Incentive Setting Device

Abstract

The purpose of the present invention is to provide an incentive setting device which imparts suitable incentives for proposals for a user.　An incentive amount calculation unit 155 calculates an incentive to affect the selection of some proposal details (search paths) proposed to a single user on the basis of approximate functions 7 and 8 derived on the basis of approximate functions (e.g., approximate functions 5 and 6) that indicate a selection trend of each user based on an evaluation value of the details of one proposal of each user (direct path) and an evaluation value of the details of another proposal (recommendation path etc.). Incentive necessity is determined by using approximate functions 1 and 2 (or approximation functions 3 and 4), and the incentive amount calculation unit 155 calculates the incentive on the basis thereof. Accordingly, the incentive amount calculation unit 155 calculates the incentive on the basis of a path selection trend of the user in a broad sense.

Description

[Technical field]

[0001] The present invention relates to an incentive setting device that gives an incentive to a user for a suggestion. [Background technology]

[0002] Patent Document 1 describes a technique for providing a technology that increases the likelihood that a user will actually change their behavior by guiding the user with incentives corresponding to a number of suggested travel routes, and granting the user an incentive for the route when the user takes action related to selecting that route. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-25968 Summary of the Invention [Problem to be solved by the invention]

[0004] In the invention described in Patent Document 1, the problem is how to set the incentive. In other words, if the incentive is high, behavior is more likely to change, but giving excessive incentives is not desirable. Therefore, it is desirable to set incentives that are necessary but not excessive.

[0005] In order to solve the above-mentioned problems, an object of the present invention is to provide an incentive setting device that provides an appropriate incentive for a suggestion made by a user. [Means for solving the problem]

[0006] The incentive setting device of the present invention comprises a memory unit that stores log data including an evaluation value of one proposal content proposed by each user, an evaluation value of the other proposal content, and selection information indicating which of the proposal contents was selected, and an incentive setting unit that sets an incentive that influences the selection of several proposal contents proposed to a user, based on the selection tendency of each user based on the evaluation value of the one proposal content of the user and the evaluation values ​​of the other proposal contents. Effect of the Invention

[0007] According to the present invention, appropriate incentives can be set, facilitating behavioral change in users. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 illustrates the processing concept of the present disclosure. [Diagram 2] 1 is a block diagram showing a functional configuration of a route search device 100 according to the present disclosure. [Diagram 3] FIG. 13 is a diagram illustrating a process for calculating a loss function and an overall evaluation. [Figure 4] 1 is a table in which various costs and efficiency coefficients calculated by the cost calculation unit 102 are associated with each other, as well as loss values, relative coefficients, and the like. [Diagram 5] 4 is a flowchart showing the operation of the route search device 100. [Figure 6] FIG. 13 is an explanatory diagram showing the concept of incentive setting. [Figure 7] FIG. 2 is a diagram illustrating a functional configuration of an incentive calculation unit 150 of the route search device 100 according to the present disclosure. [Figure 8] FIG. 13 is a diagram showing a specific example of a user log database 156. [Figure 9] FIG. 2 is an explanatory diagram showing the calculation process of approximation functions (1) to (6). [Figure 10] FIG. 1 illustrates the generation of the boundary function (1). [Figure 11] FIG. 13 is a diagram showing a determination process for an incentive fulfillment ratio. [Figure 12] FIG. 13 is a diagram for explaining the risk of lack of incentives. [Figure 13] 1 is a flowchart (part 1) showing the process of incentive calculation by the evaluation calculation unit 103 and the incentive calculation unit 150 of the road searching device 100. [Figure 14] 13 is a flowchart (part 2) showing the process of incentive calculation by the evaluation calculation unit 103 and the incentive calculation unit 150 of the road searching device 100. [Figure 15] FIG. 13 is a diagram for explaining an evaluation when incentive correction is performed for each proposal. [Figure 16] 1 is a diagram illustrating an example of a hardware configuration of a route search device 100 according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] The embodiments of the present disclosure will be described with reference to the accompanying drawings. Whenever possible, the same parts are designated by the same reference numerals and redundant description will be omitted.

[0010] FIG. 1 is a diagram showing the processing concept of the present disclosure. The vehicle 200 communicates with the route search device 100 and is provided with route information of the route T1 or the routes T2 and T3 and their overall evaluation. The current location A indicates the location of the vehicle. The driver of the vehicle 200 can choose to go from the current location A to the destination B via the route T1, or to go from the current location A to the detour C via the route T2, and then to the destination B via the route T3. In the present disclosure, it is assumed that the route T1 takes longer than usual due to traffic congestion, etc. In that case, the driver of the vehicle chooses to go to the detour C via the route T2 instead of the route T1. The driver can choose to stay at the detour C for the facility stay time Tc, and then go to the destination B via the route T3.

[0011] The routes T1 to T3, the stay time Tc at the detour C, and the overall evaluation of each route are calculated by the route search device 100 and provided to the driver.

[0012] 2 is a block diagram showing a functional configuration of the route search device 100 of the present disclosure. As shown in the figure, the route search device 100 includes a route search unit 101, a cost calculation unit 102, an evaluation calculation unit 103, a presentation unit 104, a map DB 105, a traffic DB 106, a cost DB 107, and an incentive calculation unit 150.

[0013] The route search unit 101 is a part that receives a request from the vehicle 200 and calculates one or more routes from the current location of the vehicle 200 to a destination in response to the request. The request from the vehicle 200 includes information on the current location of the vehicle 200 and the destination.

[0014] More specifically, the route search unit 101 refers to the map DB 105 and the traffic DB 106 to calculate one or more routes based on the current location and the destination. When calculating the route, the route search unit 101 searches for a detour based on the current location and the destination of the vehicle 200, and calculates a route that passes through the detour. The search for a detour may be determined based on the driver's hobbies, preferences, personality, and other attributes such as gender and age. The driver may also set the detour. In the description of FIG. 1, the number of detours is one, but there may be multiple detours.

[0015] The cost calculation unit 102 is a part that calculates the cost of each route calculated by the route search unit 101. The cost calculation unit 102 has a function of converting wasted time into a monetary value and creating a quantitative evaluation that also takes into account a relative evaluation with respect to an ideal time.

[0016] The cost calculation unit 102 creates or acquires in advance a loss function that indicates that X minutes of traffic congestion time is worth Y yen to the user. FIG. 3(a) is a diagram showing a specific example of a loss function. As shown in the figure, the horizontal axis is time and the vertical axis is amount of money, and this function outputs an amount of money according to time. This loss function is defined for each user, and is defined by conducting a questionnaire with the users in advance.

[0017] Moreover, the cost calculation unit 102 calculates the loss value and the efficiency coefficient. The route search unit 101 determines the traffic congestion time on various routes, and calculates the loss value from the loss value function based on the traffic congestion time. It is preferable that this loss value is set to increase according to the length of the traffic congestion time. In addition, when the traffic congestion lasts for a certain period of time or longer, the loss value may be further increased. For example, the loss value may be corrected by adding a predetermined value. In this disclosure, a large loss value means that the loss is large for the user.

[0018] In addition, the cost calculation unit 102 calculates the efficiency coefficient by the following formula. The efficiency coefficient is a coefficient that indicates how efficiently the proposed route for today's travel can be traveled compared to travel on a route at a reference time (here, a route without traffic jams). The larger this coefficient is, the worse the travel efficiency for that day is.

[0019] Travel time of the searched route ÷ Travel time of the route without past traffic jams (1) For example, as shown in Figure 3(b), Travel time from current location A to destination B When moving: T1=110 No traffic jam: T1_min=60 Travel time from current location A to detour location C When moving: T2=30 No traffic jam: T2_min=20 Travel time from detour C to destination B When moving: T3=50 No traffic jam: T3_min=50 Let Tx and Tx_min denote the travel time (in minutes) of route x.

[0020] In this case, the cost calculation unit 102 calculates the efficiency coefficient from the current location A to the destination B as T1 during travel / T1_min without traffic jam, for example, to obtain 110 / 60 ≈ 1.83. The efficiency coefficients are calculated similarly for other routes. FIG. 3(c) shows the traffic jam time, loss value, and efficiency coefficient when travelling from the current location A to the destination B via a direct route, and the traffic jam time, loss value, and efficiency coefficient when travelling via the detour C. As will be described later, a comprehensive evaluation is calculated based on these loss values ​​and efficiency coefficients. Note that the efficiency coefficient here is a value based on the absence of traffic jams, but is not limited to this, and may be the average travel time in a given period of time in the past.

[0021] The cost calculation unit 102 may further calculate a cost for a comprehensive evaluation that takes into account the cost of the entire route other than the travel efficiency described above.

[0022] For example, the cost calculation unit 102 calculates the vehicle movement additional cost and the vehicle movement cost efficiency coefficient. First, the cost calculation unit 102 calculates the difference in fuel cost between a route when there is no traffic jam (reference time) and the searched proposed route for today as the "vehicle movement additional cost" (fuel efficiency is poor during traffic jams, and gasoline costs increase). In addition, the cost calculation unit 102 calculates "fuel cost Y yen of the searched proposed route ÷ fuel cost X yen of the direct route when there is no traffic jam (reference time)" as the vehicle movement cost efficiency coefficient. The fuel consumption per km is calculated based on the total travel distance, traffic jam distance, and travel time, and the fuel cost is calculated based on this. For example, it is assumed that fuel consumption is 1.5 times higher during traffic jams.

[0023] The cost calculation unit 102 also calculates the detour loss value and the efficiency coefficient of the detour. The cost calculation unit 102 estimates the waiting time at the detour facility (detour location C) from external information, and calculates the loss value for the waiting time from the detour loss value function. The detour loss value is, for example, a value obtained by converting the waiting time at the detour location C into a monetary value. The cost calculation unit 102 acquires the average stay time at the detour facility from external information, etc., and calculates "1 + waiting time at facility ÷ average stay time" as the efficiency coefficient of the detour. The waiting time and average stay time are information provided from external information, and are the waiting time to enter the facility, the usage time, etc. These pieces of information are, for example, information provided from a management server managed by the facility. The "waiting time at facility" is the predicted waiting time at the facility at the scheduled arrival time.

[0024] Furthermore, the cost calculation unit 102 may use the stay cost in addition to or instead of the stay time such as the waiting time. That is, when the stay cost exceeds a certain cost, a loss function for that purpose may be used to use the loss value. This loss function is determined in advance according to the price range and stay time of the facility (e.g., a store) in the detour area C. For example, the average stay time in a general coffee shop is 30 minutes and the average amount is 600 yen, and the average amount is 60 minutes and 900 yen in a family restaurant. Based on such information, a loss function with the horizontal axis being "yen / minute" and the vertical axis being "loss value (yen)" may be prepared in advance. Note that a table that uniquely defines the loss value according to the type of facility (restaurant, souvenir shop, leisure facility) in the detour area C may be used.

[0025] Additionally, the cost calculation unit 102 calculates the loss value of an arrival delay at destination B and the relative coefficient of the delay. The cost calculation unit 102 has a "basic arrival time" that each user does not want to be later than.

[0026] In the present disclosure, the cost calculation unit 102 acquires user information including a basic arrival time. For example, the cost calculation unit 102 acquires a basic arrival time of the user information from a route search request from the vehicle 200 acquired by the route search unit 101. The cost calculation unit 102 calculates a loss value from a loss function of arrival delay according to the time later than the basic arrival time. That is, in this case, a loss function for when an arrival delay occurs other than the basic loss function is prepared, and the cost calculation unit 102 calculates the loss value using it. Then, the cost calculation unit 102 multiplies the loss value by a relative coefficient.

[0027] The relative coefficient for the direct route plan is set to 1, and the other detour route plans are defined according to the following cases: Case 1: If the direct route plan arrives before the basic arrival time → 1 Case 2: If the direct route plan is after the base arrival time → "Excess time of the detour route plan ÷ excess time of the direct route plan" (excess time indicates the time exceeded from the base arrival time)

[0028] 4 is a table in which various costs and efficiency coefficients calculated by the cost calculation unit 102 are associated with other loss values, relative coefficients, etc. As shown in the figure, they are further associated with and managed in relation to the overall evaluation calculated based on the loss value and efficiency coefficient of travel.

[0029] In FIG. 4, the overall evaluation is calculated as the sum of the overall evaluation in evaluation v1, the product of the vehicle movement additional cost and the vehicle movement cost efficiency coefficient, the product of the loss value of the detour and the efficiency coefficient of the detour, and the product of the loss value of the delay home and the relative coefficient of the delay home.

[0030] For convenience of explanation, the evaluation v1 indicates a comprehensive evaluation based on the loss for travel and its efficiency, and the evaluation v2 indicates a comprehensive evaluation based on the entire proposal including the additional cost of making a detour, but there is no need to distinguish between them. The evaluation calculation unit 103 can calculate the evaluation with only one of them. In FIG. 4, since the proposal 1 is a direct route proposal, the cost of the detour is omitted.

[0031] The evaluation calculation unit 103 functions as an evaluation unit and calculates an overall evaluation based on the loss value and efficiency coefficient calculated by the cost calculation unit 102. Specifically, the evaluation calculation unit 103 calculates the product of the loss value and the efficiency coefficient (or relative coefficient) calculated for each item as the evaluation for each item, and calculates the sum of these as the overall evaluation.

[0032] Furthermore, the evaluation calculation unit 103 may correct the overall evaluation by adding conditions other than those mentioned above, such as the traffic jam time being equal to or longer than a predetermined time, the stay cost exceeding a certain cost, the arrival time being later than a predetermined time, etc. As a function of the cost calculation unit 102, the loss value may be calculated according to these conditions, or the evaluation calculation unit 103 may correct the overall evaluation by taking these conditions into consideration.

[0033] The map DB 105 is a database that stores map information for route searches. This map information includes roads and facilities. The facilities are locations for detours, and indicate the names or types of the facilities.

[0034] The traffic DB 106 is a database that stores information about traffic congestion on roads.

[0035] The cost DB 107 is a database that stores the loss function and the cost of the detour place (stay cost of the facility). As described above, the cost of the detour place is information given from the outside as external information, so the route search device 100 may be configured not to have the cost DB 107 and to acquire it from an external server.

[0036] The incentive calculation unit 150 is a part that calculates and sets an incentive as necessary for each route searched by the route search unit 101. The evaluation calculation unit 103 calculates an overall evaluation that takes the incentive into account by subtracting the calculated incentive from the overall evaluation. In addition, the presentation unit 104 presents the incentive to the driver together with the overall evaluation (evaluation that takes the incentive into account) of each route.

[0037] Next, the operation of the route search device 100 of the present disclosure will be described. Fig. 5 is a flowchart showing the operation of the route search device 100. The route search unit 101 acquires a current location from the vehicle 200 (S101). Note that in the present disclosure, a direct route to a destination is calculated, and for example, the destination is assumed to be a home. Note that, naturally, the destination is not limited to a home.

[0038] The route search unit 101 acquires a direct route to home based on the current location (S102).The route search unit 101 acquires a detour route based on the current location (S103).

[0039] The cost calculation unit 102 calculates the cost of the direct route, the cost of the detour route, and the cost of the detour points (places of stay) on the detour route (S104).

[0040] The evaluation calculation unit 103 calculates an overall evaluation of the direct route and the detour route based on the cost (S105). The evaluation calculation unit 103 further narrows down the detour routes to x routes with high overall evaluations based on the overall evaluations (S106).

[0041] The presentation unit 104 presents the direct route and the x number of detour routes to the driver (S107).

[0042] As described above, the route search device 100 can derive a plurality of routes and provide the driver with an overall evaluation of each route.

[0043] In the table shown in FIG. 4, when there are multiple proposed routes, whether direct or detour, depending on whether or not an expressway is used, the expressway route is used as the standard for the direct route. Then, the routes can be divided into cases where a general road is used as the direct route, where an expressway is used as a detour route, and where a general road is used as a detour route. In this case, the presence or absence of an expressway may be taken into consideration as the travel cost. In other words, the toll for the expressway may be added to the travel cost.

[0044] Also, the loss value may be calculated by treating the additional time as traffic jam time based on the shortest route using the expressway, or a value number for using the expressway may be created in advance in addition to the loss value for traffic jam time. In general, the value of a 30-minute traffic jam on an expressway is different from that of a 30-minute traffic jam on an ordinary road. In general, the loss value of a traffic jam on an expressway is considered to be large, and the loss value on an ordinary road may be multiplied by 1.5.

[0045] The overall evaluation may be presented to the user not as a number but as a rank (e.g., 3000 or more is Bad, 500 or less is Good). In addition, evaluation correction may be added to the multiplication result of the loss value and the efficiency coefficient in the following cases:

[0046] Detour locations may be grouped by fixed area (such as within a five-minute drive) rather than by facility such as a store.

[0047] Loss value functions are created from surveys, etc. For example, a loss value function can be created from a survey asking questions such as how much would you be willing to pay if there was a fast lane that would allow you to avoid 10 minutes of traffic on the highway, or would you use the highway if it cost 100 yen and saved you 10 minutes? In other words, by conducting such a survey of each user, the creator of the loss value can grasp the monetary value corresponding to the user's time and create a loss value function.

[0048] Also, there may be loss functions corresponding to travel, stay at a detour, and delay in returning home. Also, the loss function may not be common to all users, but may be calculated by a machine learning model with attribute data as feature values. That is, an estimation model learned by machine learning may be used with attribute data (age, sex, hobbies, preferences, personality, etc.) and time as explanatory variables and monetary value as a target variable. Time and monetary value are determined based on the survey results taken for each user, as described above.

[0049] The time spent at the facility Tc may be calculated by aggregating the average time spent at the facility for each time period from the past logs of all drivers, or by using a prediction function for the time spent at the facility, as shown below. The prediction function determines the minimum time spent at the facility from the facility category, and calculates the time spent at the facility by finding the earliest time when the traffic jam time of the detour plan is equal to or shorter than a certain time.

[0050] In addition, the favorability of the detour (facility) may be taken into account in the overall evaluation. For example, the driver's preferences may be surveyed in advance, and if the driver likes a particular facility category, the evaluation value may be adjusted by subtracting a uniform X from the evaluation value and adding an additional coefficient (1 or less).

[0051] One of the features of the route search device 100 disclosed herein is that it is provided with an incentive calculation unit 150. As described above, the evaluation calculation unit 103 may add the incentive calculated by the incentive calculation unit 150 to the overall evaluation. In order to alleviate traffic congestion in an area where a driver is going to pass and to revitalize the area, a client such as a local government may want to set a recommended route or a detour location. The incentive setting is used for this purpose.

[0052] For example, by giving an incentive to the driver for a proposed route recommended by the route search device 100 (or the government or company that manages this device), the driver is motivated to select the proposed route. The route search device 100 (rating calculation unit 103) converts the incentive into an rating (for example, multiplying a monetary value by an efficiency coefficient) and subtracts the rating from the overall rating to obtain the overall rating. In other words, when the driver selects the proposed route with the incentive, the route search device 100 gives the driver a cashback or points that can be exchanged for cash.

[0053] After route search, the route search device 100 has the driver input which route to select, or performs GPS positioning or the like constantly or at an appropriate time, and determines whether the driver has traveled a route for which an incentive is to be granted. Then, the route search device 100 determines the granting conditions when the driver arrives at the destination. If it is determined that the incentive should be granted, the incentive is granted. Here, the route search device 100 grants the incentive, but the above operation may be performed by another incentive management device. In the above explanation, the incentive is a preset value, but it is preferable to always calculate an appropriate value, as will be described later.

[0054] Next, the setting of incentives will be described in detail. At the time of this disclosure, the setting of incentives is performed together with the calculation of the overall evaluation in step S105 of FIG.

[0055] The route search unit 101 searches for a route based on the driver's location, either by the driver (vehicle 200) or actively. The route search unit 101 searches for a direct route and a detour route. The presentation unit 105 presents each route. In the following disclosure, if there is no incentive for a detour route, the proposed route with the highest overall evaluation becomes the recommended route. As described later, the recommended route is not necessarily set based on the overall evaluation. If there is an incentive, the detour route taking the incentive into account becomes the recommended route.

[0056] For example, if the direct route to be proposed includes a congestion relief section requested by the client, the route search device 100 (presentation unit 105) in the present disclosure notifies the client of a recommended route (detour route) in addition to the direct route, and the amount of incentive to be granted if the proposal is implemented.

[0057] At this time, the incentive achievement condition (such as passing through XX section (= section to be avoided) between X and Y) is also notified to the user. This incentive achievement condition is set when the traffic congestion forecast information for the road desired by the client corresponds to a traffic congestion time or section that is greater than the threshold.

[0058] It is also assumed that the route search device 100 constantly acquires route search and notification logs, and movement logs based on GPS or the like.

[0059] Regardless of whether or not a recommended route proposal has been notified, the route search device 100 determines which proposal was made for the movement from the use of the search function to the arrival at the destination. For example, the route search device 100 determines whether or not the driver has used a recommended route.

[0060] Then, the route search device 100 judges whether or not the incentive achievement condition exists when judging the arrival, and, for example, if the stay time at the detour (circumnavigation) satisfies the achievement condition, an incentive is granted (presented).

[0061] As mentioned above, providing incentives depending on the proposal's contribution to improving the traffic conditions of the proposed route, such as easing congestion, meets the needs of clients such as local governments and road operators, but there is also a desire to minimize the incentive cost. To achieve this, it is necessary to set incentives appropriately.

[0062] FIG. 6 is an explanatory diagram showing the concept of incentive setting. FIG. 6(a) shows a comparison between proposal P when there is no incentive and proposal Q when there is an incentive. In proposal P, proposal 1 indicates the overall evaluation of the recommended route, and proposal 2 indicates the overall evaluation of the direct route. Here, the recommended route is the detour route with the highest overall evaluation among the detour routes when the direct route is in a specified state (such as traffic jam). Proposal P shows the starting point of the concept of the incentive setting process in this disclosure. In this case, if the overall evaluation of proposal 1 in proposal P is sufficiently good, no incentive is necessary. On the other hand, if the overall evaluation is not good, incentive provision is necessary.

[0063] Therefore, the route search device 100 judges whether or not the overall evaluation is appropriate based on the user log data without an incentive.

[0064] Proposal Q is the proposal content when it is determined that an incentive is necessary based on proposal P. Here, the incentive fulfillment rate and amount are determined. If an incentive is provided, how high should the overall evaluation of the recommended proposal be? This is determined based on user log data with and without an incentive. Even if the overall evaluation is the same, there may be a discrepancy in the actual user's judgment with and without an incentive.

[0065] On the other hand, Figure 6(b) shows the proposals when there are three or more proposals, and shows the case where the overall evaluation of the recommended route differs from that of the best route (highest overall evaluation). In proposal P1, when setting incentives, we would like to recommend proposal 1, but if proposal 3 has a high overall evaluation, we consider how much difference the driver will be willing to make in order to select the recommended route. Note that the recommended route here is not the one with the highest overall evaluation. As mentioned above, the recommended route will be one that meets the client's requests (for example, does not include sections with reduced congestion).

[0066] Proposal Q1 shows the case where incentives are set based on this.

[0067] As in Fig. 6(a), a decision is made as to whether or not an incentive is required, and the value of that incentive is calculated. Fig. 6 shows the concept of incentive setting, and the following process is actually carried out.

[0068] The incentive calculation unit 150 calculates and sets an appropriate incentive. Here, the incentive calculation unit 150 will be described. Fig. 7 is a diagram showing the functional configuration of the incentive calculation unit 150 of the route search device 100 of the present disclosure.

[0069] As shown in the figure, the incentive calculation unit 150 is composed of a function calculation unit 151, an incentive need determination unit 152, an incentive fulfillment ratio calculation unit 153, an incentive deficiency risk estimation unit 154, an incentive amount calculation unit 155, and a user log database 156.

[0070] The user log database 156 is a storage unit that stores driver log data. FIG. 8 is a diagram showing a specific example. As shown in the diagram, for each user ID, selection information, evaluation value of a direct route, evaluation value of a detour route, the difference between the evaluation values, best evaluation value, whether or not it is recommended, and incentives are stored. This log data is data that associates routes selected by some or all drivers with the evaluation values ​​of the routes, etc. This user log data is information that is fed back and stored based on the results of judgments made by each driver or the route search device 100.

[0071] In this user log data, the selection information is information indicating which of the proposed routes was selected. This selection information describes either the direct route, the recommended route, or the best route. The best route is the route with the highest overall evaluation value. For example, when the direct route includes a specified condition (e.g., traffic congestion), the recommended route is a proposal that avoids the condition. In addition, the recommended route may be a route that satisfies conditions indicated by a client (such as a local government). Usually, the recommended route and the best route are often the same, but there are of course cases where they are different. In this disclosure, unless otherwise specified, the recommended route is the same as the best route.

[0072] The evaluation value of the direct route and the evaluation value of the recommended route are the evaluation values ​​of the overall evaluation of each route at that time.

[0073] The evaluation value difference indicates the difference in overall evaluation between the recommended route (or the best route) and the direct route.

[0074] The rating value Best indicates the rating of the best route.

[0075] The presence or absence of a recommended route indicates the presence or absence of a recommended route. If the direct return route is not in a predetermined state (such as traffic jam), the recommended route may not be set in the proposed route.

[0076] The incentive indicates the value of an incentive if one is set in the proposal.

[0077] The function calculation unit 151 is a part that calculates various approximation functions used for incentive setting, using the user log data stored in the user log database 156. The approximation functions are information that indicates the route selection tendency of the user.

[0078] FIG. 9 is an explanatory diagram showing the calculation process of the approximation functions (1) to (6). In FIG. 9, each piece of user log data is plotted with the horizontal axis representing the direct route proposal evaluation value and the vertical axis representing the evaluation value difference between the direct route and another route (with the highest evaluation value). In FIG. 9, the recommended route is a route that avoids a traffic condition (such as traffic jam) set by a client or the like when there is a direct route that satisfies the traffic condition. Apart from that, a best route with a high overall evaluation is proposed, but in FIG. 9, the recommended route is the route with the best overall evaluation. In the figure, selecting the best overall evaluation means selecting the route with the best overall evaluation value.

[0079] FIG. 9(a) is a diagram showing the process of calculating approximation functions (1) and (2). As shown in the figure, the premise is that there are no recommended routes and the log data consists of proposals with no incentives. In FIG. 9(a), black circles indicate direct route selection, and white circles indicate best overall evaluation selection. Approximation functions (1) and (2) for white circles are calculated, respectively. These approximation functions (1) and (2) are information generated from user log data with no recommended routes and no incentives, and show the route selection tendency of users in that case.

[0080] FIG. 9(b) is a diagram showing the process of calculating the approximation functions (3) and (4). As shown in the diagram, the premise is that there is a recommended route, there is an incentive, and the user log data consists of a proposal with the recommended route being the best overall evaluation (best route). In FIG. 9(b), black circles indicate direct route selection, and white circles indicate the best overall evaluation selection. Approximation functions (3) and (4) of the white circles are calculated, respectively. Note that in FIG. 9(b), the evaluation value difference on the vertical axis is a value including incentive correction. These approximation functions (3) and (4) are information generated from user log data with a recommended route and with an incentive, and show the route selection tendency of users when incentives are taken into consideration.

[0081] FIG. 9(c) is a diagram showing the process of calculating the approximation functions (5) and (6). As shown in the figure, the premise is that there is a recommended route, there is an incentive, and the user log data consists of a proposal with the best overall evaluation (best route). In FIG. 9(c), black circles indicate direct route selection, and white circles indicate best route selection. Approximation functions (5) and (6) of white circles are calculated, respectively. Note that in FIG. 9(c), the evaluation value difference on the vertical axis is a value that does not include incentive correction. These approximation functions (5) and (6) are information generated from user log data with recommended routes and incentives, and show the route selection tendency of users when incentives are not taken into consideration.

[0082] The incentive need determination unit 152 is a part that determines whether or not an incentive is required, using the approximation functions (1) to (4) calculated by the function calculation unit 151. The incentive need determination unit 152 calculates the boundary function (1) for selection change as follows (see FIG. 10).

[0083] Approximation function(1)*a+approximation function(2)*(1-a)…(1) The value of a is a so-called weighting coefficient, and is determined based on whether the emphasis is placed on approximation function (1) or approximation function (2). This a is set based on the standard deviations of approximation function (1) and approximation function (2), etc.

[0084] This boundary function is information that represents a boundary line determined based on approximation function (1) and approximation function (2), and is information that indicates a boundary based on the user's selection tendency as to which route the user will choose.

[0085] Then, the incentive need determination unit 152 substitutes the overall evaluation of the currently proposed direct route into the boundary function (1) for selection change to obtain a threshold value. If the current evaluation value difference is greater than the threshold value, the incentive need determination unit 152 predicts that the recommended route will be selected (a detour will be made), and determines that an incentive is not necessary. If not, the incentive need determination unit 152 determines that an incentive is necessary.

[0086] If an incentive is needed here, the incentive need determination unit 152 can calculate a boundary function (1a) for selection change in the same manner using the approximation function (3) and the approximation function (4), and determine the incentive according to that boundary function, but this is not necessarily required.

[0087] The incentive fulfillment ratio calculation unit 153 is a part that calculates an approximation function of the incentive fulfillment ratio. This approximation function is a function for determining the influence of the incentive correction on the route selection of the driver.

[0088] Fig. 11 is a diagram showing the process of determining the incentive fulfillment ratio. The user log data handled in Fig. 11 is data with recommendations and incentives.

[0089] Fig. 11(a) is based on Fig. 9(c). In Fig. 11(a), the boundary function (1) has already been calculated from the approximation function (1) and the approximation function (2), and the boundary function (1) is overlaid.

[0090] In FIG. 11(a), the incentive fulfillment ratio calculation unit 153 converts each user log data (white circle, black circle) into data with the vertical axis representing the "fulfilled incentive ratio" (FIG. 11(b)), to obtain the graph in FIG. 11(b). The "fulfilled incentive ratio" indicates how much of the shortfall has been filled by the incentive relative to the boundary of the selection change. The incentive fulfillment ratio calculation unit 153 calculates the "fulfilled incentive ratio", approximate function (7), and approximate function (8) for all user log data (white circle and black circle, respectively) from the following formulas.

[0091] Satisfying incentive ratio for approximation function (7): Incentive correction value of each user log data (white circle) ÷ (value of each user log data (white circle) - value of boundary function (1) for selection change) ... (2) Satisfying incentive ratio for approximation function (8): Incentive correction value of each user log data (black circle) ÷ (value of each user log data (black circle) - value of boundary function (1) of selection change) ... (3) The above values ​​of each user log data indicate the evaluation value difference before incentive correction. The value of the boundary function (1) for selection change is calculated by substituting the evaluation value of the direct route plan in the user log data. The incentive correction value indicates the overall evaluation of the incentive in the user log data, and indicates the value given to the overall evaluation already calculated.

[0092] The incentive amount calculation unit 155 is a part that calculates the overall evaluation of the incentive using an approximation function and a boundary function, converts the overall evaluation into an incentive amount, and sets the incentive amount. This incentive amount calculation unit 155 functions as an incentive setting unit. The incentive amount calculation unit 155 calculates the overall evaluation using the following formula, and calculates the amount corresponding to it.

[0093] The boundary function (2) of the selection change in the incentive satisfaction ratio is Approximation function (7)*b + Approximation function (8)*(1-b)…(4) The boundary function indicates the degree of incentive satisfaction.

[0094] The value of b is a weighting coefficient that indicates which of the approximation functions (7) and (8) should be weighted. The value of b is set based on the standard deviations of the approximation functions (7) and (8), etc.

[0095] Then, the incentive amount calculation unit 155 calculates the total evaluation required as an incentive as follows: Boundary function for selection change (2) * (difference in evaluation value of recommended route - boundary function for selection change (1)) ... (5) Calculate.

[0096] The incentive amount calculation unit 155 converts the overall evaluation into an incentive amount. The incentive amount calculation unit 155 may calculate the incentive amount by treating the overall evaluation as an inverse function.

[0097] The incentive shortage risk estimation unit 154 is a part that estimates an incentive shortage risk, and functions as a risk determination unit. Prior to the estimation process, the incentive shortage risk estimation unit 154 performs the process shown in FIG.

[0098] Fig. 12(a) shows a graph of user log data with the evaluation value difference on the vertical axis and the best overall evaluation value (best route value) on the horizontal axis. This user log data is data with a recommended route and without an incentive, and further, data for a case where the actually selected route was the recommended route. The incentive insufficiency risk estimation unit 154 finds an approximation function (1) for this best overall evaluation data.

[0099] This process is for finding out how much the evaluation value can drop. If the evaluation value difference is larger than the approximation function (1), there is a high risk that a route other than the best route plan will be selected. This approximation function may be based on a value corrected by a predetermined coefficient. For example, the correction may be made by adding the standard error of the user log data.

[0100] FIG. 12(b) shows a graph of user log data, with the evaluation value difference on the vertical axis and the best overall evaluation value (value of the best route) on the horizontal axis. The evaluation value difference here is the evaluation value after incentives have been taken into account. This log data is data with and without recommended routes. The incentive deficiency risk estimation unit 154 finds an approximation function (2) for this best overall evaluation (evaluation value of the best route).

[0101] Prior to the estimation process, the incentive insufficiency risk estimation unit 154 utilizes the determination result of the incentive necessity determination unit 152. If the result is "incentive unnecessary," the incentive insufficiency risk estimation unit 154 determines the possibility that the best route plan will not be selected.

[0102] The incentive shortage risk estimation unit 154 compares value 1, calculated by substituting the evaluation value of the best route currently presented to the user into the approximation function (1), with value 2, the actual evaluation value difference (the difference between the recommended route plan and the best route plan), and if value 1>value 2, it determines that the recommended route plan is sufficiently selected and no incentive is required. If value 1<value 2, it determines that an incentive is necessary, and calculates and grants the incentive. The amount of incentive to be granted at this time should be set based on the value of value 2-value 1. For example, the amount of incentive to be granted may be calculated by multiplying the difference between value 1 and value 2 by a predetermined coefficient.

[0103] In addition, the incentive shortfall risk estimation unit 154 determines the possibility that the best route plan will not be selected when the incentive necessity determination unit 152 determines that an "incentive is necessary" and performs incentive design.

[0104] The incentive insufficiency risk estimation unit 154 determines that the recommended route plan is sufficiently selected and no additional incentive is necessary if value 3>value 4, which is the actual evaluation value difference (between the recommended route plan with the incentive added and the best route plan) calculated from the evaluation value of the current best proposal and the approximation function (2). If value 3<value 4, the incentive insufficiency risk estimation unit 154 adds an additional incentive. The amount of incentive granted at this time should be set based on value 4-value 3. For example, the incentive grant amount may be calculated by multiplying the difference between value 3 and value 4 by a predetermined coefficient.

[0105] The incentive amount calculation unit 155 calculates an incentive amount based on the values ​​1 to 4 calculated by the incentive shortfall risk estimation unit 154, and adds them together to calculate the final incentive amount to be awarded.

[0106] Next, the operation of the route search device 100 of the present disclosure will be described. Figures 13 and 14 are flowcharts showing the process of incentive calculation by the evaluation calculation unit 103 and the incentive calculation unit 150 of the route search device 100. This process is performed together with the process of S105 in Figure 5. The evaluation calculation unit 103 calculates the evaluation of each searched route (direct route and detour route) (S105).

[0107] Here, it is determined whether the recommended route is the best overall evaluation (best route) (S201). This determination is performed by the function calculation unit 151, but may be performed by another unit.

[0108] The function calculation unit 151 and the incentive need determination unit 152 calculate and obtain the approximation functions (1) to (6) and the boundary function (1) (S202). The incentive need determination unit 152 determines whether an incentive is necessary using the approximation functions (1) to (6) and the boundary function (1) (S203).

[0109] The incentive fulfillment ratio calculation unit 153 obtains the approximation functions (7) to (8) of the incentive fulfillment ratio, and calculates the boundary function (2) of the selection change using them (S204).

[0110] The incentive amount calculation unit 155 calculates the overall evaluation using the boundary function (2), and calculates the incentive amount therefrom (S205).

[0111] Next, the process when the recommended route is not the best overall route (step S201: NO) will be described with reference to FIG.

[0112] If the recommended route is not the best rated route (step S201: NO), the function calculation unit 151 and the incentive need determination unit 152 calculate and obtain an approximation function and a boundary function (S207). This is the same as step S202. In addition, the incentive shortage risk estimation unit 154 obtains an approximation function using the best rated route (the evaluation value of the best route) (S208).

[0113] Then, the incentive need determination unit 152 determines whether or not an incentive is needed by using the approximation functions (1) to (6) (S209), which is the same as the process S203.

[0114] Then, when it is determined that an incentive is necessary, the incentive insufficiency risk estimation unit 154 determines whether or not an additional incentive is necessary based on an approximation function using the best overall evaluation (evaluation of the best route) (S210). Here, when it is determined that an additional incentive is necessary, the incentive insufficiency risk estimation unit 154 adds an incentive (S211). Adding an incentive includes the same processing as in process S204, and is a processing of calculating the additional incentive and adding it to the incentive obtained by performing the same processing as in process S204.

[0115] The incentive amount calculation unit 155 calculates the amount of the incentive (S214).

[0116] Moreover, if it is determined in step S209 that the incentive is not required, the incentive shortfall risk estimation unit 154 further determines whether or not the incentive is not required (S212).

[0117] If the incentive shortage risk estimation unit 154 determines that an incentive is necessary, it grants the incentive (S213). If it determines that an incentive is unnecessary, it does not grant the incentive.

[0118] According to the above process, it is possible to set an appropriate incentive, that is, it is possible to motivate the driver to select a proposal with an incentive set while suppressing costs.

[0119] In the present disclosure, the proposed route for a vehicle searched by the route search device 100 is the subject of incentive granting, but the route may be a route for bicycle, walking, etc. In addition, the present invention is not limited to route proposals as long as it is something that is proposed to the user. For example, it is sufficient if it is something that can provide some kind of proposal for the user's behavior that can obtain the user's log data, such as the purchase item when purchasing something.

[0120] For example, suppose there is a product A from a company with high brand recognition and a product B from a company with low brand recognition. This method can be used to design the required incentive for the product price as is, or for the overall evaluation calculated by multiplying the product price by a coefficient calculated from brand recognition, word-of-mouth evaluation, etc.

[0121] Detour routes may be proposed not on a store-by-store basis but on a subordinate area-by-area basis. Also, incentives may be added on a store-by-store basis. There may be multiple clients. The client specified the "congested section to be avoided" and did not request a specific "plan to be guided," but this may be taken into consideration.

[0122] The calculation process of the boundary function in the incentive shortfall risk estimation unit 154 and the incentive amount calculation unit 155 may be performed by the following multiple regression analysis based on the incentive.

[0123] Boundary function p = α + (β1 * (overall evaluation of direct route plan) + β2 * (difference in evaluation value between direct route plan and recommended route plan) + β3 * (incentive correction value)) Here, α, β1, β2, and β3 are coefficients determined by multiple regression analysis.

[0124] Here, the incentive correction value will be described. Fig. 15 is a diagram for explaining the evaluation when the incentive correction for each proposal is performed. In the present disclosure, the incentive set as described above is added to or subtracted from the evaluation of each route, and the driver selects a route.

[0125] For example, in Figure 15, even if Proposal 2 is not actually proposed and Proposal 3 is proposed (when proposed with incentives), the calculation process still has the same contents as Proposal 2. The contents before incentive correction are essentially the same as Proposal 2.

[0126] To calculate proposal 3, first calculate the overall evaluation from the traffic congestion time, etc., equivalent to proposal 2. This overall evaluation is the evaluation value before incentive correction. In Figure 15, the "incentive amount" is the "30 yen" that was actually granted. The "incentive correction value" is the direct amount multiplied by the efficiency coefficient, which is 30*1.1=33. The "evaluation value difference (between the direct route plan and the detour route plan to store A) before incentive correction" is 5886-114=5772. The "evaluation value difference (between the direct route plan and the detour route plan to store A) after incentive correction" is 5886-81=5805.

[0127] The incentive correction calculation formula is merely an example, and other patterns may be used.

[0128] Next, the effects of the route search device 100 of the present disclosure will be described. The route search device 100 of the present disclosure functions as an incentive setting device that sets an incentive. As described above, the present disclosure is not limited to suggesting a route search.

[0129] In the route search device 100 of the present disclosure, the user log database 156 stores user log data including an evaluation value of one proposed content (direct route) by each user, an evaluation value of another proposed content (recommended route = best route), and selection information indicating which proposed content was selected.

[0130] Then, the incentive amount calculation unit 155 calculates an incentive that influences the selection of some proposed contents (search routes) proposed to one user based on approximation functions (7) and (8) derived based on approximation functions (for example, approximation functions (5) and (6)) that indicate the selection tendency of each user based on the evaluation value of one proposed content (direct route) of each user and the evaluation values ​​of other proposed contents (recommended routes, etc.). As will be described later, the necessity of an incentive is determined using approximation functions (1) and (2) (or approximation functions (3) and (4)), and the incentive amount calculation unit 155 calculates the incentive based on that. Therefore, the incentive amount calculation unit 155 calculates the incentive based on the selection tendency of the user's route in a broad sense.

[0131] This allows appropriate incentives to be set based on past user log data.

[0132] In addition, the incentive amount calculation unit 155 sets incentives based on the difference between the evaluation value of one proposed content (direct route) and the evaluation value of another proposed content (recommended route). By using this difference, it is possible to grasp the evaluation and selection tendency, and set appropriate incentives.

[0133] In addition, the route search device 100 of the present disclosure includes a presentation unit 104 that presents the conditions for providing an incentive to the driver. This allows the driver to know the incentive and motivate him or her to select a recommended route or the like.

[0134] Furthermore, in the route search device 100 of the present disclosure, the function calculation unit 151 (selection tendency information generation unit) generates a selection tendency (approximation function (1) and approximation function (2)) for each selection information (whether the direct route was selected or the recommended route was selected) based on a difference between the evaluation value of one proposal content (direct route) and the evaluation value of another proposal content (recommended route) in the user log data and an overall evaluation of the one proposal content (direct route). The incentive necessity determination unit 152 determines whether an incentive is necessary based on this selection tendency (approximation function (1) and approximation function (2)).

[0135] The incentive amount calculation unit 155 sets the incentive amount according to the determination of whether or not the incentive is required.

[0136] This makes it possible to set appropriate incentives according to the need for incentives. Therefore, incentives are not given when they are not needed, and excessive incentives can be avoided, which ultimately leads to cost reduction.

[0137] Furthermore, in the route search device 100 of the present disclosure, the incentive need determination unit 152 generates boundary information (boundary function (1)) on which proposal content each user will select depending on the selection tendency (approximation function (1) and approximation function (2)), and determines whether or not an incentive is needed based on the boundary information (boundary function (1)).

[0138] If no incentive is required, it is determined that the incentive is sufficient and no incentive is set.

[0139] In addition, in the route search device 100, the user log data includes evaluation values ​​of multiple other proposed contents (recommended route and best route). The incentive insufficiency risk estimation unit 154 uses user log data in which a proposed content (searched route) other than the other proposed content (best route) with a high evaluation value among one proposed content (direct route) and multiple other proposed contents (recommended route, best route, other routes) is selected, to determine the possibility that the user will select a proposed content (proposed route) other than the proposed content with a high evaluation value (best route). That is, the incentive insufficiency risk when incentives are provided and the incentive insufficiency risk for the recommended route are estimated.

[0140] The incentive amount calculation unit 155 then sets the incentive based on the result of the determination of whether or not the incentive is necessary and the result of the determination of the risk of shortage.

[0141] For example, when it is determined that an incentive is unnecessary, the incentive shortage risk estimation unit 154 uses the user log data without an incentive to determine the possibility that the user will select a proposal content (proposal route) other than the proposal content (best route) with a high overall evaluation, and calculates the amount of the grant.The incentive amount calculation unit 155 then sets the incentive according to this possibility.

[0142] Furthermore, when it is determined that an incentive is necessary, the incentive shortage risk estimation unit 154 uses the user log data with an incentive among the user log data to determine the possibility that the user will select a proposal content (proposal route) other than the proposal content (best route) with a high overall evaluation, and calculates the amount of the incentive to be granted.The incentive amount calculation unit 155 then sets an additional incentive according to the possibility.

[0143] This allows the amount of incentive shortfall to be estimated, enabling appropriate incentives to be set.

[0144] The route search device 100 of the present disclosure further includes an incentive fulfillment ratio calculation unit 153 that functions as an incentive fulfillment degree derivation unit that derives the incentive fulfillment degree from the selection tendency. The incentive amount calculation unit 155 sets the incentive based on the incentive fulfillment degree.

[0145] For example, the incentive fulfillment ratio calculation unit 153 calculates the boundary function (3) based on the approximation function (5) and the approximation function (6) generated based on the difference value before incentive correction between the overall evaluation of the direct route and the overall evaluation of the recommended route (best route) in the user log data with incentive among the user log data. The incentive fulfillment ratio calculation unit 153 generates the approximation function (7) and the approximation function (8) of the fulfillment incentive ratio based on the boundary function (3), thereby deriving the incentive fulfillment degree (boundary function (2)) for the user selection.

[0146] The incentive amount calculation unit 155 sets the incentive based on the incentive satisfaction level (boundary function (2)).

[0147] This enables incentives to be set according to the degree of incentive satisfaction, making it possible to set appropriate incentives.

[0148] The block diagrams used in the description of the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of at least one of hardware and software. The method of realizing each functional block is not particularly limited. That is, each functional block may be realized by using one device that is physically or logically combined, or may be realized by using two or more devices that are physically or logically separated and directly or indirectly connected (for example, by wire, wireless, etc.). The functional block may be realized by combining the one device or the multiple devices with software.

[0149] Functions include, but are not limited to, judgment, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocation, mapping, assignment, etc. For example, a functional block (component) that performs the function of transmission is called a transmitting unit or a transmitter. In either case, as described above, there is no particular limitation on the method of realization.

[0150] For example, the route search device 100 according to an embodiment of the present disclosure may function as a computer that performs processing of the route search method of the present disclosure. Fig. 16 is a diagram illustrating an example of a hardware configuration of the route search device 100 according to an embodiment of the present disclosure. The above-mentioned route search device 100 may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, and the like.

[0151] In the following description, the term "apparatus" may be replaced with a circuit, a device, a unit, etc. The hardware configuration of the route search apparatus 100 may be configured to include one or more of the apparatuses shown in the drawings, or may be configured to exclude some of the apparatuses.

[0152] Each function in the route search device 100 is realized by loading a specific software (program) onto hardware such as the processor 1001 and memory 1002, causing the processor 1001 to perform calculations, control communications by the communication device 1004, and control at least one of the reading and writing of data in the memory 1002 and storage 1003.

[0153] The processor 1001 controls the entire computer by running an operating system, for example. The processor 1001 may be configured with a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. For example, the above-mentioned route search unit 101, cost calculation unit 102, evaluation calculation unit 103, etc. may be realized by the processor 1001.

[0154] Moreover, the processor 1001 reads out a program (program code), a software module, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002, and executes various processes according to the read out programs. As the program, a program that causes a computer to execute at least a part of the operations described in the above-mentioned embodiment is used. For example, the route search unit 101, the cost calculation unit 102, and the evaluation calculation unit 103 may be realized by a control program stored in the memory 1002 and operated in the processor 1001, and other functional blocks may be realized in a similar manner. Although the above-mentioned various processes have been described as being executed by one processor 1001, they may be executed by two or more processors 1001 simultaneously or sequentially. The processor 1001 may be implemented by one or more chips. The program may be transmitted from a network via a telecommunication line.

[0155] The memory 1002 is a computer-readable recording medium, and may be configured by at least one of, for example, a read only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a random access memory (RAM), etc. The memory 1002 may be called a register, a cache, a main memory (primary storage device), etc. The memory 1002 can store a program (program code), a software module, etc. that can be executed to implement a path search method according to an embodiment of the present disclosure.

[0156] Storage 1003 is a computer-readable recording medium, and may be composed of at least one of, for example, an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, a magnetic strip, and the like. Storage 1003 may be referred to as an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, a server, or other suitable medium including at least one of memory 1002 and storage 1003.

[0157] The communication device 1004 is hardware (transmission / reception device) for performing communication between computers via at least one of a wired network and a wireless network, and is also called, for example, a network device, a network controller, a network card, a communication module, etc. The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc., in order to realize at least one of Frequency Division Duplex (FDD) and Time Division Duplex (TDD). For example, a part of the receiving function of the route search unit 101 and the transmitting function of the presentation unit 104 may be realized by the communication device 1004.

[0158] The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that accepts input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, an LED lamp, etc.) that performs output to the outside. The input device 1005 and the output device 1006 may be integrated into one device (e.g., a touch panel).

[0159] In addition, each device such as the processor 1001 and the memory 1002 is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.

[0160] Furthermore, the route search device 100 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), etc., and some or all of the functional blocks may be realized by the hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

[0161] The notification of information is not limited to the aspects / embodiments described in the present disclosure, and may be performed using other methods. For example, the notification of information may be performed by physical layer signaling (e.g., Downlink Control Information (DCI), Uplink Control Information (UCI)), higher layer signaling (e.g., Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, broadcast information (Master Information Block (MIB), System Information Block (SIB))), other signals, or a combination thereof. In addition, the RRC signaling may be called an RRC message, and may be, for example, an RRC Connection Setup message, an RRC Connection Reconfiguration message, or the like.

[0162] The order of the steps, sequences, flow charts, etc. of each aspect / embodiment described in this disclosure may be changed unless inconsistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0163] The input and output information may be stored in a specific location (e.g., memory) or may be managed using a management table. The input and output information may be overwritten, updated, or added to. The output information may be deleted. The input information may be transmitted to another device.

[0164] The determination may be based on a value represented by a single bit (0 or 1), a Boolean (true or false) value, or a numerical comparison (e.g., comparison with a predetermined value).

[0165] Each aspect / embodiment described in this disclosure may be used alone, in combination, or switched according to execution. In addition, notification of specific information (e.g., notification that "X is the case") is not limited to being done explicitly, but may be done implicitly (e.g., not notifying the specific information).

[0166] Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described in the present disclosure. The present disclosure can be implemented as modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the present disclosure.

[0167] Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0168] Software, instructions, information, etc. may also be transmitted or received over a transmission medium. For example, if the software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave, etc.), then these wired and / or wireless technologies are included within the definition of transmission media.

[0169] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0170] In addition, the terms described in this disclosure and the terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings. For example, at least one of the channel and the symbol may be a signal (signaling). Also, the signal may be a message. Also, the component carrier (CC) may be called a carrier frequency, a cell, a frequency carrier, etc.

[0171] In addition, the information, parameters, etc. described in the present disclosure may be represented using absolute values, may be represented using relative values ​​from a predetermined value, or may be represented using other corresponding information. For example, a radio resource may be indicated by an index.

[0172] The names used for the above-mentioned parameters are not limiting in any way. Moreover, the formulas etc. using these parameters may differ from those explicitly disclosed in this disclosure. The various channels (e.g., PUCCH, PDCCH, etc.) and information elements may be identified by any suitable names, and therefore the various names assigned to these various channels and information elements are not limiting in any way.

[0173] In this disclosure, the terms "Mobile Station (MS)", "user terminal", "User Equipment (UE)", "terminal", etc. may be used interchangeably.

[0174] A mobile station may also be referred to by those skilled in the art as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable terminology.

[0175] As used in this disclosure, the terms "determining" and "determining" may encompass a wide variety of actions. "Determining" and "determining" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, search, inquiry (e.g., searching in a table, database, or other data structure), ascertaining, and the like. "Determining" and "determining" may also include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in a memory), and the like. In addition, "judgment" and "decision" can include considering resolving, selecting, choosing, establishing, comparing, etc., to be a "judgment" or "decision." In other words, "judgment" and "decision" can include considering some action to be a "judgment" or "decision." In addition, "judgment" can be interpreted as "assuming," "expecting," "considering," etc.

[0176] The terms "connected," "coupled," or any variation thereof, refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "access." As used in this disclosure, two elements may be considered to be "connected" or "coupled" to each other using at least one of one or more wires, cables, and printed electrical connections, as well as electromagnetic energy having wavelengths in the radio frequency range, microwave range, and optical (both visible and invisible) range, as some non-limiting and non-exhaustive examples.

[0177] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0178] Any reference to an element using a designation such as "first", "second" etc. used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must precede the second element in some way.

[0179] When "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Further, when used in this disclosure, the term "or" is not intended to be an exclusive or.

[0180] In this disclosure, where articles have been added due to translation, such as, for example, a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0181] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different." [Explanation of symbols]

[0182] 100...route search device, 101...route search unit, 102...cost calculation unit, 103...evaluation calculation unit, 104...presentation unit, 105...map DB, 106...traffic DB, 107...cost DB, 151...function calculation unit, 152...incentive need determination unit, 153...incentive fulfillment ratio calculation unit, 154...incentive deficiency risk estimation unit, 155...incentive amount calculation unit, user log database, 156...user log database.

Claims

1. a storage unit that stores log data including an evaluation value of a first proposal content proposed by each user, evaluation values ​​of other proposal contents, selection information indicating whether the first proposal content or the other proposal content was selected, whether a recommended proposal was made, and an incentive value set for the other proposal content; a selection tendency information generating unit that generates a selection tendency for each of the selection information based on a difference value between an evaluation value of the one proposed content and an evaluation value of the other proposed content in the log data and the evaluation value of the one proposed content; an incentive necessity determination unit that generates boundary information indicating a boundary line between which proposal content each user will select according to the selection tendency, and determines whether an incentive is necessary based on an evaluation value of one proposal content to be proposed to one user and the boundary information; an incentive setting unit that sets an incentive that influences a selection of some proposal contents proposed to the one user, based on the selection tendency of each of the users based on an evaluation value of the one proposal content and an evaluation value of the other proposal contents of the one user; Equipped with The selection tendency is an approximation function calculated based on an evaluation value of one proposed content for each user and an evaluation value difference between the evaluation value of the one proposed content and the evaluation value of another proposed content, and an approximation function (2) based on selection information indicating that one proposed content was selected and an approximation function (1) based on selection information indicating that another proposed content was selected are generated from log data that does not have an incentive value and a recommended proposal among the log data, The boundary information is a boundary function (1) obtained by taking a weighted average of the selection tendency generated for each of the selection information, the incentive necessity determination unit obtains a threshold by substituting an evaluation value of another proposal content proposed to the one user into the boundary information, and determines whether an incentive is necessary by comparing an evaluation value difference between the evaluation value of the other proposal content proposed to the one user and the evaluation value of the one proposal content with the threshold; the incentive setting unit sets the incentive in accordance with the determination of whether the incentive is required; Incentive setting device.

2. The incentive setting unit: An approximation function of the incentive fulfillment ratio, which indicates how much of the shortfall has been filled by incentives, Using the incentive value of the log data in which the other proposal content is selected, the difference between the evaluation value of the one proposal content before correction using this incentive value and the evaluation value of the other proposal content, and the boundary function (1), an approximation function (7) of the fulfillment incentive ratio for the one proposal content is calculated; Using the incentive value of the log data in which one proposal content is selected, the difference between the evaluation value of the one proposal content before correction using this incentive value and the evaluation value of the other proposal content, and the boundary function (1), an approximation function (8) of the fulfillment incentive ratio for the one proposal content is calculated; A boundary function (2) indicating the degree of satisfaction of the incentive is obtained by taking a weighted average of the approximation function (7) and the approximation function (8); A difference value between an evaluation value of one proposal content proposed to the one user and an evaluation value of another proposal content proposed to the one user, the boundary function (1) and the boundary function (2) are used to calculate an overall evaluation required as an incentive, and an incentive setting is performed. The incentive setting device according to claim 1 .

3. a presentation unit that presents to a user the conditions for providing the incentive; The incentive setting device of claim 1 further comprising:

4. The one proposed content is a direct route among the searched routes searched based on the current location, and the other proposed content is a detour route that passes through a predetermined intermediate point among the searched routes searched based on the current location. The incentive setting device according to claim 1 .

5. The log data includes evaluation values ​​of a plurality of other proposal contents, A risk determination unit is further provided that determines a possibility that a user may select a proposal content other than the proposal content with the highest evaluation value by using log data in which a proposal content other than the one proposal content and the other proposal content with the highest evaluation value is selected from the log data, the risk assessment unit determines an approximation function (1) for the best overall evaluation based on an evaluation value of the proposal content that has the highest evaluation value not including incentives in the log data and a difference value not including incentives between the highest evaluation value and an evaluation value of a recommended proposal content; determining an approximation function (2) for the best overall evaluation based on an evaluation value of the proposal content with the highest evaluation value including the incentive in the log data and a difference value including the incentive between the highest evaluation value and the evaluation value of the recommended proposal content; When the incentive necessity determination unit determines that an incentive is unnecessary, the unit compares a value 1 obtained by substituting the evaluation value of the proposal content with the highest evaluation value among the proposal contents to be presented into the approximation function (1) in the best overall evaluation with a value 2 which is the difference between the evaluation value of the proposal content with the highest evaluation value and the evaluation value of the recommended proposal content, and determines that an incentive is unnecessary if value 1>value 2, and determines that an incentive is necessary if value 1<value 2, since there is a possibility that the user will select a proposal content other than the proposal content with the highest evaluation value; the incentive setting unit sets an additional incentive based on a value obtained by subtracting a value of 1 from a value of 2; When the incentive necessity determination unit determines that an incentive is necessary, it compares a value 3 obtained by substituting the evaluation value of the proposal content with the highest evaluation value among the proposal contents to be presented into the approximation function (2) in the best overall evaluation with a value 4 which is the difference between the evaluation value of the recommended proposal content including the incentive and the evaluation value of the proposal content with the highest evaluation value, and determines that an additional incentive is unnecessary if value 3>value 4, and determines that an additional incentive is necessary if value 3<value 4; the incentive setting unit sets an additional incentive based on a value obtained by subtracting the value 3 from the value 4; The incentive setting device according to claim 1 .

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