JP2019032771A - Prediction device, method for prediction, and prediction program - Google Patents

Abstract

To predict demands properly.SOLUTION: The prediction device according to the present invention includes an acquisition part and a prediction part. The acquisition part acquires positional information of a user. The prediction part predicts a demand related to a target based on the positional information acquired by the acquisition part and information on an event related to the position.SELECTED DRAWING: Figure 3

Description

  The present invention relates to a prediction device, a prediction method, and a prediction program.

  Conventionally, techniques for predicting various demands have been provided. For example, a technique for determining user needs information based on user search data is provided.

JP 2017-16625 A

  However, with the above-described conventional technology, it is not always possible to predict demand appropriately. For example, in the above-described conventional technology, it may be difficult to predict the demand appropriately only by using the user's search data, such as when the user's demand is not reflected in the information used for the search by the user.

  The present application has been made in view of the above, and an object thereof is to provide a prediction device, a prediction method, and a prediction program that appropriately predict demand.

  The prediction device according to the present application relates to the target based on an acquisition unit that acquires user position information, the user position information acquired by the acquisition unit, and information on the target associated with the position. And a prediction unit for predicting demand to be performed.

  According to one aspect of the embodiment, there is an effect that demand can be appropriately predicted.

FIG. 1 is a diagram illustrating an example of a prediction process according to the embodiment. FIG. 2 is a diagram illustrating a configuration example of the prediction system according to the embodiment. FIG. 3 is a diagram illustrating a configuration example of the prediction device according to the embodiment. FIG. 4 is a diagram illustrating an example of a POI information storage unit according to the 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 the behavior information storage unit according to the embodiment. FIG. 7 is a diagram illustrating an example of the relationship information storage unit according to the embodiment. FIG. 8 is a flowchart illustrating an example of the prediction process according to the embodiment. FIG. 9 is a diagram illustrating an example of a prediction process according to the embodiment. FIG. 10 is a hardware configuration diagram illustrating an example of a computer that realizes the function of the prediction device.

  Hereinafter, modes for carrying out a prediction apparatus, a prediction method, and a prediction program according to the present application (hereinafter referred to as “embodiments”) will be described in detail with reference to the drawings. Note that the prediction device, the prediction method, and the prediction program according to the present application are not limited to the embodiment. In the following embodiments, the same portions are denoted by the same reference numerals, and redundant description is omitted.

(Embodiment)
[1. (Prediction process)
An example of the prediction process according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram illustrating an example of a prediction process according to the embodiment. Specifically, FIG. 1 is a diagram illustrating an example in which the prediction device 100 predicts a demand related to a target based on the position information of the user and information on the target associated with the position. The prediction device 100 predicts demand related to a target related to a POI (Point of Interest) at a predetermined position. The POI referred to here may include various points such as a store that provides goods and services and an embassy that issues visas necessary for travel. Further, the POI is not limited to the above, but may be various points such as educational institutions such as schools and nurseries, and administrative institutions such as Hello Work. Hereinafter, a product or service may be collectively referred to as “product”.

  In the example of FIG. 1, the prediction device 100 predicts demand related to an object with the card dealer MA as a POI. Specifically, the prediction device 100 predicts demand for a vehicle that is an offer provided by the card dealer MA.

[Configuration of prediction system]
First, the prediction system 1 shown in FIGS. 1 and 2 will be described. As illustrated in FIG. 2, the prediction system 1 includes a terminal device 10, an operator device 20, and a prediction device 100. The terminal device 10, the provider device 20, and the prediction device 100 are connected via a predetermined network N so as to be communicable by wire or wirelessly. FIG. 2 is a diagram illustrating a configuration example of the prediction system according to the embodiment. Note that the prediction system 1 illustrated in FIG. 2 may include a plurality of terminal devices 10, a plurality of business operator devices 20, and a plurality of prediction devices 100.

  The terminal device 10 is an information processing device used by a user. The terminal device 10 is realized by, for example, a smartphone, a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, a PDA (Personal Digital Assistant), or the like. In the example shown in FIG. 1, the case where the terminal device 10 is a smartphone used by the user is shown. Hereinafter, the terminal device 10 may be referred to as a user. That is, hereinafter, the user can be read as the terminal device 10.

  The terminal device 10 has a function such as a GPS (Global Positioning System) sensor, and detects and acquires user position information (sensor information). In addition, the terminal device 10 uses the location information of the base station that is performing communication, a predetermined wireless communication function such as WiFi (registered trademark) or Bluetooth (registered trademark), or a function such as a beacon. The position information may be detected and acquired. Hereinafter, the position information may be simply referred to as “position”. For example, the terminal device 10 may improve the estimation accuracy of the user's position by combining information such as GPS, the wireless communication function as described above, and a beacon.

  Moreover, the terminal device 10 may detect various sensor information not only with a GPS sensor etc. but with various sensors. Moreover, the terminal device 10 has a function of an acceleration sensor, and detects and acquires acceleration information (sensor information) in the movement of the user. Further, the terminal device 10 may have various functions such as a temperature sensor and an atmospheric pressure sensor, and may be able to detect and acquire environmental information where the user is placed such as temperature and atmospheric pressure. Moreover, the terminal device 10 may have various functions such as a heart rate sensor, and may be able to detect and acquire a user's biological information. For example, a user using the terminal device 10 may acquire a user's own context information by the terminal device 10 by wearing a wearable device that can communicate with the terminal device 10. For example, a user who uses the terminal device 10 wears a wristband type wearable device that can communicate with the terminal device 10, and the terminal device 10 acquires information on the user's own heartbeat (pulse) by the terminal device 10. It may be possible.

  Moreover, the terminal device 10 receives an operation by a user. The terminal device 10 transmits various information to the prediction device 100. For example, the terminal device 10 transmits various behavior information such as position information to the prediction device 100.

  In the example illustrated in FIG. 1, the terminal device 10 will be described as terminal devices 10-1 to 10-5 in accordance with a user who uses the terminal device 10. For example, the terminal device 10-1 is the terminal device 10 used by the user U1. For example, the terminal device 10-2 is the terminal device 10 used by the user U2. Hereinafter, the terminal devices 10-1 to 10-5 are referred to as the terminal device 10 when they are not particularly distinguished.

  As described above, when “user U * (* is an arbitrary numeric value)” is described, it indicates that the user is a user identified by the user ID “U *”. For example, when “user U2” is described, the user is a user identified by the user ID “U2”. The prediction system 1 illustrated in FIG. 1 includes a large number of terminal devices 10 such as the terminal device 10 used by the user U1 and the terminal device 10 used by the user U2.

  The prediction device 100 is an information processing device that predicts demand related to a target based on the position information of the user and information related to the target associated with the position. In addition, for example, the prediction device 100 provides information indicating demand related to the target to a business operator related to the target. For example, the prediction device 100 increases the estimation accuracy of the user's position by acquiring information on the user's position such as GPS and the above-described wireless communication functions and beacons from the terminal device 10 and combining the information. Also good.

  The business entity apparatus 20 is an information processing apparatus used by a business entity (company). As long as a company here carries out a business, various business persons, such as an individual (natural person) and a corporation, may be included. For example, the business operator may be a company that provides (sells) goods or services. The business entity apparatus 20 is realized by, for example, a smartphone, a tablet terminal, a notebook PC, a desktop PC, a mobile phone, a PDA, or the like.

  In the example of FIG. 1, the prediction device 100 acquires the position of the terminal device 10 used by the users U <b> 1 to U <b> 5 by using a function such as a GPS sensor included in the terminal device 10 used by the users U <b> 1 to U <b> 5 and the like. The map information MP1 shown in FIG. 1 schematically shows the positions of the users U1 to U5. Further, in the example of FIG. 1, the prediction target is “car”, and the demand related to the vehicle purchase contract by the user is predicted based on the information related to the visit to the user's card dealer.

  First, the prediction device 100 acquires position information from the terminal device 10-1 used by the user U1 (step S11-1). In the example of FIG. 1, the prediction device 100 acquires the position information LC1 from the terminal device 10-1 at the date and time DT1. In the example of FIG. 1, an abstract code such as the position information LC1 is illustrated, but the position information may be information (latitude / longitude information) indicating specific latitude and longitude. Further, hereinafter, “position information LC * (* is an arbitrary numerical value)” may be described as “position LC *”. For example, when “position LC1” is described, it indicates that the position corresponds to the position information LC1.

  And the prediction apparatus 100 memorize | stores in the behavior information storage part 123 the behavior information which shows that the user U1 was located in position LC1 in the date DT1. In the example of FIG. 1, the position LC1 is assumed to be a position close to the position LC10 of the card dealer MA. That is, the prediction device 100 stores behavior information indicating that the user U1 is located in the card dealer MA, which is POI, at the date and time DT1, in the behavior information storage unit 123. Thereby, the prediction apparatus 100 can predict that the user U1 visited the card dealer MA at the date and time DT1.

  In addition, the prediction device 100 acquires position information from the terminal device 10-2 used by the user U2 (step S11-2). In the example of FIG. 1, the prediction device 100 acquires the position information LC2 from the terminal device 10-2 at the date and time DT2. And the prediction apparatus 100 memorize | stores in the behavior information storage part 123 the behavior information which shows that the user U2 was located in position LC2 in the date DT2. In the example of FIG. 1, the position LC2 is assumed to be a position close to the position LC10 of the card dealer MA. That is, the prediction device 100 stores behavior information indicating that the user U2 is located in the card dealer MA at the date and time DT2 in the behavior information storage unit 123. Thereby, the prediction apparatus 100 can predict that the user U2 visited the card dealer MA at the date DT2.

  In addition, the prediction device 100 acquires position information from the terminal device 10-3 used by the user U3 (step S11-3). In the example of FIG. 1, the prediction device 100 acquires the position information LC3 from the terminal device 10-3 at the date DT3. In the example of FIG. 1, the position LC3 is assumed to be a position in the A area. And the prediction apparatus 100 memorize | stores in the action information storage part 123 the action information which shows that the user U3 was located in position LC3 in the date DT3. The position LC3 is assumed to be a position that is not close to any POI position such as the card dealer MA, the card dealer MB, and the X country embassy EM. That is, the prediction device 100 stores, in the behavior information storage unit 123, behavior information indicating that the user U3 is not located in any POI at the date and time DT3.

  Moreover, the prediction apparatus 100 acquires position information from the terminal device 10-4 used by the user U4 (step S11-4). In the example of FIG. 1, the prediction device 100 acquires the position information LC4 from the terminal device 10-4 at the date and time DT4. Then, the behavior information indicating that the user U4 is located at the position LC4 at the date DT4 is stored in the behavior information storage unit 123. In the example of FIG. 1, it is assumed that the position LC4 is close to the position LC20 of the X country embassy EM. That is, the prediction device 100 stores, in the behavior information storage unit 123, behavior information indicating that the user U4 is located in the X country embassy EM at the date DT4. Thereby, the prediction apparatus 100 can predict that the user U4 visited the X country embassy EM at the date DT4. The demand forecast based on the user located in the X country embassy EM will be described in detail in FIG.

  Moreover, the prediction apparatus 100 acquires position information from the terminal device 10-5 used by the user U5 (step S11-5). In the example of FIG. 1, the prediction device 100 acquires the position information LC5 from the terminal device 10-5 at the date and time DT5. Then, the behavior information indicating that the user U5 is located at the position LC5 at the date DT5 is stored in the behavior information storage unit 123. In the example of FIG. 1, the position LC5 is assumed to be a position close to the position LC20 of the X country embassy EM. That is, the prediction device 100 stores, in the behavior information storage unit 123, behavior information indicating that the user U5 is located in the X country embassy EM at the date DT5. Thereby, the prediction apparatus 100 can predict that the user U5 visited the X country embassy EM at the date DT5.

  Hereinafter, when it demonstrates without distinguishing step S11-1-S11-5, it will generically call step S11. Moreover, not only step S11-1-S11-5, acquisition of the position of each user may be performed in multiple times. In FIG. 1, five users U1 to U5 are illustrated, but the prediction device 100 acquires the positions of a large number of users (for example, 1 million users, 10 million users, etc.) as well as the users U1 to U5. To do.

  Thereby, the prediction apparatus 100 collects information about the user (step S12). For example, in the example of FIG. 1 in which the prediction apparatus 100 collects user position information, the prediction apparatus 100 collects positions LC1 to LC5 and the like of the users U1 to U5 and the like as shown in the behavior information storage unit 123.

  And the prediction apparatus 100 acquires a measuring object (step S13). For example, the prediction device 100 acquires a measurement target by specifying a predetermined business operator or setting an administrator of the prediction device 100. In the example of FIG. 1, the prediction apparatus 100 acquires information whose target is “automobile (car)”. Further, when the measurement target is “automobile”, the prediction device 100 acquires information on POI for measuring user position information. In the example of FIG. 1, the prediction apparatus 100 acquires information on a card dealer MA, a card dealer MB, and the like stored in the POI information storage unit 121 as a POI for measuring user position information. For example, the prediction device 100 acquires the position LC10 of the card dealer MA and the position LC101 of the card dealer MB. For example, the prediction device 100 predicts demand using information of a plurality of POIs such as a card dealer MA and a card dealer MB.

  First, the prediction device 100 generates transition information on the number of users who have visited (visited) the card dealer using the information described above (step S14). For example, the prediction device 100 is a user who has visited (visited) a card dealer in a predetermined period based on a comparison between the positions of the POIs such as the card dealer MA and the card dealer MB and the positions of the users U1 to U5. Estimate the number of

  For example, the prediction device 100 estimates that the user U1 visited the card dealer MA at the date DT1 based on the information indicating that the user U1 is located in the card dealer MA having the POI at the date DT1. Further, for example, the prediction device 100 estimates that the user U2 visited the card dealer MA at the date DT2 based on information indicating that the user U2 is located in the card dealer MA having the POI at the date DT2. Using the estimation result indicating that each user has visited the card dealer, the prediction device 100 changes the number of users who have visited (visited) the card dealer as shown in the transition graph SG11 in FIG. Generates information indicating

  In the example of FIG. 1, the prediction device 100 generates a transition graph SG11 indicating that the number of users who visited the card dealer, the number of visitors CN11 rose from the number of visitors CN11 to the number of visitors CN12 between the date DT11 and the date DT12. .

  And the prediction apparatus 100 estimates the demand regarding object "automobile" based on the information regarding the user who visited the card dealer (step S15). That is, the prediction device 100 predicts the demand related to the target “automobile” after the first action based on the first action of the user visiting the car dealer. For example, the prediction device 100 predicts the demand related to the target “automobile” based on a change in the number of users who have visited the card dealer. In the example of FIG. 1, the prediction device 100 predicts a demand related to a vehicle purchase contract by a user based on information related to a visit to the user's card dealer.

  For example, the prediction device 100 predicts a demand related to a car purchase contract by a user based on information about a relationship between a visit to a card dealer and a car purchase contract as shown in the relation information storage unit 124 in FIG. To do. As described above, the prediction device 100 predicts the second action of the car purchase contract after the first action based on the first action of the user visiting the car dealer. Thereby, the prediction apparatus 100 predicts the demand regarding the object “automobile” after the first action based on the first action of the user visiting the car dealer.

  The “target” shown in the relationship information storage unit 124 in FIG. 1 indicates the target of user demand. The “relation information” shown in the relationship information storage unit 124 in FIG. 1 indicates the user's action related to the corresponding target, the correlation between the actions, and the like. The “stage” shown in the relationship information storage unit 124 in FIG. 1 indicates the stage of action performed by the user regarding the target. For example, “stage N (N is an arbitrary numerical value)” indicates an action performed before “stage N + 1”.

  The “action type” shown in the relationship information storage unit 124 in FIG. 1 indicates the type of action corresponding to each stage. The “period” shown in the relationship information storage unit 124 in FIG. 1 indicates a period until the user's action shifts from the corresponding stage to the next stage. The “period” is abstractly illustrated as “TM11” or the like, but a specific period such as “two weeks” or “average one month” may be stored. “Correlation” indicates the correlation between the corresponding stage and the next stage. Although the “correlation information” is abstractly illustrated as “RT11” or the like, a specific relationship such as “60% of the previous stage” or “same as the previous stage” may be stored.

  For example, in the example illustrated in FIG. 1, the target “automobile” indicates that there is a stage of forecasting demand such as “N stage” to “N + 2 stage” as stages. The “N stage” of the target “car” indicates the action type “dealer visit”. In addition, “N + 1 stage” of the target “car” indicates an action type “purchase contract”. Note that the stages such as “N stage” to “N + 2 stage” may not be performed one by one. For example, N + 1 stage and N + 2 stage actions may be performed simultaneously after N stage actions. Further, without going through the N + 1 stage, after the N + 2 stage action is performed after the N stage action, the N + 1 stage action may be performed.

  In addition, the period from the action type “dealer visit” to the action type “purchase contract” for the target “automobile” is the period “TM11”. For example, when the period “TM11” is “average one month”, it indicates that the period from when the user visits the dealer to the purchase contract of the car is one month on average.

  Further, the correlation between the action type “dealer visit” and the action type “purchase contract” of the target “automobile” indicates the correlation “RT11”. For example, if the correlation “RT11” is “10% of the previous stage”, it indicates that if the visit to the dealer increases by 100%, the user also increases the car purchase contract by 10%.

  For example, the prediction device 100 may generate the relationship information as described above using a past user action history or the like. Note that the prediction device 100 may acquire various types of information stored in the related information storage unit 124 from an external information processing device.

  Further, for example, the prediction apparatus 100 may acquire information stored in the items “period” and “correlation information” shown in the relation information storage unit 124 in FIG. 1 by a method such as machine learning. For example, the prediction device 100 inputs information related to a predetermined behavior of the user, and uses a model that outputs a period until the next behavior of the behavior, using the item “indicated in the relation information storage unit 124 in FIG. Information on “period” may be acquired.

  In addition, for example, the prediction device 100 uses the model that inputs the information related to the predetermined action of the user and outputs the score related to the action next to the action, and the items shown in the relation information storage unit 124 in FIG. Information of “correlation information” may be acquired. For example, the prediction apparatus 100 uses a model that outputs a score of 0 to 1 (0% to 100%) related to the next action of the user by inputting information related to the predetermined action of the user. Information on the item “correlation information” may be acquired as the score to be output.

  In the example of FIG. 1, the prediction device 100 predicts a demand related to a vehicle purchase contract by a user based on the transition graph SG <b> 11 and related information related to the target “automobile” in the related information storage unit 124. For example, when the prediction time point is the date and time DT12, the prediction device 100 is based on the change in the number of users who visited the card dealer from the date and time DT11 to the date and time DT12 and the relationship information between the card dealer visit and the purchase contract. Predict demand related to car purchase contracts by users.

  In the example of FIG. 1, the prediction device 100 predicts that the purchase contract for the target “automobile” becomes the demand DM 11 after the period TM 11 has elapsed, as shown in the demand information DINF 11. For example, the prediction device 100 predicts that the purchase contract of the target “car” becomes the demand DM 11 indicating an increase rate corresponding to the increase rate of the card dealer visit after the period TM 11 has elapsed from the date and time DT 12. For example, for the purchase contract of the target “car”, the prediction device 100 calculates an increase rate corresponding to the increase rate of the card dealer visits in a predetermined period from the time after the period TM11 has elapsed from the date DT12 (referred to as the date DT13). The demand DM11 shown is predicted. For example, for the purchase contract of the target “car”, the prediction device 100 increases the number of visits to the card dealer from the date and time DT13 to the period between the date and time DT11 and the date and time DT12 (for example, two weeks) and the same period (for example, two weeks). It is predicted that the demand DM11 indicates an increase rate corresponding to the rate. In this case, the prediction device 100 predicts that the purchase contract for the target “automobile” becomes the demand DM 11 indicating an increase rate corresponding to the increase rate of the card dealer visits in two weeks starting from the date DT13.

  For example, when the period TM11 is “1 month” and the correlation “RT11” is “10% of the previous stage”, the prediction device 100 sets the purchase contract of the target “car” after one month from the date DT12. Expect a 10% increase in card dealer visits. For example, when the rate of increase in visits to the card dealer between the date and time DT11 and the date and time DT12 is 100%, the prediction device 100 predicts that the purchase contract for the target “car” will increase by 10% after one month from the date and time DT12. . Note that the above is an example, and the prediction device 100 may predict the target demand using various information as appropriate.

  As described above, the prediction device 100 predicts demand related to a target based on the user's position information and information regarding the target associated with the position. In the example of FIG. 1, the prediction device 100 captures a user who has visited a card dealer using the user's position information, and predicts the demand related to the purchase contract of the target “car” based on the change in the number of users. Thereby, the prediction device 100 can appropriately predict the purchase contract (second action) of the target “car” from the action of visiting the card dealer (first action) using the user's position information. Therefore, the prediction device 100 can appropriately predict the demand.

  Moreover, the prediction apparatus 100 may provide the predicted demand information to the operator. For example, the prediction device 100 may provide the predicted demand information to a company that manufactures automobiles. For example, the prediction device 100 may provide the predicted demand information to a business operator who sells automobiles. As a result, the business operator who has received the information can make a more appropriate demand prediction (determination) for his business based on the provided information.

[1-1. (Target of prediction)
[1-1-1. Other subjects)
In the example of FIG. 1, the case where the prediction device 100 predicts the demand related to the automobile provided by the card dealer MA is shown, but the prediction device 100 may predict the demand related to various objects. For example, the prediction device 100 may predict a demand related to a service provided by a predetermined operator. For example, the prediction device 100 may predict the demand for educational services provided by educational institutions such as schools and nurseries.

  For example, the prediction apparatus 100 may predict a demand for admission (entrance) to an educational institution in order to receive an educational service provided by an educational institution such as a school or a nursery school. In this case, for example, the prediction device 100 may predict the demand for admission (entrance) to an educational institution based on the number of users who participated in a briefing session of an educational institution such as a school or a nursery school.

  In this case, the prediction device 100 predicts the demand related to the target “education institution” after the first action based on the first action that the user participates in the explanatory meeting of the education institution. For example, the prediction device 100 predicts the demand related to the service provided by the target “education institution” based on the change in the number of users who participated in the explanatory meeting of the institution. For example, the prediction device 100 predicts demand related to admission to an educational institution based on information related to a user's participation in an educational institution briefing.

  In addition, for example, the prediction device 100 may be a second user (protected person) who wishes to enter (enter a park) based on the number of first users (guardians) who participated in a briefing session of the educational institution. Demand may be predicted. For example, the prediction device 100 may predict the number of children who enter (enter the school) based on the number of parents who are first users who participated in a briefing session of the educational institution. For example, when the educational institution has an examination system, the prediction apparatus 100 may predict the entrance rate of the educational institution as a demand for the educational institution based on the first user who participated in the explanatory meeting of the educational institution. .

[1-1-2. Target level (target identification)
In the example of FIG. 1, in order to simplify the explanation, a case where the prediction device 100 predicts demand related to a vehicle that is an offer provided by the card dealer MA is shown. May be predicted. For example, the prediction device 100 may predict the demand for each vehicle type provided (sold) by the card dealer MA. For example, the prediction device 100 may predict the demand for each of the five vehicle types when five types of vehicles are provided (sold) by the card dealer MA.

  For example, the prediction device 100 acquires the position information of the user in the card dealer MA, and depending on the time when the user stays at a position close to the car of each car model, the user can enter the car dealer MA for any car model. You may estimate whether you have visited. For example, when the user U1 stays at the position closest to the vehicle type “car X” among the respective car types in the card dealer MA, the user U1 aims at the car type “car X”. It may be estimated that he visited Cardira MA. For example, the prediction device 100 acquires information on the user's position such as GPS and the above-described wireless communication function and beacon from the terminal device 10, and improves the estimation accuracy of the user's position by combining the information, It may be captured which vehicle type the user stays in. As described above, the prediction device 100 identifies the target based on the position information of the user, and predicts the demand related to the identified target. For example, the prediction device 100 identifies which of the five vehicle types is targeted based on the position information of the user, and predicts demand for the identified vehicle type.

  In this case, the prediction device 100 measures that the user U1 has visited the card dealer MA for the target “car X”, and that the user U1 has visited the card dealer MA for another car type (for example, “car Y”). It is not necessary to measure. Thereby, the prediction apparatus 100 can predict the demand regarding the vehicle type which is a classification more detailed than a vehicle about the goods used as prediction object.

  Further, when the POI is a city hall or embassy or the like, the prediction apparatus 100 may identify a target (provided item) intended for the user based on the position of the user and a position where various procedures are received. Good. For example, when the POI is an embassy, the prediction device 100 may identify that the user has visited the embassy for visa application procedures when the user's location stays for a certain period of time before the visa application reception desk. Good. For example, when the POI is a city hall, the prediction device 100 is assumed that the user visits the government office for the procedure for the notification of the resident change when the user's location stays for a certain period of time in front of the reception desk for the resident transfer notification such as a transfer notification. You may specify.

[1-2. (Multi-stage demand forecast)
In the example of FIG. 1, the prediction apparatus 100 predicts the demand related to the N + 1 stage behavior based on the N stage behavior information, but the prediction apparatus 100 predicts the demand related to the N + 2 stage behavior. May be. In this case, the prediction apparatus 100 may predict the demand related to the N + 2 stage behavior based on the N stage behavior information, or may predict the demand related to the N + 2 stage behavior based on the predicted N + 1 stage behavior. May be. Further, the prediction device 100 may predict the demand related to the N + 2 stage behavior based on the N + 1 stage behavior captured by the position information.

  In the example of FIG. 1, the prediction device 100 predicts demand related to a car purchase contract from information on a car dealer visit, and then uses the prediction related to a car purchase contract to predict demand related to insurance coverage for the target “automobile”. May be. As described above, the prediction device 100 may predict the demand by chain-estimating other behaviors from the behavior information of one user based on the position information.

[1-3. Use of other information)
In the example of FIG. 1, the case where the prediction device 100 predicts demand using only the position information of the user is shown, but the prediction device 100 may predict demand using various information. For example, the prediction device 100 may predict the demand using any information as long as the information input by the user is associated with the position information. For example, the prediction device 100 may predict a demand corresponding to a predetermined area from a query used by a user for search and position information. For example, the prediction device 100 may predict a demand corresponding to a predetermined area from character information and position information communicated via a predetermined network.

  For example, the prediction device 100 may predict the demand using post information posted by the user in the social networking service and user position information at the time of posting. For example, the prediction device 100 may predict the demand using post information posted by a user on Twitter (registered trademark), Facebook (registered trademark), or the like. For example, the prediction device 100 may predict the demand using a sentence (character information) posted by the user on Twitter.

  For example, the prediction device 100 may predict demand using input information input by a user in a message service. For example, the prediction device 100 may predict demand using input information input by a user in a message service such as LINE (registered trademark). For example, the prediction device 100 may predict the demand using input information input in an e-mail.

[1-4. (Forecast of each user)
In the example of FIG. 1, the case where the prediction device 100 predicts the statistical demand for a large number of users using the information of a plurality of users is shown. However, the prediction device 100 predicts the demand for individual users. Also good. For example, the prediction device 100 may predict a demand related to each user using a model generated by a method such as machine learning.

  For example, the prediction device 100 uses a model that outputs a period until an action next to an action corresponding to the position of one user by inputting the position information of one user and attribute information of the one user. Also good. In this case, the prediction apparatus 100 outputs the period until the next action of the action corresponding to the position of the one user in the model by inputting the position information of the one user and the attribute information of the one user into the model. You may let them. Thereby, the prediction apparatus 100 may predict the period until the next action of the action corresponding to the position of one user.

  For example, the prediction device 100 outputs information indicating a correlation between an action corresponding to the position of the one user and the next action by inputting the position information of the one user and the attribute information of the one user. A model may be used. In this case, the prediction device 100 inputs the position information of one user and the attribute information of one user into the model, thereby 0 to 1 (0 to 1) regarding the next action of the action corresponding to the position of the one user in the model. A score of 0% to 100%) may be output. Thereby, the prediction apparatus 100 may predict the probability that one user will perform the next action of the action corresponding to the position of the one user.

  Note that the model learning technique is not limited to the technique described above, and any known technique can be applied. Each model may be generated using various conventional techniques relating to machine learning as appropriate. For example, the model generation may be performed using a technique related to machine learning of supervised learning such as SVM (Support Vector Machine). Further, for example, the model generation may be performed using a technique related to machine learning of unsupervised learning. For example, the generation of the model may be performed using a deep learning technique. For example, the model generation may be performed by appropriately using various deep learning techniques such as DNN (Deep Neural Network), RNN (Recurrent Neural Network), and CNN (Convolutional Neural Network). The description relating to the generation of the model is merely an example, and the generation of the model may be performed by a learning method appropriately selected according to information that can be acquired.

[1-5. (Forecast)
In the example of FIG. 1, the number of users performing the next stage (N + 1 stage) action (second action) according to the change in the number of users who performed the previous stage (N stage) action (first action). Although the case where the change is predicted is shown, the prediction device 100 may predict the demand related to the object using various information. For example, the prediction device 100 may predict the number of users performing the second action from the number of users performing the first action.

  For example, when the correlation information that the half (50%) of the users who have performed the first action performs the second action is obtained from the past user action history or the like, You may estimate the number of users who perform 2nd action from the number of users who performed action. In this case, when the number of users who performed the first action is 10,000, the prediction device 100 may predict the number of users who perform the second action as 5000. And the prediction apparatus 100 may predict the demand relevant to an object based on the number of users who perform the predicted 2nd action. For example, the prediction device 100 may predict the demand related to the target based on a comparison between the predicted number of users who perform the second action and the average number of users who perform the second action in the past. For example, the prediction device 100 may include the number of users who perform the second action in a predetermined period (for example, one week from the prediction time point) and the number of users who perform the second action in the same period (for example, one week) in the past. The demand associated with the object may be predicted based on a comparison with the average.

  For example, the prediction device 100 may predict that the demand related to the target is high when the predicted number of users who perform the second action and the average number of users who perform the second action in the past are larger. For example, the prediction device 100 may predict that the demand related to the target is low when the predicted number of users performing the second action and the average number of users performing the second action in the past are lower. Note that the above-described demand prediction is an example, and the prediction device 100 may predict the demand related to the target using various information as appropriate.

[2. Configuration of prediction device]
Next, the configuration of the prediction apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 3 is a diagram illustrating a configuration example of the prediction device according to the embodiment. As illustrated in FIG. 3, the prediction device 100 includes a communication unit 110, a storage unit 120, and a control unit 130. The prediction device 100 includes an input unit (for example, a keyboard and a mouse) that receives various operations from an administrator of the prediction device 100, and a display unit (for example, a liquid crystal display) for displaying various types of information. May be.

(Communication unit 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 to and from the terminal device 10 and the provider device 20 included in the prediction system 1, for example.

(Storage unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory device such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. As illustrated in FIG. 3, the storage unit 120 according to the embodiment includes a POI information storage unit 121, a user information storage unit 122, a behavior information storage unit 123, and a relationship information storage unit 124.

(POI information storage unit 121)
The POI information storage unit 121 according to the embodiment stores various types of information related to products. FIG. 4 is a diagram illustrating an example of a POI information storage unit according to the embodiment. The POI information storage unit 121 illustrated in FIG. 4 includes items such as “POI_ID”, “POI name”, “business type”, “location”, and “provided item”.

  “POI_ID” indicates identification information for identifying the POI. “POI name” indicates the POI identified by the corresponding POI_ID. For example, “POI name” indicates the name of the POI identified by the corresponding POI_ID. “Business type” indicates the business type of the business operator corresponding to the POI identified by the corresponding POI_ID. “Location” indicates the location of the POI identified by the corresponding POI_ID. “Provided” indicates a product or service provided by a business entity corresponding to the POI identified by the corresponding POI_ID.

  For example, in the example illustrated in FIG. 4, the POI identified by the POI_ID “EP1” indicates that the business operator is “card dealer MA” and the business type is “sales”. Further, the location of the card dealer MA indicates the position LC10 in the A area. In the example of FIG. 4, the position is illustrated by an abstract code such as “LC10”, but the position information may be specific latitude / longitude information or the like. Further, the product provided by the card dealer MA indicates that it is “automobile”.

  The POI information storage unit 121 is not limited to the above, and may store various information according to the purpose. For example, the POI information storage unit 121 may store information on the business scale such as the number of employees of the business operator and the sales amount.

(User information storage unit 122)
The user information storage unit 122 according to the embodiment stores various types of information regarding user attributes. For example, the user information storage unit 122 stores user attribute information. FIG. 5 is a diagram illustrating an example of a user information storage unit according to the embodiment. The user information storage unit 122 illustrated in FIG. 5 includes items such as “user ID”, “age”, “sex”, “home”, “work location”, and “interest”.

  “User ID” indicates identification information for identifying a user. For example, the user identified by the user ID “U1” corresponds to the user U1 illustrated in the example of FIG. “Age” indicates the age of the user identified by the user ID. The “age” may be a specific age of the user identified by the user ID, such as 35 years old. “Gender” indicates the gender of the user identified by the user ID.

  “Home” indicates location information of the user's home identified by the user ID. In the example illustrated in FIG. 5, “home” indicates an abstract code such as “LC11”, but may be latitude / longitude information or the like. For example, “home” may be a region name or an address.

  “Work location” indicates position information of the user's work location identified by the user ID. In the example illustrated in FIG. 5, “work location” illustrates an abstract code such as “LC12”, but may be latitude / longitude information or the like. Further, for example, the “work location” may be an area name or an address.

  “Interest” indicates the interest of the user identified by the user ID. That is, “interest” indicates an object that is highly interested by the user identified by the user ID. In the example shown in FIG. 5, “interest” is illustrated for each user one by one.

  For example, in the example illustrated in FIG. 5, the age of the user identified by the user ID “U1” is “20s”, and the gender is “male”. For example, the user identified by the user ID “U1” indicates that the home is “LC11”. For example, the user identified by the user ID “U1” indicates that the work location is “LC12”. For example, the user identified by the user ID “U1” indicates that he / she is interested in “sports”.

  Note that the user information storage unit 122 is not limited to the above, and may store various types of information according to the purpose. For example, the user information storage unit 122 may store information on the demographic attribute of the user and information on the psychographic attribute. For example, the user information storage unit 122 may store information such as name, family structure, income, interest, and lifestyle.

(Behavior information storage unit 123)
The behavior information storage unit 123 according to the embodiment stores various types of information regarding the user's behavior. FIG. 6 is a diagram illustrating an example of the behavior information storage unit according to the embodiment. For example, the behavior information storage unit 123 stores various behavior information including a search performed using the terminal device 10 of each user. The action information storage unit 123 illustrated in FIG. 6 includes items such as “action ID”, “user ID”, “position”, “date / time”, and “action content”.

  The “behavior ID” indicates information for identifying the user's behavior. “User ID” indicates identification information for identifying a user. For example, the user identified by the user ID “U1” corresponds to the user U1 illustrated in the example of FIG.

  The “position” indicates a position where the corresponding action is performed. For example, “position” indicates the position of the user. In the example of FIG. 6, the position is illustrated by an abstract code such as “LC1”, but the position information may be specific latitude / longitude information or the like.

  The “date and time” indicates the date and time when the corresponding user action was performed. “Date and time” is abstractly illustrated as “DT1” or the like, but a specific date and time such as “August 8, 2017 23:46:58” may be stored. Further, “action content” indicates the content of the action performed by the user at the corresponding position. For example, “behavior content” may store information such as content viewed by the user or a query used by the user for a search. The “action content” may be stored when only the position is acquired by the user.

  For example, in the example illustrated in FIG. 6, the user (user U1) identified by the user ID “U1” is located at the position LC1 on the date DT1. Further, for example, in the example shown in FIG. 6, it is indicated that the user (user U2) identified by the user ID “U2” is located at the position LC2 at the date DT2.

  The behavior information storage unit 123 is not limited to the above, and may store various information according to the purpose. Moreover, although the case where action information was memorize | stored in the action information storage part 123 for every user ID was shown in FIG. 6, action information may be memorize | stored for every user ID, for example not only in order of a date.

(Relational information storage unit 124)
The relationship information storage unit 124 according to the embodiment stores various types of information related to the relationship between the target and the demand. FIG. 7 is a diagram illustrating an example of the relationship information storage unit according to the embodiment. The relationship information storage unit 124 illustrated in FIG. 7 includes items such as “stage”, “behavior type”, “period”, and “correlation” that include items such as “target” and “relation information”. .

  “Target” indicates the target of the user's demand. The “target” is not limited to a specific product name, service name, country name, or the like, but may be a product or service category (beverage, fashion, travel, etc.).

  “Relational information” indicates a user's action related to the corresponding target, a correlation between the actions, and the like. “Stage” indicates the stage of action performed by the user regarding the target. “Stage N (N is an arbitrary numerical value)” indicates an action performed before “Stage N + 1”.

  “Action type” indicates the type of action corresponding to each stage. “Period” indicates a period until the user's action shifts from the corresponding stage to the next stage. The “period” is abstractly illustrated as “TM11” or the like, but a specific period such as “two weeks” or “average one month” may be stored. “Correlation” indicates the correlation between the corresponding stage and the next stage. Although the “correlation information” is abstractly illustrated as “RT11” or the like, a specific relationship such as “60% of the previous stage” or “same as the previous stage” may be stored.

  For example, in the example illustrated in FIG. 7, the target “automobile” indicates that there is a stage of predicting demand such as “N stage” to “N + 2 stage” as stages. The “N stage” of the target “car” indicates the action type “dealer visit”. In addition, “N + 1 stage” of the target “car” indicates an action type “purchase contract”. In addition, the period from the action type “dealer visit” to the action type “purchase contract” for the target “automobile” is the period “TM11”. Further, the correlation between the action type “dealer visit” and the action type “purchase contract” of the target “automobile” indicates the correlation “RT11”.

  Further, the relationship information storage unit 124 is not limited to the above, and may store various information according to the purpose. For example, the relationship information storage unit 124 may store information regarding the date and time when the demand information was generated and the period during which the information used for generation was collected.

(Control unit 130)
Returning to the description of FIG. 3, the control unit 130 is a controller and is stored in a storage device inside the prediction device 100 by, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like. Various programs (corresponding to an example of a prediction program) are implemented by using the RAM as a work area. The control unit 130 is a controller, and is realized by an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

  As illustrated in FIG. 3, the control unit 130 includes an acquisition unit 131, a generation unit 132, a prediction unit 133, and a provision unit 134, and realizes or executes information processing functions and operations described below. .

(Acquisition part 131)
The acquisition unit 131 acquires various types of information. For example, the acquisition unit 131 acquires various types of information from the POI information storage unit 121, the user information storage unit 122, the behavior information storage unit 123, and the relationship information storage unit 124. For example, the acquisition unit 131 may acquire various types of information stored in the relationship information storage unit 124 from an external information processing apparatus. For example, the acquisition unit 131 acquires various types of information from the terminal device 10 or the provider device 20. The acquisition unit 131 acquires user position information. For example, the acquisition unit 131 acquires information in which a position and a target are associated with each other.

  The acquisition unit 131 acquires various types of information from an external information processing apparatus. For example, the acquisition unit 131 acquires user position information from the behavior information storage unit 123. For example, the acquisition unit 131 acquires user position information from the terminal device 10.

  In the example of FIG. 1, the acquisition unit 131 acquires position information from the terminal device 10-1 used by the user U1. For example, the acquisition unit 131 acquires the position information LC1 from the terminal device 10-1 at the date DT1. For example, the acquisition unit 131 acquires position information from the terminal device 10-2 used by the user U2. For example, the acquisition unit 131 acquires the position information LC2 from the terminal device 10-2 at the date DT2. For example, the acquisition unit 131 acquires position information from the terminal device 10-3 used by the user U3. For example, the acquisition unit 131 acquires the position information LC3 from the terminal device 10-3 at the date and time DT3. For example, the acquisition unit 131 acquires position information from the terminal device 10-4 used by the user U4. For example, the acquisition unit 131 acquires the position information LC4 from the terminal device 10-4 at the date and time DT4. For example, the acquisition unit 131 acquires position information from the terminal device 10-5 used by the user U5. For example, the acquisition unit 131 acquires the position information LC5 from the terminal device 10-5 at the date DT5.

  In the example of FIG. 1, the acquisition unit 131 acquires a measurement target. For example, the acquisition unit 131 acquires a measurement target by specifying a predetermined business operator or setting an administrator of the prediction device 100. For example, the acquisition unit 131 acquires information whose target is “automobile (car)” as a measurement target. For example, when the measurement target is “automobile”, the acquisition unit 131 acquires information on the POI for measuring the position information of the user. For example, the acquisition unit 131 acquires information on the card dealer MA, the card dealer MB, and the like stored in the POI information storage unit 121 as a POI for measuring user position information. For example, the acquisition unit 131 acquires the position LC10 of the card dealer MA and the position LC101 of the card dealer MB.

(Generator 132)
The generation unit 132 generates various information. For example, the generation unit 132 generates transition information. For example, the generation unit 132 generates transition information based on the prediction (estimation) by the prediction unit 133.

  In the example of FIG. 1, the generation unit 132 generates transition information on the number of users who have visited (visited) the card dealer. For example, the generation unit 132 generates information indicating the transition of the number of users who have visited (visited) the card dealer as shown in the transition graph SG11 in FIG.

  In the example of FIG. 1, the generation unit 132 generates a transition graph SG11 indicating that the number of users visiting the card dealer has increased from the number of visitors CN11 to the number of visitors CN12 between the date and time DT11 and the date and time DT12. . For example, the generation unit 132 may generate various types of information stored in the relationship information storage unit 124 using past user behavior histories and the like.

(Prediction unit 133)
The prediction unit 133 predicts various information. For example, the prediction unit 133 predicts various information based on the information stored in the storage unit 120. For example, the prediction unit 133 predicts various information based on the information acquired from the terminal device 10.

  The prediction unit 133 predicts the demand related to the target based on the position information of the user acquired by the acquisition unit 131 and information on the target associated with the position. For example, the prediction unit 133 predicts the demand related to the target specified based on the user location information. For example, the prediction unit 133 predicts a demand related to an object related to an offer provided at a position.

  For example, the prediction unit 133 predicts a demand related to an object that is an offer provided at a position. For example, the prediction unit 133 predicts a demand related to an object that requires a provision provided at a position.

  For example, the prediction unit 133 predicts the demand related to the target after the time corresponding to the user position information. For example, the prediction unit 133 predicts the demand related to the target after the first action based on the first action of the user corresponding to the user position information. For example, the prediction unit 133 predicts a second action that is an action of the user after the first action and is an action related to the target, based on the first action of the user.

  For example, the prediction unit 133 predicts a change in the second behavior based on the change in the first behavior. For example, the prediction unit 133 predicts the second behavior of the user after a lapse of a period that varies depending on the target from the time of the first behavior of the user.

  In the example of FIG. 1, the prediction unit 133 predicts demand using information on a plurality of POIs such as a card dealer MA and a card dealer MB. For example, the predicting unit 133 visits the card dealer for a predetermined period based on a comparison between the positions of the POIs such as the card dealer MA and the card dealer MB and the positions of the users U1 to U5. Estimate the number of

  For example, the prediction unit 133 estimates that the user U1 visited the card dealer MA at the date DT1 based on the information indicating that the user U1 is located in the card dealer MA having POI at the date DT1. For example, the prediction unit 133 estimates that the user U2 has visited the card dealer MA at the date DT2 based on information indicating that the user U2 is located in the card dealer MA having the POI at the date DT2.

  In the example of FIG. 1, the prediction unit 133 predicts the demand related to the target “automobile” based on the information related to the user who has visited the card dealer. For example, the prediction unit 133 predicts the demand related to the target “automobile” after the first action based on the first action of the user visiting the car dealer. For example, the prediction unit 133 predicts the demand related to the target “automobile” based on the change in the number of users who have visited the card dealer. For example, the prediction unit 133 predicts a demand related to a vehicle purchase contract by the user based on information related to the visit to the user's card dealer.

  For example, the prediction unit 133 predicts the demand related to the vehicle purchase contract by the user based on the information regarding the relationship between the visit to the card dealer and the vehicle purchase contract as shown in the relationship information storage unit 124 in FIG. To do. For example, the prediction unit 133 predicts a second action called a car purchase contract after the first action based on the first action called a user's visit to a car dealer. For example, the prediction unit 133 predicts the demand related to the target “automobile” after the first action based on the first action of the user visiting the car dealer.

  In the example of FIG. 1, the prediction unit 133 predicts a demand related to a vehicle purchase contract by the user based on the transition graph SG <b> 11 and the relationship information regarding the target “automobile” in the relationship information storage unit 124. For example, when the prediction time point is the date and time DT12, the prediction unit 133 is based on the change in the number of users who visited the card dealer from the date and time DT11 to the date and time DT12 and the relationship information between the card dealer visit and the purchase contract. Predict demand related to car purchase contracts by users.

  In the example of FIG. 1, the prediction unit 133 predicts that the purchase contract for the target “automobile” becomes the demand DM 11 after the period TM 11 has elapsed, as shown in the demand information DINF 11. For example, the prediction unit 133 predicts that the purchase contract of the target “automobile” becomes the demand DM11 indicating the increase rate corresponding to the increase rate of the card dealer visit after the period TM11 has elapsed from the date and time DT12.

  For example, when the period TM11 is “1 month” and the correlation “RT11” is “10% of the previous stage”, the prediction unit 133 sets the purchase contract of the target “car” after one month from the date DT12. Expect a 10% increase in card dealer visits. For example, when the increase rate of the visit to the card dealer between the date and time DT11 and the date and time DT12 is 100%, the prediction unit 133 predicts that the purchase contract of the target “car” increases by 10% after one month has passed since the date and time DT12. .

(Providing unit 134)
The providing unit 134 provides various types of information to an external information processing apparatus. For example, the providing unit 134 provides various types of information to the business entity apparatus 20. For example, the providing unit 134 may provide the terminal device 10 with information indicating a search result corresponding to the query. For example, the providing unit 134 provides demand information to the business entity apparatus 20.

  For example, the providing unit 134 provides information indicating the demand related to the target to the business operator related to the target. For example, the providing unit 134 provides information indicating the demand for the product to the business operator located in the area corresponding to the position information.

  Further, the providing unit 134 may provide the demand information predicted by the prediction unit 133 to the business operator. For example, the providing unit 134 may provide the demand information predicted by the predicting unit 133 to an operator that manufactures an automobile. For example, the providing unit 134 may provide the demand information predicted by the prediction unit 133 to a business operator who sells automobiles.

[3. (Prediction process flow)
Here, the procedure of the prediction process by the prediction apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 8 is a flowchart illustrating an example of the prediction process according to the embodiment.

  As illustrated in FIG. 8, the prediction device 100 acquires user position information (step S <b> 101). In the example of FIG. 1, the prediction device 100 acquires the position information LC1 from the terminal device 10-1 at the date and time DT1. In the example of FIG. 1, the prediction device 100 acquires the position information LC2 from the terminal device 10-2 at the date and time DT2. In the example of FIG. 1, the prediction device 100 acquires the position information LC3 from the terminal device 10-3 at the date DT3. In the example of FIG. 1, the prediction device 100 acquires the position information LC4 from the terminal device 10-4 at the date and time DT4. In the example of FIG. 9, the prediction device 100 acquires the position information LC5 from the terminal device 10-5 at the date DT5.

  In addition, the prediction device 100 predicts demand related to the target based on the position information of the user and information regarding the target associated with the position (step S102). For example, in the example of FIG. 1 in which the prediction device 100 predicts demand related to an object based on the user's position information and information related to the object associated with the position, the prediction device 100 includes the users U1 to U5 and the like. Based on the position of, the demand for the target “car” is predicted.

[4. Another example of prediction processing)
In the example of FIG. 1, the case where the demand related to the automobile that is the product is predicted is shown, but the prediction device 100 may predict the demand related to various objects. This point will be described with reference to FIG. FIG. 9 is a diagram illustrating an example of a prediction process according to the embodiment. Specifically, FIG. 9 illustrates a case where the prediction apparatus 100 predicts demand related to the target “country X”. In addition, about the same point of FIG. 1, the same code | symbol is attached | subjected and description is abbreviate | omitted suitably.

  In the example of FIG. 9, the prediction device 100 acquires the position of the terminal device 10 used by the users U <b> 1 to U <b> 5 by a function such as a GPS sensor included in the terminal device 10 used by the users U <b> 1 to U <b> 5 and the like. The map information MP1 shown in FIG. 9 is the same as the map information MP1 shown in FIG. In the example of FIG. 1, the prediction target is “country X”, and the user's travel to country X is based on information about the visa application to the X country embassy (visit of the X country embassy). Predict demand.

  Steps S11 and S12 shown in the example of FIG. 9 are the same as those in FIG. First, the prediction device 100 acquires position information from the terminal device 10-1 used by the user U1 (step S11-1). In the example of FIG. 9, the prediction device 100 acquires the position information LC1 from the terminal device 10-1 at the date and time DT1. In addition, the prediction device 100 acquires position information from the terminal device 10-2 used by the user U2 (step S11-2). In the example of FIG. 9, the prediction device 100 acquires the position information LC2 from the terminal device 10-2 at the date and time DT2. In addition, the prediction device 100 acquires position information from the terminal device 10-3 used by the user U3 (step S11-3). In the example of FIG. 9, the prediction device 100 acquires the position information LC3 from the terminal device 10-3 at the date and time DT3.

  Moreover, the prediction apparatus 100 acquires position information from the terminal device 10-4 used by the user U4 (step S11-4). In the example of FIG. 9, the prediction device 100 acquires the position information LC4 from the terminal device 10-4 at the date DT4. Then, the behavior information indicating that the user U4 is located at the position LC4 at the date DT4 is stored in the behavior information storage unit 123. In the example of FIG. 9, the position LC4 is assumed to be a position close to the position LC20 of the X country embassy EM. That is, the prediction device 100 stores, in the behavior information storage unit 123, behavior information indicating that the user U4 is located in the X country embassy EM at the date DT4. Thereby, the prediction apparatus 100 can predict that the user U4 visited the X country embassy EM on the date and time DT4 and applied for a visa.

  Moreover, the prediction apparatus 100 acquires position information from the terminal device 10-5 used by the user U5 (step S11-5). In the example of FIG. 9, the prediction device 100 acquires the position information LC5 from the terminal device 10-5 at the date DT5. Then, the behavior information indicating that the user U5 is located at the position LC5 at the date DT5 is stored in the behavior information storage unit 123. In the example of FIG. 9, the position LC5 is assumed to be a position close to the position LC20 of the X country embassy EM. That is, the prediction device 100 stores, in the behavior information storage unit 123, behavior information indicating that the user U5 is located in the X country embassy EM at the date DT5. Thereby, the prediction apparatus 100 can predict that the user U5 visited the X country embassy EM at the date and time DT5 and applied for a visa.

  Thereby, the prediction apparatus 100 collects user's position information (step S12). In the example of FIG. 9, the prediction device 100 collects positions LC1 to LC5 of the users U1 to U5 and the like as shown in the behavior information storage unit 123.

  And the prediction apparatus 100 acquires a measuring object (step S23). In the example of FIG. 9, the prediction device 100 acquires information whose target is “country X” as a measurement target. Further, when the measurement target is “country X”, the prediction device 100 acquires information about the POI for measuring the position information of the user. In the example of FIG. 9, the prediction apparatus 100 acquires information on the X country embassy EM or the like stored in the POI information storage unit 121 as a POI for measuring user position information. For example, the prediction apparatus 100 acquires the position LC20 of the X country embassy EM.

  First, the prediction device 100 visits the X embassy EM using the information described above, and generates transition information on the number of users who applied for a visa (step S24). For example, the prediction apparatus 100 visits the X embassy EM for a predetermined period based on a comparison between the position of each POI such as the X embassy EM and the position of each user U1 to U5, etc. Estimate the number of users who applied.

  For example, the prediction device 100 estimates that the user U4 visited the X embassy EM at the date DT4 based on the information indicating that the user U4 was located at the X embassy EM that is the POI at the date DT4. Further, for example, the prediction device 100 estimates that the user U5 visited the X country embassy EM at the date DT5 based on the information indicating that the user U5 was located at the X country embassy EM that is the POI at the date DT5. To do. Using the estimation result indicating that each user has applied for a visa for country X, the prediction device 100 visits the X country embassy EM as shown in the transition graph SG21 in FIG. Information indicating the transition of the number of applied users is generated.

  In the example of FIG. 9, the prediction apparatus 100 increases the number of visa applicants CN21 from the number of visa applicants CN21 to the number of visa applicants CN22 between the date DT21 and the date DT22. A transition graph SG21 indicating that the above has been performed is generated.

  And the prediction apparatus 100 predicts the demand regarding object "Country X" based on the information regarding the user who visited X embassy EM and applied for a visa (step S25). That is, the prediction device 100 predicts the demand related to the target “country X” after the first behavior based on the first behavior of the user's country X visa application. For example, the prediction device 100 predicts the demand for the target “country X” based on a change in the number of users who have visited the X country embassy EM and applied for a visa. In the example of FIG. 9, the prediction device 100 predicts demand related to a user's travel to country X based on the information related to the visa application for the user's country X. In this way, the prediction device 100 predicts the demand related to the travel to the country X by the user who needs the country X visa provided in the POI (the X country embassy EM) corresponding to the position.

  For example, the prediction device 100 determines the demand related to the travel to the country X by the user based on the information on the relationship between the visa application for the country X and the travel to the country X as shown in the relationship information storage unit 124 in FIG. Predict. As described above, the prediction device 100 predicts the second action of traveling to the country X after the first action based on the first action of the visa application for the country X of the user. Thereby, the prediction apparatus 100 predicts the demand regarding the target “country X” after the first action based on the first action of visa application for the user X country.

  For example, in the example illustrated in FIG. 9, the target “Country X” indicates that there are stages for predicting demand such as “N stages” to “N + 1 stages” as stages. In addition, “N stage” of the target “country X” indicates the action type “visa application”. In addition, “N + 1 stage” of the target “country X” indicates the action type “travel”.

  Further, the period from the action type “visa application” to the action type “travel” for the target “country X” is the period “TM21”. For example, if the period “TM21” is “average one month”, it means that the period from when the user applies for a visa to traveling to country X is one month on average. For example, the period “TM21” may be an average period from visa application to issuance in country X at the Embassy of Country X.

  Further, the correlation between the action type “visa application” and the action type “travel” for the target “country X” indicates that the correlation is “RT21”. For example, when the correlation “RT21” is “90% of the previous stage”, when the visa application of the user X country is increased by 100%, the user's travel to X country is also increased by 90%.

  For example, the prediction device 100 may generate the relationship information as described above using a past user action history or the like. Note that the prediction device 100 may acquire various types of information stored in the related information storage unit 124 from an external information processing device.

  In the example of FIG. 9, the prediction device 100 predicts a user's demand for travel to country X based on the transition graph SG <b> 21 and the relationship information regarding the target “country X” in the relationship information storage unit 124. For example, when the prediction time point is the date and time DT22, the prediction device 100 is based on the change in the number of users who applied for visas from the date and time DT21 to the date and time DT22 and the relationship information between the visa application and travel in country X. Predict demand related to travel to country X by users.

  In the example of FIG. 9, the prediction device 100 predicts that the travel of the target “country X” becomes the demand DM 21 after the period TM21 has elapsed, as shown in the demand information DINF 21. For example, the prediction device 100 predicts that the demand “DM21” indicating an increase rate corresponding to the increase rate of the visa application in the country X after the period TM21 has elapsed from the date and time DT22 for the travel of the target “country X”.

  For example, when the period TM21 is “1 month” and the correlation “RT21” is “90% of the previous stage”, the prediction device 100 determines that the travel of the target “Country X” is 1 month after the date DT22. , Predict that country X will increase 90% of visa applications. For example, when the rate of increase in the visa application for country X between date DT21 and date DT22 is 100%, the prediction device 100 increases the travel of the target “country X” by 90% after one month from date DT22. Predict. In the example of FIG. 9, in order to simplify the explanation, a case where it is estimated that a user who has visited the X country embassy EM applies for a visa for country X is shown. The visit to the embassy EM and the visa application for country X may be processed as separate steps. For example, the prediction apparatus 100 may process visits to the X country embassy EM in N stages, country X visa applications in stages N + 1, and country X travel in stages N + 2. In this case, for example, the prediction apparatus 100 obtains correlation information indicating that 80% of users who have visited the X Embassy in the past apply from the past user's action history, etc. Then, the number of visa application users in country X may be estimated from the number of users visited by country X Embassy EM. Then, the prediction device 100 estimates a second action of applying for a visa in X country from the first action of a visit to the X Embassy of the user EM, and uses the information on the second action to make a first trip to X country. You may perform the prediction regarding three steps.

[5. effect〕
As described above, the prediction device 100 according to the embodiment includes the acquisition unit 131 and the prediction unit 133. The acquisition unit 131 acquires user position information. The prediction unit 133 predicts the demand related to the target based on the position information of the user acquired by the acquisition unit 131 and information on the target associated with the position.

  As described above, the prediction device 100 according to the embodiment appropriately predicts the demand by predicting the demand related to the target based on the position information of the user and the information regarding the target associated with the position. be able to.

  In the prediction device 100 according to the embodiment, the acquisition unit 131 acquires information in which a position and a target are associated with each other.

  Thus, the prediction device 100 according to the embodiment can appropriately predict demand by acquiring information in which a position and a target are associated with each other.

  Moreover, in the prediction device 100 according to the embodiment, the prediction unit 133 predicts demand related to the target specified based on the user position information.

  As described above, the prediction device 100 according to the embodiment can appropriately predict the demand by predicting the demand related to the target specified based on the position information of the user.

  Moreover, in the prediction device 100 according to the embodiment, the prediction unit 133 predicts demand related to an object related to an offer provided at a position.

  Thus, the prediction apparatus 100 which concerns on embodiment can predict a demand appropriately by estimating the demand relevant to the object relevant to the offer provided in a position.

  In the prediction device 100 according to the embodiment, the prediction unit 133 predicts a demand related to an object that is an offer provided at a position.

  Thus, the prediction apparatus 100 which concerns on embodiment can predict a demand appropriately by estimating the demand relevant to the object which is the provision provided in a position.

  Moreover, in the prediction device 100 according to the embodiment, the prediction unit 133 predicts demand related to an object that requires a provision provided at a position.

  As described above, the prediction device 100 according to the embodiment can appropriately predict the demand by predicting the demand related to the target that requires the provision provided at the position.

  In the prediction device 100 according to the embodiment, the prediction unit 133 predicts a demand related to the target after the time corresponding to the user's position information.

  As described above, the prediction device 100 according to the embodiment can appropriately predict the demand by predicting the demand related to the target after the time corresponding to the user position information.

  In the prediction device 100 according to the embodiment, the prediction unit 133 predicts a demand related to a target after the first action based on the first action of the user corresponding to the user's position information.

  As described above, the prediction device 100 according to the embodiment appropriately determines the demand by predicting the demand related to the target after the first action based on the first action of the user corresponding to the position information of the user. Can be predicted.

  Further, in the prediction device 100 according to the embodiment, the prediction unit 133 performs a second action that is an action of the user after the first action and is an action related to the target based on the first action of the user. Predict.

  As described above, the prediction device 100 according to the embodiment predicts the second action, which is the action of the user after the first action and is related to the target, based on the first action of the user. Thus, the demand can be appropriately predicted.

  In addition, the prediction apparatus 100 according to the embodiment includes a providing unit 134. The providing unit 134 provides information indicating the demand related to the target to the business operator related to the target.

  As described above, the prediction device 100 according to the embodiment can appropriately provide information related to demand to the business by providing information indicating the demand related to the target to the business related to the target.

  In the prediction device 100 according to the embodiment, the providing unit 134 provides information indicating the demand related to the target to the business operator related to the target.

  As described above, the prediction device 100 according to the embodiment can appropriately provide information related to demand to the business by providing information indicating the demand related to the target to the business related to the target.

[6. Hardware configuration)
The prediction apparatus 100 according to the embodiment described above is realized by a computer 1000 having a configuration as shown in FIG. 10, for example. FIG. 10 is a hardware configuration diagram illustrating an example of a computer that realizes the function of the prediction device. The computer 1000 includes a 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.

  The CPU 1100 operates based on a program stored in the ROM 1300 or the HDD 1400 and controls each unit. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started up, a program 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. The communication interface 1500 receives data from other devices via the network N and sends the data to the CPU 1100, and provides the data generated by the CPU 1100 to other devices via the network N.

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

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

  For example, when the computer 1000 functions as the prediction device 100 according to the embodiment, the CPU 1100 of the computer 1000 implements the function of the control unit 130 by executing a program loaded on the RAM 1200. The CPU 1100 of the computer 1000 reads these programs from the recording medium 1800 and executes them. However, as another example, these programs may be acquired from other devices via the network N.

  Although the embodiments of the present application have been described in detail with reference to the drawings, these are merely examples, and various modifications and improvements have been made based on the knowledge of those skilled in the art including the aspects described in the disclosure line of the invention. The present invention can be implemented in other forms.

[7. Others]
In addition, among the processes described in the above embodiment, all or part of the processes described as being automatically performed can be performed manually, or the processes described as being performed manually can be performed. All or a part can be automatically performed by a known method. In addition, the processing procedures, specific names, and information including various data and parameters shown in the document and drawings can be arbitrarily generated unless otherwise specified. For example, the various types of information illustrated in each drawing is not limited to the illustrated information.

  Further, each component of each illustrated apparatus is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution / integration of each device is not limited to that shown in the figure, and all or a part thereof may be functionally or physically distributed or arbitrarily distributed in arbitrary units according to various loads or usage conditions. Can be integrated and configured.

  In addition, the processes described in the above-described embodiments can be appropriately combined within a range that does not contradict the processing contents.

  In addition, the “section (module, unit)” described above can be read as “means” or “circuit”. For example, the acquisition unit can be read as acquisition means or an acquisition circuit.

DESCRIPTION OF SYMBOLS 1 Prediction system 100 Prediction apparatus 121 POI information storage part 122 User information storage part 123 Behavior information storage part 124 Relation information storage part 130 Control part 131 Acquisition part 132 Generation part 133 Prediction part 134 Providing part 10 Terminal apparatus 20 Provider apparatus N network

Claims (13)

An acquisition unit for acquiring user location information;
A prediction unit that predicts demand related to the target based on the position information of the user acquired by the acquisition unit and information on the target associated with the position;
A prediction apparatus comprising: The acquisition unit
The information according to which the position and the target are matched is acquired. The prediction device according to claim 1 characterized by things. The prediction unit
The prediction device according to claim 1, wherein a demand related to the target specified based on the user location information is predicted. The prediction unit
The demand related to the object relevant to the offer provided in the position is predicted. The prediction device according to any one of claims 1 to 3. The prediction unit
The prediction device according to claim 4, wherein a demand related to the object that is an offer provided at the position is predicted. The prediction unit
The prediction device according to claim 4, wherein a demand related to the object requiring an offer provided at the position is predicted. The prediction unit
The prediction apparatus according to claim 1, wherein a demand related to the target after a time point corresponding to the position information of the user is predicted. The prediction unit
The demand related to the target after the first action is predicted based on the first action of the user corresponding to the position information of the user. The prediction device described. The prediction unit
The second action that is an action of the user after the first action and that is related to the target is predicted based on the first action of the user. The prediction device described. A providing unit for providing information indicating a demand related to the object predicted by the prediction unit;
The prediction apparatus according to claim 1, further comprising: The providing unit includes:
The information which shows the demand relevant to the said object is provided to the provider relevant to the said object. The prediction apparatus of Claim 10 characterized by the above-mentioned. A prediction method performed by a computer,
An acquisition step of acquiring user location information;
A prediction step of predicting demand related to the target based on the position information of the user acquired by the acquisition step and information on the target associated with the position;
The prediction method characterized by including. An acquisition procedure for acquiring user location information;
A prediction procedure for predicting a demand related to the object based on the position information of the user acquired by the acquisition procedure and information related to the object associated with the position;
A prediction program that causes a computer to execute.

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