system

Abstract

The system according to this embodiment aims to efficiently manage multiple point services and utilize them at the optimal timing. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a payment unit. The collection unit collects information on point services held by the user. The analysis unit analyzes the expiration date and usable stores for each point based on the information collected by the collection unit. The generation unit selects the optimal points for payment based on the analysis results obtained by the analysis unit and generates a QR code. The payment unit makes a payment using the QR code generated by the generation unit.

Description

Technical Field

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[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 a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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, there is a problem that it is difficult to efficiently manage a plurality of point services and use them at an optimal timing.

[0005] The system according to the embodiment aims to efficiently manage a plurality of point services and use them at an optimal timing.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a payment unit. The collection unit collects information on point services held by the user. The analysis unit analyzes the expiration date and usable stores for each point based on the information collected by the collection unit. The generation unit selects the optimal points for payment based on the analysis results obtained by the analysis unit and generates a QR code (registered trademark), which is a two-dimensional code. The payment unit makes a payment using the QR code generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage multiple point services and allow them to be used at the optimal timing. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 QR code payment system according to an embodiment of the present invention is a system that manages multiple point services across different services, enabling users to use points at the optimal time. This system collects information on each point service held by the user, analyzes the expiration date and usable stores of each point based on the collected information, automatically selects the optimal points at the time of payment, and combines multiple points as needed for payment. It generates a QR code, which is a two-dimensional code, and uses it for payment at stores. For example, it collects information on each point service held by the user. At this time, it collects detailed information such as which point service the user is using, the balance and expiration date of each point. This allows for centralized management of all point information held by the user. Next, based on the collected information, it analyzes the expiration date and usable stores of each point. For example, it sorts the points in order of their expiration date approaching, or identifies points that can be used at specific stores. This analysis is performed using a generation AI. The generation AI proposes the optimal way to use points based on data such as the user's location information and past purchase history. Based on the analysis results, it automatically selects the optimal points at the time of payment so that the user can use points in the most advantageous way. For example, when a user makes a 2000 yen purchase, points with the nearest expiration date are used first, and multiple points can be combined for payment as needed. In this case, a QR code is generated and used for payment at the store. The QR code contains the selected point information, and when scanned at the store's register, the points are automatically applied. This system allows users to use their points at the optimal time without having to worry about their expiration dates. Furthermore, by centrally managing multiple points, the efficiency of point usage is improved, and the amount of points that expire is reduced. For example, if a user uses multiple point services, they are saved the trouble of individually checking the expiration date and usable stores for each point. In addition, the generating AI suggests the optimal way to use points, eliminating the need for users to think about how to use their points themselves.Thus, the present invention provides a QR code payment service that manages multiple point services across platforms, enabling users to use their points at the optimal time. This allows users to use their points at the optimal time without worrying about their expiration date, improving the efficiency of point usage. As a result, the QR code payment system can utilize the points held by users at the optimal time.

[0029] The QR code payment system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a payment unit. The collection unit collects information on point services held by the user. For example, the collection unit collects detailed information such as the balance and expiration date of each point service held by the user. The collection unit can centrally manage all point information held by the user. The analysis unit analyzes the expiration date and usable stores of each point based on the information collected by the collection unit. For example, the analysis unit sorts the points in order of their expiration date and identifies points usable at specific stores. The analysis unit uses a generation AI to propose the optimal way to use points based on data such as the user's location information and past purchase history. The generation unit selects the optimal points for payment based on the analysis results obtained by the analysis unit and generates a QR code. For example, if a user makes a payment of 2000 yen, the generation unit generates a QR code that prioritizes the use of points with the nearest expiration date and combines multiple points as needed for payment. The generation unit includes the selected point information in the QR code, and points are automatically applied when read at the store's register. The payment unit makes payments using the QR code generated by the generation unit. The payment unit automatically applies points by, for example, scanning the QR code at the store's cash register. The payment unit allows users to use points at the optimal time without having to worry about their expiration date. As a result, the QR code payment system according to the embodiment allows users to use the points they have at the optimal time. Some or all of the above-described processes in the collection unit, analysis unit, generation unit, and payment unit may be performed using AI, for example, or without AI. For example, the collection unit can input information about the point service held by the user into the AI ​​and have the AI ​​perform the information collection. The analysis unit can input the information collected by the collection unit into the AI ​​and have the AI ​​perform the analysis. The generation unit can input the analysis results obtained by the analysis unit into the AI ​​and have the AI ​​perform the QR code generation. The payment unit can input the QR code generated by the generation unit into the AI ​​and have the AI ​​perform the payment.

[0030] The data collection unit collects information on point services held by users. Specifically, it collects detailed information such as balances, expiration dates, and usage history for various point services used by the user. This allows the data collection unit to centrally manage all point information held by the user. Furthermore, with the user's consent, the data collection unit securely stores point service account information and has a function to periodically retrieve the latest information. For example, if a user registers for a new point service or if their point balance changes, the data collection unit automatically updates the information. In addition, even if a user uses multiple point services, the data collection unit integrates information from each service, allowing the user to understand their overall point status at a glance. This allows users to efficiently manage their points without worrying about expiration dates or balances. Furthermore, as a security measure, the data collection unit encrypts data and controls access to protect users' personal information. This allows the data collection unit to collect and provide point information to users safely and efficiently.

[0031] The analysis unit analyzes the expiration date and usable stores for each point based on the information collected by the data collection unit. Specifically, it sorts points by their expiration date and identifies points usable at specific stores. Using a generation AI, the analysis unit proposes the optimal way to use points based on data such as the user's location information and past purchase history. For example, it calculates the most effective way to use points based on the stores the user frequently visits and the products they purchase. The generation AI learns the user's behavior patterns and preferences, enabling it to provide individually customized suggestions. Furthermore, the analysis unit also has a function to send notifications to users when points are about to expire or when a specific campaign is running. This allows users to make the most of their points without wasting them. The analysis unit also performs trend analysis and predictions based on past data to support the optimization of future point usage. For example, it analyzes point usage trends during specific seasons or events and provides appropriate advice to users. In this way, the analysis unit can support users in effectively using points and maximize their value.

[0032] The generation unit selects the optimal points for payment based on the analysis results obtained by the analysis unit and generates a QR code. Specifically, if a user makes a payment of 2000 yen, the generation unit generates a QR code that prioritizes the use of points with the nearest expiration date and combines multiple points as needed for payment. The generation unit includes the selected point information in the QR code, and points are automatically applied when scanned at the store's register. Furthermore, if the user is eligible for a specific campaign or discount, the generation unit can also include that information in the QR code. This allows the user to apply multiple points and discounts with a single scan, resulting in a smooth payment process. The generation unit also speeds up the QR code generation process, enabling users to complete payments quickly without waiting. In this way, the generation unit can provide users with a convenient payment experience by allowing them to use points efficiently.

[0033] The payment unit uses QR codes generated by the generation unit to process payments. Specifically, points are automatically applied when the QR code is scanned at the store's register. The payment unit allows users to use points at the optimal time without having to worry about their expiration date. For example, when a user scans a QR code at the register, the system automatically checks the point balance and expiration date and applies the most appropriate points. This allows users to use points without any hassle. Furthermore, the payment unit also has a function to record payment history, which users can review later. This allows users to easily check past payment history and understand how they are using their points. In addition, the payment unit supports multiple payment methods, allowing users to pay using not only points but also credit cards, debit cards, and electronic money in combination. This allows users to flexibly choose their payment method and complete the payment in the most optimal way. Furthermore, the payment unit is equipped with security measures such as encryption of payment information and fraud detection functions to ensure user safety. This allows users to use points with peace of mind and make payments smoothly.

[0034] The collection unit can collect detailed information such as the balance and expiration date of each point service held by the user. The collection unit can centrally manage all point information held by the user. This allows for the collection of detailed information about the points held by the user. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input information about the point services held by the user into AI and have AI perform the information collection.

[0035] The analysis unit can sort points by their expiration date, or identify points usable at specific stores. For example, the analysis unit can sort points by their expiration date, or identify points usable at specific stores. Using a generation AI, the analysis unit proposes the optimal way to use points based on data such as the user's location information and past purchase history. This allows for analysis of point expiration dates and usable stores. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or without one. For example, the analysis unit can input information collected by the collection unit into the generation AI and have the generation AI perform the analysis.

[0036] The generation unit can generate a QR code that, when a user makes a 2000 yen purchase, prioritizes the use of points with the nearest expiration date and combines multiple points as needed for payment. For example, when a user makes a 2000 yen purchase, the generation unit generates a QR code that prioritizes the use of points with the nearest expiration date and combines multiple points as needed for payment. The generation unit includes the selected point information in the QR code, and when scanned at the store's register, the points are automatically applied. This allows the user to make a payment using the most appropriate points. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the analysis results obtained by the analysis unit into the generation AI and have the generation AI execute the generation of the QR code.

[0037] The payment unit can make payments at stores using the generated QR code. For example, the payment unit can automatically apply points by scanning the QR code at the store's register. The payment unit allows users to use points at the optimal time without having to worry about their expiration date. This enables payments at stores using QR codes. Some or all of the above processing in the payment unit may be performed using AI, for example, or not using AI. For example, the payment unit can input the QR code generated by the generation unit into the AI ​​and have the AI ​​execute the payment.

[0038] The collection unit can analyze the user's past point usage history and select the optimal collection method. For example, the collection unit can prioritize collecting points from services that the user has frequently used in the past. It can also prioritize collecting points that are nearing their expiration date. Furthermore, the collection unit can customize the collection method based on the frequency of point usage by the user in the past. This allows the collection unit to select the optimal collection method based on the user's past point usage history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past point usage history into AI and have the AI ​​select the optimal collection method.

[0039] The data collection unit can filter point information based on the user's current purchasing behavior and areas of interest. For example, the data collection unit can prioritize collecting point information related to products the user is currently purchasing. The data collection unit can also filter and collect relevant point information based on the user's areas of interest. Furthermore, the data collection unit can analyze the user's purchase history and collect highly relevant point information. This allows for filtering point information based on the user's current purchasing behavior and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current purchasing behavior and areas of interest into the AI ​​and have the AI ​​perform the filtering.

[0040] The collection unit can prioritize the collection of highly relevant point information by considering the user's geographical location when collecting point information. For example, the collection unit can prioritize the collection of point information available at stores close to the user's current location. The collection unit can also filter and collect highly relevant point information based on the user's geographical location. Furthermore, the collection unit can prioritize the collection of point information related to places the user frequently visits. This allows for the priority collection of highly relevant point information based on the user's geographical location. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location information into AI and have the AI ​​perform the collection of highly relevant point information.

[0041] The collection unit can analyze the user's social media activity and collect relevant point information when collecting point information. For example, the collection unit can collect point information related to products or services mentioned by the user on social media. The collection unit can also collect point information related to areas of interest from the user's social media activity. Furthermore, the collection unit can collect point information related to brands or stores that the user follows. This allows for the collection of relevant point information based on the user's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data on the user's social media activity into AI and have AI perform the collection of relevant point information.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the points during the analysis. For example, the analysis unit can perform a detailed analysis for points of high importance. It can also perform a simplified analysis for points of low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the points. This allows the level of detail of the analysis to be adjusted according to the importance of the points. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input point importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the point category during analysis. For example, the analysis unit can apply an analysis algorithm based on purchase history to shopping points. It can also apply an analysis algorithm based on visit frequency to restaurant points. Furthermore, it can apply an analysis algorithm based on travel history to travel points. This allows different analysis algorithms to be applied depending on the point category. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input point category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the expiration dates of the points during the analysis process. For example, the analysis unit may prioritize the analysis of points with approaching expiration dates. It can also postpone the analysis of points with far-off expiration dates. Furthermore, the analysis unit can adjust the priority of analysis in stages according to the expiration dates of the points. This allows the analysis priority to be determined based on the expiration dates of the points. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input point expiration date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of points during the analysis. For example, the analysis unit may prioritize the analysis of points related to the user's current purchasing behavior. It can also prioritize the analysis of points related to the user's areas of interest. Furthermore, the analysis unit can adjust the order of analysis step by step according to the relevance of points. This allows the order of analysis to be adjusted based on the relevance of points. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input point relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The generation unit can adjust the level of detail of the generated QR code based on the expiration date of the points. For example, the generation unit can prioritize generating QR codes that include points with approaching expiration dates. It can also postpone the generation of points with far-off expiration dates. Furthermore, the generation unit can adjust the level of detail of the QR code in stages according to the expiration date of the points. This allows the level of detail of the generated QR code to be adjusted based on the expiration date of the points. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input point expiration date data into the generation AI and have the generation AI perform the adjustment of the level of detail of the generated QR code.

[0047] The generation unit can apply different generation algorithms depending on the point category when generating QR codes. For example, the generation unit can apply a generation algorithm based on purchase history to shopping points. It can also apply a generation algorithm based on visit frequency to restaurant points. Furthermore, it can apply a generation algorithm based on travel history to travel points. This allows different generation algorithms to be applied depending on the point category. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input point category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0048] The generation unit can determine the generation priority based on the point expiration date when generating QR codes. For example, the generation unit can prioritize generating QR codes that include points with approaching expiration dates. It can also postpone the generation of points with far-off expiration dates. Furthermore, the generation unit can adjust the QR code generation priority in stages according to the point expiration dates. This allows the generation priority of QR codes to be determined based on the point expiration dates. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input point expiration date data into a generation AI and have the generation AI determine the QR code generation priority.

[0049] The generation unit can adjust the generation order of QR codes based on the relevance of points during generation. For example, the generation unit can generate QR codes that prioritize points related to the user's current purchasing behavior. It can also generate QR codes that prioritize points related to the user's areas of interest. Furthermore, the generation unit can adjust the generation order of QR codes in stages according to the relevance of points. This allows the generation order of QR codes to be adjusted based on the relevance of points. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input point relevance data into a generation AI and have the generation AI perform the adjustment of the QR code generation order.

[0050] The payment unit can analyze the user's past payment history to select the optimal payment method at the time of payment. For example, the payment unit may prioritize suggesting payment methods that the user has frequently used in the past. The payment unit can also select the most efficient payment method based on the user's past payment history. Furthermore, the payment unit can analyze the user's past payment history and customize the optimal payment method. This allows the payment unit to select the optimal payment method based on the user's past payment history. Some or all of the above processes in the payment unit may be performed using AI, for example, or not. For example, the payment unit can input the user's past payment history into AI and have the AI ​​select the optimal payment method.

[0051] The payment unit can customize payment methods based on the user's current purchasing behavior at the time of payment. For example, the payment unit can suggest payment methods related to the products the user is currently purchasing. The payment unit can also customize the optimal payment method based on the user's purchasing behavior. Furthermore, the payment unit can analyze the user's purchase history and suggest highly relevant payment methods. This allows for the customization of payment methods based on the user's current purchasing behavior. Some or all of the above processes in the payment unit may be performed using AI, for example, or not using AI. For example, the payment unit can input the user's current purchasing behavior data into AI and have the AI ​​perform the customization of payment methods.

[0052] The payment unit can select the optimal payment method at the time of payment, taking into account the user's geographical location. For example, the payment unit may suggest payment methods related to the user's current location. The payment unit can also select the optimal payment method based on the user's geographical location. Furthermore, the payment unit may suggest payment methods related to places the user frequently visits. This allows for the selection of the optimal payment method based on the user's geographical location. Some or all of the above processing in the payment unit may be performed using AI, for example, or without AI. For example, the payment unit can input the user's geographical location information into AI and have the AI ​​select the optimal payment method.

[0053] The payment unit can analyze the user's social media activity and suggest payment methods at the time of payment. For example, the payment unit can suggest payment methods related to products or services mentioned by the user on social media. It can also suggest payment methods related to areas of interest based on the user's social media activity. Furthermore, it can suggest payment methods related to brands or stores that the user follows. This allows for the suggestion of payment methods based on the user's social media activity. Some or all of the above processing in the payment unit may be performed using AI, for example, or not. For example, the payment unit can input data on the user's social media activity into an AI and have the AI ​​suggest payment methods.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] QR code payment systems can also include a learning unit that learns users' purchasing patterns. The learning unit analyzes users' past purchase history and point usage history to identify their purchasing patterns. For example, if a user frequently shops at a specific store on a specific day or time, the learning unit learns this pattern and suggests the optimal way to use points for the next purchase. The learning unit can also learn if a user tends to use specific points during a particular season or event, and encourage point usage at the appropriate time. Furthermore, the learning unit can predict future purchasing behavior based on the user's purchasing patterns and optimize point usage based on these predictions. This allows for the optimization of point usage based on the user's purchasing patterns.

[0056] The QR code payment system may also include a health monitoring unit that monitors the user's health status. The health monitoring unit collects user health data and optimizes point usage based on the user's health condition. For example, if the user is in good health, it may suggest prioritizing the use of health-related points. If the user's health is deteriorating, it may suggest ways to use points to reduce stress. Furthermore, the health monitoring unit can suggest ways to use points to maintain health based on the user's health data. This allows for the optimization of point usage based on the user's health condition.

[0057] The QR code payment system can also include a social analytics unit that analyzes the user's social network and suggests point sharing with friends and family. The social analytics unit collects the user's social network data and identifies point services used by the user's friends and family. For example, if a user's friend uses a specific point service, they can share that service and use points together. Similarly, if a user's family uses a specific point service, they can share points among family members for more efficient use. Furthermore, the social analytics unit can suggest optimizing point usage with friends and family based on the user's social network data. This allows for optimized point usage based on the user's social network.

[0058] The QR code payment system can also include a location information analysis unit that uses the user's location information to suggest the optimal way to use points in real time. The location information analysis unit collects the user's current location information and identifies points that can be used at nearby stores. For example, if the user approaches a specific store, it will prioritize suggesting points that can be used at that store. It can also suggest points that can be used within a specific area if the user is in that area. Furthermore, the location information analysis unit can predict and suggest points that can be used at future destinations based on the user's movement patterns. This allows for the optimization of point usage based on the user's location information.

[0059] QR code payment systems can also incorporate features that analyze users' purchase history and offer bonus points when purchasing specific products. For example, they can identify products that users have frequently purchased in the past and offer bonus points when those products are purchased again. They can also offer additional points when users purchase products in specific categories. Furthermore, they can offer bonus points when users spend a certain amount or more at specific stores. This allows for the optimization of point allocation based on the user's purchase history.

[0060] A QR code payment system can also include a prediction unit that forecasts user purchasing behavior and proposes the optimal way to use points for future purchases. The prediction unit analyzes the user's past purchase history and point usage history to predict future purchasing behavior. For example, if a user tends to purchase certain products during certain seasons or events, the prediction unit can forecast this trend and propose point usage at the appropriate time. Furthermore, if a user frequently shops at a particular store, the prediction unit can also propose point usage methods at that store in advance. In addition, the prediction unit can forecast future purchasing behavior based on the user's purchasing patterns and optimize point usage based on these predictions. This allows for the optimization of point usage based on the user's purchasing behavior.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit collects information about the point services held by the user. Step 2: The analysis unit analyzes the expiration date and usable stores for each point based on the information collected by the collection unit. For example, it sorts the points by their expiration date and identifies points that can be used at specific stores. It also uses a generation AI to suggest the optimal way to use the points based on data such as the user's location information and past purchase history. Step 3: Based on the analysis results obtained by the analysis unit, the generation unit selects the optimal points for payment and generates a QR code. For example, if a user is making a payment of 2000 yen, the generation unit will generate a QR code that prioritizes the use of points with the nearest expiration date and combines multiple points as needed for payment. The QR code contains the selected point information, and the points are automatically applied when scanned at the store's register. Step 4: The payment unit uses the QR code generated by the generation unit to make the payment. For example, by scanning the QR code at the store's cash register, points are automatically applied. This allows users to use their points at the optimal time without having to worry about their expiration date.

[0063] (Example of form 2) The QR code payment system according to an embodiment of the present invention is a system that manages multiple point services across different services, enabling users to use points at the optimal time. This system collects information on each point service held by the user, analyzes the expiration date and usable stores of each point based on the collected information, automatically selects the most suitable points at the time of payment, and combines multiple points as needed for payment. A QR code is generated and used for payment at stores. For example, information on each point service held by the user is collected. At this time, detailed information such as which point services the user is using, the balance and expiration date of each point is collected. This allows for centralized management of all point information held by the user. Next, based on the collected information, the expiration date and usable stores of each point are analyzed. For example, points may be sorted in order of approaching expiration date, or points usable at specific stores may be identified. This analysis is performed using a generation AI. The generation AI proposes the optimal way to use points based on data such as the user's location information and past purchase history. Based on the analysis results, the system automatically selects the most suitable points at the time of payment so that the user can use points in the most advantageous way. For example, if a user makes a payment of 2000 yen, points with approaching expiration dates will be used first, and multiple points will be combined as needed for payment. In this process, a QR code is generated and used for payment at the store. The QR code contains the selected point information, and points are automatically applied when scanned at the store's register. This system allows users to use their points at the optimal time without worrying about their expiration date. Furthermore, by centrally managing multiple point programs, the efficiency of point usage is improved, and the amount of points that expire is reduced. For example, if a user uses multiple point services, they can avoid the hassle of individually checking the expiration date and usable stores for each point program. In addition, the generation AI suggests the optimal way to use points, eliminating the need for users to think about how to use their points themselves.Thus, the present invention provides a QR code payment service that manages multiple point services across platforms, enabling users to use their points at the optimal time. This allows users to use their points at the optimal time without worrying about their expiration date, improving the efficiency of point usage. As a result, the QR code payment system can utilize the points held by users at the optimal time.

[0064] The QR code payment system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a payment unit. The collection unit collects information on point services held by the user. For example, the collection unit collects detailed information such as the balance and expiration date of each point service held by the user. The collection unit can centrally manage all point information held by the user. The analysis unit analyzes the expiration date and usable stores of each point based on the information collected by the collection unit. For example, the analysis unit sorts the points in order of their expiration date and identifies points usable at specific stores. The analysis unit uses a generation AI to propose the optimal way to use points based on data such as the user's location information and past purchase history. The generation unit selects the optimal points for payment based on the analysis results obtained by the analysis unit and generates a QR code. For example, if a user makes a payment of 2000 yen, the generation unit generates a QR code that prioritizes the use of points with the nearest expiration date and combines multiple points as needed for payment. The generation unit includes the selected point information in the QR code, and points are automatically applied when read at the store's register. The payment unit makes payments using the QR code generated by the generation unit. The payment unit automatically applies points by, for example, scanning the QR code at the store's cash register. The payment unit allows users to use points at the optimal time without having to worry about their expiration date. As a result, the QR code payment system according to the embodiment allows users to use the points they have at the optimal time. Some or all of the above-described processes in the collection unit, analysis unit, generation unit, and payment unit may be performed using AI, for example, or without AI. For example, the collection unit can input information about the point service held by the user into the AI ​​and have the AI ​​perform the information collection. The analysis unit can input the information collected by the collection unit into the AI ​​and have the AI ​​perform the analysis. The generation unit can input the analysis results obtained by the analysis unit into the AI ​​and have the AI ​​perform the QR code generation. The payment unit can input the QR code generated by the generation unit into the AI ​​and have the AI ​​perform the payment.

[0065] The data collection unit collects information on point services held by users. Specifically, it collects detailed information such as balances, expiration dates, and usage history for various point services used by the user. This allows the data collection unit to centrally manage all point information held by the user. Furthermore, with the user's consent, the data collection unit securely stores point service account information and has a function to periodically retrieve the latest information. For example, if a user registers for a new point service or if their point balance changes, the data collection unit automatically updates the information. In addition, even if a user uses multiple point services, the data collection unit integrates information from each service, allowing the user to understand their overall point status at a glance. This allows users to efficiently manage their points without worrying about expiration dates or balances. Furthermore, as a security measure, the data collection unit encrypts data and controls access to protect users' personal information. This allows the data collection unit to collect and provide point information to users safely and efficiently.

[0066] The analysis unit analyzes the expiration date and usable stores for each point based on the information collected by the data collection unit. Specifically, it sorts points by their expiration date and identifies points usable at specific stores. Using a generation AI, the analysis unit proposes the optimal way to use points based on data such as the user's location information and past purchase history. For example, it calculates the most effective way to use points based on the stores the user frequently visits and the products they purchase. The generation AI learns the user's behavior patterns and preferences, enabling it to provide individually customized suggestions. Furthermore, the analysis unit also has a function to send notifications to users when points are about to expire or when a specific campaign is running. This allows users to make the most of their points without wasting them. The analysis unit also performs trend analysis and predictions based on past data to support the optimization of future point usage. For example, it analyzes point usage trends during specific seasons or events and provides appropriate advice to users. In this way, the analysis unit can support users in effectively using points and maximize their value.

[0067] The generation unit selects the optimal points for payment based on the analysis results obtained by the analysis unit and generates a QR code. Specifically, if a user makes a payment of 2000 yen, the generation unit generates a QR code that prioritizes the use of points with the nearest expiration date and combines multiple points as needed for payment. The generation unit includes the selected point information in the QR code, and points are automatically applied when scanned at the store's register. Furthermore, if the user is eligible for a specific campaign or discount, the generation unit can also include that information in the QR code. This allows the user to apply multiple points and discounts with a single scan, resulting in a smooth payment process. The generation unit also speeds up the QR code generation process, enabling users to complete payments quickly without waiting. In this way, the generation unit can provide users with a convenient payment experience by allowing them to use points efficiently.

[0068] The payment unit uses QR codes generated by the generation unit to process payments. Specifically, points are automatically applied when the QR code is scanned at the store's register. The payment unit allows users to use points at the optimal time without having to worry about their expiration date. For example, when a user scans a QR code at the register, the system automatically checks the point balance and expiration date and applies the most appropriate points. This allows users to use points without any hassle. Furthermore, the payment unit also has a function to record payment history, which users can review later. This allows users to easily check past payment history and understand how they are using their points. In addition, the payment unit supports multiple payment methods, allowing users to pay using not only points but also credit cards, debit cards, and electronic money in combination. This allows users to flexibly choose their payment method and complete the payment in the most optimal way. Furthermore, the payment unit is equipped with security measures such as encryption of payment information and fraud detection functions to ensure user safety. This allows users to use points with peace of mind and make payments smoothly.

[0069] The collection unit can collect detailed information such as the balance and expiration date of each point service held by the user. The collection unit can centrally manage all point information held by the user. This allows for the collection of detailed information about the points held by the user. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input information about the point services held by the user into AI and have AI perform the information collection.

[0070] The analysis unit can sort points by their expiration date, or identify points usable at specific stores. For example, the analysis unit can sort points by their expiration date, or identify points usable at specific stores. Using a generation AI, the analysis unit proposes the optimal way to use points based on data such as the user's location information and past purchase history. This allows for analysis of point expiration dates and usable stores. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or without one. For example, the analysis unit can input information collected by the collection unit into the generation AI and have the generation AI perform the analysis.

[0071] The generation unit can generate a QR code that, when a user makes a 2000 yen purchase, prioritizes the use of points with the nearest expiration date and combines multiple points as needed for payment. For example, when a user makes a 2000 yen purchase, the generation unit generates a QR code that prioritizes the use of points with the nearest expiration date and combines multiple points as needed for payment. The generation unit includes the selected point information in the QR code, and when scanned at the store's register, the points are automatically applied. This allows the user to make a payment using the most appropriate points. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the analysis results obtained by the analysis unit into the generation AI and have the generation AI execute the generation of the QR code.

[0072] The payment unit can make payments at stores using the generated QR code. For example, the payment unit can automatically apply points by scanning the QR code at the store's register. The payment unit allows users to use points at the optimal time without having to worry about their expiration date. This enables payments at stores using QR codes. Some or all of the above processing in the payment unit may be performed using AI, for example, or not using AI. For example, the payment unit can input the QR code generated by the generation unit into the AI ​​and have the AI ​​execute the payment.

[0073] The data collection unit can estimate the user's emotions and adjust the timing of point information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay collection and collect the information when the user is relaxed. Alternatively, if the user is relaxed, the data collection unit can immediately collect point information and notify the user. If the user is in a hurry, the data collection unit can speed up collection to quickly obtain point information. This allows the timing of point information collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0074] The collection unit can analyze the user's past point usage history and select the optimal collection method. For example, the collection unit can prioritize collecting points from services that the user has frequently used in the past. It can also prioritize collecting points that are nearing their expiration date. Furthermore, the collection unit can customize the collection method based on the frequency of point usage by the user in the past. This allows the collection unit to select the optimal collection method based on the user's past point usage history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past point usage history into AI and have the AI ​​select the optimal collection method.

[0075] The data collection unit can filter point information based on the user's current purchasing behavior and areas of interest. For example, the data collection unit can prioritize collecting point information related to products the user is currently purchasing. The data collection unit can also filter and collect relevant point information based on the user's areas of interest. Furthermore, the data collection unit can analyze the user's purchase history and collect highly relevant point information. This allows for filtering point information based on the user's current purchasing behavior and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current purchasing behavior and areas of interest into the AI ​​and have the AI ​​perform the filtering.

[0076] The data collection unit can estimate the user's emotions and determine the priority of the point information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone collecting less important point information. If the user is relaxed, the data collection unit can collect all point information equally. If the user is in a hurry, the data collection unit can prioritize collecting more important point information. This allows the priority of the point information to be collected to be determined 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 data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0077] The collection unit can prioritize the collection of highly relevant point information by considering the user's geographical location when collecting point information. For example, the collection unit can prioritize the collection of point information available at stores close to the user's current location. The collection unit can also filter and collect highly relevant point information based on the user's geographical location. Furthermore, the collection unit can prioritize the collection of point information related to places the user frequently visits. This allows for the priority collection of highly relevant point information based on the user's geographical location. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location information into AI and have the AI ​​perform the collection of highly relevant point information.

[0078] The collection unit can analyze the user's social media activity and collect relevant point information when collecting point information. For example, the collection unit can collect point information related to products or services mentioned by the user on social media. The collection unit can also collect point information related to areas of interest from the user's social media activity. Furthermore, the collection unit can collect point information related to brands or stores that the user follows. This allows for the collection of relevant point information based on the user's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data on the user's social media activity into AI and have AI perform the collection of relevant point information.

[0079] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. If the user is stressed, the analysis unit can provide visually easy-to-understand analysis results. This allows the presentation of the analysis 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-described processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the points during the analysis. For example, the analysis unit can perform a detailed analysis for points of high importance. It can also perform a simplified analysis for points of low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the points. This allows the level of detail of the analysis to be adjusted according to the importance of the points. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input point importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the point category during analysis. For example, the analysis unit can apply an analysis algorithm based on purchase history to shopping points. It can also apply an analysis algorithm based on visit frequency to restaurant points. Furthermore, it can apply an analysis algorithm based on travel history to travel points. This allows different analysis algorithms to be applied depending on the point category. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input point category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is stressed, the analysis unit can also provide a visually easy-to-understand analysis result. This allows the length of the analysis 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0083] The analysis unit can determine the priority of analysis based on the expiration dates of the points during the analysis process. For example, the analysis unit may prioritize the analysis of points with approaching expiration dates. It can also postpone the analysis of points with far-off expiration dates. Furthermore, the analysis unit can adjust the priority of analysis in stages according to the expiration dates of the points. This allows the analysis priority to be determined based on the expiration dates of the points. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input point expiration date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0084] The analysis unit can adjust the order of analysis based on the relevance of points during the analysis. For example, the analysis unit may prioritize the analysis of points related to the user's current purchasing behavior. It can also prioritize the analysis of points related to the user's areas of interest. Furthermore, the analysis unit can adjust the order of analysis step by step according to the relevance of points. This allows the order of analysis to be adjusted based on the relevance of points. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input point relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0085] The generation unit can estimate the user's emotions and adjust the QR code generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a QR code containing detailed information. If the user is in a hurry, the generation unit can also generate a QR code containing concise information. Furthermore, if the user is stressed, the generation unit can generate a visually easy-to-understand QR code. This allows the QR code generation method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the QR code generation method.

[0086] The generation unit can adjust the level of detail of the generated QR code based on the expiration date of the points. For example, the generation unit can prioritize generating QR codes that include points with approaching expiration dates. It can also postpone the generation of points with far-off expiration dates. Furthermore, the generation unit can adjust the level of detail of the QR code in stages according to the expiration date of the points. This allows the level of detail of the generated QR code to be adjusted based on the expiration date of the points. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input point expiration date data into the generation AI and have the generation AI perform the adjustment of the level of detail of the generated QR code.

[0087] The generation unit can apply different generation algorithms depending on the point category when generating QR codes. For example, the generation unit can apply a generation algorithm based on purchase history to shopping points. It can also apply a generation algorithm based on visit frequency to restaurant points. Furthermore, it can apply a generation algorithm based on travel history to travel points. This allows different generation algorithms to be applied depending on the point category. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input point category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0088] The generation unit can estimate the user's emotions and adjust the length of the QR code based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise QR code. If the user is relaxed, the generation unit can generate a longer QR code containing more detailed information. If the user is stressed, the generation unit can generate a visually clear QR code. This allows the length of the QR code to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the length of the QR code.

[0089] The generation unit can determine the generation priority based on the point expiration date when generating QR codes. For example, the generation unit can prioritize generating QR codes that include points with approaching expiration dates. It can also postpone the generation of points with far-off expiration dates. Furthermore, the generation unit can adjust the QR code generation priority in stages according to the point expiration dates. This allows the generation priority of QR codes to be determined based on the point expiration dates. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input point expiration date data into a generation AI and have the generation AI determine the QR code generation priority.

[0090] The generation unit can adjust the generation order of QR codes based on the relevance of points during generation. For example, the generation unit can generate QR codes that prioritize points related to the user's current purchasing behavior. It can also generate QR codes that prioritize points related to the user's areas of interest. Furthermore, the generation unit can adjust the generation order of QR codes in stages according to the relevance of points. This allows the generation order of QR codes to be adjusted based on the relevance of points. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input point relevance data into a generation AI and have the generation AI perform the adjustment of the QR code generation order.

[0091] The payment unit can estimate the user's emotions and adjust the payment method based on the estimated emotions. For example, if the user is relaxed, the payment unit can provide a detailed payment method. If the user is in a hurry, the payment unit can provide a concise payment method. If the user is stressed, the payment unit can provide a visually easy-to-understand payment method. This allows the payment 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 payment unit may be performed using AI, for example, or not using AI. For example, the payment unit can input user emotion data into AI and have the AI ​​adjust the payment method.

[0092] The payment unit can analyze the user's past payment history to select the optimal payment method at the time of payment. For example, the payment unit may prioritize suggesting payment methods that the user has frequently used in the past. The payment unit can also select the most efficient payment method based on the user's past payment history. Furthermore, the payment unit can analyze the user's past payment history and customize the optimal payment method. This allows the payment unit to select the optimal payment method based on the user's past payment history. Some or all of the above processes in the payment unit may be performed using AI, for example, or not. For example, the payment unit can input the user's past payment history into AI and have the AI ​​select the optimal payment method.

[0093] The payment unit can customize payment methods based on the user's current purchasing behavior at the time of payment. For example, the payment unit can suggest payment methods related to the products the user is currently purchasing. The payment unit can also customize the optimal payment method based on the user's purchasing behavior. Furthermore, the payment unit can analyze the user's purchase history and suggest highly relevant payment methods. This allows for the customization of payment methods based on the user's current purchasing behavior. Some or all of the above processes in the payment unit may be performed using AI, for example, or not using AI. For example, the payment unit can input the user's current purchasing behavior data into AI and have the AI ​​perform the customization of payment methods.

[0094] The payment unit can estimate the user's emotions and determine payment priorities based on those emotions. For example, if the user is relaxed, the payment unit will suggest all payment methods equally. If the user is in a hurry, the payment unit can also prioritize suggesting payment methods that can be processed quickly. If the user is stressed, the payment unit can also prioritize suggesting visually easy-to-understand payment methods. This allows the payment priority to be determined 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 payment unit may be performed using AI or not. For example, the payment unit can input user emotion data into an AI and have the AI ​​determine the payment priority.

[0095] The payment unit can select the optimal payment method at the time of payment, taking into account the user's geographical location. For example, the payment unit may suggest payment methods related to the user's current location. The payment unit can also select the optimal payment method based on the user's geographical location. Furthermore, the payment unit may suggest payment methods related to places the user frequently visits. This allows for the selection of the optimal payment method based on the user's geographical location. Some or all of the above processing in the payment unit may be performed using AI, for example, or without AI. For example, the payment unit can input the user's geographical location information into AI and have the AI ​​select the optimal payment method.

[0096] The payment unit can analyze the user's social media activity and suggest payment methods at the time of payment. For example, the payment unit can suggest payment methods related to products or services mentioned by the user on social media. It can also suggest payment methods related to areas of interest based on the user's social media activity. Furthermore, it can suggest payment methods related to brands or stores that the user follows. This allows for the suggestion of payment methods based on the user's social media activity. Some or all of the above processing in the payment unit may be performed using AI, for example, or not. For example, the payment unit can input data on the user's social media activity into an AI and have the AI ​​suggest payment methods.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] QR code payment systems can also include a learning unit that learns users' purchasing patterns. The learning unit analyzes users' past purchase history and point usage history to identify their purchasing patterns. For example, if a user frequently shops at a specific store on a specific day or time, the learning unit learns this pattern and suggests the optimal way to use points for the next purchase. The learning unit can also learn if a user tends to use specific points during a particular season or event, and encourage point usage at the appropriate time. Furthermore, the learning unit can predict future purchasing behavior based on the user's purchasing patterns and optimize point usage based on these predictions. This allows for the optimization of point usage based on the user's purchasing patterns.

[0099] The QR code payment system may also include a health monitoring unit that monitors the user's health status. The health monitoring unit collects user health data and optimizes point usage based on the user's health condition. For example, if the user is in good health, it may suggest prioritizing the use of health-related points. If the user's health is deteriorating, it may suggest ways to use points to reduce stress. Furthermore, the health monitoring unit can suggest ways to use points to maintain health based on the user's health data. This allows for the optimization of point usage based on the user's health condition.

[0100] A QR code payment system can also include an emotion estimation unit that estimates the user's emotions and suggests point usage based on those emotions. The emotion estimation unit analyzes the user's facial expressions, voice, and text data to estimate their emotions. For example, if the user is happy, it can amplify their happiness by suggesting rewards or bonus points. If the user is sad, it can suggest ways to use points to improve their mood. Furthermore, if the user is stressed, it can suggest ways to use points to relax. This allows for the optimization of point usage based on the user's emotions.

[0101] The QR code payment system can also include a social analytics unit that analyzes the user's social network and suggests point sharing with friends and family. The social analytics unit collects the user's social network data and identifies point services used by the user's friends and family. For example, if a user's friend uses a specific point service, they can share that service and use points together. Similarly, if a user's family uses a specific point service, they can share points among family members for more efficient use. Furthermore, the social analytics unit can suggest optimizing point usage with friends and family based on the user's social network data. This allows for optimized point usage based on the user's social network.

[0102] The QR code payment system can also include a location information analysis unit that uses the user's location information to suggest the optimal way to use points in real time. The location information analysis unit collects the user's current location information and identifies points that can be used at nearby stores. For example, if the user approaches a specific store, it will prioritize suggesting points that can be used at that store. It can also suggest points that can be used within a specific area if the user is in that area. Furthermore, the location information analysis unit can predict and suggest points that can be used at future destinations based on the user's movement patterns. This allows for the optimization of point usage based on the user's location information.

[0103] QR code payment systems can also be equipped with a function to estimate the user's emotions and extend the expiration date of points based on those emotions. For example, if a user is feeling stressed, the expiration date of points can be automatically extended so that the user can use them when they are relaxed. If a user is happy, the expiration date of specific points can be extended to further amplify their happiness. Furthermore, if a user is sad, the expiration date of points can be extended so that they can use them when their mood improves. This allows for flexible adjustment of point expiration dates based on the user's emotions.

[0104] QR code payment systems can also incorporate features that analyze users' purchase history and offer bonus points when purchasing specific products. For example, they can identify products that users have frequently purchased in the past and offer bonus points when those products are purchased again. They can also offer additional points when users purchase products in specific categories. Furthermore, they can offer bonus points when users spend a certain amount or more at specific stores. This allows for the optimization of point allocation based on the user's purchase history.

[0105] QR code payment systems can also be equipped with a function to estimate the user's emotions and relax point usage restrictions based on those emotions. For example, if a user is stressed, certain point usage restrictions can be temporarily relaxed to allow the user to relax. If a user is happy, certain point usage restrictions can be relaxed to further amplify their happiness. Furthermore, if a user is sad, point usage restrictions can be relaxed to improve their mood. This allows for flexible adjustment of point usage restrictions based on the user's emotions.

[0106] A QR code payment system can also include a prediction unit that forecasts user purchasing behavior and proposes the optimal way to use points for future purchases. The prediction unit analyzes the user's past purchase history and point usage history to predict future purchasing behavior. For example, if a user tends to purchase certain products during certain seasons or events, the prediction unit can forecast this trend and propose point usage at the appropriate time. Furthermore, if a user frequently shops at a particular store, the prediction unit can also propose point usage methods at that store in advance. In addition, the prediction unit can forecast future purchasing behavior based on the user's purchasing patterns and optimize point usage based on these predictions. This allows for the optimization of point usage based on the user's purchasing behavior.

[0107] QR code payment systems can also incorporate features to estimate user emotions and adjust point awarding methods based on those emotions. For example, if a user is happy, additional points can be awarded to amplify their happiness. If a user is sad, bonus points can be awarded to improve their mood. Furthermore, if a user is stressed, additional points can be awarded to help them relax. This allows for flexible adjustment of point awarding methods based on user emotions.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The collection unit collects information about the point services held by the user. Step 2: The analysis unit analyzes the expiration date and usable stores for each point based on the information collected by the collection unit. For example, it sorts the points by their expiration date and identifies points that can be used at specific stores. It also uses a generation AI to suggest the optimal way to use the points based on data such as the user's location information and past purchase history. Step 3: Based on the analysis results obtained by the analysis unit, the generation unit selects the optimal points for payment and generates a QR code. For example, if a user is making a payment of 2000 yen, the generation unit will generate a QR code that prioritizes the use of points with the nearest expiration date and combines multiple points as needed for payment. The QR code contains the selected point information, and the points are automatically applied when scanned at the store's register. Step 4: The payment unit uses the QR code generated by the generation unit to make the payment. For example, by scanning the QR code at the store's cash register, points are automatically applied. This allows users to use their points at the optimal time without having to worry about their expiration date.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and payment unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects information on each point service held by the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the expiration date and usable stores of each point based on the collected information. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and selects the optimal points based on the analysis results and generates a QR code. The payment unit is implemented by the control unit 46A of the smart device 14 and makes a payment at a store using the generated QR code. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and payment unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information on each point service held by the user. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the expiration date and usable stores of each point based on the collected information. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and selects the optimal points based on the analysis results and generates a QR code. The payment unit is implemented, for example, by the control unit 46A of the smart glasses 214 and makes a payment at a store using the generated QR code. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and payment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information on each point service held by the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the expiration date and usable stores of each point based on the collected information. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and selects the optimal points based on the analysis results and generates a QR code. The payment unit is implemented by the control unit 46A of the headset terminal 314 and makes a payment at a store using the generated QR code. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and payment unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects information on each point service held by the user. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the expiration date and usable stores for each point based on the collected information. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and selects the optimal points based on the analysis results and generates a QR code. The payment unit is implemented by, for example, the control unit 46A of the robot 414 and makes a payment at a store using the generated QR code. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) A collection unit that collects information on point services held by users, Based on the information collected by the aforementioned collection unit, an analysis unit analyzes the expiration date and usable stores for each point, Based on the analysis results obtained by the aforementioned analysis unit, a generation unit selects the optimal points for payment and generates a QR code. The system includes a payment unit that performs payment using the QR code generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect detailed information about the user's points, such as the balance and expiration date of each point service they hold. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Sort points by expiration date (closest first) or identify points usable at specific stores. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is When a user makes a 2000 yen purchase, the system generates a QR code that prioritizes using points with the nearest expiration date and combines multiple points as needed for payment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned payment section is, Use the generated QR code to make a payment at the store. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of point data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We analyze the user's past point usage history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting points information, filtering is performed based on the user's current purchasing behavior and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and determines the priority of point information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting point information, the system prioritizes collecting highly relevant point information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting points information, we analyze users' social media activity and collect relevant points information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the points. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the points. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on the expiration date of the points. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relationships between points. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is The system estimates the user's emotions and adjusts the QR code generation method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating a QR code, adjust the level of detail based on the points' expiration date. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating QR codes, different generation algorithms are applied depending on the point category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts the length of the QR code based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating QR codes, the generation priority is determined based on the point expiration date. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating QR codes, the generation order is adjusted based on the relevance of the points. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned payment section is, It estimates the user's emotions and adjusts the payment method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned payment section is, At the time of payment, the system analyzes the user's past payment history to select the most suitable payment method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned payment section is, At checkout, the payment method is customized based on the user's current purchasing behavior. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned payment section is, It estimates the user's emotions and determines payment priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned payment section is, When making a payment, the system selects the most suitable payment method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned payment section is, When making a payment, the system analyzes the user's social media activity and suggests payment methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 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 collection unit that collects information on point services held by users, Based on the information collected by the aforementioned collection unit, an analysis unit analyzes the expiration date and usable stores for each point, Based on the analysis results obtained by the aforementioned analysis unit, a generation unit selects the optimal points for payment and generates a two-dimensional code. The system includes a payment unit that performs payment using the two-dimensional code generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect detailed information about the user's points, such as the balance and expiration date of each point service they hold. The system according to feature 1.

3. The aforementioned analysis unit, Sort points by expiration date (closest first) or identify points usable at specific stores. The system according to feature 1.

4. The generating unit is When a user makes a 2000 yen purchase, the system prioritizes using points with the nearest expiration date and generates a QR code that allows them to combine multiple points for payment as needed. The system according to feature 1.

5. The aforementioned payment section is, Use the generated QR code to make a payment at the store. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of point data collection based on the estimated user emotions. The system according to feature 1.

7. The aforementioned collection unit is We analyze the user's past point usage history and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting points information, filtering is performed based on the user's current purchasing behavior and areas of interest. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and determines the priority of point information to collect based on the estimated user emotions. The system according to feature 1.

10. The aforementioned collection unit is When collecting point information, the system prioritizes collecting highly relevant point information by considering the user's geographical location. The system according to feature 1.

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