JP6814115B2 - Specific device, specific method and specific program - Google Patents

Description

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

Conventionally, a technique for predicting congestion at a specific point or section has been known. As an example of such a technique, a technique for calculating an index of the degree of congestion based on the number of pre-search logs at the congestion prediction date and time at the congestion prediction point is known. Further, there is known a technique of predicting the degree of congestion related to a user's future behavior from a future action schedule and a past history and providing the prediction result to the user.

JP-A-2017-33440

However, the above-mentioned prior art has not identified the cause of congestion. For this reason, in the prior art, although it is possible to notify the user in advance that congestion will occur, it is not possible to notify in advance the cause of congestion such as why congestion occurs.

The present application has been made in view of the above, and an object of the present application is to identify the cause of congestion.

The specific device according to the present application has a prediction unit that predicts congestion at a predetermined location based on action schedule information indicating the future behavior of the user, and if the prediction unit predicts that congestion will occur, the predetermined device It is characterized by having a specific part that identifies an event that causes the congestion based on the user's information associated with the action schedule information related to the location of.

According to one aspect of the embodiment, the cause of congestion can be identified.

FIG. 1 is a diagram showing an example of a specific process executed by the information providing device according to the embodiment. FIG. 2 is a diagram showing a configuration example of the information providing device according to the embodiment. FIG. 3 is a diagram showing an example of information registered in the user database according to the embodiment. FIG. 4 is a diagram showing an example of information registered in the route search log database according to the embodiment. FIG. 5 is a diagram showing an example of information registered in the event database according to the embodiment. FIG. 6 is a diagram illustrating an example of processing in which the information providing device according to the embodiment predicts abnormal congestion. FIG. 7 is a diagram illustrating an example of a process in which the information providing device according to the embodiment identifies an event. FIG. 8 is a flowchart showing an example of a flow of specific processing executed by the information providing device according to the embodiment. FIG. 9 is a diagram showing an example of a hardware configuration.

Hereinafter, a specific device, a specific method, and a mode for carrying out the specific program according to the present application (hereinafter, referred to as “the embodiment”) will be described in detail with reference to the drawings. It should be noted that this embodiment does not limit the specific device, the specific method, and the specific program according to the present application. Further, in each of the following embodiments, the same parts are designated by the same reference numerals, and duplicate description is omitted.

[Embodiment]
[1-1. An example of an information providing device]
First, an example of a specific process executed by an information providing device, which is an example of the specific device, will be described with reference to FIG. FIG. 1 is a diagram showing an example of a specific process executed by the information providing device according to the embodiment. In FIG. 1, a terminal device 101 used by each user U1 to U3 (hereinafter, may be collectively referred to as “user U”) via a predetermined network N (see, for example, FIG. 2) such as the Internet. 103 (hereinafter, may be collectively referred to as "terminal device 100"), the search server 200, and the information providing device 10 can communicate with each other.

The terminal device 100 is a smart device such as a smartphone or tablet, and is a mobile terminal device capable of communicating with an arbitrary server device via a wireless communication network such as 3G (3rd Generation) or LTE (Long Term Evolution). is there. The terminal device 100 may be an information processing device such as a desktop PC (Personal Computer) or a notebook PC as well as a smart device.

Further, the terminal device 100 has a function of specifying the current position by using a positioning system such as GPS (Global Positioning System) and outputting position information indicating the specified position. Further, the terminal device 100 has a function of communicating with various wearable devices worn by the user U and acquiring various biological information such as the pulse, blood pressure, exercise amount, and brain wave of the user U. In addition, the terminal device 100 executes various processes that can be executed by a general mobile device, such as a search using the Internet, a route search, a purchase in an electronic shopping street, a use of an online auction, and a reproduction of music contents and video contents. It shall be possible.

The search server 200 is an information processing device that provides a service related to route search to the user U, and is realized by, for example, a server device or a cloud system. For example, the search server 200 accepts the departure point and the arrival point from the terminal device 100 as a route search search query. In such a case, the search server 200 searches for the movement route from the departure place to the arrival place, the means of transportation (train, bus, taxi, walking, etc.), the required time, the estimated arrival time, and the like, and obtains the search result from the terminal device 100. To provide.

Here, the search server 200 can perform a route search from an arbitrary departure point to an arbitrary arrival point. For example, the search server 200 adopts not only important points of public transportation such as bus stops and stations, but also landmarks and addresses of hotels and various facilities, places selected by map contents, etc. as departure and arrival places. be able to. Further, the search server 200 may adopt, for example, the current position acquired by the terminal device 100 using GPS as the starting point. In the following description, as an example of the route search executed by the search server 200, an example of accepting the designation of a station as a departure place or an arrival place will be described.

In addition, the search server 200 can accept the designation of the departure date and time and the arrival date and time. For example, when the search server 200 accepts the designation of the departure place and the arrival place together with the departure date and time, the movement route when departing from the designated departure place to the arrival place after the designated departure date and time. Or search for the estimated arrival time. In addition, when the search server 200 receives the designation of the departure place and the arrival place together with the arrival date and time, the search server 200 sets the movement route and the departure date and time so as to arrive at the designated arrival place before the designated arrival date and time. Search for.

In addition to the above-described processing, the search server 200 can realize an arbitrary route search. Further, it is assumed that the search server 200 holds the history of the route search by the user U as a log. For example, the search server 200 holds a log in which a search query such as a departure place, an arrival place, a departure date / time, an arrival date / time, etc. received from the terminal device 100 is associated with a route search result for the search query.

The information providing device 10 is an information processing device that executes a specific process described later, and is realized by, for example, a server device or a cloud system. For example, the information providing device 10 acquires a log of a search query held by the search server 200, and uses the acquired log to execute a specific process according to the invention.

[1-2. About the outline of specific processing]
Here, it can be said that the route search log in which a future date and time is set as a departure date and time or an arrival date and time to some extent is information indicating the future action of the user U, that is, action schedule information. By analyzing such action schedule information, it is considered that congestion at each position and movement route can be predicted. For example, if the number of logs that arrive at a predetermined station at a predetermined station and the number of logs including search results arriving at the predetermined station exceeds a predetermined threshold value, congestion occurs at that station during that time zone. It is thought that. In this way, by analyzing the log held by the search server 200 for each destination, it is possible to predict congestion.

Such congestion prediction may be performed for each facility such as a station as a destination, or may be performed for each predetermined area (for example, within a radius of 1 km from a certain position). In addition, the travel route that each user will use may be estimated from the log included in the route search, and the travel route that will cause congestion may be estimated based on the duplication of the estimated travel route. As described above, in the congestion prediction technique using the route search log, the congestion that may occur in the future can be predicted in any manner.

However, when the congestion is estimated using the route search log, it is difficult to identify the cause of the congestion, although the occurrence of the congestion can be predicted. The cause of such congestion is likely to be useful information for the user U who is presumed to visit or pass through the place where congestion occurs at the same time as the date and time when congestion occurs.

Therefore, the information providing device 10 executes the following specific processing. First, the information providing device 10 predicts congestion at a predetermined place based on the action schedule information indicating the future behavior of the user. Then, when it is predicted that congestion will occur, the information providing device 10 identifies an event that causes congestion based on the user's information associated with the action schedule information associated with the predetermined location. For example, when it is predicted that congestion will occur, the information providing device 10 identifies a user who causes congestion (hereinafter, "cause user"), and based on the information about the identified cause user, congestion occurs. Identify the event that causes the.

Here, any event can be adopted as the event that causes congestion. For example, the information providing device 10 may specify the holding of various events, the opening of a new store, or the like as an event. For example, it is extended to the effect that a character appearance event has occurred in a game application or the like using location information. It is possible to specify as an event that a location-linked event is occurring in an application using reality, free distribution of products, information on the appearance of celebrities, and the like. That is, the information providing device 10 can identify an arbitrary event regardless of whether it is virtual or real, as long as it identifies an event that causes congestion based on the information of the cause user.

For example, the information providing device 10 predicts congestion at a predetermined place based on a search query for searching a movement route from a predetermined departure place to a predetermined arrival place at a predetermined date and time. More specifically, the information providing device 10 predicts congestion at a predetermined destination included in the search query based on the search query.

[1-3. About an example of specific processing]
Hereinafter, an example of the specific processing executed by the information providing device 10 will be described with reference to FIG. For example, the search server 200 accepts the designation of the departure place and the arrival place as a search query for the route search (step S1). More specifically, the search server 200 accepts a search query including a user ID (Identifier) that identifies a station as a departure place, a station as a destination, and a user U. Here, the search server 200 may accept a search query including a departure date and time or an arrival date and time. In such a case, the search server 200 performs a route search based on the content of the search query and provides the search result to the terminal device 100 (step S2). Further, the search server 200 holds a route search log in which the departure place and the arrival place received as the search query are associated with the arrival date and time provided as the search result, that is, the date and time of arrival at the arrival place in the search result. To do.

On the other hand, the information providing device 10 acquires the route search log as action schedule information at a predetermined timing (for example, at regular intervals) (step S3). Then, the information providing device 10 executes the following specific processing for each station that is the destination. First, the information providing device 10 obtains an average pattern of a search query with a predetermined station as an arrival point, and predicts the occurrence of abnormal congestion based on the deviation from the average pattern (step S4).

For example, the information providing device 10 extracts all the logs whose arrival place is "station D" from the route search logs. Then, the information providing device 10 counts the number of logs including the arrival date and time in the predetermined period (for example, one hour). That is, the information providing device 10 coefficients the number of users who visit the station D for each predetermined period, assuming that each user U acts according to the result of the route search.

Then, the information providing device 10 calculates an average pattern of the number of users visiting the station D based on the counting result. For example, the information providing device 10 calculates the average value of the number of users visiting the station D for each time zone on weekdays, and sets the calculated average value pattern as the averaging pattern AP. The information providing device 10 also calculates the averaging pattern AP for each time zone on Saturday and for each time zone on Sunday.

Then, the information providing device 10 searches for a date and time when the number of users deviates from the averaging pattern. For example, the information providing device 10 calculates the number of users who use the station D for each time zone for each day. Then, for example, the information providing device 10 predicts that congestion will occur when the number of users who use the station D in a certain time zone is larger than the number of users indicated by the averaging pattern by a predetermined threshold value or more.

That is, the information providing device 10 is based on the difference between the action schedule information indicating the user's behavior at a predetermined place on a predetermined date and time and the action schedule information indicating the average user's behavior at the predetermined place. Predict congestion at a given date and time at a given place. More specifically, the information providing device 10 includes an average number of action schedule information indicating the user's behavior at a predetermined place and an action schedule information indicating the user's behavior at a predetermined place on a predetermined date and time. Based on the comparison result with the number, the congestion of a predetermined place at a predetermined date and time is predicted.

To give a more specific example, in the example shown in FIG. 1, the information providing device 10 has an arrival place of "station D" and an arrival date and time of "December 12, 2017" from the route search log. By counting the number of logs included in the time zone, the pattern of the number of users of "December 12, 2017" at "Station D" (hereinafter referred to as "prediction pattern PP") is predicted. Then, the information providing device 10 compares the averaging pattern AP with the prediction pattern PP, and searches for deviations from the averaging pattern AP included in the prediction pattern PP. That is, the information providing device 10 estimates the average number of users of station D and the number of users who use station D at each date and time based on the route search log, and compares the estimated numbers. This predicts sudden congestion, that is, abnormal congestion (hereinafter referred to as "abnormal congestion").

For example, the information providing device 10 includes the number of logs whose arrival date and time is "December 12, 2017" from 9:00 am to 10:00 am, and the number of logs included in the averaging pattern from 9:00 am to 10:00 am. Compare with the number of logs. That is, the information providing device 10 has an average number of action schedule information indicating the behavior of the user in a predetermined time zone at a predetermined place and the behavior of the user in a predetermined time zone at a predetermined date and time in the predetermined place. Based on the result of comparison with the number of action schedule information indicating, the congestion of a predetermined place at a predetermined date and time is predicted.

For example, the information providing device 10 includes the number of users U who visit "Station D" from 9:00 am to 10:00 am on "December 12, 2017" and "Station D" from 9:00 am to 10 am. Compare with the average number of users U used by the time. Then, in the information providing device 10, the number of users U who visit "Station D" from 9:00 am to 10:00 am on "December 12, 2017" is from 9:00 am to 10 am on "Station D". If the number of users U is greater than the average number of users U used by the time, abnormal congestion will occur between 9:00 am and 10:00 am on "December 12, 2017". I predict that. That is, the information providing device 10 determines that the number of action schedule information indicating the user's behavior at a predetermined place on a predetermined date and time is larger than the average number of action schedule information indicating the user's behavior at the predetermined place. If it is more than the threshold value of, it is predicted that congestion will occur at a predetermined place at a predetermined date and time.

Further, when the information providing device 10 predicts that abnormal congestion will occur, the information providing device 10 identifies a user who causes the abnormal congestion (hereinafter, referred to as “cause user”) (step S5). For example, when the information providing device 10 predicts that abnormal congestion will occur at a predetermined place at a predetermined date and time, the information providing device 10 identifies a user who is predicted to visit the place at that date and time as a causative user. More specifically, the information providing device 10 sets the arrival place at the place where abnormal congestion is predicted from the route search log, and the arrival date and time is set at the time zone where abnormal congestion is predicted to occur. Extract the included logs. Then, the information providing device 10 identifies the user U who has input the search query corresponding to the extracted log as the cause user. That is, the information providing device 10 uses the user U who is predicted to visit the place where the abnormal congestion occurs when the abnormal congestion occurs as the causative user.

For example, in the example shown in FIG. 1, the information providing device 10 predicts that abnormal congestion will occur at station D between 9:00 am and 10:00 am on December 12, 2017. In such a case, the information providing device 10 includes a route search in which the arrival date and time is included between 9:00 am and 10:00 am on "December 12, 2017" and the arrival destination is "station D". The user ID of the user U who entered the search query that caused the above is specified. Then, the information providing device 10 uses the users U1, U4, and U6 indicated by the specified user ID as the cause user.

Here, it is predicted that the users U1, U4, and U6, which are the causative users, have some relation with the event causing the congestion. Therefore, the information providing device 10 identifies an event that causes congestion based on the information of the cause user. More specifically, the information providing device 10 identifies the difference between the information of the cause user and the information of another general user (step S6).

More specifically, the information providing device 10 acquires various histories as information on the cause user. For example, the information providing device 10 acquires the history of the search by the cause user using the Internet, the history of the route search, and the like as the search history. Here, the search using the Internet is a concept that includes not only a search of various web pages but also a search within various service domains such as a shopping search for searching a transaction target in an electronic shopping street and a bulletin board search. is there. Further, the information providing device 10 acquires a position history which is a history of the position information acquired by the terminal device 100 used by the cause user. In addition, the information providing device 10 includes the contents and price of the transaction target purchased by the cause user in the electronic shopping district, the contents and price of the transaction target sold or purchased at the auction, and the transaction target purchased using a credit card or electronic money. Acquire various purchase histories such as the contents and prices of.

Further, the information providing device 10 randomly selects a predetermined number of users as general users from the users U other than the cause user among the users U. In addition, the information providing device 10 acquires various histories of general users as general user information. Then, the information providing device 10 identifies the difference between the information of the cause user and the information of the general user. That is, the information providing device 10 identifies information unique to the cause user.

It should be noted that various difference extraction techniques for extracting differences in user attributes and various histories can be adopted for such differences in information. For example, the information providing device 10 may purely extract the difference in information, the difference between the tendency of the information of the cause user and the tendency of the information of the general user, or the cause user as seen from the general user. The tendency of the information of the above may be extracted as a difference. Extraction of such differences is realized by various models such as SVM (Support Vector Machine) and DNN (Deep Neural Network) that have learned the characteristics of information useful for identifying the causative event among user information. You may.

Then, the information providing device 10 identifies the event related to the specified difference as the cause of the abnormal congestion (step S7). For example, the information providing device 10 identifies an event (hereinafter, referred to as “scheduled event”) scheduled to occur at a date and time when abnormal congestion is predicted to occur in the vicinity of a place where abnormal congestion is predicted to occur. To do. Then, the information providing device 10 identifies an event whose relevance to the difference specified in step S6 exceeds a predetermined threshold value among the specified scheduled events.

For example, in the information providing device 10, when the cause user purchases more products related to the idol "X" than the general user, an event in which the idol "X" appears among the scheduled events, etc. , An event that is highly related to the idol "X" may be specified. In addition, when the cause user uses the luxury hotel more frequently than the general user, the information providing device 10 has a relationship with the luxury hotel such as a new opening event of the luxury hotel among the scheduled events. High events may be identified.

Further, for example, in the information providing device 10, if the cause user frequently inputs a search query related to the predetermined soccer team "Y" as compared with the general user, the soccer team "Y" participates. Among the scheduled events such as a match, an event highly related to the soccer team "Y" may be selected. In addition, the information providing device 10 is related to the restaurant among the scheduled events such as the rotation of a new restaurant when the cause user frequently posts a character string or a photograph related to the restaurant as compared with the general user. Highly probable events may be selected. Further, the information providing device 10 identifies the amount of exercise of each user from the biological information, and when the cause user has a large amount of exercise as compared with the general user, a scheduled event such as opening a new gym or a marathon event. Of these, an event that is highly relevant to a user with a large amount of exercise may be selected.

That is, when the information providing device 10 predicts that abnormal congestion will occur, among the events that occur on the date and time when abnormal congestion occurs in the vicinity of the place where abnormal congestion occurs, the event that is highly relevant to the causative user. Is identified as the cause of abnormal congestion. Then, the information providing device 10 provides the user U with information indicating the specified event as information indicating the cause of congestion (step S8). For example, the information providing device 10 includes the arrival station as "station D" and the arrival date and time as "December 12, 2017" between 9:00 am and 10:00 am in the route search by the user U3. In this case, in order to call attention to the user U3, information indicating that abnormal congestion will occur and the cause of the abnormal congestion will be provided.

In this way, the information providing device 10 predicts congestion at a predetermined place based on a search query for searching a movement route from a predetermined departure place to a predetermined arrival place at a predetermined date and time as action schedule information. To do. For example, the information providing device 10 predicts congestion at a predetermined destination included in the search query, and identifies an event that causes congestion at the predicted predetermined destination.

Further, the information providing device 10 predicts abnormal congestion rather than simple congestion based on the number of users. For example, the information providing device 10 compares the average number of action schedule information indicating the user's behavior at a predetermined place with the number of action schedule information indicating the user's behavior at a predetermined place on a predetermined date and time. Based on the result, the congestion of a predetermined place at a predetermined date and time is predicted. To give a more specific example, in the information providing device 10, the number of action schedule information indicating the user's behavior at a predetermined place on a predetermined date and time is the action schedule information indicating the user's behavior at the predetermined place. If it is more than a predetermined threshold value than the average number, it is predicted that congestion will occur at a predetermined place at a predetermined date and time.

As a result of such processing, when abnormal congestion occurs, the information providing device 10 identifies the cause user who causes the abnormal congestion, and based on the information of the identified cause user, causes the abnormal congestion. Can be identified.

[1-4. About action schedule information]
In the above-mentioned example, the information providing device 10 uses the search query of the route search as the action schedule information. However, the embodiments are not limited to this. That is, the information providing device 10 can adopt arbitrary information as action schedule information as long as it is information suggesting the location of the user at a certain date and time in the future.

For example, the information providing device 10 specifies a place visited by the user U and a date and time when the user U visits the place from the contents of an e-mail including various reservation contents such as a hotel reservation and a train reservation made by the user U. .. In such a case, the information providing device 10 predicts congestion and identifies whether or not the user is the cause user, assuming that the user U visits the specified place from the e-mail at the specified date and time. Good.

Further, the information providing device 10 predicts the future behavior of the user U based on the posted information posted on the Internet by the user U such as SNS (Social Networking Service), and the action schedule information indicating the predicted behavior. You may predict the congestion in a predetermined place based on. For example, the information providing device 10 specifies a place where the user U visits and a date and time when the user U visits from the posted information, and predicts congestion or assumes that the user U visits the specified place at the specified date and time. , You may specify whether or not you are the cause user. Further, when the terminal device 100 has the function of a contactless IC (Integrated Circuit) card system, the information providing device 10 acquires the ticket information held in the terminal device 100 and uses the acquired ticket information. The place where the person U visits and the date and time when the person U visits the place may be specified.

Here, the information providing device 10 may adopt the information input by the user U as the action schedule information, and is the information input by another user and indicates the future action of the user U. May be adopted as action schedule information. For example, the information providing device 10 predicts congestion at a predetermined place based on the action schedule information input by the user U, and is based on the information of the user who has input the action schedule information related to the predetermined place. , The event that causes congestion may be identified.

[1-5. About the location where congestion occurs]
Here, the above-mentioned information providing device 10 predicts abnormal congestion that occurs at the station that is the destination. However, the embodiments are not limited to this. For example, the information providing device 10 generates an average pattern AP and a prediction pattern PP for each of a plurality of stations within a predetermined range, and based on the comparison result, abnormal congestion occurs at the plurality of stations within the predetermined range. May be predicted whether or not Further, when the information providing device 10 predicts that abnormal congestion will occur at a plurality of stations within a predetermined range, the user U who visits the plurality of stations at the time of congestion is set as the cause user, and the cause of the abnormal congestion is determined. May be estimated.

Further, for example, when abnormal congestion is predicted at a certain station, the information providing device 10 identifies a station in the surrounding stations where abnormal congestion occurs at the same time. Then, the information providing device 10 may estimate an event that causes abnormal congestion, with the station where abnormal congestion is predicted and the user U who visits any of the specified stations as the causative user.

Further, the information providing device 10 may predict abnormal congestion on a line-by-line basis, and when it is predicted that abnormal congestion will occur on a certain line, the user U who uses that line at the time of congestion is set as the cause user. The causative event may be identified from the scheduled events that occur around each station on the line. Further, the information providing device 10 may predict abnormal congestion and identify an event that causes congestion in a part of the route.

Further, the information providing device 10 predicts abnormal congestion at a place set in an arbitrary unit such as an arbitrary position, place, range, landmark, etc., and sets the user U who visits the place at the time of congestion as the cause user. The event that causes the abnormal congestion may be estimated.

[1-6. About abnormal congestion]
In the above example, the information providing device 10 predicts abnormal congestion by comparing the averaging pattern AP and the prediction pattern PP. However, the embodiments are not limited to this. For example, the information providing device 10 predicts that if the number of users visiting "station D" at a certain date and time exceeds a predetermined threshold value, congestion will occur at "station D" at that date and time. In such a case, the information providing device 10 performs a route search that includes the user who will visit "station D" at the time of congestion, that is, the arrival date and time at the time of congestion, and the arrival place is "station D". The event that causes congestion may be identified based on the information of the cause user, with the user U as the cause user.

[1-7. About user information]
In addition to the above-mentioned information, the information providing device 10 can adopt arbitrary information as user information. For example, the information providing device 10 may use the usage history of the function of the contactless IC card system and the function of the electronic money as the information of the user U. Further, the information providing device 10 may acquire various attributes such as a demographic attribute and a psychographic attribute (including hobbies and tastes) of the user U, and may acquire a history of biometric information of the user U. .. Further, the information providing device 10 may acquire contents such as a character string and a photograph posted by the user U on the SNS. Further, the information providing device 10 may acquire the information registered by the user U as the attribute of the user U, or may acquire the attribute predicted from the various histories described above.

Further, the information providing device 10 may identify an event that causes congestion based on the information acquired within a predetermined period from the date and time when congestion is predicted to occur among the information of the cause user. .. For example, if the information providing device 10 is predicted to be congested on December 12, 2017, the cause of the congestion is based on the cause user information acquired within one year from December 12, 2017. You may specify the event that becomes. In this way, the information providing device 10 causes congestion based on the information of the user U that is presumed to be highly related to the congestion and the event that causes the congestion. Since the event is specified, the identification accuracy can be further improved.

Further, the information providing device 10 may extract information that deviates from the average pattern of information from the information of the cause user, and identify an event that causes congestion based on the extracted information. For example, the information providing device 10 specifies an average pattern of position history and purchase history among the information of the cause user, and identifies position history and purchase history that deviate from the specified pattern. The location history and purchase history identified in this way can be said to be information indicating extraordinary behavior among the behaviors of the causative user. On the other hand, abnormal congestion can be said to be extraordinary congestion. Therefore, the information providing device 10 may further improve the identification accuracy by identifying the event that causes the abnormal congestion based on the information indicating the extraordinary behavior of the identified cause user.

The information providing device 10 may use all the information of the user U described above, or may use only a part of the information. Further, the information providing device 10 may specify an event that causes congestion by combining the above-mentioned information in any embodiment.

[1-8. About identification of the cause]
In the above example, the information providing device 10 has identified an event that causes congestion based on the difference between the information of the causative user and the information of another randomly selected user. In this way, the information providing device 10 identifies the event that causes congestion based on the difference between the information of the cause user and the information of the general user, so that the cause is based on the information unique to the cause user. As a result of identifying the event that causes congestion, the accuracy of identifying the event that causes congestion can be improved.

However, the embodiments are not limited to this. For example, the information providing device 10 searches for an event that is highly related to the information of the cause user from the events scheduled to occur at a predetermined date and time in the vicinity of a predetermined place, and sets the searched event as the cause of congestion. If so, the event causing congestion may be identified in any manner.

For example, the information providing device 10 may identify an event that causes congestion based on the commonality of information of the causative user. For example, if the information providing device 10 includes "fishing enthusiast" in the attribute of the majority of the cause users among the cause users, the information providing device 10 may select an event highly related to fishing from the scheduled events. Good.

Further, the information providing device 10 may select a general user to be compared with the cause user in any manner. For example, the information providing device 10 may select a user who visits a place where abnormal congestion is predicted to occur on a daily basis as a general user. When such a process is executed, the information providing device 10 identifies an event that causes abnormal congestion based on the information of a user who visits a place where abnormal congestion is predicted to occur on an unusual basis. , The specific accuracy can be improved.

Further, when it is predicted that abnormal congestion will occur in a certain place, the information providing device 10 is a user U who visits the place other than the user U who uses the place on a daily basis. May be specified as the cause user. That is, when abnormal congestion occurs in a certain place, the information providing device 10 makes the event causing the abnormal congestion more accurate by setting the user U who visits the place on an extraordinary basis as the causative user. Can be well identified.

[1-9. Regarding the provision of information]
Here, the information providing device 10 provides the user U with information indicating the identified cause. For example, the information providing device 10 provided information on the specified event to the user U3 who searched the route to the place where the abnormal congestion was predicted to occur at the date and time when the abnormal congestion was predicted to occur. .. Here, the information providing device 10 may provide information on such an event together with the search result, or may notify by e-mail or the like.

In addition, the information providing device 10 may widely provide the place where abnormal congestion occurs, the date and time of occurrence, and the event that causes the abnormal congestion to the general public via various portal sites. Further, the information providing device 10 is used for users who live in the vicinity of the place where the abnormal congestion occurs, users who have a workplace near the place where the abnormal congestion occurs, and other users who are related to the place where the abnormal congestion occurs. On the other hand, information indicating the identified event may be provided. Further, when the abnormal congestion is predicted, the information providing device 10 may deliver the place where the abnormal congestion occurs, the date and time, and the event causing the abnormal congestion in a news format.

In addition, the information providing device 10 may provide information related to the specified event to the user associated with the place where the abnormal congestion is predicted to occur. For example, the information providing device 10 uses the position information of the user U to identify a user who regularly uses a station predicted to cause abnormal congestion, and identifies an event specified for the specified user. Information may be provided.

Further, for example, the information providing device 10 is related to the specified event for the user U3 who has performed a route search to the place where the abnormal congestion is predicted to occur at the date and time when the abnormal congestion is predicted to occur. Product recommendation information may be provided. To give a more specific example, when the specified event is the live of the idol "X", the information providing device 10 may provide the user U3 with the goods of the idol "X", the live information, and the like. Good. That is, the information providing device 10 may provide the user U who will be related to the specified event with the information selected based on the specified event.

In addition, the information providing device 10 may provide the user U related to the specified event with information indicating the specified event or information related to the specified event. For example, when the specified event is a live performance of the idol "X", the information providing device 10 identifies a user U having a relationship with the idol "X", such as a fan of the idol "X". Then, the information providing device 10 indicates that the specified user U has a live performance of the idol "X", goods and live information of the idol "X", and abnormal congestion caused by the live performance of the idol "X". Information such as location and time of day may be provided.

That is, if the information providing device 10 provides the content related to the specified event to the user U related to the specified event, the information providing device 10 provides the content of arbitrary content to the arbitrary user U. You may.

[2. Configuration of information providing device]
Hereinafter, an example of the functional configuration of the information providing device 10 that realizes the above-mentioned learning process will be described. FIG. 2 is a diagram showing a configuration example of the information providing device according to the embodiment. As shown in FIG. 2, the information providing device 10 includes a communication unit 20, a storage unit 30, and a control unit 40.

The communication unit 20 is realized by, for example, a NIC (Network Interface Card) or the like. Then, the communication unit 20 is connected to the network N by wire or wirelessly, and transmits / receives information to / from the terminal device 100 and the search server 200.

The storage unit 30 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory (Flash Memory), or a storage device such as a hard disk or an optical disk. In addition, the storage unit 30 stores the user database 31, the route search log database 32, and the event database 33 (hereinafter, may be referred to as “each database 31 to 33”).

Hereinafter, an example of information registered in each database 31 to 33 will be described with reference to FIGS. 3 to 5. First, an example of information registered in the user database 31 will be described with reference to FIG. FIG. 3 is a diagram showing an example of information registered in the user database according to the embodiment. As shown in FIG. 3, the user database 31 has items such as "user ID", "attribute information", "search history", "purchase history", "location history", and "biological information log". Information is registered. In addition to the information shown in FIG. 3, arbitrary information regarding the user U (for example, history of posted information) may be registered in the user database 31.

Here, the "user ID" is an identifier for identifying the user U. Further, the "attribute information" is information indicating various attributes such as the name, gender, age, hobbies and tastes of the user U, and the place of residence. Further, the "search history" is a history of search queries input by the user U, and may include not only the history of search queries for searches using the Internet but also the history of search queries for route searches. .. The "purchase history" is a history of purchases made by the user U through an electronic shopping street or an auction. Further, the "position history" is a history of position information acquired by the terminal device 100 used by the user U. The "biological information log" is a history of biometric information of the user U.

For example, in the example shown in FIG. 3, in the user database 31, the user ID "U1", the attribute information "attribute information # U1", the search history "search history # U1", the purchase history "purchase history # U1", The position history "position history # U1" and the biometric information log "biological information # U1" are registered in association with each other. In such information, the attribute information of the user U1 indicated by the user ID "U1" is "attribute information # U1", the search history is "search history # U1", and the purchase history is "purchase history # U1". , The position history is "position history # U1", and the history of biometric information is "biological information # U1".

In the example shown in FIG. 3, conceptual values such as "attribute information # U1" are described, but in reality, the user database 31 contains the attributes of the user U, the history of search queries, and purchases. Character strings, numerical values, etc. indicating history, position history, biometric information history, etc. will be registered.

Next, an example of the information registered in the route search log database 32 will be described with reference to FIG. FIG. 4 is a diagram showing an example of information registered in the route search log database according to the embodiment. As shown in FIG. 4, the route search log database 32 contains the "log ID", "departure date and time", "departure place", "arrival date and time", "arrival place", "user ID", and "search date and time". Information having an item such as "" is registered. In addition to the information shown in FIG. 4, various information related to the route search may be registered in the route search log database 32.

Here, the "log ID" is an identifier that identifies the route search log. The "departure date and time" is the date and time set as the departure date and time in the route search result. The "departure place" is a departure place designated by the user U in the route search. The "arrival date and time" is the date and time set as the arrival date and time in the route search result. Further, the "arrival place" is the arrival place designated by the user U in the route search. The "user ID" is an identifier of the user U who has performed a route search. The "search date and time" is information indicating the date and time when the route search was performed.

For example, in the example shown in FIG. 4, the route search log database 32 contains the log ID "Q1", the departure date and time "2017/9/12/8: 00", the departure place "station E", and the arrival date and time "2017/9". Information such as "/ 12/9: 00", the arrival place "Station D", the user ID "U1", and the search date and time "2017/8/31/10: 00" is registered. Such information is the route from the departure place "station E" to the arrival place "station D" by the user U1 indicated by the user ID "U1" at the search date and time "2017/8/31/10: 00". The search is performed, and it is shown that the departure date and time in the search result of the route search is "2017/9/12/8: 00" and the arrival date and time is "2017/9/12/9: 00".

Next, an example of the information registered in the event database 33 will be described with reference to FIG. FIG. 5 is a diagram showing an example of information registered in the event database according to the embodiment. As shown in FIG. 5, information having items such as "event ID", "venue place", "neighboring station", "holding date and time", and "correspondence information" is registered in the event database 33. In addition to the information shown in FIG. 5, various information related to the event may be registered in the event database 33.

Here, the "event ID" is an identifier that identifies an event. The "venue place" is information indicating a place where an event occurs. Further, the "neighborhood station" is information indicating a station located in the vicinity of the place where the event occurs. The "date and time of the event" is information indicating the date and time when the event is performed. Further, the "correspondence information" is information indicating user information that is presumed to be related to the event, and is, for example, attribute information, search history, purchase history, etc. of the user U that is highly related to the event. It is information showing a tendency such as a position history and a biological information log.

For example, in the example shown in FIG. 5, in the event database 33, the event ID “F1”, the venue “place #A”, the neighboring stations “station A, station C”, the date and time “2017/12/11/9:” "00" and the correspondence information "correspondence information # 1" are registered in association with each other. Such information indicates that the event indicated by the event ID "F1" is performed at the place indicated by the venue "place #A" at the date and time indicated by the date and time "2017/12/11/9: 00". In addition, such information indicates that the neighboring stations "Station A, Station C" are located in the vicinity of the venue "Place # A". In addition, such information indicates that the event indicated by the event ID "F1" is an event related to the user U corresponding to the information indicated by the corresponding information "correspondence information # 1".

In the example shown in FIG. 5, conceptual values such as "location # A" and "correspondence information # 1" are described in the event database 33, but in reality, a character string indicating the venue of the event is described. In addition, numerical values, character strings, conditions, etc. indicating the tendency of the information of the user U are registered.

Returning to FIG. 2, the explanation will be continued. The control unit 40 is a controller, and for example, various programs stored in a storage device inside the information providing device 10 by a processor such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit) are stored in a RAM or the like. Is realized by executing as a work area. Further, the control unit 40 is a controller, and may be realized by, for example, an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

As shown in FIG. 2, the control unit 40 includes an acquisition unit 41, a prediction unit 42, a specific unit 43, and a providing unit 44. The acquisition unit 41 acquires action schedule information indicating the future behavior of the user. For example, the acquisition unit 41 acquires the route search log including the route search search query input by the user U from the search server 200 as the action schedule information. Then, the acquisition unit 41 registers the acquired route search log in the route search log database 32.

The prediction unit 42 predicts congestion at a predetermined location based on the action schedule information. For example, the prediction unit 42 predicts congestion at a predetermined location based on the action schedule information input by the user U. Further, for example, the prediction unit 42 predicts congestion at a predetermined place based on a search query for searching a movement route from a predetermined departure place to a predetermined arrival place at a predetermined date and time as action schedule information. .. More specifically, the prediction unit 42 predicts congestion at a predetermined destination included in the search query based on the search query.

For example, the prediction unit 42 determines the action schedule information indicating the behavior of the user at a predetermined date and time at a predetermined location and the action schedule information indicating the average user behavior at the predetermined location. Predict congestion at a given date and time at the location of. To give a more specific example, the prediction unit 42 has an average number of action schedule information indicating the user's behavior at a predetermined place and an action schedule indicating the user's behavior at a predetermined place on a predetermined date and time. Based on the comparison result with the number of information, the congestion of the predetermined place at the predetermined date and time is predicted. For example, the prediction unit 42 determines that the number of action schedule information indicating the user's behavior at a predetermined place on a predetermined date and time is larger than the average number of action schedule information indicating the user's behavior at the predetermined place. If it is more than the threshold value, it is predicted that congestion will occur at a predetermined place at a predetermined date and time.

For example, the prediction unit 42 selects a station to be predicted for congestion, and extracts a route search log with the selected station as the destination from the route search log database 32. Then, the prediction unit 42 predicts the congestion at the selected station by using the extracted log. For example, the prediction unit 42 estimates the average number of users in each time zone of the selected station using the extracted log. For example, the prediction unit 42 counts the number of logs whose arrival date and time is included in that time zone for each time zone, and counts the number of counted logs from the oldest arrival date and time included in the extracted log to the latest arrival date and time. Estimate the average number of users in each time zone of the selected station by dividing by the number of days. Then, the prediction unit 42 generates an averaging pattern AP based on the estimated number of users.

In addition, the prediction unit 42 counts the number of logs whose arrival date and time are included in each time zone of each day, and the number of users in each time zone in the averaging pattern and the counted logs. Compare with numbers. Then, when the number of logs in any time zone of any date and time is greater than the number of users in that time zone in the averaging pattern by a predetermined threshold value or more, the prediction unit 42 determines that date and time. It is estimated that abnormal congestion will occur during the time period. That is, the prediction unit 42 determines the average number of action schedule information indicating the behavior of the user in the predetermined time zone at the predetermined location and the behavior of the user in the predetermined time zone at the predetermined date and time in the predetermined location. Based on the comparison result with the number of action schedule information shown, the congestion of a predetermined place at a predetermined date and time is predicted.

For example, FIG. 6 is a diagram illustrating an example of a process in which the information providing device according to the embodiment predicts abnormal congestion. For example, (A) in FIG. 6 shows the number of route search logs with a certain station (hereinafter referred to as "station D") as the destination, and each time zone from 0:00 to 24:00. A graph showing the transition of the average number of logs including the arrival date and time is shown in. In addition, (B) in FIG. 6 shows the number of route search logs with station D as the destination, and includes the arrival date and time in each time zone from 0:00 to 24:00 on December 12, 2017. A graph showing the transition of the number of logs is shown. In the graphs shown in FIGS. 6A and 6B, the vertical axis direction is the number of logs, and the horizontal axis direction is the time zone including the arrival date and time.

Comparing the graph shown in FIG. 6 (A) with the graph shown in FIG. 6 (B), the arrival date and time is the period from 6:00 am to 15:00 pm on December 12, 2017, and the station. The number of route search logs with D as the destination is increasing. From such comparison results, it can be predicted that abnormal congestion will occur at station D between about 6:00 am and about 15:00 pm on December 12, 2017. Therefore, the prediction unit 42 predicts the abnormal congestion that occurs at a predetermined date and time from the comparison result between the averaging pattern AP and the prediction pattern PP at a predetermined date and time.

In addition, the prediction unit 42 predicts the future behavior of the user U based on the posted information posted by the user U, and predicts the congestion at a predetermined place based on the action schedule information indicating the predicted behavior. You may. That is, the prediction unit 42 sets the averaging pattern AP as shown in FIG. 6 based on the number of arbitrary action schedule information suggesting the place where the user U visits and the time when the user U visits the place. , The prediction pattern PP at a certain predetermined date and time may be generated, and the abnormal congestion may be predicted based on the comparison result of the generated patterns.

Returning to FIG. 2, the explanation will be continued. When it is predicted that congestion will occur, the identification unit 43 identifies an event that causes congestion based on the user's information associated with the action schedule information associated with the predetermined location. For example, the identification unit 43 identifies an event that causes congestion based on the information of the user who has input the action schedule information related to the predetermined place. For example, when the action schedule information includes a search query for route search, the specific unit 43 identifies an event that causes congestion at the destination indicated by the search query. For example, the specific unit 43 may include a history of searches by the user U using the Internet, a history of electronic commerce by the user U, a history of the position of the user U, attributes of the user U, or biometric information of the user U. Identify the event that causes congestion based on at least one.

More specifically, when it is predicted that congestion will occur at a predetermined place at a predetermined date and time, the specific unit 43 is caused by a user who is predicted to visit the predetermined place at a predetermined date and time based on the action schedule information. Estimate as a user and identify the event that causes congestion based on the estimated cause user information. Here, the identification unit 43 may identify an event that causes congestion based on the commonality of the information of the cause user, and the information of the cause user and another user U (that is, general use) may be specified. The event that causes congestion may be identified based on the difference from the information of the person).

For example, when it is predicted that abnormal congestion will occur at station D between about 6:00 am and about 15:00 pm on December 12, 2017, the specific unit 43 will refer to the route search log database 32. Identify the log in which the arrival date and time is included in each time zone from about 6:00 am to about 15:00 pm on December 12, 2017, and the arrival place is "Station D". Subsequently, the specifying unit 43 identifies the user ID included in the specified log as the user ID indicating the cause user. That is, the identification unit 43 identifies the user U who is expected to visit the station D between about 6:00 am and about 15:00 pm on December 12, 2017 as the cause user.

Subsequently, the specific unit 43 randomly selects a predetermined number of users U from the users other than the cause user, and makes the selected user U a general user. Then, the specific unit 43 acquires the information of the cause user and the information of the general user from the user database 31, and the difference between the acquired information, that is, the information of the cause user and the information of the general user. Identify the difference.

In addition, the identification unit 43 refers to the event database 33 and extracts events to be held in the vicinity of the station D after 6:00 am on December 12, 2017. In addition, the specific section 43 extracts the events to be held in the vicinity of the station D from about 6:00 am to about 15:00 pm on December 12, 2017, when abnormal congestion is predicted to occur. May be good. In addition, the specific section 43 is held in the vicinity of the station D from about 6:00 am on December 12, 2017, which is the first time zone in which abnormal congestion is predicted to occur, until a predetermined period elapses. Events may be extracted, and in the vicinity of station D from around 15:00 pm on December 12, 2017, which is the first time zone when abnormal congestion is predicted to occur, until a predetermined period elapses. The event to be held may be extracted.

Then, the specific unit 43 calculates the degree of relevance between the difference between the information of the cause user and the information of the general user and the correspondence information of the extracted event, and determines the event in which the calculated relevance exceeds a predetermined threshold value. Identify. For example, when the causative user has a tendency to have a predetermined hobby / preference as compared with the general user, the specific unit 43 identifies an event having the highest relevance to the hobby / preference.

In addition, the identification unit 43 may specify an event that causes congestion based on the information acquired within a predetermined period from a predetermined date and time among the information of the cause user. In addition, the identification unit 43 may extract information that deviates from the average pattern from the information of the cause user, and identify an event that causes congestion based on the extracted information. In addition, the specific unit 43 searches for an event that is highly related to the information of the causative user from the events scheduled to occur at a predetermined date and time in the vicinity of the predetermined location, and the searched event may be used as the cause of congestion. Good.

Here, FIG. 7 is a diagram illustrating an example of a process in which the information providing device according to the embodiment identifies an event. In the example shown in FIG. 7, the specific process executed by the information providing device 10 is conceptually shown. Further, in the example shown in FIG. 7, the users who visit the station D for the purpose of the event that causes the abnormal congestion when it occurs are shown in color, and the other users are shown without color.

For example, as shown in FIG. 7A, the prediction unit 42 identifies abnormal congestion from the search query pattern of the route search. That is, the prediction unit 42 predicts the abnormal congestion that occurs at the station D by comparing the average number of users at the station D with the number of users at the station D when the abnormal congestion occurs. Then, the specific unit 43 uses the user of the station D at the time of abnormal congestion as the cause user.

Here, as shown in FIG. 7, it is estimated that the causative users include users who regularly use station D in addition to users who aim for an event that causes abnormal congestion. Will be done. Therefore, as shown in FIG. 7B, the specific unit 43 extracts the difference between the information of the randomly selected user and the information of the cause user, thereby causing abnormal congestion among the cause users. Emphasize the tendency of user information for the purpose of the event that causes the. Then, as shown in FIG. 7C, the identification unit 43 identifies an event that causes abnormal congestion by specifying an event corresponding to the difference.

Returning to FIG. 2, the explanation will be continued. The providing unit 44 provides various contents to the user U based on the specified event. For example, the providing unit 44 uses a location related to a predetermined location, such as a user who has performed a route search with a location predicted to cause congestion as an arrival destination, or a user who regularly uses that location. Provide information to the person indicating the identified event.

In addition, the providing unit 44 may provide information related to the specified event to the user related to the predetermined place. For example, the providing unit 44 may provide advertisements and recommendation information of products related to the specified event to the user who has performed a route search with the place where congestion is predicted to occur as the destination. In addition, the providing unit 44 may provide the user U related to the specified event with information indicating the specified event or information related to the specified event. For example, the providing unit 44 may specify a user U that is presumed to be highly related to the specified event, and provide various contents related to the event to the specified user U.

[3. Processing flow of information providing device]
Next, an example of the procedure of the specific processing executed by the information providing device 10 will be described with reference to FIG. FIG. 8 is a flowchart showing an example of a flow of specific processing executed by the information providing device according to the embodiment.

First, the information providing device 10 acquires a route search log with a predetermined location as the arrival destination (step S101). Subsequently, the information providing device 10 calculates a pattern of the number of average route search logs (step S102). Then, the information providing device 10 specifies the date and time when the number of route search logs deviates from the average pattern as the date and time when the abnormal congestion occurs (step S103).

Further, the information providing device 10 identifies a user who has input a search query whose arrival place is a predetermined place at a specified date and time as a cause user (step S104). Then, the information providing device 10 identifies an event corresponding to the difference between the information of the cause user and the information of the general user selected at random (step S105). After that, the information providing device 10 provides information indicating the specified event (step S106), and ends the process.

[4. Modification example]
In the above, an example of the learning process and the generation process by the information providing device 10 has been described. However, the embodiments are not limited to this. Hereinafter, variations of the specific processing executed by the information providing device 10 will be described.

[4-1. Handling of date and time]
In the above example, the information providing device 10 uses the arrival date and time obtained as a result of the route search to predict the date and time when congestion will occur. However, the embodiments are not limited to this. For example, the information providing device 10 may predict the date and time when congestion will occur by using the arrival date and time input by the user U as a search query.

Further, when the information providing device 10 predicts congestion at a place other than the station, the distance from the station to the place to be predicted and the date and time when the user U will arrive at the station (for example, route search). The date and time when the user U is expected to arrive at the place is estimated based on the arrival date and time obtained as a result and the arrival date and time entered as the search query, and the date and time when congestion occurs is estimated based on the estimated date and time. You may predict. That is, the information providing device 10 may predict the date and time when congestion will occur based on not only the date and time information included in the route search result but also the arrival date and time of the user U estimated from this information. Good.

Further, the information providing device 10 may generate an averaging pattern with a predetermined particle size. For example, the information providing device 10 generates an averaging pattern for each day of the week, and is abnormal based on the comparison result between the prediction pattern of the day to be predicted and the day of the week to be predicted and the corresponding averaging pattern. Congestion may be predicted.

[4-2. Device configuration〕
The information providing device 10 may be communicably connected to an arbitrary number of terminal devices 100, or may be communicably connected to an arbitrary number of search servers 200. Further, the information providing device 10 may be able to communicate with an e-mail distribution server, an SNS server that accepts SNS posts, and the like. Further, the information providing device 10 may be realized by a front-end server and a back-end server that executes various processes. In such a case, the provision unit 44 shown in FIG. 2 is arranged in the front-end server, and the back-end server includes the acquisition unit 41, the prediction unit 42, and the specific unit 43 shown in FIG. .. Further, each of the databases 31 to 33 registered in the storage unit 30 may be managed by an external storage server.

[4-3. Others]
Further, among the processes described in the above-described embodiment, all or a part of the processes described as being automatically performed can be manually performed, or the processes described as being manually performed can be performed. All or part of it can be done automatically by a known method. In addition, the processing procedure, specific name, and information including various data and parameters shown in the above document and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each figure is not limited to the illustrated information.

Further, each component of each of the illustrated devices is a functional concept, and does not necessarily have to be physically configured as shown in the figure. That is, the specific form of distribution / integration of each device is not limited to the one shown in the figure, and all or part of the device is functionally or physically distributed / physically in any unit according to various loads and usage conditions. It can be integrated and configured.

In addition, the above-described embodiments can be appropriately combined as long as the processing contents do not contradict each other.

[5. program〕
Further, the information providing device 10 according to the above-described embodiment is realized by, for example, a computer 1000 having a configuration as shown in FIG. FIG. 9 is a diagram showing an example of a hardware configuration. The computer 1000 is connected to the output device 1010 and the input device 1020, and the arithmetic unit 1030, the primary storage device 1040, the secondary storage device 1050, the output IF (Interface) 1060, the input IF 1070, and the network IF 1080 are connected by the bus 1090. Has.

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

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

The input device 1020 is, for example, an optical recording medium such as a CD (Compact Disc), a DVD (Digital Versatile Disc), or a PD (Phase change rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), or a tape. It may be a device that reads information from a medium, a magnetic recording medium, a semiconductor memory, or the like. Further, the input device 1020 may be an external storage medium such as a USB memory.

The network IF1080 receives data from another device via the network N and sends it to the arithmetic unit 1030, and also transmits the data generated by the arithmetic unit 1030 to the other device via the network N.

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

For example, when the computer 1000 functions as the information providing device 10, the arithmetic unit 1030 of the computer 1000 executes the program or data (for example, the processing model M1) loaded on the primary storage device 1040, so that the control unit 40 To realize the function of. The arithmetic unit 1030 of the computer 1000 reads and executes these programs or data (for example, the processing model M1) from the primary storage device 1040, but as another example, these programs are transmitted from another device via the network N. You may get it.

[6. effect〕
As described above, the information providing device 10 predicts congestion at a predetermined place based on the action schedule information indicating the future action of the user U. Then, when the information providing device 10 is predicted to cause congestion, the information providing device 10 identifies an event that causes the congestion based on the information of the user U associated with the action schedule information related to the predetermined place. As a result, when the information providing device 10 is congested, the information providing device 10 can identify the event that causes the congestion.

Further, the information providing device 10 predicts congestion at a predetermined place based on the action schedule information input by the user U, and is based on the information of the user U who has input the action schedule information related to the predetermined place. To identify the event that causes congestion. For example, the information providing device 10 predicts congestion at a predetermined place based on a search query for searching a movement route from a predetermined departure place to a predetermined arrival place at a predetermined date and time as action schedule information. Then, the information providing device 10 predicts congestion at a predetermined arrival place included in the search query based on the search query, and identifies an event that causes congestion at the predetermined arrival place. Therefore, since the information providing device 10 identifies the event that causes congestion based on the highly reliable action schedule information, the identification accuracy can be improved.

Further, the information providing device 10 determines the difference between the action schedule information indicating the behavior of the user U at a predetermined date and time at a predetermined location and the action schedule information indicating the average behavior of the user U at the predetermined location. Based on this, the congestion at a predetermined place on a predetermined date and time is predicted. Therefore, the information providing device 10 can predict the occurrence of extraordinary congestion, that is, abnormal congestion, and identify the event that causes the predicted abnormal congestion.

Further, the information providing device 10 includes an average number of action schedule information indicating the behavior of the user U at a predetermined location and an average number of action schedule information indicating the behavior of the user U at a predetermined location on a predetermined date and time. Based on the comparison result of, the congestion of a predetermined place at a predetermined date and time is predicted. For example, in the information providing device 10, the number of action schedule information indicating the behavior of the user U at a predetermined date and time at a predetermined location is larger than the average number of action schedule information indicating the behavior of the user U at the predetermined location. If there is more than a predetermined threshold value, it is predicted that congestion will occur at a predetermined place at a predetermined date and time. Therefore, the information providing device 10 can accurately predict the occurrence of abnormal congestion.

Further, the information providing device 10 has an average number of action schedule information indicating the behavior of the user U in a predetermined time zone at a predetermined place, and the user U in a predetermined time zone at a predetermined date and time in the predetermined place. Based on the result of comparison with the number of action schedule information indicating the behavior of, the congestion of a predetermined place at a predetermined date and time is predicted. Therefore, the information providing device 10 can predict the occurrence of abnormal congestion for each time zone.

Further, when the information providing device 10 is predicted to be congested at a predetermined place at a predetermined date and time, the user U who is predicted to visit the predetermined place at the predetermined date and time from the action schedule information is the cause user. Based on the estimated cause user information, identify the event that causes the congestion. Therefore, the information providing device 10 can improve the accuracy of identifying an event that causes abnormal congestion.

For example, the information providing device 10 identifies an event that causes the congestion based on the commonality of the information of the causative user. Further, for example, the information providing device 10 identifies an event that causes congestion based on the difference between the information of the cause user and the information of another user U. Further, the information providing device 10 may identify an event that causes congestion based on the information acquired within a predetermined period from a predetermined date and time among the information of the cause user. Further, the information providing device 10 may extract information that deviates from the average pattern of the information from the information of the cause user, and identify an event that causes congestion based on the extracted information. ..

Further, the information providing device 10 is at least one of a search history by the user U, a history of electronic commerce by the user U, a history of the position of the user U, an attribute of the user U, or biometric information of the user U. Based on one, the event causing congestion may be identified. Further, the information providing device 10 searches for an event that is highly related to the information of the cause user from the events scheduled to occur at a predetermined date and time in the vicinity of the predetermined place, and sets the searched event as the cause of congestion. To do. As a result of such processing, the information providing device 10 can appropriately identify an event that causes abnormal congestion.

In addition, the information providing device 10 predicts the future behavior of the user U based on the posted information posted by the user U, and crowds at a predetermined place based on the action schedule information indicating the predicted behavior. You may predict. As a result of such processing, the information providing device 10 can appropriately predict congestion from the information posted on the SNS or the like.

In addition, the information providing device 10 provides the user U associated with the predetermined place with information indicating the specified event. In addition, the information providing device 10 provides the user U associated with the predetermined location with information related to the specified event. In addition, the information providing device 10 provides the user U related to the specified event with information indicating the specified event or information related to the specified event. Therefore, the information providing device 10 can distribute various contents in various distribution modes centering on an event that causes congestion.

Although some of the embodiments of the present application have been described in detail with reference to the drawings, these are examples, and various modifications based on the knowledge of those skilled in the art, including the embodiments described in the disclosure column of the invention. It is possible to practice the present invention in other improved forms.

In addition, the above-mentioned "section, module, unit" can be read as "means" or "circuit". For example, the distribution unit can be read as a distribution means or a distribution circuit.

10 Information provider 20 Communication unit 30 Storage unit 31 User database 32 Route search log database 33 Event database 40 Control unit 41 Acquisition unit 42 Prediction unit 43 Specific unit 44 Provision unit 100 Terminal device 200 Search server

Claims (20)

A prediction unit that predicts congestion at a predetermined location based on action schedule information that indicates the future behavior of the user,
When it is predicted by the prediction unit that congestion will occur at the predetermined place, the users who visit the predetermined place are estimated based on the action schedule information, and the estimated users are routinely used. It has a specific part that estimates the user other than the user who is using the predetermined place as the cause user, and identifies the event that causes the congestion based on the estimated cause user information. A specific device characterized by that. The prediction unit predicts congestion at the predetermined location based on the action schedule information input by the user.
The identification device according to claim 1, wherein the identification unit identifies an event that causes the congestion based on the information of a user who has input the action schedule information related to the predetermined place. As the action schedule information, the prediction unit predicts congestion at the predetermined place based on a search query for searching a movement route from a predetermined departure place to a predetermined arrival place at a predetermined date and time. The specific device according to claim 1 or 2. Based on the search query, the prediction unit predicts congestion at a predetermined destination included in the search query, and predicts congestion.
The identification device according to claim 3, wherein the identification unit identifies an event that causes congestion at the predetermined destination. The prediction unit is based on the difference between the action schedule information indicating the behavior of the user at the predetermined place on the predetermined date and time and the action schedule information indicating the average user behavior at the predetermined place. The specific device according to any one of claims 1 to 4, wherein the congestion at a predetermined date and time is predicted at a predetermined place. The prediction unit is a comparison result between the average number of action schedule information indicating the user's behavior at the predetermined place and the number of action schedule information indicating the user's behavior at the predetermined place on the predetermined date and time. The specific device according to any one of claims 1 to 5, wherein the congestion of the predetermined place at the predetermined date and time is predicted based on the above. The prediction unit determines that the number of action schedule information indicating the user's behavior at the predetermined place on the predetermined date and time is larger than the average number of action schedule information indicating the user's behavior at the predetermined place. The specific device according to claim 6, wherein when the number is more than the threshold value, it is predicted that congestion will occur at the predetermined place at a predetermined date and time. The prediction unit has an average number of action schedule information indicating the behavior of the user in the predetermined time zone in the predetermined place, and the behavior of the user in the predetermined time zone at the predetermined date and time in the predetermined place. The specific device according to claim 6 or 7, wherein the congestion of the predetermined place at the predetermined date and time is predicted based on the comparison result with the number of action schedule information indicating the above. The identification device according to any one of claims 1 to 8, wherein the identification unit identifies an event that causes the congestion based on the commonality of information of the cause user. Any one of claims 1 to 9, wherein the specific unit identifies an event that causes the congestion based on the difference between the information of the causative user and the information of another user . The specific device described in one . The specific unit identifies the event that causes the congestion based on the information acquired within a predetermined period from the predetermined date and time when the congestion is estimated to occur among the information of the cause user. The specific device according to any one of claims 1 to 10 as a feature. The specific unit is characterized in that information that deviates from the average pattern of the information is extracted from the information of the cause user, and the event that causes the congestion is specified based on the extracted information. The specific device according to any one of claims 1 to 11. The specific unit may be at least one of a search history by the user, a history of electronic commerce by the user, a history of the position of the user, an attribute of the user, or biometric information of the user. The identification device according to any one of claims 1 to 12 , wherein the event that causes the congestion is specified based on the above. The specific unit searches for and searches for events that are highly relevant to the cause user's information from among the events scheduled to occur at a predetermined date and time estimated to cause congestion in the vicinity of the predetermined location. specifying apparatus according to any one of claims 1 to 1 3 to the event, characterized in that the cause of the congestion. The prediction unit predicts the future behavior of the user based on the posted information posted by the user, and predicts the congestion at a predetermined place based on the action schedule information indicating the predicted behavior. The specific device according to any one of claims 1 to 14, which is characterized. The specification according to any one of claims 1 to 15, wherein the user has a providing unit that provides information indicating an event specified by the specific unit to a user associated with the predetermined location. apparatus. The invention according to any one of claims 1 to 15, wherein the user has a providing unit that provides information related to the event specified by the specific unit to a user associated with the predetermined place. Specific device. Having a providing unit that provides information indicating an event specified by the specific unit or information related to the event specified by the specific unit to a user related to the event specified by the specific unit. The specific device according to any one of claims 1 to 15. A specific method performed by a specific device
A prediction process that predicts congestion at a predetermined location based on action schedule information that indicates the future behavior of the user,
When it is predicted that congestion will occur at the predetermined place by the prediction process, the users who visit the predetermined place are estimated based on the action schedule information, and the estimated users are routinely used. Includes a specific process that estimates a user other than the user who is using the predetermined location as the cause user and identifies the event that causes the congestion based on the estimated cause user information. A specific method characterized by that. A prediction procedure that predicts congestion at a given location based on action schedule information that indicates the future behavior of the user,
When it is predicted that congestion will occur at the predetermined place by the prediction procedure, the users who visit the predetermined place are estimated based on the action schedule information, and the estimated users are routinely used. A computer is used to estimate a user other than the user who is using the predetermined location as the cause user, and to identify the event that causes the congestion based on the estimated cause user information. A specific program to run on.

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