system

Abstract

The system according to this embodiment aims to propose the optimal travel plan based on the user's food preferences and travel style. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, and a reservation management unit. The reception unit receives input from the user regarding their food preferences and travel style. The analysis unit analyzes the information received by the reception unit. The proposal unit proposes a travel plan based on the information analyzed by the analysis unit. The reservation management unit manages reservations based on the plan 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, and includes 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 is a problem that it is difficult to propose an optimal travel plan based on the individual preferences and travel styles of users.

[0005] The system according to the embodiment aims to propose an optimal travel plan based on the user's food preferences and travel styles.

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 management unit. The reception unit receives input from the user regarding their food preferences and travel style. The analysis unit analyzes the information received by the reception unit. The proposal unit proposes a travel plan based on the information analyzed by the analysis unit. The reservation management unit manages reservations based on the plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest an optimal travel plan based on the user's food preferences and travel style. [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 multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). <y

[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). <x

[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) The travel plan suggestion system according to an embodiment of the present invention is a system that uses AI to suggest the optimal regional gourmet travel plan based on the user's food preferences and travel style. This system suggests and makes reservations for everything from visits to undiscovered regional gourmet spots to accommodations and transportation. Specifically, when a user inputs their preferences and budget, the AI ​​creates the optimal regional gourmet travel plan and centrally manages reservations for accommodations and transportation. This makes it easy to plan a trip to enjoy hidden delicious spots in the region. For example, a user inputs information such as their food preferences, travel style, and budget. For example, they might input information such as, "I like Japanese food, and my budget is less than 10,000 yen per day." This information is input into the AI. Next, the AI ​​analyzes the input information and creates the optimal regional gourmet travel plan for the user. The AI ​​selects undiscovered regional gourmet spots, accommodations, and transportation based on the user's preferences and budget. For example, for a user who likes Japanese food, it suggests local famous restaurants and hidden gems, and selects accommodations and transportation according to the budget. Furthermore, the AI ​​centrally manages reservations for accommodations and transportation based on the created travel plan. Users can review suggested plans and make all necessary reservations at once. This simplifies travel planning and saves time. The system allows users to enjoy hidden gems in rural areas and makes travel planning easier. It also contributes to the revitalization of local tourism and food service industries. For example, visiting undiscovered local gourmet spots stimulates the local economy and spreads the appeal of the region. Thus, the travel plan suggestion system proposes optimal travel plans based on the user's food preferences and travel style, and manages reservations centrally.

[0029] The travel plan suggestion system according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, and a reservation management unit. The reception unit accepts input from the user regarding their food preferences and travel style. For example, the reception unit allows the user to input information such as their food preferences, travel style, and budget. For example, the user might input information such as "I like Japanese food, and my budget is less than 10,000 yen per day." The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes information such as the user's food preferences, travel style, and budget to create a local gourmet travel plan that is optimal for the user. The suggestion unit proposes a travel plan based on the information analyzed by the analysis unit. For example, the suggestion unit selects local undiscovered gourmet spots, accommodations, and transportation based on the user's preferences and budget. For example, for a user who likes Japanese food, it suggests local famous restaurants and hidden gems, and selects accommodations and transportation according to the budget. The reservation management unit manages reservations based on the plans proposed by the suggestion unit. For example, the reservation management unit centrally manages reservations for accommodations and transportation based on the created travel plan. As a result, the travel plan suggestion system according to the embodiment can suggest the optimal travel plan based on the user's food preferences and travel style, and centrally manage reservations.

[0030] The reception desk receives input from users regarding their food preferences and travel styles. Specifically, users can access the system and input information such as their food preferences, travel style, and budget through the interface. For example, when a user inputs information such as "I like Japanese food and my budget is less than 10,000 yen per day," the reception desk records this information in detail and stores it in the database. Furthermore, the reception desk can verify the information entered by the user and gather more detailed information by asking additional questions as needed. For example, if a user is interested in the cuisine of a particular region, the reception desk can ask about specific dishes from that region and preferred cooking methods. Regarding travel styles, the reception desk can also confirm whether the user prefers active sightseeing or a relaxing stay, and use this information to help create travel plans. The reception desk processes user input in real time and prepares it to be sent to the analysis department. This allows the reception desk to accurately understand user needs and improve the accuracy and efficiency of the entire system.

[0031] The analysis department analyzes the information received by the reception department. Specifically, it analyzes information such as the user's food preferences, travel style, and budget in detail to create the optimal local gourmet travel plan for the user. The analysis department uses AI to analyze the user's input data and generates the optimal plan based on past data and trend information. For example, for a user who likes Japanese food, it identifies local famous restaurants and hidden gems by referring to the ratings and reviews of past travelers. Also, when selecting accommodations and transportation according to the budget, it prioritizes suggesting cost-effective options. The analysis department simulates multiple plans based on the user's preferences and budget and selects the most suitable plan. Furthermore, the analysis department can customize the plan to meet individual needs by considering the user's past travel history and ratings. As a result, the analysis department can provide users with highly accurate and personalized travel plans.

[0032] The Proposal Department proposes travel plans based on information analyzed by the Analysis Department. Specifically, it selects local, undiscovered gourmet spots, accommodations, and transportation options based on the user's preferences and budget. Based on the data provided by the Analysis Department, the Proposal Department creates the optimal travel plan for the user and presents it to the user through the interface. For example, for a user who likes Japanese food, it will suggest local famous restaurants and hidden gems, and select accommodations and transportation options that fit the budget. The Proposal Department allows users to review the proposed plan and make requests for changes or additions as needed. For example, if a user wants to try a particular dish or visit a specific tourist destination, the plan can be adjusted according to their request. The Proposal Department collects user feedback and accumulates data to improve the accuracy and satisfaction of the suggestions. This enables the Proposal Department to provide users with the optimal travel plan and realize a highly satisfying travel experience.

[0033] The Reservation Management Department manages reservations based on plans proposed by the Proposal Department. Specifically, it centrally manages reservations for accommodations and transportation based on the created travel plans. The Reservation Management Department has a system that allows it to review proposed plans and automatically perform various reservation procedures. For example, when a user approves a proposed plan, the Reservation Management Department checks the availability of accommodations and transportation and confirms the reservation. The Reservation Management Department also handles changes and cancellations of reservations, and can flexibly respond to user requests. Furthermore, the Reservation Management Department updates reservation status in real time, providing users with the latest information. For example, when a reservation is confirmed, it sends a confirmation email to the user with detailed travel information. It also sends reminder notifications the day before and on the day of the trip to help users start their trip smoothly. In this way, the Reservation Management Department can centrally manage users' travel plans and provide a smooth travel experience.

[0034] The reception desk can analyze the user's past travel history and provide an auto-completion function during input. For example, the reception desk can automatically display suggested next travel destinations based on places the user has visited in the past. It can also automatically complete input content based on preferences and budgets previously entered by the user. Furthermore, the reception desk can suggest input content related to specific seasons or events based on the user's past travel history. This allows for automatic completion of input content based on past travel history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past travel history data into a generating AI and have the generating AI perform automatic completion of input content.

[0035] The input field can filter input based on the user's current health status and dietary restrictions. For example, if the user has allergies, the input field can automatically exclude foods containing allergens. It can also suggest appropriate meal options if the user has a specific health condition (e.g., diabetes). Furthermore, if the user is on a diet, the input field can prioritize displaying low-calorie meal options. This allows for input tailored to the user's health status and dietary restrictions. Some or all of the above processing in the input field may be performed using AI, for example, or not. For instance, the input field can input the user's health data into a generating AI and have the generating AI perform the filtering of the input.

[0036] The reception desk can provide region-specific input options, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can display local specialties and famous dishes as input options. The reception desk can also automatically suggest tourist attractions and events in a region when the user enters information about their travel destination. Furthermore, if the user is staying in a specific region, the reception desk can prioritize displaying information about transportation and accommodation in that region. This allows for the provision of region-specific input options based on geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI provide region-specific input options.

[0037] The reception desk can analyze a user's social media activity and suggest relevant input content. For example, it can suggest the next travel destination or dining option based on information about travel destinations and restaurants the user has shared on social media. It can also suggest relevant travel destinations and dining options based on information about accounts the user follows on social media. Furthermore, it can suggest travel destinations and dining options that the user might be interested in based on posts the user has liked or commented on on social media. This allows the reception desk to suggest relevant input content 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 the user's social media activity data into a generating AI and have the generating AI suggest relevant input content.

[0038] The analysis unit can improve the accuracy of the analysis by referring to the user's past travel history during the analysis. For example, the analysis unit can customize the analysis results based on the user's past visits to places and food preferences. The analysis unit can also provide analysis results related to specific seasons or events based on the user's past travel history. Furthermore, the analysis unit can analyze the user's past travel history and suggest the most suitable travel plan. This allows for improved analysis accuracy based on past travel history. 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 the user's past travel history data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0039] The analysis unit can customize the analysis results based on the user's current lifestyle and areas of interest during the analysis. For example, the analysis unit can suggest a suitable travel plan based on the user's current lifestyle (e.g., workload). It can also suggest relevant travel destinations and activities based on the user's areas of interest (e.g., history, nature). Furthermore, the analysis unit can customize the optimal travel plan considering the user's current lifestyle and areas of interest. This allows the analysis unit to provide analysis results tailored to the user's current lifestyle and areas of interest. 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 the user's lifestyle data into a generating AI and have the generating AI perform the customization of the analysis results.

[0040] The analysis unit can improve analysis accuracy by considering the user's geographical location information during analysis. For example, if the user is in a specific region, the analysis unit can reflect local specialties and famous dishes of that region in the analysis results. Furthermore, when the user inputs information about their travel destination, the analysis unit can also reflect information about tourist attractions and events in that region in the analysis results. Additionally, if the user is staying in a specific region, the analysis unit can reflect information about transportation and accommodations in that region in the analysis results. This allows for improved analysis accuracy based on geographical location information. 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 the user's geographical location data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0041] The analysis unit can analyze a user's social media activity during analysis and provide relevant analysis results. For example, the analysis unit can suggest the next travel destination or dining option based on information about travel destinations and restaurants that the user has shared on social media. The analysis unit can also suggest relevant travel destinations and dining options based on information about accounts that the user follows on social media. Furthermore, the analysis unit can suggest travel destinations and dining options that the user might be interested in based on posts that the user has "liked" or commented on on social media. This allows the analysis unit to provide relevant analysis results based on social media activity. 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 the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant analysis results.

[0042] The suggestion unit can improve the accuracy of its suggestions by referring to the user's past travel history. For example, the suggestion unit can customize suggestions based on the user's past visits to places and food preferences. It can also provide suggestions related to specific seasons or events based on the user's past travel history. Furthermore, the suggestion unit can analyze the user's past travel history and suggest the most suitable travel plan. This allows for improved suggestion accuracy based on past travel history. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past travel history data into a generating AI and have the generating AI perform the task of improving the accuracy of suggestions.

[0043] The suggestion unit can customize the suggested content based on the user's current lifestyle and areas of interest. For example, the suggestion unit can suggest a suitable travel plan based on the user's current lifestyle (e.g., workload). It can also suggest relevant travel destinations and activities based on the user's areas of interest (e.g., history, nature). Furthermore, the suggestion unit can customize the optimal travel plan considering the user's current lifestyle and areas of interest. This allows the unit to provide suggestions tailored to the user's current lifestyle and areas of interest. 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 user's lifestyle data into a generating AI and have the generating AI perform the customization of the suggested content.

[0044] The suggestion unit can optimize its suggestions by considering the user's geographical location information. For example, if the user is in a specific region, the suggestion unit can reflect local specialties and famous dishes in its suggestions. Furthermore, when the user inputs information about their travel destination, the suggestion unit can also reflect information about tourist attractions and events in that region. Additionally, if the user is staying in a specific region, the suggestion unit can reflect information about transportation and accommodations in that region. This allows for the optimization of suggestions based on geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input the user's geographical location data into a generating AI and have the generating AI optimize the suggestions.

[0045] The suggestion unit can analyze the user's social media activity when making suggestions and provide relevant suggestions. For example, the suggestion unit can suggest the next travel destination or dining option based on information about travel destinations and restaurants the user has shared on social media. It can also suggest relevant travel destinations and dining options based on information about accounts the user follows on social media. Furthermore, the suggestion unit can suggest travel destinations and dining options that the user might be interested in based on posts the user has liked or commented on on social media. This allows the suggestion unit to provide relevant suggestions based on social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI provide relevant suggestions.

[0046] The reservation management unit can improve the accuracy of reservations by referring to the user's past reservation history during reservation management. For example, the reservation management unit can suggest the next reservation option based on the accommodation and transportation methods the user has used in the past. The reservation management unit can also provide reservation options related to specific seasons or events based on the user's past reservation history. Furthermore, the reservation management unit can analyze the user's past reservation history and suggest the most suitable reservation option. This allows for improved reservation accuracy based on past reservation history. Some or all of the above processes in the reservation management unit may be performed using AI, for example, or not using AI. For example, the reservation management unit can input the user's past reservation history data into a generating AI and have the generating AI perform the task of improving reservation accuracy.

[0047] The reservation management unit can customize reservation details based on the user's current lifestyle and areas of interest during the reservation management process. For example, the reservation management unit can suggest suitable reservation options based on the user's current lifestyle (e.g., workload). It can also suggest relevant accommodations and transportation options based on the user's areas of interest (e.g., history, nature). Furthermore, the reservation management unit can customize the optimal reservation options considering the user's current lifestyle and areas of interest. This allows the system to provide reservation details that are tailored to the user's current lifestyle and areas of interest. Some or all of the above processes in the reservation management unit may be performed using AI, for example, or not. For example, the reservation management unit can input user lifestyle data into a generating AI and have the generating AI perform the customization of reservation details.

[0048] The reservation management unit can optimize reservation details by considering the user's geographical location information during reservation management. For example, if the user is in a specific region, the reservation management unit will prioritize displaying accommodations and transportation options in that region. Furthermore, when the user enters regional information for their travel destination, the reservation management unit can suggest accommodations and transportation options in that region. Additionally, if the user is staying in a specific region, the reservation management unit can prioritize displaying information on accommodations and transportation options in that region. This allows for the optimization of reservation details based on geographical location information. Some or all of the above processing in the reservation management unit may be performed using AI, for example, or without AI. For example, the reservation management unit can input the user's geographical location data into a generating AI and have the generating AI perform the optimization of reservation details.

[0049] The reservation management department can analyze a user's social media activity during reservation management and provide relevant reservation options. For example, the reservation management department can suggest the next reservation option based on information about travel destinations and accommodations that the user has shared on social media. It can also suggest relevant accommodations and transportation options based on information about accounts that the user follows on social media. Furthermore, the reservation management department can suggest accommodations and transportation options that the user might be interested in based on posts that the user has liked or commented on on social media. This allows the department to provide relevant reservation options based on social media activity. Some or all of the above processes in the reservation management department may be performed using AI, for example, or not. For example, the reservation management department can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant reservation options.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The reception desk can analyze the user's past travel history and provide an auto-completion function during input. For example, it can automatically display suggested next travel destinations based on places the user has visited in the past. The reception desk can also automatically complete input content based on the user's previously entered preferences and budget. Furthermore, the reception desk can suggest input content related to specific seasons or events based on the user's past travel history. This allows for automatic completion of input content based on past travel history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past travel history data into a generating AI and have the generating AI perform automatic completion of input content.

[0052] The input field can filter input based on the user's current health status and dietary restrictions. For example, if the user has allergies, it can automatically exclude foods containing allergens. The input field can also suggest appropriate meal options if the user has a specific health condition (e.g., diabetes). Furthermore, if the user is on a diet, the input field can prioritize displaying low-calorie meal options. This allows for input tailored to the user's health status and dietary restrictions. Some or all of the above processing in the input field may be performed using AI, for example, or not. For instance, the input field can input the user's health data into a generating AI and have the generating AI perform the filtering of the input.

[0053] The reception desk can provide region-specific input options, taking into account the user's geographical location. For example, if the user is in a specific region, it can display local specialties and famous dishes as input options. The reception desk can also automatically suggest tourist attractions and events in a region when the user enters information about their travel destination. Furthermore, if the user is staying in a specific region, the reception desk can prioritize displaying information about transportation and accommodation in that region. This allows for the provision of region-specific input options based on geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI provide region-specific input options.

[0054] The reception desk can analyze a user's social media activity and suggest relevant input content. For example, it can suggest the next travel destination or dining option based on information about travel destinations and restaurants the user has shared on social media. The reception desk can also suggest relevant travel destinations and dining options based on information about accounts the user follows on social media. Furthermore, the reception desk can suggest travel destinations and dining options that the user might be interested in based on posts the user has liked or commented on on social media. In this way, relevant input content can be suggested 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 the user's social media activity data into a generating AI and have the generating AI suggest relevant input content.

[0055] The analysis unit can improve the accuracy of its analysis by referring to the user's past travel history during the analysis process. For example, it can customize the analysis results based on places the user has visited in the past and their food preferences. The analysis unit can also provide analysis results related to specific seasons or events based on the user's past travel history. Furthermore, the analysis unit can analyze the user's past travel history and suggest the most suitable travel plan. This allows for improved analysis accuracy based on past travel history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past travel history data into a generating AI and have the generating AI perform the task of improving the analysis accuracy.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk receives input from the user regarding their food preferences and travel style. For example, the user might enter information such as, "I like Japanese food, and my budget is under 10,000 yen per day." Step 2: The analysis unit analyzes the information received by the reception unit. For example, it analyzes information such as the user's food preferences, travel style, and budget to create a local gourmet travel plan that is optimal for the user. Step 3: The proposal department proposes a travel plan based on the information analyzed by the analysis department. For example, it selects undiscovered local gourmet spots, accommodations, and transportation options based on the user's preferences and budget. Step 4: The Reservation Management Department manages reservations based on the plans proposed by the Proposal Department. For example, they centrally manage reservations for accommodations and transportation based on the travel plans created.

[0058] (Example of form 2) The travel plan suggestion system according to an embodiment of the present invention is a system that uses AI to suggest the optimal regional gourmet travel plan based on the user's food preferences and travel style. This system suggests and makes reservations for everything from visits to undiscovered regional gourmet spots to accommodations and transportation. Specifically, when a user inputs their preferences and budget, the AI ​​creates the optimal regional gourmet travel plan and centrally manages reservations for accommodations and transportation. This makes it easy to plan a trip to enjoy hidden delicious spots in the region. For example, a user inputs information such as their food preferences, travel style, and budget. For example, they might input information such as, "I like Japanese food, and my budget is less than 10,000 yen per day." This information is input into the AI. Next, the AI ​​analyzes the input information and creates the optimal regional gourmet travel plan for the user. The AI ​​selects undiscovered regional gourmet spots, accommodations, and transportation based on the user's preferences and budget. For example, for a user who likes Japanese food, it suggests local famous restaurants and hidden gems, and selects accommodations and transportation according to the budget. Furthermore, the AI ​​centrally manages reservations for accommodations and transportation based on the created travel plan. Users can review suggested plans and make all necessary reservations at once. This simplifies travel planning and saves time. The system allows users to enjoy hidden gems in rural areas and makes travel planning easier. It also contributes to the revitalization of local tourism and food service industries. For example, visiting undiscovered local gourmet spots stimulates the local economy and spreads the appeal of the region. Thus, the travel plan suggestion system proposes optimal travel plans based on the user's food preferences and travel style, and manages reservations centrally.

[0059] The travel plan suggestion system according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, and a reservation management unit. The reception unit accepts input from the user regarding their food preferences and travel style. For example, the reception unit allows the user to input information such as their food preferences, travel style, and budget. For example, the user might input information such as "I like Japanese food, and my budget is less than 10,000 yen per day." The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes information such as the user's food preferences, travel style, and budget to create a local gourmet travel plan that is optimal for the user. The suggestion unit proposes a travel plan based on the information analyzed by the analysis unit. For example, the suggestion unit selects local undiscovered gourmet spots, accommodations, and transportation based on the user's preferences and budget. For example, for a user who likes Japanese food, it suggests local famous restaurants and hidden gems, and selects accommodations and transportation according to the budget. The reservation management unit manages reservations based on the plans proposed by the suggestion unit. For example, the reservation management unit centrally manages reservations for accommodations and transportation based on the created travel plan. As a result, the travel plan suggestion system according to the embodiment can suggest the optimal travel plan based on the user's food preferences and travel style, and centrally manage reservations.

[0060] The reception desk receives input from users regarding their food preferences and travel styles. Specifically, users can access the system and input information such as their food preferences, travel style, and budget through the interface. For example, when a user inputs information such as "I like Japanese food and my budget is less than 10,000 yen per day," the reception desk records this information in detail and stores it in the database. Furthermore, the reception desk can verify the information entered by the user and gather more detailed information by asking additional questions as needed. For example, if a user is interested in the cuisine of a particular region, the reception desk can ask about specific dishes from that region and preferred cooking methods. Regarding travel styles, the reception desk can also confirm whether the user prefers active sightseeing or a relaxing stay, and use this information to help create travel plans. The reception desk processes user input in real time and prepares it to be sent to the analysis department. This allows the reception desk to accurately understand user needs and improve the accuracy and efficiency of the entire system.

[0061] The analysis department analyzes the information received by the reception department. Specifically, it analyzes information such as the user's food preferences, travel style, and budget in detail to create the optimal local gourmet travel plan for the user. The analysis department uses AI to analyze the user's input data and generates the optimal plan based on past data and trend information. For example, for a user who likes Japanese food, it identifies local famous restaurants and hidden gems by referring to the ratings and reviews of past travelers. Also, when selecting accommodations and transportation according to the budget, it prioritizes suggesting cost-effective options. The analysis department simulates multiple plans based on the user's preferences and budget and selects the most suitable plan. Furthermore, the analysis department can customize the plan to meet individual needs by considering the user's past travel history and ratings. As a result, the analysis department can provide users with highly accurate and personalized travel plans.

[0062] The Proposal Department proposes travel plans based on information analyzed by the Analysis Department. Specifically, it selects local, undiscovered gourmet spots, accommodations, and transportation options based on the user's preferences and budget. Based on the data provided by the Analysis Department, the Proposal Department creates the optimal travel plan for the user and presents it to the user through the interface. For example, for a user who likes Japanese food, it will suggest local famous restaurants and hidden gems, and select accommodations and transportation options that fit the budget. The Proposal Department allows users to review the proposed plan and make requests for changes or additions as needed. For example, if a user wants to try a particular dish or visit a specific tourist destination, the plan can be adjusted according to their request. The Proposal Department collects user feedback and accumulates data to improve the accuracy and satisfaction of the suggestions. This enables the Proposal Department to provide users with the optimal travel plan and realize a highly satisfying travel experience.

[0063] The Reservation Management Department manages reservations based on plans proposed by the Proposal Department. Specifically, it centrally manages reservations for accommodations and transportation based on the created travel plans. The Reservation Management Department has a system that allows it to review proposed plans and automatically perform various reservation procedures. For example, when a user approves a proposed plan, the Reservation Management Department checks the availability of accommodations and transportation and confirms the reservation. The Reservation Management Department also handles changes and cancellations of reservations, and can flexibly respond to user requests. Furthermore, the Reservation Management Department updates reservation status in real time, providing users with the latest information. For example, when a reservation is confirmed, it sends a confirmation email to the user with detailed travel information. It also sends reminder notifications the day before and on the day of the trip to help users start their trip smoothly. In this way, the Reservation Management Department can centrally manage users' travel plans and provide a smooth travel experience.

[0064] The reception desk can estimate the user's emotions and dynamically change the design of the input interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick information entry. This allows the input interface to be optimized according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines 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 user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0065] The reception desk can analyze the user's past travel history and provide an auto-completion function during input. For example, the reception desk can automatically display suggested next travel destinations based on places the user has visited in the past. It can also automatically complete input content based on preferences and budgets previously entered by the user. Furthermore, the reception desk can suggest input content related to specific seasons or events based on the user's past travel history. This allows for automatic completion of input content based on past travel history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past travel history data into a generating AI and have the generating AI perform automatic completion of input content.

[0066] The input field can filter input based on the user's current health status and dietary restrictions. For example, if the user has allergies, the input field can automatically exclude foods containing allergens. It can also suggest appropriate meal options if the user has a specific health condition (e.g., diabetes). Furthermore, if the user is on a diet, the input field can prioritize displaying low-calorie meal options. This allows for input tailored to the user's health status and dietary restrictions. Some or all of the above processing in the input field may be performed using AI, for example, or not. For instance, the input field can input the user's health data into a generating AI and have the generating AI perform the filtering of the input.

[0067] The reception desk can estimate the user's emotions and determine the priority of inputs based on the estimated emotions. For example, if the user is stressed, the reception desk can prioritize displaying important input items and postpone other items. If the user is relaxed, the reception desk can also sequentially display detailed input items, allowing the user to input freely. Furthermore, if the user is in a hurry, the reception desk can display only the most important input items, allowing for quick completion. This optimizes the priority of inputs according to the user'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 user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0068] The reception desk can provide region-specific input options, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can display local specialties and famous dishes as input options. The reception desk can also automatically suggest tourist attractions and events in a region when the user enters information about their travel destination. Furthermore, if the user is staying in a specific region, the reception desk can prioritize displaying information about transportation and accommodation in that region. This allows for the provision of region-specific input options based on geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI provide region-specific input options.

[0069] The reception desk can analyze a user's social media activity and suggest relevant input content. For example, it can suggest the next travel destination or dining option based on information about travel destinations and restaurants the user has shared on social media. It can also suggest relevant travel destinations and dining options based on information about accounts the user follows on social media. Furthermore, it can suggest travel destinations and dining options that the user might be interested in based on posts the user has liked or commented on on social media. This allows the reception desk to suggest relevant input content 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 the user's social media activity data into a generating AI and have the generating AI suggest relevant input content.

[0070] The analysis unit can estimate the user's emotions and dynamically adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can use a simple and rapid analysis algorithm. If the user is relaxed, the analysis unit can also use a more detailed analysis algorithm, providing more options. Furthermore, if the user is in a hurry, the analysis unit can perform a rapid analysis based on the most important information. This allows the analysis algorithm to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0071] The analysis unit can improve the accuracy of the analysis by referring to the user's past travel history during the analysis. For example, the analysis unit can customize the analysis results based on the user's past visits to places and food preferences. The analysis unit can also provide analysis results related to specific seasons or events based on the user's past travel history. Furthermore, the analysis unit can analyze the user's past travel history and suggest the most suitable travel plan. This allows for improved analysis accuracy based on past travel history. 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 the user's past travel history data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0072] The analysis unit can customize the analysis results based on the user's current lifestyle and areas of interest during the analysis. For example, the analysis unit can suggest a suitable travel plan based on the user's current lifestyle (e.g., workload). It can also suggest relevant travel destinations and activities based on the user's areas of interest (e.g., history, nature). Furthermore, the analysis unit can customize the optimal travel plan considering the user's current lifestyle and areas of interest. This allows the analysis unit to provide analysis results tailored to the user's current lifestyle and areas of interest. 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 the user's lifestyle data into a generating AI and have the generating AI perform the customization of the analysis results.

[0073] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. This allows the display method of the analysis results to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. 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 the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0074] The analysis unit can improve analysis accuracy by considering the user's geographical location information during analysis. For example, if the user is in a specific region, the analysis unit can reflect local specialties and famous dishes of that region in the analysis results. Furthermore, when the user inputs information about their travel destination, the analysis unit can also reflect information about tourist attractions and events in that region in the analysis results. Additionally, if the user is staying in a specific region, the analysis unit can reflect information about transportation and accommodations in that region in the analysis results. This allows for improved analysis accuracy based on geographical location information. 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 the user's geographical location data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0075] The analysis unit can analyze a user's social media activity during analysis and provide relevant analysis results. For example, the analysis unit can suggest the next travel destination or dining option based on information about travel destinations and restaurants that the user has shared on social media. The analysis unit can also suggest relevant travel destinations and dining options based on information about accounts that the user follows on social media. Furthermore, the analysis unit can suggest travel destinations and dining options that the user might be interested in based on posts that the user has "liked" or commented on on social media. This allows the analysis unit to provide relevant analysis results based on social media activity. 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 the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant analysis results.

[0076] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is presented based on the estimated emotions. For example, if the user is nervous, the suggestion unit can provide simple and highly visible suggestions. If the user is relaxed, the suggestion unit can also provide suggestions that include detailed information. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that get straight to the point. This allows the presentation of suggestions to be optimized according to the user'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 suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0077] The suggestion unit can improve the accuracy of its suggestions by referring to the user's past travel history. For example, the suggestion unit can customize suggestions based on the user's past visits to places and food preferences. It can also provide suggestions related to specific seasons or events based on the user's past travel history. Furthermore, the suggestion unit can analyze the user's past travel history and suggest the most suitable travel plan. This allows for improved suggestion accuracy based on past travel history. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past travel history data into a generating AI and have the generating AI perform the task of improving the accuracy of suggestions.

[0078] The suggestion unit can customize the suggested content based on the user's current lifestyle and areas of interest. For example, the suggestion unit can suggest a suitable travel plan based on the user's current lifestyle (e.g., workload). It can also suggest relevant travel destinations and activities based on the user's areas of interest (e.g., history, nature). Furthermore, the suggestion unit can customize the optimal travel plan considering the user's current lifestyle and areas of interest. This allows the unit to provide suggestions tailored to the user's current lifestyle and areas of interest. 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 user's lifestyle data into a generating AI and have the generating AI perform the customization of the suggested content.

[0079] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize displaying important suggestions and postpone others. If the user is relaxed, the suggestion unit can sequentially display detailed suggestions, allowing the user to choose freely. Furthermore, if the user is in a hurry, the suggestion unit can display only the most important suggestions, allowing for quick selection. This optimizes the priority of suggestions according to the user'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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The suggestion unit can optimize its suggestions by considering the user's geographical location information. For example, if the user is in a specific region, the suggestion unit can reflect local specialties and famous dishes in its suggestions. Furthermore, when the user inputs information about their travel destination, the suggestion unit can also reflect information about tourist attractions and events in that region. Additionally, if the user is staying in a specific region, the suggestion unit can reflect information about transportation and accommodations in that region. This allows for the optimization of suggestions based on geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input the user's geographical location data into a generating AI and have the generating AI optimize the suggestions.

[0081] The suggestion unit can analyze the user's social media activity when making suggestions and provide relevant suggestions. For example, the suggestion unit can suggest the next travel destination or dining option based on information about travel destinations and restaurants the user has shared on social media. It can also suggest relevant travel destinations and dining options based on information about accounts the user follows on social media. Furthermore, the suggestion unit can suggest travel destinations and dining options that the user might be interested in based on posts the user has liked or commented on on social media. This allows the suggestion unit to provide relevant suggestions based on social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI provide relevant suggestions.

[0082] The reservation management unit can estimate the user's emotions and dynamically change the reservation management interface based on the estimated emotions. For example, if the user is stressed, the reservation management unit can provide a simple and intuitive interface and minimize the reservation process. If the user is relaxed, the reservation management unit can also provide detailed reservation options and suggest a customizable reservation method. Furthermore, if the user is in a hurry, the reservation management unit can prioritize voice input to allow for quick reservation completion. This allows the reservation management interface to be optimized according to the user'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 reservation management unit may be performed using AI or not. For example, the reservation management unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The reservation management unit can improve the accuracy of reservations by referring to the user's past reservation history during reservation management. For example, the reservation management unit can suggest the next reservation option based on the accommodation and transportation methods the user has used in the past. The reservation management unit can also provide reservation options related to specific seasons or events based on the user's past reservation history. Furthermore, the reservation management unit can analyze the user's past reservation history and suggest the most suitable reservation option. This allows for improved reservation accuracy based on past reservation history. Some or all of the above processes in the reservation management unit may be performed using AI, for example, or not using AI. For example, the reservation management unit can input the user's past reservation history data into a generating AI and have the generating AI perform the task of improving reservation accuracy.

[0084] The reservation management unit can customize reservation details based on the user's current lifestyle and areas of interest during the reservation management process. For example, the reservation management unit can suggest suitable reservation options based on the user's current lifestyle (e.g., workload). It can also suggest relevant accommodations and transportation options based on the user's areas of interest (e.g., history, nature). Furthermore, the reservation management unit can customize the optimal reservation options considering the user's current lifestyle and areas of interest. This allows the system to provide reservation details that are tailored to the user's current lifestyle and areas of interest. Some or all of the above processes in the reservation management unit may be performed using AI, for example, or not. For example, the reservation management unit can input user lifestyle data into a generating AI and have the generating AI perform the customization of reservation details.

[0085] The reservation management unit can estimate the user's emotions and determine the priority of reservation management based on the estimated emotions. For example, if the user is stressed, the reservation management unit can prioritize displaying important reservation items and postpone other items. If the user is relaxed, the reservation management unit can also sequentially display detailed reservation items, allowing the user to choose freely. Furthermore, if the user is in a hurry, the reservation management unit can display only the most important reservation items, allowing for quick selection. This optimizes the priority of reservation management according to the user'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 reservation management unit may be performed using AI, or not using AI. For example, the reservation management unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0086] The reservation management unit can optimize reservation details by considering the user's geographical location information during reservation management. For example, if the user is in a specific region, the reservation management unit will prioritize displaying accommodations and transportation options in that region. Furthermore, when the user enters regional information for their travel destination, the reservation management unit can suggest accommodations and transportation options in that region. Additionally, if the user is staying in a specific region, the reservation management unit can prioritize displaying information on accommodations and transportation options in that region. This allows for the optimization of reservation details based on geographical location information. Some or all of the above processing in the reservation management unit may be performed using AI, for example, or without AI. For example, the reservation management unit can input the user's geographical location data into a generating AI and have the generating AI perform the optimization of reservation details.

[0087] The reservation management department can analyze a user's social media activity during reservation management and provide relevant reservation options. For example, the reservation management department can suggest the next reservation option based on information about travel destinations and accommodations that the user has shared on social media. It can also suggest relevant accommodations and transportation options based on information about accounts that the user follows on social media. Furthermore, the reservation management department can suggest accommodations and transportation options that the user might be interested in based on posts that the user has liked or commented on on social media. This allows the department to provide relevant reservation options based on social media activity. Some or all of the above processes in the reservation management department may be performed using AI, for example, or not. For example, the reservation management department can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant reservation options.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The analysis unit can estimate the user's emotions and dynamically adjust the accuracy of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can perform a quick and concise analysis to avoid burdening the user. If the user is relaxed, the analysis unit can perform a detailed analysis and provide more options. Furthermore, if the user is in a hurry, the analysis unit can perform a quick analysis based on the most important information. This allows the accuracy of the analysis results to be optimized according to the user's emotions. Emotion estimation is achieved using, for example, 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-described processes in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0090] The suggestion unit can estimate the user's emotions and dynamically adjust the way the suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can provide suggestions that include detailed information. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that get straight to the point. This allows the presentation of suggestions to be optimized according to the user's emotions. Emotion estimation is achieved, for example, using 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 user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0091] The reservation management unit can estimate the user's emotions and dynamically change the reservation management interface based on the estimated emotions. For example, if the user is stressed, the reservation management unit can provide a simple and intuitive interface and minimize the reservation process. If the user is relaxed, the reservation management unit can provide detailed reservation options and suggest a customizable reservation method. Furthermore, if the user is in a hurry, the reservation management unit can prioritize voice input to allow for quick reservation completion. This allows the reservation management interface to be optimized according to the user's emotions. Emotion estimation is achieved using, for example, 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 reservation management unit may be performed using AI or not. For example, the reservation management unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0092] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize important suggestions and postpone others. If the user is relaxed, the suggestion unit can sequentially display detailed suggestions, allowing the user to choose freely. Furthermore, if the user is in a hurry, the suggestion unit can display only the most important suggestions, allowing for quick selection. This optimizes the priority of suggestions according to the user's emotions. Emotion estimation is achieved using, for example, 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 user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0093] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. This allows the display method of the analysis results to be optimized according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generative AI. The 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 user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0094] The reception desk can analyze the user's past travel history and provide an auto-completion function during input. For example, it can automatically display suggested next travel destinations based on places the user has visited in the past. The reception desk can also automatically complete input content based on the user's previously entered preferences and budget. Furthermore, the reception desk can suggest input content related to specific seasons or events based on the user's past travel history. This allows for automatic completion of input content based on past travel history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past travel history data into a generating AI and have the generating AI perform automatic completion of input content.

[0095] The input field can filter input based on the user's current health status and dietary restrictions. For example, if the user has allergies, it can automatically exclude foods containing allergens. The input field can also suggest appropriate meal options if the user has a specific health condition (e.g., diabetes). Furthermore, if the user is on a diet, the input field can prioritize displaying low-calorie meal options. This allows for input tailored to the user's health status and dietary restrictions. Some or all of the above processing in the input field may be performed using AI, for example, or not. For instance, the input field can input the user's health data into a generating AI and have the generating AI perform the filtering of the input.

[0096] The reception desk can provide region-specific input options, taking into account the user's geographical location. For example, if the user is in a specific region, it can display local specialties and famous dishes as input options. The reception desk can also automatically suggest tourist attractions and events in a region when the user enters information about their travel destination. Furthermore, if the user is staying in a specific region, the reception desk can prioritize displaying information about transportation and accommodation in that region. This allows for the provision of region-specific input options based on geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI provide region-specific input options.

[0097] The reception desk can analyze a user's social media activity and suggest relevant input content. For example, it can suggest the next travel destination or dining option based on information about travel destinations and restaurants the user has shared on social media. The reception desk can also suggest relevant travel destinations and dining options based on information about accounts the user follows on social media. Furthermore, the reception desk can suggest travel destinations and dining options that the user might be interested in based on posts the user has liked or commented on on social media. In this way, relevant input content can be suggested 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 the user's social media activity data into a generating AI and have the generating AI suggest relevant input content.

[0098] The analysis unit can improve the accuracy of its analysis by referring to the user's past travel history during the analysis process. For example, it can customize the analysis results based on places the user has visited in the past and their food preferences. The analysis unit can also provide analysis results related to specific seasons or events based on the user's past travel history. Furthermore, the analysis unit can analyze the user's past travel history and suggest the most suitable travel plan. This allows for improved analysis accuracy based on past travel history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past travel history data into a generating AI and have the generating AI perform the task of improving the analysis accuracy.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The reception desk receives input from the user regarding their food preferences and travel style. For example, the user might enter information such as, "I like Japanese food, and my budget is under 10,000 yen per day." Step 2: The analysis unit analyzes the information received by the reception unit. For example, it analyzes information such as the user's food preferences, travel style, and budget to create a local gourmet travel plan that is optimal for the user. Step 3: The proposal department proposes a travel plan based on the information analyzed by the analysis department. For example, it selects undiscovered local gourmet spots, accommodations, and transportation options based on the user's preferences and budget. Step 4: The Reservation Management Department manages reservations based on the plans proposed by the Proposal Department. For example, they centrally manage reservations for accommodations and transportation based on the travel plans created.

[0101] 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.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] 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.

[0104] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and reservation management 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 reception device 38 of the smart device 14 and receives input of the user's food preferences and travel style. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a travel plan based on the analyzed information. The reservation management unit is implemented by, for example, the control unit 46A of the smart device 14 and manages reservations based on the proposed plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.).

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] 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.

[0120] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and reservation management unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives input of the user's food preferences and travel style. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a travel plan based on the analyzed information. The reservation management unit is implemented by, for example, the control unit 46A of the smart glasses 214 and manages reservations based on the proposed plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] 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.

[0136] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and reservation management 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 microphone 238 of the headset terminal 314 and receives input of the user's food preferences and travel style. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a travel plan based on the analyzed information. The reservation management unit is implemented by, for example, the control unit 46A of the headset terminal 314 and manages reservations based on the proposed plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] 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.

[0153] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and reservation management 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 microphone 238 of the robot 414 and receives input of the user's food preferences and travel style. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a travel plan based on the analyzed information. The reservation management unit is implemented by, for example, the control unit 46A of the robot 414 and manages reservations based on the proposed plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] (Note 1) A reception area that accepts user input about their food preferences and travel style, An analysis unit that analyzes the information received by the reception unit, A proposal unit proposes a travel plan based on the information analyzed by the aforementioned analysis unit, The system includes a reservation management unit that manages reservations based on the plan proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and dynamically changes the design of the input interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It analyzes the user's past travel history and provides an auto-completion function during input. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is Filter input based on the user's current health status and dietary restrictions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and determines the priority of inputs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Provide region-specific input options, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyzes users' social media activity and suggests relevant inputs. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, It estimates the user's emotions and dynamically adjusts the analysis algorithm based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The system improves analysis accuracy by referencing the user's past travel history during the analysis process. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, the results are customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The analysis accuracy is improved by taking into account the user's geographical location information during analysis. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the system analyzes the user's social media activity and provides relevant analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way the suggested content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, We improve the accuracy of suggestions by referencing the user's past travel history during the suggestion process. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, customize the content based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, It estimates the user's emotions and prioritizes suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a proposal, we optimize the proposal content by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and provide relevant suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reservation management department, It estimates the user's emotions and dynamically changes the reservation management interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reservation management department, Improve the accuracy of reservations by referencing the user's past reservation history during reservation management. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reservation management department, When managing reservations, customize reservation details based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reservation management department, It estimates the user's emotions and determines the priority of reservation management based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reservation management department, When managing reservations, the system optimizes reservation details by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reservation management department, Analyze users' social media activity during reservation management and provide relevant reservation information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 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 area that accepts user input about their food preferences and travel style, An analysis unit that analyzes the information received by the reception unit, A proposal unit proposes a travel plan based on the information analyzed by the aforementioned analysis unit, The system includes a reservation management unit that manages reservations based on the plan proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and dynamically changes the design of the input interface based on those estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is It analyzes the user's past travel history and provides an auto-completion function during input. The system according to feature 1.

4. The aforementioned reception unit is Filter input based on the user's current health status and dietary restrictions. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and determines the priority of inputs based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is Provide region-specific input options, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is Analyzes users' social media activity and suggests relevant inputs. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and dynamically adjusts the analysis algorithm based on those estimated emotions. The system according to feature 1.

9. The aforementioned analysis unit, The system improves analysis accuracy by referencing the user's past travel history during the analysis process. The system according to feature 1.

10. The aforementioned analysis unit, During analysis, the results are customized based on the user's current lifestyle and areas of interest. The system according to feature 1.

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