System

Abstract

An object of a system according to an embodiment is to easily create a travel plan and provide information on the spot.SOLUTION: A system according to an embodiment includes a reception unit, a generation unit, and a guide unit. An accepting part inputs information of a city to visit. The generation unit searches for information related to a visited city based on the information input by the reception unit, and makes a recommendation from the viewpoint of the local area. Based on the information generated by the generation unit, the guide unit provides necessary information and performs translation while staying at the site.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making travel plans and providing information on-site, which is cumbersome and burdensome for travelers.

[0005] The system according to the embodiment aims to easily create travel plans and provide information on the spot. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a guide unit. The reception unit inputs information about the city to be visited. The generation unit searches for information about the city to be visited based on the information input by the reception unit and makes recommendations from a local perspective. The guide unit provides information and translations necessary during a stay in the city based on the information generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can easily create a travel plan and provide information on the spot. [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 travel support system according to an embodiment of the present invention eliminates the hassle of planning a trip. A generation AI searches for and recommends information about cities the user will visit, much like a local would, and provides information and translations while the user is in the city. The travel support system allows the user to input the city the user will visit, and the generation AI searches for information about that city and provides recommendations from a local perspective. For example, it provides information about tourist spots, restaurants, and events. Furthermore, the generation AI functions as a personal guide while the user is in the city, providing information and translations as needed. This enhances the user's trip. For example, the travel support system allows the user to input the city the user will visit. For example, the user inputs "Paris." This information is input into the generation AI. The generation AI then searches for information about the input city. The generation AI collects information from the Internet and provides recommendations from a local perspective. For example, it provides information about tourist spots, restaurants, and events. This allows the user to obtain the latest local information. Furthermore, the generation AI functions as a personal guide while the user is in the city. For example, when the user encounters a problem while in the city, the generation AI provides the necessary information. Additionally, if a language barrier is encountered, the generative AI will translate, allowing users to enjoy their trip with peace of mind. This allows the travel support system to eliminate the hassle of planning a trip and make the user's trip more fulfilling. For example, users can obtain the latest local information and be able to deal with any issues they may have while on the spot, reducing the stress of traveling. For example, they can obtain real-time information such as the congestion status of tourist spots and the reservation status of restaurants. Furthermore, even if they do not understand the local language, the generative AI will translate, solving communication problems.

[0029] A travel support system according to an embodiment includes a reception unit, a generation unit, and a guide unit. The reception unit inputs information about a city the user will visit. The information about the city the user will visit includes, but is not limited to, tourist attractions, restaurants, and event information. The reception unit, for example, simply inputs the name of the city the user wants to visit. The generation unit uses a generation AI to search for information about the city based on the information input by the reception unit and provides recommendations from a local perspective. The generation unit, for example, collects information from the Internet and provides tourist attractions, restaurants, and event information. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, and analyzes information on the Internet to provide optimal information to the user. The guide unit provides and translates information needed during a user's stay in the local area based on the information generated by the generation unit. The guide unit provides, for example, information needed when the user encounters problems in the local area. The guide unit can also translate if the user does not understand the local language. The generation AI is, for example, a text translation AI or a voice translation AI, and performs real-time translation based on user input. As a result, the travel support system according to the embodiment can eliminate the hassle of creating a travel plan for the user and can provide information and translation on-site, thereby making the trip more fulfilling.

[0030] The reception unit allows the user to input the name of a city to visit. For example, the reception unit only requires the user to input the name of the city they want to visit. The city name may include, but is not limited to, for example, the official name, abbreviation, place name code, etc. This allows the user to easily input the name of the city they want to visit. Some or all of the above-described processing in the reception unit may be performed using or without using the generation AI. For example, the reception unit may input the city name input by the user to the generation AI, and the generation AI may analyze the city name and provide appropriate information.

[0031] The generation unit can collect information from the Internet and recommend tourist spots, restaurants, and event information from a local perspective. The generation unit, for example, collects information from the Internet and recommends tourist spots, restaurants, and event information from a local perspective. Examples of information from the Internet include, but are not limited to, websites, social media, and blogs. Examples of tourist spots include, but are not limited to, popularity and historical value. Examples of restaurants include, but are not limited to, ratings, price ranges, and types of cuisine. Examples of event information include, but are not limited to, event dates, locations, and participation fees. This allows users to obtain the latest local information. Some or all of the above-described processing by the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input information from the Internet into a generation AI, which can analyze the information and make appropriate recommendations.

[0032] The guide unit can provide the user with information they need while they are staying at the local area. For example, the guide unit provides information they need when they encounter a problem at the local area. Necessary information includes, but is not limited to, traffic information, emergency contact information, medical institution information, etc. This allows the user to obtain the information they need when they encounter a problem at the local area. Some or all of the above-mentioned processing in the guide unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the guide unit can input information they need when they encounter a problem at the local area into the generation AI, and the generation AI can analyze the information and provide appropriate information.

[0033] The guide unit can perform translation when the user does not understand the local language. The guide unit can perform translation when the user does not understand the local language, for example. Translation includes, but is not limited to, text translation, voice translation, real-time translation, etc. This allows the user to solve communication problems even if they do not understand the local language. Some or all of the above-mentioned processing in the guide unit may be performed using or without the generation AI. For example, the guide unit can input a translation to the generation AI when the user does not understand the local language, and the generation AI can perform the translation.

[0034] The reception unit can analyze the user's past travel history and automatically complete the input of cities to be visited. For example, the reception unit analyzes the user's past travel history and automatically completes the input of cities to be visited. Past travel history includes, but is not limited to, cities visited, length of stay, and travel purpose. Automatic completion includes, but is not limited to, predictions based on past data and the use of machine learning algorithms. For example, the reception unit can automatically suggest the next destination based on cities the user has previously visited. It can also prioritize frequently visited cities based on the user's past travel history. It can also analyze the user's past travel patterns and predict and suggest cities that the user is likely to visit next. This allows the next destination to be automatically suggested based on the user's past travel history. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past travel history into the generation AI, which can analyze the history and suggest appropriate destinations.

[0035] The reception unit can suggest nearby cities based on the user's current location information. The reception unit can suggest nearby cities based on, for example, the user's current location information. Current location information can include, for example, GPS data and Wi-Fi location information, but is not limited to these examples. Nearby cities can include, for example, distance, transportation access, and tourist value, but is not limited to these examples. For example, nearby tourist destinations can be suggested based on the user's current location. Cities that are easily accessible from the user's current location can also be preferentially displayed. Nearby event information can also be provided based on the user's current location. This makes it possible to suggest nearby tourist destinations based on the user's current location. Some or all of the above-described processing in the reception unit can be performed using or without the generation AI. For example, the reception unit can input the user's current location information into the generation AI, which can analyze the location information and suggest appropriate nearby cities.

[0036] The reception unit can customize the input interface according to the user's travel purpose. The reception unit customizes the input interface according to, for example, the user's travel purpose (sightseeing, business, vacation, etc.). Travel purposes include, but are not limited to, sightseeing, business, vacation, etc. Examples of customizing the input interface include, but are not limited to, changing input items and changing the interface design. For example, if the user's travel purpose is sightseeing, an interface that prioritizes input of tourist spots can be provided. Also, if the user's travel purpose is business, an interface that prioritizes input of conference facilities and business hotels can be provided. Also, if the user's travel purpose is vacation, an interface that prioritizes input of relaxation places can be provided. This allows the user to input more appropriate information by providing an input interface according to the user's travel purpose. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's travel purpose into the generation AI, which can analyze the purpose and provide an appropriate input interface.

[0037] The reception unit can analyze the user's social media activity and suggest related cities. For example, the reception unit analyzes the user's social media activity and suggests related cities. Social media activity includes, but is not limited to, the content of posts, the number of likes, the number of followers, etc. For example, related cities include, but are not limited to, the user's interests and past visit history. For example, related cities can be suggested based on locations where the user has checked in on social media. Furthermore, the content of the user's social media posts can be analyzed to suggest cities that the user may be interested in. Furthermore, related cities can be suggested based on the social media activity of the user's friends. In this way, related cities can be suggested based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's social media activity into the generation AI, which can analyze the activity and suggest appropriate cities.

[0038] The reception unit can customize the input method by reflecting the user's past feedback. The reception unit, for example, customizes the input method by reflecting the user's past feedback. Past feedback includes, for example, user ratings, comments, and improvement requests, but is not limited to these examples. Customizing the input method includes, for example, changing input fields and changing the interface design, but is not limited to these examples. For example, the reception unit can prioritize input methods that the user previously preferred. The input interface can also be improved based on the user's past feedback. The input interface can also be customized to avoid input methods that the user previously dissatisfied with. This allows the input interface to be improved based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past feedback into the generation AI, which can analyze the feedback and provide an appropriate input method.

[0039] The reception unit can provide an optimal input interface by taking into account the user's device information. The reception unit, for example, provides the optimal input interface by taking into account the user's device information. Device information includes, but is not limited to, the device type, OS version, screen size, etc. The optimal input interface can include, but is not limited to, adjustments based on usability test results and device characteristics. For example, if the user is using a smartphone, an interface optimized for touch operation can be provided. Also, if the user is using a tablet, an interface optimized for a large screen can be provided. Also, if the user is using a desktop, an interface optimized for keyboard input can be provided. This makes it possible to provide an optimal input interface based on the user's device information. Some or all of the above-described processing in the reception unit may be performed using a generation AI or without a generation AI. For example, the reception unit can input the user's device information into a generation AI, which can analyze the information and provide an appropriate input interface.

[0040] The generation unit can prioritize providing the latest event information for the city to be visited when making a recommendation. For example, the generation unit prioritizes providing the latest event information for the city to be visited when making a recommendation. The latest event information includes, but is not limited to, obtaining information from official websites and collecting information from social media, for example. For example, the generation unit prioritizes displaying the latest event information held in the city to be visited. The generation unit can also provide the latest festival and concert information for the city to be visited. The generation unit can also provide the latest exhibition and trade show information for the city to be visited. This allows the user to obtain the latest information by preferentially providing the latest event information for the city to be visited. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the latest event information for the city to be visited into the generation AI, which can analyze the information and provide appropriate event information.

[0041] The generation unit can provide customized information by referring to the user's past travel history when making recommendations. For example, the generation unit can provide customized information by referring to the user's past travel history when making recommendations. Past travel history includes, but is not limited to, cities visited, length of stay, and travel purpose. Customized information includes, but is not limited to, information based on the user's interests and suggestions based on past behavioral patterns. For example, the generation unit can customize and suggest the next destination based on information about cities the user has previously visited. It can also suggest tourist spots that the user is likely to be interested in based on the user's past travel history. It can also analyze the user's past travel patterns and suggest places the user is likely to visit next. This allows the generation unit to customize and suggest the next destination based on the user's past travel history. Some or all of the above-described processing in the generation unit can be performed using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's past travel history into the generation AI, which can analyze the history and suggest appropriate destinations.

[0042] The generation unit can apply different recommendation algorithms depending on the user's travel purpose when making recommendations. For example, the generation unit can apply different recommendation algorithms depending on the user's travel purpose when making recommendations. Travel purposes include, but are not limited to, sightseeing, business, and vacation. Recommendation algorithms include, but are not limited to, collaborative filtering and content-based filtering. For example, if the user's travel purpose is sightseeing, an algorithm that prioritizes recommending tourist spots can be applied. Also, if the user's travel purpose is business, an algorithm that prioritizes recommending conference facilities and business hotels can be applied. Also, if the user's travel purpose is vacation, an algorithm that prioritizes recommending relaxation spots can be applied. This allows for providing more appropriate information by providing a recommendation algorithm according to the user's travel purpose. Some or all of the above-described processing in the generation unit can be performed using or without a generation AI. For example, the generation unit can input the user's travel purpose into the generation AI, which can analyze the purpose and apply an appropriate recommendation algorithm.

[0043] The generation unit can provide information taking into consideration the season and weather information of the city to be visited when making recommendations. For example, the generation unit provides information taking into consideration the season and weather information of the city to be visited when making recommendations. Season and weather information includes, but is not limited to, weather data, seasonal event information, and the like. For example, tourist spots according to the season of the city to be visited can be suggested. Indoor and outdoor activities can also be suggested based on the weather information of the city to be visited. Clothing advice can also be provided according to the season and weather information of the city to be visited. This makes it possible to provide appropriate information based on the season and weather information of the city to be visited. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input season and weather information of the city to be visited into the generation AI, which can analyze the information and provide appropriate information.

[0044] The generation unit can determine the priority of information based on the user's interests when making recommendations. For example, the generation unit determines the priority of information based on the user's interests when making recommendations. Interests include, but are not limited to, hobbies, past behavioral patterns, and social media activity. Information priority includes, but is not limited to, the user's level of interest and the importance of the information. For example, theme parks in which the user is interested can be preferentially recommended. Historical buildings in which the user is interested can also be preferentially recommended. Places likely to be of interest based on the user's past search history can also be preferentially recommended. This allows information based on the user's interests to be preferentially provided. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's interests into the generation AI, which can analyze the information and provide appropriate information priorities.

[0045] The generation unit can adjust the level of detail of the information according to the user's level of expertise when making a recommendation. For example, the generation unit adjusts the level of detail of the information according to the user's level of expertise when making a recommendation. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. The level of detail of the information includes, but is not limited to, basic information, detailed technical information, and the like. For example, if the user is a beginner, basic information can be provided. Also, if the user is an intermediate user, detailed information can be provided. Also, if the user is an advanced user, specialized information can be provided. This allows for providing more appropriate information by providing information according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's level of expertise into the generation AI, which can analyze the information and provide the appropriate level of detail of the information.

[0046] The guide unit can prioritize providing information for responding to local emergencies. For example, the guide unit prioritizes providing information for responding to local emergencies. Examples of emergencies include, but are not limited to, natural disasters, accidents, and illnesses. Prioritizing the provision of information can include, but are not limited to, the importance and urgency of the information. For example, local emergency contact information can be provided. Information on the nearest local medical institution can also be provided. Information on local emergency evacuation sites can also be provided. This prioritizes the provision of information for responding to local emergencies, thereby ensuring the safety of the user. Some or all of the above-described processing in the guide unit may be performed using or without the generation AI. For example, the guide unit can input information for responding to local emergencies into the generation AI, which can analyze the information and provide appropriate information.

[0047] The guide unit can provide local traffic information in real time. The guide unit, for example, provides local traffic information in real time. Traffic information includes, for example, but is not limited to, the operation status of public transportation and traffic congestion information. Real-time information includes, for example, but is not limited to, the frequency of information updates and the method of data acquisition. For example, the operation status of local public transportation can be provided in real time. Local traffic congestion information can also be provided in real time. Local taxi and ride-sharing service usage status can also be provided in real time. This allows users to travel efficiently by providing local traffic information in real time. Some or all of the above-mentioned processing in the guide unit may be performed using or without the generation AI. For example, the guide unit can input local traffic information into the generation AI, which can analyze the information and provide appropriate traffic information.

[0048] The guide unit can provide information about local culture and customs. The guide unit provides, for example, information about local culture and customs. Examples of culture and customs include, but are not limited to, traditional events, manners, and food culture. For example, the guide unit can provide information about local cultural manners and customs. It can also provide information about local traditional events and festivals. It can also provide information about local food culture and local specialties. By providing information about local culture and customs, it is possible to make it easier for users to understand the local culture. Some or all of the above-mentioned processing in the guide unit may be performed using or without the generation AI. For example, the guide unit can input information about local culture and customs into the generation AI, which can analyze the information and provide appropriate information.

[0049] The guide unit can provide local shopping information. The guide unit provides, for example, local shopping information. Shopping information includes, but is not limited to, store information, sale information, and product reviews. For example, information on popular local shopping areas can be provided. Information on local specialties and souvenirs can also be provided. Local sale and discount information can also be provided. By providing local shopping information, the user can enjoy shopping locally. Some or all of the above-described processing in the guide unit may be performed using or without the generation AI. For example, the guide unit can input local shopping information into the generation AI, which can analyze the information and provide appropriate shopping information.

[0050] The guide unit can provide information on local medical institutions. The guide unit provides, for example, information on local medical institutions. Medical institution information includes, for example, hospital locations, medical specialties, emergency contact information, etc., but is not limited to these examples. For example, information on the nearest local hospital or clinic can be provided. Information on local pharmacies and drugstores can also be provided. Contact information for local emergency medical services can also be provided. By providing information on local medical institutions, the user can use an appropriate medical institution in an emergency. Some or all of the above-mentioned processing in the guide unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the guide unit can input information on local medical institutions into a generation AI, which can analyze the information and provide information on appropriate medical institutions.

[0051] The guide unit can update local event information in real time. For example, the guide unit updates local event information in real time. Event information includes, but is not limited to, the date and time of the event, the location, and the participation fee. Real-time includes, but is not limited to, the frequency of information updates and the method of data acquisition. For example, the guide unit can provide a schedule of local events in real time. It can also provide the latest local festival and concert information in real time. It can also provide information on local exhibitions and trade shows in real time. By updating local event information in real time, the user can obtain the latest event information. Some or all of the above-described processing in the guide unit may be performed using or without the generation AI. For example, the guide unit can input local event information into the generation AI, which can analyze the information and provide appropriate event information.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The reception unit can monitor the user's health condition and suggest cities to visit based on that condition. For example, if the user has high blood pressure, it can suggest avoiding cities with high altitudes. Also, if the user has allergies, it can suggest cities with low levels of that allergen. Furthermore, if the user is undergoing rehabilitation, it can suggest cities with good rehabilitation facilities. This makes it possible to provide travel plans that are tailored to the user's health condition.

[0054] The reception unit can analyze the user's past travel behavior patterns and suggest cities to visit. For example, the reception unit can suggest the next city to visit based on the user's behavior patterns in cities the user has visited in the past. The reception unit can also suggest cities with a wide range of activities based on the user's past favorite activities. Furthermore, the reception unit can suggest cities that the user should avoid based on places the user has avoided in the past. This makes it possible to provide a travel plan based on the user's past behavior patterns.

[0055] The reception unit can adjust the input interface taking into account the remaining battery level of the user's device. For example, when the battery level is low, a simple, power-saving interface can be provided. When the battery level is sufficient, detailed input options can be provided. Furthermore, when the battery level is very low, voice input can be prioritized to complete input quickly. This makes it possible to provide an optimal input interface according to the remaining battery level of the user's device.

[0056] The guide unit monitors the user's current activity level and can provide information according to that activity level. For example, if the user is walking, it can provide information about nearby tourist spots. If the user is taking a break, it can provide information about cafes and parks where the user can relax. Furthermore, if the user is driving, it can provide traffic information and information about the nearest parking lot. This makes it possible to provide optimal information according to the user's current activity level.

[0057] The reception unit can suggest cities to visit taking into account the user's travel budget. For example, if the user has a limited budget, it can suggest cities with good cost performance. If the user has a sufficient budget, it can also suggest cities where the user can enjoy a luxurious trip. It can also suggest accommodation and meal options according to the user's budget. This makes it possible to provide the user with an optimal travel plan that suits their travel budget.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit inputs information about the city the user is visiting. The information about the city the user is visiting includes tourist attractions, restaurants, event information, etc. The user only needs to input the name of the city they want to visit. Step 2: The generation unit searches for information about the city to be visited based on the information entered by the reception unit and makes recommendations from a local's perspective. The generation unit collects information from the internet and provides information on tourist spots, restaurants, events, etc. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, which analyzes information on the internet and provides the most appropriate information to the user. Step 3: The guide unit provides necessary information and translations while the user is in the local area based on the information generated by the generation unit. The guide unit provides necessary information when the user encounters problems in the local area and can also translate if the user does not understand the local language. The generation AI is a text translation AI or a voice translation AI, and translates in real time based on the user's input.

[0060] (Example 2) A travel support system according to an embodiment of the present invention eliminates the hassle of planning a trip. A generation AI searches for and recommends information about cities the user will visit, much like a local would, and provides information and translations while the user is in the city. The travel support system allows the user to input the city the user will visit, and the generation AI searches for information about that city and provides recommendations from a local perspective. For example, it provides information about tourist spots, restaurants, and events. Furthermore, the generation AI functions as a personal guide while the user is in the city, providing information and translations as needed. This enhances the user's trip. For example, the travel support system allows the user to input the city the user will visit. For example, the user inputs "Paris." This information is input into the generation AI. The generation AI then searches for information about the input city. The generation AI collects information from the Internet and provides recommendations from a local perspective. For example, it provides information about tourist spots, restaurants, and events. This allows the user to obtain the latest local information. Furthermore, the generation AI functions as a personal guide while the user is in the city. For example, when the user encounters a problem while in the city, the generation AI provides the necessary information. Additionally, if a language barrier is encountered, the generative AI will translate, allowing users to enjoy their trip with peace of mind. This allows the travel support system to eliminate the hassle of planning a trip and make the user's trip more fulfilling. For example, users can obtain the latest local information and be able to deal with any issues they may have while on the spot, reducing the stress of traveling. For example, they can obtain real-time information such as the congestion status of tourist spots and the reservation status of restaurants. Furthermore, even if they do not understand the local language, the generative AI will translate, solving communication problems.

[0061] A travel support system according to an embodiment includes a reception unit, a generation unit, and a guide unit. The reception unit inputs information about a city the user will visit. The information about the city the user will visit includes, but is not limited to, tourist attractions, restaurants, and event information. The reception unit, for example, simply inputs the name of the city the user wants to visit. The generation unit uses a generation AI to search for information about the city based on the information input by the reception unit and provides recommendations from a local perspective. The generation unit, for example, collects information from the Internet and provides tourist attractions, restaurants, and event information. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, and analyzes information on the Internet to provide optimal information to the user. The guide unit provides and translates information needed during a user's stay in the local area based on the information generated by the generation unit. The guide unit provides, for example, information needed when the user encounters problems in the local area. The guide unit can also translate if the user does not understand the local language. The generation AI is, for example, a text translation AI or a voice translation AI, and performs real-time translation based on user input. As a result, the travel support system according to the embodiment can eliminate the hassle of creating a travel plan for the user and can provide information and translation on-site, thereby making the trip more fulfilling.

[0062] The reception unit allows the user to input the name of a city to visit. For example, the reception unit only requires the user to input the name of the city they want to visit. The city name may include, but is not limited to, for example, the official name, abbreviation, place name code, etc. This allows the user to easily input the name of the city they want to visit. Some or all of the above-described processing in the reception unit may be performed using or without using the generation AI. For example, the reception unit may input the city name input by the user to the generation AI, and the generation AI may analyze the city name and provide appropriate information.

[0063] The generation unit can collect information from the Internet and recommend tourist spots, restaurants, and event information from a local perspective. The generation unit, for example, collects information from the Internet and recommends tourist spots, restaurants, and event information from a local perspective. Examples of information from the Internet include, but are not limited to, websites, social media, and blogs. Examples of tourist spots include, but are not limited to, popularity and historical value. Examples of restaurants include, but are not limited to, ratings, price ranges, and types of cuisine. Examples of event information include, but are not limited to, event dates, locations, and participation fees. This allows users to obtain the latest local information. Some or all of the above-described processing by the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input information from the Internet into a generation AI, which can analyze the information and make appropriate recommendations.

[0064] The guide unit can provide the user with information they need while they are staying at the local area. For example, the guide unit provides information they need when they encounter a problem at the local area. Necessary information includes, but is not limited to, traffic information, emergency contact information, medical institution information, etc. This allows the user to obtain the information they need when they encounter a problem at the local area. Some or all of the above-mentioned processing in the guide unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the guide unit can input information they need when they encounter a problem at the local area into the generation AI, and the generation AI can analyze the information and provide appropriate information.

[0065] The guide unit can perform translation when the user does not understand the local language. The guide unit can perform translation when the user does not understand the local language, for example. Translation includes, but is not limited to, text translation, voice translation, real-time translation, etc. This allows the user to solve communication problems even if they do not understand the local language. Some or all of the above-mentioned processing in the guide unit may be performed using or without the generation AI. For example, the guide unit can input a translation to the generation AI when the user does not understand the local language, and the generation AI can perform the translation.

[0066] The reception unit can estimate the user's emotions and adjust the input method for the cities to be visited based on the emotions. The reception unit, for example, estimates the user's emotions and adjusts the input method for the cities to be visited based on the emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. Examples of adjusting the input method include, but are not limited to, changing the input interface and simplifying the input procedure. For example, if the user is stressed, a simple interface can be provided to minimize the input procedure. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Alternatively, if the user is in a hurry, voice input can be prioritized to allow the user to quickly input the cities to be visited. This allows for a more comfortable input experience by providing an input method that corresponds 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using the generation AI, or without the generation AI. For example, the reception unit can input the user's emotions into the generation AI, which can then analyze the emotions and provide an appropriate input method.

[0067] The reception unit can analyze the user's past travel history and automatically complete the input of cities to be visited. For example, the reception unit analyzes the user's past travel history and automatically completes the input of cities to be visited. Past travel history includes, but is not limited to, cities visited, length of stay, and travel purpose. Automatic completion includes, but is not limited to, predictions based on past data and the use of machine learning algorithms. For example, the reception unit can automatically suggest the next destination based on cities the user has previously visited. It can also prioritize frequently visited cities based on the user's past travel history. It can also analyze the user's past travel patterns and predict and suggest cities that the user is likely to visit next. This allows the next destination to be automatically suggested based on the user's past travel history. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past travel history into the generation AI, which can analyze the history and suggest appropriate destinations.

[0068] The reception unit can suggest nearby cities based on the user's current location information. The reception unit can suggest nearby cities based on, for example, the user's current location information. Current location information can include, for example, GPS data and Wi-Fi location information, but is not limited to these examples. Nearby cities can include, for example, distance, transportation access, and tourist value, but is not limited to these examples. For example, nearby tourist destinations can be suggested based on the user's current location. Cities that are easily accessible from the user's current location can also be preferentially displayed. Nearby event information can also be provided based on the user's current location. This makes it possible to suggest nearby tourist destinations based on the user's current location. Some or all of the above-described processing in the reception unit can be performed using or without the generation AI. For example, the reception unit can input the user's current location information into the generation AI, which can analyze the location information and suggest appropriate nearby cities.

[0069] The reception unit can customize the input interface according to the user's travel purpose. The reception unit customizes the input interface according to, for example, the user's travel purpose (sightseeing, business, vacation, etc.). Travel purposes include, but are not limited to, sightseeing, business, vacation, etc. Examples of customizing the input interface include, but are not limited to, changing input items and changing the interface design. For example, if the user's travel purpose is sightseeing, an interface that prioritizes input of tourist spots can be provided. Also, if the user's travel purpose is business, an interface that prioritizes input of conference facilities and business hotels can be provided. Also, if the user's travel purpose is vacation, an interface that prioritizes input of relaxation places can be provided. This allows the user to input more appropriate information by providing an input interface according to the user's travel purpose. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's travel purpose into the generation AI, which can analyze the purpose and provide an appropriate input interface.

[0070] The reception unit can estimate the user's emotions and determine the input priority of cities to visit based on the emotions. The reception unit, for example, estimates the user's emotions and determines the input priority of cities to visit based on the emotions. Emotions include, but are not limited to, joy, sadness, and anger. Input priority can be determined based on, but is not limited to, importance, urgency, and the user's interest. For example, if the user is excited, popular tourist destinations can be preferentially suggested. Also, if the user is tired, relaxing cities can be preferentially suggested. Also, if the user is stressed, quiet cities can be preferentially suggested. This provides an input priority based on the user's emotions, thereby providing a more comfortable input experience. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using the generation AI or without the generation AI. For example, the reception unit can input the user's emotions into the generation AI, which can then analyze the emotions and provide appropriate input priorities.

[0071] The reception unit can analyze the user's social media activity and suggest related cities. For example, the reception unit analyzes the user's social media activity and suggests related cities. Social media activity includes, but is not limited to, the content of posts, the number of likes, the number of followers, etc. For example, related cities include, but are not limited to, the user's interests and past visit history. For example, related cities can be suggested based on locations where the user has checked in on social media. Furthermore, the content of the user's social media posts can be analyzed to suggest cities that the user may be interested in. Furthermore, related cities can be suggested based on the social media activity of the user's friends. In this way, related cities can be suggested based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's social media activity into the generation AI, which can analyze the activity and suggest appropriate cities.

[0072] The reception unit can customize the input method by reflecting the user's past feedback. The reception unit, for example, customizes the input method by reflecting the user's past feedback. Past feedback includes, for example, user ratings, comments, and improvement requests, but is not limited to these examples. Customizing the input method includes, for example, changing input fields and changing the interface design, but is not limited to these examples. For example, the reception unit can prioritize input methods that the user previously preferred. The input interface can also be improved based on the user's past feedback. The input interface can also be customized to avoid input methods that the user previously dissatisfied with. This allows the input interface to be improved based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past feedback into the generation AI, which can analyze the feedback and provide an appropriate input method.

[0073] The reception unit can provide an optimal input interface by taking into account the user's device information. The reception unit, for example, provides the optimal input interface by taking into account the user's device information. Device information includes, but is not limited to, the device type, OS version, screen size, etc. The optimal input interface can include, but is not limited to, adjustments based on usability test results and device characteristics. For example, if the user is using a smartphone, an interface optimized for touch operation can be provided. Also, if the user is using a tablet, an interface optimized for a large screen can be provided. Also, if the user is using a desktop, an interface optimized for keyboard input can be provided. This makes it possible to provide an optimal input interface based on the user's device information. Some or all of the above-described processing in the reception unit may be performed using a generation AI or without a generation AI. For example, the reception unit can input the user's device information into a generation AI, which can analyze the information and provide an appropriate input interface.

[0074] The generation unit can estimate the user's emotions and adjust the way recommendations are presented in accordance with the emotions. For example, the generation unit estimates the user's emotions and adjusts the way recommendations are presented in accordance with the emotions. Emotions include, but are not limited to, joy, sadness, anger, and the like. Recommendation presentation methods include, but are not limited to, text tone, image selection, and level of detail. For example, if the user is relaxed, a relaxed recommendation can be provided. If the user is in a hurry, a concise recommendation that focuses on the main points can be provided. If the user is excited, a recommendation that adds a visually stimulating effect can be provided. This allows for a more comfortable recommendation experience by providing a recommendation presentation method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using or without the generation AI. For example, the generation unit may input the user's emotions into the generation AI, which may then analyze the emotions and provide an appropriate way of expressing recommendations.

[0075] The generation unit can prioritize providing the latest event information for the city to be visited when making a recommendation. For example, the generation unit prioritizes providing the latest event information for the city to be visited when making a recommendation. The latest event information includes, but is not limited to, obtaining information from official websites and collecting information from social media, for example. For example, the generation unit prioritizes displaying the latest event information held in the city to be visited. The generation unit can also provide the latest festival and concert information for the city to be visited. The generation unit can also provide the latest exhibition and trade show information for the city to be visited. This allows the user to obtain the latest information by preferentially providing the latest event information for the city to be visited. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the latest event information for the city to be visited into the generation AI, which can analyze the information and provide appropriate event information.

[0076] The generation unit can provide customized information by referring to the user's past travel history when making recommendations. For example, the generation unit can provide customized information by referring to the user's past travel history when making recommendations. Past travel history includes, but is not limited to, cities visited, length of stay, and travel purpose. Customized information includes, but is not limited to, information based on the user's interests and suggestions based on past behavioral patterns. For example, the generation unit can customize and suggest the next destination based on information about cities the user has previously visited. It can also suggest tourist spots that the user is likely to be interested in based on the user's past travel history. It can also analyze the user's past travel patterns and suggest places the user is likely to visit next. This allows the generation unit to customize and suggest the next destination based on the user's past travel history. Some or all of the above-described processing in the generation unit can be performed using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's past travel history into the generation AI, which can analyze the history and suggest appropriate destinations.

[0077] The generation unit can apply different recommendation algorithms depending on the user's travel purpose when making recommendations. For example, the generation unit can apply different recommendation algorithms depending on the user's travel purpose when making recommendations. Travel purposes include, but are not limited to, sightseeing, business, and vacation. Recommendation algorithms include, but are not limited to, collaborative filtering and content-based filtering. For example, if the user's travel purpose is sightseeing, an algorithm that prioritizes recommending tourist spots can be applied. Also, if the user's travel purpose is business, an algorithm that prioritizes recommending conference facilities and business hotels can be applied. Also, if the user's travel purpose is vacation, an algorithm that prioritizes recommending relaxation spots can be applied. This allows for providing more appropriate information by providing a recommendation algorithm according to the user's travel purpose. Some or all of the above-described processing in the generation unit can be performed using or without a generation AI. For example, the generation unit can input the user's travel purpose into the generation AI, which can analyze the purpose and apply an appropriate recommendation algorithm.

[0078] The generation unit can estimate the user's emotion and adjust the length of the recommendation according to the emotion. For example, the generation unit estimates the user's emotion and adjusts the length of the recommendation according to the emotion. Emotions include, but are not limited to, joy, sadness, anger, etc. The length of the recommendation includes, but is not limited to, the importance of the information and the user's level of interest. For example, if the user is in a hurry, a short and to-the-point recommendation can be provided. If the user is relaxed, a longer recommendation with detailed explanations can be provided. If the user is excited, a recommendation with visually stimulating effects can be provided. This allows the user to enjoy a more comfortable recommendation experience by providing a recommendation length that corresponds to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using or without the generation AI. For example, the generation unit may input the user's emotions into the generation AI, which may then analyze the emotions and provide an appropriate recommendation length.

[0079] The generation unit can provide information taking into consideration the season and weather information of the city to be visited when making recommendations. For example, the generation unit provides information taking into consideration the season and weather information of the city to be visited when making recommendations. Season and weather information includes, but is not limited to, weather data, seasonal event information, and the like. For example, tourist spots according to the season of the city to be visited can be suggested. Indoor and outdoor activities can also be suggested based on the weather information of the city to be visited. Clothing advice can also be provided according to the season and weather information of the city to be visited. This makes it possible to provide appropriate information based on the season and weather information of the city to be visited. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input season and weather information of the city to be visited into the generation AI, which can analyze the information and provide appropriate information.

[0080] The generation unit can determine the priority of information based on the user's interests when making recommendations. For example, the generation unit determines the priority of information based on the user's interests when making recommendations. Interests include, but are not limited to, hobbies, past behavioral patterns, and social media activity. Information priority includes, but is not limited to, the user's level of interest and the importance of the information. For example, theme parks in which the user is interested can be preferentially recommended. Historical buildings in which the user is interested can also be preferentially recommended. Places likely to be of interest based on the user's past search history can also be preferentially recommended. This allows information based on the user's interests to be preferentially provided. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's interests into the generation AI, which can analyze the information and provide appropriate information priorities.

[0081] The generation unit can adjust the level of detail of the information according to the user's level of expertise when making a recommendation. For example, the generation unit adjusts the level of detail of the information according to the user's level of expertise when making a recommendation. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. The level of detail of the information includes, but is not limited to, basic information, detailed technical information, and the like. For example, if the user is a beginner, basic information can be provided. Also, if the user is an intermediate user, detailed information can be provided. Also, if the user is an advanced user, specialized information can be provided. This allows for providing more appropriate information by providing information according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's level of expertise into the generation AI, which can analyze the information and provide the appropriate level of detail of the information.

[0082] The guide unit can estimate the user's emotions and adjust the information provision method according to the emotions. For example, the guide unit can estimate the user's emotions and adjust the information provision method according to the emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. Information provision methods include, but are not limited to, text, audio, video, etc. For example, if the user is nervous, simple, highly visible information can be provided. If the user is relaxed, detailed information can be provided. If the user is in a hurry, information can be provided that focuses on the main points. This allows for a more comfortable information provision experience by providing an information provision method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the guide unit can be performed using the generation AI or without the generation AI. For example, the guide unit can input the user's emotions into the generation AI, which can then analyze the emotions and provide an appropriate method of providing information.

[0083] The guide unit can prioritize providing information for responding to local emergencies. For example, the guide unit prioritizes providing information for responding to local emergencies. Examples of emergencies include, but are not limited to, natural disasters, accidents, and illnesses. Prioritizing the provision of information can include, but are not limited to, the importance and urgency of the information. For example, local emergency contact information can be provided. Information on the nearest local medical institution can also be provided. Information on local emergency evacuation sites can also be provided. This prioritizes the provision of information for responding to local emergencies, thereby ensuring the safety of the user. Some or all of the above-described processing in the guide unit may be performed using or without the generation AI. For example, the guide unit can input information for responding to local emergencies into the generation AI, which can analyze the information and provide appropriate information.

[0084] The guide unit can provide local traffic information in real time. The guide unit, for example, provides local traffic information in real time. Traffic information includes, for example, but is not limited to, the operation status of public transportation and traffic congestion information. Real-time information includes, for example, but is not limited to, the frequency of information updates and the method of data acquisition. For example, the operation status of local public transportation can be provided in real time. Local traffic congestion information can also be provided in real time. Local taxi and ride-sharing service usage status can also be provided in real time. This allows users to travel efficiently by providing local traffic information in real time. Some or all of the above-mentioned processing in the guide unit may be performed using or without the generation AI. For example, the guide unit can input local traffic information into the generation AI, which can analyze the information and provide appropriate traffic information.

[0085] The guide unit can provide information about local culture and customs. The guide unit provides, for example, information about local culture and customs. Examples of culture and customs include, but are not limited to, traditional events, manners, and food culture. For example, the guide unit can provide information about local cultural manners and customs. It can also provide information about local traditional events and festivals. It can also provide information about local food culture and local specialties. By providing information about local culture and customs, it is possible to make it easier for users to understand the local culture. Some or all of the above-mentioned processing in the guide unit may be performed using or without the generation AI. For example, the guide unit can input information about local culture and customs into the generation AI, which can analyze the information and provide appropriate information.

[0086] The guide unit can estimate the user's emotions and determine the priority of information provision based on the emotions. The guide unit, for example, estimates the user's emotions and determines the priority of information provision based on the emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. The priority of information provision includes, but is not limited to, the importance and urgency of the information. For example, if the user is nervous, emergency contact information and medical institution information can be provided preferentially. Also, if the user is relaxed, tourist spot and restaurant information can be provided preferentially. Also, if the user is in a hurry, traffic information and shortest route information can be provided preferentially. This allows for a more comfortable information provision experience by providing information provision priorities based on 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 guide unit may be performed using the generation AI or without the generation AI. For example, the guide unit can input the user's emotions into the generation AI, which can then analyze the emotions and provide appropriate information provision priorities.

[0087] The guide unit can provide local shopping information. The guide unit provides, for example, local shopping information. Shopping information includes, but is not limited to, store information, sale information, and product reviews. For example, information on popular local shopping areas can be provided. Information on local specialties and souvenirs can also be provided. Local sale and discount information can also be provided. By providing local shopping information, the user can enjoy shopping locally. Some or all of the above-described processing in the guide unit may be performed using or without the generation AI. For example, the guide unit can input local shopping information into the generation AI, which can analyze the information and provide appropriate shopping information.

[0088] The guide unit can provide information on local medical institutions. The guide unit provides, for example, information on local medical institutions. Medical institution information includes, for example, hospital locations, medical specialties, emergency contact information, etc., but is not limited to these examples. For example, information on the nearest local hospital or clinic can be provided. Information on local pharmacies and drugstores can also be provided. Contact information for local emergency medical services can also be provided. By providing information on local medical institutions, the user can use an appropriate medical institution in an emergency. Some or all of the above-mentioned processing in the guide unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the guide unit can input information on local medical institutions into a generation AI, which can analyze the information and provide information on appropriate medical institutions.

[0089] The guide unit can update local event information in real time. For example, the guide unit updates local event information in real time. Event information includes, but is not limited to, the date and time of the event, the location, and the participation fee. Real-time includes, but is not limited to, the frequency of information updates and the method of data acquisition. For example, the guide unit can provide a schedule of local events in real time. It can also provide the latest local festival and concert information in real time. It can also provide information on local exhibitions and trade shows in real time. By updating local event information in real time, the user can obtain the latest event information. Some or all of the above-described processing in the guide unit may be performed using or without the generation AI. For example, the guide unit can input local event information into the generation AI, which can analyze the information and provide appropriate event information. === Hard Collateral 1-1 === Each of the multiple elements including the reception unit, generation unit, and guide 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 is realized by the reception device 38 of the smart device 14, and inputs information about the city the user will visit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and searches for information about the city to be visited using a generation AI and makes recommendations from a local perspective. The guide unit is realized, for example, by the control unit 46A of the smart device 14, and provides information and translations necessary during the user's stay in the local area. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, generation unit, and guide 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 is realized by the microphone 238 of the smart glasses 214, and inputs information about the city the user will visit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses a generation AI to search for information about the city to be visited and make recommendations from a local perspective. The guide unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides information and translations necessary during the user's stay in the local area. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and guide unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314, and inputs information about the city the user will visit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses a generation AI to search for information about the city to be visited and make recommendations from the perspective of a local resident. The guide unit is realized, for example, by the control unit 46A of the headset terminal 314, and provides information and translations necessary during the user's stay in the local area. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, generation unit, and guide 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 is realized by the microphone 238 of the robot 414, and inputs information about the city the user will visit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses a generation AI to search for information about the city to be visited and make recommendations from a local person's perspective. The guide unit is realized, for example, by the control unit 46A of the robot 414, and provides information and translations necessary during the user's stay in the local area.

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

[0091] The reception unit can monitor the user's health condition and suggest cities to visit based on that condition. For example, if the user has high blood pressure, it can suggest avoiding cities with high altitudes. Also, if the user has allergies, it can suggest cities with low levels of that allergen. Furthermore, if the user is undergoing rehabilitation, it can suggest cities with good rehabilitation facilities. This makes it possible to provide travel plans that are tailored to the user's health condition.

[0092] The generation unit can estimate the user's emotions and adjust the timing of recommendations according to those emotions. For example, if the user is feeling stressed, recommendations can be made at a time when the user can relax. Also, if the user is excited, recommendations can be made immediately. Furthermore, if the user is tired, recommendations can be made after the user has rested. This makes it possible to provide recommendations at the optimal timing according to the user's emotions.

[0093] The guide unit can estimate the user's emotions and adjust the frequency of information provision according to those emotions. For example, if the user is relaxed, the frequency of information provision can be reduced. Also, if the user is excited, the frequency of information provision can be increased. Furthermore, if the user is feeling stressed, the information provided can be limited to the minimum necessary. In this way, by adjusting the frequency of information provision according to the user's emotions, a more comfortable information provision experience can be provided.

[0094] The reception unit can analyze the user's past travel behavior patterns and suggest cities to visit. For example, the reception unit can suggest the next city to visit based on the user's behavior patterns in cities the user has visited in the past. The reception unit can also suggest cities with a wide range of activities based on the user's past favorite activities. Furthermore, the reception unit can suggest cities that the user should avoid based on places the user has avoided in the past. This makes it possible to provide a travel plan based on the user's past behavior patterns.

[0095] The generator can estimate the user's emotions and adjust the content of recommendations according to those emotions. For example, if the user is sad, it can recommend tourist spots that will lift their spirits. If the user is happy, it can provide event information that will further enhance those emotions. Furthermore, if the user is angry, it can recommend places where they can relax. This makes it possible to provide optimal recommendations according to the user's emotions.

[0096] The guide unit can estimate the user's emotions and adjust the translation tone according to those emotions. For example, if the user is nervous, the translation can be performed in a gentle tone. If the user is relaxed, the translation can be performed in a casual tone. Furthermore, if the user is excited, the translation can be performed in an energetic tone. This makes it possible to provide the optimal translation tone according to the user's emotions.

[0097] The reception unit can adjust the input interface taking into account the remaining battery level of the user's device. For example, when the battery level is low, a simple, power-saving interface can be provided. When the battery level is sufficient, detailed input options can be provided. Furthermore, when the battery level is very low, voice input can be prioritized to complete input quickly. This makes it possible to provide an optimal input interface according to the remaining battery level of the user's device.

[0098] The generation unit can estimate the user's emotions and adjust the order of recommendations according to those emotions. For example, if the user is excited, the most exciting tourist spots can be recommended first. If the user is relaxed, relaxing tourist spots can be recommended first. Furthermore, if the user is stressed, relaxing places can be recommended first. This makes it possible to provide the optimal recommendation order according to the user's emotions.

[0099] The guide unit monitors the user's current activity level and can provide information according to that activity level. For example, if the user is walking, it can provide information about nearby tourist spots. If the user is taking a break, it can provide information about cafes and parks where the user can relax. Furthermore, if the user is driving, it can provide traffic information and information about the nearest parking lot. This makes it possible to provide optimal information according to the user's current activity level.

[0100] The reception unit can suggest cities to visit taking into account the user's travel budget. For example, if the user has a limited budget, it can suggest cities with good cost performance. If the user has a sufficient budget, it can also suggest cities where the user can enjoy a luxurious trip. It can also suggest accommodation and meal options according to the user's budget. This makes it possible to provide the user with an optimal travel plan that suits their travel budget.

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

[0102] Step 1: The reception unit inputs information about the city the user is visiting. The information about the city the user is visiting includes tourist attractions, restaurants, event information, etc. The user only needs to input the name of the city they want to visit. Step 2: The generation unit searches for information about the city to be visited based on the information entered by the reception unit and makes recommendations from a local's perspective. The generation unit collects information from the internet and provides information on tourist spots, restaurants, events, etc. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, which analyzes information on the internet and provides the most appropriate information to the user. Step 3: The guide unit provides necessary information and translations while the user is in the local area based on the information generated by the generation unit. The guide unit provides necessary information when the user encounters problems in the local area and can also translate if the user does not understand the local language. The generation AI is a text translation AI or a voice translation AI, and translates in real time based on the user's input.

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

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (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.

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

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 where you can enter information about the city you are visiting, a generating unit that searches for information about cities to visit based on the information input by the receiving unit and makes recommendations from a local perspective; a guide unit that provides and translates information required during a stay at the local location based on the information generated by the generation unit. A system characterized by:

2. The reception unit Enter the name of the city the user is visiting 2. The system of claim 1.

3. The generation unit Collects information from the internet and recommends tourist spots, restaurants, and event information from a local perspective 2. The system of claim 1.

4. The guide portion is Providing users with the information they need while they are in the area 2. The system of claim 1.

5. The guide portion is Translate if you don't understand the local language 2. The system of claim 1.

6. The reception unit Inferring the user's emotions and adjusting the way they input the cities they want to visit based on those emotions 2. The system of claim 1.

7. The reception unit Analyzes the user's past travel history and auto-completes the city they want to visit.

2. The system of claim 1.

8. The reception unit Suggest nearby cities based on the user's current location 2. The system of claim 1.

9. The reception unit Customize the input interface according to the user's travel purpose 2. The system of claim 1.

10. The reception unit Estimate the user's emotions and prioritize the cities to visit based on those emotions.

2. The system of claim 1.

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