JP6461230B2 - Determination device, determination method, and determination program - Google Patents

Description

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

  Some services provided to the user change the efficiency of the service depending on whether or not the user is at home. Taking a home delivery service as an example, if the user is at home, the delivered package can be delivered smoothly, but if the user is not present, the delivery of the package cannot be completed. Such inconveniences can occur.

  In recent years, a technique for changing the operation of a device installed in a home based on whether or not the user is at home has been proposed. For example, a technology relating to a home server that determines the number of inhabitants at home based on a pre-registered home schedule and realizes more rational hot water management has been proposed.

Japanese Patent Laying-Open No. 2015-218933

  However, with the above-described conventional technology, it is difficult to accurately determine the possibility of the user being at home. Specifically, in the above-described conventional technology, registration of the home schedule needs to be received from the user in advance. For this reason, in the above-described conventional technology, for example, it is difficult to determine the possibility of staying of a user who has not performed schedule registration or to determine the possibility of staying in accordance with a situation that changes in real time.

  The present application has been made in view of the above, and an object thereof is to provide a determination device, a determination method, and a determination program that can accurately determine the possibility of a user being at home.

  The determination device according to the present application is based on a relationship between an acquisition unit that acquires a usage status in a service provided to a user, a usage status acquired by the acquisition unit, and the possibility that the user is at home. And a determination unit for determining the possibility of being at home.

  According to one aspect of the embodiment, there is an effect that it is possible to accurately determine the possibility of the user being at home.

FIG. 1 is a diagram illustrating an example of a determination process according to the embodiment. FIG. 2 is a diagram illustrating a configuration example of a determination processing system according to the embodiment. FIG. 3 is a diagram illustrating a configuration example of the determination apparatus according to the embodiment. FIG. 4 is a diagram illustrating an example of an attribute table according to the embodiment. FIG. 5 is a diagram illustrating an example of a usage status table according to the embodiment. FIG. 6 is a diagram illustrating an example of the definition table according to the embodiment. FIG. 7 is a diagram illustrating an example of a learning data table according to the embodiment. FIG. 8 is a diagram illustrating an example of a model table according to the embodiment. FIG. 9 is a diagram illustrating an example of a provision schedule storage unit according to the embodiment. FIG. 10 is a diagram illustrating a configuration example of a user terminal according to the embodiment. FIG. 11 is a flowchart (1) illustrating a processing procedure according to the embodiment. FIG. 12 is a flowchart (2) illustrating a processing procedure according to the embodiment. FIG. 13 is a flowchart (3) illustrating a processing procedure according to the embodiment. FIG. 14 is a flowchart (4) illustrating a processing procedure according to the embodiment. FIG. 15 is a hardware configuration diagram illustrating an example of a computer that realizes the function of the determination apparatus.

  Hereinafter, modes for carrying out a determination apparatus, a determination method, and a determination program according to the present application (hereinafter referred to as “embodiments”) will be described in detail with reference to the drawings. Note that the determination device, the determination method, and the determination program according to the present application are not limited by this embodiment. In addition, the embodiments can be appropriately combined within a range that does not contradict processing contents. In the following embodiments, the same portions are denoted by the same reference numerals, and redundant description is omitted.

[1. Example of judgment process)
First, an example of the determination process according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram illustrating an example of a determination process according to the embodiment. In FIG. 1, an example in which the determination device 100 according to the present application performs a process of determining a user's at-home possibility based on a relationship between a usage situation in a service provided to the user and the user's at-home possibility. Show.

  The determination apparatus 100 illustrated in FIG. 1 acquires the usage status in a service provided to a user via a network, and determines the user's home potential based on the relationship between the acquired usage status and the user's home potential. It is a server device that performs processing for determination. In the embodiment, various web services provided via a network such as the Internet are taken as examples of services.

  A service server 30 shown in FIG. 1 is a server device that provides a service to a user. For example, the service server 30 is a web server that provides a web service. As an example, when the service server 30 receives a login from a user, the service server 30 provides a portal service that provides a portal site customized for the user. The service server 30 registers a search service, a shopping service, an auction service, an information providing service (for example, a map information service, a navigation service, a news service, a weather forecast service), a task setting, and a schedule. Various services such as a calendar service may be provided. Although illustration in FIG. 1 is omitted, the service server 30 according to the embodiment is not limited to one, and a plurality of service servers 30 may exist.

  A user terminal 10 illustrated in FIG. 1 is an information processing terminal such as a smartphone used by a user. A user U01 shown in FIG. 1 is an example of a user who uses a service provided from the service server 30. In the example illustrated in FIG. 1, the user terminal 10 is used by the user U01. Although illustration in FIG. 1 is omitted, the number of user terminals 10 according to the embodiment is not limited to one, and a plurality of user terminals 10 may exist. That is, the user U01 may have a plurality of user terminals 10. Hereinafter, the user may be read as the user terminal 10. For example, the description “user U01 accesses service server 30” may actually indicate a situation “user terminal 10 used by user U01 accesses service server 30”.

  The service provider 40 shown in FIG. 1 is a provider who provides a service to the user U01 offline. In the example of FIG. 1, the service provider 40 provides a home delivery service for delivering a package to the user U01. It is assumed that the service provider 40 possesses and uses a service provider device 50 (not shown in FIG. 1) that is an information processing terminal that can communicate with the determination device 100.

  In the example illustrated in FIG. 1, the determination device 100 determines the possibility of the user U01 being at home, and transmits the determined result to the service provider 40. That is, when the service provider 40 tries to deliver a package to the home of the user U01, the determination apparatus 100 notifies in advance that the user U01 is absent, or is more likely to be at home than the user U01. Notify other users' information. Thereby, the determination apparatus 100 enables provision of a smooth service. Hereinafter, the flow of the determination process according to the embodiment by the determination apparatus 100 will be described with reference to FIG.

  First, the user U01 uses various services provided from the service server 30 via the user terminal 10 (step S11). For example, the user U01 uses a portal service provided by the service server 30. The user U01 may use a reservation service that reserves restaurants, transportation facilities, and the like, a calendar service, and a shopping service. The determination apparatus 100 acquires the usage status of the service when the user U01 uses the service (step S12). The determination apparatus 100 stores the acquired usage status in the usage status storage unit 121.

  In the embodiment, the usage status in the service means log information (behavior history or usage history) taken by the user U01 in the service, information transmitted / received by the user U01 when using the service, or when using the service. Information on the context of the user U01. In addition, the usage status in the service may include attribute information of the user U01 who uses the service (age and sex of the user U01, place of residence, occupation, workplace, etc.).

  For example, the determination apparatus 100 acquires position information of the user U01 when using the service. Specifically, the determination apparatus 100 uses the GPS (Global Positioning System) function of the user terminal 10 to acquire the position information acquired when the service is logged in or every predetermined time during login (for example, Every 1 minute). The position information acquired by the determination apparatus 100 is not limited to the position information acquired by the GPS function, and may be position information estimated from the IP address of the user terminal 10 that has accessed the service, for example. Further, the position information may be a specific numerical value indicating longitude or latitude, or address information indicating a predetermined area. In this case, for example, the determination apparatus 100 refers to a definition database or the like for associating position information and address information, and specifies address information from the acquired position information.

  In the embodiment, the determination apparatus 100 may specify the position of the user U01 such as the home or work place by various methods. For example, the determination apparatus 100 may identify the address and work place of the user U01 based on user information registered by the user U01 in a predetermined service (for example, information presented to the service side in member registration for the service). Good. Alternatively, the determination apparatus 100 estimates the position indicated by the position information frequently observed early in the morning or at night in the position information continuously acquired from the user terminal 10, and specifies the position of the user U01's home. Also good. Further, the determination apparatus 100 may estimate the position indicated by the position information frequently observed during the weekday daytime as the work place, and specify the position of the work place of the user U01.

  Moreover, the determination apparatus 100 may acquire schedule information on which the user U01 has made a reservation in a reservation service such as a restaurant, a transportation facility, or an accommodation facility as the usage status. Moreover, the determination apparatus 100 may acquire schedule information that specifies that the user U01 has purchased a product in the shopping service and the delivery date and time of the product as the usage status. Moreover, the determination apparatus 100 may acquire the communication status of the user terminal 10 when using the service as the usage status. For example, the determination apparatus 100 acquires a situation in which the user terminal 10 performs short-range communication such as Bluetooth (registered trademark) with an IoT (Internet of Things) device installed in the home of the user U01. Thus, the determination apparatus 100 acquires various information that can be acquired from the user terminal 10 when using the service, as the usage status. Note that the determination apparatus 100 may acquire the usage status of the service of the user U01 from the user terminal 10 or the service server 30.

  Further, the determination apparatus 100 may continuously acquire the usage status of the user U01 over a predetermined period (for example, one month or one year) in association with each day of the week or time period. The determination apparatus 100 can accumulate the usage status of the user U01 in the service provided to the user U01 in association with each day of the week or time period by continuously acquiring the usage status.

  Then, the determination apparatus 100 determines the possibility that the user U01 is at home based on the usage situation (step S13). For example, the determination apparatus 100 holds a database (denoted as “definition file” in the example of FIG. 1) in which a definition for defining the relationship between the acquired usage status and the possibility of being at home of the user U01 is described. To do. The contents of the definition file are set by, for example, the administrator of the determination apparatus 100.

  In the definition file, the relationship between the usage status of the service and the possibility of being at home is defined. Specifically, the definition file defines an index value (score) that indicates how likely you are at home when a specific usage status of a service is observed at the time to be judged. It is done. For example, in the example of FIG. 1, it is assumed that the definition file indicates the possibility that the user is at home when a certain usage situation is observed as a score from “−100” to “100”. In this example, the greater the score, the higher the likelihood that the user U01 will be at home. In addition, when a plurality of usage situations are observed at the time to be determined (hereinafter referred to as “target date and time”), the determination apparatus 100 adds the scores and determines the user to be determined The possibility of staying at home shall be determined. Although details will be described later, the score is not necessarily fixed, and may change dynamically by learning, for example. Moreover, a score is not common with respect to all the users, and may change dynamically for every user. The score only indicates the possibility of staying at home, and the numerical value itself may not have an absolute meaning.

  As an example, in the definition file, when the position information acquired from the user terminal 10 is within a predetermined range (for example, a range within several tens of meters from the home) of the user U01 at the target date and time. Describes a definition that the score indicating the possibility of staying at home of the user U01 is “60”. This is because it is estimated that the possibility that the user U01 is at home is relatively high when the position information acquired from the user terminal 10 indicates the vicinity of the home of the user U01 at the target date and time. Show.

  The definition file may describe a score related to the usage status estimated at the target date and time. For example, in the definition file, position information acquired from the user terminal 10 a predetermined time before the target date and time is within a predetermined range from the position of the user U01's home (for example, the user U01 can return home after the predetermined time) In such a case, the definition that the score indicating the possibility of staying at home of the user U01 is “40” may be described. Alternatively, the definition file indicates the possibility that the user U01 is at home when the position information acquired from the user terminal 10 is outside the predetermined range from the position of the user U01 at a predetermined time before the target date and time. The definition that the score is “−40” may be described. Note that these scores may vary depending on the position where the user U01 is located. For example, it is assumed that the position information acquired from the user terminal 10 a predetermined time before the target date and time is position information that indicates a position far away from the home position of the user U01 (for example, an overseas country). In this case, since it is estimated that the user U01 cannot stay at the target date and time, the score indicating the possibility that the user U01 is at home may be defined as “−100”.

  As another example, in the definition file, when the user U01 registers schedule information such as a restaurant or accommodation facility reservation at the target date and time, a score indicating the possibility of the user U01 being at home at the target date and time A definition such that is “−30” may be described. This is because if the target date and time overlaps with the time when the user U01 reserved a restaurant or the like, it is estimated that the possibility that the user U01 is at home is relatively low at the target date and time. .

  As another example, in the definition file, when the delivery of the product purchased by the user U01 using the shopping service is designated at the target date and time, the score indicating the possibility that the user U01 is at home at the target date and time is “ 50 ”is described. This indicates that if the target date and time overlaps with the time when the user U01 specified delivery, it is estimated that the user U01 is relatively likely to be at home at the target date and time. . As described above, the determination apparatus 100 holds a plurality of definitions indicating the relationship between the usage status in the service and the possibility of staying at the user U01.

  Then, the determination apparatus 100 determines the possibility that the user U01 is at home at the time to be determined based on these definition files. For example, the determination apparatus 100 determines the possibility that the user U01 is at home using the time specified by the service provider 40 as the target date and time.

  For example, it is assumed that the determination apparatus 100 is requested by the service provider 40 to determine the possibility that the user U01 is at home “two hours after the current time”. In this case, the determination apparatus 100 extracts a usage situation that is related to the possibility of being at home from the usage situation in the service of the user U01, and calculates a score corresponding to the extracted usage situation for 2 hours from the present time. The possibility that the user U01 will be at home later is determined.

  For example, it is assumed that the position information of the user U01 at the present time indicates the vicinity of the user U01's home, or indicates a position where the user U01 is estimated to be able to return home within two hours. In this case, the determination apparatus 100 adds “60” or “40”, which is the above-described score, to the score indicating the possibility that the user U01 is at home. Further, it is assumed that the user U01 has specified delivery of a product purchased using a shopping service at the target date and time (that is, two hours after the current time). In this case, the determination apparatus 100 adds “50”, which is the above-described score, to the score indicating the possibility that the user U01 is at home. Then, it is assumed that the usage status used in the service of the user U01 and used for measuring the possibility of the user U01 being at home is not extracted except for the above. In this case, the determination apparatus 100 determines that the score indicating the possibility that the user U01 is at home two hours after the current time is “110” or “90”.

  Then, the determination apparatus 100 transmits the possibility of staying at the user U01 determined based on the calculated score to the service provider 40 (step S14). For example, the determination apparatus 100 transmits the possibility of being at home expressed as a percentage by normalizing the calculated score to the service provider apparatus 50 possessed by the service provider 40.

  The service provider 40 provides a service based on the possibility of being at home transmitted from the determination device 100 (step S15). For example, when the service provider 40 receives a determination result that the possibility that the user U01 is at home is relatively high from the determination apparatus 100, the service provider 40 actually visits the home of the user U01 and provides a service for delivering the package. To do. Alternatively, when the service provider 40 receives the determination result that the possibility that the user U01 is at home is relatively low from the determination apparatus 100, the service provider 40 loads the package at the user U01's home at another date and time instead of the target date and time. A home delivery service may be provided. Alternatively, the service provider 40 may call the user U01 to confirm that the user U01 is at home before visiting the home. Alternatively, the service provider 40 may have a higher possibility of being at home than the user U01, and when another user is located in the vicinity, the service provider 40 may preferentially visit the other user.

  Note that the determination apparatus 100 may not only determine the possibility of being at home based on a predetermined determination element, but also perform a predetermined learning process to improve the determination accuracy.

  For example, in step S15, the service provider 40 can confirm that the user U01 is at home by actually providing the service to the user U01. That is, the service provider 40 can obtain result information indicating whether or not the user U01 is actually at home. In other words, the service provider 40 acquires result information indicating whether or not the result determined by the determination device 100 is correct.

  The service provider 40 transmits result information indicating whether or not the user U01 is at home via the service provider device 50, for example (step S16).

  Based on the result information transmitted from the service provider 40, the determination apparatus 100 learns the correlation between the usage status used for determining the possibility of staying at the user U01 and the result information. Then, the determination device 100 reflects the learning and generates a calculation formula (model) for determining the possibility of the user U01 being at home (step S17). Although details will be described later, as an example, the determination apparatus 100 performs learning by a regression analysis method using the result information indicating whether or not the user U01 is at home as an objective variable, and using each usage state used in the determination as an explanatory variable. . Thereby, the determination apparatus 100 can derive information such as what explanatory variable (usage status) contributed to the determination of the possibility of the user U01 being at home. The determination apparatus 100 stores the generated model in the model storage unit 125.

  The determination apparatus 100 generates the above model for each user. If the user U01 has a strong tendency to go out with the user terminal 10 placed at home, the position information acquired from the user terminal 10 is likely not to contribute to the possibility of being at home. Even if the user U01 registers a schedule in the service, if the tendency to act according to the schedule is weak, the registered schedule information is highly likely not to contribute to the possibility of being at home. In this way, the determination apparatus 100 generates a model for each user so that the possibility of being at home can be derived based on the usage status of each user's service and the characteristics of individual behavior. In this case, the determination apparatus 100 may update the model stored in the model storage unit 125 every time the result information is acquired.

  Moreover, the determination apparatus 100 may update the definition file applied to the user U01 as the learning progresses. For example, as described above, for a user who has a strong tendency that the position information acquired from the user terminal 10 does not contribute to the determination of the possibility of being at home, the determination apparatus 100 determines the score corresponding to the position information according to the learning result. However, a weight that is calculated to be lower than other users may be given. Alternatively, the determination apparatus 100 assigns a weight such that a score corresponding to the usage status of a user who accurately observes a registered schedule or a specified delivery date is calculated higher than other users. May be.

  Moreover, the determination apparatus 100 may perform determination in consideration of a day of the week or a time zone. Even if the usage status corresponding to the target date and time cannot be acquired from the user U01, the determination device 100 determines the possibility of being at home on the same day in the past or the same time period in the past, and acquires the result information. There is a possibility. In this case, for example, the determination apparatus 100 may determine the possibility of staying at the user U01 at the target date and time by reflecting the possibility of staying at the user U01 on the same day in the past or the same time period in the past. Specifically, when the user U01 has been at home on the same day of the week or at the same time in the past, the determination apparatus 100 performs adjustment such as calculating a relatively high possibility of staying at the current target date and time. May be.

  Note that the determination device 100 does not necessarily acquire the result information from the service provider 40, but based on the information acquired from the user terminal 10, an event that has a high probability of the user U01 being at home is established. In addition, the learning may be performed on the assumption that the user U01 is at home at that time.

  Specifically, it is assumed that the determination apparatus 100 determines the possibility that the user U01 is at home “two hours after the current time”. Then, it is assumed that the position information of the user terminal 10 acquired two hours after the current time (target date and time) is position information indicating the home of the user U01. In this case, the probability that the user U01 was at home at the target date and time is extremely high. At this time, the determination apparatus 100 may perform learning by assuming that the result at the target date and time is “at home”. Thereby, the determination apparatus 100 can reinforce the model with only the data acquired by itself without receiving feedback from the service provider 40. Note that the determination apparatus 100 may perform learning using information other than position information. For example, the determination apparatus 100 may be an event that the IoT device installed in the user U01's home and the user terminal 10 have performed short-range communication at the target date and time, or an event that a response has been made in the user's U01 home intercom. (For example, when input of voice information is observed in the interphone and the information is acquired by the determination apparatus 100 via the network) is assumed to be correct data indicating that “user U01 was at home”. Also good.

  As described above, as described with reference to FIG. 1, the determination apparatus 100 according to the embodiment acquires the usage status in the service provided to the user U01. Then, the determination apparatus 100 determines the possibility of staying at the user U01 based on the relationship between the acquired usage situation and the possibility of staying at the user U01.

  Thus, since the determination apparatus 100 determines the possibility of the user U01 being at home based on the usage status in the service, the user U01 does not consciously perform home registration or the like, but based on the daily behavior of the user U01. Thus, the possibility of the user U01 being at home can be determined. In addition, since the determination apparatus 100 makes a determination based on the usage status of the service, for example, the user U01 is determined to determine real-time information such as the timing when the user U01 registered the schedule and the timing when the delivery date / time is specified, so The possibility can be determined. Thereby, the determination apparatus 100 can determine the possibility of staying at a user who has not performed schedule registration, or can determine the possibility of being at home according to a situation that changes in real time. Hereinafter, the configuration of the determination apparatus 100 that performs such processing and the determination processing system 1 including the determination apparatus 100 will be described in detail.

[2. Judgment processing system configuration]
The configuration of the determination processing system 1 including the determination device 100 according to the embodiment will be described with reference to FIG. FIG. 2 is a diagram illustrating a configuration example of the determination processing system 1 according to the embodiment. As illustrated in FIG. 2, the determination processing system 1 according to the embodiment includes a user terminal 10, a service server 30, a service provider device 50, and a determination device 100. These various devices are communicably connected via a network N by wire or wireless. The determination processing system 1 illustrated in FIG. 2 may include a plurality of user terminals 10, a plurality of service servers 30, and a plurality of service provider devices 50.

  The user terminal 10 is, for example, an information processing apparatus such as a smartphone, a desktop PC (Personal Computer), a notebook PC, a tablet terminal, a mobile phone, a PDA (Personal Digital Assistant), or a wearable device. It is. Furthermore, the user terminal 10 may include various smart devices having an information processing function. For example, the user terminal 10 may include a smart home appliance such as a TV (television) or a stationary speaker, a smart vehicle such as an automobile, a drone, a home robot, and the like.

  The user terminal 10 acquires a web page from a website provided from the service server 30 by accessing the service server 30 in accordance with an operation by the user. Then, the user terminal 10 displays the acquired web page on a display device (for example, a liquid crystal display). Further, when the user uses a service, the user terminal 10 may detect information regarding the service and transmit the detected information to the service side. For example, the user terminal 10 may transmit the detected position information to the service server 30 at a timing when the user uses a predetermined service.

  The service server 30 is a server device that provides various services when accessed from the user terminal 10. The service server 30 is, for example, a portal site, news site, weather forecast site, shopping site, auction site, finance (stock price) site, route search site, map providing site, travel information site, restaurant introduction site, restaurant, etc. Various services are provided through various web pages related to reservation sites, web blogs, SNS (Social Networking Service), and the like.

  Further, the service server 30 acquires the usage status of the user in the service. For example, when the service server 30 provides a service related to a portal site, the service server 30 acquires user registration information (attribute information such as gender and age), location information at the time of login, and the like as the usage status. To do. Further, when the service server 30 provides a service relating to purchase such as a shopping site or an auction site, the service server 30 uses the usage status as the frequency or purchase price at which the user purchases the product, or the delivery of the product. Get information such as the date and time to be specified. Further, the service server 30 acquires various usage statuses of the service every time the user uses the service in services other than the above. In addition, the service server 30 may transmit the usage status of the user in the service to the determination apparatus 100.

  The service provider device 50 is an information processing device used by the service provider 40. The service provider device 50 may be a terminal used by each of the service providers 40 or may be a server installed on the service side. Further, the service provider device 50 is not configured as a single device, but may be a combination of a server and a terminal.

  For example, the service provider device 50 receives the possibility of being at home for each user transmitted from the determination device 100. Further, the service provider device 50 may transmit result information indicating whether or not the user is actually at home to the determination device 100 in accordance with an operation by the service provider 40.

  The determination device 100 is a server device that acquires a usage status in a service provided to a user, and determines the user's home potential based on the relationship between the acquired usage status and the user's home potential. Note that the determination apparatus 100 may also function as the service server 30 that provides a service to the user.

[3. (Configuration of judgment device)
Next, the configuration of the determination apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 3 is a diagram illustrating a configuration example of the determination apparatus 100 according to the embodiment. As illustrated in FIG. 3, the determination apparatus 100 includes a communication unit 110, a storage unit 120, and a control unit 130. The determination device 100 includes an input unit (for example, a keyboard and a mouse) that receives various operations from an administrator who uses the determination device 100, and a display unit (for example, a liquid crystal display) that displays various types of information. You may have.

(About the communication unit 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card). The communication unit 110 is connected to the network N in a wired or wireless manner, and transmits / receives information to / from the user terminal 10, the service server 30, and the service provider device 50 via the network N.

(About the storage unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory device such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 120 includes a usage status storage unit 121, a model storage unit 125, and a provision schedule storage unit 129.

(About usage status storage unit 121)
The usage status storage unit 121 stores information related to usage status in the service. As illustrated in FIG. 3, the usage status storage unit 121 includes an attribute table 122, a usage status table 123, and a definition table 124 as data tables for storing information.

(About attribute table 122)
The attribute table 122 stores attribute information of users registered in the service. FIG. 4 shows an example of the attribute table 122 according to the embodiment. FIG. 4 is a diagram illustrating an example of the attribute table 122 according to the embodiment. In the example illustrated in FIG. 4, the attribute table 122 includes items such as “user ID”, “owned terminal ID”, “sex”, “age”, “home position”, “profession”, and “working time”.

  “User ID” is identification information for identifying a user. The “owned terminal ID” is identification information for identifying the user terminal 10 used by the user. The owned terminal ID is acquired, for example, when the service server 30 or the like recognizes unique identification information (device ID or the like) of the user terminal 10 when accessing the service using the user terminal 10. . Alternatively, the owned terminal ID may be acquired on the service side by a user using the service registering on the service side.

  Note that the information registered as the owned terminal ID may include the type of terminal. Specifically, in the information registered as the owned terminal ID, the terminal is a terminal (smart phone or wearable device) carried by the user, or a terminal that is generally not carried (installed at home). Such as a speaker or an IoT device such as a speaker or a desktop personal computer). In the present specification, identification information as shown in FIG. 4 may be used as a reference symbol. For example, the user identified by the user ID “U01” may be referred to as “user U01”.

  “Gender” indicates the gender of the user who uses the service. “Age” indicates the age of the user. “Home position” indicates the position where the user's home is located. The “home position” may store not the specific address but position information (for example, longitude and latitude) corresponding to the user's home. “Job title” indicates the job title of the user. “Working time” indicates the working time of the user. The job type and working hours may be information acquired by the service by the user registering with the service, or may be information estimated based on the usage status of the user. For example, the determination apparatus 100 identifies the position of the work place of the user U01 based on the position information frequently observed during the weekday day among the position information transmitted from the user terminal 10. Furthermore, the determination apparatus 100 specifies a company name located at the position, a job type to which the company belongs, based on map information and the like held by the service. Then, the determination apparatus 100 stores the identified information in the attribute table 122 in association with the user.

  That is, the example of the data shown in FIG. 4 is attribute information related to the user U01 identified by the user ID “U01”, and the user terminal 10 used by the user U01 has the owned terminal ID “A01” or “A02”. Indicates that it will be identified. In FIG. 4, the gender of the user U01 is “male”, the age is “30s”, the address is “A prefecture...”, And the occupation is “company employee (day shift)”. It indicates that the working hours are “9:00 to 17:00”.

  The attribute information stored in the attribute table 122 is not necessarily accurate information. For example, the determination apparatus 100 may store “estimated sex”, “estimated age”, and the like estimated from the service usage status in the attribute table 122.

(About the usage status table 123)
Next, FIG. 5 shows an example of the usage status table 123 according to the embodiment. FIG. 5 is a diagram illustrating an example of the usage status table 123 according to the embodiment. The usage status table 123 stores the usage status of the user in the service. In the example illustrated in FIG. 5, the usage status table 123 includes items such as “user ID”, “usage log ID”, “acquisition date / time”, “service”, “information type”, “acquisition information”, and “day information”. Have

  The “user ID” corresponds to the same item shown in FIG. “Usage log ID” indicates identification information for identifying an action history (log) in which a user has used a service. “Acquisition date” indicates the date and time when the user's usage log was acquired. For example, the acquisition date and time is the date and time when the user logged into the service, the date and time when the user registered the schedule with the service, the date and time when the reservation to the restaurant was registered, or the like. For this reason, the acquisition date / time may not be related to the date / time to be determined for the possibility of being at home. For example, in the case of schedule information, the schedule registered as a schedule, not the timing at which the schedule is registered, has a relationship with the date and time that is a target of determination of the possibility of being at home.

  “Service” indicates the type of service used by the user. “Information type” indicates the type of information acquired as the usage status. “Acquired information” indicates specific contents of the acquired usage status. In other words, the acquired information may include information indicating a relationship with the date and time that is a determination target of the at-home possibility when determining the at-home possibility of the user. For example, in the case of schedule information, the schedule registered as a schedule has a relationship with the date and time that is the target of being at home. In the example shown in FIG. 5, a part of the acquired information is indicated by a conceptual notation such as “C01”. However, in actuality, when the acquired information is position information, a specific latitude or Location information indicating longitude and information indicating address and area are stored. “Day of the week information” indicates a day of the week that has a relationship with the date and time that is subject to determination of the possibility of being at home.

  That is, the example of the data illustrated in FIG. 5 indicates that the usage log B01 identified by the usage log ID “B01” is stored as a log in the service of the user U01. Further, the usage log B01 is an acquisition date “April 20, 2017 12:00” and is a log in a service related to “map / traffic”, and the information type acquired as the usage status is “location information”. Yes, the acquired information indicates “C01”. In addition, the information acquired as the usage log B01 indicates that the day of the week to be determined to be at home is “Thursday”.

(About the definition table 124)
Next, FIG. 6 shows an example of the definition table 124 according to the embodiment. FIG. 6 is a diagram illustrating an example of the definition table 124 according to the embodiment. The definition table 124 stores information defining the relationship between the possibility of staying at home and the usage status. For example, the information stored in the definition table 124 corresponds to the information described in the definition file shown in the description of FIG. In the example illustrated in FIG. 6, the definition table 124 includes items such as “definition ID”, “usage status”, and “score”.

  “Definition ID” indicates identification information for identifying each definition. “Usage status” indicates the specific usage status of the service. “Score” indicates a numerical value that is added as a score indicating the possibility of staying at home when a defined usage situation is acquired at the date and time of determination.

  That is, in the example of the data illustrated in FIG. 6, the definition K01 identified by the definition ID “K01” has acquired the usage status that “location information is in the vicinity of home” in any service at the target date and time. In this case, “60” is added as a score indicating the possibility that the user is at home. As another example, the definition K11 is “−30” as a score indicating the possibility that the user is at home when the usage status that “schedule registration” is acquired by an arbitrary service at the target date and time is acquired. Is added. As another example, if the user has a history of being at home on the same day in the past at the target date and time, the definition K21 sets “5” as a score indicating the user's possibility of being at home. It shows adding.

(About the model storage unit 125)
The model storage unit 125 stores learning about the relationship between the usage situation and the possibility of being at home and information about the model generated as a result of the learning. As illustrated in FIG. 3, the model storage unit 125 includes a learning data table 126 and a model table 127 as data tables for storing information.

(About the learning data table 126)
The learning data table 126 stores information related to learning for generating a model for determining the possibility of being at home. FIG. 7 shows an example of the learning data table 126 according to the embodiment. FIG. 7 is a diagram illustrating an example of the learning data table 126 according to the embodiment. In the example illustrated in FIG. 7, the learning data table 126 includes “learning data ID”, “user ID”, “target date / time”, “target day of the week”, “at-home result information (objective variable)”, “usage status data ( (Explanatory variable) ”.

  “Learning data ID” indicates identification information for identifying learning data for generating a model. Note that the learning data is accumulated for each user, for example. “User ID” corresponds to the similar item shown in FIG.

  “Target date and time” indicates the date and time when the possibility of being at home is determined. “Target day of the week” indicates the day of the week for which the possibility of being at home is determined.

  “At-home result information (objective variable)” is result information indicating whether the answer is correct or incorrect at the date and time of determination. For example, if the user is at home at the date and time when the determination is made, “1” is stored in the home result information. On the other hand, if the user is not at home at the date and time when the determination is made, “0” is stored in the home result information. Note that the at-home result information is not limited to the above example. For example, when the determination apparatus 100 determines whether the user is at home based on “Yes” or “No”, the at-home result information includes Information regarding whether the determination is correct (in this case, “1” is stored) or incorrect (in this case, “0” is stored) may be stored.

  “Usage status data (explanatory variable)” indicates user usage status data at the date and time of determination. In the example of FIG. 7, the usage status data is conceptually expressed as “G01”, but actually, the usage status data stores data in which a plurality of usage statuses in the service are listed. Is done. That is, the usage status data is a collection of feature information extracted from usage status in the user's service, and includes all usage statuses used to determine the user's possibility of being at home.

  That is, in the example of the data illustrated in FIG. 7, the learning data E01 identified by the learning data ID “E01” indicates that the learning target is the user U01. As an example of the learning data included in the learning data E01, the target date and time of determination is “April 13, 2017 12:00”, the target day of the week is “Thursday”, and the at-home result information is “1”. It is shown that the usage status data is “G01”.

(About the model table 127)
Next, FIG. 8 shows an example of the model table 127 according to the embodiment. FIG. 8 is a diagram illustrating an example of the model table 127 according to the embodiment. As illustrated in FIG. 8, the model table 127 includes items such as “model ID” and “user ID”.

  “Model ID” indicates identification information for identifying a model. “User ID” indicates identification information for identifying a user corresponding to the model.

  That is, in the example of the data illustrated in FIG. 8, the model M01 identified by the model ID “M01” is a model corresponding to the user U01, and the model M02 identified by the model ID “M02” is assigned to the user U02. It shows that it is a corresponding model. Thus, a model is generated for each user.

(About the provision schedule storage unit 129)
The provision schedule storage unit 129 stores information provided to the service provider 40. Specifically, the information stored in the provision schedule storage unit 129 is a list indicating the possibility of each user being at home with respect to the service provider 40, and on which schedule the service should be provided (in other words, which schedule The service provider 40 recognizes whether the user should visit his / her home. FIG. 9 shows an example of the provision schedule storage unit 129 according to the embodiment. FIG. 9 is a diagram illustrating an example of the provision schedule storage unit 129 according to the embodiment. As illustrated in FIG. 9, the provision schedule storage unit 129 includes items such as “service ID”, “target date / time”, “determination date / time”, “providing target user ID”, and “at home”.

  “Service ID” indicates identification information for identifying a service. The service shown in FIG. 9 is, for example, an offline service that requires a visit to the user's home, such as a delivery service or a visiting sales service.

  “Target date and time” indicates a date and time that is a target for determining the possibility of the user being at home. For example, the target date and time is set based on a request from the service. Specifically, when the determination apparatus 100 receives a request from the service H01 that provides a delivery service for determination of the possibility of being home for each user at “April 30, 2017 12:00”, the determination device 100 sets the target date and time. The possibility of being at home is determined as “April 30, 2017 12:00”. The “determination date / time” is the date / time when the user's possibility of being at home is determined. Note that the determination date and time may not be the timing itself for performing the determination process, but may be a deadline for issuing a determination result. For example, in the example of FIG. 9, the determination apparatus 100 repeatedly obtains the usage status of the user and determines the possibility of being at home until “April 30, 2017, 10:00”. In addition, at the time of “April 30, 2017 10:00”, the determination result of the possibility of staying at the user may be output. Further, the same date and time may be set as the target date and time and the determination date and time. In this case, for example, the determination apparatus 100 continues to determine the user's ability to stay at home until the service provider 40 provides a service to the user.

  “Provision target user ID” indicates identification information for identifying a user to whom a service is provided. “Possibility to stay home” indicates the possibility of staying home for each user. For example, “at-home possibility” normalizes the score calculated by the determination apparatus 100 and stores a numerical value expressed as a percentage. Note that the possibility of being at home is not necessarily stored as a fixed numerical value, but may be updated each time the determination process is performed based on the acquired usage status until the determination date and time elapses.

  That is, in the example of the data illustrated in FIG. 9, regarding the customer user of the service H01 identified by the service ID “H01”, the possibility of being at home on the target date “April 30, 2017 12:00” was determined. An example is shown. In this case, the determination date is “April 30, 2017 10:00”. As a result of the determination, for example, it is derived that the possibility that the user U13 identified by the provision target user ID “U13” is “95”% and that the user U08 is “93”%. It has been shown.

(About the control unit 130)
Returning to FIG. 3, the description will be continued. The control unit 130 is, for example, a controller, and includes various programs (an example of a determination program) stored in a storage device inside the determination apparatus 100 by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like. This is realized by executing the RAM as a work area. The control unit 130 is a controller, and is realized by an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

  As illustrated in FIG. 3, the control unit 130 includes an acquisition unit 131, a determination unit 132, a generation unit 133, a determination unit 134, and a transmission unit 135, and functions and functions of information processing described below. Realize or execute. Note that the internal configuration of the control unit 130 is not limited to the configuration illustrated in FIG. 3, and may be another configuration as long as the information processing described below is performed. Further, the connection relationship between the processing units included in the control unit 130 is not limited to the connection relationship illustrated in FIG. 3, and may be another connection relationship.

(About the acquisition unit 131)
The acquisition unit 131 acquires various types of information. For example, the acquisition unit 131 acquires a usage status in a service provided to the user. Specifically, the acquisition unit 131 is a service that can be used via a network, and acquires, for example, a usage status of a web service that is used via the user terminal 10. The service is not limited to the web service, and may be a service used via an application program (hereinafter, referred to as “application”) installed in the user terminal 10, for example.

  For example, the acquisition unit 131 acquires the position information of the user when using the service as the usage status. Further, the acquisition unit 131 may acquire position information indicating a position serving as a user's base, such as position information indicating the position of the user's home, position information indicating the position of the work place, and the like. For example, the acquisition unit 131 specifies the user's home or work place based on information registered in the service, and acquires position information of the specified position.

  Further, the acquisition unit 131 may acquire time information that is estimated to elapse when the user moves from the position indicated by the acquired position information to the user's home. For example, the acquisition unit 131 acquires time information estimated to elapse when the user moves by acquiring distance information between position information indicating the current location where the user is located and position information indicating the user's home. get. In this case, for example, the acquisition unit 131 may estimate the user's moving means from the transition of the position information, and estimate the time required for the movement, assuming that the estimated moving means is adopted.

  Further, the acquisition unit 131 can acquire more accurate time information depending on the service used by the user. For example, when the car navigation application is executed on the user terminal 10, the acquisition unit 131 moves from the service server 30 that provides the car navigation application or the user terminal 10 on which the car navigation application is executed to the home. It is possible to acquire time information estimated to elapse in some cases.

  Further, the acquisition unit 131 may acquire information related to a delivery schedule specified by the user in using a service related to purchase. For example, when the user orders a product, the acquisition unit 131 acquires information related to the schedule specified as the delivery date.

  Further, the acquisition unit 131 may acquire schedule information registered by the user in using a service related to schedule management. For example, the acquisition unit 131 acquires, as schedule information, a schedule registered by the user when using a service related to schedule management provided via a calendar application, a task management application, or the like.

  In addition, the acquisition part 131 may acquire not only the information regarding a schedule but various information as schedule information. For example, when a location is registered in the schedule, the acquisition unit 131 may acquire location information regarding the location. In this case, in the determination process described later, for example, if the place where the schedule is registered is the user's home or the like, it can be determined that the user is likely to be at home at the registered date and time. Alternatively, if the place where the schedule is registered is other than the user's home, it can be determined that the possibility that the user is at home at the registered date and time is low.

  Moreover, the acquisition part 131 may acquire the information regarding the reservation schedule which the user reserved in the utilization of the service which concerns on at least any one of a transportation, a travel, a dining facility, or an accommodation facility. Note that the acquisition unit 131 may acquire position information as reservation information in the same manner as the schedule information.

  Moreover, the acquisition part 131 may acquire the information regarding a user's occupation or working time among the attribute information which the user registered into the service as a usage condition in a service.

  The acquisition unit 131 may be an information device used for using a service, and may acquire information related to a communication state in an information device installed at a user's home. For example, the acquisition unit 131 acquires information regarding a communication state such that near field communication is performed between the user terminal 10 carried by the user and an IoT device installed at the user's home. Note that the acquisition unit 131 is not limited to a home, and may acquire the communication status with information devices and access points installed in various places. Thereby, even when the user is out, the acquisition unit 131 can acquire information such as at which position communication is performed, and as a result, the acquisition unit 131 can acquire the position where the user is located. it can.

  The acquisition unit 131 may appropriately acquire the usage status of the user even in services other than those exemplified above. The acquisition unit 131 stores the acquired information in the usage status storage unit 121.

(About determination unit 132)
The determination unit 132 determines the possibility of staying at the user based on the relationship between the usage status acquired by the acquisition unit 131 and the possibility of staying at the user. For example, the determination unit 132 refers to a definition file in which information defining the relationship between the usage status in the service and the at-home possibility is described, and determines the at-home possibility of the user.

  For example, the determination unit 132 determines the possibility that the user is at home based on the position information of the user. Specifically, when the position information of the user acquired from the user terminal 10 indicates the vicinity of the user's home at the target date and time, the determination unit 132 determines that the user is likely to be at home relatively high.

  In addition, when the time information estimated to elapse when the user moves from the position indicated by the acquired position information to the user's home is acquired, the determination unit 132 acquires a predetermined time based on the time information. You may determine the possibility that the user will be at home later. That is, the determination unit 132 says that the user is located in a range where the user can reach the home by moving from the current time to the target date and time, for example, even if information indicating that the user is located at the current date and time is not obtained. If the information is obtained, it is determined that the user is likely to be at home after a predetermined time (that is, the target date and time).

  In addition, when information regarding a delivery schedule specified by the user in using a service related to purchase is acquired, the determination unit 132 may determine the user's possibility of being at home based on the information regarding the delivery schedule. In general, it can be said that the date and time when the user specified the delivery schedule is likely to be at home. For this reason, the determination part 132 can determine a user's at-home possibility accurately by performing a determination process based on the information regarding a delivery schedule.

  Further, the determination unit 132 may determine the user's possibility of being at home based on the schedule information when the schedule information registered by the user is acquired in the use of the service related to the schedule management. Generally, when a user registers a schedule, it can be said that the possibility that the user is at home is low. For this reason, the determination part 132 can determine a user's at-home possibility accurately by performing a determination process based on schedule information.

  In addition, the determination unit 132 may use a reservation schedule when information on a reservation schedule reserved by the user is acquired in the use of a service related to at least one of transportation, travel, a dining facility, or an accommodation facility. Based on the information regarding the user, the user's possibility of being at home may be determined.

  Moreover, the determination part 132 may determine a user's at-home possibility based on the information regarding a user's occupation or working time. For example, the determination unit 132 specifies a time zone during which the user is working based on the job type or working time of the user, and determines that the possibility of being at home is relatively low during the specified time. Moreover, the determination part 132 may estimate a user's commuting time based on a user's home and work location information. Furthermore, the determination unit 132 may estimate the timing at which the user goes to the office or the timing to go home based on the estimated commuting time and the user's working time. Then, the determination unit 132 determines that the possibility of being at home is low at the time when the user is estimated to be away from the home, and the possibility of being at home at the time when the user is estimated to have returned from the work place to the home. May be determined high.

  In addition, the determination unit 132 is an information device used for using the service, and when information regarding the communication status of the information device installed at the user's home is acquired, the determination unit 132 is based on the information regarding the communication status. The possibility of staying at home may be determined. Generally, when short-distance communication or the like is performed between the user terminal 10 assumed to be carried by the user and an information device installed at home, the user is assumed to be at home. For this reason, the determination part 132 can determine a user's at-home possibility accurately by performing a determination process based on the communication condition with the information apparatus installed in the house.

  In the determination process, the determination unit 132 may perform the determination process by appropriately combining the above usage situations. For example, if the usage status that can be used for the determination overlaps at the determination target date and time, the determination unit 132 adds the scores corresponding to the overlapping usage status and determines the possibility of being at home by the user. To do. Specifically, the determination unit 132 uses a schedule registered for a determination target date and time, and is used for a user who is located far from the home and has made a reservation at an accommodation facility. The score corresponding to the situation is added to determine the possibility of being at home. The determination unit 132 can improve the accuracy of determination by performing determination based on a plurality of usage situations that are assumed to indicate user behavior.

  Note that the determination unit 132 may determine a user's possibility of being at home using a model generated by the generation unit 133 through a predetermined learning process and generated for each user.

  Moreover, the determination part 132 may determine a user's at-home possibility based on the past at-home history. For example, the determination unit 132 may determine the possibility of staying at home for each day of the week. Moreover, the determination part 132 may determine a user's home possibility for every time slot | zone.

  In other words, if there is a user home history on the same day in the past at the date and time of determination, the determination unit 132 may estimate whether or not the user is home based on the tendency of the home history. Good. Similarly, when the user's home history exists in the same time period in the past (for example, a time zone corresponding to one hour before and after the determination target date and time) in the determination target date and time, based on the tendency of the home history The determination unit 132 may estimate whether the user is at home. As will be described later, the determination unit 132 can perform determination using the model generated by the generation unit 133 in consideration of the past user's home tendency and the like as described above.

  Note that the determination unit 132 may receive a request from the service provider 40 that performs the service offline, and determine the possibility that the user who is the customer of the service will be at home. Sex may be determined.

  For example, when the determination unit 132 receives a request from the service provider 40 and performs the determination, for example, the determination unit 132 acquires a list of users to be determined and information on the date and time to be determined. Then, the determination unit 132 determines the possibility that the user who is the determination target is at home at the date and time that is the determination target, when the request is received. For example, the determination unit 132 receives a request from the service provider 40 who intends to perform a home delivery service “two hours after the current time”. In this case, the determination unit 132 sets “two hours after the current time” as the determination target date and time (corresponding to the “target date and time” illustrated in FIG. 9), and a predetermined time before the determination target date and time (for example, 10 minutes before) ) Is set as the determination deadline date (corresponding to the “determination date” shown in FIG. 9). Then, the determination unit 132 determines the possibility of being at home for the customer user of the service provider 40.

(About the generator 133)
The generation unit 133 generates a model for calculating the possibility of being at home by learning the relationship between the usage status acquired by the acquisition unit 131 and the result information indicating whether or not the user is at home. . Specifically, the generation unit 133 contributes to the determination as to what usage status was actually at home based on the usage status used for the determination target and the result information in the determination. Learn the trend of whether you were doing.

  For example, different scores may be more appropriate for the home-presence score defined in advance for the usage status depending on the user. Specifically, the determination target user may be a user who does not tend to take an action as registered in the schedule, or may be a user who does not tend to be home at a specified delivery date and time. On the other hand, the determination target user may be a user who has a tendency to accurately execute the action registered in the schedule. Thus, even when the same usage status is acquired for each user, it is assumed that the usage status that contributes to determine the possibility of being at home differs depending on each user.

  Therefore, the generation unit 133 acquires the result information (that is, correct answer data) such as whether or not the user is actually at home, and learns the relationship between the usage situation used for the determination and the result, thereby learning. Generate a reflected model for each user. Then, the determination unit 132 performs determination using the model generated by the generation unit 133. Thereby, the determination part 132 can improve the precision of determination more.

  The model generation will be specifically described below. Note that the learning methods and models shown below are examples, and the generation unit 133 may generate any model using various known methods. In other words, the generation unit 133 may use any learning method as long as the result of whether or not the user is at home can be fed back to the determination processing according to the embodiment.

  For example, the generation unit 133 sets the result information indicating whether or not the user is at home on the date and time of the determination target as the objective variable in the regression analysis. And the production | generation part 133 makes the various utilization condition used for determination the explanatory variable in regression analysis. And the production | generation part 133 produces | generates the model for determining home presence possibility using an objective variable and an explanatory variable. In the example described below, the usage status described in the definition table 124 shown in FIG. 6 is used for processing, but the numerical value of each score is not considered. In addition, an example is shown in which learning is performed with a case where the user is eventually at home as a positive example and a case where the user is eventually not at home as a negative example.

  For example, the generation unit 133 generates an expression indicating the relationship between whether or not the user is actually at home and the usage status used for the determination. Further, the generation unit 133 calculates what weight each usage status of the service has for the event that the user is at home. Thereby, the production | generation part 133 can acquire the information of how much each explanatory variable contributes with respect to the phenomenon that a user is at home. For example, the generation unit 133 generates the following equation (1) when generating a model related to the user U01 which is an example of the user.

y _{(user U01)} = ω ₁ · x ₁ + ω ₂ · x ₂ + ω ₃ · x ₃ ... + ω _N · x _N (1) (N is an arbitrary number)

In the above formula (1), “y _{(user U01)} ” indicates an event “whether or not the user U01 was actually at home”. For example, in the example of the above formula (1), “y” is represented by “1” (has been at home) or “−1” (has been at home). Note that the generation unit 133 may appropriately use a numerical value other than “−1” and “1” as the value of y in order to facilitate calculation.

In the above formula (1), “x” is an explanatory variable and corresponds to the usage status of the service of the user U01. Specifically, “x ₁ ” in the above equation (1) is assumed to be position information of the user U01 (more specifically, “position information near the home”). For example, “x ₁ ” indicates whether or not “location information near the home” of the user U01 is acquired at the date and time of the determination target. Further, “x ₂ ” in the above equation (1) is the schedule information of the user U01. For example, “x ₂ ” indicates whether or not the user U01 has registered a schedule at the determination target date and time. Further, “x ₃ ” in the above formula (1) is information relating to the delivery schedule of the user U01. For example, “x ₃ ” indicates whether or not the user U01 has designated delivery of goods at the date and time of determination. That is, the right side of the above equation (1) corresponds to various information extracted from the usage status in the service used by the user U01 as shown in FIG.

In the above formula (1), “ω” is a coefficient of “x” and indicates a predetermined weight value. Specifically, “ω ₁ ” is a weight value of “x ₁ ”, “ω ₂ ” is a weight value of “x ₂ ”, and “ω ₃ ” is a weight value of “x ₃ ”. It is. In this way, the above equation (1) is a variable including the explanatory variable “x” corresponding to various information extracted from the usage status and the predetermined weight value “ω” (for example, “ω ₁ · x _1”). ).

  For example, it is assumed that the date and time of determination is “T1”. Then, it is assumed that the information acquired as the usage status in the service of the user U01 at T1 is “location information indicating the vicinity of the home” and “schedule information”. In T1, it is assumed that “designation of delivery schedule” has not been performed. Then, at T1, it is assumed that information indicating that the user U01 was “at home” was obtained as a result. In this case, the above formula (1) is expressed as the following formula (2).

y (= 1) _{(users U01, T1)} = ω ₁ · x ₁ (position information indicating the vicinity of the home) + ω ₂ × x ₂ (schedule information) + ω ₃ · 0 (2)

As shown in the above formula (2), a value of “0” is substituted for “x ₃ ” for which the usage status has not been acquired. In this case, at least the information that contributed to the determination of the positive example (y = 1) is “position information indicating the vicinity of the home” or “schedule information”.

  Further, the date and time of the determination target is temporarily “T2”. In T2, it is assumed that the information acquired as the usage status of the service of the user U01 is “schedule information” and “designation of delivery schedule”. Then, at T2, it is assumed that information indicating that the user U01 is “not at home” is obtained as a result. In this case, the above formula (1) is expressed as the following formula (3).

y (= -1) _{(Users U01, T2)} = ω ₁ · 0 + ω ₂ · x ₂ (Schedule information) + ω ₃ · x ₃ (Designation of delivery schedule) (3)

As shown in the above equation (3), a value of “0” is substituted for “x ₁ ” and “x ₂ ” for which the usage status has not been acquired. In this case, at least the information contributing to the determination of the negative example (y = −1) is “schedule information” or “designation of delivery schedule”.

  And the production | generation part 133 produces | generates a formula for every date and time of determination object like said Formula (2) and (3), and makes the produced | generated formula the sample of regression analysis. Then, the generation unit 133 derives a value corresponding to the predetermined weight value “ω” by performing calculation processing of the sample expression. Moreover, the production | generation part 133 produces | generates the formula used as a sample like said Formula (2) at any time. Then, the generation unit 133 recursively determines a predetermined weight value “ω” that satisfies the expressions (2) and (3) according to the increase in the generated expressions. In other words, the generation unit 133 determines the weight value “ω” indicating the influence of the predetermined explanatory variable on the objective variable “y”.

If the “location information indicating the vicinity of the home” contributes to the event that the user U01 is “at home” as compared with other variables, the “location information indicating the vicinity of the home” is set. The value of the corresponding weight value “ω ₁ ” is estimated to be a large positive value compared to other variables. Further, if the “schedule information” contributes to the event that the user U01 is “not at home” as compared with other variables, the weight value “ω ₂ ” corresponding to the “schedule information”. The value of is estimated to be a large negative value compared to other variables. Also, if “designation of delivery schedule” does not contribute to the event of whether or not the user U01 is at home as compared with other variables, the weight value corresponding to “designation of delivery schedule” The value of “ω ₃ ” is estimated to gradually approach “0” as learning progresses.

  In the above example, three types of usage situations are shown as explanatory variables. Actually, however, the above formulas (2) and (3) have various usage situations corresponding to various usage situations acquired by the acquisition unit 131. The explanatory variables are included. That is, the generation unit 133 generates a model using various information extracted from the usage situation as illustrated in FIGS. 5 and 6 as explanatory variables.

  As described above, the generation unit 133 generates a model that associates the phenomenon that the user is at home with the usage status of the service. In the calculation process using the above formula (2), the left side is not set to “1” or “−1”, but a predetermined error is assumed, and a value obtained by squaring the difference from the error is the minimum value. The optimal solution of “ω” may be calculated using a method such as a least square method that approximates to

  In addition, when substituting information extracted from the usage status into the generated model, the generation unit 133 sets “1” for variables determined by “present” or “none” such as “schedule information”. "Or" 0 "is substituted. Further, for the dynamic value such as the position information, the generation unit 133 may appropriately substitute a numerical value corresponding to the distance to the home, for example. For example, the generation unit 133 may apply various known methods such as normalization so that the usage situation that can be an explanatory variable such as position information can be handled by a model according to a known method.

  The generation unit 133 may update the model as needed when the acquisition unit 131 acquires a new usage state after generating the model. Thereby, the production | generation part 133 can optimize the model for determining a user's at-home possibility.

  In addition, as described above, the generation unit 133 may reflect the learning result in the definition table 124 when learning the relationship between the usage status and the possibility of being at home. As described above, the generation unit 133 indicates a value (in this example, the weight value ω) that contributes to whether each usage state is at home of the user by a positive value or a negative value. Can do. For this reason, the generation unit 133 can give an appropriate score even for a usage situation that is not defined in advance, for example, by calculating a score corresponding to each usage situation based on the calculated weight value. . More specifically, according to learning, a positive score is assigned to a usage situation that contributes to the determination that the user is at home, and a negative usage situation that contributes to the determination that the user is not at home. A score is given.

  The generation unit 133 stores information related to learning and the generated model in the model storage unit 125. And the determination part 132 may perform determination using the model like said Formula (1) by which the weight value was substituted as a result of learning, and the score was defined (adjusted) according to the user. The determination may be made using information in the definition table 124.

  Note that the generation unit 133 does not necessarily need to use the result information when the offline service is provided by the service provider 40 as correct data. For example, the generation unit 133 may acquire information with a very high probability that the user is at home, and acquire result information indicating whether the user was at home based on the acquired information. Thereby, since the production | generation part 133 can learn even if an offline service is not actually provided to a user, it can compensate for the shortage of the sample number of result information.

(About the determination unit 134)
The determination unit 134 determines a mode of providing a predetermined service (meaning an offline service such as a home delivery service) to the user based on the user's possibility of being at home determined by the determination unit 132.

  For example, the determination unit 134 determines the order in which the predetermined service is provided based on the possibility of staying at a customer user who is provided with the predetermined service. Specifically, the determination unit 134 sorts the possibility of staying at a customer user of a predetermined service in descending order at the determination target date and time. Then, the determination unit 134 creates a transmittable file using the sorted information as a list.

  Further, the determination unit 134 may determine a schedule such as which timing a predetermined service should be provided for a user who is a determination target. In other words, the determination unit 134 extracts the date and time when the user is likely to be at home among the several determination target dates and times determined by the determination unit 132. And the determination part 134 determines what timing (schedule) should provide a predetermined service with respect to the said user by sorting the date with high possibility of being at home in order.

  When the determination unit 134 determines a mode for providing a predetermined service, the determination unit 134 transmits the determined information to the transmission unit 135.

(About transmitter 135)
The transmission unit 135 transmits information regarding the mode determined by the determination unit 134 to a provider who provides a predetermined service.

  For example, the transmission unit 135 transmits to the service provider 40 a list indicating the order in which a predetermined service is provided to a customer user during a certain time period. Or the transmission part 135 transmits the list | wrist which showed the schedule suitable as a timing which provides a predetermined | prescribed service regarding a certain user to the service provider 40. FIG.

[4. Configuration of user terminal]
Next, the configuration of the user terminal 10 according to the embodiment will be described with reference to FIG. FIG. 10 is a diagram illustrating a configuration example of the user terminal 10 according to the embodiment. As illustrated in FIG. 10, the user terminal 10 includes a communication unit 11, an input unit 12, a display unit 13, a detection unit 14, a storage unit 15, and a control unit 16. Note that the connection relationship between the processing units included in the user terminal 10 is not limited to the connection relationship illustrated in FIG. 10, and may be another connection relationship.

  The communication unit 11 is connected to the network N by wire or wireless, and transmits / receives information to / from the service server 30 and the determination apparatus 100. For example, the communication unit 11 is realized by a NIC or the like.

  The input unit 12 is an input device that receives various operations from the user. For example, the input unit 12 is realized by an operation key or the like provided in the user terminal 10. The input unit 12 may include an imaging device (such as a camera) for capturing an image and a sound collection device (such as a microphone) that collects sound.

  The display unit 13 is a display device for displaying various information. For example, the display unit 13 is realized by a liquid crystal display or the like. In addition, when a touch panel is employ | adopted for the user terminal 10, a part of input part 12 and the display part 13 are integrated.

  The detection unit 14 detects various operations on the user terminal 10 and environmental information around the user terminal 10. For example, the detection unit 14 is realized by a sensor or an antenna that detects various types of information. Specifically, the detection unit 14 includes a communication status related to a device connected to the user terminal 10, illuminance and noise around the user terminal 10, physical movement of the user terminal 10, position information of the user terminal 10, and the like. Is detected.

  For example, the detection unit 14 detects a user operation based on information input to the input unit 12. In other words, the detection unit 14 detects that there has been an input of an operation for touching the screen to the input unit 12, an input of a voice, or the like. Moreover, the detection part 14 may detect that the predetermined application was started by the user. When such an application is an application that operates an imaging function (for example, a camera) in the user terminal 10, the detection unit 14 detects that the imaging function is used by the user. Further, the detection unit 14 may detect an operation in which the user terminal 10 itself is moved based on data detected by an acceleration sensor, a gyro sensor, or the like provided in the user terminal 10. For example, the detection unit 14 detects that the user terminal 10 is in the user's hand, the user is handling the user terminal 10 with one hand, based on data detected by a gyro sensor or the like.

  Further, the detection unit 14 detects the current position of the user terminal 10. Specifically, the detection unit 14 receives radio waves transmitted from a GPS (Global Positioning System) satellite, and obtains position information (for example, latitude and longitude) indicating the current position of the user terminal 10 based on the received radio waves. get.

  In addition, the detection part 14 may acquire position information by various methods other than GPS. For example, when the user terminal 10 has a function equivalent to that of a contactless IC card used at a station ticket gate or a store (or the user terminal 10 has a function of reading a history of the contactless IC card) ), The used position is recorded together with the information on the settlement of the boarding fee at the station by the user terminal 10. The detection unit 14 detects such information and acquires it as position information. Moreover, the detection part 14 may detect the positional information acquirable from an access point, when the user terminal 10 communicates with a specific access point. The position information may be acquired by an optical sensor, an infrared sensor, a magnetic sensor, or the like included in the user terminal 10.

  The detection unit 14 detects an external device connected to the user terminal 10. For example, the detection unit 14 detects the external device based on exchange of communication packets with the external device, a signal generated by the external device, and the like. Specifically, the detection unit 14 detects radio waves such as WiFi and Bluetooth used by the external device. The detection unit 14 may detect the type of connection with the external device when communication with the external device is established. For example, the detection unit 14 detects whether it is connected to an external device by wire or wireless communication. Moreover, the detection part 14 may detect the communication system etc. which are used by radio | wireless communication. The detection unit 14 may detect the external device based on information acquired by a radio wave sensor that detects a radio wave emitted by the external device, an electromagnetic wave sensor that detects an electromagnetic wave, or the like. An example of the external device is another device (another user terminal 10) used by a user who uses the user terminal 10, and is, for example, a wearable device or a stationary IoT device.

  In addition, the detection unit 14 detects an environment in the user terminal 10. The detection unit 14 uses various sensors and functions provided in the user terminal 10 to detect information about the environment. For example, the detection unit 14 is a microphone that collects sound around the user terminal 10, an illuminance sensor that detects illuminance around the user terminal 10, and an acceleration sensor that detects physical movement of the user terminal 10 (or A gyro sensor), a humidity sensor that detects the humidity around the user terminal 10, a geomagnetic sensor that detects a magnetic field at the location of the user terminal 10, and the like. And the detection part 14 detects various information using various sensors. For example, the detection unit 14 detects a noise level around the user terminal 10 and whether the surroundings of the user terminal 10 have illuminance suitable for imaging the user's iris. Further, the detection unit 14 may detect surrounding environment information based on a photograph or video taken by the camera.

  The user terminal 10 may acquire context information indicating the context of the user terminal 10 based on information detected by the detection unit 14. As described above, the user terminal 10 has various sensors such as position, acceleration, temperature, gravity, rotation (angular velocity), illuminance, geomagnetism, pressure, proximity, humidity, and rotation vector by various built-in sensors (detection unit 14). Is obtained as context information. In addition, the user terminal 10 may acquire, as context information, a connection status (for example, information regarding establishment of communication or a communication standard used) with various devices by using a built-in communication function. .

(About the storage unit 15)
The storage unit 15 stores various information. The storage unit 15 is realized by, for example, a semiconductor memory device such as a RAM or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 15 includes a service information storage unit 151.

  The service information storage unit 151 stores, for example, information related to services used by the user. Specifically, the service information storage unit 151 stores an action history (log) in the service used by the user. For example, the service information storage unit 151 stores a usage history of applications installed in the user terminal 10. Note that the user terminal 10 may upload the information stored in the service information storage unit 151 to the determination apparatus 100 at regular intervals, for example, in accordance with an instruction from the determination apparatus 100.

  The control unit 16 is a controller, and is realized, for example, by executing various programs stored in a storage device inside the user terminal 10 using the RAM as a work area by a CPU, an MPU, or the like. The control unit 16 is a controller, and is realized by an integrated circuit such as an ASIC or FPGA, for example.

  The control unit 16 controls various processes performed in the user terminal 10. As illustrated in FIG. 10, the control unit 16 includes a reception unit 161, an acquisition unit 162, and a transmission unit 163, and implements or executes information processing functions and operations described below.

  The receiving unit 161 receives various information. For example, the reception unit 161 receives information transmitted from the service server 30 or the determination device 100. The receiving unit 161 receives various information detected by the detecting unit 14.

  The acquisition unit 162 acquires various information and data. For example, the acquisition unit 162 acquires the web page that the user desires to browse by accessing the service server 30. In addition, the acquisition unit 162 acquires information about the application to be used for using the service via the application download site or the like.

  The transmission unit 163 transmits various information. For example, the transmission unit 163 transmits information regarding the usage status of the user terminal 10 detected by the detection unit 14 to the service server 30 and the determination device 100. In addition, the transmission unit 163 refers to the storage unit 15 and the like, and transmits information regarding the service usage status accumulated in the user terminal 10 to the determination apparatus 100.

[5. Processing procedure)
Next, a processing procedure performed by the determination apparatus 100 according to the embodiment will be described with reference to FIGS. 11, 12, 13, and 14. First, a processing procedure for determining the possibility of being at home will be described with reference to FIG. FIG. 11 is a flowchart (1) illustrating a processing procedure according to the embodiment.

  As illustrated in FIG. 11, the determination apparatus 100 acquires the usage status in the service (step S <b> 101). Then, the determination apparatus 100 refers to, for example, the definition table 124 and the like to refer to the relationship between the usage status at the target date and time to be determined and the possibility of being at home (step S102).

  Subsequently, the determination apparatus 100 calculates a score that is an index value indicating the possibility of being at home for each user based on the referenced information (step S103). And the determination apparatus 100 determines a user's at-home possibility based on the calculated score (step S104).

  Next, a processing procedure related to model generation will be described with reference to FIG. FIG. 12 is a flowchart (2) illustrating a processing procedure according to the embodiment.

  The determination apparatus 100 acquires the usage status in the service (step S201). And the determination apparatus 100 accumulate | stores a utilization condition (step S202). In addition, the determination apparatus 100 shall accumulate | store also the result information for every utilization condition in arbitrary determination object dates with a utilization condition.

  Then, the determination apparatus 100 determines whether or not a usage situation sufficient for learning has been accumulated (step S203). When the usage status sufficient for learning is not accumulated (step S203; No), the determination apparatus 100 continues the process of acquiring the usage status.

  On the other hand, when the usage situation sufficient for learning is accumulated (step S203; Yes), the determination apparatus 100 generates a model for each user based on the learning result (step S204). The determination apparatus 100 stores the generated model in the storage unit 120 (step S205).

  Next, a processing procedure related to model update will be described with reference to FIG. FIG. 13 is a flowchart (3) illustrating a processing procedure according to the embodiment.

  The determination apparatus 100 determines whether a determination request has been received from, for example, a service provider (step S301). When the determination request is not received (step S301; No), the determination apparatus 100 waits until the determination request is received.

  On the other hand, when the determination request is received (step S301; Yes), the determination apparatus 100 acquires the usage status of the user service (step S302). And the determination apparatus 100 inputs the acquired use condition into a model, and determines the at-home possibility of the said user using the model corresponding to a user (step S303).

  Thereafter, the determination apparatus 100 determines whether or not the result information has been acquired (step S304). When the result information is not acquired (step S304; No), the determination apparatus 100 waits until acquisition. On the other hand, when the result status is acquired (step S304; Yes), the determination apparatus 100 updates the model based on the acquired result information (step S305). The determination apparatus 100 generates a model optimized for each user by repeating the processing from step S301 to step S305.

  Next, a processing procedure for transmitting information to the service provider 40 that provides a predetermined service will be described with reference to FIG. FIG. 14 is a flowchart (4) illustrating a processing procedure according to the embodiment.

  First, when receiving a request from the service provider 40, the determination apparatus 100 determines the possibility of staying at each user who is a customer of the predetermined service (step S401). It is assumed that the determination apparatus 100 has acquired the usage status of each user at the time of step S401.

  And the determination apparatus 100 arranges a user in order of the determined at-home possibility (step S402). Specifically, the determination apparatus 100 generates a list in which users are sorted (sorted) in descending order of the likelihood of being at home.

  And the determination apparatus 100 determines whether the determination date set by the service provider 40 passed, for example (step S403). When the determination date / time has not elapsed (step S403; No), the determination apparatus 100 continues the process of determining the possibility of being home for each user at the determination target date / time.

  On the other hand, when the determination date has passed (step S403; Yes), the determination apparatus 100 determines the service provision order in descending order of the likelihood of being at home (step S404). Then, the determination apparatus 100 transmits the determined information to the service provider 40 (Step S405).

[6. (Modification)
The determination apparatus 100 described above may be implemented in various different forms other than the above embodiment. Therefore, in the following, another embodiment of the determination apparatus 100 will be described.

[6-1. Usage situation〕
The determination apparatus 100 may acquire various usage situations other than the usage situations exemplified in the above-described embodiment.

  For example, the determination apparatus 100 may acquire the user's SNS writing or SNS posting information as the usage status in the service. For example, the location information of the user terminal 10 may be included in the writing to the SNS and post information.

  Further, the determination apparatus 100 may acquire schedule information from writing to the SNS or the like using text analysis or the like. For example, the SNS writing may include text such as “I went to AAA prefecture on the 20th” or “I went to BBB prefecture on the 15th”. The determination apparatus 100 may convert these texts into schedule information using morphological analysis or the like, and acquire the schedule information based on these texts.

  Moreover, the determination apparatus 100 may acquire a search history or the like in the route search service as the usage status in the service. For example, when a user searches for a route in a region away from his / her home, it can be estimated that the user is not likely to be at home at the date and time related to the search. For this reason, the determination apparatus 100 may use the route search log as an element of determination processing for the possibility of being at home.

  Further, the determination apparatus 100 may acquire a search history in the weather information service, etc., as the usage status in the service. For example, when a user searches for weather in a region away from home, it can be estimated that the user is not likely to be at home at the date and time related to the search. For this reason, the determination apparatus 100 may use the log of the weather information service as an element of determination processing for the possibility of being at home.

  Further, the determination apparatus 100 may acquire information related to an application activated on the user terminal 10. For example, when the user activates the car navigation application, it can be estimated that the user is not likely to be at home. Or when the user has started the application which operates the household appliance installed in the home, it can be estimated that the user is highly likely to be at home. As described above, the determination apparatus 100 may determine the possibility of being at home based on the activation of the application and the operation information.

[6-2. Enjoying the service)
In the embodiment, the process for determining the possibility of the user staying at home has been described. However, for example, when the service is a home delivery service or the like, the user may be able to enjoy the service even if the user is not necessarily at home.

  Specifically, when the user installs a delivery box or the person who lives with the user is at home, the user can enjoy the service even if the user is not at home. For this reason, the determination apparatus 100 acquires information such as whether or not the user has installed a delivery box, or whether or not the user is a living person, and performs a comprehensive determination based on the acquired information. Also good.

  For example, when a predetermined number of pieces of result information such as “home delivery is completed even when the user is not at home” is acquired based on the result information transmitted from the home delivery service, the determination apparatus 100 It is determined that a delivery box is installed. Then, the determination apparatus 100 considers such information, and determines that the user is likely to be at home when the service that requested the determination of at-home possibility is a home delivery service or the like.

  Alternatively, when a predetermined number of pieces of result information such as “home delivery is completed even when the user is not at home” is acquired based on the result information transmitted from the home delivery service, the determination apparatus 100 receives the information It is determined that there is a living person. Then, the determination device 100 considers such information and determines that the user is likely to be at home when the service that requested the determination of at-home possibility is a home delivery service, a visiting sales service, a collection service, or the like.

  As described above, the determination apparatus 100 does not necessarily determine only the possibility of the user being at home, but may determine “whether or not the user can enjoy the service”. That is, the determination apparatus 100 acquires the usage status in the service provided to the user, and the user determines whether the acquired usage status is related to the possibility that the user can enjoy the home delivery service. The possibility of indicating whether or not the delivery service can be enjoyed may be determined.

  Thereby, since the determination apparatus 100 can transmit the information to the service provider 40 as long as the user can enjoy the service, not only the possibility of being at home, but useful information for smoothly promoting the service. Can be provided.

[6-3. Use of overall information)
In the said embodiment, the example which produces | generates the model corresponding to a user using the usage condition for every user was shown. Here, the determination apparatus 100 may perform a determination process that reflects a tendency or the like acquired from the entire user who is a determination target, as well as information about the individual user.

  For example, in the determination of the possibility of being at home, it is assumed that what kind of information contributes more in the usage situation has a tendency for each individual and also for the entire user. For this reason, the determination apparatus 100 may perform learning of a specific user by using the relationship between the usage status of other users and the result information.

  For example, the determination apparatus 100 extracts a user having an attribute similar to the user to be determined. Specifically, the determination apparatus 100 extracts a user whose gender, age, place of residence, and the like are similar to the user to be determined. And the determination apparatus 100 produces | generates the formula which shows the relationship between the utilization condition acquired from the extracted user, and result information. And the determination apparatus 100 utilizes the produced | generated formula for the learning of the user used as determination object. Thereby, the determination apparatus 100 can generate a model reflecting not only an individual user but also the overall tendency.

[6-4. (Possibility to stay at home)
In the embodiment, an example is shown in which the possibility of being at home is expressed as a percentage. However, the determination apparatus 100 does not necessarily need to indicate the possibility of being at home in a ratio such as a percentage. For example, the determination apparatus 100 does not indicate the possibility of being at home with a specific numerical value, but indicates the relative possibility of being at home among all users who are provided with the predetermined service (for example, priority order for providing the predetermined service). Just be fine.

[6-5. (User terminal)
In the said embodiment, although the structural example of the user terminal 10 was shown using FIG. 10, the user terminal 10 does not necessarily need to have all the structures shown in FIG. As described above, the user terminal 10 includes not only smart devices such as smartphones and tablet terminals, but also various wearable devices such as eyeglass-type terminals having a communication function, or heart rate measuring devices for storing a user's heart rate. Is included. In this case, the user terminal 10 does not necessarily receive an input from the user, but may have a function of automatically acquiring the usage status of the user service and transmitting the acquired information to the communication network. That is, the user terminal 10 does not necessarily have the configuration illustrated in FIG. 10 as long as it is a device having a predetermined communication function that realizes so-called IoT.

[7. Hardware configuration)
The determination apparatus 100 and the user terminal 10 according to the above-described embodiments are realized by a computer 1000 having a configuration as shown in FIG. 15, for example. Hereinafter, the determination apparatus 100 will be described as an example. FIG. 15 is a hardware configuration diagram illustrating an example of a computer 1000 that implements the functions of the determination apparatus 100. The computer 1000 includes a CPU 1100, RAM 1200, ROM 1300, HDD 1400, communication interface (I / F) 1500, input / output interface (I / F) 1600, and media interface (I / F) 1700.

  The CPU 1100 operates based on a program stored in the ROM 1300 or the HDD 1400 and controls each unit. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started up, a program depending on the hardware of the computer 1000, and the like.

  The HDD 1400 stores a program executed by the CPU 1100, data used by the program, and the like. The communication interface 1500 receives data from other devices via the communication network 500 (corresponding to the network N shown in FIG. 2) and sends the data to the CPU 1100, and the data generated by the CPU 1100 is transferred to other devices via the communication network 500. Send to device.

  The CPU 1100 controls an output device such as a display and a printer and an input device such as a keyboard and a mouse via the input / output interface 1600. The CPU 1100 acquires data from the input device via the input / output interface 1600. Further, the CPU 1100 outputs the data generated via the input / output interface 1600 to the output device.

  The media interface 1700 reads a program or data stored in the recording medium 1800 and provides it to the CPU 1100 via the RAM 1200. The CPU 1100 loads such a program from the recording medium 1800 onto the RAM 1200 via the media interface 1700, and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or PD (Phase change rewritable disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. Etc.

  For example, when the computer 1000 functions as the determination apparatus 100 according to the embodiment, the CPU 1100 of the computer 1000 implements the function of the control unit 130 by executing a program loaded on the RAM 1200. The HDD 1400 stores data in the storage unit 120. The CPU 1100 of the computer 1000 reads these programs from the recording medium 1800 and executes them, but as another example, these programs may be acquired from other devices via the communication network 500.

[8. Others]
In addition, among the processes described in the above embodiment, all or part of the processes described as being automatically performed can be performed manually, or the processes described as being performed manually can be performed. All or a part can be automatically performed by a known method. In addition, the processing procedures, specific names, and information including various data and parameters shown in the document and drawings can be arbitrarily changed unless otherwise specified. For example, the various types of information illustrated in each drawing is not limited to the illustrated information.

  Further, each component of each illustrated apparatus is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution / integration of each device is not limited to that shown in the figure, and all or a part thereof may be functionally or physically distributed or arbitrarily distributed in arbitrary units according to various loads or usage conditions. Can be integrated and configured. For example, the determination unit 132 and the determination unit 134 illustrated in FIG. 3 may be integrated. Further, for example, information stored in the storage unit 120 may be stored in a predetermined storage device provided outside via the network N.

  Moreover, in the said embodiment, the determination apparatus 100 showed the example which performs the acquisition process which acquires a utilization condition, the determination process which determines the presence at home, and the production | generation process which produces | generates a model, for example. However, the determination apparatus 100 described above may be separated into an acquisition apparatus that performs an acquisition process, a determination apparatus that performs a determination process, and a generation apparatus that performs a generation process. In this case, the acquisition device includes at least the acquisition unit 131. The determination device has at least a determination unit 132. The generation apparatus has at least a generation unit 133. And the process by said determination apparatus 100 is implement | achieved by the determination processing system 1 which has each apparatus of a reception apparatus, a determination apparatus, and a delivery apparatus.

  In addition, the above-described embodiments and modifications can be combined as appropriate within a range that does not contradict processing contents.

[9. effect〕
As described above, the determination apparatus 100 according to the embodiment includes the acquisition unit 131 and the determination unit 132. The acquisition unit 131 acquires the usage status in the service provided to the user. The determination unit 132 determines the possibility of staying at the user based on the relationship between the usage status acquired by the acquisition unit 131 and the likelihood of being at home.

  As described above, the determination apparatus 100 according to the embodiment determines a user's possibility of being at home based on various information that is assumed to indicate the user's behavior, such as a usage situation in a service provided to the user. Thereby, the determination apparatus 100 can determine a user's possibility of being at home accurately.

  In addition, the determination apparatus 100 according to the embodiment calculates the possibility of staying at the user by learning the relationship between the usage status acquired by the acquisition unit 131 and the result information indicating whether or not the user is at home. It further has a generation unit 133 that generates a model to be used. The determination unit 132 determines the possibility that the user is at home using the model generated by the generation unit 133.

  As described above, the determination apparatus 100 according to the embodiment may determine the possibility of staying at the user by using a model in which the result of staying home is reflected by learning as well as the predefined relationship. Thereby, the determination apparatus 100 can determine a user's possibility of being at home accurately.

  Moreover, the determination part 132 determines a user's at-home possibility for every day of the week. Moreover, the determination part 132 determines a user's at-home possibility for every time slot | zone.

  As described above, the determination apparatus 100 according to the embodiment determines the possibility of being at home for each day of the week or time period. Thereby, since the determination apparatus 100 can determine based on the past user's action history etc., it can improve the precision of determination.

  The acquisition unit 131 acquires user position information. The determination unit 132 determines the possibility that the user is at home based on the position information.

  Thus, the determination apparatus 100 according to the embodiment can determine the user's possibility of being at home based on the daily action information of the user based on the use of the service. Thereby, the determination apparatus 100 can perform the determination process without imposing a burden such as home registration on the user.

  The acquisition unit 131 acquires time information that is estimated to elapse when the user moves from the position indicated by the position information acquired by the acquisition unit 131 to the user's home. Based on the time information, the determination unit 132 determines the possibility of the user staying at home after a predetermined time.

  As described above, the determination apparatus 100 according to the embodiment can perform the determination process by reflecting the distance to the target date and time and the travel time instead of simply using the position information. Thereby, the determination apparatus 100 can improve the accuracy of the determination process.

  In addition, the acquisition unit 131 acquires information on a delivery schedule specified by the user in using a service related to purchase. The determination unit 132 determines the possibility that the user is at home based on the information regarding the delivery schedule.

  As described above, the determination apparatus 100 according to the embodiment can determine the possibility of staying at the user by reflecting real-time information such as the timing when the delivery date and time is specified. Thereby, the determination apparatus 100 can perform a determination process that accurately reflects the user's behavior.

  Further, the acquisition unit 131 acquires schedule information registered by the user in using a service related to schedule management. The determination unit 132 determines the possibility that the user is at home based on the schedule information.

  As described above, the determination apparatus 100 according to the embodiment can determine real-time information such as the timing of schedule registration and determine the possibility of the user being at home. Thereby, the determination apparatus 100 can perform a determination process that accurately reflects the user's behavior.

  In addition, the acquisition unit 131 acquires information related to a reservation schedule reserved by the user in using a service related to at least one of transportation, travel, eating and drinking facilities, or accommodation facilities. The determination unit 132 determines the possibility that the user is at home based on information related to the reservation schedule.

  As described above, the determination apparatus 100 according to the embodiment performs the determination process based on the daily service usage status of the user such as reservation information using the service. That is, the determination apparatus 100 can acquire information used for the determination process without particularly burdening the user.

  Moreover, the acquisition part 131 acquires the information regarding a user's occupation or working time among the attribute information which the user registered into the service as a utilization condition. The determination part 132 determines a user's possibility to stay at home based on a user's occupation or working hours.

  Thus, the determination apparatus 100 according to the embodiment can improve the accuracy of the determination process by performing the process using the user attribute information.

  The acquisition unit 131 is an information device used for using the service, and acquires information regarding the communication status of the information device installed at the user's home. The determination unit 132 determines the possibility of the user being at home based on information regarding the communication status.

  As described above, the determination apparatus 100 according to the embodiment can accurately determine whether or not the user is communicating at home by acquiring the communication status between the information devices such as the user terminal 10. As a result, the determination apparatus 100 can improve the determination accuracy of the at-home possibility.

  In addition, the determination apparatus 100 according to the embodiment provides a predetermined service and a determination unit 134 that determines a mode of providing a predetermined service to the user based on the possibility of staying at the user determined by the determination unit 132. It further has a transmission unit 135 that transmits information regarding the mode determined by the determination unit 134 to the provider.

  Thus, the determination apparatus 100 according to the embodiment may transmit the determination result to a service provider that provides an offline service or the like. Thereby, the determination apparatus 100 can provide useful information for a service whose efficiency varies depending on whether or not the user is at home.

  In addition, the determination apparatus 100 according to the embodiment acquires the usage state in the service provided to the user, the usage state acquired by the acquisition unit 131, and whether or not the user can enjoy the home delivery service. The determination unit 132 may determine the possibility of indicating whether or not the user can enjoy the home delivery service based on the relationship with the possibility of indicating.

  Thus, the determination apparatus 100 according to the embodiment may determine whether the user can enjoy the service as well as the possibility of being at home. Thereby, the determination apparatus 100 can provide useful information for the service from various viewpoints.

  The embodiment of the present application has been described in detail with reference to the drawings. However, this is an exemplification, and various modifications and improvements are made based on the knowledge of those skilled in the art including the aspects described in the column of the disclosure of the invention. The present invention can be implemented in other forms.

  In addition, the “section (module, unit)” described above can be read as “means” or “circuit”. For example, the acquisition unit can be read as acquisition means or an acquisition circuit.

DESCRIPTION OF SYMBOLS 1 Judgment processing system 10 User terminal 30 Service server 40 Service provider 50 Service provider apparatus 100 Judgment apparatus 110 Communication part 120 Storage part 121 Usage condition storage part 122 Attribute table 123 Usage condition table 124 Definition table 125 Model storage part 126 Learning data Table 127 Model table 129 Provision schedule storage unit 130 Control unit 131 Acquisition unit 132 Determination unit 133 Generation unit 134 Determination unit 135 Transmission unit

Claims (19)

An acquisition unit for acquiring the usage status of the service provided to the user;
A determination unit that determines the possibility of staying at the user based on the relationship between the usage situation acquired by the acquisition unit and the possibility of staying at the user;
A model for calculating the possibility of staying at a user by learning the relationship between the usage status acquired by the acquisition unit and result information indicating whether or not the user is at home, wherein the result information A generation unit that generates a model in which information indicating a tendency that the usage status contributes to the event indicated by the usage status is set for each usage status;
With
The determination unit
Using the model generated by the generation unit, determine the possibility of staying at the user,
A determination apparatus characterized by that. An acquisition unit for acquiring the usage status of the service provided to the user;
Each usage acquired by the pre-Symbol obtaining unit, by the user to learn the relationship between result information indicating whether or not a home, said each usage of the index value indicating the home possibility of the user And determining whether the user is at home using a model in which information indicating a tendency that the usage status contributes to an event indicated by the result information is set for each usage status. And
A determination apparatus comprising: By learning the relationship between the usage status acquired by the acquisition unit and the result information indicating whether or not the user is at home, an index value indicating the likelihood of the user being home is calculated for each usage status A generating unit that generates a model in which information indicating a tendency of the usage status to contribute to the event indicated by the result information is set for each usage status ;
Further comprising
The determination unit
Using the model generated by the generation unit, determine the possibility of staying at the user,
The determination apparatus according to claim 2, wherein: The determination unit
Determining the user's likelihood of being home for each day of the week,
The determination apparatus according to any one of claims 1 to 3, wherein The determination unit
Determining the likelihood of the user being at home for each time period;
The determination apparatus according to claim 1, wherein: The acquisition unit
Obtaining location information of the user;
The determination unit
Based on the location information, the possibility of being at home of the user is determined.
The determination apparatus according to claim 1, wherein: The acquisition unit
Acquiring time information estimated to elapse when the user moves from the position indicated by the position information acquired by the acquisition unit to the user's home;
The determination unit
Based on the time information, the possibility that the user is at home after a predetermined time is determined.
The determination apparatus according to claim 6. The acquisition unit
In the use of services related to purchasing, obtain information on the delivery schedule specified by the user,
The determination unit
Based on the information regarding the delivery schedule, the possibility of staying at the user is determined.
The determination apparatus according to claim 1, wherein The acquisition unit
In the use of services related to schedule management, the schedule information registered by the user is acquired,
The determination unit
Based on the schedule information, the possibility of being at home of the user is determined.
The determination apparatus according to claim 1, wherein: The acquisition unit
In the use of a service related to reservation of at least one of transportation, travel, eating and drinking facilities, or accommodation facilities, obtain information on the reservation schedule reserved by the user,
The determination unit
Based on the information related to the reservation schedule, the possibility of staying at the user is determined.
The determination apparatus according to claim 1, wherein: The acquisition unit
As the usage status, among the attribute information registered by the user in the service, obtain information on the job type or working time of the user,
The determination unit
Based on the job type or working hours of the user, the possibility of being at home for the user is determined.
The determination apparatus according to claim 1, wherein: The acquisition unit
An information device used for use of the service, obtains information about a communication status in the information device installed at the user's home,
The determination unit
Based on the information related to the communication status, the possibility of being at home of the user is determined.
The determination apparatus according to claim 1, wherein A determination unit that determines a mode of providing a predetermined service to the user based on the possibility of staying at the user determined by the determination unit;
A transmission unit that transmits information regarding the mode determined by the determination unit to a provider that provides the predetermined service;
The determination apparatus according to claim 1, further comprising: An acquisition unit for acquiring the usage status of the service provided to the user;
Possibility of indicating whether or not the user can enjoy the delivery service based on the relationship between the usage status acquired by the acquisition unit and the possibility of indicating whether or not the user can enjoy the delivery service A determination unit for determining
Whether or not the user can enjoy the delivery service by learning the relationship between the usage status acquired by the acquisition unit and the result information indicating whether or not the user has enjoyed the delivery service. A generating unit that generates a model for calculating a possibility of indicating, and a model in which information indicating a tendency that the usage status contributes to an event indicated by the result information is set for each usage status;
With
The determination unit
Using the model generated by the generation unit, determine the possibility that the user can enjoy the home delivery service,
A determination apparatus characterized by that. An acquisition unit for acquiring the usage status of the service provided to the user;
Each usage acquired by the pre-Symbol acquiring unit, by learning the relationship between the result information which the user indicates whether it is possible to enjoy a courier service, the user whether it is possible to enjoy the delivery service A model that calculates an index value for each usage situation, and a model in which information indicating a tendency that the usage situation contributes to an event indicated by the result information is set for each usage situation, A determination unit that determines whether or not the user can enjoy the delivery service;
A determination apparatus comprising: A determination method executed by a computer,
An acquisition step of acquiring the usage status of the service provided to the user;
A determination step of determining the at-home possibility of the user based on the relationship between the usage situation acquired by the acquisition step and the at-home possibility of the user;
A model for calculating the possibility of staying at a user by learning the relationship between the usage status acquired by the acquisition step and the result information indicating whether or not the user is at home, wherein the result information A generation step of generating a model in which information indicating a tendency that the usage status contributes to the event indicated by the usage status is set for each usage status;
Including
The determination step includes
Using the model generated by the generating step, determine the possibility of staying at the user,
The determination method characterized by this. An acquisition procedure for acquiring the usage status of the service provided to the user;
A determination procedure for determining the possibility of staying at the user based on the relationship between the usage situation obtained by the obtaining procedure and the possibility of staying at the user;
A model for calculating the possibility of staying at the user by learning the relationship between the usage status acquired by the acquisition procedure and the result information indicating whether or not the user is at home, wherein the result information A generation procedure for generating a model in which information indicating a tendency of the usage status to contribute to the event indicated by the usage status is set for each usage status;
To the computer,
The determination procedure is as follows:
Using the model generated by the generation procedure to determine the likelihood of the user being at home;
Judgment program characterized by that. A determination method executed by a computer,
An acquisition step of acquiring the usage status of the service provided to the user;
Each usage acquired by the pre-Symbol acquisition step, by the user to learn the relationship between result information indicating whether or not a home, said each usage of the index value indicating the home possibility of the user And determining whether the user is at home using a model in which information indicating a tendency that the usage status contributes to an event indicated by the result information is set for each usage status. Process,
The determination method characterized by including. An acquisition procedure for acquiring the usage status of the service provided to the user;
Each usage acquired by the previous SL acquisition procedure, by which the user learns the relationship between result information indicating whether or not a home, said each usage of the index value indicating the home possibility of the user And determining whether the user is at home using a model in which information indicating a tendency that the usage status contributes to an event indicated by the result information is set for each usage status. Procedure and
The determination program characterized by causing a computer to execute.

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