System

Abstract

An object of a system according to an embodiment is to provide optimal services and interpreters to foreigners visiting Japan.SOLUTION: A system includes a reception unit, an analysis unit, and a provision unit. The reception unit receives an input of a destination or a desired service from a foreigner visiting Japan. The analysis unit analyzes the information received by the reception unit and proposes a service or an interpreter. The providing unit provides the service or the interpreter proposed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a problem that it was difficult for foreign visitors to Japan to have a comfortable travel experience due to the language barrier.

[0005] The system according to the embodiment aims to provide optimal services and interpretation to foreign visitors to Japan. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives input from foreign visitors to Japan about their destination and desired services. The analysis unit analyzes the information received by the reception unit and proposes services and interpretations. The provision unit provides the services and interpretations proposed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal services and interpretation to foreign visitors to Japan. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A smart concierge service according to an embodiment of the present invention is a system that provides optimal services and interpretation to foreign visitors to Japan. In this system, foreign visitors input their destination and desired services into their smartphones, and a generation AI analyzes the information and suggests optimal services and interpretations. Examples of such services include tourist attraction guides, restaurant reservations, and transportation information. Real-time interpretation services are also provided to eliminate language barriers. This allows foreign visitors to enjoy a comfortable travel experience. For example, by simply inputting their destination and desired services, the generation AI analyzes the information and suggests optimal services and interpretations. This allows foreign visitors to intuitively receive optimal services and interpretations without complex operations. For example, tourist attraction guides, restaurant reservations, and transportation information can be smoothly provided, reducing stress during travel. Furthermore, the real-time interpretation service allows visitors to understand local information without experiencing language barriers.

[0029] A smart concierge service according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit accepts input of a destination and desired services from a foreign visitor to Japan. The information entered by the foreign visitor to Japan includes, but is not limited to, tourist attractions, restaurants, and transportation. The reception unit accepts input, for example, via a smartphone application. The reception unit can also accept voice input and text input. The analysis unit uses a generation AI to analyze the information accepted by the reception unit and propose optimal services and interpretations. The analysis can be performed, for example, using natural language processing or a machine learning algorithm, but is not limited to, examples. For example, the generation AI proposes tourist attraction guides, restaurant reservations, and transportation information tailored to the needs of the foreign visitor to Japan. The analysis unit can also analyze the language entered by the foreign visitor to Japan and translate information about destinations and services into the foreign visitor's native language. For example, the generation AI can translate menus written in Japanese into English in real time so that an English-speaking foreign visitor to Japan can understand them. The provision unit provides the services and interpretations proposed by the analysis unit. The provision of information includes, for example, real-time interpretation services, tourist attraction guides, restaurant reservations, and transportation information, but is not limited to these examples. For example, the provision unit provides tourist attraction guides to foreign visitors to Japan. The provision unit can also make restaurant reservations for foreign visitors to Japan. The provision unit can also provide transportation information to foreign visitors to Japan. As a result, the smart concierge service according to the embodiment can provide optimal services and interpretation to foreign visitors to Japan, supporting a comfortable travel experience.

[0030] The reception unit can accept information about destinations and desired services entered by foreign visitors to Japan. The reception unit accepts input, for example, through a smartphone application. Information entered by foreign visitors to Japan includes, but is not limited to, tourist attractions, restaurants, and transportation options. The reception unit can accept, for example, voice input or text input. For example, the reception unit can accept destination information when a foreign visitor simply enters, "I want to go to Tokyo Tower," into their smartphone. The reception unit can also accept desired service information when a foreign visitor simply enters, "I want to make a reservation at a Japanese restaurant." This allows the information entered by foreign visitors to Japan to be accurately accepted. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the information entered by the foreign visitor to a generation AI and have the generation AI accept the information.

[0031] The analysis unit can propose tourist information, restaurant reservations, and transportation information tailored to the needs of foreign visitors to Japan. The analysis unit uses a generation AI to propose tourist information, restaurant reservations, and transportation information tailored to the needs of foreign visitors to Japan. The analysis can be performed using, for example, natural language processing or machine learning algorithms, but is not limited to these examples. For example, if a foreign visitor inputs "I want to go to Tokyo Tower," the generation AI can propose directions to Tokyo Tower and nearby tourist attractions. Furthermore, if a foreign visitor inputs "I want to make a reservation at a Japanese restaurant," the generation AI can also propose recommended Japanese restaurants and reservation methods. Furthermore, if a foreign visitor inputs "I want to know the transportation options from the nearest station," the generation AI can also propose transportation options from the nearest station. This enables optimal proposals tailored to the needs of foreign visitors to Japan. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input information entered by the foreign visitor to the generation AI and have the generation AI execute optimal proposals.

[0032] The providing unit can provide a real-time interpretation service. The providing unit, for example, provides a real-time interpretation service. Interpretation services include, but are not limited to, voice interpretation and text interpretation. For example, the providing unit translates a menu written in Japanese into English in real time so that a foreign visitor to Japan can understand it. The providing unit can also provide real-time interpretation when a foreign visitor to Japan communicates with local people. The providing unit can also provide real-time interpretation when a foreign visitor to Japan receives guidance at tourist spots. This eliminates language barriers and allows foreign visitors to Japan to travel comfortably. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input information entered by a foreign visitor to a generation AI and have the generation AI perform real-time interpretation.

[0033] The analysis unit can analyze the language input by the foreign visitor and translate information about destinations or services into the foreign visitor's native language. The analysis unit uses the generation AI to analyze the language input by the foreign visitor and translate information about destinations or services into the foreign visitor's native language. The analysis can be performed using, for example, natural language processing or a machine learning algorithm, but is not limited to these examples. For example, the generation AI can translate a menu written in Japanese into English in real time so that an English-speaking foreign visitor can understand it. The generation AI can also analyze the information input by the foreign visitor and translate information about destinations or services into the foreign visitor's native language. For example, if a foreign visitor inputs "I want to go to Tokyo Tower," the generation AI can translate that information into the foreign visitor's native language. If a foreign visitor inputs "I want to make a reservation at a Japanese restaurant," the generation AI can also translate that information into the foreign visitor's native language. This allows the foreign visitor to understand the information in their native language. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input information entered by foreign visitors to Japan into the generation AI and have the generation AI translate the information.

[0034] The providing unit can provide foreign visitors to Japan with information on tourist spots, restaurant reservations, and transportation. The providing unit, for example, provides foreign visitors to Japan with information on tourist spots. Examples of such information include, but are not limited to, information on tourist spots and route guidance. For example, the providing unit provides information on Tokyo Tower to foreign visitors to Japan. The providing unit can also make restaurant reservations for foreign visitors to Japan. Examples of such reservations include, but are not limited to, online reservations and telephone reservations. For example, the providing unit can make reservations at Japanese restaurants for foreign visitors to Japan. The providing unit can also provide information on transportation to foreign visitors to Japan. Examples of such information include, but are not limited to, information on trains, buses, and taxis. For example, the providing unit provides information on transportation from the nearest station to foreign visitors to Japan. This allows foreign visitors to Japan to smoothly obtain the information they need. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the provision unit can input information entered by foreign visitors to Japan into the generation AI and have the generation AI provide optimal guidance and make reservations.

[0035] The reception unit can analyze the user's past input history and automatically complete frequently input information. The reception unit, for example, adds a function of analyzing the user's past input history and automatically completing frequently input information. The analysis of the input history is performed, for example, using past input data or frequency analysis, but is not limited to such examples. For example, the reception unit automatically displays destinations and services that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. The reception unit can also predict and suggest destinations and services to be used during a specific time period based on the user's past input history. This improves the efficiency of the user's input work. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input data into a generation AI and cause the generation AI to automatically complete frequently input information.

[0036] The reception unit can automatically acquire the user's current location information upon input and display the distance and required time to the destination. For example, the reception unit can automatically acquire the user's current location information upon input and display the distance and required time to the destination. The acquisition of location information can be performed, for example, using GPS or Wi-Fi location information, but is not limited to such examples. For example, when the user opens the app, the reception unit automatically acquires the user's current location and sets it as the starting point. Furthermore, when the user inputs a destination, the reception unit can also suggest optimal candidate locations taking into account the distance from the current location. Furthermore, when the user uses the app while traveling, the reception unit can update the current location in real time and reflect it as the starting point. This allows the user to easily acquire information from the current location to the destination. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current location information to the generation AI and cause the generation AI to display the distance and required time.

[0037] The reception unit can analyze a user's voice input at the time of input and convert it into text using natural language processing. The reception unit, for example, adds a function of analyzing a user's voice input at the time of input and converting it into text using natural language processing. The analysis of the voice input can be performed using, for example, voice recognition technology or noise reduction, but is not limited to these examples. For example, the reception unit can automatically set a destination when the user simply inputs, "I want to go to Tokyo Tower" by voice. The reception unit can also allow the user to easily set a destination or service by performing a specific gesture on the smartphone screen. The reception unit can also allow the user to combine voice input and gesture input to more intuitively set a destination or service. This allows the user to easily set a destination or service by voice. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's voice data to a generation AI and have the generation AI convert it into text.

[0038] The reception unit can refer to the user's past travel history at the time of input and suggest related destinations and services. For example, the reception unit can refer to the user's past travel history at the time of input and suggest related destinations and services. Referencing the travel history can be performed, for example, using past travel data or records of visited places, but is not limited to such examples. For example, the reception unit can suggest related tourist spots based on tourist spots the user has visited in the past. The reception unit can also suggest related restaurants based on restaurants the user has used in the past. The reception unit can also predict and suggest tourist spots and services that should be visited during a specific time period based on the user's past travel history. This enables optimal suggestions based on the user's past travel history. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past travel data into a generation AI and cause the generation AI to suggest related destinations and services.

[0039] The reception unit can analyze the user's social media activity at the time of input and suggest related tourist attractions and services. For example, the reception unit can analyze the user's social media activity at the time of input and suggest related tourist attractions and services. The analysis of social media activity can be performed, for example, using the content of posts and the number of likes, but is not limited to such examples. For example, the reception unit can suggest related tourist attractions based on the locations where the user checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related tourist attractions and services. The reception unit can also suggest related tourist attractions and services based on the activities of the user's friends on social media. This enables optimal suggestions based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI suggest related tourist attractions and services.

[0040] The reception unit can make the input interface multilingual in accordance with the user's language setting at the time of input. The reception unit, for example, makes the input interface multilingual in accordance with the user's language setting at the time of input. The language setting can be acquired, for example, using the user's device settings or app settings, but is not limited to such examples. For example, the reception unit automatically sets the language of the input interface based on the language setting of the user's device. The reception unit can also provide a language switching function when the user uses multiple languages. The reception unit can also provide an input interface in a specific language when the user selects that language. This provides an interface in accordance with the user's language setting. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's language setting data to a generation AI and cause the generation AI to execute a multilingual interface.

[0041] The analysis unit can refer to the user's past selection history during analysis to improve the accuracy of suggestions. The analysis unit uses the generation AI to refer to the user's past selection history during analysis to improve the accuracy of suggestions. The reference to the selection history can be performed, for example, using past selection data or selection frequency, but is not limited to such examples. For example, the analysis unit causes the generation AI to make relevant suggestions based on tourist destinations and services previously selected by the user. The analysis unit can also cause the generation AI to make suggestions to avoid crowds based on the user's past selection history. The analysis unit can also analyze the user's past selection history and cause the generation AI to make the most efficient suggestions. This enables optimal suggestions based on the user's past selection history. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's past selection data into the generation AI and cause the generation AI to improve the accuracy of suggestions.

[0042] The analysis unit can make optimal suggestions by taking into account the user's current stay time and schedule during analysis. The analysis unit can make optimal suggestions by using the generation AI during analysis by taking into account the user's current stay time and schedule. The stay time and schedule can be taken into account using, for example, calendar information or reservation information, but is not limited to these examples. For example, if the user's stay time is short, the analysis unit can suggest tourist spots that can be enjoyed in a short amount of time. The analysis unit can also suggest efficient sightseeing routes that match the user's schedule. The analysis unit can also suggest optimal restaurant reservation times by taking into account the user's stay time and schedule. This enables optimal suggestions based on the user's stay time and schedule. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's calendar information into the generation AI and cause the generation AI to execute optimal suggestions.

[0043] The analysis unit can customize the proposal content based on the user's interests during analysis. The analysis unit uses the generation AI to customize the proposal content based on the user's interests during analysis. The interests can be acquired using, for example, survey results or past behavioral data, but are not limited to these examples. For example, the analysis unit makes the generation AI make related proposals based on tourist spots that the user is interested in. The analysis unit can also make related proposals based on stores that the user frequently visits. The analysis unit can also suggest places that the user may be interested in based on the user's past search history. This enables proposals based on the user's interests. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's interest data into the generation AI and have the generation AI customize the proposal content.

[0044] The analysis unit can make highly relevant suggestions by taking into account the user's geographical location information during analysis. The analysis unit can make highly relevant suggestions by using the generation AI to take into account the user's geographical location information during analysis. The geographical location information can be acquired, for example, using GPS or Wi-Fi location information, but is not limited to these examples. For example, the analysis unit can suggest tourist spots and services close to the user's current location. The analysis unit can also make optimal suggestions by taking into account transportation from the user's current location. The analysis unit can also suggest event information in real time based on the user's current location. This enables optimal suggestions based on the user's geographical location information. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's location information into the generation AI and cause the generation AI to make highly relevant suggestions.

[0045] The analysis unit can analyze the user's social media activity during analysis and make relevant suggestions. The analysis unit can use the generation AI to analyze the user's social media activity during analysis and make relevant suggestions. Analysis of social media activity can be performed, for example, using the content of posts and the number of likes, but is not limited to such examples. For example, the analysis unit can suggest related tourist attractions based on the locations where the user checked in on social media. The analysis unit can also analyze the content of the user's social media posts and suggest related tourist attractions and services. The analysis unit can also suggest related tourist attractions and services based on the activities of the user's friends on social media. This enables optimal suggestions based on the user's social media activity. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's social media data into the generation AI and cause the generation AI to execute related suggestions.

[0046] The analysis unit can improve the proposal content by reflecting the user's past feedback during analysis. The analysis unit uses the generation AI to improve the proposal content by reflecting the user's past feedback during analysis. Reflecting the feedback is performed, for example, using the user's ratings and comments, but is not limited to such examples. For example, the analysis unit improves the proposal content based on feedback provided by the user in the past. The analysis unit can also make proposals that will result in high satisfaction based on the user's past feedback. The analysis unit can also analyze the user's past feedback and make optimal proposals. This enables proposals that will result in higher satisfaction by improving the proposal content based on the user's past feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's feedback data into the generation AI and cause the generation AI to improve the proposal content.

[0047] The providing unit can refer to the user's past usage history when providing a service and provide the optimal service. For example, the providing unit can refer to the user's past usage history when providing a service and provide the optimal service. Referencing the usage history can be performed, for example, using past usage data or usage frequency, but is not limited to such examples. For example, the providing unit can provide a related service based on a service the user has used in the past. The providing unit can also provide a service that provides high satisfaction based on the user's past usage history. The providing unit can also analyze the user's past usage history and provide the most efficient service. In this way, the optimal service is provided based on the user's past usage history, thereby providing a service that provides high satisfaction. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's usage history data into a generation AI and cause the generation AI to provide the optimal service.

[0048] The providing unit can customize the service content based on the user's current situation and environment when providing the service. For example, the providing unit customizes the service content based on the user's current situation and environment when providing the service. The situation and environment can be acquired using, for example, weather information or surrounding environmental data, but is not limited to such examples. For example, the providing unit provides an optimal service based on the user's current location. The providing unit can also customize the service content taking into account the user's current situation (weather, time of day, etc.). The providing unit can also customize the service content taking into account the user's current environment (congestion level, etc.). This allows for providing a more appropriate service by providing a service that is tailored to the user's current situation and environment. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's environmental data into a generating AI and cause the generating AI to customize the service content.

[0049] The providing unit can collect user feedback in real time when providing a service and immediately improve the service content. For example, the providing unit can collect user feedback in real time when providing a service and immediately improve the service content. The collection of feedback can be performed, for example, using user ratings and comments, but is not limited to such examples. For example, the providing unit can collect feedback immediately after a user uses a service and reflect the feedback in the next service. The providing unit can also immediately improve the service content based on the user's real-time feedback. The providing unit can also analyze the user's feedback and improve the quality of the service. In this way, the quality of the service is improved by reflecting the user's feedback in real time. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and cause the generating AI to improve the service content.

[0050] The providing unit can provide an optimal service by taking into account the user's geographical location information when providing a service. The providing unit can provide an optimal service by taking into account the user's geographical location information when providing a service, for example. The geographical location information can be acquired using, for example, GPS or Wi-Fi location information, but is not limited to such examples. For example, the providing unit can provide tourist spots and services close to the user's current location. The providing unit can also provide an optimal service by taking into account transportation from the user's current location. The providing unit can also provide real-time event information based on the user's current location. This allows for more appropriate services to be provided by providing an optimal service based on the user's geographical location information. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's location information into a generating AI and cause the generating AI to provide an optimal service.

[0051] The providing unit may analyze the user's social media activities and provide related services when providing a service. For example, the providing unit may analyze the user's social media activities and provide related services when providing a service. The analysis of social media activities may be performed, for example, using the content of posts and the number of likes, but is not limited to such examples. For example, the providing unit may provide information about places where the user has checked in on social media. The providing unit may also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The providing unit may also provide information about related places and events based on the activities of the user's friends on social media. This allows for more appropriate services to be provided by optimally utilizing the user's social media activities. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's social media data into a generating AI and cause the generating AI to provide related services.

[0052] The providing unit can customize the service content by reflecting the user's past feedback when providing the service. For example, the providing unit customizes the service content by reflecting the user's past feedback when providing the service. Reflecting the feedback can be performed, for example, using the user's ratings and comments, but is not limited to such examples. For example, the providing unit customizes the service content based on feedback provided by the user in the past. The providing unit can also provide a service that provides high satisfaction based on the user's past feedback. The providing unit can also analyze the user's past feedback and provide an optimal service. In this way, the service content can be customized based on the user's past feedback, thereby providing a service that provides high satisfaction. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's feedback data into a generating AI and cause the generating AI to customize the service content.

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

[0054] The analysis unit can also monitor the health status of foreign visitors to Japan and adjust the suggestions based on their health condition. For example, if a foreign visitor is tired, it can suggest tourist spots and services where they can relax. If a foreign visitor has allergies, it can also suggest restaurants that cater to allergies. Furthermore, if a foreign visitor complains of feeling unwell, it can also guide them to nearby medical institutions. This makes it possible to make optimal suggestions based on the health status of foreign visitors to Japan.

[0055] The provision department can also customize the content of the service taking into account the cultural background of foreign visitors to Japan. For example, if a foreign visitor has a specific religion, it can suggest restaurants and tourist spots that correspond to that religion. Also, if a foreign visitor is interested in a specific cultural event, it can provide information related to that event. Furthermore, if a foreign visitor has specific cultural customs, it can provide services that take those customs into consideration. This makes it possible to provide the optimal service according to the cultural background of foreign visitors to Japan.

[0056] The analysis unit can also customize the content of suggestions based on the travel purpose of the foreign visitor. For example, if the foreign visitor is visiting for business purposes, it can suggest locations and services suitable for business meetings. If the foreign visitor is visiting for sightseeing purposes, it can also suggest tourist spots and activities. Furthermore, if the foreign visitor is visiting for family vacations, it can suggest tourist spots and services suitable for families. This makes it possible to make optimal suggestions based on the travel purpose of the foreign visitor.

[0057] The provision department can also customize the content of services according to the budget of foreign visitors to Japan. For example, if a foreign visitor is traveling on a low budget, it can suggest cost-effective tourist spots and restaurants. If a foreign visitor is traveling on a high budget, it can also suggest luxury services and high-end restaurants. Furthermore, if a foreign visitor is planning a trip within a specific budget, it can also provide the optimal service according to that budget. This makes it possible to provide the optimal service according to the budget of foreign visitors to Japan.

[0058] The provision unit can also adjust the content of services based on the travel schedule of foreign visitors to Japan. For example, if the foreign visitor is staying in Japan for a short period of time, it can suggest tourist spots and services that can be enjoyed in a short amount of time. On the other hand, if the foreign visitor is staying in Japan for a long period of time, it can suggest tourist spots and services that can be enjoyed at a leisurely pace. Furthermore, if the foreign visitor plans to participate in a specific event, it can also provide information and services related to that event. This makes it possible to provide the optimal service according to the travel schedule of the foreign visitor to Japan.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit accepts input from foreign visitors about their destination and desired services. Information entered by foreign visitors includes tourist spots, restaurants, transportation options, etc. The reception unit can accept input via a smartphone application, and can also accept voice input and text input. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and propose the most appropriate services and interpretations. The analysis is carried out using natural language processing and machine learning algorithms. For example, the generation AI proposes tourist information, restaurant reservations, and transportation information tailored to the needs of foreign visitors to Japan. The analysis unit can also analyze the language input by foreign visitors to Japan and translate information about destinations and services into their native language. Step 3: The provision unit provides the services and interpretations proposed by the analysis unit. The provision may include real-time interpretation services, tourist information, restaurant reservations, and transportation information. For example, the provision unit may provide tourist information, restaurant reservations, and transportation information to foreign visitors to Japan.

[0061] (Example 2) A smart concierge service according to an embodiment of the present invention is a system that provides optimal services and interpretation to foreign visitors to Japan. In this system, foreign visitors input their destination and desired services into their smartphones, and a generation AI analyzes the information and suggests optimal services and interpretations. Examples of such services include tourist attraction guides, restaurant reservations, and transportation information. Real-time interpretation services are also provided to eliminate language barriers. This allows foreign visitors to enjoy a comfortable travel experience. For example, by simply inputting their destination and desired services, the generation AI analyzes the information and suggests optimal services and interpretations. This allows foreign visitors to intuitively receive optimal services and interpretations without complex operations. For example, tourist attraction guides, restaurant reservations, and transportation information can be smoothly provided, reducing stress during travel. Furthermore, the real-time interpretation service allows visitors to understand local information without experiencing language barriers.

[0062] A smart concierge service according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit accepts input of a destination and desired services from a foreign visitor to Japan. The information entered by the foreign visitor to Japan includes, but is not limited to, tourist attractions, restaurants, and transportation. The reception unit accepts input, for example, via a smartphone application. The reception unit can also accept voice input and text input. The analysis unit uses a generation AI to analyze the information accepted by the reception unit and propose optimal services and interpretations. The analysis can be performed, for example, using natural language processing or a machine learning algorithm, but is not limited to, examples. For example, the generation AI proposes tourist attraction guides, restaurant reservations, and transportation information tailored to the needs of the foreign visitor to Japan. The analysis unit can also analyze the language entered by the foreign visitor to Japan and translate information about destinations and services into the foreign visitor's native language. For example, the generation AI can translate menus written in Japanese into English in real time so that an English-speaking foreign visitor to Japan can understand them. The provision unit provides the services and interpretations proposed by the analysis unit. The provision of information includes, for example, real-time interpretation services, tourist attraction guides, restaurant reservations, and transportation information, but is not limited to these examples. For example, the provision unit provides tourist attraction guides to foreign visitors to Japan. The provision unit can also make restaurant reservations for foreign visitors to Japan. The provision unit can also provide transportation information to foreign visitors to Japan. As a result, the smart concierge service according to the embodiment can provide optimal services and interpretation to foreign visitors to Japan, supporting a comfortable travel experience.

[0063] The reception unit can accept information about destinations and desired services entered by foreign visitors to Japan. The reception unit accepts input, for example, through a smartphone application. Information entered by foreign visitors to Japan includes, but is not limited to, tourist attractions, restaurants, and transportation options. The reception unit can accept, for example, voice input or text input. For example, the reception unit can accept destination information when a foreign visitor simply enters, "I want to go to Tokyo Tower," into their smartphone. The reception unit can also accept desired service information when a foreign visitor simply enters, "I want to make a reservation at a Japanese restaurant." This allows the information entered by foreign visitors to Japan to be accurately accepted. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the information entered by the foreign visitor to a generation AI and have the generation AI accept the information.

[0064] The analysis unit can propose tourist information, restaurant reservations, and transportation information tailored to the needs of foreign visitors to Japan. The analysis unit uses a generation AI to propose tourist information, restaurant reservations, and transportation information tailored to the needs of foreign visitors to Japan. The analysis can be performed using, for example, natural language processing or machine learning algorithms, but is not limited to these examples. For example, if a foreign visitor inputs "I want to go to Tokyo Tower," the generation AI can propose directions to Tokyo Tower and nearby tourist attractions. Furthermore, if a foreign visitor inputs "I want to make a reservation at a Japanese restaurant," the generation AI can also propose recommended Japanese restaurants and reservation methods. Furthermore, if a foreign visitor inputs "I want to know the transportation options from the nearest station," the generation AI can also propose transportation options from the nearest station. This enables optimal proposals tailored to the needs of foreign visitors to Japan. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input information entered by the foreign visitor to the generation AI and have the generation AI execute optimal proposals.

[0065] The providing unit can provide a real-time interpretation service. The providing unit, for example, provides a real-time interpretation service. Interpretation services include, but are not limited to, voice interpretation and text interpretation. For example, the providing unit translates a menu written in Japanese into English in real time so that a foreign visitor to Japan can understand it. The providing unit can also provide real-time interpretation when a foreign visitor to Japan communicates with local people. The providing unit can also provide real-time interpretation when a foreign visitor to Japan receives guidance at tourist spots. This eliminates language barriers and allows foreign visitors to Japan to travel comfortably. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input information entered by a foreign visitor to a generation AI and have the generation AI perform real-time interpretation.

[0066] The analysis unit can analyze the language input by the foreign visitor and translate information about destinations or services into the foreign visitor's native language. The analysis unit uses the generation AI to analyze the language input by the foreign visitor and translate information about destinations or services into the foreign visitor's native language. The analysis can be performed using, for example, natural language processing or a machine learning algorithm, but is not limited to these examples. For example, the generation AI can translate a menu written in Japanese into English in real time so that an English-speaking foreign visitor can understand it. The generation AI can also analyze the information input by the foreign visitor and translate information about destinations or services into the foreign visitor's native language. For example, if a foreign visitor inputs "I want to go to Tokyo Tower," the generation AI can translate that information into the foreign visitor's native language. If a foreign visitor inputs "I want to make a reservation at a Japanese restaurant," the generation AI can also translate that information into the foreign visitor's native language. This allows the foreign visitor to understand the information in their native language. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input information entered by foreign visitors to Japan into the generation AI and have the generation AI translate the information.

[0067] The providing unit can provide foreign visitors to Japan with information on tourist spots, restaurant reservations, and transportation. The providing unit, for example, provides foreign visitors to Japan with information on tourist spots. Examples of such information include, but are not limited to, information on tourist spots and route guidance. For example, the providing unit provides information on Tokyo Tower to foreign visitors to Japan. The providing unit can also make restaurant reservations for foreign visitors to Japan. Examples of such reservations include, but are not limited to, online reservations and telephone reservations. For example, the providing unit can make reservations at Japanese restaurants for foreign visitors to Japan. The providing unit can also provide information on transportation to foreign visitors to Japan. Examples of such information include, but are not limited to, information on trains, buses, and taxis. For example, the providing unit provides information on transportation from the nearest station to foreign visitors to Japan. This allows foreign visitors to Japan to smoothly obtain the information they need. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the provision unit can input information entered by foreign visitors to Japan into the generation AI and have the generation AI provide optimal guidance and make reservations.

[0068] The reception unit can estimate the user's emotion and change the design of the input interface based on the estimated user emotion. The reception unit, for example, estimates the user's emotion and dynamically changes the design of the input interface based on the estimated user emotion. Emotion estimation can be performed using, for example, facial expression recognition or voice analysis, but is not limited to these examples. For example, if the user is nervous, the reception unit can provide an interface with subdued colors to reduce visual stress. If the user is having fun, the reception unit can provide an interface with bright colors to make input work more enjoyable. If the user is tired, the reception unit can provide a simple, highly visible interface to make input work easier. This provides an interface that corresponds to the user's emotion and makes input work more comfortable. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0069] The reception unit can analyze the user's past input history and automatically complete frequently input information. The reception unit, for example, adds a function of analyzing the user's past input history and automatically completing frequently input information. The analysis of the input history is performed, for example, using past input data or frequency analysis, but is not limited to such examples. For example, the reception unit automatically displays destinations and services that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. The reception unit can also predict and suggest destinations and services to be used during a specific time period based on the user's past input history. This improves the efficiency of the user's input work. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input data into a generation AI and cause the generation AI to automatically complete frequently input information.

[0070] The reception unit can automatically acquire the user's current location information upon input and display the distance and required time to the destination. For example, the reception unit can automatically acquire the user's current location information upon input and display the distance and required time to the destination. The acquisition of location information can be performed, for example, using GPS or Wi-Fi location information, but is not limited to such examples. For example, when the user opens the app, the reception unit automatically acquires the user's current location and sets it as the starting point. Furthermore, when the user inputs a destination, the reception unit can also suggest optimal candidate locations taking into account the distance from the current location. Furthermore, when the user uses the app while traveling, the reception unit can update the current location in real time and reflect it as the starting point. This allows the user to easily acquire information from the current location to the destination. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current location information to the generation AI and cause the generation AI to display the distance and required time.

[0071] The reception unit can analyze a user's voice input at the time of input and convert it into text using natural language processing. The reception unit, for example, adds a function of analyzing a user's voice input at the time of input and converting it into text using natural language processing. The analysis of the voice input can be performed using, for example, voice recognition technology or noise reduction, but is not limited to these examples. For example, the reception unit can automatically set a destination when the user simply inputs, "I want to go to Tokyo Tower" by voice. The reception unit can also allow the user to easily set a destination or service by performing a specific gesture on the smartphone screen. The reception unit can also allow the user to combine voice input and gesture input to more intuitively set a destination or service. This allows the user to easily set a destination or service by voice. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's voice data to a generation AI and have the generation AI convert it into text.

[0072] The reception unit can estimate the user's emotions and adjust the priority of input content based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and automatically adjusts the priority of input content based on the estimated user emotions. Emotion estimation can be performed using, for example, facial expression recognition or voice analysis, but is not limited to these examples. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick entry of destinations and services. This adjusts the input content based on the priority according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0073] The reception unit can refer to the user's past travel history at the time of input and suggest related destinations and services. For example, the reception unit can refer to the user's past travel history at the time of input and suggest related destinations and services. Referencing the travel history can be performed, for example, using past travel data or records of visited places, but is not limited to such examples. For example, the reception unit can suggest related tourist spots based on tourist spots the user has visited in the past. The reception unit can also suggest related restaurants based on restaurants the user has used in the past. The reception unit can also predict and suggest tourist spots and services that should be visited during a specific time period based on the user's past travel history. This enables optimal suggestions based on the user's past travel history. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past travel data into a generation AI and cause the generation AI to suggest related destinations and services.

[0074] The reception unit can analyze the user's social media activity at the time of input and suggest related tourist attractions and services. For example, the reception unit can analyze the user's social media activity at the time of input and suggest related tourist attractions and services. The analysis of social media activity can be performed, for example, using the content of posts and the number of likes, but is not limited to such examples. For example, the reception unit can suggest related tourist attractions based on the locations where the user checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related tourist attractions and services. The reception unit can also suggest related tourist attractions and services based on the activities of the user's friends on social media. This enables optimal suggestions based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI suggest related tourist attractions and services.

[0075] The reception unit can make the input interface multilingual in accordance with the user's language setting at the time of input. The reception unit, for example, makes the input interface multilingual in accordance with the user's language setting at the time of input. The language setting can be acquired, for example, using the user's device settings or app settings, but is not limited to such examples. For example, the reception unit automatically sets the language of the input interface based on the language setting of the user's device. The reception unit can also provide a language switching function when the user uses multiple languages. The reception unit can also provide an input interface in a specific language when the user selects that language. This provides an interface in accordance with the user's language setting. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's language setting data to a generation AI and cause the generation AI to execute a multilingual interface.

[0076] The analysis unit can estimate the user's emotions and change the way the suggestions are presented based on the estimated user emotions. The analysis unit can use a generation AI to estimate the user's emotions and adjust the way the suggestions are presented based on the estimated user emotions. The emotion estimation can be performed using, for example, facial expression recognition or voice analysis, but is not limited to these examples. For example, if the user is relaxed, the analysis unit can cause the generation AI to provide suggestions that progress at a leisurely pace. If the user is in a hurry, the analysis unit can also cause the generation AI to provide suggestions that emphasize the shortest route. If the user is excited, the analysis unit can also provide suggestions that add visually stimulating effects. This allows the suggestions to be provided according to the user's emotions. The emotion estimation can be achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0077] The analysis unit can refer to the user's past selection history during analysis to improve the accuracy of suggestions. The analysis unit uses the generation AI to refer to the user's past selection history during analysis to improve the accuracy of suggestions. The reference to the selection history can be performed, for example, using past selection data or selection frequency, but is not limited to such examples. For example, the analysis unit causes the generation AI to make relevant suggestions based on tourist destinations and services previously selected by the user. The analysis unit can also cause the generation AI to make suggestions to avoid crowds based on the user's past selection history. The analysis unit can also analyze the user's past selection history and cause the generation AI to make the most efficient suggestions. This enables optimal suggestions based on the user's past selection history. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's past selection data into the generation AI and cause the generation AI to improve the accuracy of suggestions.

[0078] The analysis unit can make optimal suggestions by taking into account the user's current stay time and schedule during analysis. The analysis unit can make optimal suggestions by using the generation AI during analysis by taking into account the user's current stay time and schedule. The stay time and schedule can be taken into account using, for example, calendar information or reservation information, but is not limited to these examples. For example, if the user's stay time is short, the analysis unit can suggest tourist spots that can be enjoyed in a short amount of time. The analysis unit can also suggest efficient sightseeing routes that match the user's schedule. The analysis unit can also suggest optimal restaurant reservation times by taking into account the user's stay time and schedule. This enables optimal suggestions based on the user's stay time and schedule. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's calendar information into the generation AI and cause the generation AI to execute optimal suggestions.

[0079] The analysis unit can customize the proposal content based on the user's interests during analysis. The analysis unit uses the generation AI to customize the proposal content based on the user's interests during analysis. The interests can be acquired using, for example, survey results or past behavioral data, but are not limited to these examples. For example, the analysis unit makes the generation AI make related proposals based on tourist spots that the user is interested in. The analysis unit can also make related proposals based on stores that the user frequently visits. The analysis unit can also suggest places that the user may be interested in based on the user's past search history. This enables proposals based on the user's interests. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's interest data into the generation AI and have the generation AI customize the proposal content.

[0080] The analysis unit can estimate the user's emotions and change the priority of the suggestions based on the estimated user emotions. The analysis unit can estimate the user's emotions using a generation AI and adjust the priority of the suggestions based on the estimated user emotions. Emotion estimation can be performed using, for example, facial expression recognition or voice analysis, but is not limited to these examples. For example, if the user is feeling stressed, the analysis unit can prioritize suggesting relaxing tourist spots and services. Also, if the user is having fun, the analysis unit can prioritize suggesting active tourist spots and services. Also, if the user is tired, the analysis unit can prioritize suggesting places where the user can rest and services. In this way, the suggestions are adjusted based on the priority according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0081] The analysis unit can make highly relevant suggestions by taking into account the user's geographical location information during analysis. The analysis unit can make highly relevant suggestions by using the generation AI to take into account the user's geographical location information during analysis. The geographical location information can be acquired, for example, using GPS or Wi-Fi location information, but is not limited to these examples. For example, the analysis unit can suggest tourist spots and services close to the user's current location. The analysis unit can also make optimal suggestions by taking into account transportation from the user's current location. The analysis unit can also suggest event information in real time based on the user's current location. This enables optimal suggestions based on the user's geographical location information. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's location information into the generation AI and cause the generation AI to make highly relevant suggestions.

[0082] The analysis unit can analyze the user's social media activity during analysis and make relevant suggestions. The analysis unit can use the generation AI to analyze the user's social media activity during analysis and make relevant suggestions. Analysis of social media activity can be performed, for example, using the content of posts and the number of likes, but is not limited to such examples. For example, the analysis unit can suggest related tourist attractions based on the locations where the user checked in on social media. The analysis unit can also analyze the content of the user's social media posts and suggest related tourist attractions and services. The analysis unit can also suggest related tourist attractions and services based on the activities of the user's friends on social media. This enables optimal suggestions based on the user's social media activity. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's social media data into the generation AI and cause the generation AI to execute related suggestions.

[0083] The analysis unit can improve the proposal content by reflecting the user's past feedback during analysis. The analysis unit uses the generation AI to improve the proposal content by reflecting the user's past feedback during analysis. Reflecting the feedback is performed, for example, using the user's ratings and comments, but is not limited to such examples. For example, the analysis unit improves the proposal content based on feedback provided by the user in the past. The analysis unit can also make proposals that will result in high satisfaction based on the user's past feedback. The analysis unit can also analyze the user's past feedback and make optimal proposals. This enables proposals that will result in higher satisfaction by improving the proposal content based on the user's past feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's feedback data into the generation AI and cause the generation AI to improve the proposal content.

[0084] The providing unit can estimate the user's emotion and change the timing of service provision based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the timing of service provision based on the estimated user's emotion. Emotion estimation is performed using, for example, facial expression recognition or voice analysis, but is not limited to these examples. For example, the providing unit can provide the service at a leisurely pace when the user is relaxed. Furthermore, the providing unit can provide the service quickly when the user is in a hurry. Furthermore, the providing unit can provide the service with a visually stimulating effect when the user is excited. This provides a more comfortable service experience by providing the service at a timing that corresponds to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0085] The providing unit can refer to the user's past usage history when providing a service and provide the optimal service. For example, the providing unit can refer to the user's past usage history when providing a service and provide the optimal service. Referencing the usage history can be performed, for example, using past usage data or usage frequency, but is not limited to such examples. For example, the providing unit can provide a related service based on a service the user has used in the past. The providing unit can also provide a service that provides high satisfaction based on the user's past usage history. The providing unit can also analyze the user's past usage history and provide the most efficient service. In this way, the optimal service is provided based on the user's past usage history, thereby providing a service that provides high satisfaction. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's usage history data into a generation AI and cause the generation AI to provide the optimal service.

[0086] The providing unit can customize the service content based on the user's current situation and environment when providing the service. For example, the providing unit customizes the service content based on the user's current situation and environment when providing the service. The situation and environment can be acquired using, for example, weather information or surrounding environmental data, but is not limited to such examples. For example, the providing unit provides an optimal service based on the user's current location. The providing unit can also customize the service content taking into account the user's current situation (weather, time of day, etc.). The providing unit can also customize the service content taking into account the user's current environment (congestion level, etc.). This allows for providing a more appropriate service by providing a service that is tailored to the user's current situation and environment. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's environmental data into a generating AI and cause the generating AI to customize the service content.

[0087] The providing unit can collect user feedback in real time when providing a service and immediately improve the service content. For example, the providing unit can collect user feedback in real time when providing a service and immediately improve the service content. The collection of feedback can be performed, for example, using user ratings and comments, but is not limited to such examples. For example, the providing unit can collect feedback immediately after a user uses a service and reflect the feedback in the next service. The providing unit can also immediately improve the service content based on the user's real-time feedback. The providing unit can also analyze the user's feedback and improve the quality of the service. In this way, the quality of the service is improved by reflecting the user's feedback in real time. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and cause the generating AI to improve the service content.

[0088] The providing unit can estimate the user's emotions and set a priority order for providing services based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines a priority order for providing services based on the estimated user emotions. Emotion estimation is performed, for example, using facial expression recognition or voice analysis, but is not limited to these examples. For example, if the user is feeling stressed, the providing unit can prioritize providing relaxing services. Furthermore, if the user is enjoying themselves, the providing unit can prioritize active services. Furthermore, if the user is tired, the providing unit can prioritize resting services. This provides more appropriate services by prioritizing services according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0089] The providing unit can provide an optimal service by taking into account the user's geographical location information when providing a service. The providing unit can provide an optimal service by taking into account the user's geographical location information when providing a service, for example. The geographical location information can be acquired using, for example, GPS or Wi-Fi location information, but is not limited to such examples. For example, the providing unit can provide tourist spots and services close to the user's current location. The providing unit can also provide an optimal service by taking into account transportation from the user's current location. The providing unit can also provide real-time event information based on the user's current location. This allows for more appropriate services to be provided by providing an optimal service based on the user's geographical location information. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's location information into a generating AI and cause the generating AI to provide an optimal service.

[0090] The providing unit may analyze the user's social media activities and provide related services when providing a service. For example, the providing unit may analyze the user's social media activities and provide related services when providing a service. The analysis of social media activities may be performed, for example, using the content of posts and the number of likes, but is not limited to such examples. For example, the providing unit may provide information about places where the user has checked in on social media. The providing unit may also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The providing unit may also provide information about related places and events based on the activities of the user's friends on social media. This allows for more appropriate services to be provided by optimally utilizing the user's social media activities. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's social media data into a generating AI and cause the generating AI to provide related services.

[0091] The providing unit can customize the service content by reflecting the user's past feedback when providing the service. For example, the providing unit customizes the service content by reflecting the user's past feedback when providing the service. Reflecting the feedback can be performed, for example, using the user's ratings and comments, but is not limited to such examples. For example, the providing unit customizes the service content based on feedback provided by the user in the past. The providing unit can also provide a service that provides high satisfaction based on the user's past feedback. The providing unit can also analyze the user's past feedback and provide an optimal service. In this way, the service content can be customized based on the user's past feedback, thereby providing a service that provides high satisfaction. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's feedback data into a generating AI and cause the generating AI to customize the service content. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive input of destinations and desired services from foreign visitors to Japan using the reception device 38 of the smart device 14. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12, and can analyze the information received by the reception unit using a generation AI and propose optimal services and interpretations. For example, the provision unit can provide the services and interpretations proposed by the analysis unit using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive input of a destination and desired services from a foreign visitor to Japan using the microphone 238 of the smart glasses 214. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12, and can analyze the information received by the reception unit using a generation AI and propose optimal services and interpretations. For example, the provision unit can provide the services and interpretations proposed by the analysis unit using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the reception unit, analysis unit, and provision unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit can receive input of a destination and desired services from a foreign visitor to Japan using the microphone 238 of the headset terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the information received by the reception unit using a generation AI and proposes optimal services and interpretations. For example, the provision unit can provide the services and interpretations proposed by the analysis unit using the speaker 240 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, analysis unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive input of a destination and desired services from a foreign visitor to Japan using the microphone 238 of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the information received by the reception unit using a generation AI and proposes optimal services and interpretations. For example, the provision unit can provide the services and interpretations proposed by the analysis unit using the speaker 240 of the robot 414.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The analysis unit can also monitor the health status of foreign visitors to Japan and adjust the suggestions based on their health condition. For example, if a foreign visitor is tired, it can suggest tourist spots and services where they can relax. If a foreign visitor has allergies, it can also suggest restaurants that cater to allergies. Furthermore, if a foreign visitor complains of feeling unwell, it can also guide them to nearby medical institutions. This makes it possible to make optimal suggestions based on the health status of foreign visitors to Japan.

[0094] The provision department can also customize the content of the service taking into account the cultural background of foreign visitors to Japan. For example, if a foreign visitor has a specific religion, it can suggest restaurants and tourist spots that correspond to that religion. Also, if a foreign visitor is interested in a specific cultural event, it can provide information related to that event. Furthermore, if a foreign visitor has specific cultural customs, it can provide services that take those customs into consideration. This makes it possible to provide the optimal service according to the cultural background of foreign visitors to Japan.

[0095] The analysis unit can also customize the content of suggestions based on the travel purpose of the foreign visitor. For example, if the foreign visitor is visiting for business purposes, it can suggest locations and services suitable for business meetings. If the foreign visitor is visiting for sightseeing purposes, it can also suggest tourist spots and activities. Furthermore, if the foreign visitor is visiting for family vacations, it can suggest tourist spots and services suitable for families. This makes it possible to make optimal suggestions based on the travel purpose of the foreign visitor.

[0096] The provision department can also customize the content of services according to the budget of foreign visitors to Japan. For example, if a foreign visitor is traveling on a low budget, it can suggest cost-effective tourist spots and restaurants. If a foreign visitor is traveling on a high budget, it can also suggest luxury services and high-end restaurants. Furthermore, if a foreign visitor is planning a trip within a specific budget, it can also provide the optimal service according to that budget. This makes it possible to provide the optimal service according to the budget of foreign visitors to Japan.

[0097] The provision unit can also adjust the content of services based on the travel schedule of foreign visitors to Japan. For example, if the foreign visitor is staying in Japan for a short period of time, it can suggest tourist spots and services that can be enjoyed in a short amount of time. On the other hand, if the foreign visitor is staying in Japan for a long period of time, it can suggest tourist spots and services that can be enjoyed at a leisurely pace. Furthermore, if the foreign visitor plans to participate in a specific event, it can also provide information and services related to that event. This makes it possible to provide the optimal service according to the travel schedule of the foreign visitor to Japan.

[0098] The analysis unit can estimate the user's emotions and adjust the tone of the suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide relaxing suggestions. If the user is excited, the analysis unit can provide active suggestions. If the user is sad, the analysis unit can provide comforting suggestions. This makes it possible to provide optimal suggestions according to the user's emotions.

[0099] The providing unit can estimate the user's emotion and adjust the order of service provision based on the estimated user's emotion. For example, if the user is tired, a relaxing service can be provided first. Also, if the user is excited, an active service can be provided first. Furthermore, if the user is stressed, a stress-relieving service can be provided first. In this way, it is possible to set an optimal order of service provision according to the user's emotion.

[0100] The analysis unit can estimate the user's emotions and adjust the level of detail of the suggestions based on the estimated user's emotions. For example, if the user is relaxed, detailed suggestions can be provided. If the user is in a hurry, concise suggestions can be provided. Furthermore, if the user is excited, visually appealing suggestions can be provided. In this way, the level of detail of the suggestions can be optimized according to the user's emotions.

[0101] The providing unit can estimate the user's emotions and adjust the method of providing the service based on the estimated user's emotions. For example, if the user is relaxed, the service can be provided at a leisurely pace. If the user is in a hurry, the service can be provided quickly. Furthermore, if the user is excited, the service can be provided with a visually stimulating effect. This makes it possible to set the optimal method of providing the service according to the user's emotions.

[0102] The analysis unit can estimate the user's emotions and adjust the priority of suggested content based on the estimated user's emotions. For example, if the user is feeling stressed, suggested content that helps them relax can be provided preferentially. Also, if the user is having fun, suggested content that helps them stay active can be provided preferentially. Furthermore, if the user is tired, places where they can rest and services can be provided preferentially. This makes it possible to set the optimal priority of suggested content according to the user's emotions.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The reception unit accepts input from foreign visitors about their destination and desired services. Information entered by foreign visitors includes tourist spots, restaurants, transportation options, etc. The reception unit can accept input via a smartphone application, and can also accept voice input and text input. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and propose the most appropriate services and interpretations. The analysis is carried out using natural language processing and machine learning algorithms. For example, the generation AI proposes tourist information, restaurant reservations, and transportation information tailored to the needs of foreign visitors to Japan. The analysis unit can also analyze the language input by foreign visitors to Japan and translate information about destinations and services into their native language. Step 3: The provision unit provides the services and interpretations proposed by the analysis unit. The provision may include real-time interpretation services, tourist information, restaurant reservations, and transportation information. For example, the provision unit may provide tourist information, restaurant reservations, and transportation information to foreign visitors to Japan.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0149] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] 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.

[0168] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk that accepts input of destinations and desired services from foreign visitors to Japan; an analysis unit that analyzes the information received by the reception unit and proposes services and interpretations; a providing unit that provides the services and interpretations suggested by the analysis unit. A system characterized by:

2. The reception unit Accepts information entered by foreign visitors to Japan about their destination and desired services 2. The system of claim 1.

3. The analysis unit Providing tourist information, restaurant reservations, and transportation information to meet the needs of foreign visitors to Japan 2. The system of claim 1.

4. The providing unit Providing real-time interpretation services 2. The system of claim 1.

5. The analysis unit Analyze the language entered by foreign visitors to Japan and translate information about destinations or services into the foreign visitors' native language 2. The system of claim 1.

6. The providing unit Providing tourist information, restaurant reservations, and transportation information to foreign visitors to Japan 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and change the design of the input interface based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Add a feature to analyze the user's past input history and auto-complete frequently entered information.

2. The system of claim 1.

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