JP7660491B2 - Information processing device - Google Patents

Description

The present invention relates to a technology for recommending products or services to users.

When recommending a product or service to a user, it is important to recommend products or services that suit the user from the vast number of products or services available. For example, Patent Literature 1 discloses a mechanism for determining the services to be provided to a store visitor based on the results of an analysis of the visitor's means of visiting the store, the visitor's actions before and after visiting the store, the personnel composition of the store visitor, and the visitor's attributes.

International Publication No. 2018/110077

The present invention has been made in consideration of the above-mentioned background, and aims to recommend products or services to a user who visits to carry out a procedure based on the user's psychological state.

In order to solve the above problem, the present invention provides an information processing device comprising: an acquisition unit that acquires information regarding the waiting time a user waits until a procedure for the user is completed; and a generation unit that generates recommendation information for recommending a product or service to the user according to the acquired waiting time, wherein the generation unit generates the recommendation information according to the likelihood that the user will purchase the recommended product or service based on an objective variable obtained by inputting the acquired information about the waiting time as an explanatory variable into a learned algorithm generated by machine learning that includes information regarding the waiting time the user waits until a procedure for the user is completed as an explanatory variable and information regarding the result of a recommendation to the user as an objective variable; and an output unit that outputs the generated recommendation information.

According to the present invention, it is possible to recommend products or services to a user who visits to carry out a procedure depending on the psychological state of the user.

1 is a block diagram showing an example of an overall configuration of an information processing system 1 according to an embodiment of the present invention. FIG. 2 is a block diagram showing an example of a hardware configuration of a server device 20 according to the embodiment. FIG. 2 is a block diagram showing an example of a hardware configuration of a store terminal 100 according to the embodiment. FIG. 2 is a block diagram showing an example of a functional configuration of a server device 20 according to the embodiment. FIG. 4 is a diagram showing an example of information stored in the server device 20 according to the embodiment. FIG. 4 is a diagram showing an example of information stored in the server device 20 according to the embodiment. 5 is a flowchart showing an example of an operation of the server device 20 according to the embodiment. 5 is a flowchart showing an example of an operation of the server device 20 according to the embodiment. FIG. 11 is a diagram illustrating an example of recommendation information generated in the embodiment. FIG. 11 is a diagram showing an example of information stored in a server device 20 according to a modified example. FIG. 11 is a diagram showing an example of information stored in a server device 20 according to a modified example.

[composition]
FIG. 1 is a diagram showing an example of an information processing system 1 according to an embodiment of the present invention. The information processing system 1 includes a store system 10 installed in each of a plurality of stores, a server device 20 functioning as an information processing device according to the present invention, and a communication network 2 including a wireless communication network or a wired communication network. The store system 10 includes a store terminal 100 and a ticket issuing machine 110, as well as a communication network (not shown) that communicatively connects them. The store terminal 100 may be a computer that a user can carry with them, such as a tablet terminal, or may be a fixed computer, such as a personal computer. A plurality of store terminals 100 may be present within one store system 10.

The store exemplified in this embodiment is a store operated by a telecommunications carrier that mainly provides communication services using various communication terminals such as smartphones and mobile phones owned by users. Users visit this store to carry out procedures related to various products or services provided by the telecommunications carrier. In other words, the store corresponds to a place where users carry out various procedures. These procedures may be any procedures related to products or services provided by the telecommunications carrier, but examples of such procedures include procedures related to the purchase, repair, change, or return of various products (including various communication terminals and their accessories), and procedures related to the application, change, suspension, or cancellation of various services (including communication services and services associated therewith).

A single store may be visited by a large number of users during a certain period of time (e.g., the users' lunch break at work), but the number of operators available at the store to serve users and carry out the procedures requested by the users is limited. For this reason, users who visit a store may have to wait for a certain amount of time before their procedures can begin. If the waiting time is too long, it is thought that the length of the wait time will affect the user's psychological state, for example, making them feel that they need to return to work early or even making them irritable.

Meanwhile, while the user is completing the procedure they requested, the operator may engage in so-called sales activities, such as recommending new products or services to the user. However, as mentioned above, depending on the length of the waiting time, it is possible that the user may not show interest in the recommended products or services, or may find it difficult to become interested in them.

Therefore, in this embodiment, recommendation information for recommending products or services to a user is generated and output according to the user's waiting time in a store. This makes it possible to support or realize sales activities by an operator that are more likely to be successful according to the user's psychological state.

Figure 2 is a diagram showing the hardware configuration of the server device 20. The server device 20 is physically configured as a computer device including a processor 2001, a memory 2002, a storage 2003, a communication device 2004, an input device 2005, an output device 2006, and a bus connecting these devices. Each of these devices operates with power supplied from a battery (not shown). In the following description, the word "device" can be interpreted as a circuit, device, unit, etc. The hardware configuration of the server device 20 may be configured to include one or more of the devices shown in the figure, or may be configured without including some of the devices. In addition, the server device 20 may be configured by communicating and connecting multiple devices each having a different housing.

Each function of the server device 20 is realized by loading a specific software (program) onto hardware such as the processor 2001 and memory 2002, causing the processor 2001 to perform calculations, control communications via the communication device 2004, and control at least one of the reading and writing of data in the memory 2002 and storage 2003.

The processor 2001, for example, operates an operating system to control the entire computer. The processor 2001 may be configured with a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, registers, etc. Also, for example, a baseband signal processing unit, a call processing unit, etc. may be realized by the processor 2001.

The processor 2001 reads out programs (program codes), software modules, data, etc. from at least one of the storage 2003 and the communication device 2004 into the memory 2002, and executes various processes according to these. The programs used are those that cause a computer to execute at least some of the operations described below. The functional blocks of the server device 20 may be realized by a control program stored in the memory 2002 and running on the processor 2001. The various processes may be executed by one processor 2001, or may be executed simultaneously or sequentially by two or more processors 2001. The processor 2001 may be implemented by one or more chips. The programs may be transmitted to the server device 20 via a telecommunications line.

The memory 2002 is a computer-readable recording medium, and may be composed of, for example, at least one of a ROM (Read Only Memory), an EPROM (Erasable Programmable ROM), an EEPROM (Electrically Erasable Programmable ROM), a RAM (Random Access Memory), etc. The memory 2002 may also be called a register, a cache, a main memory (primary storage device), etc. The memory 2002 can store executable programs (program codes), software modules, etc. for implementing the method according to this embodiment.

Storage 2003 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 2003 may also be called an auxiliary storage device.

The communication device 2004 is, for example, a network device, a network controller, a network card, a communication module, etc., and performs communication via the communication network 2.

The input device 2005 is an input device (e.g., a key, a microphone, a switch, a button, etc.) that accepts input from the outside. The output device 2006 is an output device (e.g., a display, a speaker, an LED lamp, etc.) that performs output to the outside. Note that the input device 2005 and the output device 2006 may be integrated into one structure (e.g., a touch panel).

Each device, such as the processor 2001 and memory 2002, is connected by a bus for communicating information. The bus may be configured using a single bus, or may be configured using different buses between each device.

The server device 20 may 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 2001 may be implemented using at least one of these pieces of hardware.

Figure 3 is a diagram showing the hardware configuration of the store terminal 100. The store terminal 100 is physically configured as a computer device including a processor 1001, memory 1002, storage 1003, communication device 1004, input device 1005, output device 1006, and a bus connecting these. Each of these devices operates with power supplied from a battery (not shown). The hardware configuration of the store terminal 100 may be configured to include one or more of the devices shown in the figure, or may be configured to exclude some of the devices. In addition, the store terminal 100 may be configured by communicating and connecting multiple devices each having a different housing.

The processor 1001, memory 1002, storage 1003, communication device 1004, input device 1005, output device 1006, and the bus connecting them of the store terminal 100 are similar in hardware to the processor 2001, memory 2002, storage 2003, communication device 2004, input device 2005, output device 2006, and the bus connecting them described for the server device 20. The communication device 1004 includes a function for communicating with a ticket issuing machine via a communication network (not shown), and a function for communicating with the server device 20 via communication network 2. The output device 2006 includes a display device for displaying various information to the store operator.

The functions of the store terminal 100 are realized by loading specific software (programs) onto hardware such as the processor 1001 and memory 1002, causing the processor 1001 to perform calculations, control communications via the communication device 1004, and control at least one of the reading and writing of data in the memory 1002 and storage 1003.

Figure 4 is a diagram showing an example of the functional configuration of the server device 20. Each function realized by the server device 20 is realized by loading predetermined software (programs) onto hardware such as the processor 2001 and memory 2002, causing the processor 2001 to perform calculations, control the communication device 2004, and control at least one of reading and writing of data in the memory 2002 and storage 2003. Specifically, the functions realized in the server device 20 are an acquisition unit 21, a machine learning unit 22, a storage unit 23, a generation unit 24, and an output unit 25.

The acquisition unit 21 is a function for acquiring various information, and for example, acquires information regarding the waiting time of a user at a store. The waiting time here refers to the time the user waits until the procedure for the user is completed. In this embodiment, this waiting time includes the time from when the user arrives at the place where the procedure is performed (i.e., the store) to when the procedure for the user is started. When the user comes to the store, he or she first receives a ticket issued by the ticket issuing machine 110. Since the operator starts to interact with the user in the order of the ticket issuing number printed on the ticket (i.e., in the order of the ticket issuing date and time), the date and time when the user arrives at the store can be specified by the ticket issuing date and time by the ticket issuing machine 110. Furthermore, when the operator starts to interact with the user, the operator performs a predetermined operation on the store terminal 100, which means the start of the interaction. The date and time when the procedure for the user is started is the date and time when such an operator operation is performed. In other words, in this embodiment, the time when the operator starts to interact with the user is considered to be the time when the procedure for the user is started. In this way, the waiting time of each user at the store can be specified by the store system 10. The acquisition unit 21 acquires information about each user's waiting time from the store system 10 via the communication network 2.

Specifically, the acquisition unit 21 acquires information such as that illustrated in FIG. 5 as information regarding the waiting time of each user. This information includes the ticket issue number of the ticket issued to each user, the date and time of issue, a user ID for identifying the user to whom the ticket was issued, the procedure start date and time when the procedure for the user was started, and the waiting time X from when the user arrived at the store until the procedure for the user was started. In the example of FIG. 5, for example, a ticket with the ticket issue number "10001" was issued to a user at the date and time of issue "2021/12/21 10:00", the user ID of the user is "U01005", and the procedure for the user was started at "2021/12/21 10:15". Therefore, the waiting time for this user is "0:15" (minutes) from "2021/12/21 10:00" to "2021/12/21 10:15".

The ticket issuing number and the ticket issuing date and time are identified by the ticket issuing machine 110. The procedure start date and time is the date and time when the operator performs a specified operation on the store terminal 100 when starting to deal with the user, and the user ID is recognized by the operator as the user's user ID and input to the store terminal 100. In other words, the procedure start date and time and the user ID are identified by the store terminal 100. The waiting time X is the time from the ticket issuing date and time to the procedure start date and time, and is identified by a calculation by the store terminal 100.

The machine learning unit 22 generates a learned algorithm using a supervised machine learning algorithm such as a neural network or deep learning. This machine learning uses information about the waiting time that the user waited until the procedure for the user is completed as an explanatory variable, and information about the result recommended to the user as an objective variable. The explanatory variable (i.e., the information about the waiting time) is, for example, the length of the waiting time of each user in the store as described above. The objective variable (i.e., the information about the result of the recommendation to the user) is, for example, information on whether the user purchased or used the recommended product or service (i.e., the success or failure of the sale) as a result of the operator recommending (i.e., selling) various products or services to a user who has waited for the above-mentioned waiting time. This machine learning makes it possible to grasp the relationship between the two, that is, what product or service should be recommended to a user who has waited for how long in the store to make the user more likely to purchase or use such product or service.

Furthermore, the explanatory variables may include information about the user's attributes. Information about the user's attributes may include, for example, the user's physical characteristics (height and weight), occupation, school, organization to which the user belongs, current activities or actions, qualifications held, hobbies and preferences, address, family composition, assets, and history of the user's location and actions (for example, information about areas or stores the user has visited, content browsing history, or product or service purchase history, usage history, etc.).

By using a huge number of sets of explanatory variables and objective variables as described above, it is possible to generate a trained algorithm that can more accurately determine what products or services should be recommended to users who have been waiting for a certain amount of time in order to determine the likelihood that the user will purchase or use such products or services.

The storage unit 23 stores the learned algorithm generated by the machine learning unit 22. The storage unit 23 also stores information necessary for the processing executed by the server device 20, such as the information illustrated in FIG. 6 in addition to the information illustrated in FIG. 5 described above. The information illustrated in FIG. 6 is information related to the attributes of each user. Data related to these attributes is transmitted to the server device 20 via the communication network 2 from, for example, a user database (not shown) owned by the telecommunications carrier, and is stored in the storage unit 23.

The generation unit 24 inputs information about the user's waiting time as an explanatory variable into the learned algorithm stored in the memory unit 23, and generates recommendation information based on the information obtained as the objective variable (i.e., the probability that a sale of each product or service to the user will be successful). This recommendation information includes information about the content of the product or service recommended to the user, such as the name and summary of the product or service, and other information.

The output unit 25 outputs the recommendation information generated by the generation unit 24, for example, by display or other methods. The operator refers to this recommendation information to recommend products or services to the user, i.e., to perform sales activities.

[Action]
Next, the operation of the embodiment will be described. In the following description, for example, when the server device 20 is described as the subject of processing, this specifically means that the processor 2001 performs calculations by loading predetermined software (programs) onto hardware such as the processor 2001 and memory 2002, and controls communication by the communication device 2004 and reading and/or writing of data in the memory 2002 and storage 2003, thereby executing processing. The same applies to the store terminal 100.

First, referring to FIG. 7, the operation of the machine learning performed by the server device 20 will be described. When a user comes to a store, he or she first receives a ticket issued by the ticket issuing machine 110, and the operator starts to serve the user in the order of issuance. When the operator starts serving the user, the operator performs an operation on the store terminal 100 indicating the start of serving and inputs the user ID of the user. Then, while the user is performing the procedure desired by the user, the operator performs sales activities related to various products and services. Then, during or after this serving, the operator inputs the name of the product or service recommended to the user and the success or failure of the sales to the store terminal 100. The acquisition unit 21 of the server device 20 acquires the information obtained as described above (i.e., a set of information related to the waiting time of each user and information related to the recommended results for the user) from the store terminal 100 via the communication network 2 (step S11). Such information acquisition is performed one by one for a large number of users who visit the store, and a huge number of sets of information related to the waiting time of each user and information related to the recommended results for the user are acquired.

The machine learning unit 22 executes a machine learning algorithm using information on the waiting time of each user as an explanatory variable and information on the results recommended to each user as an objective variable to generate a trained algorithm (step S12). At this time, it is desirable to include information on the attributes of the user as the explanatory variables from the viewpoint of improving accuracy.

The memory unit 23 stores the learned algorithm generated by the machine learning unit 22 (step S13).

Next, referring to FIG. 8, the operation of generating recommendation information performed by the server device 20 will be described. When a user visits a store, he or she first receives a ticket issued by the ticket issuing machine 110, and an operator begins to serve the user in the order in which the tickets were issued. When the operator begins serving the user, the operator performs an operation on the store terminal 100 indicating the start of the service and inputs the user ID of the user. The acquisition unit 21 acquires information related to the user's waiting time, together with the user ID, etc., from the store system 10 via the communication network 2 (step S21).

Next, the generation unit 24 inputs information about the waiting time as an explanatory variable to the learned algorithm stored in the memory unit 23 (step S22), and as a result, the probability that a sale to the user for each product or service will be successful is obtained as the objective variable. The generation unit 24 generates recommendation information based on the obtained information (step S23). The output unit 25 outputs the recommendation information generated by the generation unit 24 to the store terminal 100 via the communication network 2 (step S24). This recommendation information is output from the store terminal 100, for example, by a method such as display.

9 is a diagram illustrating an example of recommendation information output (displayed) on the store terminal 100. This recommendation information includes a "user name" that identifies the user who visited the store, the "attributes" of the user, the "waiting time" that the user waited, the "product or service name" that is likely to be purchased or used when recommended to the user, and the "average explanation time" required to explain the product or service to be recommended (here, a guideline of short, medium, or long). The "priority" illustrated in FIG. 9 means the order of likelihood of purchase or use when recommended to the user, and the first priority means the highest likelihood of purchase or use when recommended to the user. When information about the user's waiting time is input as an explanatory variable to the trained algorithm, the probability of success when selling each product or service to the user is obtained as an objective variable, so the above order can be identified from that probability. The operator uses this recommendation information as a reference to conduct sales activities for the user.

Then, while or after serving the user, the operator inputs into the store terminal 100 the success or failure of the sale of each product or service sold to the user. In the "recommended result" illustrated in FIG. 9, "Not" means that no sale was made, "Success" means that the sale to the user was successful, and "No" means that the sale to the user was unsuccessful. Such a pair of "recommended result" and the user's "waiting time" is reused as a pair of objective variables and explanatory variables for machine learning by the machine learning unit 22. This further improves the accuracy of machine learning.

According to the embodiment described above, recommendation information for recommending products or services to a user is generated and output according to the waiting time of the user in the store. Because waiting time affects the psychological state of the user, it is possible to support or realize sales activities of an operator that are more likely to be successful according to the psychological state of the user.

[Modification]
The present invention is not limited to the above-described embodiment. The above-described embodiment may be modified as follows. In addition, two or more of the following modifications may be combined and implemented.
[Modification 1]
The waiting time may be the time that the user waits until the procedure for the user is completed. As an example, in the above embodiment, the time from when the user arrives at the place where the procedure is performed to when the procedure for the user is started is exemplified. Not limited to this example, the waiting time may be, for example, the time from when the procedure for the user is started to when the procedure is completed, or the sum of the time from when the user arrives at the place where the procedure is performed to when the procedure for the user is started and the time from when the procedure for the user is started to when the procedure is completed. The date and time when the procedure for the user is started can be specified in the same manner as in the above embodiment. The date and time when the procedure is completed may be, for example, the date and time when the operator performs a predetermined operation on the store terminal 100 when the operator finishes dealing with the user. In this way, the procedure start date and time and the procedure end date and time can be specified by the store terminal 100, and these are acquired by the acquisition unit 21 of the server device 20 via the communication network 2.

Also, as the waiting time, the time from when the user arrives at the place where the procedure is to be performed until the procedure for that user starts, and the time from when the procedure for that user starts until the current time may each be used as an explanatory variable. In this case, information such as that shown in FIG. 10 is stored in the storage unit 23. This information includes the ticket issue number of the ticket issued to each user, the issue date and time, a user ID for identifying the user to whom the ticket was issued, the procedure start date and time when the procedure for that user starts, the waiting time X from when the user arrives at the store until the procedure for that user starts, and the waiting time Y, which is the elapsed time from when the procedure starts until the current time (the current time is 11:00 in FIG. 10). As described above, various times can be used as the time that the user waits until the procedure for the user is completed.

[Modification 2]
The explanatory variables of the machine learning may include information other than the examples of the embodiment. For example, the explanatory variables may include information on the progress of the procedure. Specifically, the explanatory variables may be information on the ratio between a standard time determined as the time required from the start of a procedure for a user to the completion of the procedure and the actual elapsed time from the start of the procedure for the user to the current time. To achieve this, as illustrated in FIG. 11, the storage unit 23 stores a procedure ID for identifying each procedure in association with a standard time determined as the time required from the start of the procedure to the completion of the procedure. The standard time is specified by a simulation for each procedure, etc. When the operator starts a procedure, the acquisition unit 21 acquires the procedure ID of the procedure for the user by inputting the procedure ID of the procedure to the store terminal 100. Thereafter, until the procedure is completed, the generation unit 24 periodically inputs the ratio between the standard time of the procedure and the actual elapsed time from the start of the procedure for the user to the current time into the learned algorithm, and generates recommended information based on the information obtained as the objective variable as a result. This trained algorithm was generated by machine learning, and includes, as explanatory variables, the ratio between the standard time of each procedure and the actual time elapsed since the start of each procedure to the present time, and, as objective variables, information on the sales results of various products or services made by the operator to the user up to the present time. This makes it possible to support or realize sales activities with a high probability of success, based on knowledge such as, for example, that the sales success rate of a certain product recommended at an early stage in the standard time of procedure A is high, that the sales success rate of a certain product recommended within the standard time of procedure A is high, or that the sales success rate of a certain service recommended beyond the standard time of procedure A is low.

[Modification 3]
The explanatory variables may include information about the weather when the user arrives at the place where the procedure is to be performed. The information about the weather may be acquired by the acquisition unit 21 via the communication network 2 from a server device that publishes weather information for various locations. The generation unit 24 inputs information about the weather when the user arrives at the place where the procedure is to be performed into the trained algorithm, and generates recommendation information based on the information obtained as the objective variable. This trained algorithm is generated by machine learning that includes, as an explanatory variable, information about the weather when the user arrives at the place where the procedure is to be performed, and includes, as an objective variable, information about the sales results of various products or services made by the operator to the user up to the present time. This makes it possible to support or realize sales activities with a high probability of success, for example, based on knowledge such as a high sales success rate for a certain product recommended when the weather is fine, or a low sales success rate for a certain service recommended when the weather is rainy.

[Modification 4]
The explanatory variables may include information about the time zone when the user arrives at the place where the procedure is to be performed. The information about the time zone may be measured by the internal clock function of the server device 20 and acquired by the acquisition unit 21. The generation unit 24 inputs information about the time zone when the user arrives at the place where the procedure is to be performed into the learned algorithm, and generates recommendation information based on the information obtained as the objective variable. This learned algorithm is generated by machine learning that includes, as an explanatory variable, information about the time zone when the user arrives at the place where the procedure is to be performed, and includes, as an objective variable, information about the sales results of various products or services made by the operator to the user up to the present time. This makes it possible to support or realize sales activities with a high probability of success, for example, based on knowledge such as a high sales success rate of a certain product recommended during the day or a low sales success rate of a certain service recommended at night.

[Modification 5]
In the above embodiment, the recommended information is presented to the operator in the order of likelihood of purchase or use when recommended to the user, but the order in which products or services should be recommended to the user may be presented to the operator. Specifically, the priority order illustrated in FIG. 9 may mean the order in which the operator recommends products or services to the user.

[Modification 6]
The method of measuring the waiting time of the user is not limited to the example of the embodiment. For example, an imaging device may be installed in the store, and the store system 10 may recognize the captured image and analyze the movements of the user and the operator to identify the date and time when the user visited the store, the date and time when the procedure started, and the date and time when the procedure ended, and the acquisition unit 21 of the server device 20 may acquire these.

[Modification 7]
The store is not limited to the example of the embodiment, and may be any type or size of store as long as it is a place where procedures for users can be performed. Furthermore, the products or services are not limited to the example of the embodiment.

The store terminal 100 may also perform some or all of the processing performed by the server device 20. When the store terminal 100 performs all of the processing performed by the server device 20, the store terminal 100 corresponds to the information processing device according to the present invention. When the store terminal 100 performs some of the processing performed by the server device 20, the server device 20 and the store terminal 100 correspond to the information processing device according to the present invention.

[Other Modifications]
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. The method of realizing each functional block is not particularly limited. That is, each functional block may be realized using one device that is physically or logically coupled, or may be realized using two or more devices that are physically or logically separated and directly or indirectly connected (for example, using wires, wirelessly, etc.). The functional blocks may be realized by combining the one device or the multiple devices with software.

Functions include, but are not limited to, judgement, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, regard, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs the transmission function is called a transmitting unit or a transmitter. As mentioned above, there are no particular limitations on the method of realization for either of these.

For example, the server device 20 in one embodiment of the present disclosure may function as a computer that performs the processing of the present disclosure.

Each aspect/embodiment described in this disclosure may be applied to at least one of LTE (Long Term Evolution), LTE-A (LTE-Advanced), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), FRA (Future Radio Access), NR (new Radio), 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 802.20, UWB (Ultra-WideBand), Bluetooth (registered trademark), and other suitable systems, and next-generation systems extended based on these. In addition, multiple systems may be combined (for example, a combination of at least one of LTE and LTE-A with 5G, etc.).

The processing steps, sequences, flow charts, etc. of each aspect/embodiment described in this disclosure may be reordered unless inconsistent. 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.

The input and output information may be stored in a specific location (e.g., memory) or may be managed using a management table. The input and output information may be overwritten, updated, or added to. The output information may be deleted. The input information may be transmitted to another device.

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

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 as an illustrative example and does not have any limiting meaning on the present disclosure.

Software should 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, and the like, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise. Software, instructions, information, and the like may also be transmitted or received over a transmission medium. For example, if the 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, and the like), then these wired and/or wireless technologies are included within the definition of a transmission medium.

The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, the 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.
In addition, terms explained in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings.

In addition, the information, parameters, etc. described in this 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.

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."

Any reference to elements using designations such as "first," "second," etc., used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish 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 precede the second element in some way.

The "part" in the configuration of each of the above devices may be replaced with "means," "circuit," "device," etc.

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." Additionally, the term "or," as used in this disclosure, is not intended to be an exclusive or.

In this disclosure, where articles have been added through translation, such as a, an, and the in English, this disclosure may include that the nouns following these articles are in the plural form.

1...information processing system, 2...communication network, 10...store system, 100...store terminal, 110...ticket issuing machine, 1001...processor, 1002...memory, 1003...storage, 1004...communication device, 1005...input device, 1006...output device, 20...server device, 21...acquisition unit, 22...machine learning unit, 23...storage unit, 24...generation unit, 25...output unit, 2001...processor, 2002...memory, 2003...storage, 2004...communication device, 2005...input device, 2006...output device.

Claims (9)

an acquisition unit that acquires information regarding a waiting time that a user has waited until a procedure for the user is completed;
a generation unit that generates recommendation information for recommending a product or service to the user according to the acquired waiting time, the generation unit generating the recommendation information according to the likelihood that the user will purchase the recommended product or service based on an objective variable obtained by inputting the acquired information about the waiting time into a trained algorithm generated by machine learning, the trained algorithm including, as an explanatory variable, information about the waiting time the user waited until a procedure for the user was completed, and information about a result of a recommendation to the user, as an objective variable;
and an output unit that outputs the generated recommendation information. The information regarding the waiting time is
The information processing apparatus according to claim 1 , further comprising a time from when the user arrives at a place where the procedure is to be performed to when the procedure for the user is started. The information regarding the waiting time is
3. The information processing apparatus according to claim 1, further comprising a time from when the procedure for the user was started to a current time. the acquisition unit acquires information regarding a ratio between a standard time determined as a time required from the start of the procedure for the user to the completion of the procedure and an elapsed time from the start of the procedure for the user to a current time,
The explanatory variables include information about the obtained ratio.
4. The information processing apparatus according to claim 3. 5. The information processing apparatus according to claim 1 , wherein the explanatory variables include information relating to attributes of the users. The information processing device according to any one of claims 1 to 5 , wherein the explanatory variables include information regarding weather when the user arrives at a location where the procedure is to be performed or before and after the arrival. The information processing device according to claim 1 , wherein the explanatory variables include information regarding a time period when the user arrives at a location where the procedure is to be performed. The information processing device according to claim 1 , wherein the recommendation information includes information regarding the contents of a product or service recommended to the user. The information processing device according to claim 1 , wherein the recommendation information includes information regarding an order in which a plurality of products or services are to be recommended to the user.

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