system

Abstract

The system according to this embodiment aims to provide senior citizens with a new payment method and to deliver peace of mind in cooperation with their families. [Solution] The system according to the embodiment comprises a supply unit, a monitoring unit, and a notification unit. The supply unit provides a keychain-type touch payment device. The monitoring unit monitors the purchase pattern of the device provided by the supply unit. The notification unit notifies the family based on the purchase pattern monitored by the monitoring unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, since the senior layer is reluctant to have a smartphone or a card, there is a problem that it is difficult to use digital payment means.

[0005] The system according to the embodiment aims to provide a new payment means to the senior layer and convey a sense of security in cooperation with the family.

Means for Solving the Problems

[0006] The system according to the embodiment includes a providing unit, a monitoring unit, and a notification unit. The providing unit provides a keyholder type touch payment device. The monitoring unit monitors the purchase pattern of the device provided by the providing unit. The notification unit notifies the family based on the purchase pattern monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide senior citizens with a new payment method and deliver peace of mind in cooperation with their families. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The payment system according to an embodiment of the present invention is a system that provides a new payment method to reassure seniors and their families who are reluctant to carry smartphones or cards in today's increasingly digitized society. This payment system provides a keychain-type touch payment device that attaches to keys, eliminating the need for seniors to carry wallets or smartphones. This device is prepaid, which helps prevent overspending. Next, an AI agent monitors the purchase patterns of this device, reads the purchase amount and number of purchases, and notifies the family as needed. For example, if a senior makes a suspicious purchase, the AI ​​agent notifies the family, who can then restrict, suspend, or allow the use of the device. Furthermore, the family can instruct the AI ​​agent to top up the device. This allows seniors to use the device with peace of mind. This mechanism eliminates the need for seniors to carry smartphones or cards, and allows families to check the senior's purchase history and take appropriate action. This provides peace of mind to seniors and their families. In this way, the payment system allows seniors to use the device with peace of mind, and allows families to check the purchase history and take appropriate action.

[0029] The payment system according to this embodiment comprises a provisioning unit, a monitoring unit, and a notification unit. The provisioning unit provides a keychain-type touch payment device. The provisioning unit provides a device that can be attached to keys, for example, so that seniors do not have to carry wallets or smartphones. The provisioning unit has a prepaid charging function that can prevent overspending. For example, the provisioning unit can receive charging instructions from family members. The monitoring unit monitors the purchase patterns of the device provided by the provisioning unit. For example, the monitoring unit monitors the purchase amount and the number of purchases. The monitoring unit can monitor the purchase patterns in detail. For example, the monitoring unit can monitor the total amount within a certain period and the amount of a single purchase. The notification unit notifies family members based on the purchase patterns monitored by the monitoring unit. For example, the notification unit provides notifications that allow family members to restrict, suspend, or allow the use of the device. The notification unit enables family members to manage the use of the device. For example, the notification unit can notify them of restrictions on usage time and restrictions on the number of uses. As a result, the payment system according to this embodiment allows seniors to use the device with peace of mind, and family members to check the purchase history and take action as needed.

[0030] The provider offers a keychain-type touch payment device. For example, the device can be attached to keys, eliminating the need for seniors to carry wallets or smartphones. This device is lightweight and compact, easily attaching to keys carried daily. The device features a prepaid charging function to prevent overspending. Specifically, the device incorporates NFC (Near Field Communication) technology, allowing for payment completion with a simple touch. Charging can be done through a dedicated application or website, and family members can remotely issue charging instructions. For example, family members can charge a fixed amount for seniors, preventing overspending while supporting necessary usage. Furthermore, the device has a balance check function, allowing seniors to easily check their current balance. In this way, the provider provides an environment where seniors can use the device with peace of mind and where families can manage it appropriately.

[0031] The monitoring unit monitors the purchase patterns of devices provided by the service provider. For example, the monitoring unit monitors purchase amounts and purchase frequency. Specifically, the monitoring unit has a system that collects and analyzes transaction data in real time when devices are used. The monitoring unit can monitor purchase patterns in detail. For example, it can monitor the total amount spent within a certain period and the amount spent on individual purchases. This allows for the early detection of abnormal purchase patterns and signs of fraudulent use. Furthermore, the monitoring unit can utilize AI-based machine learning algorithms to automatically identify abnormal patterns that differ from normal purchase patterns. For example, if an abnormal pattern is detected, such as a sudden increase in the amount spent compared to normal purchase patterns, or a large number of purchases made in a short period, an alert can be issued immediately. This allows the monitoring unit to gain a detailed understanding of device usage among senior citizens and respond quickly as needed.

[0032] The notification unit notifies family members based on purchase patterns monitored by the monitoring unit. For example, the notification unit can send notifications to family members allowing them to restrict, suspend, or permit device usage. Specifically, the notification unit has a system that receives alerts from the monitoring unit and notifies family members in real time. The notification unit enables family members to manage device usage. For example, it can notify them of usage time limits or usage frequency limits. Families can check device usage and set usage restrictions as needed through a dedicated application or web portal. For example, they can set restrictions to allow device use only during specific time periods or limit the number of uses per day. Furthermore, if an abnormal purchase pattern is detected, the notification unit can immediately notify family members to encourage prompt action. For example, if a series of high-value purchases are made or abnormal usage differing from normal usage patterns is detected, the notification unit can warn family members and prompt them to temporarily suspend or restrict device usage. This allows the notification unit to provide an environment where families can properly manage and safely use devices for seniors.

[0033] The service provider can be equipped with a prepaid charging function. The service provider can, for example, set a maximum charge amount. The service provider can provide an online charging method. For example, the service provider can allow charging via the internet. The service provider can also provide an offline charging method. For example, the service provider can allow charging at a convenience store. This helps prevent overspending. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use AI to analyze the user's usage history and suggest an appropriate maximum charge amount in order to set a maximum charge amount.

[0034] The monitoring unit can monitor purchase amounts and purchase frequency. For example, the monitoring unit can monitor the total amount within a certain period. The monitoring unit can also monitor the amount of individual purchases. For example, the monitoring unit can monitor purchases exceeding a certain amount. The monitoring unit can also monitor the number of purchases within a certain period. For example, the monitoring unit can monitor purchases exceeding a certain number of times. This allows for detailed monitoring of purchase patterns. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input purchase amount and purchase frequency data into the AI ​​and have the AI ​​perform analysis to detect abnormal purchase patterns.

[0035] The notification unit can send notifications to family members that allow them to restrict, suspend, or allow device use. For example, the notification unit can send notifications to limit usage time. The notification unit can also send notifications to limit the number of uses. For example, the notification unit can send notifications to restrict use during specific time periods. The notification unit can also send notifications to suspend use under specific conditions. For example, the notification unit can send notifications to suspend use if an abnormal purchase pattern is detected. Furthermore, the notification unit can also send notifications to allow use under specific conditions. For example, the notification unit can send notifications to resume use if the family grants permission. This allows the family to manage device use. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the conditions for usage restrictions or suspensions into the AI ​​and have the AI ​​perform analysis to send appropriate notifications.

[0036] The service provider can receive charge instructions from family members. The service provider can, for example, accept a charge amount specified by the family. The service provider can also specify the timing of the charge. For example, the service provider can perform the charge at a date and time specified by the family. The service provider can also specify the method of the charge. For example, the service provider can accept online charge instructions. This allows family members to instruct the device to charge. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input charge instructions from family members into AI and have the AI ​​perform an analysis to suggest an appropriate charging method.

[0037] The service provider can analyze a user's past purchase history and select the optimal method for providing a device. For example, the service provider can provide a device based on the assumption that the user will use it at stores they have frequently visited in the past. If the service provider's past purchase history indicates that the user frequently uses a device during a specific time period, they can provide a device tailored to that time period. If the service provider analyzes a user's past purchase history and finds that they tend to purchase products in a particular category, they can provide a device specialized for that category. This allows the service provider to provide the optimal device based on the user's past purchase history. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's past purchase history data into a generating AI and have the generating AI perform an analysis to select the optimal method for providing a device.

[0038] The service provider can filter devices based on the user's current lifestyle and areas of interest when providing them. For example, if a user is interested in health, the service provider can provide a device with health-related benefits. If a user travels frequently, the service provider can provide a device that can be used at their travel destination. If a user has a specific hobby, the service provider can provide a device with benefits related to that hobby. This allows the service provider to provide devices that are tailored to the user's lifestyle and areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0039] The service provider can prioritize providing highly relevant devices by considering the user's geographical location when providing devices. For example, if a user lives in a specific region, the service provider can provide a device with benefits usable in that region. If a user is traveling, the service provider can provide a device usable at their travel destination. If a user frequently uses a specific store, the service provider can provide a device usable at that store. This allows the service provider to provide highly relevant devices based on the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI perform analysis to select highly relevant devices.

[0040] The service provider can analyze a user's social media activity when providing a device and provide a relevant device. For example, if a user mentions a particular brand on social media, the service provider can provide a device with benefits from that brand. If a user participates in a particular event on social media, the service provider can provide a device that can be used at that event. If a user mentions a particular hobby on social media, the service provider can provide a device with benefits related to that hobby. This allows the service provider to provide relevant devices based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user social media activity data into a generating AI and have the generating AI perform analysis to select a relevant device.

[0041] The monitoring unit can improve the accuracy of its monitoring by considering the interrelationships of purchase patterns during monitoring. For example, if a user frequently makes purchases at a particular store, the monitoring unit can focus on monitoring the purchase patterns at that store. If a user tends to make purchases during a particular time period, the monitoring unit can focus on monitoring the purchase patterns during that time period. If a user tends to purchase products in a particular category, the monitoring unit can focus on monitoring products in that category. This improves the accuracy of monitoring by considering the interrelationships of purchase patterns. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input purchase pattern data into a generating AI and have the generating AI perform analysis to improve the accuracy of monitoring by considering the interrelationships.

[0042] The monitoring unit can perform monitoring while considering the buyer's attribute information. For example, the monitoring unit can focus on monitoring purchase patterns related to specific attributes based on the user's age and gender. The monitoring unit can focus on monitoring purchase patterns in a user's residential area based on their location. The monitoring unit can focus on monitoring purchase patterns related to specific attributes based on the user's occupation and lifestyle. This improves the accuracy of monitoring by performing monitoring based on the buyer's attribute information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input buyer attribute information data into a generating AI and have the generating AI perform analysis to improve the accuracy of monitoring.

[0043] The monitoring unit can perform monitoring while considering the geographical distribution of purchase patterns. For example, if a user frequently makes purchases in a particular area, the monitoring unit can focus on monitoring purchase patterns in that area. If a user is traveling, the monitoring unit can focus on monitoring purchase patterns at their travel destination. If a user frequently uses a particular store, the monitoring unit can focus on monitoring purchase patterns at that store. This improves the accuracy of monitoring by performing monitoring based on the geographical distribution of purchase patterns. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input geographical distribution data of purchase patterns into a generating AI and have the generating AI perform analysis to improve the accuracy of monitoring.

[0044] The monitoring unit can improve the accuracy of its monitoring by referring to relevant literature on purchase patterns during monitoring. For example, the monitoring unit can improve the accuracy of its monitoring by referring to the latest research on purchase patterns. The monitoring unit can improve the accuracy of its monitoring by referring to historical data on purchase patterns. The monitoring unit can improve the accuracy of its monitoring by referring to industry best practices on purchase patterns. This improves the accuracy of monitoring by referring to relevant literature on purchase patterns. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input relevant literature data on purchase patterns into a generating AI and have the generating AI perform analysis to improve the accuracy of monitoring.

[0045] The notification unit can optimize the current notification content by referring to past notification data when a notification is sent. For example, the notification unit can provide the most suitable notification content based on the notifications the user has received in the past. The notification unit can provide the most suitable notification content for a specific time period based on the user's past notification history. The notification unit can analyze the user's past notification history and provide the most effective notification content. This allows the current notification content to be optimized by referring to past notification data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past notification data into a generating AI and have the generating AI perform an analysis to optimize the current notification content.

[0046] The notification unit can apply different notification methods depending on the purchase pattern category when issuing notifications. For example, the notification unit can provide a simple and quick notification method for food purchases. For clothing purchases, it can provide a notification method that includes detailed information. For electronic device purchases, it can provide a visually easy-to-understand notification method. By applying different notification methods for each purchase pattern category, the accuracy of notifications is improved. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input purchase pattern category data into a generating AI and have the generating AI perform an analysis to select the optimal notification method.

[0047] The notification unit can determine the priority of notifications based on the timing of purchase pattern submissions. For example, if a user tends to make purchases during a specific time period, the notification unit can prioritize displaying notifications that are most suitable for that time period. If a user tends to make purchases on a specific day of the week, the notification unit can prioritize displaying notifications that are most suitable for that day of the week. If a user makes a purchase related to a specific event, the notification unit can prioritize displaying notifications that are most suitable for that event. This allows the notification unit to determine the priority of notifications based on the timing of purchase pattern submissions. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input purchase pattern submission timing data into a generating AI and have the generating AI perform analysis to determine the priority of notifications.

[0048] The notification unit can optimize notification content by referring to relevant market data on purchase patterns at the time of notification. For example, the notification unit can refer to the latest market data on purchase patterns to provide optimal notification content. The notification unit can refer to historical market data on purchase patterns to provide optimal notification content. The notification unit can refer to industry best practices on purchase patterns to provide optimal notification content. This allows for optimization of notification content by referring to relevant market data on purchase patterns. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input relevant market data on purchase patterns into a generating AI and have the generating AI perform analysis to optimize the notification content.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The service provider can monitor the user's health status and adjust the device delivery method accordingly. For example, based on the results of a health checkup, the service provider can deliver the device using the standard method if the user's health is good, and quickly deliver the device if the user's health has deteriorated. Furthermore, if the service provider has set specific health goals, they can provide a device tailored to those goals. For example, if a user is aiming to lose weight, they can be provided with a device that includes a calorie management function. This enables the provision of the optimal device for each user's health condition.

[0051] The notification unit can analyze a user's past notification history to determine the optimal notification timing. For example, it can analyze the time periods when a user has previously received notifications and send notifications at those times. It can also avoid sending notifications during times when a user has previously ignored them. Furthermore, if a user prefers to receive notifications related to a specific event, the notification unit can time notifications to coincide with that event. This allows the system to provide optimal notification timing based on the user's past notification history.

[0052] The monitoring unit can analyze users' purchase history and set criteria for detecting abnormal purchase patterns. For example, the monitoring unit can determine if a user purchases an expensive item that they do not normally buy as abnormal. Furthermore, if a user frequently makes purchases during a specific time period, the monitoring unit can focus on monitoring purchase patterns during that time. Additionally, if a user tends to purchase items in a particular category, the monitoring unit can focus on monitoring items in that category. This enables the detection of abnormal purchase patterns based on the user's purchase history.

[0053] The service provider can select the optimal device delivery method by considering the user's geographical location. For example, if a user lives in a specific region, the service provider can provide a device with benefits usable in that region. If a user is traveling, the service provider can provide a device usable at their travel destination. Furthermore, if a user frequently uses a particular store, the service provider can provide a device usable at that store. This enables the provision of the most suitable device based on the user's geographical location.

[0054] The notification unit can analyze a user's social media activity and provide relevant notifications. For example, if a user mentions a specific brand on social media, the notification unit can send notifications about that brand's offers. It can also send notifications related to events if a user participates in a particular event on social media. Furthermore, if a user mentions a specific hobby on social media, it can send notifications related to that hobby. This allows the system to provide optimal notifications based on the user's social media activity.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The provider will provide a keychain-type touch payment device. The provider will provide a device that can be attached to keys so that seniors do not have to carry wallets or smartphones. The provider will have a prepaid charging function to prevent overspending. For example, the provider can receive charging instructions from family members. Step 2: The monitoring unit monitors the purchase patterns of devices provided by the supply unit. The monitoring unit monitors the purchase amount and number of purchases, and can monitor the total amount within a certain period and the amount of a single purchase in detail. Step 3: The notification unit notifies the family based on the purchase patterns monitored by the monitoring unit. The notification unit allows the family to manage device usage by providing notifications that enable them to restrict, suspend, or allow device usage. For example, the notification unit can notify about limiting usage time or the number of uses.

[0057] (Example of form 2) The payment system according to an embodiment of the present invention is a system that provides a new payment method to reassure seniors and their families who are reluctant to carry smartphones or cards in today's increasingly digitized society. This payment system provides a keychain-type touch payment device that attaches to keys, eliminating the need for seniors to carry wallets or smartphones. This device is prepaid, which helps prevent overspending. Next, an AI agent monitors the purchase patterns of this device, reads the purchase amount and number of purchases, and notifies the family as needed. For example, if a senior makes a suspicious purchase, the AI ​​agent notifies the family, who can then restrict, suspend, or allow the use of the device. Furthermore, the family can instruct the AI ​​agent to top up the device. This allows seniors to use the device with peace of mind. This mechanism eliminates the need for seniors to carry smartphones or cards, and allows families to check the senior's purchase history and take appropriate action. This provides peace of mind to seniors and their families. In this way, the payment system allows seniors to use the device with peace of mind, and allows families to check the purchase history and take appropriate action.

[0058] The payment system according to this embodiment comprises a provisioning unit, a monitoring unit, and a notification unit. The provisioning unit provides a keychain-type touch payment device. The provisioning unit provides a device that can be attached to keys, for example, so that seniors do not have to carry wallets or smartphones. The provisioning unit has a prepaid charging function that can prevent overspending. For example, the provisioning unit can receive charging instructions from family members. The monitoring unit monitors the purchase patterns of the device provided by the provisioning unit. For example, the monitoring unit monitors the purchase amount and the number of purchases. The monitoring unit can monitor the purchase patterns in detail. For example, the monitoring unit can monitor the total amount within a certain period and the amount of a single purchase. The notification unit notifies family members based on the purchase patterns monitored by the monitoring unit. For example, the notification unit provides notifications that allow family members to restrict, suspend, or allow the use of the device. The notification unit enables family members to manage the use of the device. For example, the notification unit can notify them of restrictions on usage time and restrictions on the number of uses. As a result, the payment system according to this embodiment allows seniors to use the device with peace of mind, and family members to check the purchase history and take action as needed.

[0059] The provider offers a keychain-type touch payment device. For example, the device can be attached to keys, eliminating the need for seniors to carry wallets or smartphones. This device is lightweight and compact, easily attaching to keys carried daily. The device features a prepaid charging function to prevent overspending. Specifically, the device incorporates NFC (Near Field Communication) technology, allowing for payment completion with a simple touch. Charging can be done through a dedicated application or website, and family members can remotely issue charging instructions. For example, family members can charge a fixed amount for seniors, preventing overspending while supporting necessary usage. Furthermore, the device has a balance check function, allowing seniors to easily check their current balance. In this way, the provider provides an environment where seniors can use the device with peace of mind and where families can manage it appropriately.

[0060] The monitoring unit monitors the purchase patterns of devices provided by the service provider. For example, the monitoring unit monitors purchase amounts and purchase frequency. Specifically, the monitoring unit has a system that collects and analyzes transaction data in real time when devices are used. The monitoring unit can monitor purchase patterns in detail. For example, it can monitor the total amount spent within a certain period and the amount spent on individual purchases. This allows for the early detection of abnormal purchase patterns and signs of fraudulent use. Furthermore, the monitoring unit can utilize AI-based machine learning algorithms to automatically identify abnormal patterns that differ from normal purchase patterns. For example, if an abnormal pattern is detected, such as a sudden increase in the amount spent compared to normal purchase patterns, or a large number of purchases made in a short period, an alert can be issued immediately. This allows the monitoring unit to gain a detailed understanding of device usage among senior citizens and respond quickly as needed.

[0061] The notification unit notifies family members based on purchase patterns monitored by the monitoring unit. For example, the notification unit can send notifications to family members allowing them to restrict, suspend, or permit device usage. Specifically, the notification unit has a system that receives alerts from the monitoring unit and notifies family members in real time. The notification unit enables family members to manage device usage. For example, it can notify them of usage time limits or usage frequency limits. Families can check device usage and set usage restrictions as needed through a dedicated application or web portal. For example, they can set restrictions to allow device use only during specific time periods or limit the number of uses per day. Furthermore, if an abnormal purchase pattern is detected, the notification unit can immediately notify family members to encourage prompt action. For example, if a series of high-value purchases are made or abnormal usage differing from normal usage patterns is detected, the notification unit can warn family members and prompt them to temporarily suspend or restrict device usage. This allows the notification unit to provide an environment where families can properly manage and safely use devices for seniors.

[0062] The service provider can be equipped with a prepaid charging function. The service provider can, for example, set a maximum charge amount. The service provider can provide an online charging method. For example, the service provider can allow charging via the internet. The service provider can also provide an offline charging method. For example, the service provider can allow charging at a convenience store. This helps prevent overspending. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use AI to analyze the user's usage history and suggest an appropriate maximum charge amount in order to set a maximum charge amount.

[0063] The monitoring unit can monitor purchase amounts and purchase frequency. For example, the monitoring unit can monitor the total amount within a certain period. The monitoring unit can also monitor the amount of individual purchases. For example, the monitoring unit can monitor purchases exceeding a certain amount. The monitoring unit can also monitor the number of purchases within a certain period. For example, the monitoring unit can monitor purchases exceeding a certain number of times. This allows for detailed monitoring of purchase patterns. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input purchase amount and purchase frequency data into the AI ​​and have the AI ​​perform analysis to detect abnormal purchase patterns.

[0064] The notification unit can send notifications to family members that allow them to restrict, suspend, or allow device use. For example, the notification unit can send notifications to limit usage time. The notification unit can also send notifications to limit the number of uses. For example, the notification unit can send notifications to restrict use during specific time periods. The notification unit can also send notifications to suspend use under specific conditions. For example, the notification unit can send notifications to suspend use if an abnormal purchase pattern is detected. Furthermore, the notification unit can also send notifications to allow use under specific conditions. For example, the notification unit can send notifications to resume use if the family grants permission. This allows the family to manage device use. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the conditions for usage restrictions or suspensions into the AI ​​and have the AI ​​perform analysis to send appropriate notifications.

[0065] The service provider can receive charge instructions from family members. The service provider can, for example, accept a charge amount specified by the family. The service provider can also specify the timing of the charge. For example, the service provider can perform the charge at a date and time specified by the family. The service provider can also specify the method of the charge. For example, the service provider can accept online charge instructions. This allows family members to instruct the device to charge. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input charge instructions from family members into AI and have the AI ​​perform an analysis to suggest an appropriate charging method.

[0066] The service provider can estimate the user's emotions and adjust the timing of device delivery based on the estimated emotions. For example, if the user is feeling anxious, the service provider can quickly deliver the device to provide reassurance. If the user is relaxed, the service provider can deliver the device slowly and provide detailed explanations. If the user is in a hurry, the service provider can deliver the device quickly and provide only the bare minimum of explanations. This allows the service provider to adjust the timing of device delivery according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0067] The service provider can analyze a user's past purchase history and select the optimal method for providing a device. For example, the service provider can provide a device based on the assumption that the user will use it at stores they have frequently visited in the past. If the service provider's past purchase history indicates that the user frequently uses a device during a specific time period, they can provide a device tailored to that time period. If the service provider analyzes a user's past purchase history and finds that they tend to purchase products in a particular category, they can provide a device specialized for that category. This allows the service provider to provide the optimal device based on the user's past purchase history. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's past purchase history data into a generating AI and have the generating AI perform an analysis to select the optimal method for providing a device.

[0068] The service provider can filter devices based on the user's current lifestyle and areas of interest when providing them. For example, if a user is interested in health, the service provider can provide a device with health-related benefits. If a user travels frequently, the service provider can provide a device that can be used at their travel destination. If a user has a specific hobby, the service provider can provide a device with benefits related to that hobby. This allows the service provider to provide devices that are tailored to the user's lifestyle and areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0069] The service provider can estimate the user's emotions and determine the priority of devices to offer based on the estimated emotions. For example, if the user is feeling anxious, the service provider can prioritize offering the most reliable device to provide a sense of security. If the user is relaxed, the service provider can prioritize offering devices tailored to the user's interests. If the user is in a hurry, the service provider can prioritize offering devices that can be delivered quickly. This allows for the prioritization of devices according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0070] The service provider can prioritize providing highly relevant devices by considering the user's geographical location when providing devices. For example, if a user lives in a specific region, the service provider can provide a device with benefits usable in that region. If a user is traveling, the service provider can provide a device usable at their travel destination. If a user frequently uses a specific store, the service provider can provide a device usable at that store. This allows the service provider to provide highly relevant devices based on the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI perform analysis to select highly relevant devices.

[0071] The service provider can analyze a user's social media activity when providing a device and provide a relevant device. For example, if a user mentions a particular brand on social media, the service provider can provide a device with benefits from that brand. If a user participates in a particular event on social media, the service provider can provide a device that can be used at that event. If a user mentions a particular hobby on social media, the service provider can provide a device with benefits related to that hobby. This allows the service provider to provide relevant devices based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user social media activity data into a generating AI and have the generating AI perform analysis to select a relevant device.

[0072] The monitoring unit can estimate the user's emotions and adjust monitoring criteria based on the estimated emotions. For example, if the user is feeling anxious, the monitoring unit can increase the monitoring frequency to provide reassurance. If the user is relaxed, the monitoring unit can reduce the monitoring frequency to respect privacy. If the user is in a hurry, the monitoring unit can provide monitoring results quickly. This allows the monitoring criteria to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The monitoring unit can improve the accuracy of its monitoring by considering the interrelationships of purchase patterns during monitoring. For example, if a user frequently makes purchases at a particular store, the monitoring unit can focus on monitoring the purchase patterns at that store. If a user tends to make purchases during a particular time period, the monitoring unit can focus on monitoring the purchase patterns during that time period. If a user tends to purchase products in a particular category, the monitoring unit can focus on monitoring products in that category. This improves the accuracy of monitoring by considering the interrelationships of purchase patterns. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input purchase pattern data into a generating AI and have the generating AI perform analysis to improve the accuracy of monitoring by considering the interrelationships.

[0074] The monitoring unit can perform monitoring while considering the buyer's attribute information. For example, the monitoring unit can focus on monitoring purchase patterns related to specific attributes based on the user's age and gender. The monitoring unit can focus on monitoring purchase patterns in a user's residential area based on their location. The monitoring unit can focus on monitoring purchase patterns related to specific attributes based on the user's occupation and lifestyle. This improves the accuracy of monitoring by performing monitoring based on the buyer's attribute information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input buyer attribute information data into a generating AI and have the generating AI perform analysis to improve the accuracy of monitoring.

[0075] The monitoring unit can estimate the user's emotions and adjust the order in which monitoring results are displayed based on the estimated emotions. For example, if the user is feeling anxious, the monitoring unit can prioritize displaying important monitoring results. If the user is relaxed, the monitoring unit can sequentially display detailed monitoring results. If the user is in a hurry, the monitoring unit can quickly display concise monitoring results. This allows the display order of monitoring results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The monitoring unit can perform monitoring while considering the geographical distribution of purchase patterns. For example, if a user frequently makes purchases in a particular area, the monitoring unit can focus on monitoring purchase patterns in that area. If a user is traveling, the monitoring unit can focus on monitoring purchase patterns at their travel destination. If a user frequently uses a particular store, the monitoring unit can focus on monitoring purchase patterns at that store. This improves the accuracy of monitoring by performing monitoring based on the geographical distribution of purchase patterns. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input geographical distribution data of purchase patterns into a generating AI and have the generating AI perform analysis to improve the accuracy of monitoring.

[0077] The monitoring unit can improve the accuracy of its monitoring by referring to relevant literature on purchase patterns during monitoring. For example, the monitoring unit can improve the accuracy of its monitoring by referring to the latest research on purchase patterns. The monitoring unit can improve the accuracy of its monitoring by referring to historical data on purchase patterns. The monitoring unit can improve the accuracy of its monitoring by referring to industry best practices on purchase patterns. This improves the accuracy of monitoring by referring to relevant literature on purchase patterns. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input relevant literature data on purchase patterns into a generating AI and have the generating AI perform analysis to improve the accuracy of monitoring.

[0078] The notification unit can estimate the user's emotions and adjust the way notifications are displayed based on the estimated emotions. For example, if the user is feeling anxious, the notification unit can provide a simple and highly visible display method. If the user is relaxed, the notification unit can provide a display method that includes detailed information. If the user is in a hurry, the notification unit can provide a display method that gets straight to the point. This allows the notification display method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The notification unit can optimize the current notification content by referring to past notification data when a notification is sent. For example, the notification unit can provide the most suitable notification content based on the notifications the user has received in the past. The notification unit can provide the most suitable notification content for a specific time period based on the user's past notification history. The notification unit can analyze the user's past notification history and provide the most effective notification content. This allows the current notification content to be optimized by referring to past notification data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past notification data into a generating AI and have the generating AI perform an analysis to optimize the current notification content.

[0080] The notification unit can apply different notification methods depending on the purchase pattern category when issuing notifications. For example, the notification unit can provide a simple and quick notification method for food purchases. For clothing purchases, it can provide a notification method that includes detailed information. For electronic device purchases, it can provide a visually easy-to-understand notification method. By applying different notification methods for each purchase pattern category, the accuracy of notifications is improved. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input purchase pattern category data into a generating AI and have the generating AI perform an analysis to select the optimal notification method.

[0081] The notification unit can estimate the user's emotions and adjust the importance of notifications based on the estimated emotions. For example, if the user is feeling anxious, the notification unit can prioritize displaying important notifications. If the user is relaxed, the notification unit can display detailed notifications sequentially. If the user is in a hurry, the notification unit can quickly display concise and important notifications. This allows the importance of notifications to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The notification unit can determine the priority of notifications based on the timing of purchase pattern submissions. For example, if a user tends to make purchases during a specific time period, the notification unit can prioritize displaying notifications that are most suitable for that time period. If a user tends to make purchases on a specific day of the week, the notification unit can prioritize displaying notifications that are most suitable for that day of the week. If a user makes a purchase related to a specific event, the notification unit can prioritize displaying notifications that are most suitable for that event. This allows the notification unit to determine the priority of notifications based on the timing of purchase pattern submissions. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input purchase pattern submission timing data into a generating AI and have the generating AI perform analysis to determine the priority of notifications.

[0083] The notification unit can optimize notification content by referring to relevant market data on purchase patterns at the time of notification. For example, the notification unit can refer to the latest market data on purchase patterns to provide optimal notification content. The notification unit can refer to historical market data on purchase patterns to provide optimal notification content. The notification unit can refer to industry best practices on purchase patterns to provide optimal notification content. This allows for optimization of notification content by referring to relevant market data on purchase patterns. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input relevant market data on purchase patterns into a generating AI and have the generating AI perform analysis to optimize the notification content.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The service provider can monitor the user's health status and adjust the device delivery method accordingly. For example, based on the results of a health checkup, the service provider can deliver the device using the standard method if the user's health is good, and quickly deliver the device if the user's health has deteriorated. Furthermore, if the service provider has set specific health goals, they can provide a device tailored to those goals. For example, if a user is aiming to lose weight, they can be provided with a device that includes a calorie management function. This enables the provision of the optimal device for each user's health condition.

[0086] The monitoring unit can estimate the user's emotions and adjust the intensity of monitoring based on those estimates. For example, if the user is stressed, the monitoring unit can reduce the frequency of monitoring, respecting the user's privacy. If the user is at ease, the monitoring unit can perform normal monitoring and only notify the user if an anomaly occurs. Furthermore, if the user is in a hurry, the monitoring unit can quickly provide monitoring results, allowing the user to obtain the necessary information immediately. This enables flexible monitoring that responds to the user's emotions.

[0087] The notification unit can analyze a user's past notification history to determine the optimal notification timing. For example, it can analyze the time periods when a user has previously received notifications and send notifications at those times. It can also avoid sending notifications during times when a user has previously ignored them. Furthermore, if a user prefers to receive notifications related to a specific event, the notification unit can time notifications to coincide with that event. This allows the system to provide optimal notification timing based on the user's past notification history.

[0088] The service provider can estimate the user's emotions and customize the device delivery based on those emotions. For example, if the user is feeling anxious, the service provider can provide detailed explanations about how to use the device and its convenience to reassure them. If the user is relaxed, the service provider can deliver the device smoothly and provide only the necessary explanations. Furthermore, if the user is in a hurry, the service provider can deliver the device quickly and provide detailed explanations later. This makes it possible to provide the optimal device delivery tailored to the user's emotions.

[0089] The monitoring unit can analyze users' purchase history and set criteria for detecting abnormal purchase patterns. For example, the monitoring unit can determine if a user purchases an expensive item that they do not normally buy as abnormal. Furthermore, if a user frequently makes purchases during a specific time period, the monitoring unit can focus on monitoring purchase patterns during that time. Additionally, if a user tends to purchase items in a particular category, the monitoring unit can focus on monitoring items in that category. This enables the detection of abnormal purchase patterns based on the user's purchase history.

[0090] The notification unit can estimate the user's emotions and adjust the content of notifications based on those estimates. For example, if the user is feeling anxious, the notification unit can provide a simple, highly visible notification so that the user can quickly obtain the necessary information. If the user is relaxed, the notification unit can provide a notification with detailed information so that the user can obtain sufficient information. Furthermore, if the user is in a hurry, the notification unit can provide a concise notification that gets straight to the point, so that the user can respond quickly. This allows for the provision of optimal notification content tailored to the user's emotions.

[0091] The service provider can select the optimal device delivery method by considering the user's geographical location. For example, if a user lives in a specific region, the service provider can provide a device with benefits usable in that region. If a user is traveling, the service provider can provide a device usable at their travel destination. Furthermore, if a user frequently uses a particular store, the service provider can provide a device usable at that store. This enables the provision of the most suitable device based on the user's geographical location.

[0092] The monitoring unit can estimate the user's emotions and adjust how it displays monitoring results based on those emotions. For example, if the user is feeling anxious, the unit can prioritize displaying important monitoring results to reassure the user. If the user is relaxed, the unit can sequentially display detailed monitoring results to ensure the user receives sufficient information. Furthermore, if the user is in a hurry, the unit can quickly display concise monitoring results to ensure the user immediately obtains the necessary information. This enables the display of optimal monitoring results tailored to the user's emotions.

[0093] The notification unit can analyze a user's social media activity and provide relevant notifications. For example, if a user mentions a specific brand on social media, the notification unit can send notifications about that brand's offers. It can also send notifications related to events if a user participates in a particular event on social media. Furthermore, if a user mentions a specific hobby on social media, it can send notifications related to that hobby. This allows the system to provide optimal notifications based on the user's social media activity.

[0094] The service provider can estimate the user's emotions and prioritize the devices to provide based on those emotions. For example, if the user is feeling anxious, the service provider can prioritize providing the most reliable device to alleviate their anxiety. If the user is relaxed, the service provider can prioritize providing devices that match their interests and concerns. Furthermore, if the user is in a hurry, the service provider can prioritize providing devices that can be delivered quickly. This enables the provision of the most suitable device in response to the user's emotions.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The provider will provide a keychain-type touch payment device. The provider will provide a device that can be attached to keys so that seniors do not have to carry wallets or smartphones. The provider will have a prepaid charging function to prevent overspending. For example, the provider can receive charging instructions from family members. Step 2: The monitoring unit monitors the purchase patterns of devices provided by the supply unit. The monitoring unit monitors the purchase amount and number of purchases, and can monitor the total amount within a certain period and the amount of a single purchase in detail. Step 3: The notification unit notifies the family based on the purchase patterns monitored by the monitoring unit. The notification unit allows the family to manage device usage by providing notifications that enable them to restrict, suspend, or allow device usage. For example, the notification unit can notify about limiting usage time or the number of uses.

[0097] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0098] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0099] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0100] Each of the multiple elements described above, including the providing unit, monitoring unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the providing unit is implemented by the control unit 46A of the smart device 14 and provides a keychain-type touch payment device. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors the purchase pattern of the device provided by the providing unit. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and notifies the family based on the purchase pattern monitored by the monitoring unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] As shown in Figure 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.

[0103] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0107] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0108] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0109] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0110] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0111] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0113] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0115] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the providing unit, monitoring unit, and notification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the providing unit is implemented by the control unit 46A of the smart glasses 214 and provides a keychain-type touch payment device. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and monitors the purchase pattern of the device provided by the providing unit. The notification unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and notifies the family based on the purchase pattern monitored by the monitoring unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the providing unit, monitoring unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the providing unit is implemented by the control unit 46A of the headset terminal 314 and provides a keychain-type touch payment device. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors the purchase pattern of the device provided by the providing unit. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and notifies the family based on the purchase pattern monitored by the monitoring unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] As shown in Figure 7, the 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.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0141] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the providing unit, monitoring unit, and notification unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the providing unit is implemented by the control unit 46A of the robot 414 and provides a keychain-type touch payment device. The monitoring unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and monitors the purchase pattern of the device provided by the providing unit. The notification unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and notifies the family based on the purchase pattern monitored by the monitoring unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0152] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0153] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0154] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0158] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0159] 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.

[0160] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0161] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0162] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0163] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0165] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0166] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0167] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0168] (Note 1) A supply unit that provides a keychain-type touch payment device, A monitoring unit that monitors the purchase patterns of devices provided by the aforementioned supply unit, The system includes a notification unit that notifies family members based on purchase patterns monitored by the aforementioned monitoring unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, It features a prepaid charging function. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned monitoring unit, Monitor purchase amount and number of purchases The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, Send notifications to family members so they can restrict, suspend, or allow device usage. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Accepts charging instructions from family members. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, It estimates the user's emotions and adjusts the timing of device delivery based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Analyze the user's past purchase history to select the optimal method for providing devices. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, When providing devices, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the devices to offer based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned supply unit is, When providing devices, we prioritize providing the most relevant devices by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned supply unit is, When providing devices, we analyze the user's social media activity and provide relevant devices. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned monitoring unit, We estimate user sentiment and adjust monitoring criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned monitoring unit, During monitoring, we improve the accuracy of monitoring by considering the interrelationships of purchase patterns. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned monitoring unit, During monitoring, the monitor's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned monitoring unit, It estimates the user's sentiment and adjusts the order in which monitoring results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned monitoring unit, During monitoring, the geographical distribution of purchase patterns is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned monitoring unit, During monitoring, we improve the accuracy of monitoring by referring to relevant literature on purchase patterns. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned notification unit, It estimates the user's emotions and adjusts how notifications are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, When sending a notification, the system optimizes the current notification content by referencing past notification data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, When sending notifications, different notification methods will be applied depending on the purchase pattern category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, It estimates the user's emotions and adjusts the importance of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, When sending notifications, we prioritize them based on when the purchase pattern was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, When sending notifications, the notification content is optimized by referring to relevant market data on purchase patterns. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A supply unit that provides a keychain-type touch payment device, A monitoring unit that monitors the purchase patterns of devices provided by the aforementioned supply unit, The system includes a notification unit that notifies family members based on purchase patterns monitored by the aforementioned monitoring unit. A system characterized by the following features.

2. The aforementioned supply unit is, It features a prepaid charging function. The system according to feature 1.

3. The aforementioned monitoring unit, Monitor purchase amount and number of purchases The system according to feature 1.

4. The aforementioned notification unit, Send notifications to family members so they can restrict, suspend, or allow device usage. The system according to feature 1.

5. The aforementioned supply unit is, Accepts charging instructions from family members. The system according to feature 1.

6. The aforementioned supply unit is, It estimates the user's emotions and adjusts the timing of device delivery based on the estimated user emotions. The system according to feature 1.

7. The aforementioned supply unit is, Analyze the user's past purchase history to select the optimal method for providing devices. The system according to feature 1.

8. The aforementioned supply unit is, When providing devices, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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