JP7514878B2 - Information processing device, information processing method, and program - Google Patents

Description

The present invention relates to an information processing device, an information processing method, and a program, and in particular to a technology related to predicting the possibility of a user being at home.

With the recent spread of electronic commerce, the demand for package delivery is rapidly increasing. If the user is not at home, the delivery company must redeliver the package, and there is no guarantee that the user will be at home when the package re-deliveries, which places a heavy burden on the delivery company. As a technology to address this issue, Patent Document 1 discloses a technology that determines the possibility of a user being at home using a learning model that is trained using the relationship between the user's service usage status and result information indicating whether the user was at home.

JP 2019-016411 A

The technology disclosed in Patent Document 1 determines whether a target user is at home based on information about the user's behavior, but this information is only localized and does not determine whether other users who may live with the user are at home.

The present invention has been made in consideration of the above problems, and aims to provide a technology for accurately predicting the possibility of successful delivery based on comprehensive relationships between a user and multiple other users.

In order to solve the above problem, one aspect of an information processing device according to the present invention has an acquisition means for acquiring factual characteristics of each of a plurality of users as user characteristics, a target user setting means for setting a target user among the plurality of users, a related user setting means for setting one or more users among the plurality of users who live with the target user as related users based on a relationship graph showing social relationships between the plurality of users , and a prediction means for predicting the possibility of successful delivery of a package addressed to the target user at a specified date and time based on the user characteristics of the target user and the user characteristics of the related users.

The prediction means may input the user characteristics of the target user and the user characteristics of the related users, and use a machine learning model configured to output the possibility of successful delivery of the package at the specified date and time to predict the possibility of successful delivery of the package at the specified date and time.

The likelihood of successful delivery of the package at the specified date and time can be expressed as a number between 0 and 1, with 1 being the maximum likelihood.

In the relationship graph, each user may be represented by a user node, and each user node may be connected by links based on the fact characteristics.

In the relationship graph , pairs of user nodes having the same factual characteristics may be connected by explicit links, and based on multiple pairs of user nodes connected by the explicit links, pairs of user nodes that are not connected by the explicit links may be connected by implicit links.

The relationship graph may have an affinity assigned to pairs of connected user nodes based on one or more factual characteristics shared by the pairs .

The user characteristics may include demographic information about the user and information about the user's use of web services.

In order to solve the above problem, one aspect of the information processing method of the present invention includes an acquisition step of acquiring factual characteristics of each of a plurality of users as user characteristics; a target user setting step of setting a target user from the plurality of users; a related user setting step of setting one or more users from the plurality of users who live with the target user as related users based on a relationship graph showing social relationships between the plurality of users ; and a prediction step of predicting the possibility of successful delivery of a package addressed to the target user at a specified date and time based on the user characteristics of the target user and the user characteristics of the related users.

In order to solve the above problem, one aspect of the program of the present invention is an information processing program for causing a computer to execute information processing, the program causing the computer to execute processes including an acquisition process for acquiring factual characteristics of each of a plurality of users as user characteristics, a target user setting process for setting a target user among the plurality of users, a related user setting process for setting one or more users among the plurality of users who live with the target user as related users based on a relationship graph showing social relationships between the plurality of users , and a prediction process for predicting the possibility of successful delivery of a package addressed to the target user at a specified date and time based on the user characteristics of the target user and the user characteristics of the related users.

According to the present invention, it is possible to accurately predict the likelihood of successful delivery of a merchandise parcel.
The above-mentioned objects, aspects, and advantages of the present invention, as well as objects, aspects, and advantages of the present invention not described above, will be understood by those skilled in the art from the following detailed description of the invention by referring to the accompanying drawings and the description of the claims.

FIG. 1 shows an example of the configuration of an information processing system. FIG. 2 shows an example of a functional configuration of the information processing device 10 according to the embodiment. FIG. 3 shows a flowchart of the process of creating a relationship graph. FIG. 4A is a diagram for explaining an explicit link. FIG. 4B is a diagram for explaining an explicit link. FIG. 4C is a diagram for explaining an explicit link. FIG. 4D is a diagram for explaining an implicit link. FIG. 5A is a diagram for explaining the process of inferring the relationship between links. FIG. 5B shows a flow chart of an example process for grouping pairs into clusters. FIG. 6A shows a conceptual diagram of a score (closeness score) based on the closeness of the relationship for a user pair. FIG. 6B shows a schematic architecture of the score prediction model 112 . FIG. 7 shows a conceptual diagram of a relationship graph. FIG. 8 shows a schematic architecture of the in-house prediction model 111. FIG. 9 shows an example of the hardware configuration of the information processing device 10 and the user device 11. FIG. 10 shows a flowchart of the process executed by the information processing device 10.

Below, an embodiment for carrying out the present invention will be described in detail with reference to the attached drawings. Among the components disclosed below, those having the same functions are given the same reference numerals, and their description will be omitted. Note that the embodiment disclosed below is one example of a means for realizing the present invention, and should be appropriately modified or changed depending on the configuration of the device to which the present invention is applied and various conditions, and the present invention is not limited to the following embodiment. Furthermore, not all of the combinations of features described in this embodiment are necessarily essential to the solution of the present invention.

[Functional configuration of information processing device]
Fig. 1 shows an example of the configuration of an information processing system according to this embodiment. As an example, as shown in Fig. 1, this information processing system includes an information processing device 10 and multiple user devices 11-1 to 11-N (N>1) used by multiple arbitrary users 1 to N. In the following description, unless otherwise specified, the user devices 11-1 to 11-N may be collectively referred to as user device 11. In the following description, the terms user device and user may be used synonymously.

The user device 11 is, for example, a device such as a smartphone or a tablet, and is configured to be able to communicate with the information processing device 10 via a public network such as LTE (Long Term Evolution) or a wireless communication network such as a wireless LAN (Local Area Network). The user device 11 has a display unit (display surface) such as a liquid crystal display, and each user can perform various operations using a GUI (Graphic User Interface) equipped on the liquid crystal display. The operations include various operations on content such as images displayed on the screen, such as tapping, sliding, and scrolling using a finger or a stylus.
The user device 11 is not limited to the device shown in Fig. 1, but may be a device such as a desktop PC (Personal Computer) or a notebook PC. In this case, each user may perform operations using an input device such as a mouse or a keyboard. The user device 11 may also be provided with a separate display screen.

The user device 11 can log in to a web service (Internet-related service) provided from the information processing device 10 or from another device (not shown) via the information processing device 10, and use the service. The web service can include online malls, online supermarkets, or services related to communications, finance, real estate, sports, and travel, all of which are provided via the Internet. By using such web services, the user device 11 can transmit information about the user of the user device 11 to the information processing device 10.

For example, the user device 11 can transmit to the information processing device 10 information on characteristics of the user device and the user, such as the IP (Internet Protocol) address of the user device 11, the user's address, and the user's name.
In addition, the user device 11 can perform positioning calculations based on signals received from GPS (Global Positioning System) satellites (not shown), generate the information obtained by the calculations as location information of the user device 11, and transmit the information to the information processing device 10.
The information processing device 10 acquires various information from the user device 11, and creates a relationship graph network (hereinafter, relationship graph) showing social relationships between users based on the information. The information processing device 10 then predicts the possibility of successful delivery of a package addressed to a target user at a specified date and time using the created relationship graph.

[Functional configuration of information processing device 10]
The information processing device 10 according to this embodiment first acquires various information from the user devices 11-1 to 11-N, and creates a relationship graph showing the social relationships between the users 1 to N. Then, the information processing device 10 identifies a target user (a user to whom a package is to be delivered) among the users 1 to N, and acquires information about the target user and information about users related to the target user based on the created relationship graph. The information processing device 10 predicts the possibility of successful delivery of the package to the target user at a specified date and time by applying the acquired information to a trained machine learning model. The package may include any item as long as it can be delivered (distributed).

FIG. 2 shows an example of the functional configuration of the information processing device 10 according to the present embodiment.
The information processing device 10 shown in Fig. 2 includes a user feature acquisition unit 101, a graph creation unit 102, a target user feature setting unit 103, a related user feature setting unit 104, a prediction unit 105, a learning unit 106, an output unit 107, a learning model storage unit 110, and a user feature storage unit 120. The learning model storage unit 110 stores a presence-at-home prediction model 111 and a score prediction model 112. The various learning models will be described later. In addition, the user feature storage unit 120 stores a user feature 121.

The user feature acquisition unit 101 acquires factual features (factual information) (hereinafter, user features) about each of the user devices 11-1 to 11-N regarding the user device or user. User features are factual features (information) that are actually or objectively obtained from the user device or user. The user feature acquisition unit 101 can acquire user features directly from the user device 11, for example. The user feature acquisition unit 101 can also acquire user features as information registered in a specified web service by the user of the user device 11.

The user characteristics include the IP address of the user device, the user's address and name, the credit card number held by the user, and the user's demographic information (demographic user attributes such as gender, age, residential area, occupation, and family structure). The user characteristics also include the location information of the user device for each time slot (hereinafter referred to as geographic data).

The user characteristics may also include a registration number or a registered name when using a specific web service. The user characteristics may also include a call history, a delivery address other than the user's address when using a specific web service, a usage status when using a specific web service, a usage history, a search history, and information about points that can be accumulated by using a service. In this way, the user characteristics may include any information, including information related to the user device or the user himself/herself, and information about the use of a specific service via communication.
The user feature acquiring unit 101 stores the acquired user features in the user feature storage unit 120 as user features 121 .

The graph creation unit 102 creates a relationship graph using the various user features acquired by the user feature acquisition unit 101. The relationship graph will be described later.

The target user characteristic setting unit 103 sets the user to whom the package is to be delivered (hereinafter, the target user). The target user may be set by an operator through an input operation using the input unit (input unit 95 in FIG. 9), may be set in advance in the system, or may be set by any program stored in the storage unit (ROM 92 or RAM 93 in FIG. 9). Furthermore, the target user characteristic setting unit 103 acquires the user characteristics of the target user from the user characteristics 121 and sets them in the prediction unit 105.

The related user characteristic setting unit 104 determines, as related users, one or more other users associated with the target user set by the target user characteristic setting unit 103, based on the relationship graph created by the graph creation unit 102. In this embodiment, the related user characteristic setting unit 104 determines, as related users, one or more other users who have the same address as the target user. The related user characteristic setting unit 104 may also determine, as related users, one or more users who live in the neighborhood of the target user. Furthermore, the related user characteristic setting unit 104 acquires the user characteristics of the related user from the user characteristics 121 and sets them in the prediction unit 105.

The prediction unit 105 predicts the possibility of successful delivery of a package addressed to the target user at a specified date and time based on the user characteristics of the target user set by the target user characteristic setting unit 10 and the user characteristics of related users set by the related user characteristic setting unit 104. In this embodiment, the prediction unit 105 predicts the possibility of successful delivery using the at-home prediction model 111 trained by the learning unit 106. The process of predicting the possibility of successful delivery will be described later.

The learning unit 106 trains the at-home prediction model 111 and the score prediction model 112, and stores the trained at-home prediction model 111 and score prediction model 112 in the learning model storage unit 110. The learning process for each learning model will be described later.

The output unit 107 outputs the prediction result of the possibility of successful delivery predicted by the prediction unit 105. The output unit 107 may generate and output information related to the predicted possibility of successful delivery. The output may be any output process, and may be output to an external device via a communication I/F (communication I/F 97 in FIG. 9) or may be displayed on a display unit (display unit 96 in FIG. 9).

[Steps for creating a relationship graph]
Next, a procedure for creating a relationship graph according to this embodiment will be described. In the following description, users A to E are users referred to for the purpose of explanation, and may be users of the user device 11. The relationship graph is composed of connections between the user nodes circled in Figures 4A to 4D, and in the following description, the user nodes are simply referred to as users. Figure 3 shows a flowchart of the relationship graph creation process executed by the graph creation unit 102 according to this embodiment. Each step of the process in Figure 3 will be described below.

<S31: Creating a link>
In S31, the graph creation unit 102 predicts and creates links between a plurality of users.
The link creation process will be described with reference to Figs. 4A to 4D. Figs. 4A to 4C are diagrams for explaining an explicit link, and Fig. 4D is a diagram for explaining an implicit link. An explicit link is a link created based on an explicit common feature between two users (a user pair). An implicit link is a link created as an indirect relationship by utilizing an explicit link that has already been created, even though the existence of an explicit common feature between the user pair is unclear. In this way, links between users are distinguished as explicit links and implicit links.

Figure 4A shows an example of creating an explicit link using the IP addresses of the users' user devices as a common feature. Figure 4A shows an example in which the web services available to users A to C include an online mall 41, a golf course reservation service 42, a travel-related reservation service 43, and a card management system 44. Although Figures 4A to 4C show these four web services, the number of web services is not limited to a specific number.

The online mall 41 is a shopping mall available online (using the Internet) and can provide a wide variety of products and services, such as fashion, books, food, concert tickets, and real estate.
The golf course reservation service 42 is operated as a website that provides online services related to golf courses, and can provide, for example, golf course search and reservation and lesson information.
The travel-related reservation service 43 is operated as a website that provides various travel services available online. The travel-related reservation service 43 can provide, for example, hotel and travel tour reservations, airline ticket and rental car reservations, tourist information, and information on hotels and the surrounding areas.
The card management system 44 is operated on a website that provides services related to credit cards issued and managed by a specific card management company. The card management system 44 may provide services in association with at least one of the online mall 41, the golf course reservation service 42, and the travel-related reservation service 43.

4A, users A to C each use the same IP address (=198.45.66.xx) to access an online mall 41, a golf course reservation service 42, and a travel-related reservation service 43. IP address information can be acquired by the user characteristic acquisition unit 101.
In such a case, the graph creator 102 creates explicit links (for example, link L1 between user A and user C) between users A to C with the same IP address characteristic, as shown in link state 45.

Fig. 4B shows an example of creating an explicit link using the features of users' addresses as common features. Like Fig. 4A, Fig. 4B shows an example in which an online mall 41, a golf course reservation service 42, a travel-related reservation service 43, and a card management system 44 exist as web services available to users A to C. Here, users A to C each register the same address (delivery address) and use the online mall 41, the golf course reservation service 42, and the travel-related reservation service 43. Address information can be acquired by the user feature acquisition unit 101.
In such a case, the graph creation unit 102 creates explicit links (for example, link L1 between user A and user C) between users A to C using the same address characteristics, as shown in link state 46.

Fig. 4C shows an example of creating an explicit link using the characteristics of the credit card numbers used by users as a common characteristic. Fig. 4B shows an example in which, like Fig. 4A, there are an online mall 41, a golf course reservation service 42, a travel-related reservation service 43, and a card management system 44 as web services available to users A to C. Here, users A to C each register the same credit card and use the online mall 41, the golf course reservation service 42, and the travel-related reservation service 43. Information including the credit card numbers can be acquired by the user characteristic acquisition unit 101.
In such a case, the graph creation unit 102 creates explicit links (for example, link L1 between user A and user C) between users A to C using the same card characteristics, as shown in link state 47.

Figure 4D shows an example of creating an implicit link between users. In the example of Figure 4D, user C, user D, and user E are connected to user A by explicit links, and user C, user D, and user E are connected to user B by explicit links. Such link features (features indicating the relationship between links) are embedded in a common feature space, and links inferred as implicitly building relationships between each user (each node) are created (established) as implicit links. In the example of Figure 4D, user A and user B are not connected by an explicit link, but an implicit link L2 is created as a result of inferring that they have a relationship in the common feature space. The graph creation unit 102 predicts and creates implicit links between users by learning (representation learning, relationship learning, embedding learning, knowledge graph embedding) the user relationship graph composed of nodes (users) connected by explicit links. At this time, the graph creation unit 102 may perform the learning based on a known embedding model or its extension as appropriate.

<S32: Inference of Relationship Between Links>
In S32, the graph creation unit 102 infers the relationship between the links predicted and created in S31. The inference process of the relationship between links will be described with reference to Fig. 5A and Fig. 5B. Fig. 5A is a diagram for explaining the inference process of the relationship between links, and shows an example of inferring the relationship between a link between user A and user B connected by an explicit link.

The graph creation unit 102 treats pairs of users connected by the links created in S31 as data points, and groups the pairs (data points) into clusters that represent a common type using various information acquired by the user feature acquisition unit 101. The various information may be information such as IP address, address, credit card, age, gender, and friends. Each cluster may be a cluster with a relationship such as spouse, parent and child, neighbor, same household, colleague, friend, same-gender sibling, or different-gender sibling. In the example of FIG. 5A, user pairs are indicated by crosses, and the following clusters into which the pairs may be grouped are indicated: parent-child cluster 51, spouse cluster 52, same-gender sibling cluster 53, friend cluster 54, and colleague cluster. Note that although five clusters are shown in FIG. 5A, the number of clusters is not limited to a specific number.

For example, if user A and user B have (share) the following characteristics 50: the same surname, an age difference of less than 10 years, opposite gender, and the same address, the graph creation unit 102 can group the pair of user A and user B into a cluster (spouse cluster 52) that represents the relationship between husband and wife (spouse).

FIG. 5B shows a flowchart of an example of a process performed by the graph creator 102 for grouping pairs into clusters.
At the start of S51, the pair to be grouped is assumed to have the characteristics of the same address and the same last name. In S52, the graph creation unit 102 judges whether the pair has the characteristics of the same gender. If the pair has the characteristics of the same gender (Yes in S52), in S53, the graph creation unit 102 judges whether the age difference of the pair is equal to or less than a predetermined threshold (=X value). If the age difference of the pair is greater than the X value (No in S53), the graph creation unit 102 groups the pair into a parent-child cluster 51. If the age difference is equal to or less than the X value (Yes in S53), the graph creation unit 102 groups the pair into a same-gender sibling cluster 53. If the pair does not have the characteristics of the same gender (No in S52), in S54, the graph creation unit 102 judges whether the age difference of the pair is equal to or less than a predetermined threshold (=Y value). If the age difference is greater than the Y value (No in S54), the graph creation unit 102 groups the target pair into a parent-child cluster 51. If the age difference is equal to or less than the Y value (Yes in S54), the graph creation unit 102 groups the target pair into a spouse cluster 52.

<S33: Assigning a score based on the closeness of the relationship>
In S33, the graph creation unit 102 predicts a score based on the intimacy of the relationship for the pair inferred in S32, and assigns the score to the pair. In this embodiment, the score is a value between 0 and 1, but there is no particular limit to the values that the score can take. Fig. 6A shows a conceptual diagram of a score based on the intimacy of the relationship for a user pair (hereinafter, an intimacy score).

In the example of FIG. 6A, the intimacy of the relationship between the user pair changes depending on the characteristics shared by user A and user B who are connected by an explicit link. In the upper part of FIG. 6A, if user A and user B have characteristics 60, such as siblings of the same gender, the same address, 1200 call histories, and 50 gift exchanges, the intimacy of the relationship between the user pair (i.e., the intimacy score) is high. On the other hand, in the lower part of FIG. 6A, if user A and user B have characteristics 61, such as siblings of the same gender, different addresses, 30 call histories, and 2 gift exchanges, the intimacy of the relationship between the user pair (i.e., the intimacy score) is low. Thus, as in the example of FIG. 6A, even if user A and user B are siblings of the same gender, the intimacy of the relationship between the pair will differ depending on other characteristics shared by the user pair. It is observed that pairs with high intimacy of the relationship are close to each other in social distance and have high influence. On the other hand, pairs with low levels of relationship intimacy are observed to have a large social distance from each other and are not closely related.

In this embodiment, the intimacy score for a user pair is predicted using a score prediction model 112. FIG. 6B shows a schematic architecture of the score prediction model 112. The score prediction model 112 is a learning model that uses the features 63 of the user pair as input and predicts the intimacy score 64 for the features 63.

The score prediction model 112 is, for example, a learning model that performs weakly supervised learning, for example, a learning model using a convolutional neural network (CNN). In this embodiment, the score prediction model 112 is a learning model that is trained using intimacy scores (0 to 1) assigned to multiple features for a user pair as teacher data, as shown in FIG. 6A. For example, in the learning stage, combined data such as an intimacy score close to 1 set for feature 60 in FIG. 6A and an intimacy score close to 0 set for feature 61 is used as teacher data. The learning process is performed by the learning unit 108. Note that the score prediction model 112 may differ for each type of relationship between user pairs, and may be a learning model trained according to one type of relationship.

In this embodiment, the intimacy score for a user pair is predicted using the score prediction model 112, but the graph creation unit 102 may be configured to predict the score using other methods.

Through the above process, explicit or implicit links are formed between multiple users, an intimacy score is assigned between each link, and a relationship graph is created. A conceptual diagram of the relationship graph is shown in Figure 7. Each of users 71 to 73 has multiple characteristics, and pairs of users are assigned an intimacy score predicted as described above.

[Prediction of the probability of successful delivery of a package addressed to a user]
Next, a process for predicting the possibility of successful delivery of a package addressed to a user according to this embodiment will be described. In this embodiment, the prediction unit 105 predicts the possibility of successful delivery of a package addressed to a target user using a trained at-home prediction model 111. FIG. 8 shows a schematic architecture of the at-home prediction model 111. The at-home prediction model 111 is, for example, a learning model using a convolutional neural network (CNN).

In the learning stage, the learning unit 106 uses a combination of the user features acquired by the user feature acquisition unit 101 and past delivery history (correct answer data (labels)) linked to at least one of the user features as training data to train the at-home prediction model 111. Specifically, the past delivery history includes delivery date and time information when delivery of a product previously purchased by a user using a web service was completed (successful delivery). The delivery date and time information includes, for example, the day of delivery (e.g., a specific day (day of the week) in a week, a public holiday, etc.) and the time slot (time slot) of delivery (e.g., morning, 6:00 p.m. to 8:00 p.m., etc.).

The trained at-home prediction model 111 is configured to predict the possibility of successful delivery of a package addressed to the target user, using the user characteristics of the target user and the user characteristics of one or more other users (related users) who have the same address as the target user and who are identified based on the created relationship graph as inputs. As described above, the user characteristics of the target user are set in the prediction unit 105 by the target user characteristic setting unit 103, and the user characteristics of the related users are set by the related user characteristic setting unit 104. The related user characteristic setting unit 104 may determine one or more other users who have the same address as the target user as related users, for example, based on the parent-child cluster 51 and the spouse cluster 52 in FIG. 5A. Note that even if the addresses of multiple users are registered at different addresses, one or more other users who are recognized as having the same address as the target user may be determined as related users based on the approximate distance between the user devices ascertained by the IP addresses of each user device.

In the prediction stage, the prediction unit 105 inputs the user characteristics of the target user, the user characteristics of the related users identified based on the created relationship graph, and the information of the target time slot into the at-home prediction model 111 to predict the possibility of the target user and/or the related user being at home. Even if the target user is absent, a package addressed to the target user can be received by another user living with the target user, and the possibility of the target user and/or the related user being at home is synonymous with the possibility of successful delivery, or can be applied to the possibility of successful delivery. The information of the target time slot includes not only information on the time slot (time) but also information on the date (i.e., information on the date and time). In this embodiment, the at-home prediction model 111 is configured to output the possibility of successful delivery as a numerical value from 0 to 1 (1 being the maximum possibility). In this embodiment, the at-home prediction model 111 may be configured for each time slot. Here, the prediction unit 105 may use the at-home prediction model 111 according to the time slot.

In this embodiment, as shown in FIG. 8, the user characteristics 82 of the target user, the user characteristics 83 of the spouse of the target user who lives with the target user (i.e., has the same address), the user characteristics 84 of the parent of the target user who lives with the target user, and the target time slot 85 (information on a specified date and time) are input to the at-home prediction model 111, and the possibility 86 of successful delivery in the time slot 85 is predicted. Note that if the at-home prediction model 111 is configured for each time slot, it is not necessary to input the time slot 85 to the at-home prediction model 111.

As described above, in this embodiment, the possibility of successful delivery is expressed, for example, by a numerical value between 0 and 1. If the possibility of successful delivery 86 output from the at-home prediction model 111 is higher than a predetermined threshold, the prediction unit 105 can determine that the possibility of successful delivery is high. The predetermined threshold is, for example, 0.7.

In FIG. 8, to predict the possibility of successful delivery of a package addressed to a target user, demographic information (demographic user attributes such as gender, age, residential area, occupation, and family structure), geodata (location information of the user device for each time slot), and the IP address (source address) of the user device are used for each user. Note that the input data to the presence-at-home prediction model 111 is not limited to the data shown in FIG. 8 (demographic information, geodata, IP address), and data on all characteristics that each user may have, such as information regarding the user's use of web services, may be input to the presence-at-home prediction model 111.

The user characteristics of related users may be weighted before use. For example, a high weighting may be applied to the user characteristics of users with high intimacy scores, and a low weighting may be applied to the user characteristics of users with low intimacy scores, and the resulting data may be input to the presence-at-home prediction model 111.

In the above description, the spouse and parents of the target user are selected (determined) as related users related to the target user, but a user living in the vicinity of the target user may be selected. For example, if the target user allows delivery to the home of a user living in the vicinity instead of his or her own home through a web service, the prediction unit 105 may predict the possibility that the user living in the vicinity will be at home. In this case, in FIG. 8, the characteristics of the user living in the vicinity are used instead of/or in addition to the characteristics of the target user, and the possibility of successful delivery of a package addressed to the target user may be predicted using the characteristics of the user living in the vicinity and the characteristics of one or more users living together with the user living in the vicinity.

[Hardware configuration of information processing device 10]
FIG. 9 is a block diagram showing an example of a hardware configuration of an information processing device 10 according to the present embodiment.
The information processing apparatus 10 according to the present embodiment can be implemented on any single or multiple computers, mobile devices, or any other processing platform.
9, the information processing device 10 is illustrated as being implemented in a single computer, but the information processing device 10 according to the present embodiment may be implemented in a computer system including multiple computers. The multiple computers may be connected to each other via a wired or wireless network so as to be able to communicate with each other.

9, the information processing device 10 may include a CPU 91, a ROM 92, a RAM 93, a HDD 94, an input unit 95, a display unit 96, a communication I/F 97, and a system bus 98. The information processing device 10 may also include an external memory.
A CPU (Central Processing Unit) 91 generally controls the operations of the information processing device 10, and controls each of the components (92 to 97) via a system bus 98, which is a data transmission path.

The ROM (Read Only Memory) 92 is a non-volatile memory that stores control programs and the like necessary for the CPU 91 to execute processing. Note that the programs may be stored in a non-volatile memory such as a HDD (Hard Disk Drive) 94 or an SSD (Solid State Drive) or in an external memory such as a removable storage medium (not shown).
The RAM (Random Access Memory) 93 is a volatile memory and functions as a main memory, a work area, etc. of the CPU 91. That is, the CPU 91 loads necessary programs, etc. from the ROM 92 to the RAM 93 when executing processing, and realizes various functional operations by executing the programs, etc. The learning model storage unit 110 and the user feature storage unit 120 shown in FIG. 2 can be configured by the RAM 93.

The HDD 94 stores, for example, various data and various information required when the CPU 91 performs processing using a program. The HDD 94 also stores, for example, various data and various information obtained when the CPU 91 performs processing using a program.
The input unit 95 is composed of a keyboard and a pointing device such as a mouse.
The display unit 96 is configured with a monitor such as a liquid crystal display (LCD) etc. The display unit 86 may be configured in combination with the input unit 95 to function as a GUI (Graphical User Interface).

The communication I/F 97 is an interface that controls communication between the information processing device 10 and external devices.
The communication I/F 97 provides an interface with a network and executes communication with an external device via the network. Various data, various parameters, etc. are transmitted and received between the external device and the communication I/F 97. In this embodiment, the communication I/F 87 may execute communication via a wired LAN (Local Area Network) or a dedicated line that complies with a communication standard such as Ethernet (registered trademark). However, the network that can be used in this embodiment is not limited to this, and may be configured as a wireless network. This wireless network includes wireless PANs (Personal Area Networks) such as Bluetooth (registered trademark), ZigBee (registered trademark), and UWB (Ultra Wide Band). It also includes wireless LANs (Local Area Networks) such as Wi-Fi (Wireless Fidelity) (registered trademark) and wireless MANs (Metropolitan Area Networks) such as WiMAX (registered trademark). It also includes wireless WANs (Wide Area Networks) such as LTE/3G, 4G, and 5G. Note that the network only needs to be able to connect devices to each other and communicate with each other, and the communication standard, scale, and configuration are not limited to those described above.

At least some of the functions of each element of the information processing device 10 shown in FIG. 2 can be realized by the CPU 91 executing a program. However, at least some of the functions of each element of the information processing device 10 shown in FIG. 2 may be operated as dedicated hardware. In this case, the dedicated hardware operates under the control of the CPU 91.

[Hardware Configuration of User Device 11]
The hardware configuration of the user device 11 shown in Fig. 1 may be the same as that of Fig. 9. That is, the user device 10 may include a CPU 91, a ROM 92, a RAM 93, a HDD 94, an input unit 95, a display unit 96, a communication I/F 97, and a system bus 98. The user device 11 can display various information provided by the information processing device 10 on the display unit 96 and perform processing corresponding to an input operation received from a user via a GUI (configured by the input unit 95 and the display unit 96).

<Processing flow>
Fig. 10 shows a flowchart of the process executed by the information processing device 10 according to this embodiment. The process shown in Fig. 9 can be realized by the CPU 91 of the information processing device 10 loading a program stored in the ROM 92 or the like into the RAM 93 and executing it. For the explanation of Fig. 10, the information processing system shown in Fig. 1 will be referred to. It is assumed that the presence-at-home prediction model 111 and the score prediction model 112, which have been trained by the training unit 106, are stored in the training model storage unit 110.

In S101, the user characteristic acquisition unit 101 acquires the user characteristics of each user from the user devices 11-1 to 11-N and stores them in the user characteristic storage unit 120 as user characteristics 121. The process of S101 may be a process of acquiring (collecting) user characteristics for a certain period of time in the past.

In S102, the graph creation unit 102 creates a relationship graph for users 1 to N using the various user features acquired by the user feature acquisition unit 101. The procedure for creating the relationship graph is as described above.

In S103, the target user characteristic setting unit 103 sets a user (target user) to whom the package is to be delivered from among users 1 to N. As described above, the target user may be set by an operator through an input operation using the input unit 95, may be set in advance in the system, or may be set by any program stored in the ROM 92 or RAM 93. Furthermore, in S103, the target user characteristic setting unit 103 acquires the user characteristics of the target user from the user characteristics 121 and sets them in the prediction unit 105.

In S104, the related user characteristic setting unit 104 determines, based on the relationship graph created by the graph creation unit 102, one or more other users who have the same address as the target user as related users. Furthermore, the related user characteristic setting unit 104 acquires the user characteristics of the related users from the user characteristics 121 and sets them in the prediction unit 105.

In S105, the prediction unit 105 inputs the user characteristics of the target user and the related user set in S103 and S104, as well as the target time slot (date and time), into the at-home prediction model 111, and predicts the possibility of successful delivery of a package addressed to the target user in the target time slot.

In S106, the output unit 107 outputs the prediction result of the possibility of successful delivery predicted in S105. The output unit 107 may generate information regarding the prediction result and output it to an external device (not shown).

In this way, the information processing device 10 creates a relationship graph network (relationship graph) that indicates the social relationships between users from the user characteristics of multiple users, and predicts the possibility of successful delivery of a package addressed to a target user based on the relationship graph. This can improve the accuracy of predictions of the possibility of successful delivery of a package addressed to a target user, and reduce the risk of redelivery by the delivery company.

Although specific embodiments have been described above, these embodiments are merely examples and are not intended to limit the scope of the present invention. The devices and methods described herein may be embodied in forms other than those described above. Furthermore, the above-described embodiments may be omitted, substituted, or modified as appropriate without departing from the scope of the present invention. Forms in which such omissions, substitutions, or modifications have been made are included within the scope of the claims and their equivalents, and belong to the technical scope of the present invention.

1 to N: User, 10: Information processing device, 11-1 to 11-N: User device, 101: User feature acquisition unit, 102: Graph creation unit, 103: Target user feature setting unit, 104: Related user feature setting unit, 105: Prediction unit, 106: Learning unit, 107: Output unit, 110: Learning model storage unit, 111: Presence prediction model, 112: Score prediction model, 120: User feature storage unit, 121: User feature

Claims (9)

An acquisition means for acquiring factual features of each of a plurality of users as user features;
a target user setting means for setting a target user among the plurality of users;
a related user setting means for setting, among the plurality of users, one or more users who live together with the target user as related users based on a relationship graph showing social relationships between the plurality of users ;
A prediction means for predicting a possibility of successful delivery of a package addressed to the target user at a predetermined date and time based on user characteristics of the target user and user characteristics of the related users;
13. An information processing device comprising: The information processing device according to claim 1, characterized in that the prediction means predicts the possibility of successful delivery of the package at the specified date and time using a machine learning model configured to input user characteristics of the target user and user characteristics of the related user and to output the possibility of successful delivery of the package at the specified date and time. The information processing device according to claim 1 or 2, characterized in that the possibility of successful delivery of the package at the specified date and time is expressed as a number between 0 and 1, with 1 being the maximum possibility. 4. The information processing apparatus according to claim 1, wherein in the relationship graph, each user is represented by a user node, and the user nodes are connected by links based on the fact characteristics. The information processing device according to claim 4, characterized in that, in the relationship graph , pairs of user nodes having the same fact characteristics are connected by explicit links, and based on the multiple pairs of user nodes connected by the explicit links, pairs of user nodes that are not connected by the explicit links are connected by implicit links. 6. The information processing apparatus according to claim 4 , wherein the relationship graph has an affinity assigned to pairs of connected user nodes based on one or more factual features shared by the pairs. The information processing device according to any one of claims 1 to 6, characterized in that the user characteristics include demographic information of the user and information regarding the user's use of web services. An information processing method executed by an information processing device,
An acquisition step of acquiring factual features of each of a plurality of users as user features;
a target user setting step of setting a target user from among the plurality of users;
a related user setting step of setting, as related users, one or more users who live together with the target user among the plurality of users based on a relationship graph showing social relationships between the plurality of users ;
a prediction step of predicting a possibility of successful delivery of a package addressed to the target user at a predetermined date and time based on user characteristics of the target user and user characteristics of the related users;
13. An information processing method comprising: An information processing program for causing a computer to execute information processing, the program comprising:
An acquisition process of acquiring factual features of each of a plurality of users as user features;
A target user setting process for setting a target user among the plurality of users;
a related user setting process for setting one or more users who live together with the target user among the plurality of users as related users based on a relationship graph showing social relationships between the plurality of users ;
A prediction process for predicting the possibility of successful delivery of a package addressed to the target user at a predetermined date and time based on the user characteristics of the target user and the user characteristics of the related users.
Information processing program.

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