system

Abstract

The system according to this embodiment aims to propose individually optimized travel plans and experiences to travelers. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, and a reservation unit. The reception unit receives input information from travelers. The analysis unit analyzes travel data or reviews of tourist spots based on the information received by the reception unit. The proposal unit proposes a travel plan or experience to travelers based on the analysis results obtained by the analysis unit. The reservation unit provides reservation support based on the travel plan or experience proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to propose an optimal travel plan and experience for each traveler individually.

[0005] The system according to the embodiment aims to propose an optimal travel plan and experience for each traveler individually.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a reservation unit. The reception unit receives input information from travelers. The analysis unit analyzes travel data or reviews of tourist spots based on the information received by the reception unit. The proposal unit proposes a travel plan or experience to travelers based on the analysis results obtained by the analysis unit. The reservation unit provides reservation support based on the travel plan or experience proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose individually optimized travel plans and experiences to travelers. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI platform according to an embodiment of the present invention is a system that analyzes travel data and tourist spot reviews to propose travel plans and experiences suitable for individual travelers. This AI platform works by having travelers input their travel destination and desired experiences. The AI ​​then analyzes travel data and tourist spot reviews to identify tourist spots and experiences that match the traveler's preferences. Furthermore, the AI ​​platform provides travelers with customized travel guides and booking support. For example, when a traveler inputs their travel destination and desired experiences, the AI ​​platform analyzes travel data and tourist spot reviews based on that information to propose the most suitable tourist spots and experiences. Next, the AI ​​platform provides travelers with customized travel guides and supports booking the desired experiences. The AI ​​platform can also accumulate travelers' past travel data and utilize it for future travel planning. This allows travelers to create travel plans that meet their needs, improving travel satisfaction. Thus, the AI ​​platform can propose optimal travel plans and experiences and provide booking support based on the traveler's input information.

[0029] The AI ​​platform according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, and a booking unit. The reception unit receives input from travelers regarding their travel destination and desired experiences. The information entered by travelers includes, but is not limited to, destinations, dates, budget, and activities of interest. The reception unit can, for example, store the information entered by travelers in a database so that the analysis unit can use it later. The reception unit can also transmit the information entered by travelers to the analysis unit in real time. The analysis unit uses AI to analyze travel data and reviews of tourist spots based on the information received by the reception unit. The analysis unit uses, for example, techniques such as text mining, statistical analysis, and machine learning algorithms to identify tourist spots and experiences that match the traveler's preferences. For example, the analysis unit analyzes past travel data and reviews of tourist spots based on the information entered by travelers to identify the most suitable tourist spots and experiences. The suggestion unit proposes the most suitable travel plan and experiences to travelers based on the analysis results obtained by the analysis unit. The suggestion unit can, for example, provide travelers with customized tour guides and support bookings for desired experiences. The suggestion department can accumulate travelers' past travel data and use it to plan future trips. The booking department supports travelers in booking desired experiences based on the travel plans and experiences suggested by the suggestion department. For example, the booking department can book accommodations, arrange transportation, and book activities. As a result, the AI ​​platform according to this embodiment can suggest optimal travel plans and experiences and provide booking support based on the traveler's input information.

[0030] The reception desk can analyze a traveler's past input history and suggest the optimal input method. For example, the reception desk can automatically display destinations and experiences that the traveler has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the traveler has used in the past. Furthermore, the reception desk can predict and suggest destinations and experiences that the traveler will use at a specific time of day based on their past input history. This improves input efficiency by suggesting the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the traveler's past input history data into a generating AI and have the generating AI suggest the optimal input method.

[0031] The reception unit can acquire the traveler's current location information and present input suggestions based on that location. For example, when a traveler opens the app, the reception unit can automatically acquire their current location and set it as their departure point. The reception unit can also suggest the most suitable destination by considering the distance from the current location when the traveler enters their destination. Furthermore, if the traveler uses the app while on the move, the reception unit can update their current location in real time and reflect it as their departure point. This improves input accuracy by presenting input suggestions based on the current location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the traveler's current location data into a generating AI and have the generating AI perform the task of presenting input suggestions.

[0032] The reception desk can analyze travelers' social media activity and suggest relevant destinations and experiences as input options. For example, the reception desk can analyze photos and posts shared by travelers on social media and suggest relevant tourist spots. It can also analyze posts from influencers that travelers follow and suggest similar places or experiences. Furthermore, the reception desk can analyze travelers' interests on social media and suggest relevant experiences. This allows for suggestions tailored to travelers' interests by presenting input options based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input travelers' social media data into a generating AI and have the generating AI suggest relevant destinations and experiences.

[0033] The reception desk can refer to the traveler's past travel data and automatically complete the input content. For example, the reception desk can automatically suggest the next travel destination based on places the traveler has visited in the past. It can also automatically suggest related experiences based on activities the traveler has experienced in the past. Furthermore, the reception desk can analyze the traveler's past travel data and automatically provide information useful for planning the next trip. This improves the efficiency of input by automatically completing the input content based on past travel data. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the traveler's past travel data into a generating AI and have the generating AI perform automatic completion of the input content.

[0034] The analysis department can collect travel data and tourist spot reviews in real time and perform analysis based on the latest information. For example, the analysis department can collect tourist spot reviews that are updated in real time and analyze the latest popular spots. The analysis department can also collect travel data that is updated in real time and analyze the latest trends. Furthermore, the analysis department can collect transportation information that is updated in real time and analyze the optimal mode of transportation. This allows for more accurate recommendations by collecting and analyzing the latest information in real time. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input data collected in real time into a generating AI and have the generating AI perform analysis based on the latest information.

[0035] The analysis unit can refer to travelers' past travel data and perform analyses based on individual preferences. For example, the analysis unit can analyze tourist spots that match individual preferences based on places travelers have visited in the past. The analysis unit can also analyze experiences that match individual preferences based on activities travelers have experienced in the past. Furthermore, the analysis unit can analyze travelers' past travel data and provide information useful for planning future trips. This allows for more appropriate suggestions by performing analyses based on past travel data and individual preferences. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input travelers' past travel data into a generating AI and have the generating AI perform analyses based on individual preferences.

[0036] The analysis unit can analyze the popularity of tourist spots in each region, taking into account the geographical distribution of travelers. For example, the analysis unit can analyze the popularity of tourist spots in each region based on the geographical distribution of travelers. The analysis unit can also analyze trends in tourist spots in each region based on the geographical distribution of travelers. Furthermore, the analysis unit can analyze the evaluation of tourist spots in each region based on the geographical distribution of travelers. This allows for the understanding of regional trends by analyzing popularity based on geographical distribution. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical distribution data of travelers into a generating AI and analyze the popularity of tourist spots in each region.

[0037] The analytics department can analyze travelers' social media activity and collect relevant reviews and ratings to incorporate into the analysis. For example, the analytics department can analyze travelers' social media posts and collect relevant reviews. It can also analyze posts from influencers that travelers follow and collect relevant ratings. Furthermore, the analytics department can analyze travelers' interests on social media and collect relevant reviews and ratings. This allows for more accurate analysis by collecting reviews and ratings based on social media activity. Some or all of the above processes in the analytics department may be performed using AI, for example, or not. For example, the analytics department can input travelers' social media data into a generating AI and have the generating AI collect relevant reviews and ratings.

[0038] The suggestion unit can make personalized suggestions based on the traveler's preferences by referring to the traveler's past travel data. For example, the suggestion unit can suggest tourist spots that match the traveler's preferences based on places the traveler has visited in the past. It can also suggest experiences that match the traveler's preferences based on activities the traveler has experienced in the past. Furthermore, the suggestion unit can analyze the traveler's past travel data and provide information useful for planning future trips. This allows for more appropriate suggestions by making personalized suggestions based on past travel data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the traveler's past travel data into a generating AI and have the generating AI execute suggestions based on the traveler's preferences.

[0039] The suggestion unit can propose an optimal travel plan by considering the traveler's current location information. For example, the suggestion unit can suggest tourist spots close to the traveler's current location. The suggestion unit can also propose an optimal travel plan by considering the traveler's means of transportation from their current location. Furthermore, the suggestion unit can propose an optimal travel plan by considering the weather information at the traveler's current location. This improves the traveler's convenience by proposing an optimal travel plan based on their current location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the traveler's current location information data into a generating AI and have the generating AI execute the proposal of an optimal travel plan.

[0040] The suggestion unit can analyze a traveler's social media activity and suggest relevant travel destinations and experiences. For example, it can analyze photos and posts shared by travelers on social media and suggest relevant tourist spots. It can also analyze posts from influencers followed by travelers and suggest similar places or experiences. Furthermore, it can analyze travelers' interests on social media and suggest relevant experiences. This allows for suggestions tailored to travelers' interests by basing them on social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the traveler's social media data into a generating AI and have the AI ​​generate suggestions for relevant travel destinations and experiences.

[0041] The suggestion unit can refer to the traveler's past travel data when making suggestions and utilize it for future travel planning. For example, the suggestion unit can suggest a next travel destination based on places the traveler has visited in the past. It can also suggest a next travel plan based on activities the traveler has experienced in the past. Furthermore, the suggestion unit can analyze the traveler's past travel data and provide information useful for future travel planning. This improves the traveler's convenience by suggesting future travel plans based on past travel data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the traveler's past travel data into a generating AI and have the generating AI generate suggestions for the next travel plan.

[0042] The booking department can suggest the most suitable booking method by referring to the traveler's past booking history during the booking process. For example, the booking department can automatically display booking methods that the traveler has frequently used in the past as options. It can also prioritize suggesting booking sites or apps that the traveler has used in the past. Furthermore, the booking department can predict and suggest booking methods to be used during specific time periods based on the traveler's past booking history. This improves booking efficiency by suggesting the most suitable booking method based on past booking history. Some or all of the above processes in the booking department may be performed using AI, for example, or not. For example, the booking department can input the traveler's past booking history data into a generating AI and have the generating AI suggest the most suitable booking method.

[0043] The booking department can suggest the most suitable accommodations when a reservation is made, taking into account the traveler's current location. For example, the booking department can suggest accommodations close to the traveler's current location. It can also suggest the most suitable accommodations considering the traveler's means of transportation from their current location. Furthermore, it can suggest the most suitable accommodations considering the weather information at the traveler's current location. This improves the convenience for travelers by suggesting the most suitable accommodations based on their current location. Some or all of the above processing in the booking department may be performed using AI, for example, or without AI. For example, the booking department can input the traveler's current location data into a generating AI and have the generating AI perform the task of suggesting the most suitable accommodations.

[0044] The booking department can analyze travelers' social media activity and suggest relevant bookings and experiences. For example, it can analyze photos and posts shared by travelers on social media and suggest relevant bookings. It can also analyze posts from influencers that travelers follow and suggest similar places or experiences. Furthermore, it can analyze travelers' interests on social media and suggest relevant experiences. This allows for suggestions tailored to travelers' interests by basing bookings and experiences on social media activity. Some or all of the above processing in the booking department may be performed using AI, for example, or not. For example, the booking department can input travelers' social media data into a generating AI and have the AI ​​generate suggestions for relevant bookings and experiences.

[0045] The booking department can refer to a traveler's past travel data and use it to inform future bookings. For example, the booking department can suggest future booking destinations based on places the traveler has visited in the past. It can also suggest future bookings based on activities the traveler has experienced in the past. Furthermore, the booking department can analyze a traveler's past travel data and provide information useful for future bookings. This improves the convenience for travelers by suggesting future bookings based on past travel data. Some or all of the above processes in the booking department may be performed using AI, for example, or not. For example, the booking department can input a traveler's past travel data into a generating AI and have the generating AI generate suggestions for future bookings.

[0046] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0047] The reception desk can also acquire travelers' health data and adjust travel plans based on their health status. For example, if a traveler is feeling tired, it can prioritize suggesting relaxing sightseeing spots and experiences. Conversely, if a traveler is feeling active, it can suggest adventurous activities. Furthermore, if a traveler has a specific health problem, it can propose a travel plan that addresses that problem. By providing travel plans tailored to travelers' health conditions, this system improves travel satisfaction.

[0048] The reception desk can analyze travelers' past travel data and suggest destinations based on their preferences. For example, it can suggest similar tourist spots based on places travelers have visited in the past. It can also suggest related experiences based on activities travelers have participated in in the past. Furthermore, it can analyze travelers' past travel data and provide information useful for planning future trips. By suggesting destinations based on past travel data, this system can improve travel satisfaction.

[0049] The reception desk can acquire the traveler's current location information and suggest the most suitable tourist spots in real time based on that location. For example, it can suggest tourist spots close to the traveler's current location. Furthermore, if the traveler uses the app while on the move, it can update their current location in real time and suggest the most suitable tourist spots. In addition, it can also suggest the most suitable tourist spots considering the weather information of the traveler's current location. This improves travel satisfaction by suggesting the most suitable tourist spots in real time based on the current location information.

[0050] The reception desk can also analyze travelers' social media activity and suggest relevant destinations and experiences. For example, it can analyze photos and posts travelers share on social media and suggest related tourist spots. It can also analyze posts from influencers travelers follow and suggest similar places and experiences. Furthermore, it can analyze travelers' interests on social media and suggest relevant experiences. By suggesting destinations and experiences based on social media activity, this system can improve travel satisfaction.

[0051] The reception desk can refer to travelers' past travel data and automatically complete the input information. For example, it can automatically suggest the next travel destination based on places the traveler has visited in the past. It can also automatically suggest related experiences based on activities the traveler has participated in in the past. Furthermore, it can analyze the traveler's past travel data and automatically provide information that will be useful for planning their next trip. In this way, by automatically completing the input information based on past travel data, travel satisfaction is improved.

[0052] The analytics department can collect travel data and tourist spot reviews in real time and perform analysis based on the latest information. For example, it can collect real-time updated tourist spot reviews and analyze the latest popular spots. It can also collect real-time updated travel data and analyze the latest trends. Furthermore, it can collect real-time updated transportation information and analyze the optimal mode of transportation. By collecting and analyzing the latest information in real time, travel satisfaction can be improved.

[0053] The analytics department can also refer to travelers' past travel data and perform analyses based on individual preferences. For example, it can analyze tourist spots that match individual preferences based on places travelers have visited in the past. It can also analyze experiences that match individual preferences based on activities travelers have participated in in the past. Furthermore, it can analyze travelers' past travel data and provide information that can be used to plan future trips. In this way, by performing analyses tailored to individual preferences based on past travel data, travel satisfaction can be improved.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The reception desk receives information from travelers about their travel destination and desired experiences. This information includes destination, dates, budget, and activities of interest. The reception desk stores the traveler's information in a database for later use by the analytics department. The reception desk can also transmit the traveler's information to the analytics department in real time. Step 2: The analysis department uses AI to analyze travel data and tourist spot reviews based on the information received by the reception department. The analysis department uses techniques such as text mining, statistical analysis, and machine learning algorithms to identify tourist spots and experiences that match the traveler's preferences. For example, based on the information entered by the traveler, it analyzes past travel data and tourist spot reviews to identify the most suitable tourist spots and experiences. Step 3: Based on the analysis results obtained by the analysis department, the proposal department suggests the most suitable travel plans and experiences for travelers. The proposal department provides travelers with customized tour guides and supports them in booking the experiences they desire. The proposal department can also accumulate travelers' past travel data and use it to plan future trips. Step 4: The Reservations Department supports travelers in booking their desired experiences based on the travel plans and experiences proposed by the Proposal Department. The Reservations Department handles accommodation bookings, transportation arrangements, and activity bookings.

[0056] (Example of form 2) An AI platform according to an embodiment of the present invention is a system that analyzes travel data and tourist spot reviews to propose travel plans and experiences suitable for individual travelers. This AI platform works by having travelers input their travel destination and desired experiences. The AI ​​then analyzes travel data and tourist spot reviews to identify tourist spots and experiences that match the traveler's preferences. Furthermore, the AI ​​platform provides travelers with customized travel guides and booking support. For example, when a traveler inputs their travel destination and desired experiences, the AI ​​platform analyzes travel data and tourist spot reviews based on that information to propose the most suitable tourist spots and experiences. Next, the AI ​​platform provides travelers with customized travel guides and supports booking the desired experiences. The AI ​​platform can also accumulate travelers' past travel data and utilize it for future travel planning. This allows travelers to create travel plans that meet their needs, improving travel satisfaction. Thus, the AI ​​platform can propose optimal travel plans and experiences and provide booking support based on the traveler's input information.

[0057] The AI ​​platform according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, and a booking unit. The reception unit receives input from travelers regarding their travel destination and desired experiences. The information entered by travelers includes, but is not limited to, destinations, dates, budget, and activities of interest. The reception unit can, for example, store the information entered by travelers in a database so that the analysis unit can use it later. The reception unit can also transmit the information entered by travelers to the analysis unit in real time. The analysis unit uses AI to analyze travel data and reviews of tourist spots based on the information received by the reception unit. The analysis unit uses, for example, techniques such as text mining, statistical analysis, and machine learning algorithms to identify tourist spots and experiences that match the traveler's preferences. For example, the analysis unit analyzes past travel data and reviews of tourist spots based on the information entered by travelers to identify the most suitable tourist spots and experiences. The suggestion unit proposes the most suitable travel plan and experiences to travelers based on the analysis results obtained by the analysis unit. The suggestion unit can, for example, provide travelers with customized tour guides and support bookings for desired experiences. The suggestion department can accumulate travelers' past travel data and use it to plan future trips. The booking department supports travelers in booking desired experiences based on the travel plans and experiences suggested by the suggestion department. For example, the booking department can book accommodations, arrange transportation, and book activities. As a result, the AI ​​platform according to this embodiment can suggest optimal travel plans and experiences and provide booking support based on the traveler's input information.

[0058] The reception desk can estimate the traveler's emotions and adjust the display of the input interface based on the estimated emotions. For example, if the traveler is stressed, the reception desk can provide a simple interface and minimize the input steps. If the traveler is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the traveler is in a hurry, the reception desk can prioritize voice input, allowing them to quickly input their travel destination and desired experiences. This improves usability by providing an input interface that responds to the traveler's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0059] The reception desk can analyze a traveler's past input history and suggest the optimal input method. For example, the reception desk can automatically display destinations and experiences that the traveler has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the traveler has used in the past. Furthermore, the reception desk can predict and suggest destinations and experiences that the traveler will use at a specific time of day based on their past input history. This improves input efficiency by suggesting the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the traveler's past input history data into a generating AI and have the generating AI suggest the optimal input method.

[0060] The reception unit can acquire the traveler's current location information and present input suggestions based on that location. For example, when a traveler opens the app, the reception unit can automatically acquire their current location and set it as their departure point. The reception unit can also suggest the most suitable destination by considering the distance from the current location when the traveler enters their destination. Furthermore, if the traveler uses the app while on the move, the reception unit can update their current location in real time and reflect it as their departure point. This improves input accuracy by presenting input suggestions based on the current location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the traveler's current location data into a generating AI and have the generating AI perform the task of presenting input suggestions.

[0061] The reception desk can estimate the traveler's emotions and prioritize input content based on the estimated emotions. For example, if the traveler is excited, the reception desk will prioritize displaying activities and adventurous experiences. If the traveler is relaxed, the reception desk can also prioritize displaying relaxing sightseeing spots and experiences. If the traveler is tired, the reception desk can also prioritize displaying restful and refreshing experiences. This allows for more appropriate input by prioritizing input content according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0062] The reception desk can analyze travelers' social media activity and suggest relevant destinations and experiences as input options. For example, the reception desk can analyze photos and posts shared by travelers on social media and suggest relevant tourist spots. It can also analyze posts from influencers that travelers follow and suggest similar places or experiences. Furthermore, the reception desk can analyze travelers' interests on social media and suggest relevant experiences. This allows for suggestions tailored to travelers' interests by presenting input options based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input travelers' social media data into a generating AI and have the generating AI suggest relevant destinations and experiences.

[0063] The reception desk can refer to the traveler's past travel data and automatically complete the input content. For example, the reception desk can automatically suggest the next travel destination based on places the traveler has visited in the past. It can also automatically suggest related experiences based on activities the traveler has experienced in the past. Furthermore, the reception desk can analyze the traveler's past travel data and automatically provide information useful for planning the next trip. This improves the efficiency of input by automatically completing the input content based on past travel data. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the traveler's past travel data into a generating AI and have the generating AI perform automatic completion of the input content.

[0064] The analysis unit can estimate the traveler's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the traveler is excited, the analysis unit will focus on adventurous experiences. If the traveler is relaxed, the analysis unit can focus on relaxing tourist spots. If the traveler is tired, the analysis unit can focus on restful and refreshing experiences. This improves the accuracy of the analysis by providing an analysis algorithm that is tailored to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0065] The analysis department can collect travel data and tourist spot reviews in real time and perform analysis based on the latest information. For example, the analysis department can collect tourist spot reviews that are updated in real time and analyze the latest popular spots. The analysis department can also collect travel data that is updated in real time and analyze the latest trends. Furthermore, the analysis department can collect transportation information that is updated in real time and analyze the optimal mode of transportation. This allows for more accurate recommendations by collecting and analyzing the latest information in real time. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input data collected in real time into a generating AI and have the generating AI perform analysis based on the latest information.

[0066] The analysis unit can refer to travelers' past travel data and perform analyses based on individual preferences. For example, the analysis unit can analyze tourist spots that match individual preferences based on places travelers have visited in the past. The analysis unit can also analyze experiences that match individual preferences based on activities travelers have experienced in the past. Furthermore, the analysis unit can analyze travelers' past travel data and provide information useful for planning future trips. This allows for more appropriate suggestions by performing analyses based on past travel data and individual preferences. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input travelers' past travel data into a generating AI and have the generating AI perform analyses based on individual preferences.

[0067] The analysis unit can estimate the traveler's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the traveler is excited, the analysis unit can provide a visually stimulating display method. If the traveler is relaxed, the analysis unit can provide a calm display method. If the traveler is tired, the analysis unit can provide a simple and easy-to-read display method. This improves usability by providing a display method that matches the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input the traveler's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0068] The analysis unit can analyze the popularity of tourist spots in each region, taking into account the geographical distribution of travelers. For example, the analysis unit can analyze the popularity of tourist spots in each region based on the geographical distribution of travelers. The analysis unit can also analyze trends in tourist spots in each region based on the geographical distribution of travelers. Furthermore, the analysis unit can analyze the evaluation of tourist spots in each region based on the geographical distribution of travelers. This allows for the understanding of regional trends by analyzing popularity based on geographical distribution. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical distribution data of travelers into a generating AI and analyze the popularity of tourist spots in each region.

[0069] The analytics department can analyze travelers' social media activity and collect relevant reviews and ratings to incorporate into the analysis. For example, the analytics department can analyze travelers' social media posts and collect relevant reviews. It can also analyze posts from influencers that travelers follow and collect relevant ratings. Furthermore, the analytics department can analyze travelers' interests on social media and collect relevant reviews and ratings. This allows for more accurate analysis by collecting reviews and ratings based on social media activity. Some or all of the above processes in the analytics department may be performed using AI, for example, or not. For example, the analytics department can input travelers' social media data into a generating AI and have the generating AI collect relevant reviews and ratings.

[0070] The suggestion unit can estimate the traveler's emotions and adjust the way it presents suggestions based on those emotions. For example, if the traveler is excited, the suggestion unit can provide visually stimulating suggestions. If the traveler is relaxed, it can provide calming suggestions. If the traveler is tired, it can provide simple and easily understandable suggestions. This improves usability by providing suggestions tailored to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the traveler's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0071] The suggestion unit can make personalized suggestions based on the traveler's preferences by referring to the traveler's past travel data. For example, the suggestion unit can suggest tourist spots that match the traveler's preferences based on places the traveler has visited in the past. It can also suggest experiences that match the traveler's preferences based on activities the traveler has experienced in the past. Furthermore, the suggestion unit can analyze the traveler's past travel data and provide information useful for planning future trips. This allows for more appropriate suggestions by making personalized suggestions based on past travel data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the traveler's past travel data into a generating AI and have the generating AI execute suggestions based on the traveler's preferences.

[0072] The suggestion unit can propose an optimal travel plan by considering the traveler's current location information. For example, the suggestion unit can suggest tourist spots close to the traveler's current location. The suggestion unit can also propose an optimal travel plan by considering the traveler's means of transportation from their current location. Furthermore, the suggestion unit can propose an optimal travel plan by considering the weather information at the traveler's current location. This improves the traveler's convenience by proposing an optimal travel plan based on their current location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the traveler's current location information data into a generating AI and have the generating AI execute the proposal of an optimal travel plan.

[0073] The suggestion unit can estimate the traveler's emotions and prioritize suggestions based on those emotions. For example, if the traveler is excited, the suggestion unit will prioritize suggesting adventurous experiences. If the traveler is relaxed, the suggestion unit can prioritize suggesting relaxing experiences. If the traveler is tired, the suggestion unit can prioritize suggesting restful or refreshing experiences. By prioritizing suggestions according to the traveler's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0074] The suggestion unit can analyze a traveler's social media activity and suggest relevant travel destinations and experiences. For example, it can analyze photos and posts shared by travelers on social media and suggest relevant tourist spots. It can also analyze posts from influencers followed by travelers and suggest similar places or experiences. Furthermore, it can analyze travelers' interests on social media and suggest relevant experiences. This allows for suggestions tailored to travelers' interests by basing them on social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the traveler's social media data into a generating AI and have the AI ​​generate suggestions for relevant travel destinations and experiences.

[0075] The suggestion unit can refer to the traveler's past travel data when making suggestions and utilize it for future travel planning. For example, the suggestion unit can suggest a next travel destination based on places the traveler has visited in the past. It can also suggest a next travel plan based on activities the traveler has experienced in the past. Furthermore, the suggestion unit can analyze the traveler's past travel data and provide information useful for future travel planning. This improves the traveler's convenience by suggesting future travel plans based on past travel data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the traveler's past travel data into a generating AI and have the generating AI generate suggestions for the next travel plan.

[0076] The booking system can estimate the traveler's emotions and adjust how the booking process is displayed based on those emotions. For example, if the traveler is stressed, the booking system can provide a simple interface and minimize the booking process. If the traveler is relaxed, the booking system can also provide detailed booking options and suggest a customizable booking method. If the traveler is in a hurry, the booking system can prioritize voice input to allow for a quick booking process. This improves usability by providing a booking process display tailored to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the booking system may be performed using AI or not. For example, the booking system can input traveler facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The booking department can suggest the most suitable booking method by referring to the traveler's past booking history during the booking process. For example, the booking department can automatically display booking methods that the traveler has frequently used in the past as options. It can also prioritize suggesting booking sites or apps that the traveler has used in the past. Furthermore, the booking department can predict and suggest booking methods to be used during specific time periods based on the traveler's past booking history. This improves booking efficiency by suggesting the most suitable booking method based on past booking history. Some or all of the above processes in the booking department may be performed using AI, for example, or not. For example, the booking department can input the traveler's past booking history data into a generating AI and have the generating AI suggest the most suitable booking method.

[0078] The booking department can suggest the most suitable accommodations when a reservation is made, taking into account the traveler's current location. For example, the booking department can suggest accommodations close to the traveler's current location. It can also suggest the most suitable accommodations considering the traveler's means of transportation from their current location. Furthermore, it can suggest the most suitable accommodations considering the weather information at the traveler's current location. This improves the convenience for travelers by suggesting the most suitable accommodations based on their current location. Some or all of the above processing in the booking department may be performed using AI, for example, or without AI. For example, the booking department can input the traveler's current location data into a generating AI and have the generating AI perform the task of suggesting the most suitable accommodations.

[0079] The booking system can estimate a traveler's emotions and prioritize bookings based on those emotions. For example, if a traveler is excited, the booking system might prioritize booking adventurous experiences. If a traveler is relaxed, it might prioritize booking relaxing experiences. If a traveler is tired, it might prioritize booking restful or refreshing experiences. By prioritizing bookings according to the traveler's emotions, more appropriate bookings can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the booking system may be performed using AI or not. For example, the booking system could input traveler facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The booking department can analyze travelers' social media activity and suggest relevant bookings and experiences. For example, it can analyze photos and posts shared by travelers on social media and suggest relevant bookings. It can also analyze posts from influencers that travelers follow and suggest similar places or experiences. Furthermore, it can analyze travelers' interests on social media and suggest relevant experiences. This allows for suggestions tailored to travelers' interests by basing bookings and experiences on social media activity. Some or all of the above processing in the booking department may be performed using AI, for example, or not. For example, the booking department can input travelers' social media data into a generating AI and have the AI ​​generate suggestions for relevant bookings and experiences.

[0081] The booking department can refer to a traveler's past travel data and use it to inform future bookings. For example, the booking department can suggest future booking destinations based on places the traveler has visited in the past. It can also suggest future bookings based on activities the traveler has experienced in the past. Furthermore, the booking department can analyze a traveler's past travel data and provide information useful for future bookings. This improves the convenience for travelers by suggesting future bookings based on past travel data. Some or all of the above processes in the booking department may be performed using AI, for example, or not. For example, the booking department can input a traveler's past travel data into a generating AI and have the generating AI generate suggestions for future bookings. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and reservation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, where the traveler inputs their travel destination and desired experiences. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, where travel data and reviews of tourist spots are analyzed. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12, where the traveler is proposed the most suitable travel plan and experiences. The reservation unit is implemented by the control unit 46A of the smart device 14, where the traveler is supported in making reservations for desired experiences. Furthermore, the reception unit can estimate the traveler's emotions and adjust the display method of the input interface based on the estimated emotions. Emotion estimation is implemented by the identification processing unit 290 of the data processing unit 12. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and reservation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, where the traveler inputs their travel destination and desired experiences. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, where travel data and reviews of tourist spots are analyzed. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12, where the traveler is proposed the most suitable travel plan and experiences. The reservation unit is implemented by the control unit 46A of the smart glasses 214, where the traveler is supported in making reservations for desired experiences. Furthermore, the reception unit can estimate the traveler's emotions and adjust the display method of the input interface based on the estimated emotions. Emotion estimation is implemented by the identification processing unit 290 of the data processing unit 12. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and reservation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, where the traveler inputs their travel destination and desired experiences. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, where travel data and reviews of tourist spots are analyzed. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, where the traveler is proposed the most suitable travel plan and experiences. The reservation unit is implemented by the control unit 46A of the headset terminal 314, where the traveler is supported in making reservations for desired experiences. Furthermore, the reception unit can estimate the traveler's emotions and adjust the display method of the input interface based on the estimated emotions. Emotion estimation is implemented by the specific processing unit 290 of the data processing unit 12. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and reservation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, where the traveler inputs their travel destination and desired experiences. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, where travel data and reviews of tourist spots are analyzed. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12, where the traveler is proposed the most suitable travel plan and experiences. The reservation unit is implemented by the control unit 46A of the robot 414, where the traveler is supported in making reservations for desired experiences. Furthermore, the reception unit can estimate the traveler's emotions and adjust the display method of the input interface based on the estimated emotions. Emotion estimation is implemented by the identification processing unit 290 of the data processing unit 12.

[0082] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0083] The reception desk can also acquire travelers' health data and adjust travel plans based on their health status. For example, if a traveler is feeling tired, it can prioritize suggesting relaxing sightseeing spots and experiences. Conversely, if a traveler is feeling active, it can suggest adventurous activities. Furthermore, if a traveler has a specific health problem, it can propose a travel plan that addresses that problem. By providing travel plans tailored to travelers' health conditions, this system improves travel satisfaction.

[0084] The reception desk can estimate the traveler's emotions and suggest destinations based on those estimates. For example, if a traveler is feeling stressed, it can suggest a relaxing resort. If the traveler is excited, it can suggest an adventurous destination. Furthermore, if the traveler is sad, it can suggest a tourist spot that will lift their spirits. By suggesting destinations that match the traveler's emotions, the system can improve their travel satisfaction.

[0085] The reception desk can analyze travelers' past travel data and suggest destinations based on their preferences. For example, it can suggest similar tourist spots based on places travelers have visited in the past. It can also suggest related experiences based on activities travelers have participated in in the past. Furthermore, it can analyze travelers' past travel data and provide information useful for planning future trips. By suggesting destinations based on past travel data, this system can improve travel satisfaction.

[0086] The reception desk can acquire the traveler's current location information and suggest the most suitable tourist spots in real time based on that location. For example, it can suggest tourist spots close to the traveler's current location. Furthermore, if the traveler uses the app while on the move, it can update their current location in real time and suggest the most suitable tourist spots. In addition, it can also suggest the most suitable tourist spots considering the weather information of the traveler's current location. This improves travel satisfaction by suggesting the most suitable tourist spots in real time based on the current location information.

[0087] The reception desk can also estimate the traveler's emotions and prioritize travel plans based on those emotions. For example, if the traveler is excited, adventurous experiences will be prioritized. If the traveler is relaxed, relaxing experiences will be prioritized. Furthermore, if the traveler is tired, restful and refreshing experiences can be prioritized. By prioritizing travel plans according to the traveler's emotions, travel satisfaction can be improved.

[0088] The reception desk can also analyze travelers' social media activity and suggest relevant destinations and experiences. For example, it can analyze photos and posts travelers share on social media and suggest related tourist spots. It can also analyze posts from influencers travelers follow and suggest similar places and experiences. Furthermore, it can analyze travelers' interests on social media and suggest relevant experiences. By suggesting destinations and experiences based on social media activity, this system can improve travel satisfaction.

[0089] The reception desk can refer to travelers' past travel data and automatically complete the input information. For example, it can automatically suggest the next travel destination based on places the traveler has visited in the past. It can also automatically suggest related experiences based on activities the traveler has participated in in the past. Furthermore, it can analyze the traveler's past travel data and automatically provide information that will be useful for planning their next trip. In this way, by automatically completing the input information based on past travel data, travel satisfaction is improved.

[0090] The analytics department can estimate travelers' emotions and adjust the analysis algorithm based on those estimates. For example, if a traveler is excited, the analysis will prioritize adventurous experiences. If a traveler is relaxed, the analysis will prioritize relaxing tourist spots. Furthermore, if a traveler is tired, the analysis can prioritize restful and refreshing experiences. By providing an analysis algorithm tailored to travelers' emotions, the satisfaction of their trips can be improved.

[0091] The analytics department can collect travel data and tourist spot reviews in real time and perform analysis based on the latest information. For example, it can collect real-time updated tourist spot reviews and analyze the latest popular spots. It can also collect real-time updated travel data and analyze the latest trends. Furthermore, it can collect real-time updated transportation information and analyze the optimal mode of transportation. By collecting and analyzing the latest information in real time, travel satisfaction can be improved.

[0092] The analytics department can also refer to travelers' past travel data and perform analyses based on individual preferences. For example, it can analyze tourist spots that match individual preferences based on places travelers have visited in the past. It can also analyze experiences that match individual preferences based on activities travelers have participated in in the past. Furthermore, it can analyze travelers' past travel data and provide information that can be used to plan future trips. In this way, by performing analyses tailored to individual preferences based on past travel data, travel satisfaction can be improved.

[0093] The following briefly describes the processing flow for example form 2.

[0094] Step 1: The reception desk receives information from travelers about their travel destination and desired experiences. This information includes destination, dates, budget, and activities of interest. The reception desk stores the traveler's information in a database for later use by the analytics department. The reception desk can also transmit the traveler's information to the analytics department in real time. Step 2: The analysis department uses AI to analyze travel data and tourist spot reviews based on the information received by the reception department. The analysis department uses techniques such as text mining, statistical analysis, and machine learning algorithms to identify tourist spots and experiences that match the traveler's preferences. For example, based on the information entered by the traveler, it analyzes past travel data and tourist spot reviews to identify the most suitable tourist spots and experiences. Step 3: Based on the analysis results obtained by the analysis department, the proposal department suggests the most suitable travel plans and experiences for travelers. The proposal department provides travelers with customized tour guides and supports them in booking the experiences they desire. The proposal department can also accumulate travelers' past travel data and use it to plan future trips. Step 4: The Reservations Department supports travelers in booking their desired experiences based on the travel plans and experiences proposed by the Proposal Department. The Reservations Department handles accommodation bookings, transportation arrangements, and activity bookings.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0097] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0098] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0100] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0102] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0103] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0105] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0106] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0107] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0108] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0109] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0115] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0116] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0131] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0132] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0139] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0148] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0150] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0151] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0152] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0156] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0157] 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.

[0158] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0159] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0160] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0161] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0163] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0164] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0165] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0166] [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts travelers' input information, An analysis department analyzes travel data or reviews of tourist spots based on the information received by the aforementioned reception department. Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes a travel plan or experience to the traveler. A reservation department provides reservation support based on the travel plan or experience proposed by the aforementioned proposal department, Equipped with A system characterized by the following features.

2. The aforementioned reception unit is Estimating the traveler's emotions and adjusting the display of the input interface based on those estimated emotions. The system according to claim 1, characterized by the following:

3. The aforementioned reception unit is Analyzes travelers' past input history and suggests input methods. The system according to feature 1.

4. The aforementioned reception unit is Obtain the traveler's current location information and suggest input options based on that location information. The system according to claim 1, characterized by the following:

5. The aforementioned reception unit is Estimating the traveler's emotions and prioritizing input content based on those estimated emotions. The system according to claim 1, characterized by the following:

6. The aforementioned reception unit is Analyze travelers' social media activity and suggest relevant travel destinations and experiences as input options. The system according to claim 1, characterized by the following:

7. The aforementioned reception unit is Referencing the traveler's past travel data, the system automatically completes the input content. The system according to feature 1.

8. The aforementioned analysis unit is It estimates the traveler's emotions and adjusts the analysis algorithm based on the estimated traveler's emotions. The system according to feature 1.

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