JP7123099B2 - Provision device, provision method and provision program - Google Patents

Description

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

In recent years, navigation services related to means of transportation from a starting point to a destination have been provided. Railroad transfer guidance services, car navigation systems for automobiles, and the like have come to provide information on the congestion status of railroads and roads.

JP 2013-190867 A

However, although the conventional technology described above provides general congestion conditions and their predictions, it does not provide information that takes into consideration the diversity of relationships between users and causes of congestion.

In view of the above, an object of the present application is to identify users who have little relevance to the cause of congestion and to provide information about the cause to the identified users.

A provision device according to the present application includes an acquisition unit that acquires activity schedule information indicating an activity schedule of a user and attribute information that indicates an attribute of the user; A specifying unit that specifies a user, a determining unit that determines a target user to whom the information on the congestion is provided, among the users specified by the specifying unit, based on the attribute indicated by the attribute information; and a providing unit that provides information about the congestion to the target user.

According to one aspect of the embodiment, it is possible to identify a user who is less relevant to the cause of congestion and to provide the identified user with information about the cause.

FIG. 1 is a diagram illustrating an example of a provisioning process for identifying navigation applications and providing congestion aspects and congestion causes. FIG. 2 is a diagram showing an example of a search history related to events. FIG. 3 is a diagram showing an example of travel route history data of a car navigation application. FIG. 4 is a diagram showing an example of transfer history data of a railway transfer guidance application. FIG. 5 is a diagram showing an example of event data. FIG. 6 is a diagram illustrating a configuration example of a provision device according to an embodiment; FIG. 7 is a flowchart illustrating a processing procedure for providing a congestion state, which is executed by the providing device according to the embodiment; FIG. 8 is a diagram showing an example of a hardware configuration.

Embodiments for implementing the provision device, the provision method, and the provision program (hereinafter referred to as "embodiments") according to the present application will be described in detail below with reference to the drawings. Note that the provision device, the provision method, and the provision program according to the present application are not limited by this embodiment. The details of one or more embodiments are set forth in the following description and drawings. Also, each of one or more embodiments can be appropriately combined within a range that does not contradict the processing content. Also, in one or more embodiments below, the same parts are denoted by the same reference numerals, and overlapping descriptions are omitted.

[1. Provision process]
First, an example of provision processing according to an embodiment will be described with reference to FIG. FIG. 1 is a diagram showing an example of processing for providing a congestion mode corresponding to the transportation system used by the user to a user who has a low relevance to the cause of the congestion. In the example shown in FIG. 1, the providing system 1 according to the embodiment provides a user with a low degree of relevance to the cause of congestion with a congestion mode corresponding to the transportation system used by the user. The execution example of is described.

As shown in FIG. 1, the estimation system 1 includes user terminal devices 10 ₁ to 10 _n and an estimation device 100 (n is any natural number). In this specification, the user terminal devices 10 ₁ to 10 _n are referred to as “user terminal devices 10” when there is no need to distinguish between the user terminal devices 10 ₁ to 10 _n . Although not shown in FIG. 1 , the provision system 1 may include a plurality of estimation devices 100 .

A user terminal device 10 is an information processing device used by a user. The user terminal device 10 may be any type of information processing device including smart phones, desktop PCs (Personal Computers), notebook PCs, and tablet PCs.

In the example of FIG. 1, the user terminal device 10 is a client device used by the user. In this example, the user terminal device 10 is a smart phone in which a navigation application is installed. Navigation applications (hereinafter referred to as navigation applications) include car navigation applications (hereinafter referred to as car navigation applications), railway transfer guidance applications (hereinafter referred to as transfer guidance applications), and the like. In the case of a car navigation application, the smartphone is installed in the user's vehicle, for example. To give a more specific example, such a smartphone can be attached to an in-vehicle holder mounted on a user's vehicle. The term "user terminal device 10 installed in the user's vehicle" can include "the user terminal device 10 mounted in the user's vehicle". That is, the term "installation of the user terminal device 10" can encompass "permanent installation of the user terminal device 10 (eg, mounting the user terminal device 10)".

The estimating device 100 is an information processing device that estimates the state of congestion and the cause of congestion based on search history data, registered route data, and the like. The estimating device 100 may be any type of information processing device, including a server. Although not shown in FIG. 1, the estimation device 100 communicates with the user terminal device 10 by wire or wirelessly via a network (for example, the Internet).

In the example of FIG. 1, the estimation device 100 identifies the navigation application used by the user terminal device 10 . For example, it is assumed that at least one of a car navigation application and a transit guidance application is installed in the user terminal 10 . For example, when the user inputs the departure point and the destination in the navigation application (steps S10 and S20), the estimation device 100 determines whether the navigation application used by the user terminal device 10 owned by the user is a car navigation application or It is identified whether it is a transfer guidance application (steps S11 and S12).

In the example of FIG. 1, the estimating device 100 searches for destinations or events within a predetermined period of time based on search histories using general search engines, including other users as shown in FIG. Alternatively, the number of searches is acquired for each category related to the event. The estimating apparatus 100 estimates that destinations for which the number of searches is greater than a predetermined value are congested according to the number of searches.

In the example of FIG. 1, the estimation device 100 estimates the occurrence of an event that causes congestion, as described below. The estimating device 100 estimates the occurrence of an event that causes congestion based on the number of searches related to the vicinity of the destination by any search engine used by the user, SNS information, etc., or an event schedule obtained in advance (step S30).

Next, the estimating device 100 identifies users who are less relevant to the cause of congestion. Here, the users who are less relevant to the cause of the congestion are those users who are involved in the congestion and who do not know the event that causes the congestion. Such users are highly likely not to know why they are involved in the congestion when they are involved in a sudden congestion such as a live performance. On the other hand, users who are highly relevant to events that cause congestion are users who are likely to participate in events that cause congestion, such as live performances. Even if congestion information is provided to users who are highly related to such congestion, it can be said that it is unnecessary information. Therefore, the estimating device 100 provides congestion-related information to users who are less relevant to the event that causes the congestion among the users involved in the congestion.

In the example of FIG. 1, the estimation device 100 estimates whether or not the candidate route provided by the navigation app is a route registered in the navigation app, based on the departure point and the destination. For example, in the case of a car navigation app, the estimating device 100 refers to the routes registered by the user in the car navigation app, and determines whether any of the candidate routes are the same as or partly overlap with the registered route. to estimate In the case of a transfer guidance application, for example, referring to the registered route of the commuter pass in the transfer guidance application, based on the specified departure station and arrival station, the candidate route is the same as the registered route or the registered route. First, estimate whether there are duplicate routes. In any case, if any of the candidate routes is substantially the same as the registered route in terms of the ratio of identical or overlapping portions, the estimation device 100 determines that the route is the one that the user uses on a daily basis. It is estimated that the route is

On the other hand, the estimating device 100 acquires a candidate route from the navigation app based on the departure point and the destination, and if the route is not substantially the same as the route registered in the navigation app, In the route search history, if the number of searches within the navigation application related to stations or positions near the destination is less than or equal to a predetermined value, the estimation device 100 determines that the route is not a route that the user uses on a daily basis. We estimate that Then, the estimating device 100 regards users who routinely use routes that cause congestion as users involved in the congestion.

Furthermore, the estimating apparatus 100 determines whether or not the user has searched for events, venues, etc. using a general search engine a predetermined number of times or more in a predetermined period based on the search history. , if there is no search history, it is determined that the user has a low relevance to congestion. That is, estimating apparatus 100 identifies users who may not be aware of the occurrence of an event that causes congestion as users to whom information is provided.

Then, the estimating device 100 selects a user whose candidate route provided by the navigation application and the user's registered route is the same or substantially the same and whose search history related to events and the like is equal to or less than a predetermined amount as the target user. To provide a congestion mode to a user. In addition, when the candidate route provided by the navigation application and the user's registered route are not substantially the same, and the search history related to events, etc. As a user with a low V, the state of congestion is provided (steps S12, S22).

On the other hand, the estimating device 100 can be used for a user whose candidate route provided by the navigation application and the user's registered route are not substantially the same, or for a user whose candidate route provided by the navigation application and the user's registered route are substantially the same. Even if there is, if the search history related to events or the like exceeds a predetermined amount, the user is considered to be highly relevant to the cause of the congestion, that is, the user who knows the occurrence of the congestion, and is excluded from the information providing destination.

In the example of FIG. 1, the estimating device 100 provides the user terminal application with information on the cause of the congestion and the distance of the congestion when the navigation application is a car navigation application for a user who has a low relevance to the cause of the congestion. (step S12), and in the case of the transfer guidance application, the cause of the congestion and the congested railroad section are provided to the user terminal application (step S22).

[2. Event Estimation]
After identifying the navigation app, the estimating device 100 estimates events that cause congestion on roads or railways. For example, with regard to events, if there is usually a pre-scheduled event as shown in FIG. 5, the estimating device 100 searches for and identifies the event based on the schedule information by date and time and place. In this case, the estimation device 100 may refer to past schedule information. In addition, the estimation device 100 may acquire the event name from search histories of other users during a predetermined time from the navigation application, search histories using a search engine from the network, and SNS information.

Note that the estimation apparatus 100 may estimate a predetermined position, facility, or the like from the event as a place where congestion will occur. For example, the estimating device 100 may set stations and highways within a predetermined range from the event venue as congestion occurrence points. In addition, the estimating apparatus 100 sets a predetermined range from the date and time of the event, such as 2 hours before the start time of the event or 3 hours after the end time of the event, as a date and time when congestion occurs, and at this date and time, a predetermined range from the event. It is also possible to estimate that congestion will occur in the area, and to estimate users involved in the estimated congestion.

Note that the estimation device 100 may appropriately adjust the range of congestion, the degree of congestion, the period of congestion, etc. according to the type of event. For example, when an idle event occurs, the estimating apparatus 100 determines the extent of congestion, the degree of congestion, and the degree of congestion based on the content of congestion when the same or similar idle event occurred in the past. can be estimated.

Although the estimating apparatus 100 can use any event as a cause of congestion, it is desirable that such an event be different from an event that occurs on a daily basis. For example, the estimating apparatus 100 desirably causes congestion to occur discretely and unexpectedly rather than regularly occurring events such as idol live performances and various sports-related events.

[3. Estimation of event content]
If there is no prior event schedule information or past schedule information, or if the event name is unknown, the estimating device 100 collects the event event information from the user's search history at a predetermined time from the navigation application and the search engine from the network. It is specified from the search history and SNS information used. Based on the estimated event content, that is, the type of event, the estimation device 100 estimates whether congestion will occur in a personal means of transportation such as a car or whether public transportation such as a train will be crowded. It may be estimated whether a user moving by means of transportation will be involved in congestion.

[4. About the scale of the event]
Based on the event name, the estimation device 100 estimates the scale of the event based on the number of spectators in the past and the means of transportation used by the participants. In addition, the estimating apparatus 100 acquires the scale of the event from search histories of other users at a predetermined time from the navigation application, search histories using any search engine from the network, and SNS information.

[5. About variations of information provision processing]
In the above example, the estimation device 100 uses route search information, a search query, etc. as action plan information indicating the future action of the user, and based on the action plan information, the occurrence of congestion and the occurrence of events that cause congestion. We estimated the occurrence of events such as Then, an example of the process of notifying the occurrence of congestion by the estimating device 100 when a congestion that has a low relevance to the user occurs on the travel route used on a given date has been described. That is, in the above-described example, the estimation device 100 regards the movement route that the user uses on a daily basis as the attribute of the user, and when congestion with low relevance to this attribute occurs, Provided information about the event that caused it. However, embodiments are not so limited.

For example, the estimating device 100 may estimate the time and place of occurrence of congestion using various known congestion occurrence techniques. For example, the estimating device 100 may estimate the occurrence of congestion based on a history of route searches in which future dates and times are set. The estimating device 100 may also search for an event that is highly relevant to the estimated location and date of congestion, and estimate the cause of the congestion based on the search results.

In addition, the estimating device 100 determines the event and relevance of the user that causes congestion based on various demographic attributes and psychographic attributes of the user, and attributes based on the purchase history and search history of the transaction target in the electronic shopping mall. It may be estimated whether the user is a user with a low For example, when the cause of congestion in which a user is involved is a predetermined idol's live performance, the estimation device 100 estimates whether or not the user is a fan of the predetermined idol. For example, the estimation device 100 estimates whether or not the user is a fan of a predetermined idol based on the purchase history of the user. Then, if the user is not a fan of a certain idol, the estimation device 100 treats the user as a user who does not know the occurrence of such an event, that is, a user who is less relevant to the cause of the congestion. Information about congestion may be notified.

In addition, the estimation device 100 may use information on future route searches as action schedule information to estimate whether the user will be involved in congestion in the future. For example, the estimation device 100 acquires the route search result for the future date and time when the route search for the future date and time is performed. Then, the estimating device 100 estimates whether the user will be involved in congestion in the obtained route search result. Subsequently, when it is estimated that the user will be involved in the congestion, the estimation device 100 estimates whether or not the user is highly relevant to the event that causes the congestion. In addition to the result of the above, information indicating that congestion will occur and event information may be provided.

Note that the estimation device 100 may provide different messages depending on the relationship between the user and congestion. For example, the estimation device 100 calculates a score indicating the relationship between an event that causes congestion and the user. A technique for estimating various relationships can be employed for such score calculation. Estimation device 100 does not provide information about congestion when the calculated score value is high, and provides information indicating that congestion will occur when the score value is medium. If it is low, information about the event that caused the congestion may be provided along with the fact that congestion will occur. In addition, the estimating device 100 may provide a highly relevant person with information on a web page related to the event, etc., when performing a route search for the event occurrence date and time.

[6. About incentives]
Also, the estimation device 100 may provide various incentives. For example, the estimation device 100 makes a proposal for avoiding congestion to the target user. To give a more specific example, the estimation device 100 may propose to the user information indicating a route for avoiding congestion.

In addition, the estimating apparatus 100 may provide various incentives such as a predetermined coupon to the user when the user actually avoids congestion using the user's location history or the like. .

[7. Configuration of estimation device]
Next, a configuration example of the estimation device 100 according to the embodiment will be described with reference to FIG. FIG. 6 is a diagram illustrating a configuration example of the estimation device 100 according to the embodiment. As shown in FIG. 6 , estimation device 100 includes communication section 210 , storage section 220 , and control section 230 . Note that the estimation apparatus 100 has an input unit (for example, keyboard, mouse, etc.) that receives various operations from an administrator or the like who uses the estimation apparatus 100, and a display unit (liquid crystal display, etc.) for displaying various information. may

(Communication unit 210)
The communication unit 210 is implemented by, for example, a NIC (Network Interface Card) or the like. The communication unit 210 is connected to a network by wire or wirelessly, and transmits and receives information to and from the user device 10 via the network.

(storage unit 220)
The storage unit 220 is implemented by, for example, a semiconductor memory device such as a RAM (Random Access Memory) or flash memory, or a storage device such as a hard disk or an optical disk. As shown in FIG. 6 , the storage unit 220 has a search history data storage unit 221 , a travel route history data storage unit 222 , a transfer history data storage unit 223 and an event data storage unit 224 .

(Search history storage unit 221)
The search history storage unit 221 stores search history data. The search history data storage unit 221 stores, for example, time, user ID, search tool, search keyword, search engine, etc. as shown in FIG. For example, when the same or similar search keywords are concentrated in a predetermined time period, the estimation device 100 estimates that transportation to an event related to the search keyword is congested. Moreover, the estimation apparatus 100 may estimate that an event not described in the event schedule has occurred, based on the search history data.

(Traveling route history data storage unit 222)
The travel route history data storage unit 222 stores, for example, the user ID, departure point, destination, travel route, travel time, distance, date, etc. as shown in FIG. For example, when the user inputs the starting point and the destination in the car navigation application, the estimating device 100 estimates from the user's past travel history whether the congestion related to the destination is high or low. do. If the destination is not the nearest location to the event site, past travel history may be used to estimate the relevance of the event to congestion.

(Transfer history data storage unit 223)
The transfer history storage unit 223 stores the history of railway transfers. For example, the user ID, search time, departure station, transit station, arrival station, etc. as shown in FIG. 4 are stored. For example, when the user inputs the departure station and the arrival station in the transfer guidance application, the estimation device 100 determines whether the travel route estimated by the application is the registered route related to the user's commuter pass, or is substantially Predict whether it is the same as the registered route. If the arrival station is not the nearest station to the event venue, past boarding history may be used to estimate the relevance of the event to congestion.

(Event data storage unit 224)
The event data storage unit 224 stores, for example, the event category, event name, date and time of occurrence, location, event area, number of spectators, etc. as shown in FIG. For example, the estimation device 100 estimates an event name by referring to event data. Also, the estimation device 100 may estimate the congestion state of the current event using congestion states of similar events.

In addition to the information shown in FIG. 6, various databases for providing route guidance are registered in the storage unit 220, for example. It is assumed that such a database stores information similar to well-known techniques related to various types of route guidance, and a description thereof will be omitted.

(control unit 230)
The control unit 230 is a controller. For example, a processor such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit) executes various programs (estimation programs) stored in a storage device inside the estimation device 100. (equivalent to one example) is implemented by executing the RAM or the like as a work area. Also, the control unit 230 is a controller, and may be realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

As shown in FIG. 6, the control unit 230 includes an acquisition unit 231, an identification unit 232, a determination unit 233, and a provision unit 234, and implements or executes information processing functions and actions described below. . Note that the internal configuration of the control unit 230 is not limited to the configuration shown in FIG. 6, and may be another configuration as long as it performs information processing to be described later.

(Acquisition unit 231)
The acquisition unit 231 acquires action schedule information indicating a user's action schedule and attribute information indicating attributes of the user. For example, the acquisition unit 231 acquires, as the action plan information, information input by the user in a guidance service that provides information on a route to a destination. For example, the acquisition unit 231 acquires, from the terminal device 10 of the user, information indicating a departure point, a destination, and a travel date and time as a search query for route search. Note that the acquisition unit 231 may use the current location of the terminal device 10 as the starting point.

In such a case, the acquisition unit 231 uses various route guidance techniques to identify a moving route to be guided to the user. For example, the acquisition unit 231 identifies a movement route for traveling from a designated starting point to a designated destination on a designated date and time.

The acquisition unit 231 also acquires attribute information indicating attributes of the user. For example, the acquisition unit 231 may acquire user attribute information from various external servers (not shown) that manage user attributes. Alternatively, the acquisition unit 231 may acquire attribute information registered in advance by the user via an application.

Further, the acquisition unit 231 may, for example, acquire various search histories, purchase histories, etc. of the user from a log server or the like, and estimate attributes of the user based on the acquired search histories, purchase histories, etc. A user's attribute may be estimated based on the user's location history. It should be noted that such attributes are not limited to various demographic attributes and psychographic attributes, and may be attributes related to events that cause congestion.

In addition, the acquisition unit 231 may refer to the travel route history data storage unit 222, for example, to specify a route that the user uses on a daily basis. That is, the acquisition unit 231 may set the route that the user uses on a daily basis as the route that the user is likely to travel in the future, and may set the information indicating the route as the activity plan information.

(Specifying unit 232)
The identifying unit 232 identifies users who are estimated to be involved in congestion from the activity schedule information. For example, the identification unit 232 refers to the event data storage unit 224 and the like, and determines whether or not congestion will occur on the movement route identified by the acquisition unit 231 . Then, when the identification unit 232 determines that congestion will occur on the movement route, the identification unit 232 determines that the user who is the source of the activity schedule information is the user involved in the congestion.

For example, the identifying unit 232 identifies the date and place of congestion from the information registered in the event data storage unit 224. Identify route information included in search results. Then, the identification unit 232 identifies users involved in congestion based on matching results between the route that the user normally travels, the route included in the route search result, and the time and place where the congestion occurs. . That is, the specifying unit 232 estimates the movement mode of the user from the activity schedule information, and specifies the user who is estimated to be involved in the congestion based on the estimated movement mode.

(Determination unit 233)
Based on the attribute indicated by the attribute information, the determination unit 233 determines a target user to whom information about congestion is provided, among the users identified by the identification unit 232 . For example, the determination unit 233 identifies a user who is estimated to be involved in congestion based on the type of event that causes congestion and the mode of movement. Further, for example, the determining unit 233 determines, as the target user, a user who has an attribute different from the attribute corresponding to the transaction object corresponding to the phenomenon causing the congestion.

For example, the determination unit 233 receives from the identification unit 232 a notification of a user estimated to be involved in congestion. In such a case, the determining unit 233 acquires the notified attribute information of the user from the acquiring unit 231 . Then, the determination unit 233 estimates the relevance between the user and the event that causes the congestion based on the attribute indicated by the attribute information, and targets the user whose relevance is estimated to be lower than a predetermined threshold. User.

For example, the determination unit 233 refers to the event data storage unit 224 and acquires information on an event that causes congestion and is estimated to involve the user. Also, the determination unit 233 calculates a score indicating the relationship between the user and the event based on the user's attribute and the event information. Then, the determining unit 233 selects a user whose calculated score satisfies a predetermined condition as a target user. For example, the determination unit 233 selects, as target users, users whose calculated scores are below a predetermined threshold, that is, users who are involved in congestion and who are less relevant to events that cause congestion. .

(Providing unit 234)
The providing unit 234 provides information on congestion to the target user. For example, the providing unit 234 provides only the route search results to users other than the target user. On the other hand, the providing unit 234 provides the target user with the result of the route search, information indicating that congestion will occur, and information on events that cause congestion.

Note that the providing unit 234 may provide different information based on the degree of association between the attribute of the target user and the event that causes the congestion. For example, the providing unit 234 may provide a user with a high relevance score with a URL of a website related to an event, etc., and a user with a low relevance score with an avoidance URL for avoiding congestion. May provide route information.

In addition, the providing unit 234 provides, for example, information suggesting an action for avoiding congestion to the target therapist. Then, the providing unit 234 may provide the target user with a predetermined profit such as a coupon when the target user follows the action.

[8. Estimation process flow]
Next, a procedure of estimation processing by the estimation device 100 according to the embodiment will be described.

FIG. 7 is a flowchart showing a processing procedure for providing a congestion state, which is executed by the estimation device 100 according to the embodiment.

First, the acquisition unit 231 of the estimation device 100 acquires action schedule information indicating an action plan of a user and attribute information indicating an attribute of the user (step S101).

Next, the identifying unit 232 of the estimating device 100 identifies users who are estimated to be involved in congestion from the activity schedule information (step S102).

Next, the identifying unit 232 of the estimating device 100 identifies the date, time and location of congestion from the information recorded in the event data storage unit 224 (step S103).

Next, the determining unit 233 of the estimating device 100 determines, among the users identified by the identifying unit 232, the target users to whom the congestion-related information is to be provided, based on the attribute indicated by the attribute information (step S104). .

Next, the providing unit 234 of the estimation device 100 provides the target user with information on congestion (step 105).

[9. others〕
Also, among the processes described in the above embodiments, some of the processes described as being automatically performed can also be performed manually. Alternatively, all or part of the processes described as being performed manually can be performed automatically by known methods. In addition, information including processing procedures, specific names, various data and parameters shown in the above documents and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each drawing is not limited to the illustrated information.

Also, each component of each device illustrated is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution and integration of each device is not limited to the one shown in the figure, and all or part of them can be functionally or physically distributed and integrated in arbitrary units according to various loads and usage conditions. Can be integrated and configured.

For example, part or all of the storage unit 220 shown in FIG. 6 may be held in a storage server or the like instead of being held by the estimation device 100 . In this case, the estimation device 100 acquires various information such as travel information by accessing the storage server.

[10. Hardware configuration]
Also, the estimation apparatus 100 according to the embodiments described above is realized by a computer 1000 configured as shown in FIG. 8, for example. FIG. 8 is a diagram illustrating an example of a hardware configuration; A computer 1000 is connected to an output device 1010 and an input device 1020, and an arithmetic device 1030, a primary storage device 1040, a secondary storage device 1050, an output IF (Interface) 1060, an input IF 1070, and a network IF 1080 are connected via a bus 1090. have

The arithmetic device 1030 operates based on programs stored in the primary storage device 1040 and the secondary storage device 1050, programs read from the input device 1020, and the like, and executes various processes. The primary storage device 1040 is a memory device such as a RAM that temporarily stores data used by the arithmetic device 1030 for various calculations. The secondary storage device 1050 is a storage device in which data used for various calculations by the arithmetic device 1030 and various databases are registered, and is implemented by a ROM (Read Only Memory), HDD, flash memory, or the like.

The output IF 1060 is an interface for transmitting information to be output to the output device 1010 that outputs various types of information such as a monitor and a printer. It is realized by a connector conforming to a standard such as HDMI (registered trademark) (High Definition Multimedia Interface). Also, the input IF 1070 is an interface for receiving information from various input devices 1020 such as a mouse, keyboard, scanner, etc., and is realized by, for example, USB.

Note that the input device 1020 includes, for example, optical recording media such as CDs (Compact Discs), DVDs (Digital Versatile Discs), PDs (Phase change rewritable discs), magneto-optical recording media such as MOs (Magneto-Optical discs), and tapes. It may be a device that reads information from a medium, a magnetic recording medium, a semiconductor memory, or the like. Also, the input device 1020 may be an external storage medium such as a USB memory.

Network IF 1080 receives data from other devices via network N and sends the data to arithmetic device 1030, and also transmits data generated by arithmetic device 1030 via network N to other devices.

The arithmetic device 1030 controls the output device 1010 and the input device 1020 via the output IF 1060 and the input IF 1070 . For example, arithmetic device 1030 loads a program from input device 1020 or secondary storage device 1050 onto primary storage device 1040 and executes the loaded program.

For example, when the computer 1000 functions as the estimation device 100 , the arithmetic device 1030 of the computer 1000 implements the functions of the control unit 230 by executing a program loaded on the primary storage device 1040 .

[11. effect〕
As described above, the estimation device 100 according to the embodiment has the acquisition unit 231, the identification unit 232, the determination unit 233, and the provision unit 234.

In the estimation device 100 according to the embodiment, the acquisition unit 231 acquires action schedule information indicating the action schedule of the user and attribute information indicating attributes of the user, and the specifying unit 232 acquires, from the action schedule information, A user who is estimated to be involved in congestion is specified, and the determination unit 233 selects a target user to whom information on congestion is provided among the users specified by the specifying unit based on the attribute indicated by the attribute information. is determined, and the providing unit 234 provides the target user with information about the congestion.

Further, in the estimation device 100 according to the embodiment, the determining unit 233 estimates the relevance between the user and an event that causes congestion based on the attribute indicated by the attribute information, and the relevance is higher than a predetermined threshold. A user estimated to be low may be the target user.

Moreover, in the estimation device 100 according to the embodiment, the acquisition unit 231 may acquire, as the action plan information, information input by the user in a guidance service that provides information on a route to a destination.

In addition, in the estimation device 100 according to the embodiment, the specifying unit 232 estimates the date and place where congestion occurs based on the activity schedule information, and identifies users who are estimated to be at the estimated location at the estimated date and time. may be specified.

In addition, in the estimation device 100 according to the embodiment, the identifying unit 232 estimates the movement mode of the user from the activity schedule information, and identifies the user who is estimated to be involved in the congestion based on the estimated movement mode. You may

Further, in the estimation device 100 according to the embodiment, the determination unit 233 may identify users who are estimated to be involved in congestion based on the type of event that causes the congestion and the mode of movement.

In addition, in the estimation device 100 according to the embodiment, the acquisition unit 231 acquires, as attribute information, attribute information indicating an attribute estimated based on the purchase history of the user, and the determination unit 233 causes congestion. A user having an attribute different from the attribute corresponding to the transaction object corresponding to the event may be the target user.

Moreover, in the estimation device 100 according to the embodiment, the providing unit 234 may provide different information based on the degree of association between the attribute of the target user and the event that causes the congestion.

In addition, in the estimation device 100 according to the embodiment, the providing unit 234 may provide the target user with information about events that cause congestion.

In addition, in the estimation device 100 according to the embodiment, the providing unit 234 provides the target user with information proposing an action to avoid congestion, and when the target user follows the action, A predetermined benefit may be provided to the target user.

Through the processes described above, the estimating device 100 can identify users who are less relevant to the cause of the congestion and provide the identified users with information about the cause.

As described above, some of the embodiments of the present application have been described in detail based on the drawings. It is possible to carry out the invention in other forms with modifications.

In addition, the above-described estimating apparatus 100 may be implemented by a plurality of server computers, and depending on the function, may be implemented by calling an external platform or the like using an API (Application Programming Interface), network computing, or the like. Flexible to change.

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

1 estimation system 10 user terminal device 100 estimation device 210 communication unit 220 storage unit 221 search history data storage unit 222 traveling route history data storage unit 223 transfer history data storage unit 224 event data storage unit 230 control unit 231 acquisition unit 232 identification unit 233 determination unit 234 provision unit

Claims (12)

an acquisition unit that acquires action schedule information indicating a user's action schedule, attribute information indicating attributes of the user, registration information in a predetermined transportation application related to the user, and a predetermined search history ;
Based on the predetermined search history of the user and other users, the registration information of the user in the predetermined transportation application, and the action schedule information of the user, the search history in the predetermined search history An identification unit that identifies a user whose movement mode is estimated to be related to a predetermined congestion mode based on the search situation and who has a low relevance to an event related to the predetermined congestion mode. When,
Among the users specified by the specifying unit, the target users to whom the information on the congestion mode is provided are determined based on the attributes indicated by the attribute information and the predetermined search history of the users. a decision unit to
and a providing unit that provides the information about the congestion state to the target user. The determination unit estimates a relevance between the user and an event related to the congestion mode based on the attribute indicated by the attribute information and the predetermined search history by the user, and determines that the relevance is predetermined. The providing apparatus according to claim 1, wherein a user estimated to be lower than a threshold is set as the target user. 3. The providing device according to claim 1, wherein the acquisition unit acquires, as the action plan information, information input by the user in a guidance service that provides information on a route to a destination. The specifying unit estimates a date and place related to a predetermined congestion mode based on the action schedule information, and specifies a user who is estimated to be at the estimated place on the estimated date and time. The provision device according to any one of claims 1 to 3. The specifying unit estimates a mode of movement of the user from the activity schedule information, and specifies a user who is estimated to be involved in the congestion mode based on the estimated mode of movement. Item 5. The providing device according to any one of items 1 to 4. 6. The method according to claim 5, wherein the determining unit identifies a user who is estimated to be involved in the congestion mode based on the type of event that causes the congestion mode and the movement mode. providing equipment. The acquisition unit acquires, as the attribute information, attribute information indicating an attribute estimated based on the purchase history of the user,
7. The determining unit determines, as the target user, a user who has an attribute different from an attribute corresponding to a transaction object corresponding to an event causing the congestion mode . or a provider according to one of the preceding claims. The providing unit provides different information based on the degree of relevance between the attribute of the target user and an event that causes the congestion mode . 2. The delivery device described in . The providing device according to any one of claims 1 to 8, wherein the providing unit provides the target user with information about an event that causes the congestion mode . The providing unit provides the target user with information proposing an action for avoiding the congestion mode , and when the target user follows the action, a predetermined A provider according to any one of claims 1 to 8, characterized in that it provides the benefit of: A computer-implemented method of providing, comprising:
an acquisition step of acquiring activity schedule information indicating an activity schedule of a user, attribute information indicating an attribute of the user, registration information in a predetermined transportation application related to the user, and a predetermined search history ;
Based on the predetermined search history of the user and other users, the registration information of the user in the predetermined transportation application, and the action schedule information of the user, the search history in the predetermined search history An identifying step of identifying a user whose movement mode is estimated to be related to a predetermined congestion mode based on the search situation and who has a low relevance to an event related to the predetermined congestion mode. When,
Among the users specified in the specifying step, the target users to whom the information on the congestion mode is to be provided are determined based on the attributes indicated by the attribute information and the predetermined search history of the users. a decision process to
and a providing step of providing information on the congestion mode to the target user. Acquisition means for acquiring activity schedule information indicating an activity schedule of a user, attribute information indicating an attribute of the user, registration information in a predetermined transportation application related to the user, and a predetermined search history ;
Based on the predetermined search history of the user and other users, the registration information of the user in the predetermined transportation application, and the action schedule information of the user, the search history in the predetermined search history Identifying means for identifying a user whose movement mode is estimated to be related to a predetermined congestion mode based on the search situation and who has a low relevance to the event related to the predetermined congestion mode When,
Among the users specified by the specifying means, the target users to whom the information on the congestion mode is provided are determined based on the attributes indicated by the attribute information and the predetermined search history of the users. a determining means to
A providing program for causing a computer to execute providing means for providing information about the congestion mode to the target user.

Images