Information processing device and information processing method

Abstract

An information processing device (10) comprises: a model generation part (12) that, on the basis of stay information which is generated on the basis of position information of a first user existing in a first region where facility category information related to a category of a facility is acquired and which represents a feature of a stay of the first user for each of first unit areas resulting from dividing the first region, and facility category information for each of the first unit areas, generates a model for estimating facility category information from stay information; and an estimation part (13) that, on the basis of the generated model and stay information which is generated on the basis of position information of a second user existing in a second region for which facility category information is to be estimated, and which represents a feature of a stay of the second user for each of second unit areas resulting from dividing the second region, estimates facility category information for each of the second unit areas.

Description

Information processing device and information processing method

[0001] The present disclosure relates to an information processing device and an information processing method.

[0002] Patent Literature 1 listed below proposes a technology for accurately estimating the facilities that a user has actually visited based on the user's location information and a facility database. If the facilities that a user has visited are known, the user's interests can be estimated, and information to be recommended can be appropriately selected.

[0003] International Publication No. WO2019 / 202786

[0004] However, when considering a service for users overseas, it is not always possible to obtain accurate facility information (location information, category information, etc.) regarding various facilities overseas. Therefore, even in situations where the above-mentioned facility information cannot be obtained, it is desirable to be able to estimate the facility category of a facility that a user is visiting from the user's location information without relying on facility information.

[0005] Therefore, an object of the present disclosure is to estimate what facility category a user is visiting from the user's location information, without relying on facility information.

[0006] The information processing device according to the present disclosure includes: a model generation unit that generates a model for estimating facility category information from stay information based on stay information generated based on location information of a first user present in a first region from which facility category information related to a facility category is acquired, the stay information representing characteristics of the stay of the first user for each first unit area into which the first region is divided, and the facility category information for each first unit area; and an estimation unit that estimates the facility category information for each second unit area based on stay information generated based on location information of a second user present in a second region from which the facility category information is to be estimated, the stay information representing characteristics of the stay of the second user for each second unit area into which the second region is divided, and the generated model.

[0007] According to the present disclosure, it is possible to estimate what facility category a user is visiting from the user's location information without relying on facility information.

[0008] FIG. 1 is a functional block configuration diagram of an information processing device according to an embodiment of the present invention. FIG. 2 is a flow diagram showing processing executed in a learning phase. FIG. 3 is a diagram showing an example of user stay information. FIG. 4 is a diagram explaining generation of a stay vector. FIG. 5 is a diagram showing an example of facility information. FIG. 6 is a diagram showing an example of a facility category vector. FIG. 7 is a diagram explaining input data and correct answer data in machine learning. FIG. 8 is a diagram explaining machine learning. FIG. 9 is a flow diagram showing processing executed in an estimation phase. FIG. 10 is a diagram explaining estimation of a facility category vector. FIG. 11 is a diagram showing an example of the hardware configuration of an information processing device.

[0009] An embodiment of an information processing device according to the present disclosure will be described below with reference to the drawings. In the following embodiment, information relating to a facility category (synonymous with genre, type, or type) will be referred to as "facility category information," and the term "facility" herein refers to various buildings, facilities, etc., such as commercial, residential, industrial, and tourist buildings visited by humans. Furthermore, a region from which the facility category information is acquired will be referred to as a "first region," and a region from which the facility category information is estimated will be referred to as a "second region." In the following, an embodiment will be described in which the "second region" is assumed to be a region outside Japan (hereinafter referred to as "overseas"), and the "first region" is assumed to be a region within Japan (hereinafter referred to as "domestic").

[0010] 1, the information processing device 10 in this embodiment includes, as functional blocks, an acquisition unit 11, a model generation unit 12, an estimation unit 13, a domestic geographic mesh database (DB) 14, an overseas geographic mesh database (DB) 15, and an output unit 16. Below, the functions of each unit will be outlined, and the details of the functions will be described later together with processing explanations using the flow charts in FIGS.

[0011] The domestic geographic mesh DB14 is a database that pre-stores various information (such as ID for each mesh, location information (latitude and longitude), information on boundaries, etc.) regarding meshes (first unit areas) formed by dividing the country (first region) according to predetermined rules.

[0012] The overseas geographic mesh DB15 is a database that pre-stores various information (such as ID for each mesh, location information (latitude and longitude), information on boundaries, etc.) regarding meshes (second unit areas) formed by dividing overseas (second region) according to predetermined rules.

[0013] The acquisition unit 11 is a functional unit that acquires location information (e.g., information including latitude, longitude, positioning time, etc.) of users residing in Japan (referred to as "domestic users"), facility category information regarding categories of facilities located in Japan, and location information (e.g., information including latitude, longitude, positioning time, etc.) of users residing overseas (referred to as "overseas users") from an external server, etc.

[0014] The model generation unit 12 is a functional unit that generates a model M for estimating facility category information from stay information based on the stay information, which is generated based on the location information of domestic users (first users) and represents the characteristics of the stay of domestic users for each domestic mesh (first unit area), and on the facility category information for each domestic mesh. Specifically, the model generation unit 12 generates the model M by machine learning, for example, using a neural network algorithm, using the domestic user stay information for each domestic mesh as input data and the facility category information for each domestic mesh as correct answer data. The generated model M is stored in an internal memory (not shown) of the information processing device 10 and can be accessed by the estimation unit 13 (described later). The model generation unit 12 also generates, from the location information of domestic users, a stay information vector that represents the probability of occurrence of one or more combinations of stay duration, day of the week, weekday / holiday, time, and time period, as stay information representing the characteristics of the domestic user's stay. Furthermore, the model generation unit 12 has the function of generating the above-mentioned stay information vector for domestic users based on domestic user characteristic information that represents the characteristics of domestic users (including various information such as gender, age, birthplace, hobbies, preferences, height, weight, educational background, occupation, annual income, whether or not they have a spouse, whether or not they have children), in addition to the location information of domestic users; this function will be described later as a modified example.

[0015] The estimation unit 13 is a functional unit that estimates facility category information for each overseas mesh based on stay information generated based on the location information of the overseas user (second user), the stay information representing the characteristics of the overseas user's stay in each overseas mesh (second unit area), and the generated model M. Specifically, the estimation unit 13 inputs the stay information of the overseas user for each overseas mesh into the model M, and obtains an estimated value of facility category information for each overseas mesh as its output. The estimation unit 13 also generates a stay information vector indicating the probability of occurrence of one or more combinations of stay duration, day of the week, weekday / holiday, time, and time period as stay information representing the characteristics of the overseas user's stay. Furthermore, the estimation unit 13 also has a function to generate the stay information vector for the overseas user based on overseas user characteristic information representing the characteristics of the overseas user (e.g., gender, age, birthplace, hobbies, preferences, height, weight, educational background, occupation, annual income, marital status, presence of children, and various other information) in addition to the location information of the overseas user; this function will be described later as a modified example.

[0016] The output unit 16 is a functional unit that outputs estimated values ​​of facility category information for each overseas mesh obtained by the estimation unit 13. Note that "output" can take various forms, such as display output, print output, or data transmission to an external device outside the information processing device 10.

[0017] (Regarding the processing performed in the information processing device) Below, we will explain the processing performed in the information processing device 10 of this embodiment in order: (1) the processing performed in the learning phase to generate a model, and (2) the processing performed in the estimation phase to make an estimation using the model.

[0018] 2 shows a processing flow of the processing executed in the learning phase. First, the acquisition unit 11 acquires time-series location information of domestic users for a predetermined period and facility category information relating to the categories of facilities present in domestic meshes from an external server or the like (step S1 in FIG. 2), and passes these to the model generation unit 12.

[0019] The model generation unit 12 generates stay vectors representing the characteristics of domestic users' stays in each domestic mesh from the acquired time-series domestic user location information (step S2). Specifically, the model generation unit 12 first references mesh information (such as latitude and longitude and boundary information) stored in the domestic geographic mesh DB 14 and determines, from the time-series domestic user location information, in which mesh the domestic user stayed, from when, and for how long, thereby generating the stay information shown in Fig. 3. Fig. 3 shows, for example, stay information indicating that domestic user "user1" stayed in mesh 2 for 10 minutes from a certain start time, and stay information indicating that domestic user "user2" stayed in mesh 2 for 20 minutes from a certain start time. Next, as shown in Figure 4, the model generation unit 12 generates a combination of "Mesh flag: 1 for mesh 2 only" and "Stay information: 10-30 minute weekday stay data flag only" from the generated stay information, which represents "stay of 10-30 minutes in mesh 2 on weekdays," and generates a stay vector representing the characteristics of domestic users' stays for each domestic mesh using a machine learning algorithm (for example, a neural network algorithm) with the mesh flag as input data and the stay information as ground truth data. The stay vector illustrated in the lower part of Figure 4 shows example data for mesh 2, where the proportion (realization probability) of stays of less than 10 minutes on weekdays is 0.015, the proportion (realization probability) of stays of 10-30 minutes on weekdays is 0.043, ..., and the proportion (realization probability) of stays of 3 hours or more on holidays is 0.082.

[0020] The model generation unit 12 then generates a facility category vector for each domestic mesh from the facility category information regarding the categories of facilities present in the domestic meshes acquired in step S1 (step S3). For example, if facility category information such as "mesh 1 is a convenience store, mesh 2 is a restaurant, mesh 3 is a supermarket, and mesh 4 is a restaurant" is acquired, the model generation unit 12 converts this information into information representing the correspondence between domestic meshes and facility categories, as shown in Figure 5, i.e., into tabular data in which the categories of facilities present in each domestic mesh are flagged with "1," and generates a facility category vector for each domestic mesh from the converted data. For example, as shown in Figure 6, for mesh 2, a facility category vector (0,1,...,0) is generated in which only the category "restaurant" is set to "1."

[0021] Furthermore, the model generation unit 12 generates a model M for estimating a facility category vector from the stay vector using a machine learning algorithm (e.g., a neural network algorithm) (step S4). Specifically, the model generation unit 12 generates the model M by performing machine learning using the stay vector for each domestic mesh generated in step S2 as input data and the facility category vector for the corresponding domestic mesh generated in step S3 as correct answer data. Here, as shown in FIG. 7 , the model generation unit 12 associates the input data (stay vector) with the correct answer data (facility category vector) for each domestic mesh, and generates the model M by performing machine learning using the stay vector of a certain mesh (e.g., mesh 2) as input data and the facility category vector of the same mesh (e.g., mesh 2) as correct answer data, as shown in FIG. 8 . The model M generated by the above process is stored in an internal memory (not shown) of the information processing device 10 and can be accessed by the estimation unit 13, which will be described later.

[0022] 9 shows a processing flow of processing executed in the estimation phase. First, the acquisition unit 11 acquires time-series location information of overseas users for a predetermined period from an external server or the like (step S11 in FIG. 9 ), and passes the information to the estimation unit 13.

[0023] The estimation unit 13 generates a stay vector representing the characteristics of the overseas user's stay in each overseas mesh from the acquired time-series location information of the overseas user (step S12). Specifically, similar to the process of step S2 in FIG. 2 , the estimation unit 13 first references mesh information (such as latitude and longitude, boundary information, etc.) stored in the overseas geographic mesh DB 15 and determines, from the time-series location information of the overseas user, in which mesh the overseas user stayed, from when, and for how long, thereby generating the stay information shown in FIG. 3 . Next, as shown in FIG. 4 , the estimation unit 13 generates a combination of "mesh flag: 1 for mesh 2 only" and "stay information: 10-30 minute weekday stay data flag only" from the generated stay information of FIG. 3 , representing a "stay of 10-30 minutes in mesh 2 on weekdays." Using the mesh flag as input data and the stay information as ground truth data, the estimation unit 13 generates a stay vector representing the characteristics of the overseas user's stay in each overseas mesh using a machine learning algorithm (e.g., a neural network algorithm).

[0024] Furthermore, the estimation unit 13 inputs the stay vector for each overseas mesh generated in step S12 into model M generated in the learning phase, thereby estimating facility category information for each overseas mesh (step S13). Specifically, as shown in Fig. 10, if a stay vector (0.014, 0.044, ..., 0.083) is generated for overseas mesh S, for example, the estimation unit 13 inputs the stay vector for this mesh S into model M, and obtains, as its output (output from model M), a facility category vector (0.088, 0.577, ..., 0.126), which is an estimated value of the facility category information for mesh S. Similarly, facility category vectors are obtained for overseas meshes other than mesh S.

[0025] Then, the output unit outputs the estimation results obtained in step S13, that is, facility category information (estimated values) for each overseas mesh (step S14).

[0026] According to the embodiment described above, even for overseas locations (second regions) from which facility category information is to be estimated, it is possible to estimate the facility category of a facility that an overseas user is visiting from the location information of the overseas user using a model generated from the location information of the domestic user and the domestic facility category information. Furthermore, the overseas facility category information estimated in this manner can be used to estimate the overseas user's interests, purpose of stay, etc.

[0027] Furthermore, in the learning phase, the model generation unit 12 generates, as the stay information of a domestic user (first user), a stay information vector indicating the probability of occurrence for each combination of stay duration and weekday / holiday type, and uses the generated stay information vector as input data for model generation by machine learning using a neural network algorithm, thereby enabling smooth execution of machine learning. Furthermore, in the estimation phase, the estimation unit 13 generates, as the stay information of an overseas user (second user), a stay information vector indicating the probability of occurrence for each combination of stay duration and weekday / holiday type, and uses the generated stay information vector as input data for the model, thereby enabling smooth utilization of the model generated by machine learning to obtain overseas facility category information (estimated values) as an output from the model.

[0028] (Various Modifications (Variations)) As described above, the model generation unit 12 has a function to generate the stay information vector for a domestic user based on domestic user characteristic information that represents the characteristics of the domestic user (including various information such as gender, age, birthplace, hobbies, preferences, height, weight, educational background, occupation, annual income, whether or not the user is married, and whether or not the user has children) in addition to the location information of the domestic user. The estimation unit 13 has a function to generate the stay information vector for an overseas user based on overseas user characteristic information that represents the characteristics of the overseas user (including various information such as gender, age, birthplace, hobbies, preferences, height, weight, educational background, occupation, annual income, whether or not the user is married, and whether or not the user has children) in addition to the location information of the overseas user. By using the above function, for example, if a large number of young people in their teens tend to stay in a certain mesh for more than three hours on weekdays, it is possible to more accurately estimate that the facility category for the mesh is likely to be "school" based on the additional user characteristic of "being in the teens" and to generate a model that achieves this accurate estimation.

[0029] In addition, in the above embodiment, Figure 2 (learning phase processing) shows an example in which step S3 (generation of facility category vector) is executed after step S2 (generation of stay vector), but the execution order of steps S2 and S3 is not limited thereto, and they may be executed in the reverse order to that shown in Figure 2, or may be executed simultaneously.

[0030] In addition, in the above embodiment, the first region is assumed to be "within Japan" and the second region is assumed to be "overseas," but this is not limited to this. For example, the first region may be assumed to be "major cities in Japan and overseas (e.g., New York, London, Paris, etc.)" and the second region may be assumed to be "regions overseas other than the above major cities."

[0031] Furthermore, an example has been shown in which a stay information vector is generated for each combination of stay duration and weekday / holiday type as stay information representing the characteristics of the stay of domestic / overseas users. However, in addition to the above, requirements such as day of the week, time, and time period may be added, and a stay information vector may be generated for each combination of one or more of stay duration, day of the week, weekday / holiday, time, and time period.

[0032] The gist of the present disclosure lies in the following [1] to [8]. [1] An information processing device comprising: a model generation unit that generates a model for estimating facility category information from stay information, based on stay information generated based on location information of a first user present in a first region from which facility category information related to a facility category is acquired, the stay information representing characteristics of the stay of the first user for each first unit area obtained by dividing the first region, and the facility category information for each first unit area; and an estimation unit that estimates the facility category information for each second unit area, based on stay information generated based on location information of a second user present in a second region from which the facility category information is to be estimated, the stay information representing characteristics of the stay of the second user for each second unit area obtained by dividing the second region, and the generated model. [2] The information processing device described in [1], in which the model generation unit generates the model by performing machine learning using the stay information of the first user for each first unit area as input data and the facility category information for each first unit area as correct answer data. [3] The information processing device according to [1] or [2], wherein the model generation unit generates, as the stay information of the first user, a stay information vector indicating the probability of occurrence for each combination of one or more of stay duration, day of the week, time, and time period. [4] The information processing device according to [3], wherein the model generation unit generates the stay information vector for the first user based on first user characteristic information representing characteristics of the first user in addition to location information of the first user. [5] The information processing device according to [3] or [4], wherein the estimation unit generates, as the stay information of the second user, a stay information vector indicating the probability of occurrence for each combination of one or more of stay duration, day of the week, time, and time period. [6] The information processing device according to [5], wherein the estimation unit generates the stay information vector for the second user based on second user characteristic information representing characteristics of the second user in addition to location information of the second user. [7] The information processing device according to any one of [1] to [6], wherein the first region is a region within Japan and the second region is a region outside Japan.[8] An information processing method comprising: a step in which an information processing device generates stay information based on location information of a first user who is present in a first region where facility category information related to a facility category is acquired, the stay information representing characteristics of the stay of the first user for each first unit area into which the first region is divided, and a model for estimating the facility category information from the stay information, based on the facility category information for each first unit area; and a step in which the information processing device estimates the facility category information for each second unit area based on the stay information generated based on location information of a second user who is present in a second region where the facility category information is to be estimated, the stay information representing characteristics of the stay of the second user for each second unit area into which the second region is divided, and the generated model.

[0033] [Explanation of Terms, Explanation of Hardware Configuration (FIG. 11), etc.] The block diagrams used in the description of the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method of realizing each functional block is not particularly limited. That is, each functional block may be realized using a single device that is physically or logically coupled, or may be realized using two or more physically or logically separated devices that are directly or indirectly connected (e.g., wired, wireless, etc.) and these multiple devices. The functional block may be realized by combining the single device or the multiple devices with software.

[0034] Functions include, but are not limited to, judgment, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs transmission is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on how these functions are implemented.

[0035] For example, an information processing device according to an embodiment of the present disclosure may function as a computer that executes the processes of the present disclosure. Fig. 11 is a diagram illustrating an example of a hardware configuration of an information processing device 10 according to an embodiment of the present disclosure. The information processing device 10 described above may be physically configured as a computer including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, and the like.

[0036] In the following description, the term "apparatus" can be interpreted as a circuit, a device, a unit, etc. The hardware configuration of the information processing device 10 may be configured to include one or more of the apparatuses shown in the drawings, or may be configured to exclude some of the apparatuses.

[0037] Each function of the information processing device 10 is realized by loading specified software (programs) onto hardware such as the processor 1001 and memory 1002, causing the processor 1001 to perform calculations, control communication via the communication device 1004, and control at least one of reading and writing data in the memory 1002 and storage 1003.

[0038] The processor 1001 controls the entire computer by running, for example, an operating system, and may be configured as a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc.

[0039] The processor 1001 also reads programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002 and executes various processes in accordance with these. The programs used are those that cause a computer to execute at least some of the operations described in the above-described embodiments. While the various processes have been described as being executed by one processor 1001, they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The programs may be transmitted from a network via a telecommunications line.

[0040] The memory 1002 is a computer-readable recording medium and may be configured by, for example, at least one of a read-only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a random access memory (RAM), etc. The memory 1002 may also be called a register, a cache, a main memory (primary storage device), etc. The memory 1002 can store executable programs (program codes), software modules, etc. for implementing a wireless communication method according to an embodiment of the present disclosure.

[0041] Storage 1003 is a computer-readable recording medium, and may be composed of at least one of, for example, an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, a magnetic strip, etc. Storage 1003 may also be referred to as an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, a server, or other appropriate medium including at least one of memory 1002 and storage 1003.

[0042] The communication device 1004 is hardware (transmission / reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as, for example, a network device, a network controller, a network card, a communication module, etc. The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc. to realize at least one of frequency division duplex (FDD) and time division duplex (TDD).

[0043] The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that receives input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, an LED lamp, etc.) that outputs to the outside. The input device 1005 and the output device 1006 may be integrated into one device (e.g., a touch panel).

[0044] Furthermore, each device, such as the processor 1001 and the memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.

[0045] The information processing device 10 may also be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

[0046] The notification of information is not limited to the aspects / embodiments described in the present disclosure and may be performed using other methods. For example, the notification of information may be performed by physical layer signaling (e.g., Downlink Control Information (DCI) and Uplink Control Information (UCI)), higher layer signaling (e.g., Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, broadcast information (Master Information Block (MIB) and System Information Block (SIB))), other signals, or a combination thereof. Furthermore, the RRC signaling may be referred to as an RRC message, and may be, for example, an RRC Connection Setup message, an RRC Connection Reconfiguration message, or the like.

[0047] Each aspect / embodiment described in the present disclosure may be implemented using any of the following standards: LTE (Long Term Evolution), LTE-Advanced (LTE-A), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), 6th generation mobile communication system (6G), xth generation mobile communication system (xG) (xG (x is, for example, an integer or a decimal number)), FRA (Future Radio Access), NR (new Radio), New radio access (NX), Future generation radio access (FX), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.17 (WiMAX (registered trademark)), IEEE 802.19 (WiMAX (registered trademark)), IEEE 802.20 (WiMAX (registered trademark)), IEEE 802.21 (Wi-Fi (registered trademark)), IEEE 802.22 (WiMAX (registered trademark)), IEEE 802.23 (WiMAX (registered trademark)), IEEE 802.24 (WiMAX (registered trademark)), IEEE 802.25 (WiMAX (registered trademark)), IEEE 802.26 (WiMAX (registered trademark)), IEEE 802.27 (WiMAX (registered trademark)), IEEE 802.28 (WiMAX (registered trademark)), IEEE 802.29 (WiMAX (registered trademark)), IEEE 802.30 (WiMAX (registered trademark)), IEEE 802.31 (Wi-Fi (registered trademark)), IEEE 802.32 (WiMAX (registered trademark)), IEEE 802.33 (WiMAX (registered trademark)), IEEE 802.34 ( The present invention may be applied to at least one of systems using 802.20, UWB (Ultra-Wide Band), Bluetooth (registered trademark), or other suitable systems, and next-generation systems that are extended, modified, created, or defined based on these systems. The present invention may also be applied to a combination of multiple systems (e.g., a combination of LTE and / or LTE-A with 5G).

[0048] The order of the procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be changed unless it is consistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0049] Input and output information may be stored in a specific location (for example, memory) or may be managed using a management table. Input and output information may be overwritten, updated, or added to. Output information may be deleted. Input information may be transmitted to another device.

[0050] The determination may be made based on a value represented by one bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0051] The aspects / embodiments described in this disclosure may be used alone, in combination, or switched depending on the implementation. Notification of predetermined information (e.g., notification that "X is true") is not limited to explicit notification, but may be implicit (e.g., not notifying the predetermined information).

[0052] Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the present disclosure.

[0053] Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0054] Software, instructions, information, etc. may also be transmitted or received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), then these wired and / or wireless technologies are included within the definition of transmission media.

[0055] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0056] Note that terms described in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings. For example, at least one of a channel and a symbol may be a signal (signaling). Furthermore, a signal may be a message. Furthermore, a component carrier (CC) may be called a carrier frequency, a cell, a frequency carrier, etc.

[0057] As used in this disclosure, the terms "system" and "network" are used interchangeably.

[0058] Furthermore, the information, parameters, etc. described in the present disclosure may be expressed using absolute values, may be expressed using relative values ​​from a predetermined value, or may be expressed using other corresponding information. For example, a radio resource may be indicated by an index.

[0059] The names used for the above-described parameters are not intended to be limiting in any way. Furthermore, the mathematical expressions using these parameters may differ from those explicitly disclosed in this disclosure. The various channels (e.g., PUCCH, PDCCH, etc.) and information elements may be identified by any suitable names, and therefore the various names assigned to these various channels and information elements are not intended to be limiting in any way.

[0060] As used in this disclosure, the terms "determining" and "determining" may encompass a wide variety of actions. "Determining" and "determining" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiring (e.g., searching in a table, database, or other data structure), ascertaining, and the like. "Determining" and "determining" may also include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), and the like. Furthermore, "judgment" and "decision" can include regarding resolving, selecting, choosing, establishing, comparing, etc. as having been "judged" or "decided." In other words, "judgment" and "decision" can include regarding some action as having been "judged" or "decided." Furthermore, "judgment (decision)" can be interpreted as "assuming," "expecting," "considering," etc.

[0061] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0062] As used in this disclosure, any reference to an element using a designation such as "first," "second," etc. does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must in some way precede the second element.

[0063] When the terms "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Furthermore, when the term "or" is used in this disclosure, it is not intended to be an exclusive or.

[0064] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0065] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different."

[0066] 10...information processing device, 11...acquisition unit, 12...model generation unit, 13...estimation unit, 14...domestic geographic mesh DB, 15...overseas geographic mesh DB, 16...output unit, 1001...processor, 1002...memory, 1003...storage, 1004...communication device, 1005...input device, 1006...output device, 1007...bus.

Claims

1. An information processing device comprising: a model generation unit that generates a model for estimating facility category information from stay information based on stay information generated based on location information of a first user present in a first region from which facility category information related to a facility category is obtained, the stay information representing characteristics of the stay of the first user for each first unit area into which the first region is divided, and the facility category information for each first unit area; and an estimation unit that estimates the facility category information for each second unit area based on stay information generated based on location information of a second user present in a second region from which the facility category information is to be estimated, the stay information representing characteristics of the stay of the second user for each second unit area into which the second region is divided, and the generated model.

2. The information processing device according to claim 1, wherein the model generation unit generates the model by performing machine learning using the stay information of the first user for each of the first unit areas as input data and the facility category information for each of the first unit areas as correct answer data.

3. The information processing device according to claim 1, wherein the model generation unit generates a stay information vector indicating the probability of occurrence of one or more combinations of stay duration, day of the week, time of day, and time period as the stay information of the first user.

4. The information processing device according to claim 3, wherein the model generation unit generates the stay information vector regarding the first user based on first user characteristic information representing characteristics of the first user in addition to the location information of the first user.

5. The information processing device according to claim 3, wherein the estimation unit generates a stay information vector indicating the probability of occurrence of one or more combinations of stay duration, day of the week, time of day, and time period as the stay information of the second user.

6. The information processing device according to claim 5, wherein the estimation unit generates the stay information vector regarding the second user based on second user characteristic information representing characteristics of the second user in addition to the location information of the second user.

7. The information processing device according to claim 1, wherein the first region is a region within Japan, and the second region is a region outside Japan.

8. An information processing method comprising: a step of generating, by an information processing device, a model for estimating the facility category information from the stay information, based on stay information generated based on location information of a first user present in a first area from which facility category information related to a facility category is acquired, the stay information representing characteristics of the stay of the first user for each first unit area into which the first area is divided, and the facility category information for each first unit area; and a step of estimating, by the information processing device, the facility category information for each second unit area, based on stay information generated based on location information of a second user present in a second area from which the facility category information is to be estimated, the stay information representing characteristics of the stay of the second user for each second unit area into which the second area is divided, and the generated model.

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