Information processing device, provision control system, provision control method, and provision control program

Abstract

An information processing device (100) comprises: an acquisition unit (120) that acquires a departure point, a destination, a departure time, and map information; a specification unit (130) that uses the departure point, the destination, and the map information and specifies a plurality of establishments present between the departure point and the destination; a value calculation unit (140); an extraction unit (150); and a provision control unit (190). The acquisition unit (120) acquires, a congestion level of each of the establishments at a time based on the departure time, the number of visitors of each of the establishments at a time based on the departure time, or the sales of each of the establishments in a time zone including the departure time. The value calculation unit (140) calculates an establishment value of each of the establishments using the congestion level, the number of visitors, or the sales of each of the establishments. The extraction unit (150) uses a predetermined value and extracts an establishment with a high establishment value from among the plurality of establishments. The provision control unit (190) executes control for providing information indicating the establishment with the high establishment value.

Description

Information processing device, provision control system, provision control method, and provision control program

[0001] The present disclosure relates to an information processing device, a provision control system, a provision control method, and a provision control program.

[0002] There are known techniques for providing information to users. For example, store information is provided to users. Also, a technique for allowing users to discover places of interest has been proposed (see Patent Document 1).

[0003] Japanese Patent Application Laid-Open No. 2006-119132

[0004] Meanwhile, store value, which is the value of a store, fluctuates. For example, if a store is a restaurant, the store's sales increase during lunchtime and dinnertime. Therefore, the store value of the store during lunchtime and dinnertime can be said to be high. In this way, store value fluctuates. However, there is a problem in that information about stores with high store value is not being provided to users.

[0005] An object of the present disclosure is to provide information on stores with high store value.

[0006] An information processing device according to one aspect of the present disclosure is provided. The information processing device includes an acquisition unit that acquires a departure point, a destination, a departure time, and map information; an identification unit that identifies multiple stores located between the departure point and the destination using the departure point, the destination, and the map information; a value calculation unit; an extraction unit; and a provision control unit. The acquisition unit acquires the congestion level of each of the multiple stores at a time based on the departure time, the number of customers at each of the multiple stores at a time based on the departure time, or the sales of each of the multiple stores during a time period including the departure time. The value calculation unit calculates the store value of each of the multiple stores using the congestion level, number of customers, or sales of each of the multiple stores. The extraction unit extracts stores with high store value from the multiple stores using a predetermined value. The provision control unit executes control to provide information indicating the stores with high store value.

[0007] According to the present disclosure, it is possible to provide information on stores with high store value.

[0008] 1 is a diagram showing an example of an overview of the first embodiment. FIG. 2 is a diagram showing hardware included in an information processing device of the first embodiment. FIG. 3 is a block diagram showing functions of the information processing device of the first embodiment. FIG. 4 is a flowchart showing an example (part 1) of processing executed by the information processing device of the first embodiment. FIG. 5 is a flowchart showing an example (part 2) of processing executed by the information processing device of the first embodiment. FIG. 6 is a flowchart showing an example (part 3) of processing executed by the information processing device of the first embodiment. FIG. 7 is a diagram showing examples of a plurality of Pareto solutions of the first embodiment. FIG. 8 is a block diagram showing functions of an information processing device of the second embodiment. FIG. 9 is a flowchart showing an example of processing executed by the information processing device of the second embodiment. FIG. 10 is a flowchart showing an example of processing executed by the information processing device of the third embodiment. FIG. 11 is a diagram showing a specific example of provision of the third embodiment. FIG. 12 is a flowchart showing an example of processing executed by an information processing device of the fourth embodiment. FIG. 13 is a flowchart showing an example of processing executed by an information processing device of the fifth embodiment. FIG. 14 is a flowchart showing an example of processing executed by an information processing device of the sixth embodiment. FIG. 15 is a diagram showing an example of extraction processing of the sixth embodiment. FIG. 16 is a diagram showing provision control systems according to modified examples of the first to sixth embodiments.

[0009] Hereinafter, an embodiment will be described with reference to the drawings.

[0010] Embodiment 1. FIG. 1 is a diagram showing an example of an overview of embodiment 1. For example, a user inputs a departure point, a destination, and a departure time into an information processing device (e.g., a smartphone). The information processing device displays establishments with high store values ​​at the departure time. For example, in the case of FIG. 1, the establishments with high store values ​​at the departure time are restaurants. Therefore, the information processing device displays information about the restaurants. This allows the information processing device to provide the user with information about establishments with high store values. Furthermore, the information processing device can display a route from the departure point to the destination via establishments with high store values ​​at the departure time. Furthermore, the information processing device can display a route that takes into account the degree of congestion.

[0011] Furthermore, the embodiments may be implemented outdoors or indoors. For example, when a user travels from their home (i.e., the departure point) to a facility (i.e., the destination), the user inputs their home, the facility, and the departure time into the information processing device. The information processing device then displays, among multiple stores located between their home and the facility, a store with a high store value at the departure time. Furthermore, for example, when a user travels from a first location (i.e., the departure point) indoors, such as a shopping mall or train station, to a second location (i.e., the destination), the user inputs the first location, the second location, and the departure time into the information processing device. The information processing device then displays, among multiple stores located between the first location and the second location, a store with a high store value at the departure time. In this way, the information processing device can provide information on stores with high store value. The processing of the information processing device will be described in detail below.

[0012] First, the hardware of the information processing device will be described. Fig. 2 is a diagram showing the hardware of the information processing device of embodiment 1. The information processing device 100 is a device that executes the provision control method. The information processing device 100 is also called a computer. For example, the information processing device 100 is a smartphone or a tablet terminal. The information processing device 100 has a processor 101, a volatile storage device 102, and a non-volatile storage device 103.

[0013] The processor 101 controls the entire information processing device 100. For example, the processor 101 is a central processing unit (CPU) or a field programmable gate array (FPGA). The processor 101 may be a multiprocessor. The information processing device 100 may also include a processing circuit.

[0014] The volatile storage device 102 is a main storage device of the information processing device 100. For example, the volatile storage device 102 is a random access memory (RAM). The nonvolatile storage device 103 is an auxiliary storage device of the information processing device 100. For example, the nonvolatile storage device 103 is a hard disk drive (HDD) or a solid state drive (SSD).

[0015] Next, a description will be given of the functions of the information processing device 100. Fig. 3 is a block diagram showing the functions of the information processing device of embodiment 1. The information processing device 100 has a storage unit 110, an acquisition unit 120, an identification unit 130, a value calculation unit 140, an extraction unit 150, a cost calculation unit 160, a path calculation unit 170, a selection unit 180, and a provision control unit 190.

[0016] The storage unit 110 may be realized as a storage area secured in the volatile storage device 102 or the non-volatile storage device 103. Some or all of the acquisition unit 120, identification unit 130, value calculation unit 140, extraction unit 150, cost calculation unit 160, path calculation unit 170, selection unit 180, and delivery control unit 190 may be realized by a processing circuit. Furthermore, some or all of the acquisition unit 120, identification unit 130, value calculation unit 140, extraction unit 150, cost calculation unit 160, path calculation unit 170, selection unit 180, and delivery control unit 190 may be realized as program modules executed by the processor 101. For example, the program executed by the processor 101 is also referred to as a delivery control program or a delivery control program product. For example, the delivery control program is recorded on a recording medium.

[0017] The storage unit 110 stores various information. The functions of the acquisition unit 120, the identification unit 130, the value calculation unit 140, the extraction unit 150, the cost calculation unit 160, the path calculation unit 170, the selection unit 180, and the provision control unit 190 will be described later.

[0018] Next, a description will be given of a process executed by the information processing device 100 using a flowchart. Fig. 4 is a flowchart showing an example (part 1) of a process executed by the information processing device according to the first embodiment.

[0019] (Step S11) The acquisition unit 120 acquires the departure point, destination, and departure time. For example, the acquisition unit 120 acquires the departure point, destination, and departure time through a user's input operation. The departure time may be the current time. The departure time may be a future time. (Step S12) The acquisition unit 120 acquires map information. For example, the acquisition unit 120 acquires the map information from the storage unit 110. Also, for example, the acquisition unit 120 acquires the map information from an external device. Note that the external device is a device that exists outside the information processing device 100. For example, the external device is a cloud server. The external device is not illustrated. The map information may include the congestion degree of roads present on the map. If the congestion degree is not included in the map information, the acquisition unit 120 acquires the congestion degree from the storage unit 110 or the external device.

[0020] (Step S13) The identification unit 130 uses the departure point, the destination, and map information to identify multiple stores located between the departure point and the destination.

[0021] (Step S14) The acquisition unit 120 acquires the degree of congestion or the number of customers at each of the multiple stores at the time based on the departure time. For example, the acquisition unit 120 acquires the degree of congestion or the number of customers from an external device. Also, for example, the acquisition unit 120 acquires the degree of congestion or the number of customers based on video captured by a camera installed in the store.

[0022] Here, the time based on the departure time will be explained. For example, the time based on the departure time is the departure time. Also, for example, the time based on the departure time may be one second before the departure time, ten seconds before the departure time, or one minute before the departure time. In this way, the time based on the departure time is a time that does not contradict the purpose of providing information on stores with high store value at the departure time. For example, it is considered that the store value at a departure time of "11:00:00" is the same as the store value at a departure time of "11:00:01". Therefore, the time based on the departure time is not limited to the departure time. In the following explanation, the time based on the departure time is the departure time.

[0023] The acquisition unit 120 may also acquire the sales of each of a plurality of stores for a time period including the departure time. For example, the acquisition unit 120 acquires the sales of the stores from an external device. For example, if the departure time is 5:15 PM, the time period including the departure time is the time period from 5 PM to 6 PM.

[0024] If the departure time is a future time, the acquisition unit 120 acquires the congestion level or number of customers of each of the multiple stores at a time based on the future time from past history information. For example, if the current time is 10:00 and the departure time is 11:00, the acquisition unit 120 acquires the congestion level or number of customers of each of the multiple stores at 11:00 from past history information. Furthermore, if the departure time is a future time, the acquisition unit 120 acquires the sales of each of the multiple stores for a time period including the future time from past history information. For example, if the current time is 10:00 and the departure time is 11:00, the acquisition unit 120 acquires the sales of each of the multiple stores for a time period including 11:00 from past history information.

[0025] (Step S15) The value calculation unit 140 selects one store from among the multiple stores. (Step S16) The value calculation unit 140 calculates the store value of the selected store using the congestion level or number of customers of the selected store. When the congestion level is acquired, the value calculation unit 140 calculates the store value using the congestion level. For example, the value calculation unit 140 calculates the store value using equation (1). The maximum congestion level is obtained from past history. The maximum congestion level is the maximum congestion level in the time period including the departure time.

[0026]

[0027] When the number of visitors is acquired, the value calculation unit 140 calculates the store value using the number of visitors. For example, the value calculation unit 140 calculates the store value using equation (2). The maximum number of visitors is obtained from past history. The maximum number of visitors is the maximum number of visitors in a time period including the departure time.

[0028]

[0029] When the sales are acquired, the value calculation unit 140 calculates the store value using the sales. For example, the value calculation unit 140 calculates the store value using equation (3). The maximum sales is obtained from past history. The maximum sales is the maximum sales in the time period including the departure time.

[0030]

[0031] The larger the store value, the higher the store value.

[0032] (Step S17) The value calculation unit 140 determines whether all stores have been selected. If all stores have been selected, the process proceeds to step S18. If not all stores have been selected, the process proceeds to step S15. (Step S18) The extraction unit 150 uses a predetermined value to extract stores with high store value from among the multiple stores. For example, the predetermined value is a threshold or a percentage. Specifically, the extraction unit 150 extracts stores with a store value equal to or greater than a threshold from among the multiple stores as stores with high store value. Furthermore, the extraction unit 150 extracts stores with a store value in the top 30% from among the multiple stores as stores with high store value. Then, the process proceeds to step S21.

[0033] 5 is a flowchart showing an example (part 2) of processing executed by the information processing device of the first embodiment. (Step S21) The cost calculation unit 160 calculates the cost of a road included in the map using map information and congestion level. Note that, for example, a road is a road from one intersection to another. In other words, a road is not a long distance like a route from a starting point to a destination. A road is a short distance. For example, a road may be expressed as a partial route. A road may also be a road surrounding a route from a starting point to a destination. For example, the cost calculation unit 160 calculates the cost using equation (4). C indicates the cost. j indicates the road. t indicates the departure time. H indicates the congestion level. ω(H) is a function. D indicates the distance of the road.

[0034]

[0035] The cost calculation unit 160 similarly calculates the costs of all roads. In this way, the cost calculation unit 160 calculates the costs of multiple roads using the congestion levels of multiple roads included in the map indicated by the map information and the distances of the multiple roads. When the cost calculation unit 160 has calculated the costs of all roads, the process proceeds to step S22. In the following description, it is assumed that multiple stores with high store value are extracted in step S18.

[0036] (Step S22) The path calculation unit 170 creates combinations based on multiple stores with high store value. For example, if the multiple stores with high store value are "Store A," "Store B," and "Store C," the path calculation unit 170 creates a combination of only "Store A," a combination of only "Store B," a combination of only "Store C," a combination of "Store A" and "Store B," a combination of "Store A" and "Store C," a combination of "Store B" and "Store C," and a combination of "Store A," "Store B," and "Store C." In this way, the path calculation unit 170 creates all possible combinations.

[0037] (Step S23) The route calculation unit 170 selects one combination. (Step S24) The route calculation unit 170 uses map information to identify the location information of the stores belonging to the selected combination. (Step S25) The route calculation unit 170 determines the visiting order based on the relative positions of the departure point, destination, and stores.

[0038] (Step S26) The route calculation unit 170 calculates a route for traveling from the departure point to the destination by passing through the stores belonging to the selected combination in the order of visiting. When calculating the route, the route calculation unit 170 uses the costs calculated in step S21 to calculate a route with the smallest total cost. More specifically, the route calculation unit 170 calculates the route using a shortest route search algorithm. Note that the shortest route search algorithm is, for example, Dijkstra's algorithm.

[0039] (Step S27) The path calculation unit 170 calculates the total store value of the stores belonging to the selected combination. The path calculation unit 170 associates the total store value and the total cost with the path. (Step S28) The path calculation unit 170 determines whether or not all combinations have been selected. If all combinations have been selected, the process proceeds to step S31. If all combinations have not been selected, the process proceeds to step S23.

[0040] 6 is a flowchart showing an example (part 3) of the process executed by the information processing device of the first embodiment. (Step S31) The selection unit 180 selects one Pareto solution from among a plurality of Pareto solutions based on the total store value and the total cost of all combinations. Here, an example of the plurality of Pareto solutions is shown.

[0041] FIG. 7 is a diagram illustrating an example of multiple Pareto solutions according to the first embodiment. The vertical axis of the graph represents the total store value. The horizontal axis of the graph represents the total cost. FIG. 7 illustrates multiple Pareto solutions. For example, the selection unit 180 selects a Pareto solution from the multiple Pareto solutions that has a good balance between the total store value and the total cost. For example, a Pareto solution that has a good balance between the total store value and the total cost is a Pareto solution that is closest to the point corresponding to the average or median of the total store value of the multiple Pareto solutions and the average or median of the total cost of the multiple Pareto solutions. For example, the selection unit 180 selects a Pareto solution 11 that is closest to point 10 that corresponds to the average of the total store value of the multiple Pareto solutions and the average of the total cost of the multiple Pareto solutions.

[0042] (Step S32) The provision control unit 190 executes control to provide a route corresponding to the selected Pareto solution and information indicating stores with high store value that are located on the route. In detail, the provision control unit 190 executes control to display the route and information indicating stores with high store value on the display of the information processing device 100.

[0043] Steps S13 to S18 may be executed after steps S21 to S28. Steps S13 to S18 and steps S21 to S28 may be executed in parallel.

[0044] According to the first embodiment, the information processing device 100 can provide the user with information on stores with high store value. Furthermore, the information processing device 100 provides the user with a route from a departure point to a destination that passes through stores with high store value. This allows the user to recognize the route. Furthermore, the information processing device 100 can provide the user with a route that takes into account the degree of congestion.

[0045] Furthermore, by selecting a well-balanced Pareto solution, the information processing device 100 can provide the user with a route that has a good balance between the total store value and the total cost.

[0046] The provision control unit 190 may execute control to provide information indicating the store with high store value extracted in step S18. That is, the information processing device 100 may execute control to provide information indicating the store with high store value extracted in step S18 without executing steps S21 to S32.

[0047] Furthermore, after step S18, the route calculation unit 170 may use map information to calculate a route from the departure point to the destination via stores with high store value. The provision control unit 190 may execute control to provide the route.

[0048] Second Embodiment Next, a second embodiment will be described. In the second embodiment, differences from the first embodiment will be mainly described. Furthermore, in the second embodiment, description of matters common to the first embodiment will be omitted. FIG. 8 is a block diagram showing the functions of an information processing device of the second embodiment. The information processing device 100 further includes a preference estimation unit 191. Part or all of the preference estimation unit 191 may be realized by a processing circuit. Furthermore, part or all of the preference estimation unit 191 may be realized as a program module executed by the processor 101. The function of the preference estimation unit 191 will be described later.

[0049] Next, the processing executed by the information processing device 100 will be described using a flowchart. Fig. 9 is a flowchart showing an example of the processing executed by the information processing device of embodiment 2. The processing in Fig. 9 differs from the processing in Fig. 4 in that steps S13a to S13c and 16a are executed. Therefore, steps S13a to S13c and 16a will be described in Fig. 9. Further, a description of the processing other than steps S13a to S13c and 16a will be omitted.

[0050] (Step S13a) The acquiring unit 120 acquires information for estimating a user's preferences from the storage unit 110 or an external device. For example, the information for estimating a user's preferences is a questionnaire, a store visit history, or the contents of a bookmark.

[0051] (Step S13b) The preference estimation unit 191 estimates the user's preference based on information for estimating the user's preference. For example, the information for estimating the user's preference is a questionnaire. If the questionnaire answer is "I like coffee," the preference estimation unit 191 estimates that the user likes coffee. If the questionnaire answer is "I really like coffee," the preference estimation unit 191 estimates that the user really likes coffee.

[0052] (Step S13c) The preference estimation unit 191 associates a preference value indicating a preference with each of a plurality of shops located between the departure point and the destination based on the estimated preference. For example, if the user likes coffee, the preference estimation unit 191 associates a preference value of "7" with the cafe. For example, if the user really likes coffee, the preference estimation unit 191 associates a preference value of "10" with the cafe. In this way, the higher the user's preference level, the higher the preference value.

[0053] (Step S16a) The value calculation unit 140 calculates the store value V of the selected store using the congestion level or number of customers of the selected store. The value calculation unit 140 may also calculate the store value V of the selected store using the sales of the selected store. The value calculation unit 140 calculates the store value Q based on the store value V of the store and the preference value α associated with the store. Specifically, the value calculation unit 140 calculates the store value Q using equation (5).

[0054]

[0055] The value calculation unit 140 may calculate the store value Q by adding the preference value α and the store value V.

[0056] In this way, the value calculation unit 140 calculates the store value of each of the multiple stores. That is, the value calculation unit 140 calculates the store value Q of each of the multiple stores using the congestion level, number of customers, or sales of each of the multiple stores and the preference value associated with each of the multiple stores. The preference value α is reflected in the store value Q. The larger the preference value α, the larger the value of the store value Q. Therefore, in step S18, stores that are preferred by the user are more likely to be extracted.

[0057] According to the second embodiment, the information processing device 100 can provide the user with information on stores that are the user's favorite and have high store value.

[0058] Furthermore, the preference estimation unit 191 may estimate a travel distance avoidance value, which is a value indicating the user's aversion to travel distance, based on information for estimating the user's preferences. For example, the information for estimating the user's preferences is a questionnaire. For example, if the questionnaire is, "I dislike walking long distances," the preference estimation unit 191 estimates a travel distance avoidance value of "7." If the questionnaire is, "I really dislike walking long distances," the preference estimation unit 191 estimates a travel distance avoidance value of "10."

[0059] Furthermore, the preference estimation unit 191 may estimate a congestion avoidance value, which is a value indicating the user's aversion to congestion, based on information for estimating the user's preferences. For example, if the questionnaire answer is "I hate congestion," the preference estimation unit 191 estimates a congestion avoidance value of "7." If the questionnaire answer is "I really hate congestion," the preference estimation unit 191 estimates a congestion avoidance value of "10."

[0060] When the travel distance avoidable value is estimated, the cost calculation unit 160 calculates the cost using the map information, the congestion level, and the travel distance avoidable value. Specifically, the cost calculation unit 160 calculates the cost using equation (6), where β is the travel distance avoidable value.

[0061]

[0062] When the congestion avoidance value is estimated, the cost calculation unit 160 calculates the cost using the map information, the congestion degree, and the congestion avoidance value. Specifically, the cost calculation unit 160 calculates the cost using equation (7), where γ is the congestion avoidance value.

[0063]

[0064] When the travel distance avoidance value and the congestion avoidance value are estimated, the cost calculation unit 160 calculates the cost using the map information, the congestion level, the travel distance avoidance value, and the congestion avoidance value. Specifically, the cost calculation unit 160 calculates the cost using equation (8).

[0065]

[0066] In this way, the cost calculation unit 160 calculates the costs of multiple roads using at least one of the travel distance avoidance value and the congestion avoidance value, the congestion levels of the multiple roads, and the distances of the multiple roads. When the travel distance avoidance value is estimated, the information processing device 100 can provide the user with a route that takes the travel distance avoidance value into consideration. When the congestion avoidance value is estimated, the information processing device 100 can provide the user with a route that takes the congestion avoidance value into consideration. When the travel distance avoidance value and the congestion avoidance value are estimated, the information processing device 100 can provide the user with a route that takes the travel distance avoidance value and the congestion avoidance value into consideration.

[0067] Embodiment 3 Next, embodiment 3 will be described. In embodiment 3, differences from embodiments 1 and 2 will be mainly described. Furthermore, in embodiment 3, descriptions of matters common to embodiments 1 and 2 will be omitted.

[0068] In the first embodiment, a case where one Pareto solution is selected has been described. In the third embodiment, a case where multiple Pareto solutions are selected will be described.

[0069] Fig. 10 is a flowchart showing an example of processing executed by the information processing device of embodiment 3. The processing in Fig. 10 differs from the processing in Fig. 6 in that steps S31a and S32a are executed. Therefore, steps S31a and S32a will be described in Fig. 10. Description of processing other than steps S31a and S32a will be omitted.

[0070] (Step S31a) The selection unit 180 selects multiple Pareto solutions from multiple Pareto solutions based on the total store value and the total cost of all combinations. For example, the selection unit 180 selects multiple Pareto solutions at equal intervals. Also, for example, the selection unit 180 selects multiple Pareto solutions so that the differences between the total store value and the total cost are equal. The number of Pareto solutions to be selected may be determined in advance. The selection unit 180 determines multiple routes corresponding to the selected multiple Pareto solutions as routes to be provided to the user.

[0071] (Step S32a) The provision control unit 190 provides the user with a plurality of routes corresponding to the plurality of selected Pareto solutions and information indicating stores with high store value located on the routes. A specific example of provision will be given below.

[0072] 11 is a diagram showing a specific example of provision in embodiment 3. FIG. 11 shows a case where three Pareto solutions are selected. The provision control unit 190 provides the user with multiple routes corresponding to the three Pareto solutions and information indicating stores with high store value located on the routes.

[0073] In the third embodiment, the information processing device 100 provides the user with multiple routes and information indicating stores with high store value among stores located on the routes. In this way, the information processing device 100 can give the user room for choice by providing multiple routes.

[0074] Embodiment 4 Next, embodiment 4 will be described. In embodiment 4, differences from embodiments 1 to 3 will be mainly described. Furthermore, in embodiment 4, descriptions of matters common to embodiments 1 to 3 will be omitted.

[0075] Fig. 12 is a flowchart showing an example of processing executed by the information processing device of embodiment 4. The processing in Fig. 12 differs from the processing in Fig. 4 in that step S17a is executed. Therefore, step S17a will be described in Fig. 12. Description of processing other than step S17a will be omitted.

[0076] (Step S17a) The acquisition unit 120 acquires at least one of information indicating a store preferred by the user and information indicating a store to be excluded from the storage unit 110 or an external device. When information indicating a store preferred by the user is acquired, if the store indicated by the information is included in the multiple stores identified in step S13, the value calculation unit 140 changes the store value of the store indicated by the information to the maximum value. When information indicating a store to be excluded is acquired, if the store indicated by the information is included in the multiple stores identified in step S13, the value calculation unit 140 changes the store value of the store indicated by the information to the minimum value. By executing the store value adjustment process, the user's favorite store is extracted in step S18. Furthermore, by executing the store value adjustment process, the store to be excluded in step S18 is no longer extracted.

[0077] According to the fourth embodiment, the information processing device 100 can reflect the user's requests. In the second embodiment, the preference value is reflected, which makes it easier to extract stores that the user likes. The preference value is determined based on a questionnaire or the like. Therefore, the preference value may reflect the mood at the time when the questionnaire or the like is completed. Therefore, in the second embodiment, stores based on the mood at the time when the questionnaire or the like is completed may be extracted. On the other hand, in the fourth embodiment, the user's favorite stores or the like are set in advance. Therefore, in the fourth embodiment, stores based on the preferences of a normal user are extracted. This is the difference between the second embodiment and the fourth embodiment.

[0078] It is also possible to combine the second embodiment with the fourth embodiment. Specifically, after step S17 in FIG.

[0079] Embodiment 5 Next, embodiment 5 will be described. In embodiment 5, differences from embodiments 1 and 3 will be mainly described. Furthermore, in embodiment 5, descriptions of matters common to embodiments 1 and 3 will be omitted.

[0080] In the second embodiment, a case where the preferences of one user are reflected is described. In the fifth embodiment, a case where the preferences of multiple users are reflected is described. For example, the multiple users are a family. For example, the information processing device 100 provides information on stores with high store value when the family travels from a departure point to a destination. The fifth embodiment will be described in detail.

[0081] Fig. 13 is a flowchart showing an example of processing executed by the information processing device of embodiment 5. The processing in Fig. 13 differs from the processing in Fig. 4 in that steps S13d to S13f and 16b are executed. Therefore, steps S13d to S13f and 16b will be explained in Fig. 13. Explanation of processing other than steps S13d to S13f and 16b will be omitted.

[0082] (Step S13d) The acquisition unit 120 acquires information for estimating the preferences of each of the multiple users from the storage unit 110 or an external device. For example, the information is a questionnaire, a store visit history, or the contents of a bookmark. An example of the acquisition process will be described. The acquisition unit 120 acquires a user ID (identifier) ​​of a user who uses the information processing device 100. The acquisition unit 120 acquires a group ID linked to the user ID. The acquisition unit 120 acquires information for estimating the preferences of each of the multiple users corresponding to the group ID from the storage unit 110 or an external device.

[0083] (Step S13e) The preference estimation unit 191 estimates the preferences of each of the multiple users based on the information for estimating the preferences of each of the multiple users. (Step S13f) The preference estimation unit 191 associates a preference value with each of the multiple shops located between the departure point and the destination based on the estimated preferences. For example, if the multiple users are a father, a mother, and a child, and the father, mother, and child all like coffee, the preference estimation unit 191 associates three preference values ​​(note that the preference values ​​are higher) with cafes.

[0084] (Step S16b) The value calculation unit 140 calculates the store value V of the selected store using the congestion level or number of customers of the selected store. The value calculation unit 140 may also calculate the store value V of the selected store using the sales of the selected store. The value calculation unit 140 calculates the store value Q based on the store value V of the store and multiple preference values ​​α associated with the store. For example, if preference values ​​α1, α2, and α3 are associated with the store, the value calculation unit 140 calculates the store value Q using equation (9).

[0085]

[0086] The value calculation unit 140 may calculate the store value Q by adding the preference value and the store value V. Multiple preference values ​​may be multiplied. The value calculation unit 140 may calculate the store value Q by adding the multiplied preference value and the store value V. The value calculation unit 140 may divide a value based on the preference value and the store value V by the number of people in the group.

[0087] The value calculation unit 140 may calculate the store value F using equation (10). ｕｓｅｒＩＤ is the store value Q calculated by equation (5) for each of the multiple users. δ is a weight. δ does not have to be included in equation (10). n is the number of people in the group.

[0088]

[0089] The value calculation unit 140 calculates the store value of each of the multiple stores. That is, the value calculation unit 140 calculates the store value of each of the multiple stores using the congestion level, number of customers, or sales of each of the multiple stores and the preference value associated with each of the multiple stores.

[0090] In this way, the store value reflects the preference values ​​of multiple users, which makes it easier to extract stores that are favorites of multiple users in step S18.

[0091] According to the fifth embodiment, the information processing device 100 can provide information on stores that are favorites of multiple users and have high store value.

[0092] Furthermore, the preference estimation unit 191 may estimate a plurality of travel distance avoidance values, which are values ​​indicating the plurality of users' respective aversion to travel distance, based on information for estimating the preferences of the plurality of users. Furthermore, the preference estimation unit 191 may estimate a plurality of congestion avoidance values, which are values ​​indicating the plurality of users' respective aversion to congestion, based on information for estimating the preferences of the plurality of users.

[0093] When at least one of the plurality of travel distance avoidance values ​​and the plurality of congestion avoidance values ​​is estimated, the cost calculation unit 160 calculates the cost CF using the map information, the congestion degree, and at least one of the plurality of travel distance avoidance values ​​and the plurality of congestion avoidance values. Specifically, the cost calculation unit 160 calculates the cost using equation (11). C ｕｓｅｒＩＤ is the cost C of each of the multiple users calculated by equation (6), equation (7), or equation (8). ε is a weight. ε does not have to be included in equation (11). n is the number of people in the group.

[0094]

[0095] In this way, the cost calculation unit 160 calculates the costs of multiple roads using at least one of the multiple travel distance avoidance values ​​and the multiple congestion avoidance values, the congestion levels of the multiple roads, and the distances of the multiple roads. Therefore, the cost CF reflects the preferences of multiple users. Therefore, the information processing device 100 can provide routes that take the preferences of multiple users into consideration.

[0096] Sixth Embodiment Next, a sixth embodiment will be described. In the sixth embodiment, differences from the first and third embodiments will be mainly described. In the sixth embodiment, descriptions of the commonalities between the first and third embodiments will be omitted.

[0097] Fig. 14 is a flowchart showing an example of processing executed by the information processing device of embodiment 6. The processing in Fig. 14 differs from the processing in Fig. 4 in that steps S13g, 13h, and 18a are executed. Therefore, steps S13g, 13h, and 18a will be described in Fig. 14. Description of processing other than steps S13g, 13h, and 18a will be omitted.

[0098] (Step S13g) The acquisition unit 120 acquires information for estimating the preferences of each of multiple users from the storage unit 110 or an external device. For example, the information is a questionnaire, a store visit history, or the contents of bookmarks. An example of the acquisition process will be described. The acquisition unit 120 acquires a user ID of a user who uses the information processing device 100. The acquisition unit 120 acquires a group ID linked to the user ID. The acquisition unit 120 acquires information for estimating the preferences of each of multiple users corresponding to the group ID from the storage unit 110 or an external device.

[0099] (Step S13h) The preference estimation unit 191 estimates the preferences of each of the multiple users based on the information for estimating the preferences of each of the multiple users.

[0100] (Step S18a) When multiple stores with high store value have been extracted, the extraction unit 150 sorts the multiple stores extracted in step S18 in descending order based on the preferences of each user. The extraction unit 150 extracts a predetermined number of stores from the top of the stores sorted for each user. A specific example of the extraction process is shown below.

[0101] FIG. 15 is a diagram showing an example of the extraction process of the sixth embodiment. The multiple stores extracted in step S18 are Restaurant A to Japanese confectionery store G. The extraction unit 150 sorts Restaurants A to Japanese confectionery store G in descending order based on preference for each user. For example, User X is very fond of Western food, so Restaurant A is ranked first. The extraction unit 150 extracts a predetermined number of stores from the top of the stores sorted for each user. The predetermined number is "6." The extraction unit 150 extracts the top two stores for Users X, Y, and Z.

[0102] According to the sixth embodiment, the information processing device 100 can provide stores that have high store value and are highly preferred by each of a plurality of users.

[0103] Modifications of Embodiments 1 to 6. For example, the information processing device 100 of Embodiments 1 to 6 has been described as being a smartphone or a tablet terminal. The information processing device 100 of Embodiments 1 to 6 may also be a server. For example, the acquisition unit 120 of the server acquires the departure point, destination, and departure time from the terminal used by the user. The server then executes processing. The provision control unit 190 of the server executes control to provide the determined route and information indicating stores with high store value among stores located on the route. In detail, the provision control unit 190 of the server transmits the route, information indicating stores with high store value, and a display instruction to the terminal. As a result, the terminal displays the route and information indicating stores with high store value.

[0104] In the first to sixth embodiments, the case where the present invention is realized by one information processing device has been described. However, the first to sixth embodiments may be realized by a provision control system. An example of the provision control system will be described below.

[0105] 16 is a diagram showing a provision control system according to a modification of the first to sixth embodiments. The provision control system includes a terminal 200 and a server 300. The terminal 200 and the server 300 communicate with each other via a network. The terminal 200 is a terminal used by a user. The server 300 is also called an information processing device.

[0106] The functions of the information processing device 100 may be realized by a terminal 200 and a server 300. For example, the terminal 200 has an acquisition unit 120, an identification unit 130, a value calculation unit 140, and an extraction unit 150. The server 300 has a cost calculation unit 160, a path calculation unit 170, a selection unit 180, and a provision control unit 190. Thus, the provision control system can realize the first to sixth embodiments.

[0107] Various modifications of each embodiment are possible within the scope of the present disclosure, and the features of each embodiment can be combined with each other as appropriate.

[0108] 10 points, 11 Pareto solutions, 100 information processing device, 101 processor, 102 volatile storage device, 103 non-volatile storage device, 110 storage unit, 120 acquisition unit, 130 identification unit, 140 value calculation unit, 150 extraction unit, 160 cost calculation unit, 170 route calculation unit, 180 selection unit, 190 provision control unit, 191 preference estimation unit, 200 terminal, 300 server.

Claims

1. An information processing device having an acquisition unit that acquires a departure point, a destination, a departure time, and map information; an identification unit that identifies a plurality of stores located between the departure point and the destination using the departure point, the destination, and the map information; a value calculation unit; an extraction unit; and a provision control unit, wherein the acquisition unit acquires the congestion level of each of the plurality of stores at a time based on the departure time, the number of customers at each of the plurality of stores at a time based on the departure time, or the sales of each of the plurality of stores in a time period including the departure time; the value calculation unit calculates the store value of each of the plurality of stores using the congestion level, number of customers, or sales of each of the plurality of stores; the extraction unit uses a predetermined value to extract stores with high store value from among the plurality of stores; and the provision control unit executes control to provide information indicating the stores with high store value.

2. The information processing device according to claim 1, further comprising a route calculation unit that uses the map information to calculate a route from the departure point to the destination via stores with high store value, and the provision control unit executes control to provide the route.

3. An information processing device as described in claim 2, further comprising: a cost calculation unit; and a selection unit, wherein the cost calculation unit calculates the costs of multiple roads using the congestion levels of multiple roads included in the map indicated by the map information and the distances of the multiple roads; the route calculation unit, when multiple stores with high store value are extracted, creates combinations based on the multiple stores with high store value, calculates a route from the departure point to the destination for each combination via the stores belonging to the combination, and associates the total store value and the total cost with the route for each combination; the selection unit selects one or more Pareto solutions from multiple Pareto solutions based on the total store value and the total cost of all combinations; and the provision control unit executes control to provide a route corresponding to the selected Pareto solution and information indicating stores with high store value that are located on the route.

4. The information processing device according to claim 3, wherein when selecting one Pareto solution, the selection unit selects from the plurality of Pareto solutions a Pareto solution that has a good balance between the total store value and the total cost.

5. An information processing device as described in claim 3 or 4, further comprising a preference estimation unit, wherein the acquisition unit acquires information for estimating the user's preferences, the preference estimation unit estimates at least one of a travel distance avoidance value, which is a value indicating the user's aversion to travel distance, and a congestion avoidance value, which is a value indicating the user's aversion to congestion, based on the information for estimating the user's preferences, and the cost calculation unit calculates the costs of the multiple roads using at least one of the travel distance avoidance value and the congestion avoidance value, the congestion levels of the multiple roads, and the distances of the multiple roads.

6. The information processing device described in claim 5, wherein the acquisition unit acquires information for estimating the preferences of each of the multiple users, the preference estimation unit estimates at least one of a plurality of travel distance avoidance values, which are values ​​indicating the plurality of users' aversion to travel distance, and a plurality of congestion avoidance values, which are values ​​indicating the plurality of users' aversion to congestion, based on the information for estimating the preferences of each of the multiple users, and the cost calculation unit calculates the costs of the multiple roads using at least one of the plurality of travel distance avoidance values ​​and the plurality of congestion avoidance values, the congestion levels of the multiple roads, and the distances of the multiple roads.

7. An information processing device as described in any one of claims 1 to 5, wherein the acquisition unit acquires at least one of information indicating a store that the user likes and information indicating a store to be excluded, and the value calculation unit, when information indicating a store that the user likes is acquired and the store indicated by the information is included in the plurality of stores, changes the store value of the store indicated by the information to the maximum value, and when information indicating the store to be excluded is acquired and the store indicated by the information is included in the plurality of stores, changes the store value of the store indicated by the information to the minimum value.

8. An information processing device as described in any one of claims 1 to 5, further comprising a preference estimation unit, wherein the acquisition unit acquires information for estimating the user's preferences, the preference estimation unit estimates the user's preferences based on the information for estimating the user's preferences, and associates a preference value indicating the preference with each of the multiple stores located between the departure point and the destination based on the estimated preferences, and the value calculation unit calculates the store value of each of the multiple stores using the congestion level, number of customers, or sales of each of the multiple stores and the preference value associated with each of the multiple stores.

9. The information processing device described in claim 8, wherein the acquisition unit acquires information for estimating the preferences of each of the multiple users, the preference estimation unit estimates the preferences of each of the multiple users based on the information for estimating the preferences of each of the multiple users, and associates a preference value indicating the preference with each of the multiple stores located between the departure point and the destination based on the estimated preferences, and the value calculation unit calculates the store value of each of the multiple stores using the congestion level, number of customers, or sales of each of the multiple stores and the preference value associated with each of the multiple stores.

10. The information processing device described in claim 8, wherein the acquisition unit acquires information for estimating the preferences of each of the multiple users, the preference estimation unit estimates the preferences of each of the multiple users based on the information for estimating the preferences of each of the multiple users, and when multiple stores with high store value have been extracted, the extraction unit sorts the multiple stores with high store value in descending order based on preference for each user, and extracts a predetermined number of stores from the top of the stores sorted for each user.

11. A provision control system including a terminal and an information processing device, comprising: an acquisition unit that acquires a departure point, a destination, a departure time, and map information; an identification unit that identifies a plurality of stores located between the departure point and the destination using the departure point, the destination, and the map information; a value calculation unit; an extraction unit; and a provision control unit, wherein the acquisition unit acquires the congestion level of each of the plurality of stores at a time based on the departure time, the number of customers at each of the plurality of stores at a time based on the departure time, or the sales of each of the plurality of stores in a time period including the departure time; the value calculation unit calculates the store value of each of the plurality of stores using the congestion level, number of customers, or sales of each of the plurality of stores; the extraction unit uses a predetermined value to extract stores with high store value from among the plurality of stores; and the provision control unit executes control to provide information indicating the stores with high store value.

12. A provision control method in which an information processing device acquires a departure point, a destination, a departure time, and map information, identifies a plurality of stores located between the departure point and the destination using the departure point, the destination, and the map information, acquires the degree of congestion of each of the plurality of stores at a time based on the departure time, the number of customers of each of the plurality of stores at a time based on the departure time, or the sales of each of the plurality of stores during a time period including the departure time, calculates the store value of each of the plurality of stores using the degree of congestion, number of customers, or sales of each of the plurality of stores, extracts stores with high store value from among the plurality of stores using a predetermined value, and executes control to provide information indicating the stores with high store value.

13. A provision control program that causes an information processing device to execute the following processes: acquire a departure point, destination, departure time, and map information; identify multiple stores located between the departure point and the destination using the departure point, destination, and map information; acquire the congestion level of each of the multiple stores at a time based on the departure time, the number of customers at each of the multiple stores at a time based on the departure time, or the sales of each of the multiple stores during a time period including the departure time; calculate the store value of each of the multiple stores using the congestion level, number of customers, or sales of each of the multiple stores; extract stores with high store value from among the multiple stores using a predetermined value; and execute control to provide information indicating the stores with high store value.

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