system

Abstract

The system according to this embodiment aims to automatically change recurring payments to cashless payments simply by having the user input basic information. [Solution] The system according to the embodiment comprises a reception unit, an acquisition unit, an analysis unit, a generation unit, and a switching unit. The reception unit receives the user's basic information. The acquisition unit acquires bank payment information based on the information received by the reception unit. The analysis unit analyzes the information acquired by the acquisition unit. The generation unit creates a payment code based on the information analyzed by the analysis unit. The switching unit automatically switches to cashless payment based on the payment code generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003] ​​​​​​​​​​​​​​​​​​​​​​​​The system according to this embodiment comprises a reception unit, an acquisition unit, an analysis unit, a generation unit, and a switching unit. The reception unit receives the user's basic information. The acquisition unit obtains bank payment information based on the information received by the reception unit. The analysis unit analyzes the information obtained by the acquisition unit. The generation unit creates a payment code based on the information analyzed by the analysis unit. The switching unit automatically switches to cashless payment based on the payment code generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to automatically change recurring payments to cashless payments simply by entering their basic information. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 payment change system according to an embodiment of the present invention is a system that utilizes a generating AI to automate the process of changing regular direct debits, such as loans and utility bills, from bank payments to cashless payments. The payment change system allows the user to simply input basic information, and the generating AI automatically handles the subsequent change procedures. For example, the user inputs basic information into the payment change system. At this time, the user only needs to input their bank payment information. Next, the payment change system uses the generating AI to analyze the input information and obtain the bank payment information. The generating AI analyzes the obtained information and creates a payment code based on the payment code selected by the user. The generated payment code can be automatically switched to cashless payment with a single click. This mechanism allows the user to change their payment method without any hassle. As a result, the payment change system significantly reduces the effort required from the user and allows for quick and efficient changes to payment methods.

[0029] The payment change system according to the embodiment comprises a reception unit, an acquisition unit, an analysis unit, a generation unit, and a switching unit. The reception unit receives the user's basic information. The user's basic information includes, but is not limited to, names, addresses, and contact information. The reception unit stores the basic information entered by the user in a database, for example. The reception unit can also store the basic information entered by the user in an encrypted form. The acquisition unit obtains bank payment information based on the information received by the reception unit. The bank payment information includes, but is not limited to, account numbers and payment history. The acquisition unit obtains payment information by, for example, accessing the user's bank account. The acquisition unit can also obtain payment information using the bank's API. Furthermore, the acquisition unit can obtain bank payment information using authentication information provided by the user. The analysis unit analyzes the information obtained by the acquisition unit. The analysis is performed using, for example, data analysis techniques and analysis algorithms, but is not limited to such examples. The analysis unit analyzes the acquired payment information to identify the payment code selected by the user. The analysis unit can also classify the acquired payment information and analyze the details of each payment code. Furthermore, the analysis unit can analyze the user's payment patterns based on the acquired payment information. The generation unit creates payment codes based on the information analyzed by the analysis unit. Payment codes include, but are not limited to, QR codes (registered trademarks) and barcodes. The generation unit creates payment codes using, for example, generation AI. The generation unit can also generate the optimal payment code based on the payment code selected by the user. Furthermore, the generation unit can save the generated payment codes in an appropriate format for provision to the user. The switching unit automatically switches to cashless payment based on the payment code generated by the generation unit. The switch is performed, for example, with a single click, but is not limited to this example. The switching unit sends the generated payment code to a cashless payment app and automatically switches the payment method.Furthermore, the switching unit allows the user to switch payment methods by clicking a confirmation button. In addition, the switching unit can notify the user when the payment method switch is complete. As a result, the payment change system according to this embodiment can receive the user's basic information, obtain and analyze bank payment information, generate a payment code, and automatically switch to cashless payment.

[0030] The reception desk receives basic user information. This information includes, but is not limited to, name, address, and contact information. The reception desk stores the user's entered information in a database. Specifically, information entered by the user through a web form or application is sent to a server using a secure communication protocol and stored in the database. The database is managed using an appropriate database management system, such as a relational database or a NoSQL database. The reception desk can also encrypt and store the user's entered information. Strong encryption algorithms such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) are used for encryption to ensure data confidentiality. Furthermore, the reception desk regularly backs up the user's basic information to prepare for data loss or corruption. Backup data is stored in a different physical location to enable rapid recovery in the event of a disaster or system failure. This allows the reception desk to safely and efficiently receive user information and improve the reliability and security of the entire system.

[0031] The acquisition unit retrieves bank payment information based on the information received by the reception unit. Bank payment information includes, but is not limited to, account numbers and payment history. The acquisition unit retrieves payment information by, for example, accessing the user's bank account. Specifically, it accesses the bank's online banking system using authentication information provided by the user (e.g., user ID and password, two-factor authentication code, etc.) and retrieves the necessary information. The acquisition unit can also retrieve payment information using the bank's API. Bank APIs are provided in the form of RESTful APIs or SOAP APIs, and the acquisition unit retrieves payment information in real time through these APIs. Furthermore, the acquisition unit can also retrieve bank payment information using authentication information provided by the user. Authentication information is managed in a secure manner, and data security is ensured using encrypted communication such as SSL / TLS when the acquisition unit accesses it. This allows the acquisition unit to safely and efficiently retrieve the user's bank information, improving the reliability and security of the entire system.

[0032] The analysis unit analyzes the information acquired by the acquisition unit. Analysis is performed using, for example, data analysis techniques and algorithms, but is not limited to these examples. For instance, the analysis unit analyzes acquired payment information to identify the payment code selected by the user. Specifically, it uses machine learning algorithms and statistical analysis techniques to classify the acquired data and extract patterns. For example, it uses clustering algorithms to group payment history and identify the user's payment patterns. The analysis unit can also classify the acquired payment information and analyze the details of each payment code. Furthermore, the analysis unit can analyze the user's payment patterns based on the acquired payment information. For example, it can use time-series analysis to analyze trends in the user's payment history and predict future payment behavior. This allows the analysis unit to quickly and accurately analyze acquired data and understand user payment behavior. Additionally, the analysis unit can use anomaly detection algorithms to detect unusual payment patterns and fraudulent transactions, issuing early warnings. This enables the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0033] The generation unit creates payment codes based on information analyzed by the analysis unit. These payment codes include, but are not limited to, QR codes and barcodes. The generation unit can, for example, use a generation AI to create payment codes. The generation AI generates the optimal payment code based on the user's payment patterns and selected payment method. Specifically, the generation AI learns the user's payment history and preferences to generate the most appropriate payment code format. The generation unit can also generate the optimal payment code based on the payment code selected by the user. For example, if the user selects a QR code, the generation unit uses a QR code generation algorithm to encode the payment information and generate a QR code. Furthermore, the generation unit can store the generated payment code in an appropriate format for provision to the user. The generated code is delivered via the user's smartphone, email, or in-app notifications. This allows the generation unit to quickly generate and provide the optimal payment code tailored to the user's needs. Additionally, the generation unit manages the expiration date and usage limit of the generated payment code to ensure security. This allows the generation unit to provide users with a safe and convenient payment method, improving the overall reliability and usability of the system.

[0034] The switching unit automatically switches to cashless payment based on the payment code generated by the generation unit. The switch can be performed, for example, with a single click, but is not limited to this example. The switching unit can, for example, send the generated payment code to a cashless payment app and automatically switch the payment method. Specifically, it can automatically switch the payment method by having the generated QR code or barcode scanned by the cashless payment app. The switching unit can also switch the payment method when the user clicks a confirmation button. For example, when the user clicks the confirmation button on the payment screen, the switching unit sends the generated payment code to the cashless payment app and switches the payment method. Furthermore, the switching unit can notify the user that the payment method switch is complete. Notifications can be made via smartphone push notifications, email, or in-app notifications. This allows the switching unit to provide users with a quick and reliable payment method switch, improving convenience. Additionally, the switching unit can record the payment method switch history, which can be used for future troubleshooting and user support. This allows the switching unit to provide users with a safe and convenient payment method switch, improving the reliability and usability of the entire system.

[0035] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display basic information that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest basic information to be used during specific time periods based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI suggest the optimal input method.

[0036] The reception unit can simplify the input process by automatically acquiring the user's current location information when they enter basic information. For example, when a user opens the app, the reception unit can automatically acquire their current location and set it as basic information. The reception unit can also suggest optimal candidate locations by considering the distance from the user's current location when the user enters basic information. Furthermore, if the user uses the app while on the move, the reception unit can update their current location in real time and reflect it as basic information. This simplifies the input process by automatically acquiring the user's current location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's location information data into a generating AI and have the generating AI suggest optimal candidate locations.

[0037] The reception desk can automatically suggest potential destinations by referencing the user's past travel history when basic information is entered. For example, the reception desk can automatically display places the user has frequently visited in the past as potential destinations. The reception desk can also predict places the user will visit on specific days of the week or times of day and suggest them as potential destinations. Furthermore, the reception desk can analyze the user's past travel patterns and suggest the most suitable potential destinations. In this way, the suggestion of potential destinations is automated by referring to the user's past travel history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's travel history data into a generating AI and have the generating AI suggest the most suitable potential destinations.

[0038] The reception desk can make schedule-based suggestions by referring to the user's calendar information when basic information is entered. For example, the reception desk can refer to the schedule registered in the user's calendar and automatically set the basic information. The reception desk can also suggest locations related to a specific event as candidate locations based on the user's calendar information. Furthermore, the reception desk can suggest the optimal route to match the schedule based on the user's calendar information. In this way, schedule-based suggestions are possible by referring to the user's calendar information. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's calendar information into a generating AI and have the generating AI execute schedule-based suggestions.

[0039] The acquisition unit can analyze the user's past acquisition history and select the optimal acquisition method. For example, the acquisition unit can select the optimal acquisition method based on the information the user has frequently acquired in the past. The acquisition unit can also predict the information to be acquired at a specific time period based on the user's past acquisition history and select the optimal acquisition method. Furthermore, the acquisition unit can analyze the user's past acquisition history and select the most efficient acquisition method. In this way, the optimal acquisition method can be selected by analyzing the user's past acquisition history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's acquisition history data into a generating AI and have the generating AI perform the selection of the optimal acquisition method.

[0040] The data acquisition unit can filter data based on the user's current living situation and areas of interest during the acquisition process. For example, the acquisition unit can prioritize acquiring highly relevant information based on the user's current living situation. It can also prioritize acquiring highly relevant information based on the user's areas of interest. Furthermore, the acquisition unit can filter and acquire optimal information considering the user's current living situation and areas of interest. This allows for the acquisition of highly relevant information by filtering based on the user's current living situation and areas of interest. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's living situation data into a generating AI and have the generating AI perform optimal information filtering.

[0041] The data acquisition unit can prioritize acquiring highly relevant information by considering the user's geographical location information during acquisition. For example, the data acquisition unit can prioritize acquiring highly relevant information based on the user's current location. The data acquisition unit can also prioritize acquiring the most relevant information by considering the user's geographical location information. Furthermore, the data acquisition unit can prioritize acquiring the most relevant information based on the user's current location. This allows for the priority acquisition of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's location data into a generating AI and have the generating AI perform the acquisition of the most relevant information.

[0042] The data acquisition unit can analyze the user's social media activity and acquire relevant information during the acquisition process. For example, the data acquisition unit can analyze the user's social media activity and prioritize acquiring highly relevant information. The data acquisition unit can also acquire optimal information based on the user's social media activity. Furthermore, the data acquisition unit can prioritize acquiring highly relevant information by considering the user's social media activity. This allows for the acquisition of highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's social media data into a generating AI and have the generating AI acquire the optimal information.

[0043] The analysis unit can optimize the current analysis by referring to past analysis data during the analysis process. For example, the analysis unit optimizes the current analysis based on past analysis data. The analysis unit can also select the optimal analysis method by referring to past analysis data. Furthermore, the analysis unit can analyze past analysis data to make the current analysis more efficient. In this way, the current analysis can be optimized by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the current analysis.

[0044] The analysis unit can apply different analysis algorithms to each category of information during analysis. For example, the analysis unit can apply the most suitable analysis algorithm for each category of information. The analysis unit can also select different analysis algorithms depending on the category of information. Furthermore, the analysis unit can adjust the analysis algorithm for each category of information to provide the optimal result. This allows for the provision of optimal analysis results by applying different analysis algorithms to each category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of the optimal analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the timing of information submission during the analysis. For example, the analysis unit can determine the priority of analysis based on the timing of information submission. The analysis unit can also determine the optimal analysis order by considering the timing of information submission. Furthermore, the analysis unit can adjust the priority of analysis according to the timing of information submission. This enables efficient analysis by determining the priority of analysis based on the timing of information submission. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information submission timing data into a generating AI and have the generating AI determine the optimal analysis order.

[0046] The analysis unit can adjust the order of analysis based on the relationships between the information during the analysis. For example, the analysis unit adjusts the order of analysis based on the relationships between the information. The analysis unit can also determine the optimal order of analysis by considering the relationships between the information. Furthermore, the analysis unit can adjust the order of analysis according to the relationships between the information. This allows for efficient analysis by adjusting the order of analysis based on the relationships between the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relationship data of the information into a generating AI and have the generating AI perform the adjustment of the optimal order of analysis.

[0047] The generation unit can optimize its generation algorithm by referring to past generation data during generation. For example, the generation unit optimizes the current generation algorithm based on past generation data. The generation unit can also select the optimal generation method by referring to past generation data. Furthermore, the generation unit can analyze past generation data to make the current generation more efficient. In this way, the generation algorithm can be optimized by referring to past generation data. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past generation data into a generation AI and have the generation AI perform optimization of the current generation algorithm.

[0048] The generation unit can apply different generation algorithms to each category of information during generation. For example, the generation unit can apply the optimal generation algorithm for each category of information. Alternatively, the generation unit can select a different generation algorithm depending on the category of information. Furthermore, the generation unit can adjust the generation algorithm for each category of information to provide the optimal result. This allows for the provision of optimal generation results by applying different generation algorithms to each category of information. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information category data into a generation AI and have the generation AI execute the application of the optimal generation algorithm.

[0049] The generation unit can determine the generation priority based on the information submission timing during generation. For example, the generation unit can determine the generation priority based on the information submission timing. The generation unit can also determine the optimal generation order considering the information submission timing. Furthermore, the generation unit can adjust the generation priority according to the information submission timing. This enables efficient generation by determining the generation priority based on the information submission timing. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information submission timing data into a generation AI and have the generation AI determine the optimal generation order.

[0050] The generation unit can adjust the generation order based on the relevance of the information during generation. For example, the generation unit adjusts the generation order based on the relevance of the information. The generation unit can also determine the optimal generation order by considering the relevance of the information. Furthermore, the generation unit can adjust the generation order according to the relevance of the information. This allows for efficient generation by adjusting the generation order based on the relevance of the information. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information relevance data into a generation AI and have the generation AI perform the adjustment of the optimal generation order.

[0051] The switching unit can select the optimal switching method by referring to past switching history during a switch. For example, the switching unit can select the optimal switching method based on past switching history. The switching unit can also determine the optimal switching timing by referring to past switching history. Furthermore, the switching unit can analyze past switching history and select the most efficient switching method. In this way, the optimal switching method can be selected by referring to past switching history. Some or all of the above processing in the switching unit may be performed using AI, for example, or without using AI. For example, the switching unit can input past switching history data into a generating AI and have the generating AI perform the selection of the optimal switching method.

[0052] The switching unit can customize the switching method based on the user's current living situation when switching. For example, the switching unit can select the optimal switching method based on the user's current living situation. The switching unit can also customize the switching method considering the user's current living situation. Furthermore, the switching unit can provide the optimal switching method according to the user's current living situation. This makes it possible to perform an optimal switch by customizing the switching method based on the user's current living situation. Some or all of the above processing in the switching unit may be performed using AI, for example, or without using AI. For example, the switching unit can input user living situation data into a generating AI and have the generating AI select the optimal switching method.

[0053] The switching unit can select the optimal switching method when switching, taking into account the user's geographical location information. For example, the switching unit can select the optimal switching method based on the user's geographical location information. The switching unit can also determine the optimal switching timing, taking into account the user's geographical location information. Furthermore, the switching unit can select the most efficient switching method based on the user's geographical location information. In this way, the optimal switching method can be selected by taking into account the user's geographical location information. Some or all of the above processing in the switching unit may be performed using AI, for example, or without using AI. For example, the switching unit can input the user's location information data into a generating AI and have the generating AI perform the selection of the optimal switching method.

[0054] The switching unit can analyze the user's social media activity and propose a switching method during the switching process. For example, the switching unit can analyze the user's social media activity and propose the optimal switching method. The switching unit can also propose the optimal switching timing based on the user's social media activity. Furthermore, the switching unit can propose the optimal switching method considering the user's social media activity. In this way, the optimal switching method can be proposed by analyzing the user's social media activity. Some or all of the above processing in the switching unit may be performed using AI, for example, or without AI. For example, the switching unit can input the user's social media data into a generating AI and have the generating AI execute a proposal for the optimal switching method.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The payment change system can also include a notification unit. This notification unit can provide users with real-time updates on the progress of the payment change. For example, it can notify the user when the payment change process has begun. It can also send notifications to the user each time a step in the process is completed. Furthermore, it can send a final confirmation notification to the user upon completion of the process. This allows users to stay informed of the progress of their payment change and proceed with confidence.

[0057] The payment change system may also include a history management unit. This unit can store and allow users to refer to their past payment change history. For example, it can store details of past payment changes made by the user. It can also provide an interface to allow users to review their past history. Furthermore, based on the history, the history management unit can suggest ways to optimize future payment change procedures. This allows users to efficiently proceed with payment change procedures while referring to their past history.

[0058] The payment change system can also be equipped with a security enhancement unit. This unit can implement advanced security measures to protect users' personal and payment information. For example, it can encrypt data to prevent unauthorized access. It can also protect user authentication information with multi-factor authentication. Furthermore, it can detect and immediately respond to unusual access or fraudulent activity. This allows users to confidently proceed with payment change procedures.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk receives the user's basic information. This information includes the user's name, address, and contact information. The reception desk can also store the user's entered information in a database and encrypt it. Step 2: The acquisition unit retrieves bank payment information based on the information received by the reception unit. This bank payment information includes account numbers and payment history. The acquisition unit can access the user's bank account to retrieve payment information, and can also use the bank's API or authentication information provided by the user. Step 3: The analysis unit analyzes the information acquired by the acquisition unit. The analysis is performed using data analysis methods and algorithms to analyze the acquired payment information and identify the payment code selected by the user. It can also classify the acquired payment information, analyze the details of each payment code, and analyze the user's payment pattern. Step 4: The generation unit creates a payment code based on the information analyzed by the analysis unit. The payment code may include a QR code or barcode and is created using generation AI. The generation unit generates the optimal payment code based on the payment code selected by the user and saves it in an appropriate format. Step 5: The switching unit automatically switches to cashless payment based on the payment code generated by the generation unit. The switch is done with a single click, sending the generated payment code to the cashless payment app and automatically switching the payment method. Users can also switch the payment method by clicking the confirmation button, and the user is notified when the payment method switch is complete.

[0061] (Example of form 2) The payment change system according to an embodiment of the present invention is a system that utilizes a generating AI to automate the process of changing regular direct debits, such as loans and utility bills, from bank payments to cashless payments. The payment change system allows the user to simply input basic information, and the generating AI automatically handles the subsequent change procedures. For example, the user inputs basic information into the payment change system. At this time, the user only needs to input their bank payment information. Next, the payment change system uses the generating AI to analyze the input information and obtain the bank payment information. The generating AI analyzes the obtained information and creates a payment code based on the payment code selected by the user. The generated payment code can be automatically switched to cashless payment with a single click. This mechanism allows the user to change their payment method without any hassle. As a result, the payment change system significantly reduces the effort required from the user and allows for quick and efficient changes to payment methods.

[0062] The payment change system according to the embodiment comprises a reception unit, an acquisition unit, an analysis unit, a generation unit, and a switching unit. The reception unit receives the user's basic information. The user's basic information includes, but is not limited to, names, addresses, and contact information. The reception unit stores the basic information entered by the user in a database, for example. The reception unit can also store the basic information entered by the user in an encrypted form. The acquisition unit obtains bank payment information based on the information received by the reception unit. The bank payment information includes, but is not limited to, account numbers and payment history. The acquisition unit obtains payment information by, for example, accessing the user's bank account. The acquisition unit can also obtain payment information using the bank's API. Furthermore, the acquisition unit can obtain bank payment information using authentication information provided by the user. The analysis unit analyzes the information obtained by the acquisition unit. The analysis is performed using, for example, data analysis techniques and analysis algorithms, but is not limited to such examples. The analysis unit analyzes the acquired payment information to identify the payment code selected by the user. The analysis unit can also classify the acquired payment information and analyze the details of each payment code. Furthermore, the analysis unit can analyze the user's payment patterns based on the acquired payment information. The generation unit creates payment codes based on the information analyzed by the analysis unit. Payment codes include, but are not limited to, QR codes and barcodes. The generation unit creates payment codes using, for example, generation AI. The generation unit can also generate the optimal payment code based on the payment code selected by the user. Furthermore, the generation unit can save the generated payment codes in an appropriate format for provision to the user. The switching unit automatically switches to cashless payment based on the payment code generated by the generation unit. The switching can be done, for example, with a single click, but is not limited to that example. The switching unit can, for example, send the generated payment code to a cashless payment app and automatically switch the payment method.Furthermore, the switching unit allows the user to switch payment methods by clicking a confirmation button. In addition, the switching unit can notify the user when the payment method switch is complete. As a result, the payment change system according to this embodiment can receive the user's basic information, obtain and analyze bank payment information, generate a payment code, and automatically switch to cashless payment.

[0063] The reception desk receives basic user information. This information includes, but is not limited to, name, address, and contact information. The reception desk stores the user's entered information in a database. Specifically, information entered by the user through a web form or application is sent to a server using a secure communication protocol and stored in the database. The database is managed using an appropriate database management system, such as a relational database or a NoSQL database. The reception desk can also encrypt and store the user's entered information. Strong encryption algorithms such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) are used for encryption to ensure data confidentiality. Furthermore, the reception desk regularly backs up the user's basic information to prepare for data loss or corruption. Backup data is stored in a different physical location to enable rapid recovery in the event of a disaster or system failure. This allows the reception desk to safely and efficiently receive user information and improve the reliability and security of the entire system.

[0064] The acquisition unit retrieves bank payment information based on the information received by the reception unit. Bank payment information includes, but is not limited to, account numbers and payment history. The acquisition unit retrieves payment information by, for example, accessing the user's bank account. Specifically, it accesses the bank's online banking system using authentication information provided by the user (e.g., user ID and password, two-factor authentication code, etc.) and retrieves the necessary information. The acquisition unit can also retrieve payment information using the bank's API. Bank APIs are provided in the form of RESTful APIs or SOAP APIs, and the acquisition unit retrieves payment information in real time through these APIs. Furthermore, the acquisition unit can also retrieve bank payment information using authentication information provided by the user. Authentication information is managed in a secure manner, and data security is ensured using encrypted communication such as SSL / TLS when the acquisition unit accesses it. This allows the acquisition unit to safely and efficiently retrieve the user's bank information, improving the reliability and security of the entire system.

[0065] The analysis unit analyzes the information acquired by the acquisition unit. Analysis is performed using, for example, data analysis techniques and algorithms, but is not limited to these examples. For instance, the analysis unit analyzes acquired payment information to identify the payment code selected by the user. Specifically, it uses machine learning algorithms and statistical analysis techniques to classify the acquired data and extract patterns. For example, it uses clustering algorithms to group payment history and identify the user's payment patterns. The analysis unit can also classify the acquired payment information and analyze the details of each payment code. Furthermore, the analysis unit can analyze the user's payment patterns based on the acquired payment information. For example, it can use time-series analysis to analyze trends in the user's payment history and predict future payment behavior. This allows the analysis unit to quickly and accurately analyze acquired data and understand user payment behavior. Additionally, the analysis unit can use anomaly detection algorithms to detect unusual payment patterns and fraudulent transactions, issuing early warnings. This enables the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0066] The generation unit creates payment codes based on information analyzed by the analysis unit. These payment codes include, but are not limited to, QR codes and barcodes. The generation unit can, for example, use a generation AI to create payment codes. The generation AI generates the optimal payment code based on the user's payment patterns and selected payment method. Specifically, the generation AI learns the user's payment history and preferences to generate the most appropriate payment code format. The generation unit can also generate the optimal payment code based on the payment code selected by the user. For example, if the user selects a QR code, the generation unit uses a QR code generation algorithm to encode the payment information and generate a QR code. Furthermore, the generation unit can store the generated payment code in an appropriate format for provision to the user. The generated code is delivered via the user's smartphone, email, or in-app notifications. This allows the generation unit to quickly generate and provide the optimal payment code tailored to the user's needs. Additionally, the generation unit manages the expiration date and usage limit of the generated payment code to ensure security. This allows the generation unit to provide users with a safe and convenient payment method, improving the overall reliability and usability of the system.

[0067] The switching unit automatically switches to cashless payment based on the payment code generated by the generation unit. The switch can be performed, for example, with a single click, but is not limited to this example. The switching unit can, for example, send the generated payment code to a cashless payment app and automatically switch the payment method. Specifically, it can automatically switch the payment method by having the generated QR code or barcode scanned by the cashless payment app. The switching unit can also switch the payment method when the user clicks a confirmation button. For example, when the user clicks the confirmation button on the payment screen, the switching unit sends the generated payment code to the cashless payment app and switches the payment method. Furthermore, the switching unit can notify the user that the payment method switch is complete. Notifications can be made via smartphone push notifications, email, or in-app notifications. This allows the switching unit to provide users with a quick and reliable payment method switch, improving convenience. Additionally, the switching unit can record the payment method switch history, which can be used for future troubleshooting and user support. This allows the switching unit to provide users with a safe and convenient payment method switch, improving the reliability and usability of the entire system.

[0068] The reception desk can estimate the user's emotions and adjust the method of inputting basic information based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of basic information. This improves user convenience by adjusting the method of inputting basic information 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 reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0069] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display basic information that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest basic information to be used during specific time periods based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI suggest the optimal input method.

[0070] The reception unit can simplify the input process by automatically acquiring the user's current location information when they enter basic information. For example, when a user opens the app, the reception unit can automatically acquire their current location and set it as basic information. The reception unit can also suggest optimal candidate locations by considering the distance from the user's current location when the user enters basic information. Furthermore, if the user uses the app while on the move, the reception unit can update their current location in real time and reflect it as basic information. This simplifies the input process by automatically acquiring the user's current location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's location information data into a generating AI and have the generating AI suggest optimal candidate locations.

[0071] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the user is tense, the reception unit can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, the reception unit can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple and highly visible interface to facilitate the input process. This improves user convenience by adjusting the input interface design 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 reception unit may be performed using AI, or not. For example, the reception unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0072] The reception desk can automatically suggest potential destinations by referencing the user's past travel history when basic information is entered. For example, the reception desk can automatically display places the user has frequently visited in the past as potential destinations. The reception desk can also predict places the user will visit on specific days of the week or times of day and suggest them as potential destinations. Furthermore, the reception desk can analyze the user's past travel patterns and suggest the most suitable potential destinations. In this way, the suggestion of potential destinations is automated by referring to the user's past travel history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's travel history data into a generating AI and have the generating AI suggest the most suitable potential destinations.

[0073] The reception desk can make schedule-based suggestions by referring to the user's calendar information when basic information is entered. For example, the reception desk can refer to the schedule registered in the user's calendar and automatically set the basic information. The reception desk can also suggest locations related to a specific event as candidate locations based on the user's calendar information. Furthermore, the reception desk can suggest the optimal route to match the schedule based on the user's calendar information. In this way, schedule-based suggestions are possible by referring to the user's calendar information. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's calendar information into a generating AI and have the generating AI execute schedule-based suggestions.

[0074] The acquisition unit can estimate the user's emotions and adjust the acquisition timing based on the estimated emotions. For example, if the user is relaxed, the acquisition unit can delay the acquisition timing to acquire information when the user is calm. Conversely, if the user is in a hurry, the acquisition unit can quickly acquire information and immediately begin analysis. Furthermore, if the user is stressed, the acquisition unit can adjust the acquisition timing to acquire information when the user is relaxed. This improves user convenience by adjusting the acquisition timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The 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 acquisition unit may be performed using AI, or not. For example, the acquisition unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0075] The acquisition unit can analyze the user's past acquisition history and select the optimal acquisition method. For example, the acquisition unit can select the optimal acquisition method based on the information the user has frequently acquired in the past. The acquisition unit can also predict the information to be acquired at a specific time period based on the user's past acquisition history and select the optimal acquisition method. Furthermore, the acquisition unit can analyze the user's past acquisition history and select the most efficient acquisition method. In this way, the optimal acquisition method can be selected by analyzing the user's past acquisition history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's acquisition history data into a generating AI and have the generating AI perform the selection of the optimal acquisition method.

[0076] The data acquisition unit can filter data based on the user's current living situation and areas of interest during the acquisition process. For example, the acquisition unit can prioritize acquiring highly relevant information based on the user's current living situation. It can also prioritize acquiring highly relevant information based on the user's areas of interest. Furthermore, the acquisition unit can filter and acquire optimal information considering the user's current living situation and areas of interest. This allows for the acquisition of highly relevant information by filtering based on the user's current living situation and areas of interest. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's living situation data into a generating AI and have the generating AI perform optimal information filtering.

[0077] The data acquisition unit can estimate the user's emotions and determine the priority of information to acquire based on the estimated emotions. For example, if the user is relaxed, the data acquisition unit will prioritize acquiring information of lower importance. If the user is in a hurry, the data acquisition unit can also prioritize acquiring information of higher importance. Furthermore, if the user is stressed, the data acquisition unit can also prioritize acquiring information of higher importance. This improves user convenience by determining the priority of information to acquire according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The 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 acquisition unit may be performed using AI, or not using AI. For example, the data acquisition unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0078] The data acquisition unit can prioritize acquiring highly relevant information by considering the user's geographical location information during acquisition. For example, the data acquisition unit can prioritize acquiring highly relevant information based on the user's current location. The data acquisition unit can also prioritize acquiring the most relevant information by considering the user's geographical location information. Furthermore, the data acquisition unit can prioritize acquiring the most relevant information based on the user's current location. This allows for the priority acquisition of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's location data into a generating AI and have the generating AI perform the acquisition of the most relevant information.

[0079] The data acquisition unit can analyze the user's social media activity and acquire relevant information during the acquisition process. For example, the data acquisition unit can analyze the user's social media activity and prioritize acquiring highly relevant information. The data acquisition unit can also acquire optimal information based on the user's social media activity. Furthermore, the data acquisition unit can prioritize acquiring highly relevant information by considering the user's social media activity. This allows for the acquisition of highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's social media data into a generating AI and have the generating AI acquire the optimal information.

[0080] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can increase the accuracy of the analysis to provide more detailed information. If the user is in a hurry, the analysis unit can also adjust the accuracy of the analysis to provide results quickly. Furthermore, if the user is stressed, the analysis unit can adjust the accuracy of the analysis to avoid burdening the user. This improves user convenience by adjusting the accuracy of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The 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 processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0081] The analysis unit can optimize the current analysis by referring to past analysis data during the analysis process. For example, the analysis unit optimizes the current analysis based on past analysis data. The analysis unit can also select the optimal analysis method by referring to past analysis data. Furthermore, the analysis unit can analyze past analysis data to make the current analysis more efficient. In this way, the current analysis can be optimized by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the current analysis.

[0082] The analysis unit can apply different analysis algorithms to each category of information during analysis. For example, the analysis unit can apply the most suitable analysis algorithm for each category of information. The analysis unit can also select different analysis algorithms depending on the category of information. Furthermore, the analysis unit can adjust the analysis algorithm for each category of information to provide the optimal result. This allows for the provision of optimal analysis results by applying different analysis algorithms to each category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of the optimal analysis algorithm.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, user convenience is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0084] The analysis unit can determine the priority of analysis based on the timing of information submission during the analysis. For example, the analysis unit can determine the priority of analysis based on the timing of information submission. The analysis unit can also determine the optimal analysis order by considering the timing of information submission. Furthermore, the analysis unit can adjust the priority of analysis according to the timing of information submission. This enables efficient analysis by determining the priority of analysis based on the timing of information submission. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information submission timing data into a generating AI and have the generating AI determine the optimal analysis order.

[0085] The analysis unit can adjust the order of analysis based on the relationships between the information during the analysis. For example, the analysis unit adjusts the order of analysis based on the relationships between the information. The analysis unit can also determine the optimal order of analysis by considering the relationships between the information. Furthermore, the analysis unit can adjust the order of analysis according to the relationships between the information. This allows for efficient analysis by adjusting the order of analysis based on the relationships between the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relationship data of the information into a generating AI and have the generating AI perform the adjustment of the optimal order of analysis.

[0086] The generation unit can estimate the user's emotions and adjust the format of the payment code it generates based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a payment code in a visually appealing format. If the user is in a hurry, the generation unit can also generate a payment code in a simple and quick-to-read format. Furthermore, if the user is stressed, the generation unit can generate a payment code in a visually calming format. This improves user convenience by adjusting the format of the payment code according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 AI or not using AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0087] The generation unit can optimize its generation algorithm by referring to past generation data during generation. For example, the generation unit optimizes the current generation algorithm based on past generation data. The generation unit can also select the optimal generation method by referring to past generation data. Furthermore, the generation unit can analyze past generation data to make the current generation more efficient. In this way, the generation algorithm can be optimized by referring to past generation data. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past generation data into a generation AI and have the generation AI perform optimization of the current generation algorithm.

[0088] The generation unit can apply different generation algorithms to each category of information during generation. For example, the generation unit can apply the optimal generation algorithm for each category of information. Alternatively, the generation unit can select a different generation algorithm depending on the category of information. Furthermore, the generation unit can adjust the generation algorithm for each category of information to provide the optimal result. This allows for the provision of optimal generation results by applying different generation algorithms to each category of information. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information category data into a generation AI and have the generation AI execute the application of the optimal generation algorithm.

[0089] The generation unit can estimate the user's emotions and determine the priority of the payment codes to generate based on the estimated emotions. For example, if the user is relaxed, the generation unit may prioritize generating payment codes of lower importance. Conversely, if the user is in a hurry, the generation unit may prioritize generating payment codes of higher importance. Furthermore, if the user is stressed, the generation unit may prioritize generating payment codes of higher importance. This improves user convenience by prioritizing payment codes 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 may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0090] The generation unit can determine the generation priority based on the information submission timing during generation. For example, the generation unit can determine the generation priority based on the information submission timing. The generation unit can also determine the optimal generation order considering the information submission timing. Furthermore, the generation unit can adjust the generation priority according to the information submission timing. This enables efficient generation by determining the generation priority based on the information submission timing. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information submission timing data into a generation AI and have the generation AI determine the optimal generation order.

[0091] The generation unit can adjust the generation order based on the relevance of the information during generation. For example, the generation unit adjusts the generation order based on the relevance of the information. The generation unit can also determine the optimal generation order by considering the relevance of the information. Furthermore, the generation unit can adjust the generation order according to the relevance of the information. This allows for efficient generation by adjusting the generation order based on the relevance of the information. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information relevance data into a generation AI and have the generation AI perform the adjustment of the optimal generation order.

[0092] The switching unit can estimate the user's emotions and adjust the timing of the switch based on the estimated emotions. For example, if the user is relaxed, the switching unit can delay the switch until the user is calm. If the user is in a hurry, the switching unit can switch quickly and provide immediate results. Furthermore, if the user is stressed, the switching unit can adjust the timing of the switch until the user is relaxed. This improves user convenience by adjusting the timing of the switch 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-described processing in the switching unit may be performed using AI, or not. For example, the switching unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0093] The switching unit can select the optimal switching method by referring to past switching history during a switch. For example, the switching unit can select the optimal switching method based on past switching history. The switching unit can also determine the optimal switching timing by referring to past switching history. Furthermore, the switching unit can analyze past switching history and select the most efficient switching method. In this way, the optimal switching method can be selected by referring to past switching history. Some or all of the above processing in the switching unit may be performed using AI, for example, or without using AI. For example, the switching unit can input past switching history data into a generating AI and have the generating AI perform the selection of the optimal switching method.

[0094] The switching unit can customize the switching method based on the user's current living situation when switching. For example, the switching unit can select the optimal switching method based on the user's current living situation. The switching unit can also customize the switching method considering the user's current living situation. Furthermore, the switching unit can provide the optimal switching method according to the user's current living situation. This makes it possible to perform an optimal switch by customizing the switching method based on the user's current living situation. Some or all of the above processing in the switching unit may be performed using AI, for example, or without using AI. For example, the switching unit can input user living situation data into a generating AI and have the generating AI select the optimal switching method.

[0095] The switching unit can estimate the user's emotions and determine the priority of switching based on the estimated emotions. For example, if the user is relaxed, the switching unit will prioritize switching to less important items. If the user is in a hurry, the switching unit can also prioritize switching to more important items. Furthermore, if the user is stressed, the switching unit can also prioritize switching to more important items. This improves user convenience by determining the priority of switching 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 switching unit may be performed using AI, or not using AI. For example, the switching unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0096] The switching unit can select the optimal switching method when switching, taking into account the user's geographical location information. For example, the switching unit can select the optimal switching method based on the user's geographical location information. The switching unit can also determine the optimal switching timing, taking into account the user's geographical location information. Furthermore, the switching unit can select the most efficient switching method based on the user's geographical location information. In this way, the optimal switching method can be selected by taking into account the user's geographical location information. Some or all of the above processing in the switching unit may be performed using AI, for example, or without using AI. For example, the switching unit can input the user's location information data into a generating AI and have the generating AI perform the selection of the optimal switching method.

[0097] The switching unit can analyze the user's social media activity and propose a switching method during the switching process. For example, the switching unit can analyze the user's social media activity and propose the optimal switching method. The switching unit can also propose the optimal switching timing based on the user's social media activity. Furthermore, the switching unit can propose the optimal switching method considering the user's social media activity. In this way, the optimal switching method can be proposed by analyzing the user's social media activity. Some or all of the above processing in the switching unit may be performed using AI, for example, or without AI. For example, the switching unit can input the user's social media data into a generating AI and have the generating AI execute a proposal for the optimal switching method.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The payment change system can also include a notification unit. This notification unit can provide users with real-time updates on the progress of the payment change. For example, it can notify the user when the payment change process has begun. It can also send notifications to the user each time a step in the process is completed. Furthermore, it can send a final confirmation notification to the user upon completion of the process. This allows users to stay informed of the progress of their payment change and proceed with confidence.

[0100] The payment change system can also include a customer support department. This department can provide real-time support for any questions or problems users may encounter during the payment change process. For example, the customer support department can use a chatbot to automatically answer user questions. It can also connect users to human operators for more detailed support when necessary. Furthermore, the customer support department can anticipate user emotions and respond quickly if the user is experiencing stress. This allows users to proceed with the payment change process with peace of mind.

[0101] The payment change system may also include a history management unit. This unit can store and allow users to refer to their past payment change history. For example, it can store details of past payment changes made by the user. It can also provide an interface to allow users to review their past history. Furthermore, based on the history, the history management unit can suggest ways to optimize future payment change procedures. This allows users to efficiently proceed with payment change procedures while referring to their past history.

[0102] The payment change system can also be equipped with a security enhancement unit. This unit can implement advanced security measures to protect users' personal and payment information. For example, it can encrypt data to prevent unauthorized access. It can also protect user authentication information with multi-factor authentication. Furthermore, it can detect and immediately respond to unusual access or fraudulent activity. This allows users to confidently proceed with payment change procedures.

[0103] The payment change system can also include a reminder function. This reminder function can remind users of deadlines and important steps in the payment change process. For example, it can send notifications to users when the deadline for the payment change process is approaching. It can also remind users if important steps in the process have not been completed. Furthermore, the reminder function can estimate the user's emotions and adjust the frequency of reminders if they are feeling stressed. This ensures that users do not forget the process and can proceed smoothly.

[0104] The payment change system can also be equipped with a learning unit. This unit can learn user behavior patterns and historical data to improve the overall system performance. For example, it can learn when users typically initiate payment change procedures and provide suggestions at the optimal time. It can also learn what information users need and provide appropriate information. Furthermore, it can estimate user emotions and enhance support if the user is experiencing stress. This allows users to change their payments more comfortably.

[0105] The payment change system can also include a predictive unit. This unit can predict future payment change needs based on the user's past behavioral data. For example, it can analyze when a user has changed their payment method in the past and predict the timing of the next change. It can also analyze the user's lifestyle patterns and spending tendencies to suggest the optimal payment method. Furthermore, it can estimate the user's emotions and offer optimal suggestions when the user is relaxed. This allows users to make future payment changes smoothly.

[0106] The payment change system can also include an incentive section. This incentive section can offer rewards and benefits to users when they complete the payment change process. For example, the incentive section could award points to users who complete the process. It could also offer cashback to users who meet certain conditions. Furthermore, the incentive section could estimate user sentiment and offer incentives to increase motivation. This would encourage users to proactively change their payment methods.

[0107] The payment change system can also include a feedback section. This feedback section can collect user feedback and use it to improve the system. For example, it could send a survey to the user after the payment change process is complete. It could also collect user opinions and requests and use this data to improve the system's functionality. Furthermore, the feedback section could estimate the user's emotions and adjust the feedback collection method if the user is experiencing stress. This allows for system improvements that reflect user feedback.

[0108] The payment change system can also include a data analytics unit. This unit can analyze data related to users' payment change procedures and provide insights to improve system performance. For example, it can analyze the success rate and time taken for users' payment change procedures. It can also analyze user behavior patterns and suggest the optimal payment change procedure. Furthermore, it can estimate user emotions and suggest simplifying the procedure if the user is experiencing stress. This enables an optimal payment change procedure based on user data.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception desk receives the user's basic information. This information includes the user's name, address, and contact information. The reception desk can also store the user's entered information in a database and encrypt it. Step 2: The acquisition unit retrieves bank payment information based on the information received by the reception unit. This bank payment information includes account numbers and payment history. The acquisition unit can access the user's bank account to retrieve payment information, and can also use the bank's API or authentication information provided by the user. Step 3: The analysis unit analyzes the information acquired by the acquisition unit. The analysis is performed using data analysis methods and algorithms to analyze the acquired payment information and identify the payment code selected by the user. It can also classify the acquired payment information, analyze the details of each payment code, and analyze the user's payment pattern. Step 4: The generation unit creates a payment code based on the information analyzed by the analysis unit. The payment code may include a QR code or barcode and is created using generation AI. The generation unit generates the optimal payment code based on the payment code selected by the user and saves it in an appropriate format. Step 5: The switching unit automatically switches to cashless payment based on the payment code generated by the generation unit. The switch is done with a single click, sending the generated payment code to the cashless payment app and automatically switching the payment method. Users can also switch the payment method by clicking the confirmation button, and the user is notified when the payment method switch is complete.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the reception unit, acquisition unit, analysis unit, generation unit, and switching unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives the user's basic information. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and acquires bank payment information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the acquired information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a payment code. The switching unit is implemented by the control unit 46A of the smart device 14 and automatically switches to cashless payment based on the generated payment 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.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the reception unit, acquisition unit, analysis unit, generation unit, and switching unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives the user's basic information. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and acquires bank payment information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the acquired information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a payment code. The switching unit is implemented by the control unit 46A of the smart glasses 214 and automatically switches to cashless payment based on the generated payment 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.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the reception unit, acquisition unit, analysis unit, generation unit, and switching unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives the user's basic information. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and acquires bank payment information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the acquired information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a payment code. The switching unit is implemented by the control unit 46A of the headset terminal 314 and automatically switches to cashless payment based on the generated payment 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.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the reception unit, acquisition unit, analysis unit, generation unit, and switching unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives the user's basic information. The acquisition unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and acquires bank payment information. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the acquired information. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates a payment code. The switching unit is implemented by, for example, the control unit 46A of the robot 414 and automatically switches to cashless payment based on the generated payment 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A reception desk that receives basic user information, An acquisition unit that acquires bank payment information based on the information received by the aforementioned reception unit, An analysis unit analyzes the information acquired by the acquisition unit, A generation unit that creates a payment code based on the information analyzed by the analysis unit, The system includes a switching unit that automatically switches to cashless payment based on the payment code generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for basic information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When entering basic information, the system automatically retrieves the user's current location to simplify the input process. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When users enter basic information, the system automatically suggests potential locations based on their past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When basic information is entered, the system references the user's calendar information to provide suggestions based on their schedule. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, The system estimates the user's emotions and adjusts the acquisition timing based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, Analyze the user's past acquisition history and select the optimal acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, When retrieving data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, It estimates the user's emotions and determines the priority of information to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When retrieving data, the system prioritizes retrieving highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The acquisition unit is, When acquiring data, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, past analysis data is referenced to optimize the current analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied to each category of information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is We estimate the user's emotions and adjust the format of the payment code generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the generation algorithm is optimized by referring to past generation data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, different generation algorithms are applied to each category of information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It estimates the user's emotions and determines the priority of the payment codes generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the generation priority is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During generation, the order of generation is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned switching unit is It estimates the user's emotions and adjusts the timing of the switch based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned switching unit is During the switchover, the system will refer to past switchover history to select the optimal switching method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned switching unit is During the switchover, the method of switching is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned switching unit is It estimates the user's emotions and determines the priority of switching based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned switching unit is During the switchover, the optimal switching method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned switching unit is During the transition, we analyze the user's social media activity and suggest methods for switching. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 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 reception desk that receives basic user information, An acquisition unit that acquires bank payment information based on the information received by the aforementioned reception unit, An analysis unit analyzes the information acquired by the acquisition unit, A generation unit that creates a payment code based on the information analyzed by the analysis unit, The system includes a switching unit that automatically switches to cashless payment based on the payment code generated by the generation unit. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for basic information based on the estimated user emotions. The system according to feature 1.

3. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

4. The aforementioned reception unit is When entering basic information, the system automatically retrieves the user's current location to simplify the input process. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is When users enter basic information, the system automatically suggests potential locations based on their past travel history. The system according to feature 1.

7. The aforementioned reception unit is When basic information is entered, the system references the user's calendar information to provide suggestions based on their schedule. The system according to feature 1.

8. The acquisition unit is, The system estimates the user's emotions and adjusts the acquisition timing based on the estimated emotions. The system according to feature 1.

9. The acquisition unit is, Analyze the user's past acquisition history and select the optimal acquisition method. The system according to feature 1.

10. The acquisition unit is, When retrieving data, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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