System

Abstract

An object of a system according to an embodiment is to provide information on a hot spring area or a surrounding facility that can be used on a day based on a current location of a user and transportation means.SOLUTION: A system includes a current location acquiring part, a transportation means analyzing part, a hot spring place proposing part, an eating and drinking facility information providing part, and a remote work facility information providing part. The current location acquisition unit acquires a current location of a user. The transportation analyzing unit analyzes transportation based on the current position information acquired by the current position acquiring unit. A hot spring area proposal part proposes a hot spring area available on a day based on the transportation means information analyzed by the transportation means analysis part. An eating and drinking facility information providing part provides eating and drinking facility information around the hot spring place proposed by the hot spring place proposing part. A remote work facility information providing part provides remote work facility information around the hot spring place proposed by the hot spring place proposing part.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 technologies have had the problem of making it difficult to efficiently provide information on hot spring resorts and surrounding facilities that can be used for day trips based on the user's current location and mode of transportation.

[0005] The system according to the embodiment aims to provide information on hot spring resorts and surrounding facilities that can be used for day trips based on the user's current location and mode of transportation. [Means for solving the problem]

[0006] The system according to the embodiment includes a current location acquisition unit, a transportation analysis unit, a hot spring resort proposal unit, a dining facility information provision unit, and a remote work facility information provision unit. The current location acquisition unit acquires the user's current location. The transportation analysis unit analyzes transportation methods based on the current location information acquired by the current location acquisition unit. The hot spring resort proposal unit proposes hot spring resorts that can be used for day trips based on the transportation information analyzed by the transportation analysis unit. The dining facility information provision unit provides information on dining facilities around the hot spring resort proposed by the hot spring resort proposal unit. The remote work facility information provision unit provides information on remote work facilities around the hot spring resort proposed by the hot spring resort proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide information on hot spring resorts and surrounding facilities that can be used for day trips based on the user's current location and mode of transportation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The hot spring information providing system according to an embodiment of the present invention is a system that provides information on hot springs that can be used for day trips based on a user's current location and available transportation. Furthermore, information on dining facilities and remote work facilities in the vicinity of the hot springs is also provided. This allows the hot spring information providing system to provide information on hot springs that can be used for day trips and the surrounding dining facilities and remote work facilities based on a user's current location and transportation.

[0029] A hot spring information providing system according to an embodiment includes a current location acquisition unit, a transportation analysis unit, a hot spring resort suggestion unit, a dining facility information provision unit, and a remote work facility information provision unit. The current location acquisition unit acquires the user's current location. For example, the current location acquisition unit acquires the current location using a smartphone's GPS function. The current location acquisition unit can also acquire the current location using Wi-Fi location information. The current location acquisition unit can also acquire current location information manually entered by the user. The transportation analysis unit analyzes the transportation method based on the current location information acquired by the current location acquisition unit. For example, the transportation analysis unit analyzes the transportation method (car, train, bus, etc.) entered by the user. The transportation analysis unit can also analyze the user's past travel history and suggest the optimal transportation method. The transportation analysis unit can also analyze traffic conditions and weather information in real time and dynamically suggest the optimal transportation method. The hot spring resort suggestion unit suggests hot spring resorts that can be used for day trips based on the transportation information analyzed by the transportation analysis unit. For example, the hot spring resort suggestion unit suggests hot spring resorts that can be reached by car within an hour. The hot spring resort suggestion unit can also suggest hot spring resorts that can be reached within two hours by train. The hot spring resort suggestion unit can also analyze the congestion status of hot spring resorts in real time and suggest the optimal hot spring resort that avoids crowds. The dining facility information providing unit provides information on dining facilities around the hot spring resort suggested by the hot spring resort suggestion unit. For example, the dining facility information providing unit displays a list of restaurants and cafes around the hot spring resort. The dining facility information providing unit can also analyze the menus of dining facilities and suggest the optimal dining facility based on the user's preferences and allergy information. The dining facility information providing unit can also analyze reviews and ratings of dining facilities and suggest the optimal facility for the user. The remote work facility information providing unit provides information on remote work facilities around the hot spring resort suggested by the hot spring resort suggestion unit. For example, the remote work facility information providing unit displays a list of coworking spaces and cafes around the hot spring resort. The remote work facility information providing unit can also analyze the facilities and environment of remote work facilities and suggest the optimal facility for the user's work.The remote work facility information providing unit can also analyze the usage status of remote work facilities in real time and suggest available facilities. This allows the hot spring information providing system according to the embodiment to provide information on hot spring resorts and nearby dining and drinking establishments and remote work facilities that can be used for day trips based on the user's current location and mode of transportation. For example, the output unit displays the suggestion results to the user via a web application or mobile application. If the user desires feedback in paper form, the results are printed using a printer. Sending the results via email provides quick feedback by sending the results directly to the user.

[0030] The transportation analysis unit analyzes the user's past travel history and preferences, and can propose more personalized transportation methods. For example, the generation AI analyzes the user's past travel history to identify frequently used transportation methods and routes. For example, the generation AI proposes the optimal transportation method based on the train and bus routes that the user has used frequently in the past. The transportation analysis unit also analyzes the user's preferences and proposes more personalized transportation methods. For example, it makes suggestions based on the user's preferred transportation methods and routes. The transportation analysis unit also analyzes the user's past travel history and preferences in combination, and proposes the optimal transportation method. For example, it makes suggestions based on the transportation methods the user has used in the past and their preferred routes. This makes it possible to propose more personalized transportation methods based on the user's past travel history and preferences.

[0031] The transportation means analysis unit can analyze traffic conditions and weather information in real time and dynamically suggest the optimal transportation means. For example, the generation AI in the transportation means analysis unit analyzes traffic conditions in real time and suggests the optimal transportation means based on congestion and delay information. For example, if congestion occurs, it suggests a detour route. The transportation means analysis unit also analyzes weather information in real time and suggests the optimal transportation means. For example, if the weather is bad, it suggests indoor transportation means. The transportation means analysis unit also analyzes traffic conditions and weather information in real time and suggests the optimal transportation means. For example, it makes suggestions taking both congestion and bad weather into consideration. This allows traffic conditions and weather information to be analyzed in real time and the optimal transportation means to be dynamically suggested.

[0032] The transportation analysis unit can analyze the user's health condition and suggest health-conscious transportation methods. For example, the generation AI in the transportation analysis unit analyzes the user's pedometer data and suggests health-conscious transportation methods. For example, if the number of steps is low, walking or cycling is suggested. The generation AI in the transportation analysis unit also analyzes the user's heart rate monitor data and suggests health-conscious transportation methods. For example, if the heart rate is high, a relaxing transportation method is suggested. The generation AI in the transportation analysis unit also comprehensively analyzes the user's health condition data and suggests the optimal transportation method. For example, suggestions are made based on step count and heart rate data. This makes it possible to suggest health-conscious transportation methods based on the user's health condition.

[0033] The transportation analysis unit can analyze the user's schedule and suggest time-efficient transportation means. For example, the generation AI analyzes the user's schedule and suggests time-efficient transportation means. For example, it suggests a route that will minimize the travel time to the next appointment. Furthermore, the transportation analysis unit analyzes the user's schedule and suggests transportation means with less waiting time. For example, it suggests a route with fewer transfers. Furthermore, the transportation analysis unit analyzes the user's schedule and suggests time-efficient transportation means. For example, it suggests transportation means that will minimize travel time. This makes it possible to suggest time-efficient transportation means based on the user's schedule.

[0034] The hot spring resort suggestion unit can analyze the congestion status of hot spring resorts in real time and suggest the optimal hot spring resort that avoids crowds. For example, the generation AI in the hot spring resort suggestion unit analyzes the congestion status of hot spring resorts in real time and suggests the optimal hot spring resort that avoids crowds. For example, it suggests hot spring resorts based on the time of day or day of the week when it is least crowded. The hot spring resort suggestion unit also analyzes the reservation status of hot spring resorts and suggests hot spring resorts that avoid crowds. For example, it suggests hot spring resorts with few reservations. The hot spring resort suggestion unit also analyzes the congestion status and reservation status of hot spring resorts in combination and suggests the optimal hot spring resort. For example, it makes suggestions taking both congestion and reservation status into consideration. This makes it possible to analyze the congestion status of hot spring resorts in real time and suggest the optimal hot spring resort that avoids crowds.

[0035] The hot spring resort suggestion unit can analyze the user's past hot spring usage history and suggest hot spring resorts that suit their preferences. For example, the generation AI in the hot spring resort suggestion unit analyzes the user's past hot spring usage history and suggests hot spring resorts that suit their preferences. For example, it can suggest hot spring resorts with similar characteristics based on the evaluations and impressions of hot spring resorts visited in the past. The hot spring resort suggestion unit also analyzes the user's preferences and suggests the most suitable hot spring resort. For example, it makes suggestions based on the type of hot spring and facilities that the user prefers. The hot spring resort suggestion unit also analyzes the user's past hot spring usage history in combination with their preferences and suggests the most suitable hot spring resort. For example, it makes suggestions based on data on past usage history and preferences. This makes it possible to suggest hot spring resorts that suit the user's preferences based on the user's past hot spring usage history.

[0036] The hot spring resort suggestion unit can analyze the seasonal characteristics and event information of a hot spring resort and suggest hot spring resorts to visit at the optimal time. For example, the generation AI in the hot spring resort suggestion unit analyzes the seasonal characteristics of a hot spring resort and suggests hot spring resorts to visit at the optimal time. For example, it suggests hot spring resorts to visit in autumn when the leaves are beautiful. The hot spring resort suggestion unit also analyzes event information of a hot spring resort and suggests hot spring resorts to visit when specific events are held. For example, it suggests hot spring resorts to visit when local festivals or special events are held. The hot spring resort suggestion unit also analyzes the seasonal characteristics of a hot spring resort and event information in combination to suggest hot spring resorts to visit at the optimal time. For example, it makes suggestions taking into account seasonal scenery and events. In this way, it is possible to analyze the seasonal characteristics and event information of a hot spring resort and suggest hot spring resorts to visit at the optimal time.

[0037] The hot spring resort suggestion unit can analyze tourist spots around a hot spring resort and suggest hot spring resorts that are also enjoyable for sightseeing. For example, the generation AI in the hot spring resort suggestion unit analyzes tourist spots around a hot spring resort and suggests hot spring resorts that are also enjoyable for sightseeing. For example, suggestions are made based on tourist attractions and natural scenery around the hot spring resort. In addition, the hot spring resort suggestion unit can analyze activities around a hot spring resort and suggest hot spring resorts that are also enjoyable for sightseeing. For example, suggestions are made based on activities that can be enjoyed around the hot spring resort. In addition, the hot spring resort suggestion unit can analyze a combination of tourist spots and activities around a hot spring resort and suggest hot spring resorts that are also enjoyable for sightseeing. For example, suggestions are made taking tourist spots and activities into consideration. In this way, it is possible to analyze tourist spots around a hot spring resort and suggest hot spring resorts that are also enjoyable for sightseeing.

[0038] The dining facility information providing unit can analyze dining facility menus and suggest the most suitable dining facility based on the user's preferences and allergy information. In the dining facility information providing unit, for example, the generation AI analyzes dining facility menus and suggests dining facilities that suit the user's preferences. For example, suggestions are made based on the user's favorite dishes and ingredients. In addition, the dining facility information providing unit can analyze the user's allergy information and suggest dining facilities that take allergies into consideration. For example, it can suggest dining facilities that have allergy-friendly menus. In addition, the dining facility information providing unit can analyze the dining facility menu in combination with the user's preferences and allergy information and suggest the most suitable dining facility. For example, suggestions are made taking into account the user's favorite dishes and allergy-friendly menus. This makes it possible to suggest the most suitable dining facility based on the user's preferences and allergy information.

[0039] The dining facility information providing unit can analyze reviews and ratings of dining facilities and suggest the most suitable facility to the user. In the dining facility information providing unit, for example, the generation AI analyzes reviews of dining facilities and suggests the most suitable facility to the user. For example, it prioritizes suggesting dining facilities with high reviews. In addition, the dining facility information providing unit can analyze the ratings of dining facilities and suggest the most suitable facility to the user. For example, it suggests dining facilities with high star ratings. In addition, the dining facility information providing unit can analyze a combination of reviews and ratings of dining facilities and suggest the most suitable facility. For example, it makes suggestions taking into consideration both reviews and star ratings. In this way, it is possible to analyze reviews and ratings of dining facilities and suggest the most suitable facility to the user.

[0040] The dining facility information providing unit can analyze special menus and event information of dining facilities and suggest facilities where users can have a special experience. For example, the generation AI in the dining facility information providing unit analyzes special menus of dining facilities and suggests facilities where users can have a special experience. For example, it suggests dining facilities that offer seasonal menus or special dishes prepared by chefs. Furthermore, the generation AI in the dining facility information providing unit analyzes event information of dining facilities and suggests facilities where users can have a special experience. For example, it suggests dining facilities that hold live events or special dinners. Furthermore, the generation AI in the dining facility information providing unit analyzes a combination of special menus and event information of dining facilities and suggests facilities where users can have a special experience. For example, it makes suggestions taking into account seasonal menus and special events. In this way, it is possible to analyze special menus and event information of dining facilities and suggest facilities where users can have a special experience.

[0041] The remote work facility information providing unit analyzes the equipment and environment of a remote work facility and can suggest the facility that is best suited to the user's work content. For example, the generation AI in the remote work facility information providing unit analyzes the equipment of a remote work facility and suggests the facility that is best suited to the user's work content. For example, it suggests a facility that is fully equipped with high-speed internet and conference rooms. The remote work facility information providing unit also analyzes the environment of a remote work facility and suggests the facility that is best suited to the user's work content. For example, it suggests a facility that is quiet and has natural light. The remote work facility information providing unit also analyzes the equipment and environment of a remote work facility and suggests the most suitable facility. For example, it makes suggestions taking into account high-speed internet and quietness. This allows the generation AI to analyze the equipment and environment of a remote work facility and suggest the facility that is best suited to the user's work content.

[0042] The remote work facility information providing unit can analyze the usage status of remote work facilities in real time and suggest available facilities. For example, the generation AI in the remote work facility information providing unit analyzes the usage status of remote work facilities in real time and suggests available facilities. For example, suggestions are made based on time periods or days of the week when there are fewer users. The remote work facility information providing unit also analyzes the reservation status of remote work facilities and suggests available facilities. For example, it suggests facilities with few reservations. The remote work facility information providing unit also analyzes the usage status and reservation status of remote work facilities in combination and suggests the most suitable facility. For example, it makes suggestions taking into account both usage status and reservation status. This makes it possible to analyze the usage status of remote work facilities in real time and suggest available facilities.

[0043] The system can collect user feedback and make suggestions that are optimized for each individual user. For example, the system uses a generation AI to analyze user feedback and make suggestions that are optimized for each individual user. For example, the system makes suggestions that match the user's preferences based on past feedback. The system also uses a generation AI to collect user feedback and improve the accuracy of the suggestions. For example, the system makes suggestions based on the user's evaluations and impressions of hot springs, restaurants, and remote work facilities that they have visited. The system also uses a generation AI to analyze user feedback and use it to improve the system as a whole. For example, the system identifies common problems and areas for improvement and improves the system's functionality. This allows the system to collect user feedback and make suggestions that are optimized for each individual user.

[0044] The system can analyze the content of the feedback and identify common problems and areas for improvement. For example, the generation AI in the system analyzes the content of the feedback and identifies common problems. For example, it extracts common problems based on feedback from multiple users. The generation AI in the system also analyzes the content of the feedback and identifies areas for improvement. For example, it identifies areas for improvement in the user interface. The generation AI in the system also analyzes the content of the feedback and identifies a combination of common problems and areas for improvement. For example, it takes into account frequently occurring complaints and technical issues when identifying common problems and areas for improvement. In this way, the system can analyze the content of the feedback and identify common problems and areas for improvement.

[0045] The system can add information on new hot spring resorts, restaurants, and remote work facilities based on the feedback. For example, the generation AI analyzes the feedback and adds information on new hot spring resorts. For example, hot spring resorts that users have given high ratings are added to the database. The system also analyzes the feedback and adds information on new restaurants. For example, restaurants that users have given high ratings are added to the database. The system also analyzes the feedback and adds information on new remote work facilities. For example, remote work facilities that users have given high ratings are added to the database. This makes it possible to add information on new hot spring resorts, restaurants, and remote work facilities based on the feedback.

[0046] The system can continuously improve the proposal algorithm based on the feedback. For example, the generation AI in the system analyzes the feedback and continuously improves the proposal algorithm. For example, the content of the proposals is optimized based on the user's feedback. The generation AI in the system also analyzes the feedback and improves the accuracy of the proposal algorithm. For example, the user's preferences and past selection history are reflected. The generation AI in the system also analyzes the feedback and improves the performance of the proposal algorithm. For example, the speed and accuracy of the proposals are improved. This allows the proposal algorithm to continuously improve based on the feedback.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The hot spring information providing system may further include a health analysis unit that analyzes the user's health condition and suggests hot spring resorts that are good for the user's health. For example, the health analysis unit may analyze the user's pedometer data and suggest hot spring resorts that can help the user overcome a lack of exercise. The health analysis unit may also analyze the user's heart rate data and suggest hot spring resorts that are effective for relaxation. Furthermore, the health analysis unit may comprehensively analyze the user's health condition data and suggest the most suitable hot spring resort. This allows the system to suggest hot spring resorts that are good for the user's health based on the user's health condition.

[0049] The hot spring information providing system may further include a schedule analysis unit that analyzes the user's schedule and suggests hot spring resorts that are time-efficient. For example, the schedule analysis unit may analyze the time until the user's next appointment and suggest hot spring resorts that can be visited in the shortest time. The schedule analysis unit may also analyze the user's schedule and suggest hot spring resorts with short waiting times. Furthermore, the schedule analysis unit may comprehensively analyze the user's schedule and suggest the most suitable hot spring resort. This allows time-efficient hot spring resorts to be suggested based on the user's schedule.

[0050] The hot spring information providing system may further include a feedback analysis unit that collects user feedback and proposes hot spring resorts optimized for each individual user. For example, the feedback analysis unit may analyze the user's past feedback and propose hot spring resorts that match the user's preferences. The feedback analysis unit may also collect user feedback and improve the accuracy of the proposals. Furthermore, the feedback analysis unit may comprehensively analyze user feedback and use the results to improve the entire system. This allows the system to propose hot spring resorts optimized for each individual user based on the user's feedback.

[0051] The hot spring information providing system may further include a health analysis unit that analyzes the user's health condition and suggests health-conscious eating and drinking facilities. For example, the health analysis unit may analyze the user's diet history and suggest eating and drinking facilities that offer nutritionally balanced menus. The health analysis unit may also analyze the user's allergy information and suggest eating and drinking facilities that are allergy-conscious. Furthermore, the health analysis unit may comprehensively analyze the user's health condition data and suggest the most suitable eating and drinking facilities. This makes it possible to suggest health-conscious eating and drinking facilities based on the user's health condition.

[0052] The hot spring information providing system may further include a schedule analysis unit that analyzes the user's schedule and suggests time-efficient remote work facilities. For example, the schedule analysis unit may analyze the time until the user's next appointment and suggest remote work facilities that can be used in the shortest time. The schedule analysis unit may also analyze the user's schedule and suggest remote work facilities with minimal waiting time. Furthermore, the schedule analysis unit may comprehensively analyze the user's schedule and suggest the most suitable remote work facility. This allows time-efficient remote work facilities to be suggested based on the user's schedule.

[0053] The hot spring information providing system may further include a feedback analysis unit that collects user feedback and proposes remote work facilities optimized for each individual user. For example, the feedback analysis unit may analyze the user's past feedback and propose remote work facilities that match the user's preferences. The feedback analysis unit may also collect user feedback and improve the accuracy of the proposals. Furthermore, the feedback analysis unit may comprehensively analyze user feedback and use the results to improve the entire system. This allows the system to propose remote work facilities optimized for each individual user based on the user's feedback.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The current location acquisition unit acquires the user's current location. For example, the current location can be acquired using the GPS function of a smartphone. It can also acquire Wi-Fi location information or current location information manually entered by the user. Step 2: The transportation analysis unit analyzes the transportation method based on the current location information acquired by the current location acquisition unit. For example, it analyzes the transportation method (car, train, bus, etc.) entered by the user. It can also analyze past travel history and real-time traffic and weather information to dynamically suggest the optimal transportation method. Step 3: The hot spring resort suggestion unit suggests hot spring resorts that can be used for day trips based on the transportation information analyzed by the transportation analysis unit. For example, it suggests hot spring resorts that can be reached within one hour by car or two hours by train. It can also analyze the congestion status of hot spring resorts in real time and suggest the most suitable hot spring resorts that avoid crowds. Step 4: The dining facility information providing unit provides information about dining facilities around the hot spring resort suggested by the hot spring resort suggestion unit. For example, it displays a list of restaurants and cafes around the hot spring resort. It can also analyze the menus of dining facilities and suggest the most suitable dining facility based on the user's preferences and allergy information. It can also analyze reviews and ratings of dining facilities and suggest the most suitable facility for the user. Step 5: The remote work facility information provider provides information about remote work facilities around the hot spring resort proposed by the hot spring resort proposal provider. For example, it displays a list of coworking spaces and cafes around the hot spring resort. It can also analyze the equipment and environment of remote work facilities and propose facilities that are best suited to the user's work. It can also analyze the usage status of remote work facilities in real time and propose available facilities.

[0056] (Example 2) The hot spring information providing system according to an embodiment of the present invention is a system that provides information on hot springs that can be used for day trips based on a user's current location and available transportation. Furthermore, information on dining facilities and remote work facilities in the vicinity of the hot springs is also provided. This allows the hot spring information providing system to provide information on hot springs that can be used for day trips and the surrounding dining facilities and remote work facilities based on a user's current location and transportation.

[0057] A hot spring information providing system according to an embodiment includes a current location acquisition unit, a transportation analysis unit, a hot spring resort suggestion unit, a dining facility information provision unit, and a remote work facility information provision unit. The current location acquisition unit acquires the user's current location. For example, the current location acquisition unit acquires the current location using a smartphone's GPS function. The current location acquisition unit can also acquire the current location using Wi-Fi location information. The current location acquisition unit can also acquire current location information manually entered by the user. The transportation analysis unit analyzes the transportation method based on the current location information acquired by the current location acquisition unit. For example, the transportation analysis unit analyzes the transportation method (car, train, bus, etc.) entered by the user. The transportation analysis unit can also analyze the user's past travel history and suggest the optimal transportation method. The transportation analysis unit can also analyze traffic conditions and weather information in real time and dynamically suggest the optimal transportation method. The hot spring resort suggestion unit suggests hot spring resorts that can be used for day trips based on the transportation information analyzed by the transportation analysis unit. For example, the hot spring resort suggestion unit suggests hot spring resorts that can be reached by car within an hour. The hot spring resort suggestion unit can also suggest hot spring resorts that can be reached within two hours by train. The hot spring resort suggestion unit can also analyze the congestion status of hot spring resorts in real time and suggest the optimal hot spring resort that avoids crowds. The dining facility information providing unit provides information on dining facilities around the hot spring resort suggested by the hot spring resort suggestion unit. For example, the dining facility information providing unit displays a list of restaurants and cafes around the hot spring resort. The dining facility information providing unit can also analyze the menus of dining facilities and suggest the optimal dining facility based on the user's preferences and allergy information. The dining facility information providing unit can also analyze reviews and ratings of dining facilities and suggest the optimal facility for the user. The remote work facility information providing unit provides information on remote work facilities around the hot spring resort suggested by the hot spring resort suggestion unit. For example, the remote work facility information providing unit displays a list of coworking spaces and cafes around the hot spring resort. The remote work facility information providing unit can also analyze the facilities and environment of remote work facilities and suggest the optimal facility for the user's work.The remote work facility information providing unit can also analyze the usage status of remote work facilities in real time and suggest available facilities. This allows the hot spring information providing system according to the embodiment to provide information on hot spring resorts and nearby dining and drinking establishments and remote work facilities that can be used for day trips based on the user's current location and mode of transportation. For example, the output unit displays the suggestion results to the user via a web application or mobile application. If the user desires feedback in paper form, the results are printed using a printer. Sending the results via email provides quick feedback by sending the results directly to the user.

[0058] The transportation analysis unit analyzes the user's past travel history and preferences, and can propose more personalized transportation methods. For example, the generation AI analyzes the user's past travel history to identify frequently used transportation methods and routes. For example, the generation AI proposes the optimal transportation method based on the train and bus routes that the user has used frequently in the past. The transportation analysis unit also analyzes the user's preferences and proposes more personalized transportation methods. For example, it makes suggestions based on the user's preferred transportation methods and routes. The transportation analysis unit also analyzes the user's past travel history and preferences in combination, and proposes the optimal transportation method. For example, it makes suggestions based on the transportation methods the user has used in the past and their preferred routes. This makes it possible to propose more personalized transportation methods based on the user's past travel history and preferences.

[0059] The transportation means analysis unit can analyze traffic conditions and weather information in real time and dynamically suggest the optimal transportation means. For example, the generation AI in the transportation means analysis unit analyzes traffic conditions in real time and suggests the optimal transportation means based on congestion and delay information. For example, if congestion occurs, it suggests a detour route. The transportation means analysis unit also analyzes weather information in real time and suggests the optimal transportation means. For example, if the weather is bad, it suggests indoor transportation means. The transportation means analysis unit also analyzes traffic conditions and weather information in real time and suggests the optimal transportation means. For example, it makes suggestions taking both congestion and bad weather into consideration. This allows traffic conditions and weather information to be analyzed in real time and the optimal transportation means to be dynamically suggested.

[0060] The transportation means analysis unit can use the emotion estimation function to analyze the user's current emotional state and suggest less stressful transportation means. For example, the transportation means analysis unit can use the emotion estimation function to analyze the user's current emotional state and suggest less stressful transportation means. For example, if the user is tired, the transportation means analysis unit can suggest transportation means that ensure a comfortable seat. The transportation means analysis unit can also use the emotion estimation function to analyze the user's emotional state and suggest transportation means that allow relaxation. For example, the transportation means analysis unit can suggest transportation means that allow travel in a quiet environment. The transportation means analysis unit can also use the emotion estimation function to analyze the user's emotional state and suggest less stressful routes. For example, the transportation means analysis unit can suggest routes that avoid crowded areas. In this way, less stressful transportation means can be suggested based on the user's emotional state.

[0061] The transportation analysis unit can analyze the user's health condition and suggest health-conscious transportation methods. For example, the generation AI in the transportation analysis unit analyzes the user's pedometer data and suggests health-conscious transportation methods. For example, if the number of steps is low, walking or cycling is suggested. The generation AI in the transportation analysis unit also analyzes the user's heart rate monitor data and suggests health-conscious transportation methods. For example, if the heart rate is high, a relaxing transportation method is suggested. The generation AI in the transportation analysis unit also comprehensively analyzes the user's health condition data and suggests the optimal transportation method. For example, suggestions are made based on step count and heart rate data. This makes it possible to suggest health-conscious transportation methods based on the user's health condition.

[0062] The transportation analysis unit can analyze the user's schedule and suggest time-efficient transportation means. For example, the generation AI analyzes the user's schedule and suggests time-efficient transportation means. For example, it suggests a route that will minimize the travel time to the next appointment. Furthermore, the transportation analysis unit analyzes the user's schedule and suggests transportation means with less waiting time. For example, it suggests a route with fewer transfers. Furthermore, the transportation analysis unit analyzes the user's schedule and suggests time-efficient transportation means. For example, it suggests transportation means that will minimize travel time. This makes it possible to suggest time-efficient transportation means based on the user's schedule.

[0063] The hot spring resort suggestion unit can analyze the congestion status of hot spring resorts in real time and suggest the optimal hot spring resort that avoids crowds. For example, the generation AI in the hot spring resort suggestion unit analyzes the congestion status of hot spring resorts in real time and suggests the optimal hot spring resort that avoids crowds. For example, it suggests hot spring resorts based on the time of day or day of the week when it is least crowded. The hot spring resort suggestion unit also analyzes the reservation status of hot spring resorts and suggests hot spring resorts that avoid crowds. For example, it suggests hot spring resorts with few reservations. The hot spring resort suggestion unit also analyzes the congestion status and reservation status of hot spring resorts in combination and suggests the optimal hot spring resort. For example, it makes suggestions taking both congestion and reservation status into consideration. This makes it possible to analyze the congestion status of hot spring resorts in real time and suggest the optimal hot spring resort that avoids crowds.

[0064] The hot spring resort suggestion unit can analyze the user's past hot spring usage history and suggest hot spring resorts that suit their preferences. For example, the generation AI in the hot spring resort suggestion unit analyzes the user's past hot spring usage history and suggests hot spring resorts that suit their preferences. For example, it can suggest hot spring resorts with similar characteristics based on the evaluations and impressions of hot spring resorts visited in the past. The hot spring resort suggestion unit also analyzes the user's preferences and suggests the most suitable hot spring resort. For example, it makes suggestions based on the type of hot spring and facilities that the user prefers. The hot spring resort suggestion unit also analyzes the user's past hot spring usage history in combination with their preferences and suggests the most suitable hot spring resort. For example, it makes suggestions based on data on past usage history and preferences. This makes it possible to suggest hot spring resorts that suit the user's preferences based on the user's past hot spring usage history.

[0065] The hot spring resort suggestion unit can use the emotion estimation function to suggest a hot spring resort that is most suitable for the user's current emotional state. For example, the hot spring resort suggestion unit uses the emotion estimation function to analyze the user's current emotional state and suggest the most suitable hot spring resort. For example, if the user wants to relax, it suggests a quiet hot spring resort. The hot spring resort suggestion unit also uses the emotion estimation function to analyze the user's emotional state and suggest a hot spring resort where the user can refresh themselves. For example, it suggests a hot spring resort surrounded by nature. The hot spring resort suggestion unit also uses the emotion estimation function to analyze the user's emotional state and suggest a hot spring resort that is ideal for relieving stress. For example, it suggests a hot spring resort with a spacious open-air bath. In this way, the most suitable hot spring resort can be suggested based on the user's emotional state.

[0066] The hot spring resort suggestion unit can analyze the seasonal characteristics and event information of a hot spring resort and suggest hot spring resorts to visit at the optimal time. For example, the generation AI in the hot spring resort suggestion unit analyzes the seasonal characteristics of a hot spring resort and suggests hot spring resorts to visit at the optimal time. For example, it suggests hot spring resorts to visit in autumn when the leaves are beautiful. The hot spring resort suggestion unit also analyzes event information of a hot spring resort and suggests hot spring resorts to visit when specific events are held. For example, it suggests hot spring resorts to visit when local festivals or special events are held. The hot spring resort suggestion unit also analyzes the seasonal characteristics of a hot spring resort and event information in combination to suggest hot spring resorts to visit at the optimal time. For example, it makes suggestions taking into account seasonal scenery and events. In this way, it is possible to analyze the seasonal characteristics and event information of a hot spring resort and suggest hot spring resorts to visit at the optimal time.

[0067] The hot spring resort suggestion unit can analyze tourist spots around a hot spring resort and suggest hot spring resorts that are also enjoyable for sightseeing. For example, the generation AI in the hot spring resort suggestion unit analyzes tourist spots around a hot spring resort and suggests hot spring resorts that are also enjoyable for sightseeing. For example, suggestions are made based on tourist attractions and natural scenery around the hot spring resort. In addition, the hot spring resort suggestion unit can analyze activities around a hot spring resort and suggest hot spring resorts that are also enjoyable for sightseeing. For example, suggestions are made based on activities that can be enjoyed around the hot spring resort. In addition, the hot spring resort suggestion unit can analyze a combination of tourist spots and activities around a hot spring resort and suggest hot spring resorts that are also enjoyable for sightseeing. For example, suggestions are made taking tourist spots and activities into consideration. In this way, it is possible to analyze tourist spots around a hot spring resort and suggest hot spring resorts that are also enjoyable for sightseeing.

[0068] The hot spring resort suggestion unit can use the emotion estimation function to provide information on music and scents of hot spring resorts that can help the user relax. The hot spring resort suggestion unit, for example, uses the emotion estimation function to provide music information of hot spring resorts that can help the user relax. For example, it suggests relaxing music played in hot spring resorts. The hot spring resort suggestion unit also uses the emotion estimation function to provide scent information of hot spring resorts that can help the user relax. For example, it suggests aromatherapy scents that are offered in hot spring resorts. The hot spring resort suggestion unit also uses the emotion estimation function to provide a combination of music and scent information of hot spring resorts that can help the user relax. For example, it makes suggestions taking into consideration relaxing music and aromatherapy scents. In this way, it is possible to provide information on music and scents of hot spring resorts that can help the user relax.

[0069] The dining facility information providing unit can analyze dining facility menus and suggest the most suitable dining facility based on the user's preferences and allergy information. In the dining facility information providing unit, for example, the generation AI analyzes dining facility menus and suggests dining facilities that suit the user's preferences. For example, suggestions are made based on the user's favorite dishes and ingredients. In addition, the dining facility information providing unit can analyze the user's allergy information and suggest dining facilities that take allergies into consideration. For example, it can suggest dining facilities that have allergy-friendly menus. In addition, the dining facility information providing unit can analyze the dining facility menu in combination with the user's preferences and allergy information and suggest the most suitable dining facility. For example, suggestions are made taking into account the user's favorite dishes and allergy-friendly menus. This makes it possible to suggest the most suitable dining facility based on the user's preferences and allergy information.

[0070] The dining facility information providing unit can use the emotion estimation function to suggest dishes and dining facilities that match the user's current emotional state. For example, the dining facility information providing unit uses the emotion estimation function to analyze the user's current emotional state and suggest the most suitable dishes and dining facilities. For example, if the user wants to relax, a restaurant with a quiet atmosphere is suggested. The dining facility information providing unit can also use the emotion estimation function to analyze the user's emotional state and suggest dining facilities that serve invigorating dishes. For example, a dining facility that serves dishes that can replenish energy is suggested. The dining facility information providing unit can also use the emotion estimation function to analyze the user's emotional state and suggest dishes and dining facilities that are ideal for relieving stress. For example, a dining facility that serves relaxing dishes is suggested. In this way, the most suitable dishes and dining facilities can be suggested based on the user's emotional state.

[0071] The dining facility information providing unit can analyze reviews and ratings of dining facilities and suggest the most suitable facility to the user. In the dining facility information providing unit, for example, the generation AI analyzes reviews of dining facilities and suggests the most suitable facility to the user. For example, it prioritizes suggesting dining facilities with high reviews. In addition, the dining facility information providing unit can analyze the ratings of dining facilities and suggest the most suitable facility to the user. For example, it suggests dining facilities with high star ratings. In addition, the dining facility information providing unit can analyze a combination of reviews and ratings of dining facilities and suggest the most suitable facility. For example, it makes suggestions taking into consideration both reviews and star ratings. In this way, it is possible to analyze reviews and ratings of dining facilities and suggest the most suitable facility to the user.

[0072] The dining facility information providing unit can analyze special menus and event information of dining facilities and suggest facilities where users can have a special experience. For example, the generation AI in the dining facility information providing unit analyzes special menus of dining facilities and suggests facilities where users can have a special experience. For example, it suggests dining facilities that offer seasonal menus or special dishes prepared by chefs. Furthermore, the generation AI in the dining facility information providing unit analyzes event information of dining facilities and suggests facilities where users can have a special experience. For example, it suggests dining facilities that hold live events or special dinners. Furthermore, the generation AI in the dining facility information providing unit analyzes a combination of special menus and event information of dining facilities and suggests facilities where users can have a special experience. For example, it makes suggestions taking into account seasonal menus and special events. In this way, it is possible to analyze special menus and event information of dining facilities and suggest facilities where users can have a special experience.

[0073] The dining facility information providing unit can use the emotion estimation function to provide music and interior information of dining facilities that allow the user to relax. The dining facility information providing unit, for example, uses the emotion estimation function to provide music information of dining facilities that allow the user to relax. For example, it suggests relaxing music played in dining facilities. The dining facility information providing unit also uses the emotion estimation function to provide interior information of dining facilities that allow the user to relax. For example, it suggests furniture arrangement and lighting design for dining facilities. The dining facility information providing unit also uses the emotion estimation function to provide a combination of music and interior information of dining facilities that allow the user to relax. For example, it makes suggestions taking into consideration relaxing music and interior design. In this way, it is possible to provide music and interior information of dining facilities that allow the user to relax.

[0074] The remote work facility information providing unit analyzes the equipment and environment of a remote work facility and can suggest the facility that is best suited to the user's work content. For example, the generation AI in the remote work facility information providing unit analyzes the equipment of a remote work facility and suggests the facility that is best suited to the user's work content. For example, it suggests a facility that is fully equipped with high-speed internet and conference rooms. The remote work facility information providing unit also analyzes the environment of a remote work facility and suggests the facility that is best suited to the user's work content. For example, it suggests a facility that is quiet and has natural light. The remote work facility information providing unit also analyzes the equipment and environment of a remote work facility and suggests the most suitable facility. For example, it makes suggestions taking into account high-speed internet and quietness. This allows the generation AI to analyze the equipment and environment of a remote work facility and suggest the facility that is best suited to the user's work content.

[0075] The remote work facility information providing unit can analyze the usage status of remote work facilities in real time and suggest available facilities. For example, the generation AI in the remote work facility information providing unit analyzes the usage status of remote work facilities in real time and suggests available facilities. For example, suggestions are made based on time periods or days of the week when there are fewer users. The remote work facility information providing unit also analyzes the reservation status of remote work facilities and suggests available facilities. For example, it suggests facilities with few reservations. The remote work facility information providing unit also analyzes the usage status and reservation status of remote work facilities in combination and suggests the most suitable facility. For example, it makes suggestions taking into account both usage status and reservation status. This makes it possible to analyze the usage status of remote work facilities in real time and suggest available facilities.

[0076] The remote work facility information providing unit can use the emotion estimation function to suggest remote work facilities that match the user's current emotional state. For example, the remote work facility information providing unit uses the emotion estimation function to analyze the user's current emotional state and suggest the most suitable remote work facility. For example, if the user wants to relax, the unit suggests a facility with a quiet environment. The remote work facility information providing unit also uses the emotion estimation function to analyze the user's emotional state and suggest remote work facilities with an environment that allows the user to concentrate. For example, the unit suggests a facility that plays music that improves concentration. The remote work facility information providing unit also uses the emotion estimation function to analyze the user's emotional state and suggest remote work facilities that are less stressful. For example, the unit suggests a facility that lets in natural light. This makes it possible to suggest the optimal remote work facility based on the user's emotional state.

[0077] The remote work facility information providing unit can use the emotion estimation function to suggest remote work facilities that provide music and environmental sounds that help users concentrate. The remote work facility information providing unit, for example, uses the emotion estimation function to suggest remote work facilities that provide music that helps users concentrate. For example, it suggests facilities that play music that improves concentration. The remote work facility information providing unit also uses the emotion estimation function to suggest remote work facilities that provide environmental sounds that help users concentrate. For example, it suggests facilities that play white noise and natural sounds. The remote work facility information providing unit also uses the emotion estimation function to suggest remote work facilities that provide a combination of music and environmental sounds that help users concentrate. For example, it makes a suggestion taking into account music that improves concentration and white noise. This makes it possible to suggest remote work facilities that provide music and environmental sounds that help users concentrate.

[0078] The system can collect user feedback and make suggestions that are optimized for each individual user. For example, the system uses a generation AI to analyze user feedback and make suggestions that are optimized for each individual user. For example, the system makes suggestions that match the user's preferences based on past feedback. The system also uses a generation AI to collect user feedback and improve the accuracy of the suggestions. For example, the system makes suggestions based on the user's evaluations and impressions of hot springs, restaurants, and remote work facilities that they have visited. The system also uses a generation AI to analyze user feedback and use it to improve the system as a whole. For example, the system identifies common problems and areas for improvement and improves the system's functionality. This allows the system to collect user feedback and make suggestions that are optimized for each individual user.

[0079] The system can analyze the content of the feedback and identify common problems and areas for improvement. For example, the generation AI in the system analyzes the content of the feedback and identifies common problems. For example, it extracts common problems based on feedback from multiple users. The generation AI in the system also analyzes the content of the feedback and identifies areas for improvement. For example, it identifies areas for improvement in the user interface. The generation AI in the system also analyzes the content of the feedback and identifies a combination of common problems and areas for improvement. For example, it takes into account frequently occurring complaints and technical issues when identifying common problems and areas for improvement. In this way, the system can analyze the content of the feedback and identify common problems and areas for improvement.

[0080] The system uses the emotion estimation function to analyze the emotional aspects of the user's feedback and make improvements that are more in line with the emotions. For example, the system uses the emotion estimation function to analyze the emotional aspects of the user's feedback and make improvements that are more in line with the emotions. For example, the system makes improvements that suit the user's preferences based on positive feedback. The system also uses the emotion estimation function to analyze the emotional aspects of the user's feedback and make improvements in response to negative feedback. For example, the system makes improvements to resolve dissatisfaction points. The system also uses the emotion estimation function to analyze the emotional aspects of the user's feedback and make improvements by combining positive and negative emotions. For example, the system makes improvements that are more in line with the user's emotions. This allows the system to analyze the emotional aspects of the user's feedback and make improvements that are more in line with the emotions.

[0081] The system can add information on new hot spring resorts, restaurants, and remote work facilities based on the feedback. For example, the generation AI analyzes the feedback and adds information on new hot spring resorts. For example, hot spring resorts that users have given high ratings are added to the database. The system also analyzes the feedback and adds information on new restaurants. For example, restaurants that users have given high ratings are added to the database. The system also analyzes the feedback and adds information on new remote work facilities. For example, remote work facilities that users have given high ratings are added to the database. This makes it possible to add information on new hot spring resorts, restaurants, and remote work facilities based on the feedback.

[0082] The system can continuously improve the proposal algorithm based on the feedback. For example, the generation AI in the system analyzes the feedback and continuously improves the proposal algorithm. For example, the content of the proposals is optimized based on the user's feedback. The generation AI in the system also analyzes the feedback and improves the accuracy of the proposal algorithm. For example, the user's preferences and past selection history are reflected. The generation AI in the system also analyzes the feedback and improves the performance of the proposal algorithm. For example, the speed and accuracy of the proposals are improved. This allows the proposal algorithm to continuously improve based on the feedback.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The hot spring information providing system may further include a health analysis unit that analyzes the user's health condition and suggests hot spring resorts that are good for the user's health. For example, the health analysis unit may analyze the user's pedometer data and suggest hot spring resorts that can help the user overcome a lack of exercise. The health analysis unit may also analyze the user's heart rate data and suggest hot spring resorts that are effective for relaxation. Furthermore, the health analysis unit may comprehensively analyze the user's health condition data and suggest the most suitable hot spring resort. This allows the system to suggest hot spring resorts that are good for the user's health based on the user's health condition.

[0085] The hot spring information providing system can further include an emotion analysis unit that analyzes the user's emotional state and suggests hot spring resorts that are in line with the user's emotions. For example, if the user is feeling stressed, the emotion analysis unit can suggest hot spring resorts that have a high relaxing effect. Also, if the user is tired, the emotion analysis unit can suggest hot spring resorts with quiet environments. Furthermore, the emotion analysis unit can comprehensively analyze the user's emotional state and suggest the most suitable hot spring resort. This makes it possible to suggest hot spring resorts that are in line with the user's emotions based on the user's emotional state.

[0086] The hot spring information providing system may further include a schedule analysis unit that analyzes the user's schedule and suggests hot spring resorts that are time-efficient. For example, the schedule analysis unit may analyze the time until the user's next appointment and suggest hot spring resorts that can be visited in the shortest time. The schedule analysis unit may also analyze the user's schedule and suggest hot spring resorts with short waiting times. Furthermore, the schedule analysis unit may comprehensively analyze the user's schedule and suggest the most suitable hot spring resort. This allows time-efficient hot spring resorts to be suggested based on the user's schedule.

[0087] The hot spring information providing system may further include a feedback analysis unit that collects user feedback and proposes hot spring resorts optimized for each individual user. For example, the feedback analysis unit may analyze the user's past feedback and propose hot spring resorts that match the user's preferences. The feedback analysis unit may also collect user feedback and improve the accuracy of the proposals. Furthermore, the feedback analysis unit may comprehensively analyze user feedback and use the results to improve the entire system. This allows the system to propose hot spring resorts optimized for each individual user based on the user's feedback.

[0088] The hot spring information providing system may further include an emotion analysis unit that analyzes the user's emotional state and suggests dining facilities that are in tune with the user's emotions. For example, if the user wants to relax, the emotion analysis unit may suggest dining facilities with a quiet atmosphere. If the user wants to feel energized, the emotion analysis unit may also suggest dining facilities that offer food that can replenish energy. Furthermore, the emotion analysis unit may comprehensively analyze the user's emotional state and suggest the most suitable dining facility. This makes it possible to suggest dining facilities that are in tune with the user's emotions based on the user's emotional state.

[0089] The hot spring information providing system may further include a health analysis unit that analyzes the user's health condition and suggests health-conscious eating and drinking facilities. For example, the health analysis unit may analyze the user's diet history and suggest eating and drinking facilities that offer nutritionally balanced menus. The health analysis unit may also analyze the user's allergy information and suggest eating and drinking facilities that are allergy-conscious. Furthermore, the health analysis unit may comprehensively analyze the user's health condition data and suggest the most suitable eating and drinking facilities. This makes it possible to suggest health-conscious eating and drinking facilities based on the user's health condition.

[0090] The hot spring information providing system can further include an emotion analysis unit that analyzes the user's emotional state and suggests remote work facilities that are in tune with the user's emotions. For example, if the user wants to relax, the emotion analysis unit can suggest remote work facilities with a quiet environment. If the user wants to concentrate, the emotion analysis unit can also suggest remote work facilities that play music that helps improve concentration. Furthermore, the emotion analysis unit can comprehensively analyze the user's emotional state and suggest the most suitable remote work facility. This makes it possible to suggest remote work facilities that are in tune with the user's emotions based on the user's emotional state.

[0091] The hot spring information providing system may further include a schedule analysis unit that analyzes the user's schedule and suggests time-efficient remote work facilities. For example, the schedule analysis unit may analyze the time until the user's next appointment and suggest remote work facilities that can be used in the shortest time. The schedule analysis unit may also analyze the user's schedule and suggest remote work facilities with minimal waiting time. Furthermore, the schedule analysis unit may comprehensively analyze the user's schedule and suggest the most suitable remote work facility. This allows time-efficient remote work facilities to be suggested based on the user's schedule.

[0092] The hot spring information providing system may further include a feedback analysis unit that collects user feedback and proposes remote work facilities optimized for each individual user. For example, the feedback analysis unit may analyze the user's past feedback and propose remote work facilities that match the user's preferences. The feedback analysis unit may also collect user feedback and improve the accuracy of the proposals. Furthermore, the feedback analysis unit may comprehensively analyze user feedback and use the results to improve the entire system. This allows the system to propose remote work facilities optimized for each individual user based on the user's feedback.

[0093] The hot spring information providing system can further include an emotion analysis unit that analyzes the user's emotional state and provides a remote work environment that is sensitive to the user's emotions. For example, if the user wants to relax, the emotion analysis unit can suggest a remote work environment that provides relaxing music and fragrances. Also, if the user wants to concentrate, the emotion analysis unit can suggest a remote work environment that provides music and environmental sounds that will help improve concentration. Furthermore, the emotion analysis unit can comprehensively analyze the user's emotional state and suggest the optimal remote work environment. This makes it possible to provide a remote work environment that is sensitive to the user's emotions based on the user's emotional state.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The current location acquisition unit acquires the user's current location. For example, the current location can be acquired using the GPS function of a smartphone. It can also acquire Wi-Fi location information or current location information manually entered by the user. Step 2: The transportation analysis unit analyzes the transportation method based on the current location information acquired by the current location acquisition unit. For example, it analyzes the transportation method (car, train, bus, etc.) entered by the user. It can also analyze past travel history and real-time traffic and weather information to dynamically suggest the optimal transportation method. Step 3: The hot spring resort suggestion unit suggests hot spring resorts that can be used for day trips based on the transportation information analyzed by the transportation analysis unit. For example, it suggests hot spring resorts that can be reached within one hour by car or two hours by train. It can also analyze the congestion status of hot spring resorts in real time and suggest the most suitable hot spring resorts that avoid crowds. Step 4: The dining facility information providing unit provides information about dining facilities around the hot spring resort suggested by the hot spring resort suggestion unit. For example, it displays a list of restaurants and cafes around the hot spring resort. It can also analyze the menus of dining facilities and suggest the most suitable dining facility based on the user's preferences and allergy information. It can also analyze reviews and ratings of dining facilities and suggest the most suitable facility for the user. Step 5: The remote work facility information provider provides information about remote work facilities around the hot spring resort proposed by the hot spring resort proposal provider. For example, it displays a list of coworking spaces and cafes around the hot spring resort. It can also analyze the equipment and environment of remote work facilities and propose facilities that are best suited to the user's work. It can also analyze the usage status of remote work facilities in real time and propose available facilities.

[0096] 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.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] 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.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0140] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0163] 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 current location acquisition unit that acquires the current location of the user; a transportation means analysis unit that analyzes transportation means based on the current location information acquired by the current location acquisition unit; a hot spring resort suggestion unit that suggests hot spring resorts that can be used for day trips based on the transportation information analyzed by the transportation analysis unit; a dining facility information providing unit that provides information on dining facilities around the hot spring resort proposed by the hot spring resort proposal unit; a remote work facility information providing unit that provides information on remote work facilities around the hot spring resort proposed by the hot spring resort proposal unit. A system characterized by:

2. The transportation means analysis unit Analyzes traffic conditions and weather information in real time and dynamically suggests optimal transportation options 2. The system of claim 1.

3. The transportation means analysis unit Analyzes traffic conditions and weather information in real time and dynamically suggests optimal transportation options 2. The system of claim 1.

4. The dining facility information providing unit Analyzes the menus of restaurants and suggests the most suitable restaurant based on the user's preferences and allergy information 2. The system of claim 1.

5. The remote work facility information providing unit, Analyze the equipment and environment of remote work facilities and suggest the best facilities for the user's work 2. The system of claim 1.

6. The system comprises: Using emotion estimation function, the emotional aspects of the user feedback are analyzed and improvements made that are more in line with emotions are made.

2. The system of claim 1.

7. The transportation means analysis unit Using emotion estimation function, the current emotional state of the user is analyzed and a less stressful transportation method is suggested.

2. The system of claim 1.

8. The hot spring resort proposal unit Using emotion estimation function, the system proposes hot spring resorts that best suit the user's current emotional state.

2. The system of claim 1.

Images