Information processing device, information processing method, information processing program, and learning model

Abstract

To provide a mechanism for predicting the probability of successful delivery to a destination user taking into account information on a destination address of a package.SOLUTION: An information processing apparatus performs user characteristics representing characteristics of a destination user of a package to be delivered, acquires date characteristics representing characteristics of the delivery date of the package, specifies a building type of a building located at a destination address of the package, and predicts the probability of successful delivery of the package to the destination user for each of a plurality of time zones on the delivery date based on the user characteristics, the date characteristics, and the building type.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present invention relates to an information processing device, an information processing method, an information processing program, and a learning model, and in particular to a technology related to package delivery. [Background technology]

[0002] 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 even at the time of redelivery, which results in the need for further redelivery, and this places a heavy burden on the delivery company. As a technology for addressing such issues, Patent Document 1 discloses a technology for predicting (determining) 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 is at home. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2019-016411 A Summary of the Invention [Problem to be solved by the invention]

[0004] The technology disclosed in Patent Document 1 predicts the possibility of a target user being at home, i.e., the possibility of successful delivery, based on the usage status of the service by the user. In this way, in the past, the possibility of successful delivery was predicted based on user characteristics such as the usage status of the service by the user. Meanwhile, the information required for delivery includes not only the information of the destination user but also the information of the destination address of the package, but until now, no mechanism has been provided for predicting the possibility of successful delivery taking into account the characteristics of the destination address of the package.

[0005] The present invention has been made in consideration of the above problems, and aims to provide a mechanism for predicting the possibility of successful delivery to a destination user by taking into account information related to the destination address of the package. [Means for solving the problem]

[0006] In order to solve the above problem, one aspect of an information processing device according to the present invention has a user feature acquisition means for acquiring user features representing the characteristics of a destination user of a package, a date feature acquisition means for acquiring date features representing the characteristics of the delivery date of the package, an identification means for identifying a building type of a building located at the destination address of the package, and a prediction means for predicting the possibility of successful delivery of the package to the destination user for each of a number of time periods on the delivery date based on the user features, the date features, and the building type. In order to solve the above problem, another aspect of the information processing device according to the present invention includes a user feature acquisition means for acquiring user features representing characteristics of a destination user for each of a plurality of packages, a date feature acquisition means for acquiring date features representing characteristics of the delivery dates of the plurality of packages, an identification means for identifying a building type of a building located at a destination address for each of the plurality of packages, and a prediction means for predicting the possibility of successful delivery of each of the plurality of packages to the destination user for each of a plurality of time periods on the delivery date based on the user features, the date features, and the building type.

[0007] In order to solve the above problem, one aspect of the information processing method of the present invention includes a user feature acquisition step of acquiring user features representative of the characteristics of the destination user of the package, a date feature acquisition step of acquiring date features representative of the characteristics of the delivery date of the package, an identification step of identifying a building type of a building located at the destination address of the package, and a prediction step of predicting the possibility of successful delivery of the package to the destination user for each of a number of time periods on the delivery date based on the user features, the date features, and the building type.

[0008] In order to solve the above problems, one aspect of the information processing 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 a user feature acquisition process for acquiring user features representative of the characteristics of the destination user of the package, a date feature acquisition process for acquiring date features representative of the characteristics of the delivery date of the package, an identification process for identifying the building type of the building located at the destination address of the package, and a prediction process for predicting the possibility of successful delivery of the package to the destination user for each of a number of time periods on the delivery date based on the user features, the date features, and the building type. Effect of the Invention

[0009] According to the present invention, a mechanism is provided for predicting the likelihood of successful delivery to a destination user by taking into account information regarding the destination address of a package. 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 claims. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 shows an example of the configuration of an information processing system. [Diagram 2] FIG. 2 shows an example of a functional configuration of the information processing device 10 according to the embodiment. [Diagram 3] FIG. 3 shows examples of addresses and the building types identified by the addresses. [Figure 4] FIG. 4 shows a conceptual diagram of the process of predicting the probability of successful delivery. [Diagram 5] FIG. 5 shows an example of a delivery table. [Figure 6A] FIG. 6A is a diagram for explaining the delivery schedule. [Figure 6B] FIG. 6B is a diagram for explaining the delivery schedule. [Figure 7]FIG. 7 shows a flowchart of the entire process executed by the information processing device 10. [Figure 8] FIG. 8 shows an example of the hardware configuration of the information processing device 10 and the user device 11. As shown in FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, with reference to the attached drawings, an embodiment for carrying out the present invention will be described in detail. 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 an 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 the present embodiment are necessarily essential to the solution of the present invention.

[0012] [Information processing system configuration] 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, 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.

[0013] 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. Note that user device 11 is not limited to the device shown in Fig. 1, and may be a device such as a desktop PC (Personal Computer) or a notebook PC. In this case, each user may perform operations using input devices such as a mouse and a keyboard. User device 11 may also be provided with a separate display screen.

[0014] 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 can use the service. The web service can include an online mall, an online supermarket, or services related to communications, finance, real estate, sports, and travel, which are provided via the Internet. By using such a web service, 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 or the user, such as the IP (Internet Protocol) address of the user device 11, the user's address, and the user's name.

[0015] In this embodiment, in order to use the web service, each of users 1 to N is assigned a user ID for identifying the user. The user ID may be an ID for identifying the user on the web service, or may be an ID for physically identifying the user device.

[0016] The information processing device 10 acquires various information from the user device 11, acquires delivery requests for parcels addressed to multiple users, and determines a delivery schedule for the multiple parcels on a specific date. In this embodiment, the information processing device 10 is configured to determine the delivery schedule in consideration of the characteristics of the destination addresses of the multiple parcels included in the delivery requests.

[0017] [Functional configuration of information processing device 10] The information processing device 10 according to this embodiment acquires various information from the user devices 11-1 to 11-N. The information processing device 10 also acquires a delivery request for parcels addressed to multiple users. The delivery request includes a delivery date (a specified date) and information on multiple destination users and multiple destination addresses for the multiple parcels. Based on the acquired information, the information processing device 10 predicts the possibility of successful delivery in multiple time slots set for the delivery date for each of the multiple destination users. Then, based on the prediction result, the information processing device 10 determines an efficient delivery schedule for delivering the parcels to the multiple destination users on the delivery date. Note that the parcels may include any items as long as they can be delivered (delivered).

[0018] FIG. 2 shows an example of a functional configuration of the information processing device 10 according to the present embodiment. The information processing device 10 shown in FIG. 2 includes a delivery request acquisition unit 101, a user feature acquisition unit 102, an order feature acquisition unit 103, a date feature acquisition unit 104, a building type identification unit 105, a delivery success prediction unit 106, a delivery schedule determination unit 107, an output unit 108, a learning model storage unit 110, a feature storage unit 120, and a rule storage unit 130. The learning model storage unit 110 stores a first time slot prediction model 111, a second time slot prediction model 112, a third time slot prediction model 113, a fourth time slot prediction model 114, a fifth time slot prediction model 115, a sixth time slot prediction model 116, and a user feature prediction model 117. The various learning models will be described later. In addition, the feature storage unit 120 stores a user feature 121, an order feature 122, a date feature 123, and a building type (building feature) 124, and the rule storage unit 130 stores a building type identification rule 131. The various features and rules will be described later.

[0019] The delivery request acquisition unit 101 acquires delivery requests for parcels addressed to multiple users (two or more users among users 1 to N). The delivery requests may be input by an operator via an input unit (input unit 85 in FIG. 8) or via a communication unit (communication I / F 87 in FIG. 8). Alternatively, the delivery requests may be set in advance in the system, or may be set by any program stored in a storage unit (ROM 82 or RAM 83 in FIG. 8).

[0020] The delivery request includes information necessary for delivery of each of the multiple packages. In this embodiment, the delivery request includes information on multiple destination users, information on the addresses of the multiple destination users (multiple different destination addresses), and information on the delivery date, which is information on the date of scheduled delivery. In this embodiment, the delivery date is assumed to be the same date. In addition, the multiple destination users included in the delivery request are configured to be users whose addresses are close to each other (for example, the same city or town, or the delivery area of ​​a single courier). In other words, the destination address information (multiple destination addresses) included in the delivery request may include information on multiple different addresses that satisfy one or more predetermined positional relationships. The delivery request may be configured in advance to include destination users whose addresses are close to each other, or the information processing device 10 or another device may generate a delivery request to include destination users whose addresses are close to each other.

[0021] The destination address information may include information on addresses located nearby (i.e., within a predetermined distance range). The destination address information may also include information on multiple different addresses located near a common road, street, or street. The destination address information may also include information on multiple addresses that have part in common, such as a common building name or house number. The destination address information may also include information on multiple different addresses located in a common block. The destination address information may also include information on multiple different addresses whose difference in altitude above sea level (height difference) is below a predetermined value. The destination address information may also include information on multiple different addresses whose number of right or left turns on the route between the addresses is below a predetermined number. The destination address information may also include information on multiple different addresses whose number of crossroads on the route between the addresses is below a predetermined number. The destination address information may also include information on multiple different addresses whose number of traffic lights located on the route between the addresses is below a predetermined number (prediction values ​​such as expected values ​​of stop times due to traffic lights are below a predetermined time). The destination address information may include information on a plurality of addresses where the road width of a road, street, or street that constitutes a route between the addresses exceeds a predetermined value, or the destination address information may include information on a plurality of addresses where a route between the addresses does not include a predetermined road, street, or street such as a school zone.

[0022] The user feature acquisition unit 102 acquires user features of each of multiple destination users included in the delivery request, among users 1 to N. In this embodiment, the user features are factual features (factual information) about the user device or user, and are fact-based features (information) that are actually or objectively obtained from the user device or user. The user feature acquisition unit 102 can acquire the user features directly from the user device 11, for example. In addition, the user feature acquisition unit 102 can acquire the user features as information registered in a predetermined web service by the user of the user device 11. The user features are linked to the user ID of the destination user (hereinafter, destination user ID).

[0023] In this embodiment, the user feature acquisition unit 102 acquires demographic information and past purchasing behavior information in a web service as user features. The demographic information is information indicating demographic user attributes such as gender, age, residential area, occupation, and family structure. The purchasing behavior information is, for example, information on the name of a purchased item and the date and time of purchase. The user feature acquisition unit 102 may also acquire data indicating preferences registered by the user in the web service at the time of purchase as user features.

[0024] The user feature acquisition unit 102 is also configured to apply the acquired user features to the trained user feature prediction model 117 to acquire user features (estimated user features (attributes)) estimated for the user features. For example, the user feature prediction model 117 is configured to receive user features (i.e., actual features) of a destination user (each user among a plurality of destination users) as input, and output the probability (correspondence probability) that each of the plurality of user features corresponds (matches) to the destination user. The user feature acquisition unit 102 ultimately determines the estimated user features of the destination user from the correspondence probability.

[0025] For example, the user feature acquisition unit 102 inputs demographic information of the destination user and data indicating past purchase history and preferences in web services of the destination user as the user features of the destination user to the user feature prediction model 117. The user feature prediction model 117 outputs probabilities for each of a plurality of items that the destination user is estimated to purchase and a plurality of preferences that the destination user may have as the corresponding probability. Then, the user feature acquisition unit 102 can acquire items and preferences that have a probability equal to or greater than a predetermined value as the estimated user features of the destination user. Also, the probability itself may be the estimated user feature. In this embodiment, the estimated user characteristics include, for example, characteristics related to life stage, such as the probability of being working or having children, and characteristics related to lifestyle, such as the probability of living in a house or apartment (residence).

[0026] The user characteristic acquisition unit 102 stores the acquired and estimated user characteristics in the characteristic storage unit 120 as user characteristics 121. The user characteristics are stored in association with the destination user ID.

[0027] The order feature acquisition unit 103 acquires the order features (order features) of each of the multiple destination users included in the delivery request among users 1 to N. In this embodiment, the order features are information that indicates the features of items (i.e., packages) previously ordered by the destination user. The ordered items may be limited to items ordered by the destination user and delivered to the destination user. The order features are obtained, for example, from past order information (purchase behavior information) by the destination user in a web service. For example, the order features include the weight of the item, the type of the item, and the type of genre to which the item belongs. The order features are linked to the destination user ID, and the order feature acquisition unit 103 stores the acquired order features in the feature storage unit 120 as the order features 122, linked to the destination user ID.

[0028] The date feature acquisition unit 104 acquires (specifies) the features of the date (date features) from the information on the delivery date of the package included in the delivery request. The date features can be acquired from calendar information, etc. The date feature acquisition unit 104 acquires, for example, the year, month, and day of the date and the type of the date (holiday, holiday, or weekday) as date features, and stores them in the feature storage unit 120 as date features 123. The date features are stored in association with the destination user ID.

[0029] The building type identification unit 105 identifies the building type (building characteristics) of the building located at each address from information on multiple different destination addresses included in the delivery request. In this embodiment, the building type identification unit 105 analyzes the character string of the destination address and identifies the building type of the address based on the building type identification rule 131 stored in the rule storage unit 130. That is, the building type identification unit 105 applies the character string of the destination address to the building type identification rule 131 to identify the building type of the address. The procedure for identifying the building type will be described later.

[0030] Furthermore, the building type identification unit 105 can determine whether or not the building located at the destination address has a delivery box (a box for storing delivered packages) based on the information of the destination address. This determination can be performed using an online public or private service such as a predetermined web service. Based on this determination, the building type identification unit 105 generates delivery box information indicating whether or not the building located at the destination address has a delivery box.

[0031] It should be noted that a parcel locker provided at a building located at a destination address may be understood to be synonymous with a stationary or mobile storage box provided at the building that can accommodate delivered packages. The presence of a stationary or mobile storage box may also be determined using online public or private services.

[0032] The building type identification unit 105 links the identified building type with the destination user ID and stores it in the feature storage unit 120 as the building type 124. Here, the building type identification unit 105 may link the identified building type with the generated delivery box information (information indicating whether the building has a delivery box or not) and store it as the building type 124.

[0033] The delivery success prediction unit 106 predicts the possibility of successful delivery to each of multiple destination users included in the delivery request based on the user features 121, the order features 122, the date features 123, and the building type 124. The delivery success prediction unit 106 may predict the possibility of successful delivery based on the user features 121, the date features 123, and the building type 124. In this embodiment, the delivery success prediction unit 106 predicts the possibility of successful delivery for each of multiple time slots (time slots) on the delivery date. The procedure for predicting the possibility of successful delivery will be described later.

[0034] The delivery schedule determination unit 107 determines a delivery schedule for a delivery date based on the probability of successful delivery to each of the multiple destination users predicted by the delivery success prediction unit 106. In this embodiment, the delivery schedule includes a time slot (time period) for delivery, and information on the destination user and destination address. The procedure for determining the delivery schedule will be described later.

[0035] The output unit 108 outputs the delivery schedule determined by the delivery schedule determination unit 107. The output unit 108 may generate and output information related to the schedule. The output may be any output process, and may be output to an external device via a communication I / F (communication I / F 87 in FIG. 8) or may be displayed on a display unit (display unit 86 in FIG. 8).

[0036] [Procedure for identifying building type] The following describes the procedure of the building type identification process performed by the building type identification unit 105. In this embodiment, the building type identification unit 105 identifies the building type based on the building type identification rules 131 stored in the rule storage unit 130. Specifically, the building type identification unit 105 applies the character string of the address to the building type identification rules 131 to identify the building type.

[0037] The building type identification rule 131 is a pre-created rule for identifying a building type based on the characteristics of an address character string. The building type identification rule 131 may be input by an operator via an input unit (input unit 85 in FIG. 8 ) or via a communication unit (communication I / F 87 in FIG. 8 ). Alternatively, the building type identification rule 131 may be set in advance in the system, or may be set by an arbitrary program stored in a storage unit (ROM 82 or RAM 83 in FIG. 8 ). It should be noted that the building type identification rules 131 described in this embodiment are merely an example, and although there may be cases where they do not match actual examples of combinations of addresses and building types, any rule capable of identifying the building type of an address from the character string of the address can be used. For example, the building type may be identified depending on whether or not the character string of the address contains a predetermined keyword. Also, new rules may be added to the building type identification rules 131.

[0038] The building type identification rules 131 according to this embodiment include nine types of rules, (1) to (9). The rules (1) to (9) are configured to identify a building type based on the characteristics of a character string in a destination address. (1) If the address includes the name "Inc.", the building type of the address is identified as a "business building," which refers to a business or company building. Example: "1-2-3 D-cho, ABC-shi, Osaka Prefecture, inside abc Co., Ltd." is identified as a "business building."

[0039] (2) If the address contains the word "building" followed by three digits, the building type of the address is identified as a "unit building," which refers to a building, condominium, or apartment (residence) that has multiple living units (individually occupied spaces). Example: "303 abc building, 1-2-3 D-cho, ABC-ku, Tokyo" is identified as a "unit building."

[0040] (3) If an address contains the word "building" but does not contain three digits following the word "building," the building type of the address is identified as "business building." Example: "5th floor, abc building, 1-2-3, D-cho, ABC-ku, Tokyo" is identified as a "business building."

[0041] (4) If the address ends with the letter "gou," the building type of the address is identified as a "unit building." Example: "Residence abc 303, 1-2-3 D-cho, ABC-shi, Osaka Prefecture" is identified as a "unit building."

[0042] (5) If an address contains an address in the format “x1-x2-x3” where “x3” is not a three or four digit number, the building type of the address is identified as “home (single-family home, detached).” Example: "1-234-5 D-cho, ABC City, Saitama Prefecture" is identified as a "house."

[0043] (6) If the address contains an “x1-x2” format, the building type of the address is identified as a “unit building.” Example: "DE123-4, ABC-cho, Kanagawa Prefecture" is identified as a "unit building."

[0044] (7) If the address contains a single digit and ends with that digit, the building type of the address is identified as "home." Example: "1234 D-cho, ABC-shi, Chiba Prefecture" is identified as "house."

[0045] (8) If an address contains an address in the format "x1-x2-x3" or "x1-x2-x3-x4," where "x3" or "x4" is a three or four digit number, the building type of the address is identified as a "unit building." Example: "FG1-2-3-303 DE Ward, ABC City, Fukuoka Prefecture" is identified as a "unit building."

[0046] (9) If an address contains an address in the format "x1-x2-x3" or "x1-x2-x3-x4" that also contains a trailing digit in addition to the format, the building type of the address is identified as a "unit building." Example: "Residence abc 303, DE1-2-3, ABC City, Kanagawa Prefecture" is identified as a "unit building."

[0047] In addition, the building type identification unit 105 can be configured to recognize "x1-x2-x3" in the above rule as the same string as variations such as "x1-chome x banchi x3" or "x1-chome x2-x3." In addition, the building type identification unit 105 can be configured to recognize, in the above rules, "x1-x2-x3-x4" as the same string as variations such as "x1-chome x no. 2 x no. 3 (building name) x 4" or "x1-chome x 2-x3 (building name) x 4."

[0048] Figure 3 shows a list of examples of addresses listed in the above rules (1) to (9) and building types identified by the addresses. As shown in Figure 3, a destination address is classified into one of the building types "business building," "unit building," and "house" according to the characteristics of the character string contained in the address.

[0049] For an address whose building type cannot be identified by the above rules (1) to (9), the building type identification unit 105 may identify the building type of the address as a predetermined building type such as "house." Alternatively, for an address whose building type cannot be identified by the above rules (1) to (9), the building type identification unit 105 may identify the building type of the address as "unknown type."

[0050] Also, rules (1) to (9) may be applicable to some areas, such as Tokyo, but may not be applicable to other areas. Therefore, rules other than rules (1) to (9) may be created and applied to other areas. Even in areas within Tokyo, rules other than rules (1) to (9) may be created and applied.

[0051] [Procedure for predicting the probability of successful delivery] A procedure for predicting the possibility of successful delivery to a destination user by the delivery success prediction unit 106 will be described. In this embodiment, the delivery success prediction unit 106 predicts the possibility of successful delivery to a destination user by using a learning model for trained machine learning. In this embodiment, the delivery time is divided into six time slots (time slots), namely, 8:00 to 12:00 (first time slot), 12:00 to 14:00 (second time slot), 14:00 to 16:00 (third time slot), 16:00 to 18:00 (fourth time slot), 18:00 to 20:00 (fifth time slot), and 20:00 to 22:00 (sixth time slot), and the possibility of successful delivery for each slot is predicted.

[0052] The first time slot prediction model 111 through the sixth time slot prediction model 116 are used to predict the probability of successful delivery. The first time slot prediction model 111 is a learning model that predicts the probability of successful delivery from 8:00 to 12:00 (first time slot). The second time slot prediction model 112 is a learning model that predicts the probability of successful delivery from 12:00 to 14:00 (the second time slot). The third time slot prediction model 113 is a learning model that predicts the probability of successful delivery from 14:00 to 16:00 (the third time slot). The fourth time slot prediction model 114 is a learning model that predicts the probability of successful delivery from 4:00 pm to 6:00 pm (fourth time slot). The fifth time slot prediction model 115 is a learning model that predicts the probability of successful delivery from 18:00 to 20:00 (the fifth time slot). The sixth time slot prediction model 116 is a learning model that predicts the probability of successful delivery from 8 p.m. to 10 p.m. (the sixth time slot).

[0053] The first time slot prediction model 111 to the sixth time slot prediction model 116 are learning models for machine learning, for example, learning models based on CatBoost. Alternatively, the first time slot prediction model 111 to the sixth time slot prediction model 116 may be other boosting-based learning models such as XGBoost or LightGBM.

[0054] In the learning stage, a learning model corresponding to each time slot is learned by using a combination of user features 121, order features 122, date features 123, and delivery history (correct answer data (label)) linked to at least one of building type 124 stored in feature storage unit 120 for past deliveries to users 1 to N as training data. The delivery history indicates delivery success (=1) or delivery failure (=0) for each time slot. Successful delivery may include, for example, the presence of the destination user in the building of the destination address, or delivery to a delivery box provided in the building. Alternatively, successful delivery may include completion of delivery without meeting the user (for example, leaving the package at the entrance of each unit of the building or at the entrance of the house). Delivery failure may include, for example, the absence of a user in the building of the destination address, or failure to deliver to a delivery box provided in the building.

[0055] For example, in a past delivery, if delivery to user 1 was completed at 11:00, the delivery history of only the first time slot among the first to sixth time slots indicates delivery success (=1), and the delivery history of the other time slots indicates delivery failure (=0). In addition, at least one of user characteristics 121 of user 1, order characteristics 122 related to the items ordered by user 1 for the delivery, date characteristics 123 of the delivery date, and building type 124 of the delivery destination address (which may include delivery box information) is used as input data. The first time slot prediction model 111 is trained using training data that is a combination of the input data and delivery success (=1). The second time slot prediction model to the sixth time slot prediction model 116 are trained using training data that is a combination of the input data and delivery failure (=0).

[0056] The trained first time slot prediction model 111 is configured to receive as input the user features 121 and order features 122 of any target user, the date features 123 of the delivery date, and the building type 124 of the delivery address, and to output (predict) the possibility of successful delivery to the target user in the first time slot. Alternatively, the first time slot prediction model 111 may be configured to receive as input the user features 121, the date features 123, and the building type 124, and to output the possibility of successful delivery to the target user in the first time slot. Similarly, the second time slot prediction model 112 to the sixth time slot prediction model 116 are each configured to predict the probability of successful delivery to a target user in the second time slot to the sixth time slot, respectively, using the user features 121 and order features 122 of a target user, the date features 123 of a delivery date, and the building type 124 of a delivery address as input. Alternatively, the second time slot prediction model 112 to the sixth time slot prediction model 116 may each be configured to output the probability of successful delivery to the target user in the first time slot to the sixth time slot, respectively, using the user features 121, the date features 123, and the building type 124 as input. First time slot prediction model 111 through sixth time slot prediction model 116 are configured to output the probability of successful transmission in each time slot as a numerical value between 0 and 1 (1 being the maximum probability).

[0057] In the prediction stage, the probability of successful delivery to each of the multiple destination users included in the delivery request acquired by the delivery request acquisition unit 101 is predicted for each time slot. A conceptual diagram of the process of predicting the probability of successful delivery is shown in Fig. 4. The delivery success prediction unit 106 predicts the probability of successful delivery to each of the multiple destination users included in the delivery request acquired by the delivery request acquisition unit 101 for the first time slot to the sixth time slot.

[0058] Specifically, the delivery success prediction unit 106 prepares input data 40 including a user feature 121, an order feature 122, a date feature 123 of a delivery date, and a building type 124 of a delivery address for each of the multiple destination users. Note that the order feature 122 does not have to be included in the input data 40. The user characteristics 121 are the user characteristics of the destination user included in the delivery request, and are acquired by the user characteristic acquisition unit 102 . The order characteristics 122 are the order characteristics of the destination user included in the delivery request, and are acquired by the order characteristics acquisition unit 103 . The date feature 123 is a date feature of the delivery date included in the delivery request, and is acquired (specified) by the date feature specification unit 194. In this embodiment, the delivery dates included in the delivery request (i.e., the delivery dates for multiple packages) are assumed to be the same date. The building type 124 is the building type of the building located at the destination address included in the delivery request, and is identified by the building type identification unit 105. The building type 124 may also include delivery box information (information indicating whether the building located at the destination address has a delivery box or not).

[0059] Next, the delivery success prediction unit 106 inputs the input data 40 to the first time slot prediction model 111 to the sixth time slot prediction model 116, and predicts the probability of delivery success in the first time slot to the sixth time slot for each of the multiple destination users included in the delivery request.

[0060] The first time slot prediction model 111 outputs a delivery success probability 41 (a number between 0 and 1) for the destination user in the first time slot. The second time slot prediction model 112 outputs the destination user's probability of successful delivery 42 in the second time slot (a number between 0 and 1). The third time slot prediction model 113 outputs a delivery success probability 43 (a number between 0 and 1) for the destination user in the third time slot. The fourth time slot prediction model 114 outputs the destination user's probability of successful delivery in the fourth time slot 44 (a number between 0 and 1). The 5th time slot prediction model 115 outputs the delivery success probability 45 (a number between 0 and 1) for the destination user in the 5th time slot. The sixth time slot prediction model 116 outputs the destination user's probability of successful delivery 46 in the sixth time slot (a number between 0 and 1).

[0061] The delivery success prediction unit 106 determines whether delivery is possible or not possible for each destination user for each time slot based on the delivery success probabilities 41 to 46 output for all of the multiple destination users included in the delivery request. For example, the delivery success prediction unit 106 determines "OK (delivery possible)" for a time slot having a delivery success probability equal to or greater than a predetermined threshold. On the other hand, the delivery success prediction unit 106 determines "NG (delivery not possible)" for a time slot having a delivery success probability less than the predetermined threshold. The predetermined threshold is, for example, 0.5. The delivery success prediction unit 106 creates a delivery table including the "OK" or "NG" determined for each time slot for each destination user.

[0062] The delivery table 47 shown in Fig. 4 is an example of a delivery table created by the delivery success prediction unit 106. In the delivery table 47, a label (indicator) of "OK (delivery possible)" or "NG (delivery not possible)" is attached to each time slot for any of the multiple destination users included in the delivery request. The delivery success prediction unit 106 creates such a delivery table for all of the multiple destination users.

[0063] 5 shows an example of a delivery table when multiple destination users included in a delivery request are users 1 to 4. Delivery tables T1 to T4 are created based on the predicted probability of successful delivery for each time slot for each of users 1 to 4, and a label (indicator) of "OK (delivery possible)" or "NG (delivery not possible)" is attached to each time slot.

[0064] In this embodiment, one threshold is set, and a delivery table is created that includes delivery possible or impossible determined using the threshold from the predicted delivery success probability, but two or more thresholds may be set. For example, when two thresholds, a first threshold (=0.8) and a second threshold (=0.4), are set, a delivery table may be created that includes three labels (indicators): "probability of success: high", "probability of success: medium", and "probability of success: low". In other words, for a time slot with a delivery success probability equal to or greater than the first threshold, it may be determined that "probability of success: high", for a time slot with a delivery success probability equal to or greater than the second threshold and less than the first threshold, it may be determined that "probability of success: medium", and for a time slot with a delivery success probability less than the second threshold, it may be determined that "probability of success: low". Alternatively, the delivery table may use the predicted probability of successful delivery (a number between 0 and 1), or a percentage (%) of the predicted probability of successful delivery, in which case the number directly represents the probability of successful delivery.

[0065] The trained model such as the first time slot prediction model 111 may be trained using teacher data in which various data except the building type 124 is labeled, and may output the delivery success probability (a value from 0 to 1) by inputting the input data 40 except the building type 124. At this time, the delivery success prediction unit 106 may apply some coefficient to the delivery success probability output by the trained model according to the building type 124. For example, when the building type 124 is a business building, the delivery success prediction unit 106 may apply some coefficient to upwardly correct the delivery success probability output by the trained model. The trained model may be trained using teacher data in which various data except the date feature 123 is labeled, and may output the delivery success probability by inputting the input data 40 except the date feature 123. At this time, the delivery success prediction unit 106 may apply some coefficient to the delivery success probability output by the trained model according to the date feature 123 and the type of time slot. For example, if the date feature 123 indicates a weekday that may be a business day and the time slot type indicates daytime that may be within business hours, the delivery success prediction unit 106 may apply some coefficient to upwardly revise the delivery success probability output by the trained model.

[0066] [Delivery schedule determination procedure] When the delivery success prediction unit 106 predicts the delivery success probability for each time slot for all of the multiple destination users and creates a delivery table, the delivery schedule determination unit 107 determines a delivery schedule. As described above, in this embodiment, the delivery schedule includes the time slot (time period) for delivery, and information on the destination user and the destination address.

[0067] The delivery schedule determination unit 107 extracts (groups) two or more users who have the nth (in this embodiment, 1≦n≦6) time slot indicating OK in the delivery table created by the delivery success prediction unit 106. Then, the delivery schedule determination unit 107 determines a delivery schedule so that packages addressed to the two or more extracted (grouped) users are delivered in the nth time slot. Note that, if it is not possible to extract two or more users who have the nth time slot in common, the delivery schedule is determined so that delivery can be made in the time slot indicating OK.

[0068] An example of a delivery schedule determined when delivery tables T1 to T4 for users 1 to 4 are created as shown in Fig. 5 will be described with reference to Fig. 6A and Fig. 6B. Fig. 6A and Fig. 6B are diagrams for explaining the delivery schedule. In Fig. 6A and Fig. 6B, delivery tables T1 to T4 indicate delivery tables for users 1 to 4 as shown in Fig. 5, and destinations D1 to D4 indicate the destination addresses of users 1 to 4.

[0069] The delivery schedule determination unit 107 refers to delivery tables T1 to T4, recognizes that the first time slot is both OK in delivery tables T1 and T2, and decides to deliver packages addressed to users 1 and 2 in the first time slot. Note that, since the second time slot is also both OK in delivery tables T1 and T2, the delivery schedule determination unit 107 may decide to deliver packages addressed to users 1 and 2 between the first and second time slots. In addition, the delivery schedule determination unit 107 refers to delivery tables T1 to T4, recognizes that the fourth to sixth time slots both indicate OK in delivery tables T3 and T4, and determines that packages addressed to users 3 and 4 will be delivered between the fourth and sixth time slots.

[0070] Therefore, the delivery schedule determination unit 107 determines the following delivery schedule, as an example. 1st time slot: User 1 (destination D1) User 2 (Destination D2) 4th to 6th time slots: User 3 (destination D3) User 4 (Destination D4)

[0071] 6A illustrates a delivery status in the first time slot according to the determined delivery schedule, specifically showing that packages addressed to user 1 and user 2 are delivered in the first time slot. The delivery order for user 1 and user 2 can be determined arbitrarily. Alternatively, the delivery order can be determined based on the location of the delivery van (or delivery person) and the locations of destinations D1 and D2 so that delivery can be made via an efficient route. 6B illustrates the delivery status in the fourth to sixth time slots according to the determined delivery schedule, specifically showing that packages addressed to user 3 and user 4 are delivered in the fourth to sixth time slots. The delivery order for user 3 and user 4 can be determined arbitrarily. Alternatively, the delivery order can be determined based on the location of the delivery vehicle (or delivery person) and the locations of destinations D3 and D4 so as to enable delivery via an efficient route.

[0072] In this way, a plurality of destination users having a high probability of successful delivery in the same time slot are grouped, and a delivery schedule is determined so that delivery is made to the grouped plurality of destination users in the same time slot. Then, by carrying out delivery according to the delivery schedule, it becomes possible to reduce the possibility of delivery failure and to carry out efficient delivery.

[0073] For example, in the example of Figures 6A and 6B, if delivery is attempted from user 1 to user 4 in the first time slot, delivery to user 3 and user 4 is likely to fail, and redelivery to user 3 and user 4 may be required. Furthermore, if redelivery is attempted to user 3 and user 4 in the third time slot, delivery is likely to fail again, and the number of redeliveries may increase. On the other hand, according to this embodiment, the delivery schedule is determined so that deliveries are made to multiple users who have a high probability of successful delivery in the same time slot, thereby making it possible to reduce the number of redeliveries.

[0074] [Overall process flow] 7 shows a flowchart of the overall process executed by the information processing device 10 according to this embodiment. For the explanation of FIG. 7, the information processing system shown in FIG.

[0075] In S71, the delivery request acquisition unit 101 acquires a delivery request for a package addressed to multiple users (destination users) among users 1 to N. The delivery request includes information on the multiple destination users, information on the addresses of each of the multiple destination users (multiple different destination addresses), and information on the delivery date.

[0076] In S72, the user characteristic acquisition unit 102 acquires the user characteristics of each of the multiple destination users and stores them in the characteristic storage unit 120 as user characteristics 121. The process of S72 may be a process of acquiring (collecting) user characteristics in a certain period of time in the past and estimated user characteristics. The user characteristics 121 are associated with the destination user ID and stored in the characteristic storage unit 120.

[0077] In S73, the order feature acquiring unit 103 acquires the order features of each of the multiple destination users, and stores them in the feature storage unit 120 as the order features 122. In S74, the date feature acquiring unit 104 acquires the date feature of the delivery date, and stores it in the feature storage unit 120 as the date feature 123. The order feature 122 and the date feature 123 are stored in the feature storage unit 120 in association with the destination user ID.

[0078] In S75, the building type identification unit 105 identifies the building type of the building located at the destination address from the destination address information. The procedure for identifying the building type is as described above. As an option, the building type identification unit 105 determines whether the building located at the destination address is equipped with a delivery box and generates delivery box information. The building type identification unit 105 stores the identified building type in the characteristic storage unit 120 as the building type 124. In addition, when delivery box information is generated, the delivery box information is included in the building type 124. The building type 124 is associated with the destination user ID and stored in the characteristic storage unit 120. The order of the processes from S72 to S75 is not limited to the order shown in FIG. 7, and the processes may be performed in a different order or simultaneously.

[0079] In S76, the delivery success prediction unit 106 predicts the delivery success probability in the multiple time slots for the multiple destination users. The prediction is performed by applying input data including the user feature 121, the order feature 122, the date feature 123, and the building type 124 obtained or identified in S72 to S75 to the first time slot prediction model 111 to the sixth time slot prediction model 116. The input data does not have to include the order feature 122. The procedure for predicting the delivery success probability is as described above with reference to FIG. 4. After predicting the delivery success probability, the delivery success prediction unit 106 creates a delivery table for each of the multiple destination users. Examples of the delivery table are shown in FIG. 4 and FIG. 5.

[0080] In S77, the delivery schedule determination unit 107 determines a delivery schedule based on the delivery table created in S76. In the case of the delivery tables shown in Fig. 4 and Fig. 5, the delivery schedule determination unit 107 extracts two or more users who have a common n-th (in this embodiment, 1≦n≦6) time slot indicating OK. Then, the delivery schedule determination unit 107 determines a delivery schedule so that packages addressed to the two or more extracted users are delivered in the n-th time slot. In this embodiment, since the date information included in the delivery request acquired in S71 is the same date, a delivery schedule is determined in which the destination user and destination address are set for each time slot on that date.

[0081] In S78, the output unit 108 outputs the delivery schedule determined (created) in S77. The output unit 108 may generate information related to the delivery schedule and output it to an external device (not shown). The information related to the delivery schedule is, for example, information such as the position information of each household on a map and the predicted arrival time at each household based on the road congestion situation. The position information can be identified from address information.

[0082] [Hardware configuration of information processing device 10] FIG. 8 is a block diagram showing an example of a hardware configuration of the 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. 8, 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.

[0083] 8, the information processing device 10 may include a CPU 81, a ROM 82, a RAM 83, a HDD 84, an input unit 85, a display unit 86, a communication I / F 87, and a system bus 88. The information processing device 10 may also include an external memory. A CPU (Central Processing Unit) 81 generally controls the operations of the information processing device 10, and controls each of the components (82 to 87) via a system bus 88, which is a data transmission path.

[0084] The ROM (Read Only Memory) 82 is a non-volatile memory that stores control programs and the like necessary for the CPU 81 to execute processing. Note that the programs may be stored in a non-volatile memory such as an HDD (Hard Disk Drive) 84 or an SSD (Solid State Drive) or in an external memory such as a removable storage medium (not shown). The RAM (Random Access Memory) 83 is a volatile memory and functions as a main memory, a work area, etc. of the CPU 81. That is, the CPU 81 loads necessary programs, etc. from the ROM 82 to the RAM 83 when executing processing, and executes the programs, etc. to realize various functional operations. The learning model storage unit 110, the feature storage unit 120, and the rule storage unit 130 shown in FIG. 2 can be configured by the RAM 83.

[0085] The HDD 84 stores, for example, various data and various information required when the CPU 81 performs processing using a program. The HDD 84 also stores, for example, various data and various information obtained when the CPU 81 performs processing using a program. The input unit 85 is composed of a keyboard and a pointing device such as a mouse. The display unit 86 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 85 to function as a GUI (Graphical User Interface).

[0086] The communication I / F 87 is an interface that controls communication between the information processing device 10 and an external device. The communication I / F 87 provides an interface with the 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 87. In this embodiment, the communication I / F 87 may execute communication via a wired LAN (Local Area Network) or a dedicated line conforming to a communication standard such as Ethernet (registered trademark). However, the network available in this embodiment is not limited to this, and may be configured as a wireless network. This wireless network includes wireless PAN (Personal Area Network) such as Bluetooth (registered trademark), ZigBee (registered trademark), and UWB (Ultra Wide Band). It also includes wireless LAN (Local Area Network) such as Wi-Fi (Wireless Fidelity) (registered trademark), and wireless MAN (Metropolitan Area Network) such as WiMAX (registered trademark). It also includes wireless WAN (Wide Area Network) such as LTE / 3G, 4G, and 5G. Note that the network only needs to connect each device so that they can communicate with each other and be capable of communication, and the communication standard, scale, and configuration are not limited to those described above.

[0087] 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 81 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 81.

[0088] [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. 8. That is, the user device 11 may include a CPU 81, a ROM 82, a RAM 83, a HDD 84, an input unit 85, a display unit 86, a communication I / F 87, and a system bus 88. The user device 11 can display various information provided by the information processing device 10 on the display unit 86, 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 86).

[0089] In this way, the information processing device 10 predicts the possibility of successful delivery to the destination user for each of a plurality of time slots (time periods) by using a learning model for machine learning, taking into account the user characteristics of the destination user and the characteristics of the destination address. By taking into account not only the user characteristics of the destination user but also the building type of the building located at the address, which is a characteristic of the destination address, the prediction accuracy of the possibility of successful delivery can be improved. For example, in the learning stage, if the building type is a "house", the learning model may be trained using non-face-to-face delivery history (successful delivery). Furthermore, the building type may include the presence or absence of a delivery box, and the learning model may be trained using delivery history to the delivery box (successful delivery). Furthermore, in the prediction stage, even if the building type of the building at the destination address is a "business building," if the destination user lives in that building and the user characteristics include "lives in an apartment (residence)," it is possible to predict the possibility of successful delivery based on the user characteristics. This allows the learning model to improve its prediction accuracy of the probability of successful delivery by taking into account the user characteristics of the destination user and the characteristics of the destination address.

[0090] Furthermore, the information processing device 10 groups multiple destination users who have a high probability of successful delivery in the same time slot (one or more time slots), and determines a delivery schedule so that delivery is made to the grouped multiple destination users in the time slot. By performing delivery according to the delivery schedule, the number of redeliveries can be reduced, and the cost required for redelivery can be reduced.

[0091] In this embodiment, it is assumed that the delivery dates included in the delivery requests are the same date, but if different delivery dates are included, the delivery schedule determination unit 107 can determine a delivery schedule for each delivery date.

[0092] Furthermore, the first to sixth time slots set in this embodiment are merely examples, and the time width of each time slot may be determined arbitrarily, and the first to Mth time slots may be used as appropriate (M>1). For example, if a building is equipped with delivery lockers, the time slots can be set to any time of the day during the 24 hours. In addition, in this embodiment, multiple learning models, such as six learning models, are used to predict the probability of successful delivery in the first to sixth time slots, but a single learning model that integrates the multiple learning models may also be used.

[0093] In addition, in this embodiment, it is assumed that the delivery request is configured to include destination users whose addresses are close to each other (for example, the same city or town, or the delivery area of ​​a single courier), but the range of addresses of destination users included in the delivery request may be wider. In this case, for example, the information processing device 10 may predict the possibility of successful delivery to multiple destination users according to the above-mentioned procedure, and determine a delivery schedule for a single courier in the delivery area according to the prediction result.

[0094] Although specific embodiments have been described above, the embodiments are merely illustrative and are not intended to limit the scope of the present invention. The apparatus and method described in this specification can be embodied in forms other than those described above. Furthermore, the above-described embodiments can be appropriately omitted, substituted, and modified without departing from the scope of the present invention. Such omitted, substituted, and modified forms are included in the scope of the claims and their equivalents, and belong to the technical scope of the present invention.

[0095] The disclosure of this embodiment includes the following configuration. [1] An information processing device having a user feature acquisition means for acquiring user features representing the characteristics of a destination user of a package, a date feature acquisition means for acquiring date features representing the characteristics of the delivery date of the package, an identification means for identifying a building type of a building located at the destination address of the package, and a prediction means for predicting the possibility of successful delivery of the package to the destination user for each of a plurality of time periods on the delivery date based on the user features, the date features, and the building type.

[0096] [2] An information processing device having a user feature acquisition means for acquiring user features representing characteristics of a destination user for each of a plurality of packages, a date feature acquisition means for acquiring date features representing characteristics of the delivery date of the plurality of packages, an identification means for identifying a building type of a building located at the destination address of each of the plurality of packages, and a prediction means for predicting a probability of successful delivery of each of the plurality of packages to the destination user for each of a plurality of time periods on the delivery date based on the user features, the date features, and the building type.

[0097] [3] The information processing device according to [1] or [2], wherein the identification means identifies the building type based on a character string of the destination address.

[0098] [4] The information processing device according to [3], wherein the identification means identifies the building type by applying a character string of the destination address to a predetermined rule.

[0099] [5] The information processing device described in any of [1] to [4], wherein the identification means acquires delivery box information indicating whether the building located at the destination address has a delivery box, and includes the delivery box information in the building type.

[0100] [6] An information processing device as described in any of [1] to [5], further comprising an order feature acquisition means for acquiring order features representing characteristics of items previously ordered by the destination user, wherein the prediction means predicts the probability of successful delivery based on the user features, the date features, the building type, and the order features.

[0101] [7] The information processing device described in any of [1] to [5], wherein the prediction means predicts the probability of successful delivery using a learning model for machine learning, and the learning model is configured to use the user features, the date features, and the building type as inputs and output the probability of successful delivery for each of the multiple time periods.

[0102] [8] The information processing device described in [2], further comprising a determination means for determining a delivery schedule for the multiple packages based on the predicted probability of successful delivery of each of the multiple packages to a destination user for each of the multiple time periods.

[0103] [9] A learning model that is trained to output the probability of successful delivery of the package to the destination user for each of a plurality of time periods on the delivery date when user features representing the characteristics of the destination user of the package, date features representing the characteristics of the delivery date of the package, and building features representing the building type of the building located at the destination address of the package are input.

[0104]

[10] An information processing method comprising: a user feature acquisition step of acquiring user features representing characteristics of a destination user of a package; a date feature acquisition step of acquiring date features representing characteristics of a delivery date of the package; an identification step of identifying a building type of a building located at the destination address of the package; and a prediction step of predicting a possibility of successful delivery of the package to the destination user for each of a plurality of time periods on the delivery date based on the user features, the date features, and the building type.

[0105]

[11] An information processing program for causing a computer to execute information processing, the program causing the computer to execute processes including a user feature acquisition process for acquiring user features indicative of the characteristics of a destination user of a package, a date feature acquisition process for acquiring date features indicative of the characteristics of a delivery date of the package, an identification process for identifying a building type of a building located at the destination address of the package, and a prediction process for predicting the possibility of successful delivery of the package to the destination user for each of a plurality of time periods on the delivery date based on the user features, the date features, and the building type. [Explanation of symbols]

[0106] 1 to N: users, 10: information processing device, 11-1 to 11-N: user devices, 101: delivery request acquisition unit, 102: user feature acquisition unit, 103: order feature acquisition unit, 104: date feature acquisition unit, 105: building type identification unit, 106: delivery success prediction unit, 107: delivery schedule determination unit, 108: output unit, 110: learning model storage unit, 111: first time slot prediction model, 112: second time slot prediction model, 113: third time slot prediction model, 114: fourth time slot prediction model, 115: fifth time slot prediction model, 116: sixth time slot prediction model, 117: user feature prediction model, 120: feature storage unit, 121: user feature, 122: order feature, 123: date feature, 124: building type, 130: rule storage unit, 131: building type identification rule

Claims

1. A user characteristic acquisition means for acquiring a user characteristic representing a characteristic of a destination user of a package from a user device of the destination user; A date characteristic acquisition means for acquiring a date characteristic representing a characteristic of a delivery date of the package; A means for identifying the building type of the building located at the destination address of the package as one of "business building" which refers to a business or company building, "unit building" which refers to a building, condominium, or apartment having multiple living spaces, "house," or "unknown"; a prediction means for predicting a probability of successful delivery of the package to the destination user for each of a plurality of time slots on the delivery date based on the user characteristics, the date characteristics, and the building type; having The user features include factual features of the destination user and estimated features estimated from the factual features using a first learning model for machine learning; The information processing device is characterized in that the identification means identifies the building type based on predetermined rules regarding one or more predetermined characters contained in the string of the destination address, the position of the one or more characters in the string, the position and number of digits of the numbers contained in the string, and the notation format of the numbers contained in the string.

2. a user characteristic acquisition means for acquiring user characteristics representing characteristics of a destination user of each of the plurality of packages from a user device of the destination user; A date feature acquiring means for acquiring date features representing the characteristics of the delivery dates of the plurality of packages; A means for identifying the building type of the building located at the destination address of each of the plurality of packages as one of a "business building" which refers to a business or company building, a "unit building" which refers to a building, condominium, or apartment having multiple living spaces, a "house," or an "unknown type"; a prediction means for predicting a probability of successful delivery of each of the plurality of packages to a destination user for each of a plurality of time slots on the delivery date based on the user characteristics, the date characteristics, and the building type; having The user features include factual features of the destination user and estimated features of the destination user estimated from the factual features using a first learning model for machine learning; The information processing device is characterized in that the identification means identifies the building type based on predetermined rules regarding the numbers and non-numeric parts contained in the string of the destination address, the position of the numbers contained in the string, the number of numeric digits contained in the string, the position of a predetermined kanji character in the string, and the notation format of the numbers contained in the string.

3. The information processing device described in claim 1 or 2, characterized in that the factual characteristics of the destination user include demographic information of the destination user, a purchase history of the destination user in a web service, and data registered in the web service by the destination user, and the estimated characteristics of the destination user include characteristics estimated from the factual characteristics of the destination user using the first learning model.

4. The specification means acquires delivery box information indicating whether or not a building located at the destination address has a delivery box, and includes the delivery box information in the building type.

3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

5. The method further includes an order characteristic acquisition means for acquiring order characteristics representing characteristics of items previously ordered by the destination user, The prediction means predicts the likelihood of successful delivery based on the user characteristics, the date characteristics, the building type, and the order characteristics.

3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

6. The prediction means predicts the probability of successful delivery using a second learning model for machine learning; The second learning model is configured to output the probability of successful delivery for each of the plurality of time periods using the user features, the date features, and the building type as inputs.

3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

7. and determining means for determining a delivery schedule for the plurality of packages based on the predicted probability of successful delivery of each of the plurality of packages to a destination user for each of the plurality of time periods.

3. The information processing apparatus according to claim 2.

8. A learning model for causing a computer to function so as to output a probability of successful delivery of the package to the destination user for each of a plurality of time slots on the delivery date when a user feature representing a feature of a destination user of the package, a date feature representing a feature of a delivery date of the package, and a building feature representing a building type of a building located at a destination address of the package are input, the learning model comprising: The user features include factual features of the destination user and estimated features estimated from the factual features using a learning model for machine learning; The building type is a learning model that is identified based on the numbers and non-numeric parts contained in the string of the destination address, the position of the numbers in the string, the number of numeric digits contained in the string, the position of specified kanji characters in the string, and specified rules regarding the notation format of the numbers contained in the string, and is one of "business building" which refers to an office or company building, "unit building" which refers to a building, condominium, or apartment with multiple living spaces, "home," or "unknown."

9. An information processing method executed by an information processing device, a user characteristic acquisition step of acquiring a user characteristic representing a characteristic of a destination user of the package from a user device of the destination user; A date feature acquisition step of acquiring a date feature representing a characteristic of a delivery date of the package; A step of identifying the building type of the building located at the destination address of the package as one of a "business building" which refers to a business or company building, a "unit building" which refers to a building, condominium, or apartment having multiple living spaces, a "house," or an "unknown type"; a prediction step of predicting a probability of successful delivery of the package to the destination user for each of a plurality of time slots on the delivery date based on the user characteristics, the date characteristics, and the building type; having The user features include factual features of the destination user and estimated features estimated from the factual features using a first learning model for machine learning; This information processing method is characterized in that in the identification process, the building type is identified based on predetermined rules regarding one or more specified characters contained in the string of the destination address, the position of the one or more characters in the string, the position and number of digits of the numbers contained in the string, and the notation format of the numbers contained in the string.

10. An information processing program for causing a computer to execute information processing, the program comprising: a user characteristic acquisition process for acquiring a user characteristic representing a characteristic of a destination user of the package from a user device of the destination user; A date feature acquisition process for acquiring date features representing the characteristics of the delivery date of the package; A process of identifying the building type of the building located at the destination address of the package as one of "business building" which refers to a business or company building, "unit building" which refers to a building, condominium, or apartment having multiple living spaces, "house," or "unknown type"; a prediction process for predicting a probability of successful delivery of the package to the destination user for each of a plurality of time slots on the delivery date based on the user characteristics, the date characteristics, and the building type, The user features include factual features of the destination user and estimated features estimated from the factual features using a first learning model for machine learning; the identification process includes identifying the building type based on predetermined rules regarding one or more predetermined characters included in a character string of the destination address, the positions of the one or more characters in the character string, the positions and number of digits of numbers included in the character string, and a notation format of the numbers included in the character string; Information processing program.

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