JP2019049765A - Specification apparatus, specification method and specification program - Google Patents

Abstract

To provide a specification apparatus, a specification method and a specification program.SOLUTION: This specification apparatus (information provision apparatus 10) includes a prediction unit for predicting congestion at a predetermined location based on behavior schedule information indicating future behavior of a user, and a specification unit for specifying an event that causes the congestion based on user information linked to the behavior schedule information related to the predetermined location if the prediction unit predicts that the congestion occurs.SELECTED DRAWING: Figure 2

Description

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

  Conventionally, techniques for predicting congestion at a specific point or section are known. As an example of such a technique, there is known a technique of calculating an indicator of the degree of congestion based on the number of prior search logs at the congestion prediction date and time of the congestion prediction point. In addition, there is known a technology for predicting the degree of congestion of the user's future behavior from the future action schedule and the past history, and providing the prediction result to the user.

JP, 2017-33440, A

  However, the above-mentioned prior art has not identified the cause of congestion. For this reason, in the prior art, although the user can be notified in advance that congestion will occur, the cause of the congestion can not be notified in advance such as why the congestion occurs.

  This application is made in view of the above, and it aims at specifying the cause of congestion.

  In the specific device according to the present application, when it is predicted that congestion will occur by the prediction unit that predicts congestion at a predetermined place based on action schedule information indicating the user's future behavior, And a specific unit for specifying an event that is the cause of the congestion based on the information of the user linked to the action schedule information related to the place of the group.

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

FIG. 1 is a diagram illustrating an example of a specifying process performed by the information providing apparatus according to the embodiment. FIG. 2 is a diagram showing an example of the configuration of the information providing apparatus 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 for explaining an example of a process in which the information providing apparatus according to the embodiment predicts abnormal congestion. FIG. 7 is a diagram for explaining an example of a process in which the information providing apparatus according to the embodiment identifies an event. FIG. 8 is a flowchart showing an example of the flow of identification processing performed by the information providing apparatus according to the embodiment. FIG. 9 is a diagram illustrating an example of a hardware configuration.

  Hereinafter, a specific apparatus, a specific method, and a mode for carrying out a specific program according to the present application (hereinafter, referred to as “embodiment”) will be described in detail with reference to the drawings. Note that the specific device, the specific method, and the specific program according to the present application are not limited by this embodiment. Moreover, the same code | symbol is attached | subjected to the same site | part in the following each embodiment, and the overlapping description is abbreviate | omitted.

[Embodiment]
[1-1. Example of information provision device]
First, an example of a specifying process performed by an information providing apparatus, which is an example of a specifying apparatus, will be described with reference to FIG. FIG. 1 is a diagram illustrating an example of a specifying process performed by the information providing apparatus according to the embodiment. In FIG. 1, a terminal device 101 used by each of users U1 to U3 (hereinafter sometimes referred to as “user U” in some cases) via a predetermined network N such as the Internet (see, for example, FIG. 2). To 103 (hereinafter sometimes 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 a tablet, and is a portable terminal device that can communicate with any 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.

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

  The search server 200 is an information processing apparatus that provides the user U with a service related to route search, and is realized by, for example, a server apparatus or a cloud system. For example, the search server 200 accepts the departure place and the arrival place from the terminal device 100 as a search query of the route search. In such a case, the search server 200 searches for a movement route from the departure place to the arrival place, transportation means (train, bus, taxi, walk, etc.), required time, estimated arrival time, etc. 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 a bus stop or a station, but also landmarks and addresses of hotels and various facilities, places selected by map contents, etc. as departure places 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 departure point. In the following description, as an example of route search executed by the search server 200, an example of accepting designation of a station as a departure place or an arrival place will be described.

  In addition, the search server 200 can receive designation of departure date and arrival date. For example, if the search server 200 accepts the designation of the departure place and the arrival place as well as the departure date and time, the travel route in the case of departing from the designated departure place to the arrival place after the designated departure date and time. Search for the estimated arrival time, etc. 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 travel route and the departure date and time are set to arrive at the designated arrival place before the designated arrival date and time. Search for.

  The search server 200 can realize arbitrary route search other than the above-described processing. Further, it is assumed that the search server 200 holds the history of 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, an arrival date, 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 specific processing described later, and is realized by, for example, a server device, a cloud system, or the like. For example, the information providing apparatus 10 acquires the log of the search query held by the search server 200, and executes the specific process according to the invention using the acquired log.

[1-2. About outline of specific processing]
Here, it can be said that the route search log whose future date and time is set as the departure date and arrival date 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 or moving route can be predicted. For example, if the number of logs including search results arriving at a predetermined station during a certain time zone exceeds a predetermined threshold, congestion occurs in that time zone. It is thought that. In this manner, congestion can be predicted by analyzing the logs held by the search server 200 for each arrival location.

  The prediction of such congestion may be performed for each facility such as a station as an arrival place, or may be performed for each predetermined area (for example, within a 1 km radius from a certain position). Also, from the log included in the route search, the movement route that each user will use may be estimated, and the movement route that congestion may occur may be estimated based on the estimated duplication of the movement route. As described above, in the congestion prediction technique using the route search log, it is possible to predict the congestion that may occur in any future manner.

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

  Therefore, the information providing device 10 executes the following identification process. First, the information providing apparatus 10 predicts congestion at a predetermined place based on action schedule information indicating a user's future action. Then, when it is predicted that congestion will occur, the information providing apparatus 10 specifies an event that causes congestion based on the information of the user linked to the action schedule information related to the predetermined place. For example, when it is predicted that congestion will occur, the information providing apparatus 10 identifies a user who causes congestion (hereinafter, “cause user”), and the congestion is performed based on information on the identified cause user. Identify the event that is the cause of

  Here, arbitrary events can be adopted as events causing congestion. For example, the information providing apparatus 10 may specify holding of various events, opening of a new store, etc. as an event, for example, that a character appearance event has occurred in a game application using position information, etc. The fact that a position-linked event occurs in an application using reality, free distribution of goods, appearance information of celebrities, etc. may be specified as the event. That is, the information providing apparatus 10 can specify any event regardless of whether it is virtual or real, as long as the event causing the congestion is specified based on the information of the cause user.

  For example, the information providing apparatus 10 predicts congestion at a predetermined place based on a search query for searching a moving route from a predetermined departure point to a predetermined arrival point at a predetermined date and time. More specifically, the information providing apparatus 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 a specific process performed by the information providing apparatus 10 will be described with reference to FIG. For example, the search server 200 receives the designation of the departure place and the arrival place as a search query of the route search (step S1). More specifically, the search server 200 receives a search query including a station as a departure place, a station as an arrival place, and a user ID (Identifier) identifying the user U. Here, the search server 200 may receive a search query including departure date or arrival date. 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). In addition, the search server 200 holds a route search log in which the departure place and the arrival place accepted 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. Do.

  On the other hand, the information providing apparatus 10 acquires a route search log as action schedule information at a predetermined timing (for example, every predetermined period) (step S3). And the information provision apparatus 10 performs the following specific processes for every station used as an arrival place. First, the information providing apparatus 10 obtains an average pattern of search queries having a predetermined station as an arrival place, and predicts the occurrence of abnormal congestion based on the deviation from the average pattern (step S4).

  For example, the information providing apparatus 10 extracts all the logs whose arrival place is “station D” from the route search logs. Then, the information providing apparatus 10 counts, for each predetermined period (for example, one hour), the number of logs including the arrival date and time in the period. That is, assuming that each user U acts as a result of the route search, the information providing device 10 counts the number of users visiting the station D at predetermined intervals.

  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 apparatus 10 calculates an average value of the number of users visiting the station D for each time zone on a weekday, and sets a pattern of the calculated average value as an averaging pattern AP. The information providing apparatus 10 also calculates an averaging pattern AP for each Saturday time zone and for each Sunday time zone.

  Then, the information providing apparatus 10 searches for the date and time when the number of users deviates from the averaging pattern. For example, the information providing apparatus 10 calculates the number of users who use the station D for each of the time zones on a daily basis. Then, for example, the information providing apparatus 10 predicts that congestion will occur if the number of users using 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, based on the difference between the action schedule information indicating the action of the user at the predetermined date and time at the predetermined place and the action schedule information indicating the average user action at the predetermined place, the information providing device 10 Congestion at a predetermined place at a predetermined date and time is predicted. More specifically, the information providing apparatus 10 includes an average number of pieces of action schedule information indicating a user's action at a predetermined place and action schedule information indicating a user's action at a predetermined place at 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.

  As a more specific example, in the example shown in FIG. 1, the information providing apparatus 10 determines that the arrival place is “station D” and the arrival date is “Dec. 12, 2017” from the route search log. By counting the number of logs included in the time zone, a 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 averaged pattern AP with the predicted pattern PP, and searches for deviation from the averaged pattern AP included in the predicted pattern PP. That is, the information providing apparatus 10 estimates the number of average station D users and the number of users who use the station D at each date and time based on the route search log, and compares the estimated numbers. By doing so, it is possible to predict congestion that occurs suddenly, that is, abnormal congestion (hereinafter referred to as “abnormal congestion”).

  For example, the information providing apparatus 10 includes the number of logs included between 9 am and 10 am on the date of arrival “Dec. 12, 2017” and 9 am and 10 am in the averaging pattern. Compare with the number of logs being That is, the information providing apparatus 10 determines the average number of pieces of action schedule information indicating the action of the user in the predetermined time zone and the action of the user in the predetermined time zone in the predetermined place. The congestion of a predetermined place at a predetermined date and time is predicted based on the comparison result with the number of pieces of action schedule information indicating.

  For example, the information providing apparatus 10 may change the number of users U who visit “Station D” from 9 am to 10 am on “December 12, 2017”, and “Station D” from 9 am to 10 am Compare with the average number of users U who will use until the time. Then, the information providing apparatus 10 is configured such that the number of users U who visit “Station D” from 9 am to 10 am on “December 12, 2017” is “Station D” from 9 am to 10 am An abnormal congestion occurs between 9 am and 10 am on "December 12, 2017" if there are more than a predetermined threshold value than the average number of users U who will use until the hour It predicts. That is, in the information providing apparatus 10, the number of pieces of action schedule information indicating the action of the user at a predetermined place at a predetermined date and time is more than the average number of pieces of action schedule information indicating the user's actions at the predetermined place. If the number is larger than the threshold, it is predicted that congestion will occur at a predetermined place at a predetermined date and time.

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

  For example, in the example illustrated in FIG. 1, the information providing device 10 predicts that abnormal congestion will occur in the station D between 9 am and 10 am on December 12, 2017. In such a case, the information providing apparatus 10 searches for a route whose arrival date and time is included between 9 am and 10 am of "December 12, 2017" and that "Station D" is the arrival place. The user ID of the user U who has input the search query that is the cause of is specified. Then, the information providing apparatus 10 sets the users U1, U4, and U6 indicated by the specified user ID as the cause user.

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

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

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

  In addition, the difference of such information can employ | adopt the various difference extraction techniques which extract the difference of a user's attribute and various log | history. For example, the information providing apparatus 10 may extract the difference of information purely, 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 viewed from the general user You may extract the tendency etc. of the information of as a difference. Such extraction of differences is realized by various models such as SVM (Support Vector Machine) and DNN (Deep Neural Network), which have learned the features of information useful for identifying the cause of the user's information. May be

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

  For example, when the information providing apparatus 10 purchases a lot of products related to the idle "X" as compared with the general user, the information providing apparatus 10 may, for example, an event in which the idle "X" appears among scheduled events. , An event that is highly relevant to the idle "X". In addition, when the cause user frequently uses the luxury hotel as compared with the general user, the information providing apparatus 10 has a relationship with the luxury hotel such as a new store opening event of the luxury hotel among planned events. You may identify high events.

  Also, for example, when the cause user frequently inputs a search query related to a predetermined soccer team "Y" as compared with the general user, the information providing apparatus 10 participates in the soccer team "Y" Among the planned events, such as a game, an event highly relevant to the soccer team “Y” may be selected. In addition, when the cause user frequently posts a character string or a photo related to a restaurant as compared with a general user, the information providing apparatus 10 relates to the restaurant among the planned events such as rotation of a new restaurant. You may select events of high sex. Further, the information providing apparatus 10 identifies the exercise amount of each user from the biological information, and when the cause user has a large exercise amount compared with the general user, planned events such as opening a new gym or a marathon event From the above, an event that is highly relevant to the user having a large amount of exercise may be selected.

  That is, when the information providing apparatus 10 predicts that abnormal congestion occurs, among the events that occur at the date and time when abnormal congestion occurs in the vicinity of the place where abnormal congestion occurs, the event having high relevance to the cause user As a cause of abnormal congestion. Then, the information providing device 10 provides the user U with the information indicating the identified event as the information indicating the cause of the congestion (step S8). For example, in the route search by the user U3, the information provision device 10 is included from 9 am to 10 am when the arrival station is "Station D" and the arrival date and time is "Dec. 12, 2017". When the user U3 is alerted, the user U3 is notified that abnormal congestion occurs and information indicating the cause of the abnormal congestion is provided.

  As described above, the information providing apparatus 10 predicts congestion at a predetermined place based on a search query for searching for a moving route from a predetermined departure point to a predetermined arrival position at a predetermined date and time as action schedule information. Do. For example, the information providing apparatus 10 predicts congestion at a predetermined arrival site included in the search query, and identifies an event that causes congestion at the predicted predetermined arrival site.

  Further, the information providing apparatus 10 predicts abnormal congestion, not congestion due to the number of simple users. For example, the information providing apparatus 10 compares the average number of pieces of action schedule information indicating the user's action at a predetermined place with the number of pieces of action schedule information indicating the user's action at a predetermined date and time at the predetermined place. Based on the result, congestion of a predetermined place at a predetermined date and time is predicted. As a more specific example, in the information providing apparatus 10, the number of pieces of action schedule information indicating the action of the user at the predetermined date and time at the predetermined place is the action schedule information indicating the user's action at the predetermined place. When the number is larger than the average number by a predetermined threshold or more, it is predicted that congestion will occur at a predetermined place at a predetermined date and time.

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

[1-4. About action schedule information]
In the example described above, the information providing apparatus 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 apparatus 10 can adopt any information as the action schedule information as long as the information suggests the location of the user at a certain date and time in the future.

  For example, the information providing apparatus 10 identifies the place where the user U visits and the date and time when the user U visits from the contents of the e-mail including various reservation contents, such as reservation of a hotel performed by the user U and reservation of a train. . In such a case, the information providing apparatus 10 predicts congestion or specifies whether the user U is a cause user or not on the assumption that the user U visits the date and time specified in the place identified from the electronic mail. Good.

  In addition, the information providing apparatus 10 predicts future behavior of the user U based on post information posted by the user U on the Internet, such as SNS (Social Networking Service), and action schedule information indicating the predicted behavior The congestion at a given location may be predicted based on For example, the information providing apparatus 10 identifies the place where the user U visits and the date and time of visiting the place from the post information, and predicts congestion as the user U visits the date and time specified in the identified place. , You may specify whether you are the cause user. Further, when the terminal device 100 has the function of a noncontact IC (Integrated Circuit) card system, the information providing device 10 acquires the information of the ticket held in the terminal device 100 and uses it from the acquired information of the ticket. The place where the person U visits and the date and time when they visit the place may be specified.

  Here, the information providing apparatus 10 may adopt information input by the user U as action schedule information, which is information input by another user and is information indicating the future action of the user U May be adopted as action schedule information. For example, the information providing apparatus 10 predicts congestion at a predetermined place based on the action plan information input by the user U, and based on information of a user who has input the action plan information related to the predetermined place. , You may identify events that cause congestion.

[1-5. About the location where congestion occurs]
Here, the information providing apparatus 10 described above predicts anomalous congestion that occurs at the station that is the arrival site. However, the embodiments are not limited to this. For example, the information providing apparatus 10 generates the averaging pattern AP and the prediction pattern PP for each of a plurality of stations within a predetermined range, and based on the comparison result, abnormal congestion at a plurality of stations within the predetermined range Whether or not to occur may be predicted. In addition, when the information providing apparatus 10 predicts that abnormal congestion occurs in a plurality of stations within a predetermined range, the user U who visits the plurality of stations at the time of congestion is regarded as the cause user and the cause of the abnormal congestion Event may be estimated.

  In addition, for example, when abnormal congestion is predicted at a certain station, the information providing apparatus 10 specifies stations among the surrounding stations in which abnormal congestion occurs at the same time. Then, the information providing apparatus 10 may assume a user U visiting a station where abnormal congestion is predicted and any of the specified stations as a cause user and estimate an event that is a cause of the abnormal congestion.

  Further, the information providing apparatus 10 may predict abnormal congestion on a route basis, and when it is predicted that abnormal congestion occurs on a certain route, the user U who uses the route at the time of congestion may be a cause user, Among the scheduled events that occur around each station on the route, the causal event may be identified. In addition, the information providing device 10 may perform prediction of abnormal congestion or identification of an event that causes congestion in a part of a route.

  Further, the information providing apparatus 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. An event that causes abnormal congestion may be estimated.

[1-6. About abnormal congestion]
In the example described above, the information providing apparatus 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, when the number of users visiting “Station D” at a certain date and time exceeds a predetermined threshold, the information providing apparatus 10 predicts that congestion will occur at “Station D” at that date and time. In such a case, the information providing apparatus 10 performs a route search in which a user who will visit “Station D” at the time of congestion, that is, an arrival date and time at the time of congestion is included and the arrival place is “Station D”. The user U may be the cause user, and an event causing congestion may be identified based on the information of the cause user.

[1-7. About user's information]
Further, the information providing apparatus 10 can adopt any information as the information of the user other than the information described above. For example, the information providing apparatus 10 may use the function of the noncontact IC card system or the use history of the function of electronic money as the information of the user U. In addition, the information providing apparatus 10 may acquire various attributes such as demographic attributes and psychographic attributes (including tastes and preferences) of the user U, and may acquire a history of biometric information of the user U. . In addition, the information providing device 10 may acquire content such as a character string and a picture posted by the user U on the SNS. Further, the information providing apparatus 10 may acquire, as the attribute of the user U, information registered by the user U, or may acquire an attribute predicted from the various histories described above.

  In addition, the information providing apparatus 10 may specify an event that causes congestion based on 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, when it is predicted that congestion will occur on December 12, 2017, the information providing apparatus 10 causes congestion based on the information on the cause user acquired within one year from December 12, 2017. The event that becomes As described above, the information providing apparatus 10 causes congestion based on information estimated to be highly relevant to congestion or highly relevant to an event causing congestion among the information of the user U. By identifying events, the accuracy of identification can be further improved.

  Further, the information providing apparatus 10 may extract information out of the average pattern of the information among the information of the cause user, and specify an event that is a cause of congestion based on the extracted information. For example, the information providing apparatus 10 specifies an average pattern of the position history and the purchase history among the information of the cause user, and specifies the position history and the purchase history deviating from the specified pattern. The position history and the purchase history specified in this way can be said to be information indicating non-daily actions among the actions of the cause user. On the other hand, abnormal congestion can be said to be congestion that occurs on an extraordinary basis. Therefore, the information providing device 10 may further improve the identification accuracy by identifying an event that causes abnormal congestion based on the information indicating the identified non-daily action of the cause user.

  Note that the information providing apparatus 10 may use all of the information of the user U described above, or may use only part of the information. In addition, the information providing apparatus 10 may combine the above-described information in any manner to identify an event that causes congestion.

[1-8. About the identification of the cause]
In the example described above, the information providing apparatus 10 identifies an event that causes congestion based on the difference between the information on the cause user and the information on other users randomly selected. As described above, the information providing apparatus 10 identifies the event that causes the congestion based on the difference between the information on the cause user and the information on the general user, so the cause is based on the information unique to the cause user. As a result of identifying an event that is the result of the above, it is possible to improve the identification accuracy of the event that causes congestion.

  However, the embodiments are not limited to this. For example, the information providing apparatus 10 searches for events having high relevance to the information of the cause user among events scheduled to occur on a predetermined date and time in the vicinity of the predetermined place, and sets the searched events as the cause of congestion. If so, events 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 the information of the cause user. For example, if the attribute of the cause user of the majority of cause users includes "fishing lover", the information providing apparatus 10 selects an event having high relevance to fishing among the planned events. Good.

  Further, the information providing apparatus 10 may select a general user to be compared with the cause user in any manner. For example, the information providing apparatus 10 may select, as a general user, a user who routinely visits a location where it is predicted that abnormal congestion will occur. When such a process is performed, the information providing apparatus 10 identifies an event that causes abnormal congestion based on information of a user who has visited a place where abnormal congestion is predicted to occur extraordinarily. Can improve the accuracy of identification.

  Further, when it is predicted that abnormal congestion will occur in a certain place, the information providing apparatus 10 is a user other than the user U who regularly uses the place among the users U visiting the place. May be identified as the cause user. That is, when abnormal congestion occurs in a certain place, the information providing apparatus 10 uses the user U who visits the place non-dailyly as the cause user, thereby more accurate the event that causes the abnormal congestion. It can be identified well.

[1-9. About provision of information]
Here, the information providing device 10 provides the user U with information indicating the identified cause. For example, the information providing apparatus 10 provides the information of the identified event to the user U3 who has performed the route search to the location where the abnormal congestion is predicted to occur at the date and time when the abnormal congestion is predicted to occur. . Here, the information providing apparatus 10 may provide information on such an event together with the search result, or may notify the information using an email or the like.

  In addition, the information providing device 10 may generally provide the occurrence location, occurrence date, and cause event of the abnormal congestion to the general public through various portal sites. In addition, the information providing apparatus 10 is for users associated with a place where abnormal congestion occurs, such as a user who lives near the occurrence place of abnormal congestion, a user whose work location is near the occurrence place of the abnormal congestion, etc. Alternatively, information indicative of the identified event may be provided. In addition, when the abnormal congestion is predicted, the information providing device 10 may distribute the place where the abnormal congestion occurs, the date and time, and the event causing the abnormal congestion in the news format.

  In addition, the information providing apparatus 10 may provide information related to the identified event to the user associated with the place where abnormal congestion is predicted to occur. For example, the information providing apparatus 10 specifies the user who regularly uses the station where the abnormal congestion is predicted to occur, using the position information of the user U, and the specified user for the specified user Information may be provided.

  Also, for example, the information providing apparatus 10 is associated with the identified event for the user U3 who has performed the route search to the location where it is predicted that the abnormal congestion is predicted to occur at the date and time when the abnormal congestion is predicted to occur. Recommendation information of the product may be provided. As a more specific example, when the specified event is a live of the idle "X", the information providing apparatus 10 may provide the user U3 with goods, live information, etc. of the idle "X". Good. That is, the information providing apparatus 10 may provide the user U who may be associated with the identified event with information selected based on the identified event.

  In addition, the information providing device 10 may provide the user U associated with the identified event with information indicating the identified event or information associated with the identified event. For example, when the specified event is a live of the idle “X”, the information providing device 10 specifies the user U having a relationship with the idle “X”, such as a fan of the idle “X”. Then, the information providing apparatus 10 indicates that there is a live of the idol “X” for the identified user U, the goods and live information of the idol “X”, and the abnormal congestion that occurs due to the live of the idle “X”. Information such as location and time zone may be provided.

  That is, if the information providing apparatus 10 provides the user U associated with the identified event with the content associated with the identified event, the information providing apparatus 10 provides the content with any content to any user U. May be

[2. Configuration of Information Providing Device]
Hereinafter, an example of a functional configuration of the information providing apparatus 10 for realizing the above-described learning process will be described. FIG. 2 is a diagram showing an example of the configuration of the information providing apparatus 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 network interface card (NIC). The communication unit 20 is connected to the network N in a wired or wireless manner, and transmits and receives information to and from the terminal device 100 and the search server 200.

  The storage unit 30 is realized by, for example, a semiconductor memory device such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk or an optical disk. In addition, the storage unit 30 stores a user database 31, a route search log database 32, and an event database 33 (hereinafter, may be described as "each database 31 to 33").

  Hereinafter, an example of the information registered in each of the databases 31 to 33 will be described using 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”, “position history”, and “biometric information log”. Information is registered. In addition to the information illustrated in FIG. 3, arbitrary information (for example, a history of post information) may be registered in the user database 31 regarding the user U.

  Here, the “user ID” is an identifier for identifying the user U. Further, “attribute information” is information indicating various attributes such as the user U's name, gender, age, taste for taste, place of residence, and the like. The “search history” is a history of search queries input by the user U, and may include not only a history of search queries of a search using the Internet but also a history of search queries of a route search . The “purchase history” is a history of purchases made by the user U via the online shopping mall or an auction. The “position history” is a history of position information acquired by the terminal device 100 used by the user U. Further, the “biometric information log” is a history of the biometric information of the user U.

  For example, in the example shown in FIG. 3, in the user database 31, the user ID “U1”, attribute information “attribute information # U1”, search history “search history # U1”, purchase history “purchase history # U1”, The position history “position history # U1” and the biological information log “biometric information # U1” are registered in association with each other. Such information is that 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. And indicates that the position history is “position history # U1” and the history of biological information is “biological information # U1”.

  In the example illustrated in FIG. 3, conceptual values such as “attribute information # U1” and the like are described, but in actuality, the user database 31 includes the attributes of the user U, the history of search queries, and purchase A character string or a numerical value indicating a history, a history of position, a history of biological information and the like will be registered.

  Next, an example of 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, in the route search log database 32, “log ID”, “departure date”, “departure date”, “arrival date”, “arrival date”, “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 for identifying a route search log. The "departure date" is the date and time taken as the departure date in the route search result. Further, the "departure place" is a departure place designated by the user U in the route search. The "arrival date" is the date and time taken as the arrival date in the route search result. Further, the “destination” is the destination specified by the user U in the route search. Moreover, "user ID" is an identifier of the user U who performed the route search. The “search date” is information indicating the date on which the route search was performed.

  For example, in the example shown in FIG. 4, in the route search log database 32, the log ID “Q1”, the departure date “2017/9/12/8: 00”, the departure place “station E”, the arrival date “2017/9 Information such as “12/9/9: 00”, arrival place “station D”, user ID “U1”, and 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" for 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 indicated 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 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, in the event database 33, information having items such as "event ID", "location", "neighboring station", "location date", and "correspondence information" is registered. In addition to the information shown in FIG. 5, various types of information regarding the event may be registered in the event database 33.

  Here, "event ID" is an identifier that identifies an event. Also, “Holding place” is information indicating a place where an event occurs. Also, “nearby station” is information indicating a station located near a place where an event occurs. Also, “held date” is information indicating the date when the event is performed. Further, “correspondence information” is information indicating information of a user presumed to be associated with an event, and, for example, attribute information of a user U having high relevance to an event, search history, purchase history, It is information indicating a tendency of a position history, a biological information log and the like.

  For example, in the example shown in FIG. 5, in the event database 33, the event ID “F1”, the held location “place #A”, the neighboring stations “station A, station C”, the held date “2017/12/1/9: 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 on the date indicated by the holding date “2017/12/11/9: 00” at the location indicated by the holding location “place #A”. Further, such information indicates that the nearby station "Station A, Station C" is located near the holding place "Place #A". Further, such information indicates that the event indicated by the event ID “F1” is an event having relevance to the user U corresponding to the information indicated by the correspondence information “correspondence information # 1”.

  In the example shown in FIG. 5, conceptual values such as “place #A” and “correspondence information # 1” are described in the event database 33. However, actually, a character string indicating the place where the event is held Or, numerical values, character strings, conditions, etc. indicating the tendency of the information of the user U are registered.

  Return to FIG. 2 and continue the description. The control unit 40 is a controller, and for example, various programs stored in a storage device inside the information providing apparatus 10 are processed by a processor such as a central processing unit (CPU) or a micro processing unit (MPU). This is realized by being executed as a work area. The control unit 40 is a controller, and may be realized by an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

  As illustrated in FIG. 2, the control unit 40 includes an acquisition unit 41, a prediction unit 42, a specification unit 43, and a provision unit 44. The acquisition unit 41 acquires action schedule information indicating a user's future action. For example, the acquisition unit 41 acquires, from the search server 200, a route search log including a search query of a route search input by the user U as 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 place based on the action schedule information. For example, the prediction unit 42 predicts congestion at a predetermined place 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 for a moving route from a predetermined departure point to a predetermined arrival position 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 may calculate the predetermined based on the difference between the action schedule information indicating the user's action on the predetermined date and time at the predetermined place and the action schedule information indicating the average user's action at the predetermined place. Predict the congestion at a given date and time at the location of As a more specific example, the prediction unit 42 shows an average number of pieces of action schedule information indicating the user's action at a predetermined place, and an action plan indicating the user's action at a predetermined place at a predetermined date and time. The congestion of the predetermined place at a predetermined date and time is predicted based on the comparison result with the number of pieces of information. For example, in the prediction unit 42, the number of pieces of action schedule information indicating the user's action at a predetermined date and time in the predetermined place is more than the average number of pieces of action schedule information indicating the user's actions in the predetermined place. If the number is larger 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 whose congestion is to be predicted, and extracts from the route search log database 32 a route search log whose destination is the selected station. Then, the prediction unit 42 predicts congestion at the selected station using the extracted log. For example, the prediction unit 42 uses the extracted log to estimate the average number of users in each time zone of the selected station. For example, the prediction unit 42 counts, for each time zone, the number of logs whose arrival date and time is included in the time zone, and from the oldest arrival date and time to the latest arrival date and time included in the log from which the counted number of logs is extracted. By dividing by the number of days, the average number of users in each time zone of the selected station is estimated. 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 is included in the time zone for 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 the number. Then, when the number of logs in any time zone of any date and time is larger than the number of users of that time zone in the averaging pattern by the predetermined threshold or more, the prediction unit 42 It is estimated that abnormal congestion occurs in the time zone. That is, the prediction unit 42 calculates the average number of pieces of action schedule information indicating the action of the user in the predetermined time zone and the action of the user in the predetermined time zone of the predetermined date and time in the predetermined place. Based on the comparison result with the number of pieces of action schedule information to be shown, congestion of a predetermined place at a predetermined date and time is predicted.

  For example, FIG. 6 is a diagram for explaining an example of a process in which the information providing apparatus according to the embodiment predicts abnormal congestion. For example, in (A) of FIG. 6, the number of route search logs with a certain station (hereinafter referred to as "station D") as the arrival place, and each time zone from 0 o'clock to 24 o'clock The graph shows the transition of the average number of logs including the arrival date and time. Further, (B) in FIG. 6 is the number of route search logs with station D as the arrival site, and the arrival date and time is included in each time zone from 0 o'clock to 24 o'clock on December 12, 2017. The graph which shows the transition of the number of log being done was described. In the graphs shown in (A) and (B) in FIG. 6, 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 (A) in FIG. 6 with the graph shown in (B) in FIG. 6, the period from about 6:00 am to about 15:00 pm on December 12, 2017 is taken as the arrival date and time, and the station The number of route search logs whose destination is D is increasing. From such a comparison result, it can be predicted that abnormal congestion will occur at the station D between about 6:00 am and around 15:00 pm on December 12, 2017. Therefore, the prediction unit 42 predicts abnormal congestion occurring at a predetermined date and time from the comparison result of such an averaging pattern AP and the prediction pattern PP at a predetermined date and time.

  Note that the prediction unit 42 predicts the future behavior of the user U based on the post information posted by the user U, and predicts congestion at a predetermined location based on the behavior schedule information indicating the predicted behavior. May be That is, the prediction unit 42 calculates the averaging pattern AP as shown in FIG. 6 based on the number of arbitrary pieces of action schedule information indicating the place where the user U visits and the time when the user U visits the place. It is possible to generate the prediction pattern PP at a predetermined date and time, and to predict the abnormal congestion based on the comparison result of the generated patterns.

  Return to FIG. 2 and continue the description. When it is predicted that congestion will occur, the identifying unit 43 identifies an event that causes congestion based on the information of the user linked to the action schedule information associated with the predetermined place. For example, the identifying unit 43 identifies an event that causes congestion based on information of a user who has input action schedule information related to a predetermined place. For example, when the action schedule information includes a search query for route search, the specifying unit 43 specifies an event that causes congestion at the arrival location indicated by the search query. For example, the identification unit 43 may use the history of searches by the user U using the Internet, the history of electronic commerce by the user U, the history of the position of the user U, the attribute of the user U, or biometric information of the user U Identify events that cause congestion based on at least one of them.

  More specifically, when it is predicted that congestion will occur at a predetermined date and time at a predetermined location, the identification unit 43 causes a user predicted to visit the predetermined location at the predetermined date and time from the action schedule information. Based on the information of the cause user estimated as a user and the cause user identified, an event causing the congestion is identified. Here, the identifying 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 the other user U (that is, general use) Events that cause congestion may be identified based on the difference between the

  For example, when it is predicted that abnormal congestion will occur at station D between about 6:00 am and around 15:00 pm on December 12, 2017, the identifying unit 43 refers to the route search log database 32, The log whose arrival date and time is included in each time zone from around 6 am to around 15:00 on December 12, 2017, and whose arrival place is "Station D" is specified. Subsequently, the identifying unit 43 identifies the user ID included in the identified log as a user ID indicating the cause user. That is, the specifying unit 43 specifies the user U who is predicted 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 specifying unit 43 randomly selects a predetermined number of users U from the users other than the cause user, and sets the selected user U as a general user. Then, the identification unit 43 acquires the information of the cause user and the information of the general user from the user database 31, and obtains the difference between the acquired information, that is, the information of the cause user and the information of the general user. Identify the differences.

  Further, the identifying unit 43 refers to the event database 33 and extracts an event to be held in the vicinity of the station D after 6:00 am on December 12, 2017. In addition, the identification unit 43 extracts an event to be held in the vicinity of the station D between about 6:00 am to about 15:00 pm on December 12, 2017 where it is predicted that abnormal congestion will occur. It is also good. In addition, the identification unit 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 the predetermined period elapses. Event may be extracted, and in the vicinity of Station D between about 15:00 pm on December 12, 2017, which is the first time zone in which abnormal congestion is predicted to occur, and until a predetermined period elapses. You may extract the event to be held.

  Then, the identifying unit 43 calculates the degree of association 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 the calculated degree of association exceeds a predetermined threshold. Identify. For example, when there is a tendency for the cause user to have a predetermined taste preference as compared with the general user, the specifying unit 43 specifies an event having the highest relevance to the taste preference.

  The identifying unit 43 may identify an event that causes congestion based on information acquired within a predetermined period from a predetermined date and time among the information of the cause user. The identifying unit 43 may also extract information that deviates from the average pattern among the information of the cause user, and may specify an event that is a cause of congestion based on the extracted information. In addition, the identification unit 43 searches for events having high relevance to the information of the cause user among events scheduled to occur on the predetermined date and time in the vicinity of the predetermined place, and also uses the searched events as the cause of congestion. Good.

  Here, FIG. 7 is a diagram for explaining an example of processing in which the information providing apparatus according to the embodiment identifies an event. In the example shown in FIG. 7, the specific process which the information provision apparatus 10 performs was shown notionally. Moreover, in the example shown in FIG. 7, the user who visits the station D for the purpose of the event which becomes the cause when abnormal congestion generate | occur | produced was shown with a color, and other users were shown without a color.

  For example, as illustrated in (A) in FIG. 7, the prediction unit 42 specifies abnormal congestion from the search query pattern of the route search. That is, the prediction unit 42 predicts the abnormal congestion occurring in the station D by comparing the average number of users of the station D with the number of users of the station D at the time of the abnormal congestion occurrence. And specific part 43 makes a user of station D at the time of unusual congestion generating a cause user.

  Here, as shown in FIG. 7, it is estimated that the cause users include users who regularly use the station D other than the users who aim for the event causing the abnormal congestion. Be done. Therefore, as shown in (B) in FIG. 7, the identifying 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 the user's information for the purpose of the event causing the Then, as illustrated in (C) in FIG. 7, the specifying unit 43 specifies an event that causes abnormal congestion by specifying an event corresponding to the difference.

  Return to FIG. 2 and continue the description. The providing unit 44 provides the user U with various contents based on the identified event. For example, the providing unit 44 is used in association with a predetermined place, such as a user who has made a route search using a place where congestion is predicted to occur as a destination, or a user who regularly uses the place. Provide information indicating the identified event to the

  Also, the providing unit 44 may provide information related to the identified event to the user associated with the predetermined place. For example, the providing unit 44 may provide, to a user who has performed a route search whose destination is a place where congestion is predicted to occur, advertisement and recommendation information of a product related to the identified event. Further, the providing unit 44 may provide the user U associated with the identified event with information indicating the identified event or information associated with the identified event. For example, the providing unit 44 may identify the user U estimated to be highly relevant to the identified event, and provide various contents related to the event to the identified user U.

[3. Processing Flow of Information Providing Device]
Next, an example of the procedure of the identification process performed by the information providing apparatus 10 will be described with reference to FIG. FIG. 8 is a flowchart showing an example of the flow of identification processing performed by the information providing apparatus according to the embodiment.

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

  In addition, the information providing apparatus 10 specifies, as the cause user, the user who has input the search query having the predetermined place as the arrival place at the specified date and time (step S104). Then, the information providing apparatus 10 identifies an event corresponding to the difference between the information on the cause user and the information on the randomly selected general user (step S105). Thereafter, the information providing apparatus 10 provides information indicating the specified event (step S106), and ends the process.

[4. Modified 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, the variation of the specific process which the information provision apparatus 10 performs is demonstrated.

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

  Further, when the information providing apparatus 10 predicts congestion in 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 Based on the arrival date and time obtained as a result and the arrival date and time input as a search query, the date and time when the user U is predicted to arrive at the place is estimated, and based on the estimated date and time, the date and time when congestion occurs It may be predicted. That is, the information providing apparatus 10 predicts the date and time when congestion will occur based not only on the information on the date and time included in the result of the route search but also on the arrival date and time of the user U estimated from the information. Good.

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

[4-2. Device configuration〕
The information providing device 10 may be communicably connected to any number of terminal devices 100, and may be communicably connected to any number of search servers 200. In addition, the information providing apparatus 10 may be communicable with an e-mail distribution server, an SNS server that accepts SNS postings, 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 illustrated in FIG. 2 is disposed in the front end server, and the acquisition unit 41, the prediction unit 42, and the identification unit 43 illustrated in FIG. 2 are included in the back end server. . Further, each of the databases 31 to 33 registered in the storage unit 30 may be managed by an external storage server.

[4-3. Other]
Further, among the processes described in the above embodiment, all or part of the process described as being automatically performed may be manually performed, or the process described as being manually performed. All or part of them can be performed automatically by known methods. In addition, information including processing procedures, specific names, various data and parameters shown in the above-mentioned documents 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 device illustrated is functionally conceptual, and does not necessarily have to be physically configured as illustrated. That is, the specific form of the distribution and integration of each device is not limited to the illustrated one, and all or a part thereof may be functionally or physically dispersed in any unit depending on various loads, usage conditions, etc. It can be integrated and configured.

  Moreover, it is possible to combine suitably each embodiment mentioned above in the range which does not contradict process content.

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

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

  The output IF 1060 is an interface for transmitting information to be output to an output device 1010 that outputs various types of information such as a monitor and a printer. For example, USB (Universal Serial Bus), DVI (Digital Visual Interface), or the like It is realized by a connector of a standard such as HDMI (High Definition Multimedia Interface). 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 or the like.

  The input device 1020 may be, for example, an optical recording medium such as a compact disc (CD), a digital versatile disc (DVD), a phase change rewritable disc (PD), a magneto-optical recording medium such as a magneto-optical disc (MO), or a tape. It may be a device that reads information from a medium, a magnetic recording medium, a semiconductor memory, or the like. The input device 1020 may also be an external storage medium such as a USB memory.

  The network IF 1080 receives data from another device via the network N and sends it to the computing device 1030, and transmits data generated by the computing device 1030 via the network N to the other device.

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

  For example, when the computer 1000 functions as the information providing device 10, the computing device 1030 of the computer 1000 executes the program or data (for example, the processing model M1) loaded on the primary storage device 1040 to execute 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 unit 1040, but as another example, these programs may be read from other devices 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 action schedule information indicating the future action of the user U. Then, when it is predicted that congestion will occur, the information providing apparatus 10 specifies an event that causes the congestion based on the information of the user U linked to the action schedule information related to the predetermined place. As a result, when congestion occurs, the information providing apparatus 10 can identify an event that causes the congestion.

  Further, the information providing apparatus 10 predicts congestion at a predetermined place based on the action schedule information input by the user U, and based on information of the user U who has input the action schedule information related to the predetermined place. Identify events that cause congestion. For example, the information providing apparatus 10 predicts congestion at a predetermined place based on a search query for searching a moving route from a predetermined departure point to a predetermined arrival position at a predetermined date and time as action schedule information. Then, the information providing apparatus 10 predicts congestion at a predetermined arrival location included in the search query based on the search query, and identifies an event that causes congestion at the predetermined arrival location. For this reason, the information providing apparatus 10 identifies an event that causes congestion, based on highly reliable action schedule information, so that the identification accuracy can be improved.

  Further, the information providing apparatus 10 is based on the difference between the action schedule information indicating the action of the user U at the predetermined date and time at the predetermined place and the action schedule information indicating the average user U action at the predetermined place. Based on the congestion of a predetermined date and time at a predetermined place is predicted. For this reason, the information providing apparatus 10 can predict the occurrence of the abnormal congestion, that is, the occurrence of the abnormal congestion, and can identify the event that is the cause of the predicted abnormal congestion.

  In addition, the information providing device 10 includes an average number of pieces of action schedule information indicating the action of the user U in a predetermined place, and a number of pieces of action schedule information indicating the action of the user U in a predetermined place at a predetermined date Based on the comparison result of the above, the congestion of a predetermined place at a predetermined date and time is predicted. For example, in the information providing apparatus 10, the number of pieces of action schedule information indicating the action of the user U at a predetermined place at a predetermined place is more than the average number of pieces of action schedule information indicating the action of the user U Also, 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.

  In addition, the information providing apparatus 10 can calculate the average number of pieces of action schedule information indicating the action of the user U in a predetermined time zone and the user U in a predetermined time zone of a predetermined date and time in a predetermined place. The congestion of a predetermined place at a predetermined date and time is predicted based on the comparison result with the number of pieces of action schedule information indicating an action of For this reason, the information providing apparatus 10 can predict the occurrence of abnormal congestion for each time zone.

  In addition, when it is predicted that congestion will occur on a predetermined date and time at a predetermined location, the information providing apparatus 10 causes the user U who is predicted to visit the predetermined location at a predetermined date and time from the action schedule information. Based on the information of the estimated cause user, the event that causes the congestion is identified. Therefore, the information providing device 10 can improve the identification accuracy of the event causing the abnormal congestion.

  For example, the information providing apparatus 10 identifies an event that causes the congestion based on the commonality of the information of the cause user. Also, for example, the information providing apparatus 10 identifies an event that is a cause of congestion based on the difference between the information of the cause user and the information of the other user U. In addition, the information providing apparatus 10 may identify an event that causes congestion based on information acquired within a predetermined period from a predetermined date and time among the information of the cause user. Further, the information providing apparatus 10 may extract information out of the average pattern of the information among the information of the cause user, and specify an event that causes congestion based on the extracted information. .

  Further, the information providing apparatus 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, events that cause congestion may be identified. Further, the information providing apparatus 10 searches for events having high relevance to the information of the cause user among events scheduled to occur on the predetermined date and time in the vicinity of the predetermined place, and sets the searched events as the cause of congestion. Do. As a result of such processing, the information providing apparatus 10 can appropriately identify an event that causes abnormal congestion.

  In addition, the information providing apparatus 10 predicts the future behavior of the user U based on the post information posted by the user U, and based on the action schedule information indicating the predicted behavior, the congestion in the predetermined place It may be predicted. As a result of such processing, the information providing apparatus 10 can appropriately predict congestion from the information posted to the SNS or the like.

  Further, the information providing device 10 provides the user U associated with the predetermined place with information indicating the identified event. In addition, the information providing apparatus 10 provides the user U associated with the predetermined place with the information associated with the identified event. In addition, the information providing device 10 provides the user U associated with the identified event with information indicating the identified event or information associated with the identified event. For this reason, the information providing apparatus 10 can distribute various contents in various distribution modes with an event causing congestion as an axis.

  Although some of the embodiments of the present application have been described above in detail based on the drawings, these are only examples, and various modifications can be made based on the knowledge of those skilled in the art, including the aspects described in the section of the description of the invention. It is possible to implement the invention in other improved forms.

  Also, the "section (module, unit)" described above can be read as "means" or "circuit". For example, the delivery unit can be read as delivery means or delivery circuit.

DESCRIPTION OF REFERENCE NUMERALS 10 information provision device 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 identification unit 44 provision unit 100 terminal device 200 search server

Claims (21)

A prediction unit that predicts congestion at a predetermined place based on action schedule information indicating a user's future action;
And a specifying unit for specifying an event that causes the congestion based on the information of the user linked to the action schedule information related to the predetermined place when the prediction unit predicts that congestion will occur. A specific device characterized by The prediction unit predicts congestion at the predetermined place 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 information of a user who has input activity schedule information related to the predetermined place. The prediction unit predicts congestion at the predetermined place based on a search query for searching for a movement route from a predetermined departure point to a predetermined arrival position at a predetermined date and time as the action schedule information. The specific device according to claim 1, wherein the specific device is characterized. The prediction unit predicts congestion at a predetermined destination included in the search query based on the search query,
The identifying apparatus according to claim 3, wherein the identifying unit identifies an event that causes congestion at the predetermined destination. The prediction unit is configured to calculate, based on a difference between action schedule information indicating a user's action at a predetermined date and time at the predetermined place and action schedule information indicating an average user action at the predetermined place. The specific device according to any one of claims 1 to 4, wherein congestion of a predetermined date and time at a predetermined place is predicted. The prediction unit compares the average number of action schedule information indicating the user's action at the predetermined place with the number of action schedule information indicating the user's action at a predetermined date and time at the predetermined place. The identification device according to any one of claims 1 to 5, wherein congestion of the predetermined place at the predetermined date and time is predicted on the basis of. The prediction unit is configured such that the number of pieces of action schedule information indicating a user's action at a predetermined date and time at the predetermined place is more than an average number of pieces of action schedule information indicating the user's actions at the predetermined place. The identification device according to claim 6, wherein it is predicted that congestion will occur at a predetermined date and time at the predetermined place if there is a threshold value or more. The prediction unit is configured to calculate an average number of pieces of action schedule information indicating a user's action in a predetermined time zone, and an action of the user in the predetermined time zone at a predetermined date and time in the predetermined place. The identification device according to claim 6 or 7, wherein congestion of the predetermined place at the predetermined date and time is predicted based on a comparison result with the number of pieces of action schedule information indicating. When it is predicted that congestion will occur on the predetermined date and time at the predetermined place, the specifying unit causes the user predicted to visit the predetermined place at the predetermined date and time from the action schedule information to cause use The identification device according to any one of claims 5 to 8, wherein an event that is the cause of the congestion is identified based on the information of the cause user estimated as a person and estimated. The identifying apparatus according to claim 9, wherein the identifying unit identifies an event that causes the congestion based on the commonality of the information of the cause user. The identification unit according to claim 9 or 10, wherein the identification unit identifies an event that causes the congestion based on a difference between the information of the cause user and the information of another user. apparatus. The identification unit identifies an event that causes the congestion, based on the information acquired within the predetermined period from the predetermined date and time among the information of the cause user. The specific device as described in any one of -11. The identification unit extracts information out of the average pattern of the information among the information of the cause user, and identifies an event that is a cause of the congestion based on the extracted information. The specific device according to any one of claims 9 to 12. The identification 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 apparatus according to any one of claims 9 to 11, wherein an event that causes the congestion is identified based on the information. The identification unit searches for events having high relevance to the information of the cause user among events scheduled to occur on the predetermined date and time near the predetermined place, and causes the searched events to cause the congestion The identification device according to any one of claims 9 to 12, characterized in that. The prediction unit predicts future behavior of the user based on post information posted by the user, and predict congestion at a predetermined place based on action schedule information indicating the predicted behavior. The specific device according to any one of claims 1 to 14, characterized in that: The identification unit according to any one of claims 1 to 15, further comprising: a provision unit that provides information indicating an event identified by the identification unit to a user associated with the predetermined place. apparatus. The information processing apparatus according to any one of claims 1 to 15, further comprising: a providing unit that provides information related to the event specified by the specifying unit to a user associated with the predetermined place. Specific device. A provision unit is provided which provides the user associated with the event identified by the identifying unit with information indicating the event identified by the identifying unit or information associated with the event identified by the identifying unit. The specific device according to any one of claims 1 to 15, characterized by A specific method performed by a specific device,
Predicting the congestion at a predetermined place based on action schedule information indicating a user's future action;
And a specifying step of specifying an event causing the congestion based on the information of the user linked to the action schedule information associated with the predetermined place when the congestion is predicted to occur in the prediction step. A specific method characterized by A prediction procedure for predicting congestion at a predetermined place based on action schedule information indicating a user's future action;
When it is predicted that congestion will occur according to the prediction procedure, a identification procedure for identifying an event that causes the congestion based on the information of the user linked to the action schedule information associated with the predetermined place. Specific program to run on.

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