system

Abstract

The system according to this embodiment aims to enable travelers to efficiently use up any leftover local currency. [Solution] The system according to the embodiment comprises a reception unit, a proposal unit, an explanation unit, and an update unit. The reception unit receives the amount of remaining local currency from the traveler. The proposal unit proposes services and products available locally based on the amount received by the reception unit. The explanation unit provides a detailed explanation of how to use the services and products proposed by the proposal unit. The update unit updates the remaining balance in real time after the services and products explained by the explanation unit have been used and makes the next proposal.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

[0007] The system according to this embodiment allows travelers to efficiently use up any leftover local currency. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) A smartphone application according to an embodiment of the present invention is a system for travelers to efficiently use up their remaining local currency. The system allows travelers to input the amount of their remaining local currency and suggests services and products available locally based on that amount. Examples include meals at restaurants, admission tickets to tourist attractions, and transportation. These suggestions are customized based on the traveler's current location and interests. The system also provides detailed instructions on how to use the suggested services and products, making them easy for travelers to use. Furthermore, the system updates the remaining balance in real time after the traveler has used the suggested services and products and makes subsequent suggestions. This mechanism allows travelers to efficiently use up their remaining local currency. For example, if a traveler has 1000 yen in local currency, they input that amount into the system. This information is then entered into the system. Next, the system suggests services and products available locally based on the entered amount. The system suggests the most suitable services and products based on the traveler's current location and interests. For example, if the traveler is at a tourist attraction, it suggests admission tickets to that attraction or meals at nearby restaurants. Also, if the traveler needs to use transportation, it suggests how to use it and the fares. The system provides detailed instructions on how to use the suggested services and products. For example, if a restaurant meal is suggested, the system will provide detailed information about the restaurant's location, opening hours, and menu. Similarly, if an entrance ticket to a tourist attraction is suggested, the system will provide detailed information about the attraction's location, admission procedures, and fees. Furthermore, the system updates the traveler's balance in real time after they have used a suggested service or product, and then makes the next suggestion. When a traveler uses a suggested service or product, the amount spent is entered into the system, and the balance is updated in real time. For example, if a traveler dines at a restaurant, the cost of the meal is entered into the system, and the balance is updated. The system then makes the next suggestion based on the updated balance. This mechanism allows travelers to efficiently use up any remaining local currency. By using the system, travelers can efficiently use up their local currency without wasting it.Furthermore, the system provides optimal suggestions based on the traveler's current location and interests, allowing travelers to use services and products that match their interests. For example, a traveler in a tourist area can use it for admission tickets to that tourist attraction or meals at nearby restaurants. This allows travelers to efficiently use up their local currency and enjoy their trip more. In this way, the smartphone application enables travelers to efficiently use up any leftover local currency.

[0029] The smartphone application according to this embodiment comprises a reception unit, a suggestion unit, an explanation unit, and an update unit. The reception unit receives input from the traveler regarding the amount of local currency the traveler has left over. The amount of local currency the traveler has left over includes, but is not limited to, banknotes, coins, and electronic money. For example, if the traveler has 1,000 yen in local currency, the reception unit can input that amount into the system. The reception unit can also automatically recognize the type and amount of local currency the traveler has. For example, the reception unit can scan banknotes and coins using a camera and automatically recognize their value. The suggestion unit suggests services and products available locally based on the amount entered by the reception unit. For example, the suggestion unit suggests the most suitable services and products based on the traveler's current location and interests. For example, if the traveler is at a tourist destination, the suggestion unit suggests admission tickets to that tourist destination or meals at nearby restaurants. The suggestion unit can also suggest how to use public transportation and the fares if the traveler needs to use public transportation. For example, the suggestion unit suggests meals at restaurants, admission tickets to tourist destinations, and the use of public transportation. The explanation unit provides a detailed explanation of how to use the services or products suggested by the suggestion unit. For example, if a restaurant meal is suggested, the explanation unit will provide a detailed explanation of the restaurant's location, opening hours, menu, etc. Similarly, if an admission ticket to a tourist attraction is suggested, the explanation unit can provide a detailed explanation of the attraction's location, admission procedures, and fees. The explanation unit may use text, images, videos, etc., to provide a detailed explanation of how to use the services or products. The update unit updates the balance in real time after the use of the services or products explained by the explanation unit and makes the next suggestion. For example, if a traveler dines at a restaurant, the cost of the meal is entered into the system, and the balance is updated in real time. Next, the update unit makes the next suggestion based on the updated balance. The update unit can update the balance after use in real time and make the next suggestion. This allows the smartphone application according to the embodiment to efficiently use up any remaining local currency. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without AI.For example, the suggestion unit can make suggestions using an AI model that takes the traveler's current location and interests as input and outputs the most suitable services or products. Some or all of the above processing in the explanation unit may be performed using AI, or not. For example, the explanation unit can provide explanations using an AI model that takes information about the suggested services or products as input and outputs how to use them. Some or all of the above processing in the update unit may be performed using AI, or not. For example, the update unit can perform updates using an AI model that takes the remaining balance after use as input and outputs the next suggestion.

[0030] The reception desk inputs the amount of local currency the traveler has left over. This amount may include, but is not limited to, banknotes, coins, and electronic money. For example, if a traveler has 1,000 yen in local currency, the reception desk can input that amount into the system. The reception desk can also automatically recognize the type and amount of local currency the traveler has. For example, the reception desk can use a camera to scan banknotes and coins and automatically recognize their value. Furthermore, the reception desk can use NFC (Near Field Communication) technology to check the balance of electronic money. This allows the traveler to simply hold their smartphone over the electronic money reader and have the balance automatically read. The reception desk centrally manages this data and can display the total amount of all local currency the traveler has in real time. In addition, based on the amounts entered by the traveler and the amounts recognized, the reception desk can classify the currency by type and display a detailed breakdown. For example, it could show that out of 1000 yen, 500 yen is in banknotes, 300 yen is in coins, and 200 yen is in electronic money. This allows travelers to understand the detailed breakdown of the local currency they possess.

[0031] The suggestion department proposes services and products available locally based on the amount entered by the reception department. For example, the suggestion department proposes the most suitable services and products based on the traveler's current location and interests. For instance, if the traveler is at a tourist destination, the suggestion department will suggest admission tickets to that destination or meals at nearby restaurants. The suggestion department can also suggest transportation options and fares if the traveler needs to use public transport. For example, the suggestion department will suggest meals at restaurants, admission tickets to tourist destinations, and public transport. Furthermore, the suggestion department can utilize AI to learn the traveler's past behavior history and preferences. For example, it will analyze the traveler's preferences and interests based on data on places the traveler has visited and services they have used in the past, and then make customized suggestions based on that. Using an AI model, the suggestion department takes the traveler's current location and interests as input and outputs the most suitable services and products. For example, if the traveler is interested in art museums, the suggestion department will suggest admission tickets to nearby art museums. The suggestion department can also suggest services and products available within the traveler's budget based on the amount of local currency they have. This allows the proposal department to make optimal suggestions tailored to the needs of travelers, enabling them to efficiently utilize any leftover local currency.

[0032] The explanation unit provides detailed instructions on how to use the services and products suggested by the suggestion unit. For example, if a restaurant meal is suggested, the explanation unit will provide detailed information about the restaurant's location, opening hours, and menu. Similarly, if admission tickets to a tourist attraction are suggested, the explanation unit can provide detailed information about the attraction's location, admission procedures, and fees. The explanation unit can use various methods, such as text, images, and videos, to provide detailed instructions on how to use the services and products. Furthermore, the explanation unit can use AI to take information about suggested services and products as input and output instructions on how to use them. For example, based on the suggested restaurant information, the explanation unit can collect reviews and ratings for that restaurant and provide them to travelers. The explanation unit can also generate videos to explain admission procedures and important points to note at tourist attractions in detail. This allows travelers to obtain information in a visually easy-to-understand format. Additionally, the explanation unit provides detailed instructions on the procedures and important points to note when travelers use the suggested services and products. For example, it can specifically explain the procedures for purchasing admission tickets to tourist attractions and how to make reservations at restaurants. This allows travelers to smoothly utilize the suggested services and products.

[0033] The update unit updates the balance in real time after the traveler has used the services or products explained by the explanation unit and makes the next suggestion. For example, if a traveler eats at a restaurant, the cost of the meal is entered into the system and the balance is updated in real time. Next, the update unit makes the next suggestion based on the updated balance. The update unit can update the balance in real time after use and make the next suggestion. Furthermore, the update unit can use AI to take the balance after use as input and output the next suggestion. For example, if a traveler eats at a restaurant and their balance becomes 500 yen, the update unit will suggest services or products that can be used within 500 yen based on that balance. The update unit can also customize the next suggestion based on the traveler's usage history. For example, it can analyze the traveler's preferences and interests based on data of services and products the traveler has used in the past and make the next suggestion based on that. In this way, the update unit makes the best suggestion tailored to the traveler's needs and enables the traveler to efficiently use their remaining local currency. Furthermore, the update unit can collect feedback from travelers after they have used the suggested services or products and improve the suggestion based on that feedback. This allows the update department to always provide highly accurate suggestions based on the latest information, thereby improving traveler satisfaction.

[0034] The suggestion unit can suggest services and products based on the traveler's current location and interests. For example, the suggestion unit can acquire the traveler's current location using GPS, Wi-Fi, cell towers, etc., and suggest the most suitable services and products based on that information. For example, if the traveler is at a tourist destination, the suggestion unit can suggest admission tickets to that tourist destination or meals at nearby restaurants. The suggestion unit can also acquire the traveler's interests from past behavioral history and survey results, and suggest the most suitable services and products based on that information. For example, the suggestion unit can estimate the traveler's interests based on the traveler's past visits to places and services used, and make suggestions based on that. In this way, the suggestion unit can suggest the most suitable services and products based on the traveler's current location and interests. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can make suggestions using an AI model that takes the traveler's current location and interests as input and outputs the most suitable services and products.

[0035] The explanatory unit can provide detailed explanations of how to use the proposed services or products. For example, if a restaurant meal is proposed, the explanatory unit will provide detailed explanations of the restaurant's location, opening hours, menu, etc. The explanatory unit can provide detailed explanations of how to use the services or products using, for example, text, images, videos, etc. For example, if an admission ticket to a tourist attraction is proposed, the explanatory unit will provide detailed explanations of the attraction's location, admission procedures, and fees. The explanatory unit can also provide detailed explanations of how to use public transportation and the associated fares. For example, the explanatory unit will provide detailed explanations of how to ride buses and trains, and how to pay the fares. This allows the explanatory unit to easily enable travelers to use the proposed services or products. Some or all of the above processing in the explanatory unit may be performed using, for example, AI, or not. For example, the explanatory unit can provide explanations using an AI model that takes information about the proposed services or products as input and outputs instructions on how to use them.

[0036] The update unit can update the balance after use in real time and make the next suggestion. For example, if a traveler eats at a restaurant, the cost of the meal is entered into the system and the balance is updated in real time. Next, the update unit makes the next suggestion based on the updated balance. The update unit can update the balance after use in real time and make the next suggestion. In this way, the update unit can update the balance after the traveler has used the suggested service or product in real time and make the next suggestion. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can perform the update using an AI model that takes the balance after use as input and outputs the next suggestion.

[0037] The reception desk can analyze a traveler's past currency usage history and select the optimal input method. For example, the reception desk may prioritize suggesting input methods (voice, text, etc.) that the traveler has frequently used in the past. The reception desk may also predict and suggest input methods to be used at specific times of day based on the traveler's past currency usage history. For example, the reception desk may analyze patterns in amounts entered by the traveler in the past and suggest the optimal input method. In this way, the reception desk can select the optimal input method by analyzing the traveler's past currency usage history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can perform analysis using an AI model that takes the traveler's past currency usage history as input and outputs the optimal input method.

[0038] The reception desk can filter entries based on the traveler's current length of stay and plans when entering local currency. For example, if the traveler's length of stay is short, the reception desk can suggest a simple and quick input method. The reception desk can also suggest the optimal timing for input based on the traveler's plans. The reception desk can determine the priority of the currencies to be entered based on the traveler's length of stay and plans. This allows the reception desk to suggest the optimal input method based on the traveler's length of stay and plans. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can perform filtering using an AI model that takes the traveler's length of stay and plans as input and outputs the optimal input method.

[0039] The reception desk can prioritize inputting the most relevant currency when a traveler enters local currency, taking into account the traveler's geographical location. For example, if a traveler is in a specific region, the reception desk will prioritize inputting the currency most widely used in that region. The reception desk can also suggest the optimal currency option based on the traveler's current location. For example, the reception desk can automatically select the most relevant currency based on the traveler's geographical location. This allows the reception desk to prioritize inputting the most relevant currency by considering the traveler's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can perform selection using an AI model that takes the traveler's geographical location as input and outputs the most relevant currency.

[0040] The reception desk can analyze the traveler's social media activity when inputting local currency and input the relevant currency. For example, the reception desk can suggest a relevant currency based on places the traveler has shared on social media. For example, the reception desk can input a currency related to places or events of interest from the traveler's social media activity. For example, the reception desk can analyze the content of the traveler's social media posts and suggest the optimal currency option. In this way, the reception desk can input a relevant currency by analyzing the traveler's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can perform analysis using an AI model that takes the traveler's social media activity as input and outputs a relevant currency.

[0041] The suggestion function can adjust the level of detail in its suggestions based on the importance of the services and products it proposes. For example, it can provide detailed explanations for highly important services and products, or concise explanations for less important services and products. For example, it can prioritize suggesting highly important services and products based on the traveler's interests. This allows the suggestion function to make more appropriate suggestions by adjusting the level of detail based on the importance of the services and products. Some or all of the above processing in the suggestion function may be performed using AI, or not. For example, the suggestion function can make adjustments using an AI model that takes the importance of services and products as input and outputs the level of detail of the suggestions.

[0042] The suggestion unit can apply different suggestion algorithms depending on the category of service or product when making suggestions. For example, when suggesting restaurants, the suggestion unit may make suggestions based on the type of cuisine and price range. For example, when suggesting tourist destinations, the suggestion unit may make suggestions based on popularity and accessibility. For example, when suggesting transportation, the suggestion unit may make suggestions based on available times and fares. This allows the suggestion unit to make more appropriate suggestions by applying different suggestion algorithms depending on the category of service or product. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can apply this by using an AI model that takes the category of service or product as input and outputs a suggestion algorithm.

[0043] The proposal department can determine the priority of proposals based on the delivery time of services and products. For example, the proposal department may prioritize proposals for services and products with an upcoming delivery date. For example, it may postpone proposals for services and products with a distant delivery date. For example, the proposal department may determine the optimal timing for proposals based on the delivery date. This allows the proposal department to make more appropriate proposals by determining the priority of proposals based on the delivery time of services and products. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can make decisions using an AI model that takes the delivery time of services and products as input and outputs the priority of proposals.

[0044] The suggestion unit can adjust the order of suggestions based on the relevance of services and products. For example, the suggestion unit may prioritize suggesting services and products related to the traveler's current location. It may also prioritize suggesting services and products related to the traveler's interests. For example, the suggestion unit may suggest highly relevant services and products based on the traveler's past usage history. This allows the suggestion unit to make more appropriate suggestions by adjusting the order of suggestions based on the relevance of services and products. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can make adjustments using an AI model that takes the relevance of services and products as input and outputs the order of suggestions.

[0045] The explanation unit can adjust the level of detail in how to use the service or product during the explanation. For example, the explanation unit will provide detailed explanations for services or products of high importance. For example, the explanation unit can also provide concise explanations for services or products of low importance. For example, the explanation unit will provide detailed explanations based on the traveler's interests. This allows the explanation unit to provide more appropriate explanations by adjusting the level of detail in how to use the service or product. Some or all of the above processing in the explanation unit may be performed using AI, for example, or not using AI. For example, the explanation unit can make adjustments using an AI model that takes the importance of the service or product as input and outputs the level of detail in the explanation.

[0046] The explanation unit can apply different explanation algorithms depending on the category of service or product when providing explanations. For example, when explaining a restaurant, the explanation unit might base its explanation on the type of cuisine and price range. When explaining a tourist destination, for example, the explanation unit might base its explanation on popularity and accessibility. When explaining transportation, for example, the explanation unit might base its explanation on available times and fares. This allows the explanation unit to provide more appropriate explanations by applying different explanation algorithms depending on the category of service or product. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can be applied using an AI model that takes the category of service or product as input and outputs an explanation algorithm.

[0047] The explanation unit can determine the priority of explanations based on the availability of services and products during the explanation process. For example, the explanation unit will prioritize explaining services and products that are available soon. For example, it may postpone explaining services and products that are not available for a long time. The explanation unit will determine the optimal timing for explanation based on the availability period. This allows the explanation unit to provide more appropriate explanations by prioritizing explanations based on the availability period of services and products. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can make decisions using an AI model that takes the availability period of services and products as input and outputs the priority of explanations.

[0048] The explanation unit can adjust the order of explanations based on the relevance of services and products during the explanation process. For example, the explanation unit may prioritize explaining services and products related to the traveler's current location. It may also prioritize explaining services and products related to the traveler's interests. For example, the explanation unit may prioritize explaining services and products that are highly relevant based on the traveler's past usage history. This allows the explanation unit to provide more appropriate explanations by adjusting the order of explanations based on the relevance of services and products. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can perform adjustments using an AI model that takes the relevance of services and products as input and outputs the order of explanations.

[0049] The update unit can optimize the update algorithm by referring to past usage history when updating the balance. For example, the update unit can propose the optimal update algorithm based on the traveler's past usage history. The update unit can also, for example, analyze the traveler's past usage patterns and propose the optimal update timing. For example, the update unit can propose the optimal update method from the traveler's past usage history. In this way, the update unit can apply the optimal update algorithm by referring to past usage history. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can perform optimization using an AI model that takes past usage history as input and outputs an update algorithm.

[0050] The update unit can adjust the frequency of updates based on the traveler's current length of stay and plans when updating the balance. For example, the update unit will update the balance more frequently if the traveler's length of stay is short. The update unit can also suggest an optimal update frequency based on the traveler's plans. The update unit adjusts the timing of updates based on the traveler's length of stay and plans. This allows the update unit to suggest an optimal update frequency based on the traveler's length of stay and plans. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can make adjustments using an AI model that takes the traveler's length of stay and plans as input and outputs the update frequency.

[0051] The update unit can select the optimal update method when updating the balance, taking into account the traveler's geographical location. For example, if the traveler is in a specific region, the update unit will suggest the most suitable update method for that region. The update unit can also suggest the optimal update timing based on the traveler's current location. For example, the update unit can automatically select the optimal update method based on the traveler's geographical location. In this way, the update unit can select the optimal update method by taking the traveler's geographical location into consideration. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can perform the selection using an AI model that takes the traveler's geographical location as input and outputs the optimal update method.

[0052] The update unit can analyze the traveler's social media activity and suggest an update method when updating the balance. For example, the update unit can suggest the optimal update method based on the places the traveler has shared on social media. For example, the update unit can also suggest an update method related to places or events of interest based on the traveler's social media activity. For example, the update unit can analyze the content of the traveler's social media posts and suggest the optimal update method. In this way, the update unit can suggest the optimal update method by analyzing the traveler's social media activity. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can perform analysis using an AI model that takes the traveler's social media activity as input and outputs the optimal update method.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The reception desk can analyze a traveler's past currency usage history and select the optimal input method. For example, the reception desk may prioritize suggesting input methods (voice, text, etc.) that the traveler has frequently used in the past. The reception desk may also predict and suggest input methods to be used at specific times of day based on the traveler's past currency usage history. For example, the reception desk may analyze patterns in amounts entered by the traveler in the past and suggest the optimal input method. In this way, the reception desk can select the optimal input method by analyzing the traveler's past currency usage history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can perform analysis using an AI model that takes the traveler's past currency usage history as input and outputs the optimal input method.

[0055] The explanation unit can adjust the level of detail in how to use the service or product during the explanation. For example, the explanation unit will provide detailed explanations for services or products of high importance. For example, the explanation unit can also provide concise explanations for services or products of low importance. For example, the explanation unit will provide detailed explanations based on the traveler's interests. This allows the explanation unit to provide more appropriate explanations by adjusting the level of detail in how to use the service or product. Some or all of the above processing in the explanation unit may be performed using AI, for example, or not using AI. For example, the explanation unit can make adjustments using an AI model that takes the importance of the service or product as input and outputs the level of detail in the explanation.

[0056] The update unit can optimize the update algorithm by referring to past usage history when updating the balance. For example, the update unit can propose the optimal update algorithm based on the traveler's past usage history. The update unit can also, for example, analyze the traveler's past usage patterns and propose the optimal update timing. For example, the update unit can propose the optimal update method from the traveler's past usage history. In this way, the update unit can apply the optimal update algorithm by referring to past usage history. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can perform optimization using an AI model that takes past usage history as input and outputs an update algorithm.

[0057] The suggestion function can adjust the level of detail in its suggestions based on the importance of the services and products it proposes. For example, it can provide detailed explanations for highly important services and products, or concise explanations for less important services and products. For example, it can prioritize suggesting highly important services and products based on the traveler's interests. This allows the suggestion function to make more appropriate suggestions by adjusting the level of detail based on the importance of the services and products. Some or all of the above processing in the suggestion function may be performed using AI, or not. For example, the suggestion function can make adjustments using an AI model that takes the importance of services and products as input and outputs the level of detail of the suggestions.

[0058] The update unit can adjust the frequency of updates based on the traveler's current length of stay and plans when updating the balance. For example, the update unit will update the balance more frequently if the traveler's length of stay is short. The update unit can also suggest an optimal update frequency based on the traveler's plans. The update unit adjusts the timing of updates based on the traveler's length of stay and plans. This allows the update unit to suggest an optimal update frequency based on the traveler's length of stay and plans. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can make adjustments using an AI model that takes the traveler's length of stay and plans as input and outputs the update frequency.

[0059] The reception desk can prioritize inputting the most relevant currency when a traveler enters local currency, taking into account the traveler's geographical location. For example, if a traveler is in a specific region, the reception desk will prioritize inputting the currency most widely used in that region. The reception desk can also suggest the optimal currency option based on the traveler's current location. For example, the reception desk can automatically select the most relevant currency based on the traveler's geographical location. This allows the reception desk to prioritize inputting the most relevant currency by considering the traveler's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can perform selection using an AI model that takes the traveler's geographical location as input and outputs the most relevant currency.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk enters the amount of local currency the traveler has left over. This amount of local currency may include, for example, banknotes, coins, or electronic money. The reception desk can also automatically recognize the type and amount of local currency the traveler has. For example, it can use a camera to scan banknotes and coins and automatically recognize their value. Step 2: The suggestion department proposes services and products available locally based on the amount entered by the reception department. The suggestion department proposes the most suitable services and products based on the traveler's current location and interests. For example, it may suggest entrance tickets to tourist attractions, meals at nearby restaurants, and information and fares for using public transportation. Step 3: The explanation section provides a detailed explanation of how to use the service or product proposed by the proposal section. For example, it should explain the location, opening hours, and menu of a restaurant, or the location, admission procedures, and fees of a tourist attraction using text, images, videos, etc. Step 4: The update unit updates the balance in real time after the service or product described by the explanation unit has been used, and makes the next suggestion. For example, if a traveler dines at a restaurant, the cost of the meal is entered into the system, and the balance is updated in real time. Next, the system makes the next suggestion based on the updated balance.

[0062] (Example of form 2) A smartphone application according to an embodiment of the present invention is a system for travelers to efficiently use up their remaining local currency. The system allows travelers to input the amount of their remaining local currency and suggests services and products available locally based on that amount. Examples include meals at restaurants, admission tickets to tourist attractions, and transportation. These suggestions are customized based on the traveler's current location and interests. The system also provides detailed instructions on how to use the suggested services and products, making them easy for travelers to use. Furthermore, the system updates the remaining balance in real time after the traveler has used the suggested services and products and makes subsequent suggestions. This mechanism allows travelers to efficiently use up their remaining local currency. For example, if a traveler has 1000 yen in local currency, they input that amount into the system. This information is then entered into the system. Next, the system suggests services and products available locally based on the entered amount. The system suggests the most suitable services and products based on the traveler's current location and interests. For example, if the traveler is at a tourist attraction, it suggests admission tickets to that attraction or meals at nearby restaurants. Also, if the traveler needs to use transportation, it suggests how to use it and the fares. The system provides detailed instructions on how to use the suggested services and products. For example, if a restaurant meal is suggested, the system will provide detailed information about the restaurant's location, opening hours, and menu. Similarly, if an entrance ticket to a tourist attraction is suggested, the system will provide detailed information about the attraction's location, admission procedures, and fees. Furthermore, the system updates the traveler's balance in real time after they have used a suggested service or product, and then makes the next suggestion. When a traveler uses a suggested service or product, the amount spent is entered into the system, and the balance is updated in real time. For example, if a traveler dines at a restaurant, the cost of the meal is entered into the system, and the balance is updated. The system then makes the next suggestion based on the updated balance. This mechanism allows travelers to efficiently use up any remaining local currency. By using the system, travelers can efficiently use up their local currency without wasting it.Furthermore, the system provides optimal suggestions based on the traveler's current location and interests, allowing travelers to use services and products that match their interests. For example, a traveler in a tourist area can use it for admission tickets to that tourist attraction or meals at nearby restaurants. This allows travelers to efficiently use up their local currency and enjoy their trip more. In this way, the smartphone application enables travelers to efficiently use up any leftover local currency.

[0063] The smartphone application according to this embodiment comprises a reception unit, a suggestion unit, an explanation unit, and an update unit. The reception unit receives input from the traveler regarding the amount of local currency the traveler has left over. The amount of local currency the traveler has left over includes, but is not limited to, banknotes, coins, and electronic money. For example, if the traveler has 1,000 yen in local currency, the reception unit can input that amount into the system. The reception unit can also automatically recognize the type and amount of local currency the traveler has. For example, the reception unit can scan banknotes and coins using a camera and automatically recognize their value. The suggestion unit suggests services and products available locally based on the amount entered by the reception unit. For example, the suggestion unit suggests the most suitable services and products based on the traveler's current location and interests. For example, if the traveler is at a tourist destination, the suggestion unit suggests admission tickets to that tourist destination or meals at nearby restaurants. The suggestion unit can also suggest how to use public transportation and the fares if the traveler needs to use public transportation. For example, the suggestion unit suggests meals at restaurants, admission tickets to tourist destinations, and the use of public transportation. The explanation unit provides a detailed explanation of how to use the services or products suggested by the suggestion unit. For example, if a restaurant meal is suggested, the explanation unit will provide a detailed explanation of the restaurant's location, opening hours, menu, etc. Similarly, if an admission ticket to a tourist attraction is suggested, the explanation unit can provide a detailed explanation of the attraction's location, admission procedures, and fees. The explanation unit may use text, images, videos, etc., to provide a detailed explanation of how to use the services or products. The update unit updates the balance in real time after the use of the services or products explained by the explanation unit and makes the next suggestion. For example, if a traveler dines at a restaurant, the cost of the meal is entered into the system, and the balance is updated in real time. Next, the update unit makes the next suggestion based on the updated balance. The update unit can update the balance after use in real time and make the next suggestion. This allows the smartphone application according to the embodiment to efficiently use up any remaining local currency. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without AI.For example, the suggestion unit can make suggestions using an AI model that takes the traveler's current location and interests as input and outputs the most suitable services or products. Some or all of the above processing in the explanation unit may be performed using AI, or not. For example, the explanation unit can provide explanations using an AI model that takes information about the suggested services or products as input and outputs how to use them. Some or all of the above processing in the update unit may be performed using AI, or not. For example, the update unit can perform updates using an AI model that takes the remaining balance after use as input and outputs the next suggestion.

[0064] The reception desk inputs the amount of local currency the traveler has left over. This amount may include, but is not limited to, banknotes, coins, and electronic money. For example, if a traveler has 1,000 yen in local currency, the reception desk can input that amount into the system. The reception desk can also automatically recognize the type and amount of local currency the traveler has. For example, the reception desk can use a camera to scan banknotes and coins and automatically recognize their value. Furthermore, the reception desk can use NFC (Near Field Communication) technology to check the balance of electronic money. This allows the traveler to simply hold their smartphone over the electronic money reader and have the balance automatically read. The reception desk centrally manages this data and can display the total amount of all local currency the traveler has in real time. In addition, based on the amounts entered by the traveler and the amounts recognized, the reception desk can classify the currency by type and display a detailed breakdown. For example, it could show that out of 1000 yen, 500 yen is in banknotes, 300 yen is in coins, and 200 yen is in electronic money. This allows travelers to understand the detailed breakdown of the local currency they possess.

[0065] The suggestion department proposes services and products available locally based on the amount entered by the reception department. For example, the suggestion department proposes the most suitable services and products based on the traveler's current location and interests. For instance, if the traveler is at a tourist destination, the suggestion department will suggest admission tickets to that destination or meals at nearby restaurants. The suggestion department can also suggest transportation options and fares if the traveler needs to use public transport. For example, the suggestion department will suggest meals at restaurants, admission tickets to tourist destinations, and public transport. Furthermore, the suggestion department can utilize AI to learn the traveler's past behavior history and preferences. For example, it will analyze the traveler's preferences and interests based on data on places the traveler has visited and services they have used in the past, and then make customized suggestions based on that. Using an AI model, the suggestion department takes the traveler's current location and interests as input and outputs the most suitable services and products. For example, if the traveler is interested in art museums, the suggestion department will suggest admission tickets to nearby art museums. The suggestion department can also suggest services and products available within the traveler's budget based on the amount of local currency they have. This allows the proposal department to make optimal suggestions tailored to the needs of travelers, enabling them to efficiently utilize any leftover local currency.

[0066] The explanation unit provides detailed instructions on how to use the services and products suggested by the suggestion unit. For example, if a restaurant meal is suggested, the explanation unit will provide detailed information about the restaurant's location, opening hours, and menu. Similarly, if admission tickets to a tourist attraction are suggested, the explanation unit can provide detailed information about the attraction's location, admission procedures, and fees. The explanation unit can use various methods, such as text, images, and videos, to provide detailed instructions on how to use the services and products. Furthermore, the explanation unit can use AI to take information about suggested services and products as input and output instructions on how to use them. For example, based on the suggested restaurant information, the explanation unit can collect reviews and ratings for that restaurant and provide them to travelers. The explanation unit can also generate videos to explain admission procedures and important points to note at tourist attractions in detail. This allows travelers to obtain information in a visually easy-to-understand format. Additionally, the explanation unit provides detailed instructions on the procedures and important points to note when travelers use the suggested services and products. For example, it can specifically explain the procedures for purchasing admission tickets to tourist attractions and how to make reservations at restaurants. This allows travelers to smoothly utilize the suggested services and products.

[0067] The update unit updates the balance in real time after the traveler has used the services or products explained by the explanation unit and makes the next suggestion. For example, if a traveler eats at a restaurant, the cost of the meal is entered into the system and the balance is updated in real time. Next, the update unit makes the next suggestion based on the updated balance. The update unit can update the balance in real time after use and make the next suggestion. Furthermore, the update unit can use AI to take the balance after use as input and output the next suggestion. For example, if a traveler eats at a restaurant and their balance becomes 500 yen, the update unit will suggest services or products that can be used within 500 yen based on that balance. The update unit can also customize the next suggestion based on the traveler's usage history. For example, it can analyze the traveler's preferences and interests based on data of services and products the traveler has used in the past and make the next suggestion based on that. In this way, the update unit makes the best suggestion tailored to the traveler's needs and enables the traveler to efficiently use their remaining local currency. Furthermore, the update unit can collect feedback from travelers after they have used the suggested services or products and improve the suggestion based on that feedback. This allows the update department to always provide highly accurate suggestions based on the latest information, thereby improving traveler satisfaction.

[0068] The suggestion unit can suggest services and products based on the traveler's current location and interests. For example, the suggestion unit can acquire the traveler's current location using GPS, Wi-Fi, cell towers, etc., and suggest the most suitable services and products based on that information. For example, if the traveler is at a tourist destination, the suggestion unit can suggest admission tickets to that tourist destination or meals at nearby restaurants. The suggestion unit can also acquire the traveler's interests from past behavioral history and survey results, and suggest the most suitable services and products based on that information. For example, the suggestion unit can estimate the traveler's interests based on the traveler's past visits to places and services used, and make suggestions based on that. In this way, the suggestion unit can suggest the most suitable services and products based on the traveler's current location and interests. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can make suggestions using an AI model that takes the traveler's current location and interests as input and outputs the most suitable services and products.

[0069] The explanatory unit can provide detailed explanations of how to use the proposed services or products. For example, if a restaurant meal is proposed, the explanatory unit will provide detailed explanations of the restaurant's location, opening hours, menu, etc. The explanatory unit can provide detailed explanations of how to use the services or products using, for example, text, images, videos, etc. For example, if an admission ticket to a tourist attraction is proposed, the explanatory unit will provide detailed explanations of the attraction's location, admission procedures, and fees. The explanatory unit can also provide detailed explanations of how to use public transportation and the associated fares. For example, the explanatory unit will provide detailed explanations of how to ride buses and trains, and how to pay the fares. This allows the explanatory unit to easily enable travelers to use the proposed services or products. Some or all of the above processing in the explanatory unit may be performed using, for example, AI, or not. For example, the explanatory unit can provide explanations using an AI model that takes information about the proposed services or products as input and outputs instructions on how to use them.

[0070] The update unit can update the balance after use in real time and make the next suggestion. For example, if a traveler eats at a restaurant, the cost of the meal is entered into the system and the balance is updated in real time. Next, the update unit makes the next suggestion based on the updated balance. The update unit can update the balance after use in real time and make the next suggestion. In this way, the update unit can update the balance after the traveler has used the suggested service or product in real time and make the next suggestion. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can perform the update using an AI model that takes the balance after use as input and outputs the next suggestion.

[0071] The reception desk can estimate the traveler's emotions and adjust the timing of local currency input based on the estimated emotions. For example, if the traveler is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the traveler is relaxed, the reception desk can provide detailed input options and suggest customizable input methods. For example, if the traveler is in a hurry, the reception desk can prioritize voice input to allow for quick input of the local currency amount. This allows the reception desk to provide more appropriate input by adjusting the timing of local currency input according to the traveler's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0072] The reception desk can analyze a traveler's past currency usage history and select the optimal input method. For example, the reception desk may prioritize suggesting input methods (voice, text, etc.) that the traveler has frequently used in the past. The reception desk may also predict and suggest input methods to be used at specific times of day based on the traveler's past currency usage history. For example, the reception desk may analyze patterns in amounts entered by the traveler in the past and suggest the optimal input method. In this way, the reception desk can select the optimal input method by analyzing the traveler's past currency usage history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can perform analysis using an AI model that takes the traveler's past currency usage history as input and outputs the optimal input method.

[0073] The reception desk can filter entries based on the traveler's current length of stay and plans when entering local currency. For example, if the traveler's length of stay is short, the reception desk can suggest a simple and quick input method. The reception desk can also suggest the optimal timing for input based on the traveler's plans. The reception desk can determine the priority of the currencies to be entered based on the traveler's length of stay and plans. This allows the reception desk to suggest the optimal input method based on the traveler's length of stay and plans. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can perform filtering using an AI model that takes the traveler's length of stay and plans as input and outputs the optimal input method.

[0074] The reception desk can estimate the traveler's emotions and determine the priority of the currency to be entered based on the estimated emotions. For example, if the traveler is stressed, the reception desk will prioritize the currency that is easiest for them to use. If the traveler is relaxed, the reception desk may offer multiple currency options to broaden their choices. If the traveler is in a hurry, the reception desk will prioritize the currency that can be used most quickly. This allows the reception desk to make more appropriate currency inputs by prioritizing the currency to be entered according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk may input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0075] The reception desk can prioritize inputting the most relevant currency when a traveler enters local currency, taking into account the traveler's geographical location. For example, if a traveler is in a specific region, the reception desk will prioritize inputting the currency most widely used in that region. The reception desk can also suggest the optimal currency option based on the traveler's current location. For example, the reception desk can automatically select the most relevant currency based on the traveler's geographical location. This allows the reception desk to prioritize inputting the most relevant currency by considering the traveler's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can perform selection using an AI model that takes the traveler's geographical location as input and outputs the most relevant currency.

[0076] The reception desk can analyze the traveler's social media activity when inputting local currency and input the relevant currency. For example, the reception desk can suggest a relevant currency based on places the traveler has shared on social media. For example, the reception desk can input a currency related to places or events of interest from the traveler's social media activity. For example, the reception desk can analyze the content of the traveler's social media posts and suggest the optimal currency option. In this way, the reception desk can input a relevant currency by analyzing the traveler's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can perform analysis using an AI model that takes the traveler's social media activity as input and outputs a relevant currency.

[0077] The suggestion unit can estimate the traveler's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the traveler is relaxed, the suggestion unit can provide detailed suggestions and broaden the range of options. If the traveler is in a hurry, the suggestion unit can provide concise and quick suggestions. If the traveler is excited, the suggestion unit can provide visually appealing suggestions. This allows the suggestion unit to provide more appropriate suggestions by adjusting the way it presents its suggestions according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0078] The suggestion function can adjust the level of detail in its suggestions based on the importance of the services and products it proposes. For example, it can provide detailed explanations for highly important services and products, or concise explanations for less important services and products. For example, it can prioritize suggesting highly important services and products based on the traveler's interests. This allows the suggestion function to make more appropriate suggestions by adjusting the level of detail based on the importance of the services and products. Some or all of the above processing in the suggestion function may be performed using AI, or not. For example, the suggestion function can make adjustments using an AI model that takes the importance of services and products as input and outputs the level of detail of the suggestions.

[0079] The suggestion unit can apply different suggestion algorithms depending on the category of service or product when making suggestions. For example, when suggesting restaurants, the suggestion unit may make suggestions based on the type of cuisine and price range. For example, when suggesting tourist destinations, the suggestion unit may make suggestions based on popularity and accessibility. For example, when suggesting transportation, the suggestion unit may make suggestions based on available times and fares. This allows the suggestion unit to make more appropriate suggestions by applying different suggestion algorithms depending on the category of service or product. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can apply this by using an AI model that takes the category of service or product as input and outputs a suggestion algorithm.

[0080] The suggestion unit can estimate the traveler's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the traveler is relaxed, the suggestion unit will provide detailed suggestions. If the traveler is in a hurry, the suggestion unit may provide concise suggestions. If the traveler is excited, the suggestion unit may provide visually appealing suggestions. This allows the suggestion unit to provide more appropriate suggestions by adjusting the length of the suggestions according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0081] The proposal department can determine the priority of proposals based on the delivery time of services and products. For example, the proposal department may prioritize proposals for services and products with an upcoming delivery date. For example, it may postpone proposals for services and products with a distant delivery date. For example, the proposal department may determine the optimal timing for proposals based on the delivery date. This allows the proposal department to make more appropriate proposals by determining the priority of proposals based on the delivery time of services and products. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can make decisions using an AI model that takes the delivery time of services and products as input and outputs the priority of proposals.

[0082] The suggestion unit can adjust the order of suggestions based on the relevance of services and products. For example, the suggestion unit may prioritize suggesting services and products related to the traveler's current location. It may also prioritize suggesting services and products related to the traveler's interests. For example, the suggestion unit may suggest highly relevant services and products based on the traveler's past usage history. This allows the suggestion unit to make more appropriate suggestions by adjusting the order of suggestions based on the relevance of services and products. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can make adjustments using an AI model that takes the relevance of services and products as input and outputs the order of suggestions.

[0083] The explanatory unit can estimate the traveler's emotions and adjust the way it presents the explanation based on the estimated emotions. For example, if the traveler is relaxed, the explanatory unit can provide a detailed explanation. If the traveler is in a hurry, for example, the explanatory unit can provide a concise explanation. If the traveler is excited, for example, the explanatory unit can provide a visually appealing explanation. This allows the explanatory unit to provide more appropriate explanations by adjusting the way it presents the explanation according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the explanatory unit may be performed using AI or not using AI. For example, the explanatory unit can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0084] The explanation unit can adjust the level of detail in how to use the service or product during the explanation. For example, the explanation unit will provide detailed explanations for services or products of high importance. For example, the explanation unit can also provide concise explanations for services or products of low importance. For example, the explanation unit will provide detailed explanations based on the traveler's interests. This allows the explanation unit to provide more appropriate explanations by adjusting the level of detail in how to use the service or product. Some or all of the above processing in the explanation unit may be performed using AI, for example, or not using AI. For example, the explanation unit can make adjustments using an AI model that takes the importance of the service or product as input and outputs the level of detail in the explanation.

[0085] The explanation unit can apply different explanation algorithms depending on the category of service or product when providing explanations. For example, when explaining a restaurant, the explanation unit might base its explanation on the type of cuisine and price range. When explaining a tourist destination, for example, the explanation unit might base its explanation on popularity and accessibility. When explaining transportation, for example, the explanation unit might base its explanation on available times and fares. This allows the explanation unit to provide more appropriate explanations by applying different explanation algorithms depending on the category of service or product. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can be applied using an AI model that takes the category of service or product as input and outputs an explanation algorithm.

[0086] The descriptive unit can estimate the traveler's emotions and adjust the length of the description based on the estimated emotions. For example, if the traveler is relaxed, the descriptive unit will provide a detailed description. For example, if the traveler is in a hurry, the descriptive unit may provide a concise description. For example, if the traveler is excited, the descriptive unit may provide a visually appealing description. This allows the descriptive unit to provide more appropriate descriptions by adjusting the length of the description according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the descriptive unit may be performed using AI or not using AI. For example, the descriptive unit can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0087] The explanation unit can determine the priority of explanations based on the availability of services and products during the explanation process. For example, the explanation unit will prioritize explaining services and products that are available soon. For example, it may postpone explaining services and products that are not available for a long time. The explanation unit will determine the optimal timing for explanation based on the availability period. This allows the explanation unit to provide more appropriate explanations by prioritizing explanations based on the availability period of services and products. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can make decisions using an AI model that takes the availability period of services and products as input and outputs the priority of explanations.

[0088] The explanation unit can adjust the order of explanations based on the relevance of services and products during the explanation process. For example, the explanation unit may prioritize explaining services and products related to the traveler's current location. It may also prioritize explaining services and products related to the traveler's interests. For example, the explanation unit may prioritize explaining services and products that are highly relevant based on the traveler's past usage history. This allows the explanation unit to provide more appropriate explanations by adjusting the order of explanations based on the relevance of services and products. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can perform adjustments using an AI model that takes the relevance of services and products as input and outputs the order of explanations.

[0089] The update unit can estimate the traveler's emotions and adjust the timing of balance updates based on the estimated emotions. For example, if the traveler is relaxed, the update unit can perform a detailed balance update. For example, if the traveler is in a hurry, the update unit can perform a concise balance update. For example, if the traveler is excited, the update unit can perform a visually appealing balance update. This allows the update unit to perform more appropriate balance updates by adjusting the timing of balance updates according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI or not using AI. For example, the update unit can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0090] The update unit can optimize the update algorithm by referring to past usage history when updating the balance. For example, the update unit can propose the optimal update algorithm based on the traveler's past usage history. The update unit can also, for example, analyze the traveler's past usage patterns and propose the optimal update timing. For example, the update unit can propose the optimal update method from the traveler's past usage history. In this way, the update unit can apply the optimal update algorithm by referring to past usage history. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can perform optimization using an AI model that takes past usage history as input and outputs an update algorithm.

[0091] The update unit can adjust the frequency of updates based on the traveler's current length of stay and plans when updating the balance. For example, the update unit will update the balance more frequently if the traveler's length of stay is short. The update unit can also suggest an optimal update frequency based on the traveler's plans. The update unit adjusts the timing of updates based on the traveler's length of stay and plans. This allows the update unit to suggest an optimal update frequency based on the traveler's length of stay and plans. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can make adjustments using an AI model that takes the traveler's length of stay and plans as input and outputs the update frequency.

[0092] The update unit can estimate the traveler's emotions and determine the priority of balance updates based on the estimated emotions. For example, if the traveler is relaxed, the update unit may prioritize detailed balance updates. If the traveler is in a hurry, the update unit may also prioritize concise balance updates. If the traveler is excited, the update unit may prioritize visually appealing balance updates. This allows the update unit to provide more appropriate balance updates by prioritizing them according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0093] The update unit can select the optimal update method when updating the balance, taking into account the traveler's geographical location. For example, if the traveler is in a specific region, the update unit will suggest the most suitable update method for that region. The update unit can also suggest the optimal update timing based on the traveler's current location. For example, the update unit can automatically select the optimal update method based on the traveler's geographical location. In this way, the update unit can select the optimal update method by taking the traveler's geographical location into consideration. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can perform the selection using an AI model that takes the traveler's geographical location as input and outputs the optimal update method.

[0094] The update unit can analyze the traveler's social media activity and suggest an update method when updating the balance. For example, the update unit can suggest the optimal update method based on the places the traveler has shared on social media. For example, the update unit can also suggest an update method related to places or events of interest based on the traveler's social media activity. For example, the update unit can analyze the content of the traveler's social media posts and suggest the optimal update method. In this way, the update unit can suggest the optimal update method by analyzing the traveler's social media activity. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can perform analysis using an AI model that takes the traveler's social media activity as input and outputs the optimal update method.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The reception desk can analyze a traveler's past currency usage history and select the optimal input method. For example, the reception desk may prioritize suggesting input methods (voice, text, etc.) that the traveler has frequently used in the past. The reception desk may also predict and suggest input methods to be used at specific times of day based on the traveler's past currency usage history. For example, the reception desk may analyze patterns in amounts entered by the traveler in the past and suggest the optimal input method. In this way, the reception desk can select the optimal input method by analyzing the traveler's past currency usage history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can perform analysis using an AI model that takes the traveler's past currency usage history as input and outputs the optimal input method.

[0097] The suggestion unit can estimate the traveler's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the traveler is relaxed, the suggestion unit can provide detailed suggestions and broaden the range of options. If the traveler is in a hurry, the suggestion unit can provide concise and quick suggestions. If the traveler is excited, the suggestion unit can provide visually appealing suggestions. This allows the suggestion unit to provide more appropriate suggestions by adjusting the way it presents its suggestions according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0098] The explanation unit can adjust the level of detail in how to use the service or product during the explanation. For example, the explanation unit will provide detailed explanations for services or products of high importance. For example, the explanation unit can also provide concise explanations for services or products of low importance. For example, the explanation unit will provide detailed explanations based on the traveler's interests. This allows the explanation unit to provide more appropriate explanations by adjusting the level of detail in how to use the service or product. Some or all of the above processing in the explanation unit may be performed using AI, for example, or not using AI. For example, the explanation unit can make adjustments using an AI model that takes the importance of the service or product as input and outputs the level of detail in the explanation.

[0099] The update unit can optimize the update algorithm by referring to past usage history when updating the balance. For example, the update unit can propose the optimal update algorithm based on the traveler's past usage history. The update unit can also, for example, analyze the traveler's past usage patterns and propose the optimal update timing. For example, the update unit can propose the optimal update method from the traveler's past usage history. In this way, the update unit can apply the optimal update algorithm by referring to past usage history. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can perform optimization using an AI model that takes past usage history as input and outputs an update algorithm.

[0100] The reception desk can estimate the traveler's emotions and adjust the timing of local currency input based on the estimated emotions. For example, if the traveler is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the traveler is relaxed, the reception desk can provide detailed input options and suggest customizable input methods. For example, if the traveler is in a hurry, the reception desk can prioritize voice input to allow for quick input of the local currency amount. This allows the reception desk to provide more appropriate input by adjusting the timing of local currency input according to the traveler's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0101] The suggestion function can adjust the level of detail in its suggestions based on the importance of the services and products it proposes. For example, it can provide detailed explanations for highly important services and products, or concise explanations for less important services and products. For example, it can prioritize suggesting highly important services and products based on the traveler's interests. This allows the suggestion function to make more appropriate suggestions by adjusting the level of detail based on the importance of the services and products. Some or all of the above processing in the suggestion function may be performed using AI, or not. For example, the suggestion function can make adjustments using an AI model that takes the importance of services and products as input and outputs the level of detail of the suggestions.

[0102] The suggestion unit can estimate the traveler's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the traveler is relaxed, the suggestion unit will provide detailed suggestions. If the traveler is in a hurry, the suggestion unit may provide concise suggestions. If the traveler is excited, the suggestion unit may provide visually appealing suggestions. This allows the suggestion unit to provide more appropriate suggestions by adjusting the length of the suggestions according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0103] The update unit can adjust the frequency of updates based on the traveler's current length of stay and plans when updating the balance. For example, the update unit will update the balance more frequently if the traveler's length of stay is short. The update unit can also suggest an optimal update frequency based on the traveler's plans. The update unit adjusts the timing of updates based on the traveler's length of stay and plans. This allows the update unit to suggest an optimal update frequency based on the traveler's length of stay and plans. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can make adjustments using an AI model that takes the traveler's length of stay and plans as input and outputs the update frequency.

[0104] The explanatory unit can estimate the traveler's emotions and adjust the way it presents the explanation based on the estimated emotions. For example, if the traveler is relaxed, the explanatory unit can provide a detailed explanation. If the traveler is in a hurry, for example, the explanatory unit can provide a concise explanation. If the traveler is excited, for example, the explanatory unit can provide a visually appealing explanation. This allows the explanatory unit to provide more appropriate explanations by adjusting the way it presents the explanation according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the explanatory unit may be performed using AI or not using AI. For example, the explanatory unit can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0105] The reception desk can prioritize inputting the most relevant currency when a traveler enters local currency, taking into account the traveler's geographical location. For example, if a traveler is in a specific region, the reception desk will prioritize inputting the currency most widely used in that region. The reception desk can also suggest the optimal currency option based on the traveler's current location. For example, the reception desk can automatically select the most relevant currency based on the traveler's geographical location. This allows the reception desk to prioritize inputting the most relevant currency by considering the traveler's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can perform selection using an AI model that takes the traveler's geographical location as input and outputs the most relevant currency.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The reception desk enters the amount of local currency the traveler has left over. This amount of local currency may include, for example, banknotes, coins, or electronic money. The reception desk can also automatically recognize the type and amount of local currency the traveler has. For example, it can use a camera to scan banknotes and coins and automatically recognize their value. Step 2: The suggestion department proposes services and products available locally based on the amount entered by the reception department. The suggestion department proposes the most suitable services and products based on the traveler's current location and interests. For example, it may suggest entrance tickets to tourist attractions, meals at nearby restaurants, and information and fares for using public transportation. Step 3: The explanation section provides a detailed explanation of how to use the service or product proposed by the proposal section. For example, it should explain the location, opening hours, and menu of a restaurant, or the location, admission procedures, and fees of a tourist attraction using text, images, videos, etc. Step 4: The update unit updates the balance in real time after the service or product described by the explanation unit has been used, and makes the next suggestion. For example, if a traveler dines at a restaurant, the cost of the meal is entered into the system, and the balance is updated in real time. Next, the system makes the next suggestion based on the updated balance.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0110] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0111] Each of the multiple elements described above, including the reception unit, proposal unit, explanation unit, and update unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing the traveler to input the amount of remaining local currency. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to suggest the most suitable services and products based on the traveler's current location and interests. The explanation unit is implemented by the control unit 46A of the smart device 14, for example, to explain in detail how to use the suggested services and products. The update unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to update the balance after use in real time and make the next suggestion. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0115] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0116] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0118] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0119] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0120] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0121] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0122] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0126] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the reception unit, proposal unit, explanation unit, and update unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, allowing the traveler to input the amount of remaining local currency. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, suggesting the most suitable services and products based on the traveler's current location and interests. The explanation unit is implemented, for example, by the control unit 46A of the smart glasses 214, providing a detailed explanation of how to use the suggested services and products. The update unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, updating the balance after use in real time and making the next suggestion. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the reception unit, proposal unit, explanation unit, and update unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, allowing the traveler to input the amount of remaining local currency. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, suggesting the most suitable services and products based on the traveler's current location and interests. The explanation unit is implemented by, for example, the control unit 46A of the headset terminal 314, providing a detailed explanation of how to use the suggested services and products. The update unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, updating the balance after use in real time and making the next suggestion. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0152] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the reception unit, proposal unit, explanation unit, and update unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, allowing travelers to input the amount of remaining local currency. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, suggesting the most suitable services and products based on the traveler's current location and interests. The explanation unit is implemented by, for example, the control unit 46A of the robot 414, providing a detailed explanation of how to use the suggested services and products. The update unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, updating the balance after use in real time and making the next suggestion. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0161] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0162] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0163] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0164] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0165] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0166] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0167] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0168] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0169] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0170] 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.

[0171] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0172] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0173] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0174] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0175] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0176] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0177] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0178] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0179] (Note 1) A reception desk where travelers enter the amount of leftover local currency, Based on the amount entered by the reception department, the proposal department proposes services and products available locally. The aforementioned proposal section includes an explanation section that provides a detailed explanation of how to use the services and products proposed by the proposal section, The system includes an update unit that updates the balance in real time after using the services or products explained by the explanation unit and makes the next suggestion. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We suggest services and products based on the traveler's current location and interests. The system described in Appendix 1, characterized by the features described herein. (Note 3) The above explanatory section is, A detailed explanation of how to use the proposed services and products. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned update unit is, The remaining balance after use is updated in real time, and the next suggestion is made. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the traveler's emotions and adjusts the timing of local currency input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Analyze the traveler's past currency usage history to select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When entering local currency, filtering is performed based on the traveler's current length of stay and plans. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the traveler's sentiment and determines the priority of input currencies based on the estimated traveler's sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering local currency, the system prioritizes the input of the most relevant currency, taking into account the traveler's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering local currency, the system analyzes the traveler's social media activity and enters the relevant currency. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned proposal section is, We estimate the traveler's emotions and adjust the way the proposal is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the service or product. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making proposals, different proposal algorithms are applied depending on the service or product category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, Estimate the traveler's sentiment and adjust the length of the suggestion based on the estimated traveler's sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, prioritize the proposals based on the timing of service or product delivery. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the services and products. The system described in Appendix 1, characterized by the features described herein. (Note 17) The above explanatory section is, The system estimates the traveler's emotions and adjusts the way explanations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The above explanatory section is, Adjust the level of detail in the explanation regarding how to use the service or product. The system described in Appendix 1, characterized by the features described herein. (Note 19) The above explanatory section is, When providing explanations, different explanation algorithms are applied depending on the service or product category. The system described in Appendix 1, characterized by the features described herein. (Note 20) The above explanatory section is, Estimate the traveler's emotions and adjust the length of the explanation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The above explanatory section is, When giving an explanation, prioritize the explanation based on the timing of when the service or product will be available. The system described in Appendix 1, characterized by the features described herein. (Note 22) The above explanatory section is, When explaining, adjust the order of explanations based on the relevance of the services or products. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned update unit is, We estimate the traveler's sentiment and adjust the timing of balance updates based on the estimated traveler's sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned update unit is, When updating the balance, the update algorithm is optimized by referring to past transaction history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned update unit is, When updating the balance, the update frequency will be adjusted based on the traveler's current length of stay and plans. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned update unit is, The system estimates the sentiment of travelers and determines the priority of balance updates based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned update unit is, When updating the balance, the system selects the optimal update method by considering the traveler's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned update unit is, When updating your balance, we analyze your social media activity and suggest ways to update it. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where travelers enter the amount of leftover local currency, Based on the amount entered by the reception department, the proposal department proposes services and products available locally. The aforementioned proposal section includes an explanation section that provides a detailed explanation of how to use the services and products proposed by the proposal section, The system includes an update unit that updates the balance in real time after using the services or products explained by the explanation unit and makes the next suggestion. A system characterized by the following features.

2. The aforementioned proposal section is, We suggest services and products based on the traveler's current location and interests. The system according to feature 1.

3. The above explanatory section is, A detailed explanation of how to use the proposed services and products. The system according to feature 1.

4. The aforementioned update unit is, The remaining balance after use is updated in real time, and the next suggestion is made. The system according to feature 1.

5. The aforementioned reception unit is It estimates the traveler's emotions and adjusts the timing of local currency input based on the estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyze the traveler's past currency usage history to select the optimal input method. The system according to feature 1.

7. The aforementioned reception unit is When entering local currency, filtering is performed based on the traveler's current length of stay and plans. The system according to feature 1.

8. The aforementioned reception unit is It estimates the traveler's sentiment and determines the priority of input currencies based on the estimated traveler's sentiment. The system according to feature 1.

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