System

Abstract

An object of a system according to an embodiment is to intuitively and accurately grasp values of different currencies.SOLUTION: A system includes an exchange rate acquisition part, a money amount conversion part, and a bill recognition part. The exchange rate acquisition unit automatically acquires the latest exchange rate. The money amount conversion unit converts the amount of money input by the user into another currency using the exchange rate acquired by the exchange rate acquisition unit. The banknote recognition unit recognizes a banknote using a camera and converts the value of the banknote into another currency.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to accurately grasp the value of different currencies, and it has been particularly difficult to intuitively understand the value of paper money.

[0005] The system according to the embodiment aims to intuitively and accurately grasp the value of different currencies. [Means for solving the problem]

[0006] The system according to the embodiment includes an exchange rate acquisition unit, an amount conversion unit, and a banknote recognition unit. The exchange rate acquisition unit automatically acquires the latest exchange rate. The amount conversion unit converts the amount entered by the user into another currency using the exchange rate acquired by the exchange rate acquisition unit. The banknote recognition unit recognizes banknotes using a camera and converts their value into another currency. [Effects of the Invention]

[0007] The system according to the embodiment allows users to intuitively and accurately grasp the value of different currencies. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) Global Money Valuer, an embodiment of the present invention, is a mobile app that enables overseas travelers, international students, and people doing international business to accurately grasp the value of various currencies. This mobile app provides exchange rate calculation and bill recognition functions, enabling intuitive and accurate currency value conversion for users. As a result, Global Money Valuer allows users to accurately grasp and manage the value of various currencies.

[0029] The global money valuer according to the embodiment includes an exchange rate acquisition unit, an amount conversion unit, and a banknote recognition unit. The exchange rate acquisition unit automatically acquires the latest exchange rates. For example, the exchange rate acquisition unit acquires the latest exchange rates from a reliable data source via the Internet. The exchange rate acquisition unit can also periodically update the exchange rates. For example, the exchange rate acquisition unit acquires the latest exchange rates every hour and updates them within the app. The amount conversion unit converts an amount entered by a user into another currency using the exchange rate acquired by the exchange rate acquisition unit. For example, the amount conversion unit instantly converts an amount entered by a user into another currency using the latest exchange rate. The amount conversion unit can also support conversion between multiple currencies. For example, the amount conversion unit can convert between various currencies, such as dollars to euros and euros to yen. The banknote recognition unit recognizes banknotes using a camera and converts their value into another currency. For example, the banknote recognition unit analyzes a banknote photographed by a user with a camera and recognizes the value of the banknote. The banknote recognition unit can also convert the value of the recognized banknote into another currency based on the latest exchange rate. For example, the bill recognition unit recognizes Japanese yen bills and converts their value into dollars or euros. This allows the user to convert amounts into other currencies using the latest exchange rates and intuitively understand the value of bills. For example, users can instantly check the value of local currencies while traveling. It also allows for efficient currency exchange in business transactions.

[0030] The amount conversion unit uses the generation AI to analyze the user's past transaction history and propose the optimal exchange rate. The amount conversion unit, for example, uses the generation AI to analyze the user's past transaction history and extract transaction trends and patterns. For example, the generation AI predicts and proposes the timing when the user can obtain the most favorable exchange rate based on past transaction data. The amount conversion unit can also use the generation AI to analyze market trends and propose the optimal exchange rate. For example, the generation AI proposes the optimal exchange rate for the user based on current market data. In this way, the user is proposed the optimal exchange rate based on their past transaction history.

[0031] The amount conversion unit is linked to price information for the user's current location and travel destination, and provides a conversion result that takes into account the user's actual purchasing power. The amount conversion unit, for example, collects price information for the user's current location and travel destination, and reflects this information in the exchange rate calculation result. For example, the amount conversion unit calculates the user's actual purchasing power based on the local price index. The amount conversion unit can also update price information for the user's current location and travel destination in real time, and provide a conversion result based on the latest information. For example, the amount conversion unit periodically updates price information for the user's current location and travel destination, and reflects the latest purchasing power. This allows the user to obtain a conversion result that takes into account the user's actual purchasing power.

[0032] The amount conversion unit also supports cryptocurrency rate calculations, allowing users to convert between cryptocurrency and legal tender. The amount conversion unit, for example, adds a cryptocurrency rate calculation function, allowing users to convert between cryptocurrency and legal tender. For example, the amount conversion unit provides a function to convert cryptocurrencies such as Bitcoin and Ethereum into legal tender. The amount conversion unit can also update cryptocurrency rates in real time and perform conversions based on the latest rates. For example, the amount conversion unit periodically updates cryptocurrency rates and performs conversions based on the latest rates. This allows users to convert between cryptocurrency and legal tender.

[0033] The amount conversion unit is linked to the user's account information at a financial institution and performs instant currency conversion. The amount conversion unit, for example, is linked to the user's account information at a financial institution and provides a function for instant currency conversion based on the exchange rate calculation result. For example, the amount conversion unit allows the user to perform currency conversion with one click after checking the calculation result. The amount conversion unit can also perform currency conversion in real time using the financial institution's API. For example, the amount conversion unit performs instant currency conversion through the financial institution's API. This allows the user to perform instant currency conversion.

[0034] The banknote recognition unit uses generation AI to determine the authenticity of banknotes and detect counterfeit banknotes. The banknote recognition unit adds a function to determine the authenticity of banknotes using generation AI, for example. For example, the generation AI analyzes the design and security elements of banknotes to detect counterfeit banknotes. The banknote recognition unit can also use generation AI to determine the authenticity of banknotes in real time. For example, the generation AI analyzes banknotes photographed by a user with a camera and determines their authenticity on the spot. This allows the user to detect counterfeit banknotes.

[0035] The banknote verification unit compares the data with the user's past banknote verification history to improve the recognition accuracy of frequently used banknotes. For example, the banknote verification unit builds a system that improves the recognition accuracy of frequently used banknotes based on the user's past banknote verification history. For example, the banknote verification unit analyzes past verification data to improve the recognition accuracy of specific banknotes. The banknote verification unit can also update the user's banknote verification history in real time and improve the recognition accuracy based on the latest data. For example, the banknote verification unit adds data on banknotes newly recognized by the user to improve the recognition accuracy. This allows the user to improve the recognition accuracy of frequently used banknotes.

[0036] The banknote recognition unit also supports the recognition of coins and digital currency, allowing users to handle a variety of currency forms. The banknote recognition unit, for example, expands the banknote recognition function to build a system that also supports the recognition of coins and digital currency. For example, the banknote recognition unit adds a function to analyze coin designs and QR codes on digital currency. The banknote recognition unit can also use generation AI to improve the recognition accuracy of coins and digital currency. For example, the generation AI learns the characteristics of coins and digital currency and improves recognition accuracy. This allows users to handle a variety of currency forms.

[0037] The banknote recognition unit links the banknote recognition results with tourist information for the user's travel destination, providing historical and cultural information related to the banknote. For example, the banknote recognition unit builds a system that links tourist information for the user's travel destination based on the banknote recognition results. For example, the banknote recognition unit displays historical and cultural information related to the recognized banknote. The banknote recognition unit can also provide tourist spot and event information related to the banknote based on the user's current location and travel destination information. For example, the banknote recognition unit suggests tourist spots related to the country or region of the banknote recognized by the user. This allows the user to obtain historical and cultural information related to the banknote.

[0038] The currency history management unit uses the generation AI to analyze the currency history and provide personalized advice that is useful for financial management. The currency history management unit, for example, uses the generation AI to analyze the user's currency history and provide personalized advice that is useful for financial management. For example, the generation AI makes savings and investment suggestions based on past spending patterns. The currency history management unit can also use the generation AI to analyze the user's financial situation in real time and provide optimal advice. For example, the generation AI suggests a future spending plan based on the user's current spending situation. This allows the user to receive personalized advice that is useful for financial management.

[0039] The currency history management unit links the user's currency history with the user's spending patterns and makes suggestions for savings and investments. For example, the currency history management unit builds a system that links the user's currency history with the spending patterns and makes suggestions for savings and investments. For example, the currency history management unit provides advice for reducing wasteful spending based on past spending data. The currency history management unit can also analyze the user's spending patterns in real time and make optimal suggestions for savings and investments. For example, the currency history management unit suggests specific actions for saving based on the user's current spending situation. This allows the user to receive suggestions for savings and investments based on their spending patterns.

[0040] The currency history management unit links with other financial apps to achieve comprehensive financial management. For example, the currency history management unit links currency history with other financial apps to build a system that achieves comprehensive financial management. For example, the currency history management unit shares data with banking apps and investment apps to manage it centrally. Furthermore, through linkage with other financial apps, the currency history management unit can grasp the user's financial situation in real time and provide optimal advice. For example, the currency history management unit makes suggestions for balancing income and expenses based on the user's overall financial situation. This allows the user to achieve comprehensive financial management.

[0041] The currency history management unit links the user's travel history and analyzes spending trends at travel destinations. For example, the currency history management unit links the user's currency history with the travel history to build a system that analyzes spending trends at travel destinations. For example, the currency history management unit analyzes spending patterns at specific travel destinations based on past travel data. The currency history management unit can also update the user's travel history in real time and analyze spending trends based on the latest data. For example, the currency history management unit adds data on new travel destinations visited by the user and analyzes spending trends. This allows the user to analyze spending trends at travel destinations.

[0042] The currency alert function unit uses generation AI to analyze the user's trading history and market trends and notify them at the optimal timing. For example, the currency alert function unit uses generation AI to analyze the user's trading history and market trends and build a system that notifies currency alerts at the optimal timing. For example, the currency alert function unit predicts the optimal timing based on past trading data and current market data. The currency alert function unit can also use generation AI to monitor market fluctuations in real time and notify the user. For example, the currency alert function unit detects sudden market fluctuations and notifies the user immediately. This allows the user to receive currency alerts at the optimal timing.

[0043] The currency alert function unit is linked to the user's schedule and provides notifications before important events. The currency alert function unit is linked to the user's schedule, for example, to build a system that provides currency alerts before important events. For example, the currency alert function unit provides notifications before important events based on the user's calendar information. The currency alert function unit can also update the user's schedule in real time and provide notifications based on the latest information. For example, the currency alert function unit provides notifications based on event information newly added by the user. This allows the user to receive currency alerts before important events.

[0044] The currency alert function unit also responds to price fluctuations in stocks and cryptocurrencies, allowing users to manage a variety of investment targets. For example, the currency alert function unit expands the currency alert function to build a system that also responds to price fluctuations in stocks and cryptocurrencies. For example, the currency alert function unit obtains data from the stock market and cryptocurrency market in real time and notifies the user. The currency alert function unit can also predict price fluctuations using generation AI and notify the user at the optimal time. For example, the currency alert function unit predicts price fluctuations using generation AI and notifies the user. This allows users to manage a variety of investment targets.

[0045] The currency alert function unit links with the user's social media accounts, allowing the user to receive important notifications on multiple platforms. For example, the currency alert function unit links with the user's social media accounts to build a system that allows the user to receive important notifications on multiple platforms. For example, the currency alert function unit receives notifications on social media such as Facebook and Twitter. The currency alert function unit can also update the user's social media accounts in real time and send notifications based on the latest information. For example, the currency alert function unit sends notifications based on information about newly added social media accounts by the user. This allows the user to receive important notifications on multiple platforms.

[0046] The multilingual support unit adds a real-time translation function using the generation AI, allowing users to communicate smoothly in different languages. The multilingual support unit, for example, adds a real-time translation function using the generation AI, and builds a system that allows users to communicate smoothly in different languages. For example, the multilingual support unit translates the contents of chats and messages in real time. The multilingual support unit can also perform real-time translation of voice calls using the generation AI. For example, the multilingual support unit provides translation in real time when a user makes a voice call in a different language. This allows users to communicate smoothly in different languages.

[0047] The multilingual support unit works in conjunction with the user's language learning history to encourage use in the language being learned. The multilingual support unit, for example, works in conjunction with the user's language learning history to build a system that strengthens multilingual support. For example, the multilingual support unit encourages the user to use the app in the language being learned. The multilingual support unit can also provide appropriate feedback according to the user's learning progress. For example, the multilingual support unit provides advice based on the user's learning progress to encourage use in the language being learned. This allows the user to encourage use in the language being learned.

[0048] The multilingual support unit works in conjunction with the voice recognition function, allowing the user to operate the app by voice. For example, the multilingual support unit works in conjunction with the voice recognition function to build a system that allows the user to operate the app by voice. For example, the multilingual support unit recognizes voice commands and operates the app. The multilingual support unit can also improve the accuracy of voice recognition using a generation AI. For example, the generation AI learns the user's voice data and improves the accuracy of voice recognition. This allows the user to operate the app by voice.

[0049] The multilingual support unit links with the user's cultural background and performs customization according to the culture. The multilingual support unit, for example, builds a system that links multilingual support with the user's cultural background and performs customization according to the culture. For example, the multilingual support unit provides designs and content that match the user's culture. The multilingual support unit can also provide feedback based on the user's cultural habits. For example, the multilingual support unit provides advice and suggestions according to the user's cultural background. This allows the user to perform customization according to the culture.

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

[0051] The Global Money Valuer can also be equipped with a voice assistant unit. The voice assistant unit allows users to obtain exchange rates and convert amounts using voice commands. For example, if a user commands "Convert 1 dollar to euro," the voice assistant unit instantly obtains the latest exchange rate and sends a command to the amount conversion unit. The voice assistant unit can also notify the user by voice of the recognition results of a banknote photographed by the user. For example, it may say, "This banknote is 1,000 yen." This allows users to operate the device intuitively without using their hands.

[0052] Global Money Valuer can also be equipped with a purchase history analysis unit that analyzes the user's purchase history. The purchase history analysis unit suggests the optimal exchange rate and purchase timing based on the user's past purchase data. For example, if a user is considering purchasing a specific product, the purchase history analysis unit will suggest the optimal purchase timing based on past data. The purchase history analysis unit can also monitor price fluctuations of products frequently purchased by the user and send notifications when prices drop. This allows users to shop more efficiently.

[0053] Global Money Valuer can further include a travel plan linking unit that links with the user's travel plans. The travel plan linking unit provides information on optimal exchange rates and local currencies based on the user's travel schedule. For example, it notifies the user of the best times to receive favorable exchange rates for the currency they will use at their travel destination. The travel plan linking unit can also provide information to improve the recognition accuracy of bills and coins of the currency they will use at their travel destination. This allows the user to smoothly manage their currency while traveling.

[0054] Global Money Valuer can further include a learning history linking unit that links with the user's learning history. The learning history linking unit provides optimal learning advice based on the user's knowledge of the language or currency they are studying. For example, if a user wants to deepen their knowledge of a specific currency, the learning history linking unit can provide related information and learning resources. The learning history linking unit can also provide appropriate feedback according to the user's learning progress. This allows the user to study efficiently.

[0055] Global Money Valuer can further include an investment history linking unit that links with the user's investment history. The investment history linking unit suggests the optimal investment timing and investment destination based on the user's past investment data. For example, if a user is considering investing in a specific currency or cryptocurrency, the investment history linking unit will suggest the optimal timing based on past data. The investment history linking unit can also provide advice to the user when selecting an investment destination. This allows users to invest efficiently.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The exchange rate acquisition unit automatically obtains the latest exchange rates. For example, it obtains the latest exchange rates from a reliable data source via the Internet and updates them periodically. For example, it obtains the latest exchange rates every hour and updates them within the app. Step 2: The amount conversion unit converts the amount entered by the user into another currency using the exchange rate acquired by the exchange rate acquisition unit. For example, based on the amount entered by the user, the amount is instantly converted into another currency using the latest exchange rate, and conversion between multiple currencies is supported. Step 3: The bill recognition unit uses the camera to recognize bills and convert their value into other currencies. For example, the unit analyzes the bills photographed by the user, recognizes their value, and converts them into other currencies based on the latest exchange rates.

[0058] (Example 2) Global Money Valuer, an embodiment of the present invention, is a mobile app that enables overseas travelers, international students, and people doing international business to accurately grasp the value of various currencies. This mobile app provides exchange rate calculation and bill recognition functions, enabling intuitive and accurate currency value conversion for users. As a result, Global Money Valuer allows users to accurately grasp and manage the value of various currencies.

[0059] The global money valuer according to the embodiment includes an exchange rate acquisition unit, an amount conversion unit, and a banknote recognition unit. The exchange rate acquisition unit automatically acquires the latest exchange rates. For example, the exchange rate acquisition unit acquires the latest exchange rates from a reliable data source via the Internet. The exchange rate acquisition unit can also periodically update the exchange rates. For example, the exchange rate acquisition unit acquires the latest exchange rates every hour and updates them within the app. The amount conversion unit converts an amount entered by a user into another currency using the exchange rate acquired by the exchange rate acquisition unit. For example, the amount conversion unit instantly converts an amount entered by a user into another currency using the latest exchange rate. The amount conversion unit can also support conversion between multiple currencies. For example, the amount conversion unit can convert between various currencies, such as dollars to euros and euros to yen. The banknote recognition unit recognizes banknotes using a camera and converts their value into another currency. For example, the banknote recognition unit analyzes a banknote photographed by a user with a camera and recognizes the value of the banknote. The banknote recognition unit can also convert the value of the recognized banknote into another currency based on the latest exchange rate. For example, the bill recognition unit recognizes Japanese yen bills and converts their value into dollars or euros. This allows the user to convert amounts into other currencies using the latest exchange rates and intuitively understand the value of bills. For example, users can instantly check the value of local currencies while traveling. It also allows for efficient currency exchange in business transactions.

[0060] The amount conversion unit uses the generation AI to analyze the user's past transaction history and propose the optimal exchange rate. The amount conversion unit, for example, uses the generation AI to analyze the user's past transaction history and extract transaction trends and patterns. For example, the generation AI predicts and proposes the timing when the user can obtain the most favorable exchange rate based on past transaction data. The amount conversion unit can also use the generation AI to analyze market trends and propose the optimal exchange rate. For example, the generation AI proposes the optimal exchange rate for the user based on current market data. In this way, the user is proposed the optimal exchange rate based on their past transaction history.

[0061] The amount conversion unit is linked to price information for the user's current location and travel destination, and provides a conversion result that takes into account the user's actual purchasing power. The amount conversion unit, for example, collects price information for the user's current location and travel destination, and reflects this information in the exchange rate calculation result. For example, the amount conversion unit calculates the user's actual purchasing power based on the local price index. The amount conversion unit can also update price information for the user's current location and travel destination in real time, and provide a conversion result based on the latest information. For example, the amount conversion unit periodically updates price information for the user's current location and travel destination, and reflects the latest purchasing power. This allows the user to obtain a conversion result that takes into account the user's actual purchasing power.

[0062] The amount conversion unit uses the emotion estimation function to analyze the emotion the user feels about the exchange rate calculation result and provides advice to elicit positive emotions. The amount conversion unit, for example, uses the emotion estimation function to analyze the emotion the user feels about the exchange rate calculation result in real time. For example, the emotion estimation function analyzes the user's facial expressions and voice and calculates an emotion score. The amount conversion unit also uses the emotion estimation function to analyze the user's emotions and provides advice to elicit positive emotions. For example, the emotion estimation function provides advice to relax if the user feels negative emotions. This allows the user to feel positive emotions about the exchange rate calculation result.

[0063] The amount conversion unit also supports cryptocurrency rate calculations, allowing users to convert between cryptocurrency and legal tender. The amount conversion unit, for example, adds a cryptocurrency rate calculation function, allowing users to convert between cryptocurrency and legal tender. For example, the amount conversion unit provides a function to convert cryptocurrencies such as Bitcoin and Ethereum into legal tender. The amount conversion unit can also update cryptocurrency rates in real time and perform conversions based on the latest rates. For example, the amount conversion unit periodically updates cryptocurrency rates and performs conversions based on the latest rates. This allows users to convert between cryptocurrency and legal tender.

[0064] The amount conversion unit is linked to the user's account information at a financial institution and performs instant currency conversion. The amount conversion unit, for example, is linked to the user's account information at a financial institution and provides a function for instant currency conversion based on the exchange rate calculation result. For example, the amount conversion unit allows the user to perform currency conversion with one click after checking the calculation result. The amount conversion unit can also perform currency conversion in real time using the financial institution's API. For example, the amount conversion unit performs instant currency conversion through the financial institution's API. This allows the user to perform instant currency conversion.

[0065] The amount conversion unit uses the emotion estimation function to measure the stress level of the user when calculating exchange rates and makes suggestions to provide a relaxing environment. The amount conversion unit, for example, uses the emotion estimation function to measure the stress level of the user when calculating exchange rates in real time. For example, the emotion estimation function analyzes the user's facial expression and voice to calculate a stress score. The amount conversion unit also analyzes the user's stress level using the emotion estimation function and makes suggestions to provide a relaxing environment. For example, if the user shows a high stress level, the emotion estimation function makes a suggestion to play relaxing music. This allows the user to calculate exchange rates in a relaxing environment.

[0066] The banknote recognition unit uses generation AI to determine the authenticity of banknotes and detect counterfeit banknotes. The banknote recognition unit adds a function to determine the authenticity of banknotes using generation AI, for example. For example, the generation AI analyzes the design and security elements of banknotes to detect counterfeit banknotes. The banknote recognition unit can also use generation AI to determine the authenticity of banknotes in real time. For example, the generation AI analyzes banknotes photographed by a user with a camera and determines their authenticity on the spot. This allows the user to detect counterfeit banknotes.

[0067] The banknote verification unit compares the data with the user's past banknote verification history to improve the recognition accuracy of frequently used banknotes. For example, the banknote verification unit builds a system that improves the recognition accuracy of frequently used banknotes based on the user's past banknote verification history. For example, the banknote verification unit analyzes past verification data to improve the recognition accuracy of specific banknotes. The banknote verification unit can also update the user's banknote verification history in real time and improve the recognition accuracy based on the latest data. For example, the banknote verification unit adds data on banknotes newly recognized by the user to improve the recognition accuracy. This allows the user to improve the recognition accuracy of frequently used banknotes.

[0068] The banknote recognition unit uses the emotion estimation function to analyze the emotion the user feels about the banknote recognition result and provides feedback to provide a sense of security. The banknote recognition unit, for example, uses the emotion estimation function to analyze the emotion the user feels about the banknote recognition result in real time. For example, the emotion estimation function analyzes the user's facial expressions and voice and calculates an emotion score. The banknote recognition unit also uses the emotion estimation function to analyze the user's emotion and provides feedback to provide a sense of security. For example, if the user feels anxious, the emotion estimation function displays a message that provides a sense of security. This allows the user to feel a sense of security about the banknote recognition result.

[0069] The banknote recognition unit also supports the recognition of coins and digital currency, allowing users to handle a variety of currency forms. The banknote recognition unit, for example, expands the banknote recognition function to build a system that also supports the recognition of coins and digital currency. For example, the banknote recognition unit adds a function to analyze coin designs and QR codes on digital currency. The banknote recognition unit can also use generation AI to improve the recognition accuracy of coins and digital currency. For example, the generation AI learns the characteristics of coins and digital currency and improves recognition accuracy. This allows users to handle a variety of currency forms.

[0070] The banknote recognition unit links the banknote recognition results with tourist information for the user's travel destination, providing historical and cultural information related to the banknote. For example, the banknote recognition unit builds a system that links tourist information for the user's travel destination based on the banknote recognition results. For example, the banknote recognition unit displays historical and cultural information related to the recognized banknote. The banknote recognition unit can also provide tourist spot and event information related to the banknote based on the user's current location and travel destination information. For example, the banknote recognition unit suggests tourist spots related to the country or region of the banknote recognized by the user. This allows the user to obtain historical and cultural information related to the banknote.

[0071] The banknote recognition unit uses the emotion estimation function to measure the user's excitement level when recognizing a banknote and makes suggestions to provide interesting information. The banknote recognition unit, for example, uses the emotion estimation function to measure the user's excitement level when recognizing a banknote in real time. For example, the emotion estimation function analyzes the user's facial expressions and voice and calculates an excitement score. The banknote recognition unit also uses the emotion estimation function to analyze the user's excitement level and makes suggestions to provide interesting information. For example, if the user shows a high level of excitement, the emotion estimation function suggests related topics and content. This allows the user to obtain interesting information.

[0072] The currency history management unit uses the generation AI to analyze the currency history and provide personalized advice that is useful for financial management. The currency history management unit, for example, uses the generation AI to analyze the user's currency history and provide personalized advice that is useful for financial management. For example, the generation AI makes savings and investment suggestions based on past spending patterns. The currency history management unit can also use the generation AI to analyze the user's financial situation in real time and provide optimal advice. For example, the generation AI suggests a future spending plan based on the user's current spending situation. This allows the user to receive personalized advice that is useful for financial management.

[0073] The currency history management unit links the user's currency history with the user's spending patterns and makes suggestions for savings and investments. For example, the currency history management unit builds a system that links the user's currency history with the spending patterns and makes suggestions for savings and investments. For example, the currency history management unit provides advice for reducing wasteful spending based on past spending data. The currency history management unit can also analyze the user's spending patterns in real time and make optimal suggestions for savings and investments. For example, the currency history management unit suggests specific actions for saving based on the user's current spending situation. This allows the user to receive suggestions for savings and investments based on their spending patterns.

[0074] The currency history management unit uses an emotion estimation function to analyze the emotion a user feels when checking their currency history and provides feedback to elicit positive emotions. The currency history management unit, for example, uses the emotion estimation function to analyze the emotion a user feels when checking their currency history in real time. For example, the emotion estimation function analyzes the user's facial expressions and voice and calculates an emotion score. The currency history management unit also uses the emotion estimation function to analyze the user's emotion and provides feedback to elicit positive emotions. For example, the emotion estimation function displays a message that gives a sense of security if the user feels anxious. This allows the user to have positive emotions when checking their currency history.

[0075] The currency history management unit links with other financial apps to achieve comprehensive financial management. For example, the currency history management unit links currency history with other financial apps to build a system that achieves comprehensive financial management. For example, the currency history management unit shares data with banking apps and investment apps to manage it centrally. Furthermore, through linkage with other financial apps, the currency history management unit can grasp the user's financial situation in real time and provide optimal advice. For example, the currency history management unit makes suggestions for balancing income and expenses based on the user's overall financial situation. This allows the user to achieve comprehensive financial management.

[0076] The currency history management unit links the user's travel history and analyzes spending trends at travel destinations. For example, the currency history management unit links the user's currency history with the travel history to build a system that analyzes spending trends at travel destinations. For example, the currency history management unit analyzes spending patterns at specific travel destinations based on past travel data. The currency history management unit can also update the user's travel history in real time and analyze spending trends based on the latest data. For example, the currency history management unit adds data on new travel destinations visited by the user and analyzes spending trends. This allows the user to analyze spending trends at travel destinations.

[0077] The currency history management unit uses an emotion estimation function to measure the stress level of the user when checking the currency history and makes suggestions to provide a relaxing environment. The currency history management unit, for example, uses the emotion estimation function to measure the stress level of the user when checking the currency history in real time. For example, the emotion estimation function analyzes the user's facial expressions and voice and calculates a stress score. The currency history management unit also uses the emotion estimation function to analyze the user's stress level and makes suggestions to provide a relaxing environment. For example, if the user shows a high stress level, the emotion estimation function suggests playing relaxing music. This allows the user to check the currency history in a relaxing environment.

[0078] The currency alert function unit uses generation AI to analyze the user's trading history and market trends and notify them at the optimal timing. For example, the currency alert function unit uses generation AI to analyze the user's trading history and market trends and build a system that notifies currency alerts at the optimal timing. For example, the currency alert function unit predicts the optimal timing based on past trading data and current market data. The currency alert function unit can also use generation AI to monitor market fluctuations in real time and notify the user. For example, the currency alert function unit detects sudden market fluctuations and notifies the user immediately. This allows the user to receive currency alerts at the optimal timing.

[0079] The currency alert function unit is linked to the user's schedule and provides notifications before important events. The currency alert function unit is linked to the user's schedule, for example, to build a system that provides currency alerts before important events. For example, the currency alert function unit provides notifications before important events based on the user's calendar information. The currency alert function unit can also update the user's schedule in real time and provide notifications based on the latest information. For example, the currency alert function unit provides notifications based on event information newly added by the user. This allows the user to receive currency alerts before important events.

[0080] The currency alert function unit uses an emotion estimation function to analyze the emotions of the user when receiving a currency alert and provides feedback to elicit positive emotions. The currency alert function unit, for example, uses the emotion estimation function to analyze the emotions of the user when receiving a currency alert in real time. For example, the emotion estimation function analyzes the user's facial expressions and voice and calculates an emotion score. The currency alert function unit also uses the emotion estimation function to analyze the user's emotions and provides feedback to elicit positive emotions. For example, if the user is feeling anxious, the emotion estimation function displays a message that gives a sense of security. This allows the user to have positive emotions when receiving a currency alert.

[0081] The currency alert function unit also responds to price fluctuations in stocks and cryptocurrencies, allowing users to manage a variety of investment targets. For example, the currency alert function unit expands the currency alert function to build a system that also responds to price fluctuations in stocks and cryptocurrencies. For example, the currency alert function unit obtains data from the stock market and cryptocurrency market in real time and notifies the user. The currency alert function unit can also predict price fluctuations using generation AI and notify the user at the optimal time. For example, the currency alert function unit predicts price fluctuations using generation AI and notifies the user. This allows users to manage a variety of investment targets.

[0082] The currency alert function unit links with the user's social media accounts, allowing the user to receive important notifications on multiple platforms. For example, the currency alert function unit links with the user's social media accounts to build a system that allows the user to receive important notifications on multiple platforms. For example, the currency alert function unit receives notifications on social media such as Facebook and Twitter. The currency alert function unit can also update the user's social media accounts in real time and send notifications based on the latest information. For example, the currency alert function unit sends notifications based on information about newly added social media accounts by the user. This allows the user to receive important notifications on multiple platforms.

[0083] The currency alert function unit uses an emotion estimation function to measure the user's excitement level when receiving a currency alert and makes suggestions to provide interesting information. The currency alert function unit, for example, uses the emotion estimation function to measure the user's excitement level when receiving a currency alert in real time. For example, the emotion estimation function analyzes the user's facial expression and voice and calculates an excitement score. The currency alert function unit also uses the emotion estimation function to analyze the user's excitement level and makes suggestions to provide interesting information. For example, if the user shows a high level of excitement, the emotion estimation function suggests related topics and content. This allows the user to obtain interesting information.

[0084] The multilingual support unit adds a real-time translation function using the generation AI, allowing users to communicate smoothly in different languages. The multilingual support unit, for example, adds a real-time translation function using the generation AI, and builds a system that allows users to communicate smoothly in different languages. For example, the multilingual support unit translates the contents of chats and messages in real time. The multilingual support unit can also perform real-time translation of voice calls using the generation AI. For example, the multilingual support unit provides translation in real time when a user makes a voice call in a different language. This allows users to communicate smoothly in different languages.

[0085] The multilingual support unit works in conjunction with the user's language learning history to encourage use in the language being learned. The multilingual support unit, for example, works in conjunction with the user's language learning history to build a system that strengthens multilingual support. For example, the multilingual support unit encourages the user to use the app in the language being learned. The multilingual support unit can also provide appropriate feedback according to the user's learning progress. For example, the multilingual support unit provides advice based on the user's learning progress to encourage use in the language being learned. This allows the user to encourage use in the language being learned.

[0086] The multilingual support unit uses an emotion estimation function to analyze the emotions of a user when using the app in different languages ​​and provides feedback to elicit positive emotions. The multilingual support unit, for example, uses the emotion estimation function to analyze the emotions of a user when using the app in different languages ​​in real time. For example, the emotion estimation function analyzes the user's facial expressions and voice and calculates an emotion score. The multilingual support unit also uses the emotion estimation function to analyze the user's emotions and provides feedback to elicit positive emotions. For example, the emotion estimation function displays a message that gives a sense of security when the user is feeling anxious. This allows the user to have positive emotions when using the app in different languages.

[0087] The multilingual support unit works in conjunction with the voice recognition function, allowing the user to operate the app by voice. For example, the multilingual support unit works in conjunction with the voice recognition function to build a system that allows the user to operate the app by voice. For example, the multilingual support unit recognizes voice commands and operates the app. The multilingual support unit can also improve the accuracy of voice recognition using a generation AI. For example, the generation AI learns the user's voice data and improves the accuracy of voice recognition. This allows the user to operate the app by voice.

[0088] The multilingual support unit links with the user's cultural background and performs customization according to the culture. The multilingual support unit, for example, builds a system that links multilingual support with the user's cultural background and performs customization according to the culture. For example, the multilingual support unit provides designs and content that match the user's culture. The multilingual support unit can also provide feedback based on the user's cultural habits. For example, the multilingual support unit provides advice and suggestions according to the user's cultural background. This allows the user to perform customization according to the culture.

[0089] The multilingual support unit uses an emotion estimation function to measure a user's stress level when using an app in different languages ​​and makes suggestions to provide a relaxing environment. The multilingual support unit, for example, uses the emotion estimation function to measure a user's stress level in real time when using an app in different languages. For example, the emotion estimation function analyzes the user's facial expressions and voice and calculates a stress score. The multilingual support unit also uses the emotion estimation function to analyze the user's stress level and makes suggestions to provide a relaxing environment. For example, if the user indicates a high stress level, the emotion estimation function suggests playing relaxing music. This allows the user to use the app in a relaxing environment.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The Global Money Valuer can also be equipped with a voice assistant unit. The voice assistant unit allows users to obtain exchange rates and convert amounts using voice commands. For example, if a user commands "Convert 1 dollar to euro," the voice assistant unit instantly obtains the latest exchange rate and sends a command to the amount conversion unit. The voice assistant unit can also notify the user by voice of the recognition results of a banknote photographed by the user. For example, it may say, "This banknote is 1,000 yen." This allows users to operate the device intuitively without using their hands.

[0092] Global Money Valuer can also be equipped with a health management unit that links with the user's health data. The health management unit monitors the user's heart rate and stress level and takes the user's health into consideration when calculating exchange rates and recognizing banknotes. For example, if the user shows a high stress level, the health management unit can provide advice on how to relax. The health management unit can also send a notification urging the user to take a break if the user has been using the app for a long time. This allows the user to use the app while maintaining their health.

[0093] Global Money Valuer can also be equipped with a purchase history analysis unit that analyzes the user's purchase history. The purchase history analysis unit suggests the optimal exchange rate and purchase timing based on the user's past purchase data. For example, if a user is considering purchasing a specific product, the purchase history analysis unit will suggest the optimal purchase timing based on past data. The purchase history analysis unit can also monitor price fluctuations of products frequently purchased by the user and send notifications when prices drop. This allows users to shop more efficiently.

[0094] Global Money Valuer may further include a notification customization unit that estimates a user's emotions and sends customized notifications based on the estimated emotions. The notification customization unit analyzes the user's emotions in real time when calculating exchange rates or recognizing banknotes, and sends notifications to elicit positive emotions. For example, if the user is expressing negative emotions, the notification customization unit sends an encouraging message. On the other hand, if the user is expressing positive emotions, the notification customization unit sends a message to further enhance those emotions. This allows the user to always use the app with a positive mood.

[0095] Global Money Valuer can further include a travel plan linking unit that links with the user's travel plans. The travel plan linking unit provides information on optimal exchange rates and local currencies based on the user's travel schedule. For example, it notifies the user of the best times to receive favorable exchange rates for the currency they will use at their travel destination. The travel plan linking unit can also provide information to improve the recognition accuracy of bills and coins of the currency they will use at their travel destination. This allows the user to smoothly manage their currency while traveling.

[0096] Global Money Valuer may further include a relaxation environment providing unit that estimates the user's emotions and provides a relaxing environment based on the estimated emotions. The relaxation environment providing unit measures the user's stress level in real time when calculating exchange rates or recognizing banknotes, and makes suggestions to provide a relaxing environment. For example, if the user indicates a high stress level, the relaxation environment providing unit may suggest playing relaxing music. The unit may also provide advice on how to use the app in a relaxing environment, allowing the user to use the app in a relaxed state.

[0097] Global Money Valuer can further include a learning history linking unit that links with the user's learning history. The learning history linking unit provides optimal learning advice based on the user's knowledge of the language or currency they are studying. For example, if a user wants to deepen their knowledge of a specific currency, the learning history linking unit can provide related information and learning resources. The learning history linking unit can also provide appropriate feedback according to the user's learning progress. This allows the user to study efficiently.

[0098] Global Money Valuer may further include a security unit that estimates the user's emotions and provides a sense of security based on the estimated emotions. The security unit analyzes the user's emotions in real time when calculating exchange rates or recognizing banknotes, and provides feedback to provide a sense of security. For example, if the user feels anxious, the security unit displays a message that provides a sense of security. It can also provide advice to help the user have positive emotions. This allows the user to use the app with peace of mind.

[0099] Global Money Valuer can further include an investment history linking unit that links with the user's investment history. The investment history linking unit suggests the optimal investment timing and investment destination based on the user's past investment data. For example, if a user is considering investing in a specific currency or cryptocurrency, the investment history linking unit will suggest the optimal timing based on past data. The investment history linking unit can also provide advice to the user when selecting an investment destination. This allows users to invest efficiently.

[0100] Global Money Valuer may further include an interest-drawing unit that estimates a user's emotions and provides information that will interest them based on the estimated emotions. The interest-drawing unit measures the user's excitement level in real time when calculating exchange rates or recognizing banknotes, and makes suggestions to provide information that will interest them. For example, if the user shows a high level of excitement, the interest-drawing unit may suggest related topics or content. It may also provide activities that will interest the user. This allows the user to use the app with interest.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The exchange rate acquisition unit automatically obtains the latest exchange rates. For example, it obtains the latest exchange rates from a reliable data source via the Internet and updates them periodically. For example, it obtains the latest exchange rates every hour and updates them within the app. Step 2: The amount conversion unit converts the amount entered by the user into another currency using the exchange rate acquired by the exchange rate acquisition unit. For example, based on the amount entered by the user, the amount is instantly converted into another currency using the latest exchange rate, and conversion between multiple currencies is supported. Step 3: The bill recognition unit uses the camera to recognize bills and convert their value into other currencies. For example, the unit analyzes the bills photographed by the user, recognizes their value, and converts them into other currencies based on the latest exchange rates.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

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

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

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

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

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

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

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

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

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an exchange rate acquisition unit that automatically acquires the latest exchange rates; an amount conversion unit that converts the amount entered by the user into another currency using the exchange rate acquired by the exchange rate acquisition unit; a banknote recognition unit that uses a camera to recognize banknotes and convert their value into other currencies. A system characterized by:

2. The amount conversion unit It also supports cryptocurrency rate calculations, allowing users to convert between cryptocurrencies and fiat currencies.

2. The system of claim 1.

3. The banknote verification unit Generative AI is used to determine the authenticity of the banknotes and detect counterfeit banknotes.

2. The system of claim 1.

4. The Currency History Management Department: It uses generative AI to analyze currency history and provide personalized advice to help users manage their finances.

2. The system of claim 1.

5. The currency alert function section is Using generation AI, the system analyzes the user's trading history and market trends, and notifies them at the optimal time.

2. The system of claim 1.

6. The multilingual department Add real-time translation functionality using generative AI to enable users to communicate seamlessly in different languages.

2. The system of claim 1.

7. The amount conversion unit Analyze the user's feelings about the exchange rate calculation results and provide advice to elicit positive feelings 2. The system of claim 1.

8. The banknote verification unit Analyze the user's feelings about the bill recognition results and provide feedback to provide a sense of security 2. The system of claim 1.

Images