system

Abstract

The system according to the embodiment aims to automate the line reception procedure at the store. [Solution] A system according to an embodiment includes a reception unit, an input unit, an activation unit, and a notification unit. The reception unit reads a two-dimensional code. The input unit inputs user information based on the information read by the reception unit. The activation unit automatically activates a line based on the information input by the input unit. The notification unit notifies the user after the line has been activated by the activation unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the line reception procedures at stores are not automated, and there is room for improvement in efficient operation.

[0005] The system according to the embodiment aims to automate the line reception procedure at the store. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an input unit, an activation unit, and a notification unit. The reception unit reads a two-dimensional code. The input unit inputs user information based on the information read by the reception unit. The activation unit automatically activates the line based on the information input by the input unit. The notification unit notifies the user after the line is activated by the activation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automate the line reception procedure at the store. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention automates in-store line registration. The system begins when a user scans a two-dimensional code (e.g., a QR code (registered trademark)) at the store. Next, the user enters the necessary information and completes the registration process. Once the registration process is complete, the system automatically activates the line. After activation, the user is called by number and receives a terminal. This system streamlines in-store line registration and reduces user wait times. For example, a user scans a two-dimensional code at the store. To do this, the user scans the code using a smartphone. For example, registration begins by scanning a two-dimensional code displayed on a poster or information board in the store. Next, the user enters the necessary information. For example, the user enters basic information such as name, address, and contact information, as well as the desired line plan. This information is sent to the system, which then completes the registration process. Once the registration process is complete, the system automatically activates the line. For example, the system automatically configures and activates the line based on the user's desired line plan. This process does not require the user to perform any special operations. After activation, the user is called by number and receives a terminal. For example, when the number displayed on the in-store display is called, the user goes to the counter and receives a terminal. This streamlines in-store line reception. This system streamlines in-store line reception and shortens user waiting times. It also reduces the burden on in-store staff. For example, the system automatically handles questions and detailed procedures that were previously handled by in-store staff, allowing staff to focus on other tasks. Furthermore, this system is easy to operate, as users use their own smartphones to complete the procedure, similar to mobile ordering at restaurants. For example, users simply scan a 2D code using their smartphone and enter the necessary information to complete the procedure. In this way, users can easily receive line reception. This streamlines in-store line reception and shortens user waiting times. It also reduces the burden on in-store staff.

[0029] A line reception system according to an embodiment includes a reception unit, an input unit, an activation unit, and a notification unit. The reception unit reads a two-dimensional code. The two-dimensional code can be read, for example, using a smartphone camera or a dedicated scanner. For example, a user can scan a two-dimensional code displayed on a poster or signboard installed in a storefront using the smartphone camera. Alternatively, a dedicated scanner can be used to read the two-dimensional code. The input unit inputs user information. The user information includes, for example, name, address, contact information, and desired line plan. For example, a text box or a drop-down menu can be used as the input method. For example, a user can enter their name and address in a text box on their smartphone screen and select a desired line plan from a drop-down menu. The activation unit automatically configures and activates the line based on the line plan desired by the user. The line configuration and activation process includes, for example, a communication protocol and an authentication procedure. For example, the system configures a communication protocol based on the user's desired line plan and activates the line by performing an authentication procedure. The notification unit notifies the user after the line is activated. The notification method may be, for example, email, SMS, or app notification. For example, after the system completes the line activation, the system may send a notification to the user by email and instruct the user to pick up the terminal. The system may also send a notification to the user using SMS or app notification. This allows the line reception system according to the embodiment to improve the efficiency of line reception at stores and reduce user waiting time.

[0030] The reception unit can read the two-dimensional code and transmit the information to the input unit. For example, the reception unit reads the two-dimensional code with a smartphone camera and transmits the information to the input unit. For example, when a user scans a two-dimensional code displayed on a poster or information board installed in a store with the smartphone camera, the information is transmitted to the input unit. Alternatively, a dedicated scanner can be used to read the two-dimensional code and transmit the information to the input unit. This allows for smooth input of user information by reading the two-dimensional code. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can read the two-dimensional code and transmit the information using an AI model.

[0031] The input unit can input information including the user's name, address, contact information, and desired line plan. For example, the input unit allows the user to input their name and address into text boxes on the screen of their smartphone and select a desired line plan from a drop-down menu. For example, when entering their name, the user enters their name into a text box, and when entering their address, the user enters their address into a text box. Furthermore, when entering contact information, the user can enter a phone number or email address. When selecting a desired line plan, the user can select the desired plan from a drop-down menu. Thus, the procedure proceeds by entering the user's basic information and desired line plan. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can analyze the user's input information using an AI model and suggest an appropriate input method.

[0032] The activation unit can automatically set up and activate a line based on the line plan desired by the user. The activation unit, for example, sets up a communication protocol based on the line plan desired by the user and performs an authentication procedure. For example, the system activates the line by setting up a communication protocol based on the line plan desired by the user and performing an authentication procedure. Furthermore, since the activation unit automatically sets up and activates the line, the user does not need to perform any special operations. As a result, the line is automatically activated based on the line plan desired by the user. Some or all of the above-described processing in the activation unit may be performed using, for example, AI, or may be performed without using AI. For example, the activation unit can set up and activate the line using an AI model.

[0033] The notification unit can notify the user via a specific number after the line has been activated and instruct the user to pick up the device. For example, after the line has been activated, the notification unit can send a notification by email to the user and instruct the user to pick up the device. For example, after the system has completed the line activation, the notification unit can send a notification by email to the user and instruct the user to pick up the device. The notification unit can also send notifications to the user using SMS or app notifications. For example, after the system has completed the line activation, the system can send a notification by SMS to the user and instruct the user to pick up the device. This allows the user to be notified after the line has been activated and to smoothly pick up the device. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can be executed using an AI model that sends a notification to the user after the line has been activated.

[0034] The reception unit can analyze the user's past store visit history and select an appropriate display method for the 2D code. For example, if the user has visited the store frequently in the past, the reception unit can display the 2D code in a prominent location so that the user can easily find it. For example, if the user has visited the store frequently in the past, the reception unit can display the 2D code in a prominent location so that the user can easily find it. Furthermore, if the user is visiting the store for the first time, the reception unit can provide detailed instructions on how to display the 2D code so that the user can easily read it. For example, if the user is visiting the store for the first time, the reception unit can provide detailed instructions on how to display the 2D code so that the user can easily read it. Furthermore, if the user tends to visit the store during a specific time period, the reception unit can select a display method for the 2D code that suits that time period. For example, if the user tends to visit the store during a specific time period, the reception unit can select a display method for the 2D code that suits that time period. This improves user convenience by providing an optimal display method for the 2D code based on the user's past store visit history. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can use an AI model that analyzes the user's past store visit history and selects how to display the two-dimensional code.

[0035] When reading the two-dimensional code, the reception unit can provide an appropriate reception method based on the user's current location information. For example, if the user is inside a store, the reception unit can guide the user to read the two-dimensional code displayed on a signboard inside the store. For example, if the user is inside a store, the reception unit can guide the user to read the two-dimensional code displayed on a signboard inside the store. Furthermore, if the user is outside a store, the reception unit can guide the user to read the two-dimensional code displayed on a poster or signboard outside the store. For example, if the user is outside a store, the reception unit can guide the user to read the two-dimensional code displayed on a poster or signboard outside the store. Furthermore, if the user is in a parking lot, the reception unit can guide the user to read the two-dimensional code displayed on a signboard in the parking lot. For example, if the user is in a parking lot, the reception unit can guide the user to read the two-dimensional code displayed on a signboard in the parking lot. This improves user convenience by providing an optimal reception method based on the user's current location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can execute processing using an AI model that analyzes the user's current location information and provides the optimal reception method.

[0036] When reading a 2D code, the reception unit can provide an appropriate reading method taking into account the user's device information. For example, if the user is using a smartphone, the reception unit can provide a 2D code display method optimized for the smartphone. For example, if the user is using a smartphone, the reception unit can provide a 2D code display method optimized for the smartphone. Furthermore, if the user is using a tablet, the reception unit can provide a 2D code display method optimized for the tablet. For example, if the user is using a tablet, the reception unit can provide a 2D code display method optimized for the tablet. Furthermore, if the user is using a feature phone, the reception unit can provide a 2D code display method optimized for the feature phone. For example, if the user is using a feature phone, the reception unit can provide a 2D code display method optimized for the feature phone. This improves user convenience by providing an optimal reading method based on the user's device information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can execute processing using an AI model that analyzes the user's device information and provides the optimal reading method.

[0037] When the two-dimensional code is read, the reception unit can analyze the user's social media activity and provide related information. For example, if the user follows a specific store on social media, the reception unit can provide information related to the store. For example, if the user follows a specific store on social media, the reception unit can provide information related to the store. Furthermore, if the user is participating in a specific campaign on social media, the reception unit can provide information related to the campaign. For example, if the user is participating in a specific campaign on social media, the reception unit can provide information related to the campaign. Furthermore, if the user is participating in a specific event on social media, the reception unit can provide information related to the event. For example, if the user is participating in a specific event on social media, the reception unit can provide information related to the event. This improves user convenience by providing related information based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can execute processing using an AI model that analyzes the user's social media activity and provides related information.

[0038] The input unit can provide an appropriate input method by referring to the user's past input history when inputting information. For example, the input unit can automatically display information that the user has frequently input in the past as a candidate. For example, the input unit can automatically display information that the user has frequently input in the past as a candidate. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the input unit can preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest information to be used in a specific time period based on the user's past input history. For example, the input unit can predict and suggest information to be used in a specific time period based on the user's past input history. This makes input work more efficient by providing an optimal input method based on the user's past input history. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can execute the processing using an AI model that analyzes the user's past input history and provides an optimal input method.

[0039] The input unit can customize input items based on the user's current situation when inputting information. For example, when the user is inside a store, the input unit provides input items according to the situation inside the store. For example, when the user is inside the store, the input unit can provide input items according to the situation inside the store. Furthermore, when the user is outside the store, the input unit can provide input items according to the situation outside the store. For example, when the user is outside the store, the input unit can provide input items according to the situation outside the store. Furthermore, when the user is in a parking lot, the input unit can provide input items according to the situation in the parking lot. For example, when the user is in a parking lot, the input unit can provide input items according to the situation in the parking lot. This customizes the input items based on the user's current situation, thereby streamlining the input work. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can execute the processing using an AI model that analyzes the user's current situation and customizes the input items.

[0040] The input unit can provide an appropriate input method during input, taking into account device information of the user. For example, if the user is using a smartphone, the input unit can provide an input method optimized for the smartphone. For example, if the user is using a smartphone, the input unit can provide an input method optimized for the smartphone. Furthermore, if the user is using a tablet, the input unit can provide an input method optimized for the tablet. For example, if the user is using a tablet, the input unit can provide an input method optimized for the tablet. Furthermore, if the user is using a feature phone, the input unit can provide an input method optimized for the feature phone. For example, if the user is using a feature phone, the input unit can provide an input method optimized for the feature phone. This provides an optimal input method based on the user's device information, thereby improving the efficiency of input work. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can execute processing using an AI model that analyzes device information of the user and provides an optimal input method.

[0041] The input unit can analyze the user's social media activity during input and suggest related input items. For example, if the user follows a specific store on social media, the input unit can suggest input items related to the store. For example, if the user follows a specific store on social media, the input unit can suggest input items related to the store. Furthermore, if the user is participating in a specific campaign on social media, the input unit can suggest input items related to the campaign. For example, if the user is participating in a specific campaign on social media, the input unit can suggest input items related to the campaign. Furthermore, if the user is participating in a specific event on social media, the input unit can suggest input items related to the event. For example, if the user is participating in a specific event on social media, the input unit can suggest input items related to the event. This makes input work more efficient by suggesting related input items based on the user's social media activity. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can execute the processing using an AI model that analyzes the user's social media activity and suggests related input items.

[0042] When activating a line, the activation unit can provide an appropriate activation method by referring to the user's past line usage history. The activation unit can provide an optimal activation method, for example, based on a line plan used by the user in the past. For example, the activation unit can provide an optimal activation method based on a line plan used by the user in the past. The activation unit can also provide an activation method that avoids congestion based on the user's past line usage history. For example, the activation unit can provide an activation method that avoids congestion based on the user's past line usage history. The activation unit can also analyze the user's past line usage history and provide the most efficient activation method. For example, the activation unit can analyze the user's past line usage history and provide the most efficient activation method. By providing an optimal activation method based on the user's past line usage history, line activation becomes more efficient. Some or all of the above-described processing in the activation unit can be performed using, for example, AI, or without AI. For example, the activation unit can be executed using an AI model that analyzes the user's past line usage history and provides the optimal activation method.

[0043] The activation unit can customize the activation procedure based on the user's current situation when activating a line. For example, when the user is in a store, the activation unit provides an activation procedure that suits the situation inside the store. For example, when the user is in a store, the activation unit can provide an activation procedure that suits the situation inside the store. Furthermore, when the user is outside the store, the activation unit can provide an activation procedure that suits the situation outside the store. For example, when the user is outside the store, the activation unit can provide an activation procedure that suits the situation outside the store. Furthermore, when the user is in a parking lot, the activation unit can provide an activation procedure that suits the situation in the parking lot. For example, when the user is in a parking lot, the activation procedure can be provided that suits the situation in the parking lot. This customizes the activation procedure based on the user's current situation, thereby improving the efficiency of line activation. Some or all of the above-described processing in the activation unit may be performed using, for example, AI, or may be performed without using AI. For example, the activation unit can be executed using an AI model that analyzes the user's current situation and customizes the activation procedure.

[0044] The activation unit can provide an appropriate activation method by taking into account the user's device information when activating a line. For example, if the user uses a smartphone, the activation unit can provide an activation method optimized for the smartphone. For example, if the user uses a smartphone, the activation unit can provide an activation method optimized for the smartphone. Furthermore, if the user uses a tablet, the activation unit can provide an activation method optimized for the tablet. For example, if the user uses a tablet, the activation unit can provide an activation method optimized for the tablet. Furthermore, if the user uses a feature phone, the activation unit can provide an activation method optimized for the feature phone. For example, if the user uses a feature phone, the activation unit can provide an activation method optimized for the feature phone. This provides an optimal activation method based on the user's device information, thereby improving the efficiency of line activation. Some or all of the above-described processing in the activation unit may be performed using, for example, AI, or may be performed without using AI. For example, the activation unit can be executed using an AI model that analyzes the user's device information and provides an optimal activation method.

[0045] The activation unit can analyze the user's social media activity and suggest a relevant activation procedure when activating a line. For example, if the user follows a specific store on social media, the activation unit can suggest an activation procedure related to the store. For example, if the user follows a specific store on social media, the activation unit can suggest an activation procedure related to the store. Furthermore, if the user is participating in a specific campaign on social media, the activation unit can suggest an activation procedure related to the campaign. For example, if the user is participating in a specific campaign on social media, the activation unit can suggest an activation procedure related to the campaign. Furthermore, if the user is participating in a specific event on social media, the activation unit can suggest an activation procedure related to the event. For example, if the user is participating in a specific event on social media, the activation unit can suggest an activation procedure related to the event. This improves the efficiency of line activation by suggesting a relevant activation procedure based on the user's social media activity. Some or all of the above-described processing in the activation unit may be performed using, for example, AI, or may be performed without AI. For example, the activation unit can be executed using an AI model that analyzes the user's social media activity and suggests a relevant activation procedure.

[0046] The notification unit can provide an appropriate notification method by referring to the user's past notification history when providing a notification. The notification unit can provide an optimal notification method based on, for example, a notification method that the user has previously preferred. For example, the notification unit can provide an optimal notification method based on a notification method that the user has previously preferred. The notification unit can also provide a notification method that avoids congestion based on the user's past notification history. For example, the notification unit can provide a notification method that avoids congestion based on the user's past notification history. The notification unit can also analyze the user's past notification history and provide the most efficient notification method. For example, the notification unit can analyze the user's past notification history and provide the most efficient notification method. This improves notification efficiency by providing the optimal notification method based on the user's past notification history. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can execute processing using an AI model that analyzes the user's past notification history and provides the optimal notification method.

[0047] The notification unit can customize the notification content based on the user's current situation when providing a notification. For example, when the user is inside a store, the notification unit can provide notification content that corresponds to the situation inside the store. For example, when the user is inside the store, the notification unit can provide notification content that corresponds to the situation inside the store. Furthermore, when the user is outside the store, the notification unit can provide notification content that corresponds to the situation outside the store. For example, when the user is outside the store, the notification unit can provide notification content that corresponds to the situation outside the store. Furthermore, when the user is in a parking lot, the notification unit can provide notification content that corresponds to the situation in the parking lot. For example, when the user is in a parking lot, the notification unit can provide notification content that corresponds to the situation in the parking lot. This customizes the notification content based on the user's current situation, thereby improving the efficiency of notifications. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can execute the processing using an AI model that analyzes the user's current situation and customizes the notification content.

[0048] The notification unit can provide an appropriate notification method by taking into account the user's device information when providing notification. For example, if the user is using a smartphone, the notification unit can provide a notification method optimized for the smartphone. For example, if the user is using a smartphone, the notification unit can provide a notification method optimized for the smartphone. Furthermore, if the user is using a tablet, the notification unit can provide a notification method optimized for the tablet. For example, if the user is using a tablet, the notification unit can provide a notification method optimized for the tablet. Furthermore, if the user is using a feature phone, the notification unit can provide a notification method optimized for the feature phone. For example, if the user is using a feature phone, the notification unit can provide a notification method optimized for the feature phone. This provides an optimal notification method based on the user's device information, thereby improving notification efficiency. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can be executed using an AI model that analyzes the user's device information and provides the optimal notification method.

[0049] The notification unit can analyze the user's social media activity and suggest relevant notification content when sending a notification. For example, if the user follows a specific store on social media, the notification unit can suggest notification content related to the store. For example, if the user follows a specific store on social media, the notification unit can suggest notification content related to the store. Furthermore, if the user is participating in a specific campaign on social media, the notification unit can suggest notification content related to the campaign. For example, if the user is participating in a specific campaign on social media, the notification unit can suggest notification content related to the campaign. Furthermore, if the user is participating in a specific event on social media, the notification unit can suggest notification content related to the event. For example, if the user is participating in a specific event on social media, the notification unit can suggest notification content related to the event. This improves the efficiency of notifications by suggesting relevant notification content based on the user's social media activity. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can be executed using an AI model that analyzes the user's social media activity and suggests relevant notification content.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The reception unit can analyze the user's past purchase history and propose an appropriate line plan. For example, if the user previously selected a plan with a high data usage, a similar plan with a high data usage can be proposed. Also, if the user previously selected a plan with a low data usage, a plan with a low data usage can be proposed. Furthermore, if the user previously used a specific optional service, a plan including that optional service can be proposed. This improves user convenience by proposing the optimal line plan based on the user's past purchase history.

[0052] The input unit can analyze the user's input speed in real time and provide input assistance. For example, if the user's input speed is slow, the input assistance function can be enabled and input candidate content can be automatically displayed. Also, if the user's input speed is fast, the input assistance function can be disabled so as not to interfere with the user's input. Furthermore, if the user is having trouble with a particular input item, hints or guides for that item can be displayed. This provides appropriate input assistance according to the user's input speed, making input work more efficient.

[0053] The activation unit can analyze the user's current network status and provide the optimal line activation method. For example, if the user is in a high-load network environment, it can delay line activation to ensure a stable connection. Also, if the user is in a low-load network environment, it can quickly activate the line. Furthermore, if the user is traveling, it can postpone line activation until the user has completed their travel. This improves the reliability of line activation by providing the optimal line activation method according to the user's network status.

[0054] The notification unit can analyze the user's past notification history and provide the optimal notification timing. For example, if the user has previously preferred to receive notifications during a specific time period, the notification can be sent during that time period. Also, if the user has previously preferred to receive notifications on a specific day of the week, the notification can be sent on that day. Furthermore, if the user has previously preferred to receive notifications after a specific event, the notification can be sent after that event. This improves the effectiveness of notifications by providing the optimal notification timing based on the user's past notification history.

[0055] The reception unit can analyze the user's current location information and provide the optimal reception method. For example, if the user is inside a store, the reception unit can guide the user to read a two-dimensional code displayed on a signboard inside the store. If the user is outside the store, the reception unit can guide the user to read a two-dimensional code displayed on a poster or signboard outside the store. Furthermore, if the user is in a parking lot, the reception unit can guide the user to read a two-dimensional code displayed on a signboard in the parking lot. This improves user convenience by providing the optimal reception method based on the user's current location information.

[0056] The input unit can analyze the user's device information and provide the optimal input method. For example, if the user is using a smartphone, an input method optimized for the smartphone can be provided. Also, if the user is using a tablet, an input method optimized for the tablet can be provided. Furthermore, if the user is using a feature phone, an input method optimized for the feature phone can be provided. This provides the optimal input method based on the user's device information, thereby making input work more efficient.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception unit reads the 2D code. 2D codes can be read using a smartphone camera or a dedicated scanner. For example, a user can use their smartphone camera to scan a 2D code displayed on a poster or information board in a store. 2D codes can also be read using a dedicated scanner. Step 2: The input unit inputs user information based on the information read by the reception unit. The user information includes name, address, contact information, desired line plan, etc. Text boxes and drop-down menus are used as input methods. For example, the user can input their name and address into text boxes on the smartphone screen and select their desired line plan from a drop-down menu. Step 3: The activation unit automatically activates the line based on the information entered by the input unit. Setting up and activating the line includes communication protocols and authentication procedures. For example, the system activates the line by setting up communication protocols based on the line plan desired by the user and performing authentication procedures. Step 4: The notification unit notifies the user after the activation unit has completed the activation. Notification methods include email, SMS, and app notification. For example, after the system completes the line activation, it can send a notification to the user by email and instruct them to pick up the device. It can also send a notification to the user using SMS or app notification.

[0059] (Example 2) A system according to an embodiment of the present invention automates in-store line application procedures. The system begins when a user scans a two-dimensional code (e.g., a QR code) at the store. Next, the user enters the necessary information and completes the procedure. Once the procedure is complete, the system automatically activates the line. After activation, the user is called by number and receives a terminal. This system streamlines in-store line application procedures and reduces user wait times. For example, a user scans a two-dimensional code at the store. To do this, the user uses a smartphone to scan the two-dimensional code. For example, the system begins by scanning a two-dimensional code displayed on a poster or information board in the store. Next, the user enters the necessary information. For example, the user enters basic information such as name, address, and contact information, as well as the desired line plan. This information is sent to the system, which then completes the procedure. Once the procedure is complete, the system automatically activates the line. For example, the system automatically configures and activates the line based on the user's desired line plan. During this process, the user does not need to perform any special operations. After activation, the user is called by number and receives a terminal. For example, when the number displayed on the in-store display is called, the user goes to the counter and receives a terminal. This streamlines in-store line reception. This system streamlines in-store line reception and shortens user waiting times. It also reduces the burden on in-store staff. For example, the system automatically handles questions and detailed procedures that were previously handled by in-store staff, allowing staff to focus on other tasks. Furthermore, this system is easy to operate, as users use their own smartphones to complete the procedure, similar to mobile ordering at restaurants. For example, users simply scan a 2D code using their smartphone and enter the necessary information to complete the procedure. In this way, users can easily receive line reception. This streamlines in-store line reception and shortens user waiting times. It also reduces the burden on in-store staff.

[0060] A line reception system according to an embodiment includes a reception unit, an input unit, an activation unit, and a notification unit. The reception unit reads a two-dimensional code. The two-dimensional code can be read, for example, using a smartphone camera or a dedicated scanner. For example, a user can scan a two-dimensional code displayed on a poster or signboard installed in a storefront using the smartphone camera. Alternatively, a dedicated scanner can be used to read the two-dimensional code. The input unit inputs user information. The user information includes, for example, name, address, contact information, and desired line plan. For example, a text box or a drop-down menu can be used as the input method. For example, a user can enter their name and address in a text box on their smartphone screen and select a desired line plan from a drop-down menu. The activation unit automatically configures and activates the line based on the line plan desired by the user. The line configuration and activation process includes, for example, a communication protocol and an authentication procedure. For example, the system configures a communication protocol based on the user's desired line plan and activates the line by performing an authentication procedure. The notification unit notifies the user after the line is activated. The notification method may be, for example, email, SMS, or app notification. For example, after the system completes the line activation, the system may send a notification to the user by email and instruct the user to pick up the terminal. The system may also send a notification to the user using SMS or app notification. This allows the line reception system according to the embodiment to improve the efficiency of line reception at stores and reduce user waiting time.

[0061] The reception unit can read the two-dimensional code and transmit the information to the input unit. For example, the reception unit reads the two-dimensional code with a smartphone camera and transmits the information to the input unit. For example, when a user scans a two-dimensional code displayed on a poster or information board installed in a store with the smartphone camera, the information is transmitted to the input unit. Alternatively, a dedicated scanner can be used to read the two-dimensional code and transmit the information to the input unit. This allows for smooth input of user information by reading the two-dimensional code. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can read the two-dimensional code and transmit the information using an AI model.

[0062] The input unit can input information including the user's name, address, contact information, and desired line plan. For example, the input unit allows the user to input their name and address into text boxes on the screen of their smartphone and select a desired line plan from a drop-down menu. For example, when entering their name, the user enters their name into a text box, and when entering their address, the user enters their address into a text box. Furthermore, when entering contact information, the user can enter a phone number or email address. When selecting a desired line plan, the user can select the desired plan from a drop-down menu. Thus, the procedure proceeds by entering the user's basic information and desired line plan. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can analyze the user's input information using an AI model and suggest an appropriate input method.

[0063] The activation unit can automatically set up and activate a line based on the line plan desired by the user. The activation unit, for example, sets up a communication protocol based on the line plan desired by the user and performs an authentication procedure. For example, the system activates the line by setting up a communication protocol based on the line plan desired by the user and performing an authentication procedure. Furthermore, since the activation unit automatically sets up and activates the line, the user does not need to perform any special operations. As a result, the line is automatically activated based on the line plan desired by the user. Some or all of the above-described processing in the activation unit may be performed using, for example, AI, or may be performed without using AI. For example, the activation unit can set up and activate the line using an AI model.

[0064] The notification unit can notify the user via a specific number after the line has been activated and instruct the user to pick up the device. For example, after the line has been activated, the notification unit can send a notification by email to the user and instruct the user to pick up the device. For example, after the system has completed the line activation, the notification unit can send a notification by email to the user and instruct the user to pick up the device. The notification unit can also send notifications to the user using SMS or app notifications. For example, after the system has completed the line activation, the system can send a notification by SMS to the user and instruct the user to pick up the device. This allows the user to be notified after the line has been activated and to smoothly pick up the device. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can be executed using an AI model that sends a notification to the user after the line has been activated.

[0065] The reception unit can estimate the user's emotions and adjust the timing of reading the 2D code based on the estimated user emotions. For example, if the user is nervous, the reception unit can delay the timing of reading the 2D code to give the user time to relax. For example, if the user is nervous, the reception unit can delay the timing of reading the 2D code to give the user time to relax. Furthermore, if the user is in a hurry, the reception unit can advance the timing of reading the 2D code to start the reception quickly. For example, if the user is in a hurry, the reception unit can advance the timing of reading the 2D code to start the reception quickly. Furthermore, if the user is excited, the reception unit can adjust the timing of reading the 2D code to allow the user to receive the call in a calm state. For example, if the user is excited, the reception unit can adjust the timing of reading the 2D code to allow the user to receive the call in a calm state. In this way, adjusting the timing of reading the 2D code according to the user's emotions reduces the user's stress. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may use an AI model that estimates the user's emotions and adjusts the timing of reading the two-dimensional code.

[0066] The reception unit can analyze the user's past store visit history and select an appropriate display method for the 2D code. For example, if the user has visited the store frequently in the past, the reception unit can display the 2D code in a prominent location so that the user can easily find it. For example, if the user has visited the store frequently in the past, the reception unit can display the 2D code in a prominent location so that the user can easily find it. Furthermore, if the user is visiting the store for the first time, the reception unit can provide detailed instructions on how to display the 2D code so that the user can easily read it. For example, if the user is visiting the store for the first time, the reception unit can provide detailed instructions on how to display the 2D code so that the user can easily read it. Furthermore, if the user tends to visit the store during a specific time period, the reception unit can select a display method for the 2D code that suits that time period. For example, if the user tends to visit the store during a specific time period, the reception unit can select a display method for the 2D code that suits that time period. This improves user convenience by providing an optimal display method for the 2D code based on the user's past store visit history. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can use an AI model that analyzes the user's past store visit history and selects how to display the two-dimensional code.

[0067] When reading the two-dimensional code, the reception unit can provide an appropriate reception method based on the user's current location information. For example, if the user is inside a store, the reception unit can guide the user to read the two-dimensional code displayed on a signboard inside the store. For example, if the user is inside a store, the reception unit can guide the user to read the two-dimensional code displayed on a signboard inside the store. Furthermore, if the user is outside a store, the reception unit can guide the user to read the two-dimensional code displayed on a poster or signboard outside the store. For example, if the user is outside a store, the reception unit can guide the user to read the two-dimensional code displayed on a poster or signboard outside the store. Furthermore, if the user is in a parking lot, the reception unit can guide the user to read the two-dimensional code displayed on a signboard in the parking lot. For example, if the user is in a parking lot, the reception unit can guide the user to read the two-dimensional code displayed on a signboard in the parking lot. This improves user convenience by providing an optimal reception method based on the user's current location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can execute processing using an AI model that analyzes the user's current location information and provides the optimal reception method.

[0068] The reception unit can estimate the user's emotions and determine the priority of reading 2D codes based on the estimated user emotions. For example, if the user is nervous, the reception unit can prioritize reading 2D codes over other users. For example, if the user is nervous, the reception unit can prioritize reading 2D codes over other users. Furthermore, if the user is relaxed, the reception unit can read 2D codes in the normal order. For example, if the user is relaxed, the reception unit can read 2D codes in the normal order. Furthermore, if the user is in a hurry, the reception unit can prioritize reading 2D codes. For example, if the user is in a hurry, the reception unit can prioritize reading 2D codes. This reduces the user's stress by determining the priority of reading 2D codes according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can be implemented using an AI model that estimates the user's emotions and determines the priority of reading the two-dimensional code.

[0069] When reading a 2D code, the reception unit can provide an appropriate reading method taking into account the user's device information. For example, if the user is using a smartphone, the reception unit can provide a 2D code display method optimized for the smartphone. For example, if the user is using a smartphone, the reception unit can provide a 2D code display method optimized for the smartphone. Furthermore, if the user is using a tablet, the reception unit can provide a 2D code display method optimized for the tablet. For example, if the user is using a tablet, the reception unit can provide a 2D code display method optimized for the tablet. Furthermore, if the user is using a feature phone, the reception unit can provide a 2D code display method optimized for the feature phone. For example, if the user is using a feature phone, the reception unit can provide a 2D code display method optimized for the feature phone. This improves user convenience by providing an optimal reading method based on the user's device information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can execute processing using an AI model that analyzes the user's device information and provides the optimal reading method.

[0070] When the two-dimensional code is read, the reception unit can analyze the user's social media activity and provide related information. For example, if the user follows a specific store on social media, the reception unit can provide information related to the store. For example, if the user follows a specific store on social media, the reception unit can provide information related to the store. Furthermore, if the user is participating in a specific campaign on social media, the reception unit can provide information related to the campaign. For example, if the user is participating in a specific campaign on social media, the reception unit can provide information related to the campaign. Furthermore, if the user is participating in a specific event on social media, the reception unit can provide information related to the event. For example, if the user is participating in a specific event on social media, the reception unit can provide information related to the event. This improves user convenience by providing related information based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can execute processing using an AI model that analyzes the user's social media activity and provides related information.

[0071] The input unit can estimate the user's emotions and adjust the design of the input screen based on the estimated user's emotions. For example, when the user is nervous, the input unit can provide an input screen with a calm color scheme to reduce visual stress. For example, when the user is nervous, the input unit can provide an input screen with a calm color scheme to reduce visual stress. Furthermore, when the user is having fun, the input unit can provide an input screen with a bright color scheme to make input work more enjoyable. For example, when the user is having fun, the input unit can provide an input screen with a bright color scheme to make input work more enjoyable. Furthermore, when the user is tired, the input unit can provide an input screen with a simple, highly visible color scheme to make input work easier. For example, when the user is tired, the input unit can provide an input screen with a simple, highly visible color scheme to make input work easier. This reduces user stress by adjusting the design of the input screen according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit can be implemented using an AI model that estimates the user's emotions and adjusts the design of the input screen.

[0072] The input unit can provide an appropriate input method by referring to the user's past input history when inputting information. For example, the input unit can automatically display information that the user has frequently input in the past as a candidate. For example, the input unit can automatically display information that the user has frequently input in the past as a candidate. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the input unit can preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest information to be used in a specific time period based on the user's past input history. For example, the input unit can predict and suggest information to be used in a specific time period based on the user's past input history. This makes input work more efficient by providing an optimal input method based on the user's past input history. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can execute the processing using an AI model that analyzes the user's past input history and provides an optimal input method.

[0073] The input unit can customize input items based on the user's current situation when inputting information. For example, when the user is inside a store, the input unit provides input items according to the situation inside the store. For example, when the user is inside the store, the input unit can provide input items according to the situation inside the store. Furthermore, when the user is outside the store, the input unit can provide input items according to the situation outside the store. For example, when the user is outside the store, the input unit can provide input items according to the situation outside the store. Furthermore, when the user is in a parking lot, the input unit can provide input items according to the situation in the parking lot. For example, when the user is in a parking lot, the input unit can provide input items according to the situation in the parking lot. This customizes the input items based on the user's current situation, thereby streamlining the input work. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can execute the processing using an AI model that analyzes the user's current situation and customizes the input items.

[0074] The input unit can estimate the user's emotions and prioritize input items based on the estimated user emotions. For example, when the user is nervous, the input unit can prioritize displaying important input items to enable quick input. For example, when the user is nervous, the input unit can prioritize displaying important input items to enable quick input. Furthermore, when the user is relaxed, the input unit can display input items in the normal order. For example, when the user is relaxed, the input unit can display input items in the normal order. Furthermore, when the user is in a hurry, the input unit can display the most important input items with the highest priority to enable quick input. For example, when the user is in a hurry, the input unit can display the most important input items with the highest priority to enable quick input. This allows the input work to be prioritized according to the user's emotions, thereby improving the efficiency of input work. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the input unit can be implemented using an AI model that estimates the user's emotions and prioritizes input items.

[0075] The input unit can provide an appropriate input method during input, taking into account device information of the user. For example, if the user is using a smartphone, the input unit can provide an input method optimized for the smartphone. For example, if the user is using a smartphone, the input unit can provide an input method optimized for the smartphone. Furthermore, if the user is using a tablet, the input unit can provide an input method optimized for the tablet. For example, if the user is using a tablet, the input unit can provide an input method optimized for the tablet. Furthermore, if the user is using a feature phone, the input unit can provide an input method optimized for the feature phone. For example, if the user is using a feature phone, the input unit can provide an input method optimized for the feature phone. This provides an optimal input method based on the user's device information, thereby improving the efficiency of input work. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can execute processing using an AI model that analyzes device information of the user and provides an optimal input method.

[0076] The input unit can analyze the user's social media activity during input and suggest related input items. For example, if the user follows a specific store on social media, the input unit can suggest input items related to the store. For example, if the user follows a specific store on social media, the input unit can suggest input items related to the store. Furthermore, if the user is participating in a specific campaign on social media, the input unit can suggest input items related to the campaign. For example, if the user is participating in a specific campaign on social media, the input unit can suggest input items related to the campaign. Furthermore, if the user is participating in a specific event on social media, the input unit can suggest input items related to the event. For example, if the user is participating in a specific event on social media, the input unit can suggest input items related to the event. This makes input work more efficient by suggesting related input items based on the user's social media activity. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can execute the processing using an AI model that analyzes the user's social media activity and suggests related input items.

[0077] The activation unit can estimate the user's emotions and adjust the timing of line activation based on the estimated user emotions. For example, if the user is nervous, the activation unit can delay the timing of line activation to give the user time to relax. For example, if the user is nervous, the activation unit can delay the timing of line activation to give the user time to relax. Furthermore, if the user is in a hurry, the activation unit can advance the timing of line activation to quickly activate the line. For example, if the user is in a hurry, the activation unit can advance the timing of line activation to quickly activate the line. Furthermore, if the user is excited, the activation unit can adjust the timing of line activation to activate the line in a calm state. For example, if the user is excited, the activation unit can adjust the timing of line activation to activate the line in a calm state. In this way, adjusting the timing of line activation according to the user's emotions reduces the user's stress. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the opening unit may be performed using, for example, AI, or may be performed without using AI. For example, the opening unit may be executed using an AI model that estimates the user's emotions and adjusts the timing of line opening.

[0078] When activating a line, the activation unit can provide an appropriate activation method by referring to the user's past line usage history. The activation unit can provide an optimal activation method, for example, based on a line plan used by the user in the past. For example, the activation unit can provide an optimal activation method based on a line plan used by the user in the past. The activation unit can also provide an activation method that avoids congestion based on the user's past line usage history. For example, the activation unit can provide an activation method that avoids congestion based on the user's past line usage history. The activation unit can also analyze the user's past line usage history and provide the most efficient activation method. For example, the activation unit can analyze the user's past line usage history and provide the most efficient activation method. By providing an optimal activation method based on the user's past line usage history, line activation becomes more efficient. Some or all of the above-described processing in the activation unit can be performed using, for example, AI, or without AI. For example, the activation unit can be executed using an AI model that analyzes the user's past line usage history and provides the optimal activation method.

[0079] The activation unit can customize the activation procedure based on the user's current situation when activating a line. For example, when the user is in a store, the activation unit provides an activation procedure that suits the situation inside the store. For example, when the user is in a store, the activation unit can provide an activation procedure that suits the situation inside the store. Furthermore, when the user is outside the store, the activation unit can provide an activation procedure that suits the situation outside the store. For example, when the user is outside the store, the activation unit can provide an activation procedure that suits the situation outside the store. Furthermore, when the user is in a parking lot, the activation unit can provide an activation procedure that suits the situation in the parking lot. For example, when the user is in a parking lot, the activation procedure can be provided that suits the situation in the parking lot. This customizes the activation procedure based on the user's current situation, thereby improving the efficiency of line activation. Some or all of the above-described processing in the activation unit may be performed using, for example, AI, or may be performed without using AI. For example, the activation unit can be executed using an AI model that analyzes the user's current situation and customizes the activation procedure.

[0080] The opening unit can estimate the user's emotions and determine a priority for line opening based on the estimated user's emotions. For example, if the user is nervous, the opening unit can open the line with priority over other users. For example, if the user is nervous, the opening unit can open the line with priority over other users. Furthermore, if the user is relaxed, the opening unit can open the line in the normal order. For example, if the user is relaxed, the opening unit can open the line with the normal order. Furthermore, if the user is in a hurry, the opening unit can open the line with the highest priority. For example, if the user is in a hurry, the opening unit can open the line with the highest priority. In this way, by determining the priority for line opening according to the user's emotions, the user's stress is reduced. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the opening unit may be performed using, for example, AI, or without AI. For example, the activation unit can be implemented using an AI model that estimates user sentiment and prioritizes line activation.

[0081] The activation unit can provide an appropriate activation method by taking into account the user's device information when activating a line. For example, if the user uses a smartphone, the activation unit can provide an activation method optimized for the smartphone. For example, if the user uses a smartphone, the activation unit can provide an activation method optimized for the smartphone. Furthermore, if the user uses a tablet, the activation unit can provide an activation method optimized for the tablet. For example, if the user uses a tablet, the activation unit can provide an activation method optimized for the tablet. Furthermore, if the user uses a feature phone, the activation unit can provide an activation method optimized for the feature phone. For example, if the user uses a feature phone, the activation unit can provide an activation method optimized for the feature phone. This provides an optimal activation method based on the user's device information, thereby improving the efficiency of line activation. Some or all of the above-described processing in the activation unit may be performed using, for example, AI, or may be performed without using AI. For example, the activation unit can be executed using an AI model that analyzes the user's device information and provides an optimal activation method.

[0082] The activation unit can analyze the user's social media activity and suggest a relevant activation procedure when activating a line. For example, if the user follows a specific store on social media, the activation unit can suggest an activation procedure related to the store. For example, if the user follows a specific store on social media, the activation unit can suggest an activation procedure related to the store. Furthermore, if the user is participating in a specific campaign on social media, the activation unit can suggest an activation procedure related to the campaign. For example, if the user is participating in a specific campaign on social media, the activation unit can suggest an activation procedure related to the campaign. Furthermore, if the user is participating in a specific event on social media, the activation unit can suggest an activation procedure related to the event. For example, if the user is participating in a specific event on social media, the activation unit can suggest an activation procedure related to the event. This improves the efficiency of line activation by suggesting a relevant activation procedure based on the user's social media activity. Some or all of the above-described processing in the activation unit may be performed using, for example, AI, or may be performed without AI. For example, the activation unit can be executed using an AI model that analyzes the user's social media activity and suggests a relevant activation procedure.

[0083] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated user's emotions. For example, if the user is nervous, the notification unit can provide a notification in a calm tone. For example, if the user is nervous, the notification unit can provide a notification in a calm tone. Furthermore, if the user is relaxed, the notification unit can provide a notification in a bright tone. For example, if the user is relaxed, the notification unit can provide a notification in a bright tone. Furthermore, if the user is in a hurry, the notification unit can provide a quick and concise notification. For example, if the user is in a hurry, the notification unit can provide a quick and concise notification. This adjusts the notification method according to the user's emotions, thereby reducing the user's stress. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without AI. For example, the notification unit can be executed using an AI model that estimates the user's emotions and adjusts the notification method.

[0084] The notification unit can provide an appropriate notification method by referring to the user's past notification history when providing a notification. The notification unit can provide an optimal notification method based on, for example, a notification method that the user has previously preferred. For example, the notification unit can provide an optimal notification method based on a notification method that the user has previously preferred. The notification unit can also provide a notification method that avoids congestion based on the user's past notification history. For example, the notification unit can provide a notification method that avoids congestion based on the user's past notification history. The notification unit can also analyze the user's past notification history and provide the most efficient notification method. For example, the notification unit can analyze the user's past notification history and provide the most efficient notification method. This improves notification efficiency by providing the optimal notification method based on the user's past notification history. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can execute processing using an AI model that analyzes the user's past notification history and provides the optimal notification method.

[0085] The notification unit can customize the notification content based on the user's current situation when providing a notification. For example, when the user is inside a store, the notification unit can provide notification content that corresponds to the situation inside the store. For example, when the user is inside the store, the notification unit can provide notification content that corresponds to the situation inside the store. Furthermore, when the user is outside the store, the notification unit can provide notification content that corresponds to the situation outside the store. For example, when the user is outside the store, the notification unit can provide notification content that corresponds to the situation outside the store. Furthermore, when the user is in a parking lot, the notification unit can provide notification content that corresponds to the situation in the parking lot. For example, when the user is in a parking lot, the notification unit can provide notification content that corresponds to the situation in the parking lot. This customizes the notification content based on the user's current situation, thereby improving the efficiency of notifications. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can execute the processing using an AI model that analyzes the user's current situation and customizes the notification content.

[0086] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user emotions. For example, if the user is nervous, the notification unit can prioritize important notifications. For example, if the user is nervous, the notification unit can prioritize important notifications. Furthermore, if the user is relaxed, the notification unit can also perform notifications in the normal order. For example, if the user is relaxed, the notification unit can perform notifications in the normal order. Furthermore, if the user is in a hurry, the notification unit can also perform the most important notification with the highest priority. For example, if the user is in a hurry, the most important notification can be performed with the highest priority. This reduces the user's stress by determining the priority of notifications according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without AI. For example, the notification unit can be executed using an AI model that estimates the user's emotions and determines the priority of notifications.

[0087] The notification unit can provide an appropriate notification method by taking into account the user's device information when providing notification. For example, if the user is using a smartphone, the notification unit can provide a notification method optimized for the smartphone. For example, if the user is using a smartphone, the notification unit can provide a notification method optimized for the smartphone. Furthermore, if the user is using a tablet, the notification unit can provide a notification method optimized for the tablet. For example, if the user is using a tablet, the notification unit can provide a notification method optimized for the tablet. Furthermore, if the user is using a feature phone, the notification unit can provide a notification method optimized for the feature phone. For example, if the user is using a feature phone, the notification unit can provide a notification method optimized for the feature phone. This provides an optimal notification method based on the user's device information, thereby improving notification efficiency. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can be executed using an AI model that analyzes the user's device information and provides the optimal notification method.

[0088] The notification unit can analyze the user's social media activity and suggest relevant notification content when sending a notification. For example, if the user follows a specific store on social media, the notification unit can suggest notification content related to the store. For example, if the user follows a specific store on social media, the notification unit can suggest notification content related to the store. Furthermore, if the user is participating in a specific campaign on social media, the notification unit can suggest notification content related to the campaign. For example, if the user is participating in a specific campaign on social media, the notification unit can suggest notification content related to the campaign. Furthermore, if the user is participating in a specific event on social media, the notification unit can suggest notification content related to the event. For example, if the user is participating in a specific event on social media, the notification unit can suggest notification content related to the event. This improves the efficiency of notifications by suggesting relevant notification content based on the user's social media activity. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can be executed using an AI model that analyzes the user's social media activity and suggests relevant notification content. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, input unit, opening unit, and notification unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can read a two-dimensional code using the camera 42 of the smart device 14. The input unit can input user information using the touch panel 38A of the smart device 14. The opening unit sets up and opens the line using the specific processing unit 290 of the data processing device 12. The notification unit can notify the user using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, input unit, activation unit, and notification unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can read a two-dimensional code using the camera 42 of the smart glasses 214. The input unit can input user information by voice input using the microphone 238 of the smart glasses 214. The activation unit sets up and activates the line using the specific processing unit 290 of the data processing device 12. The notification unit can notify the user using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, input unit, opening unit, and notification unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit can read a two-dimensional code using the camera 42 of the headset terminal 314. The input unit can input user information by voice input using the microphone 238 of the headset terminal 314. The opening unit sets up and opens the line using the specific processing unit 290 of the data processing device 12. The notification unit can notify the user using the speaker 240 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, input unit, opening unit, and notification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can read a two-dimensional code using the camera 42 of the robot 414. The input unit can input user information by voice input using the microphone 238 of the robot 414. The opening unit sets up and opens the line using the specific processing unit 290 of the data processing device 12. The notification unit can notify the user using the speaker 240 of the robot 414.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The reception unit can analyze the user's past purchase history and propose an appropriate line plan. For example, if the user previously selected a plan with a high data usage, a similar plan with a high data usage can be proposed. Also, if the user previously selected a plan with a low data usage, a plan with a low data usage can be proposed. Furthermore, if the user previously used a specific optional service, a plan including that optional service can be proposed. This improves user convenience by proposing the optimal line plan based on the user's past purchase history.

[0091] The input unit can analyze the user's input speed in real time and provide input assistance. For example, if the user's input speed is slow, the input assistance function can be enabled and input candidate content can be automatically displayed. Also, if the user's input speed is fast, the input assistance function can be disabled so as not to interfere with the user's input. Furthermore, if the user is having trouble with a particular input item, hints or guides for that item can be displayed. This provides appropriate input assistance according to the user's input speed, making input work more efficient.

[0092] The activation unit can analyze the user's current network status and provide the optimal line activation method. For example, if the user is in a high-load network environment, it can delay line activation to ensure a stable connection. Also, if the user is in a low-load network environment, it can quickly activate the line. Furthermore, if the user is traveling, it can postpone line activation until the user has completed their travel. This improves the reliability of line activation by providing the optimal line activation method according to the user's network status.

[0093] The notification unit can analyze the user's past notification history and provide the optimal notification timing. For example, if the user has previously preferred to receive notifications during a specific time period, the notification can be sent during that time period. Also, if the user has previously preferred to receive notifications on a specific day of the week, the notification can be sent on that day. Furthermore, if the user has previously preferred to receive notifications after a specific event, the notification can be sent after that event. This improves the effectiveness of notifications by providing the optimal notification timing based on the user's past notification history.

[0094] The reception unit can estimate the user's emotions and adjust the design of the reception screen based on the estimated user's emotions. For example, if the user is nervous, a reception screen with subdued colors can be provided to reduce visual stress. If the user is having fun, a reception screen with bright colors can be provided to make the reception work more enjoyable. Furthermore, if the user is tired, a simple, highly visible reception screen can be provided to make the reception work easier. In this way, adjusting the design of the reception screen according to the user's emotions reduces user stress.

[0095] The input unit can estimate the user's emotions and adjust the order of input items based on the estimated user's emotions. For example, if the user is nervous, important input items can be displayed with priority, allowing for quick input. Alternatively, if the user is relaxed, input items can be displayed in the normal order. Furthermore, if the user is in a hurry, the most important input items can be displayed with priority, allowing for quick input. In this way, adjusting the order of input items according to the user's emotions makes input work more efficient.

[0096] The opening unit can estimate the user's emotions and adjust the method of notifying the user of the line opening based on the estimated user's emotions. For example, if the user is nervous, the notification can be made in a calm tone. If the user is relaxed, the notification can be made in a bright tone. Furthermore, if the user is in a hurry, the notification can be made quickly and concisely. In this way, adjusting the notification method according to the user's emotions reduces the user's stress.

[0097] The notification unit can estimate the user's emotions and customize the notification content based on the estimated user's emotions. For example, if the user is nervous, a notification with a detailed explanation can be sent to provide a sense of security. If the user is relaxed, a concise notification can be sent. Furthermore, if the user is in a hurry, a notification containing only the most important information can be sent. This improves the effectiveness of notifications by customizing the notification content according to the user's emotions.

[0098] The reception unit can analyze the user's current location information and provide the optimal reception method. For example, if the user is inside a store, the reception unit can guide the user to read a two-dimensional code displayed on a signboard inside the store. If the user is outside the store, the reception unit can guide the user to read a two-dimensional code displayed on a poster or signboard outside the store. Furthermore, if the user is in a parking lot, the reception unit can guide the user to read a two-dimensional code displayed on a signboard in the parking lot. This improves user convenience by providing the optimal reception method based on the user's current location information.

[0099] The input unit can analyze the user's device information and provide the optimal input method. For example, if the user is using a smartphone, an input method optimized for the smartphone can be provided. Also, if the user is using a tablet, an input method optimized for the tablet can be provided. Furthermore, if the user is using a feature phone, an input method optimized for the feature phone can be provided. This provides the optimal input method based on the user's device information, thereby making input work more efficient.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The reception unit reads the 2D code. 2D codes can be read using a smartphone camera or a dedicated scanner. For example, a user can use their smartphone camera to scan a 2D code displayed on a poster or information board in a store. 2D codes can also be read using a dedicated scanner. Step 2: The input unit inputs user information based on the information read by the reception unit. The user information includes name, address, contact information, desired line plan, etc. Text boxes and drop-down menus are used as input methods. For example, the user can input their name and address into text boxes on the smartphone screen and select their desired line plan from a drop-down menu. Step 3: The activation unit automatically activates the line based on the information entered by the input unit. Setting up and activating the line includes communication protocols and authentication procedures. For example, the system activates the line by setting up communication protocols based on the line plan desired by the user and performing authentication procedures. Step 4: The notification unit notifies the user after the activation unit has completed the activation. Notification methods include email, SMS, and app notification. For example, after the system completes the line activation, it can send a notification to the user by email and instruct them to pick up the device. It can also send a notification to the user using SMS or app notification.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0157] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0158] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0163] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0164] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0165] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0166] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0167] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0168] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0170] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0171] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0172] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0173] [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that reads the two-dimensional code; an input unit that inputs user information based on the information read by the reception unit; an opening unit that automatically opens a line based on the information input by the input unit; a notification unit that notifies a user after the service is opened by the opening unit. A system characterized by:

2. The reception unit The two-dimensional code is read and the information is sent to the input unit. The system of claim 1 .

3. The input unit Enter information including your name, address, contact details, and desired line plan The system of claim 1 .

4. The opening portion is Automatically configure and activate the line based on the line plan desired by the user The system of claim 1 .

5. The notification unit After activation, the user will be notified by a specific number and instructed to pick up the device. The system of claim 1 .

6. The reception unit The user's emotions are estimated, and the timing of reading the two-dimensional code is adjusted based on the estimated user's emotions. The system of claim 1 .

7. The reception unit Analyze the user's past store visit history and select the appropriate 2D code display method The system of claim 1 .

8. The reception unit When a 2D code is read, the appropriate reception method is provided based on the user's current location information. The system of claim 1 .

9. The reception unit The user's emotions are estimated, and the priority of reading the two-dimensional code is determined based on the estimated user's emotions. The system of claim 1 .

10. The reception unit When reading 2D codes, provide an appropriate reading method taking into account the user's device information. The system of claim 1 .

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