the information processing device 100 may estimate a store whose calculated score is 0.1 or more and less than 0.4 as a related store that is located near the user and is likely to be visited by the user. . In this case, the information processing device 100 may provide the user with information that motivates (incentives) the user to visit the related store. For example, the information processing device 100 transmits a coupon of a related store to the terminal device 10 used by the user.

[1-3. Example of generation process]
In the estimation process shown in FIG. 1 described above, polygon data of stores is used, but polygon data may not be generated depending on the store. In the case of such stores, it is difficult to appropriately perform the estimation process described above. Therefore, the information processing device 100 executes a generation process to generate store polygon data as shown below. An example of the generation process performed by the information processing apparatus 100 will be described below using FIG. 2. FIG. 2 is a diagram illustrating an example of a generation process performed by the information processing apparatus 100. Note that descriptions of points similar to those described above with reference to FIG. 1 and the like will be omitted as appropriate.

FIG. 2 shows a case where map information such as map MP1 is used as the first information. Note that the map MP1 in FIG. 2 shows only the range necessary for explanation. For example, the map MP1 includes areas AR1 and AR2 indicating structures, lines indicating roads, and the like.

First, the information processing device 100 detects that the user U1 has used a payment application (also referred to as a “payment application”) installed on the terminal device 10 to pay for a product purchased at a convenience store A. The behavior information indicated is acquired (step S11). For example, the terminal device 10 transmits to the information processing device 100 behavior information including payment information by the payment application and location information indicating the position of the user U1 at the time of payment. Thereby, the information processing device 100 acquires behavior information including payment information by the payment application and position information indicating the position of the user U1 at the time of payment. For example, the payment information includes information indicating the product purchased by the user, information indicating the payment amount, information capable of specifying the store where the payment was made (store identification information), and the like. In FIG. 2, the information processing device 100 acquires behavior information indicating behavior AC1, which is a payment made by user U1 at convenience store A, at a position within area AR1. Thereby, the information processing device 100 can determine that the A convenience store is located in the area AR1.

Then, the information processing device 100 uses the position where the action AC1 was performed to execute a generation process of generating second information regarding convenience store A, which is the target store of the action AC1. In FIG. 2, the information processing device 100 generates a polygon of a real object, that is, a convenience store A, which is a store that exists in real space.

The information processing device 100 determines a point PT11 on the map MP1 that corresponds to the user's position when the action AC1 was performed (step S12). For example, the information processing device 100 generates a point PT11 by converting a point at a first coordinate indicating the user's position in latitude and longitude when the action AC1 was performed into a point at a second coordinate on the map MP1. do. Then, the information processing device 100 performs processing using a point PT11 that maps the position where the action AC1 was performed on the map MP1.

The information processing device 100 uses the point PT11 corresponding to the action AC1 of the user U1 and the area AR1 where the point PT11 is located to determine a part (also referred to as a "related part") that has a relationship with the action AC1 in the map MP1. is determined (step S13). In FIG. 2, the information processing device 100 extends eight lines LN radially from the point PT11, and determines the intersection of each line LN and the outer periphery of the area AR1 as the related portion. As a result, the information processing device 100 determines eight points n1 to n8, which are the intersections of the line LN and the outer periphery of the area AR1, as relevant parts of the action AC1.

Then, the information processing device 100 uses the determined related portion to generate second information indicating the actual target convenience store A (step S14). In FIG. 2, the information processing apparatus 100 generates a polygon PG11 of convenience store A using eight points n1 to n8, which are the determined related parts. For example, the information processing device 100 generates a polygon PG11 of convenience store A, which is a line connecting eight adjacent points n1 to n8. Specifically, the information processing device 100 connects points n1 and n2 with a line, connects points n2 and n3 with a line, connects points n3 and n4 with a line, and connects points n4 and n4 with a line. Connect point n5 with a line, connect point n5 and point n6 with a line, connect point n6 and point n7 with a line, connect point n7 and point n8 with a line, point n8 and point n1 By connecting them with a line, a polygon PG11 of convenience store A is generated.

The information processing device 100 converts the position of each of the points n1 to n8 on the map into latitude and longitude information, and generates a polygon PG11 of convenience store A corresponding to the latitude and longitude of the points n1 to n8. For example, the information processing device 100 generates a polygon PG11 of A convenience store that includes points n1 to n8 as points and a polyline connecting the points n1 to n8. That is, the information processing apparatus 100 generates a polygon PG11 of the convenience store A, which is a polygon (octagon) having vertices at each of points n1 to n8. In this way, the information processing device 100 generates the polygon PG11 of the A convenience store using various techniques related to polygon generation. Note that generation of polygons is the same as in the conventional method, and detailed explanation will be omitted.

As described above, the information processing device 100 determines points n1 to n8 that are related parts on the map MP1 based on the position of the user U1 at the time of payment, and generates a polygon connecting the points n1 to n8. By doing so, it is possible to appropriately generate information using information regarding user behavior. Thereby, the information processing device 100 can generate information that serves as a criterion for determining whether the user has entered the store, and can appropriately measure the user's visit to the store.

[1-3-1. Lifting of restrictions on generation processing, etc.]
Note that the above is just an example, and the information processing apparatus 100 may perform the generation process using various information as appropriate. For example, although FIG. 2 shows an example in which eight lines are extended from points corresponding to the user's actions, the information processing device 100 is not limited to extending eight lines, and may extend less than eight lines (for example, four lines, etc.). Alternatively, more than eight lines (for example, 16 lines, etc.) may be extended.

Conventionally, store polygons have often been registered by the store or generated manually. On the other hand, the information processing device 100 determines the range of the store by matching with the map data. The information processing device 100 then converts the position on the map data into latitude and longitude information to generate polygon data. In the example described above, the information processing device 100 draws lines in eight directions and generates polygon data corresponding to latitude and longitude in each direction. Thereby, the information processing apparatus 100 can suppress an increase in cost required for generating polygon data.

Note that the information processing apparatus 100 may generate polygon data using other methods. For example, the information processing device 100 may automatically convert circumference information on a map that includes a position into latitude and longitude polygon data. Furthermore, when the distance in each direction exceeds a predetermined threshold, the information processing apparatus 100 may perform the generation process only within the predetermined threshold. The information processing device 100 may perform the generation process by changing the distance depending on the type of building, the type of payment store, etc. The information processing device 100 may perform generation processing using an intersection with a road, an intersection with the outside of another building, or the like as a related part.

For example, the information processing device 100 may generate polygon data according to the date and time, depending on the date and time when the payment is made and the date and time when the payment is not made. For example, the information processing device 100 may perform the generation process using information such as store information, product types for sale, prices, store surrounding information, and user posts on SNS (Social Networking Service).

Further, the information processing apparatus 100 may perform generation processing using received communication information such as Wi-Fi and beacons. For example, if payment information for multiple stores exists in the same structure (such as a building), the information processing device 100 uses information such as the type of Wi-Fi being received and the distance from the router location to You may also decide on a store.

Furthermore, if the information processing apparatus 100 can receive the frequency of transmission and reception of payment information, the information processing apparatus 100 may receive information such as the presence or absence of multiple cash registers as information added to the polygon data. Further, the second information generated by the generation process may be three-dimensional polygon data.

Further, as described above, as long as it represents an object in the real world, it may be an open object such as a polyline instead of a closed object such as a polygon. Further, the information processing apparatus 100 may associate accuracy information corresponding to the number of people with the generated second information. Further, the second information may be a closed area, an open area, a boundary line, or a geohash (area identifier).

[2. Configuration of information processing device]
Next, the configuration of the information processing device 100 according to the embodiment will be described using FIG. 4. FIG. 4 is a diagram illustrating a configuration example of the information processing device 100 according to the embodiment. As shown in FIG. 4, the information processing device 100 includes a communication section 110, a storage section 120, and a control section 130. Note that the information processing device 100 includes an input unit (for example, a keyboard, a mouse, etc.) that accepts various operations from an administrator of the information processing device 100, and a display unit (for example, a liquid crystal display, etc.) for displaying various information. May have.

(Communication Department 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card). The communication unit 110 is connected to the network by wire or wirelessly, and transmits and receives information between the terminal device 10, the store device 20, and the data providing device 50.

(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 or an optical disk. The storage unit 120 according to the embodiment includes a user information storage unit 121, a store information storage unit 122, an action history information storage unit 123, and a map information storage unit 124, as shown in FIG. Furthermore, the storage unit 120 is not limited to the above information, and may store various types of information.

(User information storage unit 121)
The user information storage unit 121 according to the embodiment stores various information regarding users. For example, the user information storage unit 121 stores various information regarding user attributes. FIG. 5 is a diagram illustrating an example of a user information storage unit according to the embodiment. The user information storage unit 121 shown in FIG. 5 includes items such as "user ID,""age,""gender,""latestposition,""range before conversion (circle)," and "range after conversion (polygon)." included.

"User ID" indicates identification information for identifying a user. For example, the user identified by user ID "U1" corresponds to user U1 shown in the example of FIG. Moreover, "age" indicates the age of the user. Note that the "age" may be a specific age of the user, such as 35 years old. Furthermore, “gender” indicates the gender of the user.

Moreover, "latest position" indicates the latest position of the user. For example, "latest location" indicates the last location obtained for the user. In addition, in FIG. 5, the "latest position" is an abstract code such as "LC1", but it is a concrete user in real space (real space), such as information indicating latitude and longitude (latitude and longitude information). This is information indicating the location of.

“Pre-conversion range (circle)” indicates a circle indicating the estimated range of the user's position. For example, the "pre-conversion range (circle)" indicates a circular range centered on the user's latest position and corresponding to a predetermined probability. The "range before conversion (circle)" indicates the range corresponding to GPS accuracy. In addition, in FIG. 5, the "range before conversion (circle)" indicates an abstract code such as "BA1", but it is a range defined by latitude and longitude, and indicates a concrete circular range in real space. It is information.

“Post-conversion range (polygon)” indicates the range of the user's position after converting the circular range into a polygon. For example, the "post-conversion range (polygon)" indicates a polygon range centered on the user's latest position. “Post-conversion range (polygon)” indicates a square range. In addition, in FIG. 5, the "post-conversion range (polygon)" is illustrated with an abstract code such as "AA1", but it is a range defined by latitude and longitude, and is not a specific polygon range in real space. This is information indicating.

For example, the age of the user (user U1) identified by the user ID "U1" is "20s" and the gender is "male." It shows that the latest location of user U1 is "LC1". Further, the pre-conversion range (circle) of the user U1 is "BA1", and the post-conversion range (polygon) of the user U1 is "AA1".

Note that the user information storage unit 121 is not limited to the above, and may store various information depending on the purpose. For example, the user information storage unit 121 may store information regarding demographic attributes and psychographic attributes of the user. For example, the user information storage unit 121 may store information such as name, home location, work location, family structure, income, interests, lifestyle, and the like. The user information storage unit 121 may store the date and time when position information was last acquired, that is, the date and time when the latest position was acquired.

(Store information storage unit 122)
The store information storage unit 122 according to the embodiment stores various information regarding a store. FIG. 6 is a diagram illustrating an example of a store information storage unit according to the embodiment. For example, the store information storage unit 122 stores information regarding various stores. The store information storage unit 122 shown in FIG. 6 includes items such as "store ID", "name", "store information", and "polygon data".

"Store ID" indicates identification information for identifying a store. "Name" indicates the specific name of the store. For example, the "name" may be a combination of a trade name and information indicating the location of the store, such as "○○ Restaurant X Town Store."

"Store information" stores various information such as the location of the corresponding store and the name of the building in which it is located. The information stored as store information for each store is not limited to the above. In addition, in FIG. 6, "store information" indicates abstract codes such as "store information #11" and "store information #12," but store information #11, store information #12, etc. are information related to each store. Contains various information. For example, store information #11 includes information indicating the location where convenience store A is located, etc.

Moreover, "polygon data" indicates polygon data generated for the corresponding store. In FIG. 6, the "polygon data" is an abstract code such as "PD11", but it is polygon data of a specific store. The "polygon data" includes information indicating the shape of a polygon, information indicating the corresponding latitude and longitude, and the like. Note that a store whose "polygon data" is "-" indicates that polygon data for that store has not yet been generated. FIG. 6 shows, as an example, a case where polygon data for moving stall B has not yet been generated.

In FIG. 6, the store (store SP11) identified by the store ID "SP11" has the name "A convenience store" and the store information is store information #11. Furthermore, the polygon data of store SP11, which is convenience store A, is "PD11".

Note that the store information storage section 122 is not limited to the above, and may store various information depending on the purpose. The store information storage unit 122 may store information indicating the category to which the store belongs. The store information storage unit 122 may store not only store information but also information on various target areas such as facilities and areas other than stores.

(Action history information storage unit 123)
The behavior history information storage unit 123 according to the embodiment stores various information regarding the user's behavior. FIG. 7 is a diagram illustrating an example of an action history information storage unit according to the embodiment. The behavior history information storage unit 123 shown in FIG. 7 includes items such as "user ID", "behavior ID", "date and time", "location", "type", and "content".

"User ID" indicates identification information for identifying a user. "Behavior ID" indicates information that identifies the user's behavior. “Date and time” indicates the date and time when the corresponding user action was performed. In addition, in FIG. 7, although it is illustrated as "dt11" or the like, a specific date and time such as "September 14, 2021 13:07:48" may be stored in the "date and time".

"Position" indicates location information at the time of the user's action. In addition, in FIG. 7, "position" is illustrated as an abstract code such as "LC11", but it may also be information indicating a specific user's position in real space, such as information indicating latitude and longitude (latitude and longitude information). be. “Type” indicates information regarding the type of behavior of the corresponding user. "Content" indicates the content targeted in the corresponding user's action.

FIG. 7 shows that the user (user U1) identified by the user ID "U1" has performed the behavior (behavior AC11) identified by the behavior ID "AC11". For example, FIG. 7 shows that the user U1 purchased X beverage (action AC11) at a location LC11 corresponding to Y convenience store at date and time dt11. For example, it indicates that the user U1 is at the location LC11 when making the payment at the Y convenience store.

Further, the user (user U3) identified by the user ID "U3" performs the action (action AC21) identified by the action ID "AC21". For example, FIG. 7 shows that the user U3 purchased Y curry (action AC21) at the position LC21 corresponding to the moving stall B at the date and time dt21. For example, it shows that user U3 is at location LC21 when making payment at mobile stall B.

Note that the action history information storage unit 123 is not limited to the above, and may store various information depending on the purpose. Further, although FIG. 7 shows a case where behavior information is stored in the behavior history information storage unit 123 for each user ID, the behavior information is not limited to each user ID, and may be stored in chronological order, for example.

(Map information storage unit 124)
The map information storage unit 124 according to the embodiment stores various information regarding maps. Although not shown, the map information storage unit 124 stores, for example, information on a map displayed by a map display application or the like. For example, the map information storage unit 124 stores image information of a map displayed by a map display application or the like. Note that the map information storage unit 124 is not limited to the above, and may store various information depending on the purpose. For example, the map information storage unit 124 stores various information regarding maps necessary for estimation processing and generation processing performed by the information processing device 100.

(Control unit 130)
Returning to the explanation of FIG. 4, the control unit 130 is a controller, and is stored in a storage device inside the information processing device 100 by, for example, a CPU (Central Processing Unit) or an MPU (Micro Processing Unit). This is realized by executing various programs (corresponding to an example of an information processing program) using the RAM as a work area. Further, the control unit 130 is a controller, and is realized by, for example, an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

As shown in FIG. 4, the control unit 130 includes an acquisition unit 131, a determination unit 132, a generation unit 133, a conversion unit 134, an estimation unit 135, and a provision unit 136, and includes information as described below. Achieve or execute a processing function or action. Note that the internal configuration of the control unit 130 is not limited to the configuration shown in FIG. 4, and may be any other configuration as long as it performs information processing to be described later.

(Acquisition unit 131)
The acquisition unit 131 acquires various information. The acquisition unit 131 acquires various information from an external device such as the terminal device 10. Further, the acquisition unit 131 acquires various information from the storage unit 120. Further, the acquisition unit 131 acquires various information from the user information storage unit 121, the store information storage unit 122, the action history information storage unit 123, the map information storage unit 124, and the like.

The acquisition unit 131 receives various information from an external information processing device via the communication unit 110. The acquisition unit 131 stores the received information in the storage unit 120. The acquisition unit 131 receives various information from the terminal device 10, the store device 20, or the data providing device 50. The acquisition unit 131 acquires information from the terminal device 10 used by the user. The acquisition unit 131 acquires the user's location information from the terminal device 10 used by the user. For example, the acquisition unit 131 acquires various information from the store device 20. For example, the acquisition unit 131 acquires various information from the data providing device 50.

The acquisition unit 131 acquires position information indicating the user's position when performing a predetermined action. The acquisition unit 131 acquires first information indicating a map. The acquisition unit 131 acquires location information indicating the user's location when the user performs a purchasing action. The acquisition unit 131 acquires location information indicating the user's location when the user made the payment. The acquisition unit 131 acquires location information indicating the user's location when the user makes a payment using the terminal device 10. The acquisition unit 131 acquires first information including image information of a map.

The acquisition unit 131 acquires circular range information indicating the estimated range of the user's position. The acquisition unit 131 acquires circular range information indicating the range of the user's position in real space. The acquisition unit 131 acquires circular range information indicating the range of the user's position. The acquisition unit 131 acquires store range information indicating the range occupied by the store. The acquisition unit 131 acquires store range information indicating the range occupied by the store in real space.

The acquisition unit 131 acquires circular range information based on sensor information detected by a sensor that detects the user's position. The acquisition unit 131 acquires circular range information based on sensor information detected by a GPS sensor. The acquisition unit 131 acquires store range information, which is store polygon data.

(Decision unit 132)
The determining unit 132 executes determination processing to determine various information. The determining unit 132 stores the determined information in the storage unit 120. For example, the determination unit 132 executes determination processing based on various information acquired by the acquisition unit 131. The determination unit 132 executes determination processing based on various information stored in the storage unit 120. For example, the determination unit 132 executes determination processing based on various information received from an external information processing device.

For example, the determining unit 132 executes a determining process based on the information generated by the generating unit 133. For example, the determining unit 132 executes the determining process based on the information converted by the converting unit 134. For example, the determining unit 132 executes the determining process based on the information estimated by the estimating unit 135.

The determining unit 132 determines, based on the user's position indicated by the position information acquired by the acquiring unit 131, a relevant portion of the map that is a portion that has relevance to a predetermined action. The determining unit 132 determines a plurality of points that are related parts that have relevance to a predetermined action.

The determining unit 132 determines a plurality of points based on the point on the map corresponding to the user's position and the line indicating the structure on the map. The determining unit 132 determines a plurality of points based on the intersection of a first line extending from a point on the map corresponding to the user's position and a second line indicating a structure on the map. The determining unit 132 determines a plurality of points that are intersection points.

(Generation unit 133)
The generation unit 133 executes generation processing to estimate various information. The generation unit 133 stores the generated information in the storage unit 120. For example, the generation unit 133 executes generation processing based on various information acquired by the acquisition unit 131. The generation unit 133 executes generation processing based on various information stored in the storage unit 120. The generation unit 133 executes generation processing based on various information received from an external information processing device.

For example, the generation unit 133 executes generation processing based on the information determined by the determination unit 132. For example, the generation unit 133 executes generation processing based on the information generated by the generation unit 133. For example, the generation unit 133 executes generation processing based on the information converted by the conversion unit 134. For example, the generation unit 133 executes generation processing based on the information estimated by the estimation unit 135.

The generation unit 133 generates second information indicating the actual object using the relevant part determined by the determination unit 132. The generation unit 133 generates second information indicating an actual area corresponding to the area on the map based on the related portion. The generation unit 133 generates second information indicating the actual store. The generation unit 133 generates second information of a different type from the first information.

The generation unit 133 generates second information that is vector data. The generation unit 133 generates second information by connecting a plurality of points with a line. The generation unit 133 generates second information by connecting a plurality of points with a line based on a predetermined criterion. The generation unit 133 generates the second information by connecting adjacent points among the plurality of points with lines. The generation unit 133 generates second information that is polyline data. The generation unit 133 generates second information that is polygon data.

The generation unit 133 generates content. For example, the generation unit 133 generates a screen (content) to be provided to the terminal device 10 using various technologies such as Java (registered trademark) as appropriate. Note that the generation unit 133 may generate a screen (content) to be provided to the terminal device 10 based on a CSS, JavaScript (registered trademark), or HTML format. Further, for example, the generation unit 133 may generate the screen (content) in various formats such as JPEG (Joint Photographic Experts Group), GIF (Graphics Interchange Format), and PNG (Portable Network Graphics).

(Conversion unit 134)
The conversion unit 134 executes conversion processing to convert various information. The conversion unit 134 stores the information converted by the conversion process in the storage unit 120. For example, the conversion unit 134 executes conversion processing based on various information acquired by the acquisition unit 131. The conversion unit 134 executes conversion processing based on various information stored in the storage unit 120. The conversion unit 134 executes conversion processing based on various information received from an external information processing device.

For example, the conversion unit 134 executes conversion processing based on the information determined by the determination unit 132. For example, the conversion unit 134 executes conversion processing based on the information generated by the generation unit 133. For example, the conversion unit 134 executes conversion processing based on the information estimated by the estimation unit 135.

The conversion unit 134 converts the user's circular range information acquired by the acquisition unit 131 into polygonal polygon range information. The conversion unit 134 converts the user's circular range information into rectangular polygon range information. The conversion unit 134 converts the user's circular range information into square polygon range information. The converter 134 converts the user's circular range information into polygonal range information corresponding to the area. The conversion unit 134 converts the user's circular range information into polygonal range information having the same area.

(Estimation unit 135)
The estimation unit 135 executes estimation processing to estimate various information. The estimation unit 135 stores information estimated by the estimation process in the storage unit 120. For example, the estimation unit 135 executes estimation processing based on various information acquired by the acquisition unit 131. The estimation unit 135 executes estimation processing based on various information stored in the storage unit 120. The estimation unit 135 performs estimation processing based on various information received from an external information processing device.

For example, the estimation unit 135 executes estimation processing based on the information determined by the determination unit 132. For example, the estimation unit 135 executes estimation processing based on the information generated by the generation unit 133. For example, the estimation unit 135 executes estimation processing based on the information converted by the conversion unit 134.

For example, the estimation unit 135 calculates various information based on the information determined by the determination unit 132. For example, the estimation unit 135 calculates various information based on the information generated by the generation unit 133. For example, the estimation unit 135 calculates various information based on the information converted by the conversion unit 134. For example, the estimation unit 135 performs various determination processes based on the information determined by the determination unit 132. For example, the estimation unit 135 performs various determination processes based on the information generated by the generation unit 133. For example, the estimation unit 135 performs various determination processes based on the information converted by the conversion unit 134. For example, the estimation unit 135 calculates the overlapping area where the polygon range information and the store range information overlap. For example, the estimation unit 135 calculates the score using the area of the polygon range and the overlapping area. For example, the estimating unit 135 uses the calculated score to estimate whether the store is an affiliated store.

The estimating unit 135 estimates a related store, which is a store that has a geographical relationship with the user, based on the polygonal range information converted by the converting unit 134 and the store range information. The estimation unit 135 estimates a related store having a predetermined positional relationship with the user. The estimation unit 135 estimates the related store that the user has entered. The estimation unit 135 estimates related stores that the user is likely to visit. The estimation unit 135 estimates nearby stores whose distance from the user is within a predetermined value as related stores.

The estimating unit 135 estimates related stores based on the range where the polygon range information and the store range information overlap. The estimating unit 135 estimates the related store using an overlapping area, which is an area where the polygon range information and the store range information overlap. The estimation unit 135 estimates the related store using the ratio of the overlapping area to the user range area, which is the area corresponding to the polygon range information. The estimating unit 135 estimates a store whose ratio of the overlapping area to the user range area is equal to or greater than a threshold value to be an associated store.

The estimating unit 135 generates a first section list indicating sections that are associated with the user based on the polygon range information, out of a plurality of sections into which the map is divided based on predetermined criteria, and stores based on the store range information. A related store is estimated based on a comparison with a second section list indicating sections associated with. The estimation unit 135 calculates the number of blocks based on a comparison between a first block list indicating blocks associated with the user and a second block list indicating blocks associated with the store among the plurality of blocks divided by the geohashing method. to estimate related stores. The estimating unit 135 estimates that a store in which the overlap between the first section list and the second section list is equal to or greater than a threshold value is a related store.

(Providing unit 136)
The providing unit 136 provides various information. The providing unit 136 transmits various information to an external information processing device via the communication unit 110. The providing unit 136 transmits various information to the terminal device 10, the store device 20, and the data providing device 50. The providing unit 136 transmits the content to the terminal device 10.

The providing unit 136 provides the information determined by the determining unit 132. The providing unit 136 provides the information generated by the generating unit 133. The providing unit 136 provides the information converted by the converting unit 134. The providing unit 136 provides the information estimated by the estimating unit 135.

The providing unit 136 provides the second information generated by the generating unit 133. The providing unit 136 provides information regarding the related stores estimated by the estimating unit 135 to the terminal device 10 used by the user. The providing unit 136 provides the terminal device 10 used by the user with coupons from related stores within a predetermined range from the user's location. The providing unit 136 provides the terminal device 10 used by the user with coupons for related stores that the user is likely to visit.

[3. Information processing flow]
Next, the procedure of information processing by the information processing system 1 according to the embodiment will be described using FIGS. 8 and 9. 8 and 9 are flowcharts illustrating an example of information processing according to the embodiment.

First, the processing shown in FIG. 8 will be explained. For example, FIG. 8 is a flowchart illustrating an example of a generation process executed by the information processing apparatus 100.

As shown in FIG. 8, the information processing device 100 acquires position information indicating the user's position when performing a predetermined action (step S101). The information processing device 100 also acquires first information indicating a map (step S102).

Then, the information processing device 100 determines a related portion, which is a portion of the map that is related to a predetermined action, based on the user's location indicated by the location information (step S103). The information processing device 100 uses the relevant portion to generate second information indicating the actual object (step S104).

Next, the process shown in FIG. 9 will be explained. For example, FIG. 9 is a flowchart illustrating an example of the estimation process executed by the information processing apparatus 100.

As shown in FIG. 9, the information processing apparatus 100 acquires circular range information indicating the estimated range of the user's position (step S201). The information processing device 100 also acquires store range information indicating the range occupied by the store (step S202).

Then, the information processing device 100 converts the user's circular range information into polygonal range information of a polygon (step S203). The information processing device 100 estimates a related store, which is a store that has a geographical relationship with the user, based on the polygon range information and the store range information (step S204).

[4. Other estimation processing methods (use of geohash, etc.)]
As described above, the information processing apparatus 100 may perform estimation processing using information generated by a geocoding method based on latitude and longitude, such as geohash. This point will be explained using FIGS. 10-14. FIG. 10 is a diagram illustrating a concept regarding another method of estimation processing. FIGS. 11 to 14 are diagrams showing examples of other methods of estimation processing. Note that geohash is only one example, and the information processing device 100 is not limited to geohash, but can adopt any method such as QuadKey, and any method can be used as long as estimation processing is possible. good.

First, the geohashing technique used in the estimation processing shown in FIGS. 10 to 14 is one of the geocoding methods, as described above. For example, geohash divides a map (land in real space) into a grid, and represents each section (also called an "area") with a short string of characters. The region HR1 superimposed on the left map MP2 in FIG. 10 is a rectangular region identified by a four-digit code CD1 "xn76." Further, a region HR11 superimposed on the map MP2 on the right side in FIG. 10 is a rectangular region identified by a five-digit code CD11 "xn76u".

Here, the region HR11 is included in the region HR1 and corresponds to a part of the region HR1. Further, the code CD11 is a code (character string) in which the upper four digits match the four digits of the code CD1, and one digit "u" is added to the four digits of the code CD1. In this way, geohash has a hierarchical data structure and can express the range of a partition with an arbitrary number of digits. For example, geohash can express a narrower area by increasing the number of digits, and can express a wider area by decreasing the number of digits. Note that since geohash itself is a well-known technology, detailed explanation thereof will be omitted.

From here, FIGS. 11 to 14 will explain an example of estimation processing using the geohash technology by the information processing apparatus 100. For example, in FIGS. 11 to 14, a case will be explained in which the lowest layer data of the hierarchical geohash data, that is, the data divided into the smallest sections (areas) is used. Note that descriptions of points similar to those described with reference to FIG. 1 and the like will be omitted as appropriate. 11 to 14, a case where the user who uses the terminal device 10 is the user U2 will be described as an example. Further, in FIGS. 11 to 14, an example in which each area is a square is shown for explanation, but the shape of each area is not limited to a square, but may be any other shape such as a rectangle.

First, FIG. 11 shows geodata GH1 in which land in a predetermined area is divided into a plurality of areas H11 to H46, etc. using geohash. Note that in FIGS. 11 to 14, only 24 regions H11 to H46 are illustrated to show the range of regions necessary for explanation.

Further, FIG. 12 shows a state in which the polygon PG2 of the store E Pharmacy is superimposed on the geodata GH1. A hatched area in FIG. 12 indicates an area where polygon PG2 overlaps among a plurality of areas such as areas H11 to H46. Specifically, 12 regions H12 to H15, H22 to H25, and H32 to H35 are regions where the polygon PG2 overlaps.

For example, the information processing device 100 acquires the polygon PG2 of Pharmacy E as information indicating the store range of Pharmacy E. Note that the information processing device 100 may acquire information on the polygon PG2 of the E pharmacy from the storage unit 120 (see FIG. 4), or may receive information on the polygon PG2 of the E pharmacy from an external device such as the data providing device 50. You may.

The information processing device 100 uses the polygon PG2 of the E pharmacy and the geodata GH1 to determine the area where the E pharmacy is located among the plurality of areas such as areas H11 to H46 (step S21). In FIG. 12, the information processing device 100 determines that 12 areas H12 to H15, H22 to H25, and H32 to H35 are the areas where the E pharmacy is located.

Then, the information processing device 100 associates the list LT1 including the areas H12 to H15, H22 to H25, and H32 to H35 with the E pharmacy as information indicating the area where the E pharmacy is located. For example, the information processing device 100 stores the list LT1 in the store information storage unit 122 (see FIG. 6) in association with the E pharmacy.

Further, FIG. 13 shows a state in which the circular range BA2, which is the user range of the user U2, is superimposed on the geodata GH1. A hatched area in FIG. 13 indicates an area where the circular range BA2 overlaps among a plurality of areas such as areas H11 to H46. Specifically, ten regions H12, H13, H21 to H23, H31 to H33, H42, and H43 are shown as regions where the circular range BA2 overlaps.

For example, the information processing device 100 acquires the circular range BA2 as the user range of the user U2. Note that the information processing device 100 may generate information on the circular range BA2, or may receive information on the circular range BA2 from an external device such as the terminal device 10.

The information processing device 100 uses the circular range BA2 of the user U2 and the geodata GH1 to select an area corresponding to the range of the estimated position (estimated position) of the user U2 from among a plurality of areas such as areas H11 to H46. is determined (step S22). In FIG. 13, the information processing apparatus 100 determines that ten areas H12, H13, H21 to H23, H31 to H33, H42, and H43 correspond to the range of the position (estimated position) of the user U2. decide. Thereby, the information processing device 100 converts the circular range BA2 of the user U2 into a polygonal range AA2. In FIG. 13, the information processing device 100 transforms the circular range BA2 of the user U2 into a polygon (for example, an octagon) consisting of 10 regions H12, H13, H21 to H23, H31 to H33, H42, and H43. The polygon range AA2 is converted to a polygon range AA2.

Then, the information processing device 100 associates the list LT2 including the regions H12, H13, H21 to H23, H31 to H33, H42, and H43 with the user U2 as information indicating the region corresponding to the range of the estimated position of the user U2. . For example, the information processing device 100 stores the list LT2 in the user information storage unit 121 (see FIG. 5) in association with the user U2.

FIG. 14 shows estimation processing based on the region of the geodata GH1 where the polygon range AA2 of the user U2 overlaps with the region where the polygon PG2 of the E pharmacy overlaps. The hatched area in FIG. 14 corresponds to both the area where the polygon range AA2 of the user U2 overlaps and the area where the polygon PG2 of the E pharmacy overlaps, among multiple areas such as areas H11 to H46 ( overlap area). Specifically, the six areas H12, H13, H22, H23, H32, and H33 correspond to both the area where the polygon range AA2 of the user U2 overlaps and the area where the polygon PG2 of the E pharmacy overlaps ( This indicates that the area is an overlapping area OH1).

Then, the information processing device 100 performs processing using the list LT1 associated with the E pharmacy and the list LT2 associated with the user U2. The information processing device 100 uses the list associated with each target of the E pharmacy and the user U2 to calculate a score indicating the degree to which the E pharmacy is an affiliated store of the user U2 (step S23).

In FIG. 14, the information processing device 100 calculates, as a score, the ratio of the area included in the list LT1 of the E pharmacy to the area in the list LT2 associated with the user U2. As shown in the calculation result SC2 in FIG. 14, the information processing device 100 sets the number "6" of regions H12, H13, H22, H23, H32, and H33 included in both the list LT1 and the list LT2 into the list LT2. The score of E-pharmacy is calculated by dividing by the total number of areas "10". Specifically, the information processing device 100 calculates the score of Pharmacy E as "0.6 (=6/10)".

Then, the information processing device 100 estimates related stores using the calculated score (step S24). In FIG. 14, the information processing device 100 uses the calculated score of the E pharmacy to estimate whether the E pharmacy is an affiliated store of the user U2.

For example, if the score of Pharmacy E is equal to or greater than the threshold, the information processing device 100 estimates that Pharmacy E is the store (related store) that user U2 entered. That is, the information processing device 100 determines that the user U2 has entered the E-pharmacy when the score of the E-pharmacy is equal to or higher than a threshold value (for example, an arbitrary value such as 0.45 or 0.6).

Further, when the score of the E pharmacy is less than the threshold value, the information processing device 100 estimates that the E pharmacy is not the store (related store) that the user U2 entered. That is, if the score of the E-pharmacy is less than the threshold, the information processing device 100 determines that the user U2 has not entered the E-pharmacy.

In FIG. 14, the following description will be made assuming that the threshold value is "0.5". In FIG. 14, the information processing device 100 estimates that the E pharmacy is the store (related store) that the user U2 entered since the score "0.6" of the E pharmacy is equal to or higher than the threshold value "0.5". . That is, the information processing device 100 determines that the user U2, who is the target user, is inside Pharmacy E, which is the target store, as shown in the estimation result RS2 in FIG.

In this way, the information processing device 100 uses geohash-related technology to estimate related stores that are geographically related to the user, thereby appropriately identifying stores that are geographically related to the user. It can be estimated. Thereby, the information processing device 100 can appropriately measure a user's visit to the store.

As described above, the information processing device 100 uses geohash technology to speed up calculations when measuring store visits using GPS and polygons, thereby improving the process of determining store visits using GPS accuracy. It is possible to achieve high speed.

For example, the information processing device 100 may use each region as an identifier (hash data) in advance and convert the GPS accuracy circle and store polygon into identifiers for comparison. Further, for example, the information processing device 100 identifies a geohash that includes a GPS accuracy circle and a store polygon, and calculates the area based on the area of the geohash.

As described above, since geohash has a hierarchical structure, the information processing apparatus 100 can refine the details in stages. For example, the information processing device 100 may identify a higher rank hash from the geohash data on the user (GPS) side, identify geohash data of a store that includes the identified higher rank hash, and compare the geohash data. For example, the information processing device 100 uses geohash data to determine an upper layer area that includes an area corresponding to the user range, and performs the above-described processing using a store located within the determined upper layer area as a target store. You may do so. Thereby, the information processing device 100 can perform estimation processing after narrowing down the target stores, and therefore can suppress an increase in calculation cost required for estimation processing.

[5. effect〕
As described above, the information processing apparatus 100 according to the embodiment includes the acquisition section 131, the generation section 133, and the determination section 132. The acquisition unit 131 acquires location information indicating the user's position when performing a predetermined action and first information indicating a map. The determining unit 132 determines, based on the user's position indicated by the position information acquired by the acquiring unit 131, a relevant portion of the map that is a portion that has relevance to a predetermined action. The generation unit 133 generates second information indicating the actual object using the relevant part determined by the determination unit 132.

In this way, the information processing device 100 according to the embodiment determines the relevant portion, which is the portion of the map that is related to the predetermined action, based on the user's position when performing the predetermined action. By generating the second information indicating the actual object using the related portion, it is possible to appropriately generate information using information regarding the user's behavior.

Furthermore, in the information processing device 100 according to the embodiment, the generation unit 133 generates second information indicating an actual area corresponding to the area on the map based on the related portion.

In this way, the information processing device 100 according to the embodiment generates the second information indicating the actual area corresponding to the area on the map based on the related portion, and thereby appropriately uses information regarding the user's behavior. can be generated.

Furthermore, in the information processing device 100 according to the embodiment, the generation unit 133 generates second information indicating the actual store.

In this way, the information processing apparatus 100 according to the embodiment can appropriately generate information using information regarding user behavior by generating the second information indicating the actual store.

Furthermore, in the information processing apparatus 100 according to the embodiment, the acquisition unit 131 acquires location information indicating the location of the user when the user performs a purchasing action.

In this way, the information processing device 100 according to the embodiment generates the second information based on the user's position when the user performs the purchasing behavior, thereby appropriately providing information using information about the user's behavior. can be generated.

Furthermore, in the information processing apparatus 100 according to the embodiment, the acquisition unit 131 acquires location information indicating the location of the user when the user makes the payment.

In this way, the information processing device 100 according to the embodiment generates the second information based on the user's location when the user makes the payment, thereby appropriately processing the information using information about the user's behavior. can be generated.

Further, in the information processing device 100 according to the embodiment, the acquisition unit 131 acquires location information indicating the location of the user when the user makes a payment using the terminal device 10.

In this way, the information processing device 100 according to the embodiment utilizes information regarding the user's behavior by generating the second information based on the user's location when the user makes a payment using the terminal device 10. information can be generated appropriately.

Furthermore, in the information processing apparatus 100 according to the embodiment, the generation unit 133 generates second information of a type different from the first information.

In this way, the information processing apparatus 100 according to the embodiment can appropriately generate information using information related to user behavior by generating second information of a type different from the first information.

Furthermore, in the information processing apparatus 100 according to the embodiment, the acquisition unit 131 acquires first information including image information of a map.

In this way, the information processing apparatus 100 according to the embodiment generates the second information using the first information including the image information of the map, thereby appropriately generating the information using the information regarding the user's behavior. be able to.

Furthermore, in the information processing apparatus 100 according to the embodiment, the generation unit 133 generates second information that is vector data.

In this way, the information processing apparatus 100 according to the embodiment can appropriately generate information using information regarding the user's behavior by generating the second information that is vector data.

Furthermore, in the information processing apparatus 100 according to the embodiment, the determining unit 132 determines a plurality of points that are related parts that have relevance to a predetermined action. The generation unit 133 generates second information by connecting a plurality of points with a line.

In this way, the information processing apparatus 100 according to the embodiment generates the second information, which is vector data that generates the second information, by connecting a plurality of points having a relationship with a predetermined action with a line. Accordingly, information can be appropriately generated using information regarding user behavior.

Furthermore, in the information processing apparatus 100 according to the embodiment, the generation unit 133 generates the second information by connecting a plurality of points with a line based on a predetermined criterion.

In this way, the information processing apparatus 100 according to the embodiment generates the second information in which a plurality of points are connected by lines based on a predetermined criterion, thereby appropriately displaying information using information regarding the user's behavior. can be generated.

Furthermore, in the information processing apparatus 100 according to the embodiment, the generation unit 133 generates the second information by connecting adjacent points among the plurality of points with a line.

In this way, the information processing apparatus 100 according to the embodiment appropriately generates information using information regarding user behavior by generating second information in which adjacent points of a plurality of points are connected by lines. can do.

Furthermore, in the information processing apparatus 100 according to the embodiment, the determining unit 132 determines a plurality of points based on a point on the map corresponding to the user's position and a line indicating a structure on the map.

In this way, the information processing apparatus 100 according to the embodiment determines a plurality of points based on the point on the map corresponding to the user's position and the line indicating the structure on the map, thereby determining the user's behavior. It is possible to appropriately generate information using information about

Furthermore, in the information processing apparatus 100 according to the embodiment, the determining unit 132 determines, based on the intersection of the first line extending from the point on the map corresponding to the user's position and the second line indicating the structure on the map, Determine multiple points.

In this way, the information processing device 100 according to the embodiment can select a plurality of points based on the intersection of the first line extending from the point on the map corresponding to the user's position and the second line indicating the structure on the map. By determining this, it is possible to appropriately generate information using information regarding user behavior.

Further, in the information processing apparatus 100 according to the embodiment, the determining unit 132 determines a plurality of points that are intersections.

In this manner, the information processing apparatus 100 according to the embodiment can appropriately generate information using information regarding the user's behavior by determining a plurality of points that are intersection points.

Furthermore, in the information processing apparatus 100 according to the embodiment, the generation unit 133 generates second information that is polyline data.

In this way, the information processing apparatus 100 according to the embodiment can appropriately generate information using information regarding the user's behavior by generating the second information that is polyline data.

Furthermore, in the information processing apparatus 100 according to the embodiment, the generation unit 133 generates second information that is polygon data.

In this manner, the information processing apparatus 100 according to the embodiment can appropriately generate information using information regarding the user's behavior by generating the second information that is polygon data.

Further, the information processing device 100 according to the embodiment includes a providing unit 136. The providing unit 136 provides the second information generated by the generating unit 133.

In this way, the information processing apparatus 100 according to the embodiment can provide appropriate information by providing the second information.

[6. Hardware configuration]
The information processing apparatus 100 according to the embodiments described above is realized, for example, by a computer 1000 having a configuration as shown in FIG. 15. FIG. 15 is a hardware configuration diagram showing an example of a computer that implements the functions of the information processing device. Computer 1000 has CPU 1100, RAM 1200, ROM 1300, HDD (Hard Disk Drive) 1400, communication interface (I/F) 1500, input/output interface (I/F) 1600, and media interface (I/F) 1700.

CPU 1100 operates based on a program stored in ROM 1300 or HDD 1400, and controls each section. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started, programs depending on the hardware of the computer 1000, and the like.

The HDD 1400 stores programs executed by the CPU 1100, data used by the programs, and the like. Communication interface 1500 receives data from other devices via network N and sends it to CPU 1100, and sends data generated by CPU 1100 to other devices via network N.

The CPU 1100 controls output devices such as a display and a printer, and input devices such as a keyboard and mouse via an input/output interface 1600. CPU 1100 obtains data from an input device via input/output interface 1600. Further, CPU 1100 outputs the generated data to an output device via input/output interface 1600.

Media interface 1700 reads programs or data stored in recording medium 1800 and provides them to CPU 1100 via RAM 1200. CPU 1100 loads this program from recording medium 1800 onto RAM 1200 via 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 a 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. etc.

For example, when the computer 1000 functions as the 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 onto the RAM 1200. The CPU 1100 of the computer 1000 reads these programs from the recording medium 1800 and executes them, but as another example, these programs may be acquired from another device via the network N.

Some of the embodiments and modifications of the present application have been described above in detail based on the drawings, but these are merely examples, and various modifications can be made based on the knowledge of those skilled in the art, including the embodiments described in the disclosure section of the invention. It is possible to implement the present invention in other forms with modifications and improvements.

[7. others〕
Furthermore, among the processes described in the above embodiments and modified examples, all or part of the processes described as being performed automatically can be performed manually, or may be described as being performed manually. All or part of this processing can also be performed automatically using known methods. In addition, information including the processing procedures, specific names, and various data and parameters shown in the above documents and drawings may be changed arbitrarily, unless otherwise specified. For example, the various information shown in each figure is not limited to the illustrated information.

Further, each component of each device shown in the drawings is functionally conceptual, and does not necessarily need to be physically configured as shown in the drawings. In other words, the specific form of distributing and integrating each device is not limited to what is shown in the diagram, and all or part of the devices can be functionally or physically distributed or integrated in arbitrary units depending on various loads and usage conditions. Can be integrated and configured.

Furthermore, the above-described embodiments and modifications can be combined as appropriate within a range that does not conflict with the processing contents.

Further, the above-mentioned "section, module, unit" can be read as "means", "circuit", etc. For example, the acquisition unit can be read as an acquisition means or an acquisition circuit.

1 Information processing system 100 Information processing device 121 User information storage unit 122 Store information storage unit 123 Action history information storage unit 124 Map information storage unit 130 Control unit 131 Acquisition unit 132 Determination unit 133 Generation unit 134 Conversion unit 135 Estimation unit 136 Provision unit 10 Terminal device 20 Store device 50 Data providing device N Network

Claims (15)

an acquisition unit that acquires location information indicating the user's position when performing a predetermined action and first information indicating a map;
a determining unit that determines a relevant part of the map that is a part that has relevance to the predetermined action based on the position of the user indicated by the location information acquired by the acquiring unit;
a generation unit that generates second information indicating an actual object using the relevant part determined by the determination unit;
an estimation unit that estimates whether the user has a predetermined relationship with the real object using the second information generated by the generation unit;
Equipped with
The determining unit is
determining a plurality of points that are intersections between each of a plurality of first lines extending from a point on the map corresponding to the user's position and a second line indicating the outline of a structure on the map; determining a plurality of points in the relevant part that have relevance to a predetermined action;
The generation unit is
The information processing device is characterized in that the second information, which is vector data, is generated by connecting the plurality of points with a line . The generation unit is
The information processing apparatus according to claim 1, wherein the second information indicating the actual area corresponding to the area on the map based on the related portion is generated. The generation unit is
The information processing device according to claim 1 or 2, wherein the second information indicating the actual store is generated. The acquisition unit includes:
The information processing device according to any one of claims 1 to 3, wherein the information processing device acquires the location information indicating the location of the user when the user performs a purchasing action. The acquisition unit includes:
The information processing device according to any one of claims 1 to 4, wherein the information processing device acquires the location information indicating the location of the user when the user makes a payment. The acquisition unit includes:
The information processing device according to any one of claims 1 to 5, wherein the information processing device acquires the location information indicating the location of the user when the user makes a payment using a terminal device. The generation unit is
The information processing apparatus according to claim 1, wherein the second information is of a different type from the first information. The acquisition unit includes:
The information processing apparatus according to any one of claims 1 to 7, wherein the first information including image information of the map is acquired. The generation unit is
The information processing device according to claim 1, wherein the second information is generated by connecting the plurality of points with the line based on a predetermined criterion. The generation unit is
The information processing device according to claim 9 , wherein the second information is generated by connecting adjacent points among the plurality of points with the line. The generation unit is
The information processing apparatus according to claim 1 , wherein the second information is polyline data. The generation unit is
The information processing apparatus according to any one of claims 1 to 10 , wherein the second information is polygon data. a providing unit that provides the second information generated by the generating unit;
The information processing device according to any one of claims 1 to 12 , further comprising: An information processing method performed by a computer, the method comprising:
an acquisition step of acquiring location information indicating the user's position when performing a predetermined action and first information indicating a map;
a determining step of determining a relevant portion of the map that is a portion that has relevance to the predetermined action based on the location of the user indicated by the location information acquired in the acquiring step;
a generation step of generating second information indicating an actual object using the relevant part determined in the determination step;
an estimation step of estimating whether the user has a predetermined relationship with the real object using the second information generated in the generation step;
including;
The determining step includes:
determining a plurality of points that are intersections between each of a plurality of first lines extending from a point on the map corresponding to the user's position and a second line indicating the outline of a structure on the map; determining a plurality of points in the relevant part that have relevance to a predetermined action;
The production step includes:
The second information, which is vector data, is generated by connecting the plurality of points with a line.
An information processing method characterized by: an acquisition procedure for acquiring location information indicating the user's position when performing a predetermined action and first information indicating a map;
a determination step of determining a relevant portion of the map that is a portion that has relevance to the predetermined action, based on the location of the user indicated by the location information acquired by the acquisition step;
a generation procedure of generating second information indicating an actual object using the relevant part determined by the determination procedure;
an estimation procedure of estimating whether the user has a predetermined relationship with the real object using the second information generated by the generation procedure;
make the computer run
The determination procedure is
determining a plurality of points that are intersections between each of a plurality of first lines extending from a point on the map corresponding to the user's position and a second line indicating the outline of a structure on the map; determining a plurality of points in the relevant part that have relevance to a predetermined action;
The generation procedure is
An information processing program characterized in that the second information, which is vector data, is generated by connecting the plurality of points with a line .

Images