system

Abstract

The system according to this embodiment aims to automatically generate optimal travel routes and schedules based on the traveler's preferences and conditions. [Solution] The system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives input from the traveler regarding their preferences and conditions. The generation unit analyzes the information received from the reception unit and generates the optimal travel route and schedule. The provision unit provides the plan generated by the generation unit to the traveler.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 conventional technology, there is a problem that it takes a lot of time and effort for a traveler to plan an optimal travel route and schedule based on their preferences and conditions.

[0005] The system according to the embodiment aims to automatically generate an optimal travel route and schedule based on the preferences and conditions of the traveler.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit inputs the preferences and conditions of the traveler. The generation unit analyzes the information input by the reception unit and generates an optimal travel route and schedule. The provision unit provides the plan generated by the generation unit to the traveler. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate optimal travel routes and schedules based on the traveler's preferences and conditions. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 generation system according to an embodiment of the present invention is a system that automatically generates optimal travel routes and schedules by utilizing an AI agent to solve the various planning challenges faced by travelers. This travel plan generation system takes the traveler's preferences and conditions as input, and the AI ​​agent analyzes this information to generate the optimal travel route and schedule. The generated plan is provided to the traveler through a smartphone app or website, allowing the traveler to enjoy their trip based on that plan. This mechanism can shorten planning time and improve the quality of travel. For example, the traveler inputs information such as desired tourist destinations, budget, travel duration, and means of transportation. This information is transmitted to the AI ​​agent through a smartphone app or website. Next, the AI ​​agent analyzes the input information. Based on the traveler's preferences and conditions, the AI ​​agent generates the optimal travel route and schedule. For example, it creates an optimal plan considering the order of desired tourist destinations, means of transportation, and length of stay. The generated plan is provided to the traveler through a smartphone app or website. The traveler can enjoy their trip based on the provided plan. For example, they can visit tourist destinations suggested by the AI ​​agent or use suggested means of transportation. This mechanism allows travelers to shorten planning time. This reduces the time spent planning trips, allowing travelers to dedicate more time to the trip itself. It also improves the quality of the trip. Because the AI ​​agent provides optimal plans based on the traveler's preferences and circumstances, travelers can enjoy a more satisfying trip. For example, for family trips, it can suggest plans that include tourist attractions and activities that children will enjoy. For business trips, it can suggest efficient travel routes and schedules. In this way, providing optimal plans tailored to the traveler's needs improves the quality of the trip. Therefore, the travel plan generation system can shorten planning time and improve the quality of the trip by automatically generating and providing optimal travel plans based on the traveler's preferences and circumstances.

[0029] The travel plan generation system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives input from the traveler's preferences and conditions. These preferences and conditions include, but are not limited to, budget, type of travel destination, and type of accommodation. The reception unit can receive input from the traveler's preferences and conditions, for example, through a smartphone app or website. The generation unit analyzes the information entered by the reception unit and generates the optimal travel route and schedule. The generation unit generates the optimal plan, for example, based on the traveler's preferences and conditions, taking into account the order of tourist destinations to be visited, means of transportation, length of stay, etc. The generation unit can use an AI agent to generate the optimal travel plan based on the traveler's preferences and conditions. Some or all of the above-described processing in the generation unit may be performed using AI or not. The provision unit provides the plan generated by the generation unit to the traveler. The provision unit can provide the generated plan to the traveler, for example, through a smartphone app or website. The provision unit enables the traveler to enjoy their trip based on the generated plan. This allows the travel plan generation system to automatically generate and provide optimal travel plans based on the traveler's preferences and conditions, thereby reducing planning time and improving the quality of the trip.

[0030] The reception desk inputs the traveler's preferences and requirements. These preferences and requirements include, but are not limited to, budget, type of destination, and type of accommodation. The reception desk can input these preferences and requirements through, for example, a smartphone app or website. Specifically, travelers can input details such as budget, type of destination (e.g., beach resort, mountainous area, city sightseeing), type of accommodation (e.g., hotel, guesthouse, campsite), duration of trip, number of companions, and preferences for specific sightseeing spots or activities through the smartphone app or website interface. Furthermore, travelers can also input their dietary preferences (e.g., vegetarian, gluten-free) and their desire to participate in specific events (e.g., music festival, sporting event). The reception desk collects this information and stores it in a database. The collected information is managed as individual profiles for each traveler and used for subsequent processing. The reception desk prioritizes user-friendliness of the user interface, employing an intuitive design to ensure that travelers can input information without stress. Furthermore, the reception desk also has a function to display a confirmation screen of the entered information to ensure its accuracy, prompting travelers to reconfirm the information. This allows the reception desk to accurately understand the diverse needs and wishes of travelers and provide the information necessary for processing in the next step, the generation desk.

[0031] The generation unit analyzes the information entered by the reception unit and generates the optimal travel route and schedule. For example, the generation unit generates the optimal plan considering the traveler's preferences and conditions, such as the order of tourist destinations to visit, means of transportation, and length of stay. The generation unit can use an AI agent to generate the optimal travel plan based on the traveler's preferences and conditions. Specifically, the generation unit uses the traveler's input information to have the AI ​​agent comprehensively analyze past travel data, tourist destination ratings, transportation options, and accommodation availability. For example, the AI ​​agent considers the congestion level and weather forecast of the tourist destinations the traveler wishes to visit to determine the optimal order of visits. Regarding means of transportation, it investigates public transport timetables and the availability of rental cars to propose an efficient travel route. Furthermore, it adjusts the length of stay based on the recommended stay time at each tourist destination and the traveler's interests. Based on this information, the generation unit automatically generates the travel plan that will give the traveler the highest level of satisfaction. Some or all of the above processing in the generation unit may be performed using AI or not. For example, if AI is not used, the plan can be generated based on pre-set rules or algorithms. This allows the generation unit to respond to the diverse needs of travelers and provide individually customized travel plans.

[0032] The service provider delivers the plans generated by the generation unit to travelers. The service provider can deliver the generated plans to travelers, for example, through a smartphone app or website. Specifically, the service provider displays the generated travel plans in a visually clear and easy-to-understand manner, making them easily comprehensible to travelers. For example, the travel plan is displayed in a timeline format divided by day, and includes detailed information on each tourist destination and activity, transportation, and accommodation. Furthermore, the service provider displays planned tourist destinations and travel routes on a map, making it easier for travelers to grasp the overall flow. The service provider also includes plan sharing and printing functions to enable travelers to enjoy their trip based on the generated plan. For example, travelers can share the generated plan with family and friends and exchange opinions. Travelers can also download the plan in PDF format, print it, and carry it with them. In addition, the service provider provides a function that allows for real-time changes and updates to the plan during the trip. For example, it allows travelers to flexibly adjust the plan in response to situations such as sudden weather changes or transportation delays. This enables the service provider to create a user-friendly and convenient travel plan service for travelers, improving the quality of their trips.

[0033] The reception desk can input travelers' preferences and requirements through a smartphone app or website. For example, travelers can use a smartphone app to input information such as desired tourist destinations, budget, travel duration, and mode of transportation. The reception desk can also receive similar information from travelers via a website. For example, travelers can use a smartphone app to select desired tourist destinations, enter a budget, and set the travel duration. The reception desk can also receive travelers via the website to select modes of transportation and accommodation types. This allows travelers to easily input their preferences and requirements through a smartphone app or website. The smartphone app and website may include, but are not limited to, compatible operating systems and the functions they offer. Some or all of the above processing at the reception desk may be performed using AI or not. For example, the reception desk sends the information entered by the traveler to an AI agent, which analyzes the information and generates an optimal plan.

[0034] The generation unit can generate an optimal plan based on the traveler's preferences and conditions, taking into account the order of tourist destinations to visit, modes of transportation, and length of stay. For example, the generation unit can determine the order of tourist destinations to visit, select the most suitable mode of transportation, and set the length of stay based on information entered by the traveler. The generation unit can use an AI agent to generate an optimal travel plan based on the traveler's preferences and conditions. For example, the generation unit can have the AI ​​agent analyze the traveler's input information and optimize the order of tourist destinations to visit. The generation unit can also have the AI ​​agent select the mode of transportation and set the length of stay. For example, the generation unit can have the AI ​​agent determine the optimal order of tourist destinations, select the most efficient mode of transportation, and optimize the length of stay based on the traveler's preferences and conditions. This improves the quality of travel by generating an optimal travel plan based on the traveler's preferences and conditions. The order of tourist destinations to visit, modes of transportation, and length of stay include, but are not limited to, popularity, type of transportation, and methods for optimizing the length of stay. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit sends the information entered by the traveler to an AI agent, which then analyzes the information and generates the optimal plan.

[0035] The service provider can provide travelers with plans generated through smartphone apps or websites. For example, the service provider can enable travelers to use a smartphone app to review the generated plans and plan their trip. The service provider can also enable travelers to access similar information through a website. For example, the service provider can enable travelers to use a smartphone app to review the generated plans and identify tourist destinations and modes of transportation they wish to visit. The service provider can also enable travelers to access the generated plans through a website and identify information on length of stay and accommodations. This makes it easy for travelers to receive plans generated through smartphone apps or websites. Smartphone apps and websites include, but are not limited to, compatible operating systems and the functions they offer. Some or all of the above-described processes in the service provider may or may not be performed using AI. For example, the service provider can send the generated plans to an AI agent, which can then analyze the information and provide it to travelers.

[0036] The reception desk can analyze a traveler's past travel history and suggest the optimal input method. For example, the reception desk can automatically display preferences and conditions that the traveler has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the traveler has used in the past. Furthermore, the reception desk can predict and suggest preferences and conditions related to specific seasons or events based on the traveler's past travel history. This allows for a more personalized input method by analyzing the traveler's past travel history. Past travel history includes, but is not limited to, places visited, length of stay, and purpose of travel. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can send the traveler's past travel history to an AI agent, which can analyze the information and suggest the optimal input method.

[0037] The reception desk can adjust the priority of input fields based on the traveler's current location and time of day. For example, the reception desk can prioritize displaying nearby tourist attractions and activities based on the traveler's current location. It can also suggest appropriate modes of transportation and schedules based on the time of day the traveler is entering their information. Furthermore, if the traveler enters their information during a specific event or festival, the reception desk can prioritize displaying information related to that event. This allows for the provision of more relevant information by adjusting the priority of input fields according to the traveler's current situation. Current location and time of day include, but are not limited to, GPS data and time-based priority changes. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk sends the traveler's current location and time of day to an AI agent, which analyzes the information and adjusts the priority of input fields.

[0038] The reception desk can analyze travelers' social media activity and automatically input relevant travel information. For example, it can suggest relevant tourist destinations and activities based on past travel photos and posts shared by travelers on social media. It can also analyze posts from travel influencers followed by travelers and automatically input relevant travel information. Furthermore, it can automatically input relevant travel information based on events and festivals that travelers have shown interest in on social media. This allows for the automatic input of relevant travel information by analyzing travelers' social media activity, saving manual input effort. Social media activity includes, but is not limited to, the content of posts, the number of likes, and follower reactions. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can send the traveler's social media activity to an AI agent, which can analyze the information and automatically input relevant travel information.

[0039] The reception desk can provide an optimal input interface by considering the traveler's device information. For example, if the traveler is using a smartphone, the reception desk can provide an input interface that matches the screen size. It can also provide an input interface optimized for a larger screen if the traveler is using a tablet. Furthermore, if the traveler is using a smartwatch, the reception desk can provide a simple and highly visible input interface. This improves the convenience of input by providing an optimal input interface according to the traveler's device information. Device information includes, but is not limited to, the device type, OS, and screen size. Some or all of the processing described above in the reception desk may be performed using AI or not. For example, the reception desk can send the traveler's device information to an AI agent, which can then analyze the information and provide an optimal input interface.

[0040] The generation unit can generate more personalized plans by referring to the traveler's past travel history. For example, the generation unit can suggest new relevant tourist destinations and activities based on the tourist destinations and activities the traveler has visited in the past. The generation unit can also generate plans related to specific seasons or events from the traveler's past travel history. Furthermore, the generation unit can analyze the traveler's past travel history and generate the most satisfying plan. This allows for the provision of more personalized plans by referring to the traveler's past travel history. Past travel history includes, but is not limited to, places visited, length of stay, and purpose of travel. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit sends the traveler's past travel history to an AI agent, which analyzes the information and generates a more personalized plan.

[0041] The generation unit can adjust the plan in real time, taking into account the traveler's current situation and weather information. For example, the generation unit adjusts indoor and outdoor activities based on weather information at the traveler's current location. It can also suggest appropriate activities considering the traveler's current situation (e.g., fatigue level and health condition). Furthermore, the generation unit can suggest the optimal travel route considering the traveler's current mode of transportation and traffic conditions. This allows for the provision of a more appropriate plan by adjusting the plan according to the traveler's current situation and weather information. Current situation and weather information includes, but is not limited to, real-time weather forecasts and current location conditions. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit sends the traveler's current situation and weather information to an AI agent, which analyzes the information and adjusts the plan in real time.

[0042] The generation unit can analyze a traveler's social media activity and incorporate relevant destinations and activities into the plan. For example, it can suggest relevant destinations and activities based on past travel photos and posts shared by the traveler on social media. It can also analyze posts from travel influencers followed by the traveler and incorporate relevant destinations and activities into the plan. Furthermore, it can incorporate relevant destinations and activities based on events and festivals that the traveler has shown interest in on social media. This allows for the incorporation of relevant destinations and activities into the plan by analyzing the traveler's social media activity, thereby providing a more personalized plan. Social media activity includes, but is not limited to, the content of posts, the number of likes, and follower reactions. Some or all of the processing described above in the generation unit may be performed using AI or not. For example, the generation unit could send the traveler's social media activity to an AI agent, which could then analyze the information and incorporate relevant destinations and activities into the plan.

[0043] The generation unit can generate an optimal plan by taking into account the traveler's device information. For example, if the traveler is using a smartphone, the generation unit will generate a plan that matches the screen size. It can also generate a plan optimized for a larger screen if the traveler is using a tablet. Furthermore, if the traveler is using a smartwatch, the generation unit can generate a concise and highly visible plan. This improves convenience by providing an optimal plan according to the traveler's device information. Device information includes, but is not limited to, the device type, OS, and screen size. Some or all of the processing described above in the generation unit may be performed using AI or not. For example, the generation unit sends the traveler's device information to an AI agent, which analyzes the information and generates an optimal plan.

[0044] The service provider can optimize the content of the plans it offers by referring to travelers' past feedback. For example, the service provider can suggest relevant tourist destinations and activities based on feedback previously provided by travelers. The service provider can also optimize plans related to specific seasons or events based on travelers' past feedback. Furthermore, the service provider can analyze travelers' past feedback and provide the most satisfying plans. This allows for the provision of more satisfying plans by referring to travelers' past feedback. Past feedback includes, but is not limited to, travelers' ratings, comments, and suggestions for improvement. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can send travelers' past feedback to an AI agent, which then analyzes the information and optimizes the content of the plans it offers.

[0045] The service provider can adjust the timing of plan delivery based on the traveler's current location and time of day. For example, it can prioritize displaying nearby tourist attractions and activities based on the traveler's current location. It can also suggest appropriate modes of transportation and schedules based on the time of day the traveler enters their information. Furthermore, if the traveler enters their information during a specific event or festival, the service provider can prioritize displaying information related to that event. This allows the service provider to provide more relevant information by adjusting the timing of plan delivery according to the traveler's current situation. Current location and time of day include, but are not limited to, GPS data and changes in priority based on time of day. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can send the traveler's current location and time of day to an AI agent, which can analyze the information and adjust the timing of plan delivery.

[0046] The service provider can analyze travelers' social media activity and add relevant information to their plans. For example, it can suggest relevant destinations and activities based on past travel photos and posts shared by travelers on social media. It can also analyze posts from travel influencers that travelers follow and add relevant destinations and activities to their plans. Furthermore, it can add relevant information to their plans based on events and festivals that travelers have shown interest in on social media. This allows the service provider to add relevant information to plans and provide more personalized itineraries by analyzing travelers' social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and follower engagement. Some or all of the processing described above by the service provider may or may not be performed using AI. For example, the service provider can send travelers' social media activity to an AI agent, which can analyze the information and add relevant information to their plans.

[0047] The service provider can select the optimal plan delivery method by considering the traveler's device information. For example, if the traveler is using a smartphone, the service provider can select a plan delivery method that matches the screen size. If the traveler is using a tablet, the service provider can also select a plan delivery method optimized for a larger screen. Furthermore, if the traveler is using a smartwatch, the service provider can select a concise and highly visible plan delivery method. This improves convenience by selecting the optimal plan delivery method according to the traveler's device information. Device information includes, but is not limited to, the device type, OS, and screen size. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can send the traveler's device information to an AI agent, which then analyzes the information and selects the optimal plan delivery method.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The reception desk can monitor the traveler's health status and propose travel plans tailored to their needs. For example, if a traveler is tired, it can prioritize suggesting relaxing tourist destinations and activities. If the traveler is healthy and active, it can also suggest plans that include activities such as hiking and sports. Furthermore, if a traveler has a specific health problem, it can suggest accommodations and transportation options that take that problem into consideration. This allows for the provision of optimal travel plans tailored to the traveler's health condition. Devices such as smartwatches and fitness trackers can be used to monitor health status.

[0050] The generation unit can create travel plans aligned with specific themes based on the traveler's hobbies and interests. For example, if a traveler is interested in history, it can suggest a plan that includes historical sites and museums. If the traveler is interested in food, it can suggest a plan that includes local specialty dishes and restaurants. Furthermore, if the traveler wants to enjoy nature, it can suggest a plan that includes nature parks and hiking trails. This allows for the provision of themed travel tailored to the traveler's hobbies and interests. Information on hobbies and interests can be obtained from the traveler's past travel history and social media activity.

[0051] The reception desk can provide the optimal input interface based on the traveler's language settings. For example, if the traveler is using English, an English input interface will be provided. If the traveler is using Japanese, a Japanese input interface can be provided. Furthermore, if the traveler is using multiple languages, an input interface supporting multiple languages ​​can be provided. This improves the convenience of input by providing the optimal input interface according to the traveler's language settings. Language setting information can be obtained from the traveler's device settings and past input history.

[0052] The service provider can analyze a traveler's past travel history and suggest destinations and activities they have previously visited. For example, if a traveler wants to revisit a destination they have previously visited, the service can suggest a plan that includes that destination. Similarly, if a traveler wants to revisit an activity they have previously participated in, the service can suggest a plan that includes that activity. Furthermore, the service can suggest new destinations and activities related to those previously visited. This allows the service to leverage a traveler's past travel history to provide more personalized plans. Information on past travel history can be obtained from the traveler's input history and social media activity.

[0053] The generation unit can adjust the plan in real time based on the traveler's current location and time of day. For example, it can adjust indoor and outdoor activities based on weather information at the traveler's current location. It can also suggest appropriate activities considering the traveler's current situation (e.g., fatigue level and health condition). Furthermore, it can suggest the optimal travel route considering the traveler's current mode of transportation and traffic conditions. This allows for the provision of more appropriate plans by adjusting the plan according to the traveler's current situation and weather information. Current situation and weather information include, but are not limited to, real-time weather forecasts and current location conditions.

[0054] The reception desk can analyze travelers' social media activity and automatically input relevant travel information. For example, it can suggest relevant tourist destinations and activities based on past travel photos and posts shared by travelers on social media. It can also analyze posts from travel influencers that travelers follow and automatically input relevant travel information. Furthermore, it can automatically input relevant travel information based on events and festivals that travelers have shown interest in on social media. This allows for the automatic input of relevant travel information by analyzing travelers' social media activity, saving manual input time. Social media activity includes, but is not limited to, the content of posts, the number of likes, and follower reactions.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The reception desk enters the traveler's preferences and requirements. These preferences and requirements include, for example, budget, type of destination, and type of accommodation. The reception desk can enter the traveler's preferences and requirements via a smartphone app or website. Step 2: The generation unit analyzes the information entered by the reception unit and generates the optimal travel route and schedule. Based on the traveler's preferences and conditions, the generation unit generates the optimal plan, taking into account the order of tourist destinations to visit, means of transportation, and length of stay. The generation unit can use an AI agent to generate the optimal travel plan based on the traveler's preferences and conditions. Step 3: The provider provides the plan generated by the generator to the traveler. The provider can provide the generated plan to the traveler via a smartphone app or website.

[0057] (Example of form 2) The travel plan generation system according to an embodiment of the present invention is a system that automatically generates optimal travel routes and schedules by utilizing an AI agent to solve the various planning challenges faced by travelers. This travel plan generation system takes the traveler's preferences and conditions as input, and the AI ​​agent analyzes this information to generate the optimal travel route and schedule. The generated plan is provided to the traveler through a smartphone app or website, allowing the traveler to enjoy their trip based on that plan. This mechanism can shorten planning time and improve the quality of travel. For example, the traveler inputs information such as desired tourist destinations, budget, travel duration, and means of transportation. This information is transmitted to the AI ​​agent through a smartphone app or website. Next, the AI ​​agent analyzes the input information. Based on the traveler's preferences and conditions, the AI ​​agent generates the optimal travel route and schedule. For example, it creates an optimal plan considering the order of desired tourist destinations, means of transportation, and length of stay. The generated plan is provided to the traveler through a smartphone app or website. The traveler can enjoy their trip based on the provided plan. For example, they can visit tourist destinations suggested by the AI ​​agent or use suggested means of transportation. This mechanism allows travelers to shorten planning time. This reduces the time spent planning trips, allowing travelers to dedicate more time to the trip itself. It also improves the quality of the trip. Because the AI ​​agent provides optimal plans based on the traveler's preferences and circumstances, travelers can enjoy a more satisfying trip. For example, for family trips, it can suggest plans that include tourist attractions and activities that children will enjoy. For business trips, it can suggest efficient travel routes and schedules. In this way, providing optimal plans tailored to the traveler's needs improves the quality of the trip. Therefore, the travel plan generation system can shorten planning time and improve the quality of the trip by automatically generating and providing optimal travel plans based on the traveler's preferences and circumstances.

[0058] The travel plan generation system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives input from the traveler's preferences and conditions. These preferences and conditions include, but are not limited to, budget, type of travel destination, and type of accommodation. The reception unit can receive input from the traveler's preferences and conditions, for example, through a smartphone app or website. The generation unit analyzes the information entered by the reception unit and generates the optimal travel route and schedule. The generation unit generates the optimal plan, for example, based on the traveler's preferences and conditions, taking into account the order of tourist destinations to be visited, means of transportation, length of stay, etc. The generation unit can use an AI agent to generate the optimal travel plan based on the traveler's preferences and conditions. Some or all of the above-described processing in the generation unit may be performed using AI or not. The provision unit provides the plan generated by the generation unit to the traveler. The provision unit can provide the generated plan to the traveler, for example, through a smartphone app or website. The provision unit enables the traveler to enjoy their trip based on the generated plan. This allows the travel plan generation system to automatically generate and provide optimal travel plans based on the traveler's preferences and conditions, thereby reducing planning time and improving the quality of the trip.

[0059] The reception desk inputs the traveler's preferences and requirements. These preferences and requirements include, but are not limited to, budget, type of destination, and type of accommodation. The reception desk can input these preferences and requirements through, for example, a smartphone app or website. Specifically, travelers can input details such as budget, type of destination (e.g., beach resort, mountainous area, city sightseeing), type of accommodation (e.g., hotel, guesthouse, campsite), duration of trip, number of companions, and preferences for specific sightseeing spots or activities through the smartphone app or website interface. Furthermore, travelers can also input their dietary preferences (e.g., vegetarian, gluten-free) and their desire to participate in specific events (e.g., music festival, sporting event). The reception desk collects this information and stores it in a database. The collected information is managed as individual profiles for each traveler and used for subsequent processing. The reception desk prioritizes user-friendliness of the user interface, employing an intuitive design to ensure that travelers can input information without stress. Furthermore, the reception desk also has a function to display a confirmation screen of the entered information to ensure its accuracy, prompting travelers to reconfirm the information. This allows the reception desk to accurately understand the diverse needs and wishes of travelers and provide the information necessary for processing in the next step, the generation desk.

[0060] The generation unit analyzes the information entered by the reception unit and generates the optimal travel route and schedule. For example, the generation unit generates the optimal plan considering the traveler's preferences and conditions, such as the order of tourist destinations to visit, means of transportation, and length of stay. The generation unit can use an AI agent to generate the optimal travel plan based on the traveler's preferences and conditions. Specifically, the generation unit uses the traveler's input information to have the AI ​​agent comprehensively analyze past travel data, tourist destination ratings, transportation options, and accommodation availability. For example, the AI ​​agent considers the congestion level and weather forecast of the tourist destinations the traveler wishes to visit to determine the optimal order of visits. Regarding means of transportation, it investigates public transport timetables and the availability of rental cars to propose an efficient travel route. Furthermore, it adjusts the length of stay based on the recommended stay time at each tourist destination and the traveler's interests. Based on this information, the generation unit automatically generates the travel plan that will give the traveler the highest level of satisfaction. Some or all of the above processing in the generation unit may be performed using AI or not. For example, if AI is not used, the plan can be generated based on pre-set rules or algorithms. This allows the generation unit to respond to the diverse needs of travelers and provide individually customized travel plans.

[0061] The service provider delivers the plans generated by the generation unit to travelers. The service provider can deliver the generated plans to travelers, for example, through a smartphone app or website. Specifically, the service provider displays the generated travel plans in a visually clear and easy-to-understand manner, making them easily comprehensible to travelers. For example, the travel plan is displayed in a timeline format divided by day, and includes detailed information on each tourist destination and activity, transportation, and accommodation. Furthermore, the service provider displays planned tourist destinations and travel routes on a map, making it easier for travelers to grasp the overall flow. The service provider also includes plan sharing and printing functions to enable travelers to enjoy their trip based on the generated plan. For example, travelers can share the generated plan with family and friends and exchange opinions. Travelers can also download the plan in PDF format, print it, and carry it with them. In addition, the service provider provides a function that allows for real-time changes and updates to the plan during the trip. For example, it allows travelers to flexibly adjust the plan in response to situations such as sudden weather changes or transportation delays. This enables the service provider to create a user-friendly and convenient travel plan service for travelers, improving the quality of their trips.

[0062] The reception desk can input travelers' preferences and requirements through a smartphone app or website. For example, travelers can use a smartphone app to input information such as desired tourist destinations, budget, travel duration, and mode of transportation. The reception desk can also receive similar information from travelers via a website. For example, travelers can use a smartphone app to select desired tourist destinations, enter a budget, and set the travel duration. The reception desk can also receive travelers via the website to select modes of transportation and accommodation types. This allows travelers to easily input their preferences and requirements through a smartphone app or website. The smartphone app and website may include, but are not limited to, compatible operating systems and the functions they offer. Some or all of the above processing at the reception desk may be performed using AI or not. For example, the reception desk sends the information entered by the traveler to an AI agent, which analyzes the information and generates an optimal plan.

[0063] The generation unit can generate an optimal plan based on the traveler's preferences and conditions, taking into account the order of tourist destinations to visit, modes of transportation, and length of stay. For example, the generation unit can determine the order of tourist destinations to visit, select the most suitable mode of transportation, and set the length of stay based on information entered by the traveler. The generation unit can use an AI agent to generate an optimal travel plan based on the traveler's preferences and conditions. For example, the generation unit can have the AI ​​agent analyze the traveler's input information and optimize the order of tourist destinations to visit. The generation unit can also have the AI ​​agent select the mode of transportation and set the length of stay. For example, the generation unit can have the AI ​​agent determine the optimal order of tourist destinations, select the most efficient mode of transportation, and optimize the length of stay based on the traveler's preferences and conditions. This improves the quality of travel by generating an optimal travel plan based on the traveler's preferences and conditions. The order of tourist destinations to visit, modes of transportation, and length of stay include, but are not limited to, popularity, type of transportation, and methods for optimizing the length of stay. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit sends the information entered by the traveler to an AI agent, which then analyzes the information and generates the optimal plan.

[0064] The service provider can provide travelers with plans generated through smartphone apps or websites. For example, the service provider can enable travelers to use a smartphone app to review the generated plans and plan their trip. The service provider can also enable travelers to access similar information through a website. For example, the service provider can enable travelers to use a smartphone app to review the generated plans and identify tourist destinations and modes of transportation they wish to visit. The service provider can also enable travelers to access the generated plans through a website and identify information on length of stay and accommodations. This makes it easy for travelers to receive plans generated through smartphone apps or websites. Smartphone apps and websites include, but are not limited to, compatible operating systems and the functions they offer. Some or all of the above-described processes in the service provider may or may not be performed using AI. For example, the service provider can send the generated plans to an AI agent, which can then analyze the information and provide it to travelers.

[0065] The reception desk can estimate the traveler's emotions and adjust the display of the input interface based on the estimated emotions. For example, if the traveler is stressed, the reception desk can provide a simple interface and minimize the input steps. If the traveler is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the traveler is in a hurry, the reception desk can prioritize voice input, allowing them to quickly enter their preferences and conditions. This allows for a more comfortable input experience by adjusting the display of the input interface according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can capture the traveler's facial expression with a camera, input it into the generative AI, and have the generative AI perform emotion estimation.

[0066] The reception desk can analyze a traveler's past travel history and suggest the optimal input method. For example, the reception desk can automatically display preferences and conditions that the traveler has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the traveler has used in the past. Furthermore, the reception desk can predict and suggest preferences and conditions related to specific seasons or events based on the traveler's past travel history. This allows for a more personalized input method by analyzing the traveler's past travel history. Past travel history includes, but is not limited to, places visited, length of stay, and purpose of travel. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can send the traveler's past travel history to an AI agent, which can analyze the information and suggest the optimal input method.

[0067] The reception desk can adjust the priority of input fields based on the traveler's current location and time of day. For example, the reception desk can prioritize displaying nearby tourist attractions and activities based on the traveler's current location. It can also suggest appropriate modes of transportation and schedules based on the time of day the traveler is entering their information. Furthermore, if the traveler enters their information during a specific event or festival, the reception desk can prioritize displaying information related to that event. This allows for the provision of more relevant information by adjusting the priority of input fields according to the traveler's current situation. Current location and time of day include, but are not limited to, GPS data and time-based priority changes. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk sends the traveler's current location and time of day to an AI agent, which analyzes the information and adjusts the priority of input fields.

[0068] The reception desk can estimate the traveler's emotions and adjust the order of input fields based on the estimated emotions. For example, if the traveler is stressed, the reception desk can display the most important input fields first and simplify the process. If the traveler is relaxed, the reception desk can also display detailed input fields in order and provide a customizable input method. Furthermore, if the traveler is in a hurry, the reception desk can prioritize voice input, allowing them to quickly enter their preferences and conditions. This provides a more comfortable input experience by adjusting the order of input fields according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can capture the traveler's facial expression with a camera, input it into the generative AI, and have the generative AI perform emotion estimation.

[0069] The reception desk can analyze travelers' social media activity and automatically input relevant travel information. For example, it can suggest relevant tourist destinations and activities based on past travel photos and posts shared by travelers on social media. It can also analyze posts from travel influencers followed by travelers and automatically input relevant travel information. Furthermore, it can automatically input relevant travel information based on events and festivals that travelers have shown interest in on social media. This allows for the automatic input of relevant travel information by analyzing travelers' social media activity, saving manual input effort. Social media activity includes, but is not limited to, the content of posts, the number of likes, and follower reactions. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can send the traveler's social media activity to an AI agent, which can analyze the information and automatically input relevant travel information.

[0070] The reception desk can provide an optimal input interface by considering the traveler's device information. For example, if the traveler is using a smartphone, the reception desk can provide an input interface that matches the screen size. It can also provide an input interface optimized for a larger screen if the traveler is using a tablet. Furthermore, if the traveler is using a smartwatch, the reception desk can provide a simple and highly visible input interface. This improves the convenience of input by providing an optimal input interface according to the traveler's device information. Device information includes, but is not limited to, the device type, OS, and screen size. Some or all of the processing described above in the reception desk may be performed using AI or not. For example, the reception desk can send the traveler's device information to an AI agent, which can then analyze the information and provide an optimal input interface.

[0071] The generation unit can estimate the traveler's emotions and adjust the level of detail in the generated plan based on the estimated emotions. For example, if the traveler is relaxed, the generation unit can generate a plan that includes detailed information about tourist attractions and activity descriptions. If the traveler is in a hurry, the generation unit can also generate a concise plan that gets straight to the point. Furthermore, if the traveler is excited, the generation unit can generate a plan with visually appealing effects. This allows for the provision of more appropriate plans by adjusting the level of detail according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI or not. For example, the generation unit can capture the traveler's facial expressions with a camera, input them into the generation AI, and have the generation AI perform emotion estimation.

[0072] The generation unit can generate more personalized plans by referring to the traveler's past travel history. For example, the generation unit can suggest new relevant tourist destinations and activities based on the tourist destinations and activities the traveler has visited in the past. The generation unit can also generate plans related to specific seasons or events from the traveler's past travel history. Furthermore, the generation unit can analyze the traveler's past travel history and generate the most satisfying plan. This allows for the provision of more personalized plans by referring to the traveler's past travel history. Past travel history includes, but is not limited to, places visited, length of stay, and purpose of travel. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit sends the traveler's past travel history to an AI agent, which analyzes the information and generates a more personalized plan.

[0073] The generation unit can adjust the plan in real time, taking into account the traveler's current situation and weather information. For example, the generation unit adjusts indoor and outdoor activities based on weather information at the traveler's current location. It can also suggest appropriate activities considering the traveler's current situation (e.g., fatigue level and health condition). Furthermore, the generation unit can suggest the optimal travel route considering the traveler's current mode of transportation and traffic conditions. This allows for the provision of a more appropriate plan by adjusting the plan according to the traveler's current situation and weather information. Current situation and weather information includes, but is not limited to, real-time weather forecasts and current location conditions. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit sends the traveler's current situation and weather information to an AI agent, which analyzes the information and adjusts the plan in real time.

[0074] The generation unit can estimate the traveler's emotions and prioritize the plan based on those emotions. For example, if the traveler is relaxed, the generation unit can adjust the priority of sightseeing spots and activities and suggest a relaxed schedule. If the traveler is in a hurry, the generation unit can also prioritize suggesting important sightseeing spots and activities. Furthermore, if the traveler is excited, the generation unit can prioritize suggesting visually appealing sightseeing spots and activities. This allows for the provision of a more appropriate plan by prioritizing the plan according to the traveler'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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can capture the traveler's facial expressions with a camera, input them into the generative AI, and have the generative AI perform emotion estimation.

[0075] The generation unit can analyze a traveler's social media activity and incorporate relevant destinations and activities into the plan. For example, it can suggest relevant destinations and activities based on past travel photos and posts shared by the traveler on social media. It can also analyze posts from travel influencers followed by the traveler and incorporate relevant destinations and activities into the plan. Furthermore, it can incorporate relevant destinations and activities based on events and festivals that the traveler has shown interest in on social media. This allows for the incorporation of relevant destinations and activities into the plan by analyzing the traveler's social media activity, thereby providing a more personalized plan. Social media activity includes, but is not limited to, the content of posts, the number of likes, and follower reactions. Some or all of the processing described above in the generation unit may be performed using AI or not. For example, the generation unit could send the traveler's social media activity to an AI agent, which could then analyze the information and incorporate relevant destinations and activities into the plan.

[0076] The generation unit can generate an optimal plan by taking into account the traveler's device information. For example, if the traveler is using a smartphone, the generation unit will generate a plan that matches the screen size. It can also generate a plan optimized for a larger screen if the traveler is using a tablet. Furthermore, if the traveler is using a smartwatch, the generation unit can generate a concise and highly visible plan. This improves convenience by providing an optimal plan according to the traveler's device information. Device information includes, but is not limited to, the device type, OS, and screen size. Some or all of the processing described above in the generation unit may be performed using AI or not. For example, the generation unit sends the traveler's device information to an AI agent, which analyzes the information and generates an optimal plan.

[0077] The service provider can estimate the traveler's emotions and adjust how the plan is displayed based on those emotions. For example, if the traveler is nervous, the service provider can provide a simple and highly visible display. If the traveler is relaxed, the service provider can also provide a display that includes detailed information. Furthermore, if the traveler is in a hurry, the service provider can provide a concise display. By adjusting how the plan is displayed according to the traveler's emotions, a more comfortable viewing experience can be provided. 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 service provider may be performed using AI or not. For example, the service provider can capture the traveler's facial expression with a camera, input it into the generative AI, and have the generative AI perform emotion estimation.

[0078] The service provider can optimize the content of the plans it offers by referring to travelers' past feedback. For example, the service provider can suggest relevant tourist destinations and activities based on feedback previously provided by travelers. The service provider can also optimize plans related to specific seasons or events based on travelers' past feedback. Furthermore, the service provider can analyze travelers' past feedback and provide the most satisfying plans. This allows for the provision of more satisfying plans by referring to travelers' past feedback. Past feedback includes, but is not limited to, travelers' ratings, comments, and suggestions for improvement. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can send travelers' past feedback to an AI agent, which then analyzes the information and optimizes the content of the plans it offers.

[0079] The service provider can adjust the timing of plan delivery based on the traveler's current location and time of day. For example, it can prioritize displaying nearby tourist attractions and activities based on the traveler's current location. It can also suggest appropriate modes of transportation and schedules based on the time of day the traveler enters their information. Furthermore, if the traveler enters their information during a specific event or festival, the service provider can prioritize displaying information related to that event. This allows the service provider to provide more relevant information by adjusting the timing of plan delivery according to the traveler's current situation. Current location and time of day include, but are not limited to, GPS data and changes in priority based on time of day. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can send the traveler's current location and time of day to an AI agent, which can analyze the information and adjust the timing of plan delivery.

[0080] The service provider can estimate the traveler's emotions and adjust the display order of the plan based on the estimated emotions. For example, if the traveler is stressed, the service provider can display the most important information first and simplify the procedure. If the traveler is relaxed, the service provider can also display detailed information in a sequential manner and provide a customizable display method. Furthermore, if the traveler is in a hurry, the service provider can prioritize voice guidance and provide information quickly. This allows for a more comfortable viewing experience by adjusting the display order of the plan according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can capture the traveler's facial expression with a camera, input it into the generative AI, and have the generative AI perform emotion estimation.

[0081] The service provider can analyze travelers' social media activity and add relevant information to their plans. For example, it can suggest relevant destinations and activities based on past travel photos and posts shared by travelers on social media. It can also analyze posts from travel influencers that travelers follow and add relevant destinations and activities to their plans. Furthermore, it can add relevant information to their plans based on events and festivals that travelers have shown interest in on social media. This allows the service provider to add relevant information to plans and provide more personalized itineraries by analyzing travelers' social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and follower engagement. Some or all of the processing described above by the service provider may or may not be performed using AI. For example, the service provider can send travelers' social media activity to an AI agent, which can analyze the information and add relevant information to their plans.

[0082] The service provider can select the optimal plan delivery method by considering the traveler's device information. For example, if the traveler is using a smartphone, the service provider can select a plan delivery method that matches the screen size. If the traveler is using a tablet, the service provider can also select a plan delivery method optimized for a larger screen. Furthermore, if the traveler is using a smartwatch, the service provider can select a concise and highly visible plan delivery method. This improves convenience by selecting the optimal plan delivery method according to the traveler's device information. Device information includes, but is not limited to, the device type, OS, and screen size. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can send the traveler's device information to an AI agent, which then analyzes the information and selects the optimal plan delivery method.

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

[0084] The reception desk can monitor the traveler's health status and propose travel plans tailored to their needs. For example, if a traveler is tired, it can prioritize suggesting relaxing tourist destinations and activities. If the traveler is healthy and active, it can also suggest plans that include activities such as hiking and sports. Furthermore, if a traveler has a specific health problem, it can suggest accommodations and transportation options that take that problem into consideration. This allows for the provision of optimal travel plans tailored to the traveler's health condition. Devices such as smartwatches and fitness trackers can be used to monitor health status.

[0085] The generation unit can create travel plans aligned with specific themes based on the traveler's hobbies and interests. For example, if a traveler is interested in history, it can suggest a plan that includes historical sites and museums. If the traveler is interested in food, it can suggest a plan that includes local specialty dishes and restaurants. Furthermore, if the traveler wants to enjoy nature, it can suggest a plan that includes nature parks and hiking trails. This allows for the provision of themed travel tailored to the traveler's hobbies and interests. Information on hobbies and interests can be obtained from the traveler's past travel history and social media activity.

[0086] The service provider can estimate the traveler's emotions and adjust the way they notify users of their plans based on those emotions. For example, if a traveler is relaxed, notifications can be kept to a minimum, allowing them to review the plan at their own pace. If a traveler is in a hurry, important information can be immediately notified, enabling them to respond quickly. Furthermore, if a traveler is excited, visually appealing notification methods can be used to enhance their excitement. This allows the service provider to offer the most appropriate notification method based on the traveler's emotions. Emotion estimation can be performed, for example, using an emotion engine or generative AI.

[0087] The reception desk can provide the optimal input interface based on the traveler's language settings. For example, if the traveler is using English, an English input interface will be provided. If the traveler is using Japanese, a Japanese input interface can be provided. Furthermore, if the traveler is using multiple languages, an input interface supporting multiple languages ​​can be provided. This improves the convenience of input by providing the optimal input interface according to the traveler's language settings. Language setting information can be obtained from the traveler's device settings and past input history.

[0088] The generation unit can estimate the traveler's emotions and adjust the flexibility of the plan based on those emotions. For example, if the traveler is relaxed, it can suggest a more flexible plan, allowing them to enjoy their trip at their own pace. If the traveler is in a hurry, it can suggest efficient routes and schedules, helping them make the most of their time. Furthermore, if the traveler is excited, it can increase the range of activity options, allowing them to freely choose activities that interest them. This enables the provision of flexible plans tailored to the traveler's emotions. Emotion estimation can be performed using, for example, an emotion engine or generative AI.

[0089] The service provider can analyze a traveler's past travel history and suggest destinations and activities they have previously visited. For example, if a traveler wants to revisit a destination they have previously visited, the service can suggest a plan that includes that destination. Similarly, if a traveler wants to revisit an activity they have previously participated in, the service can suggest a plan that includes that activity. Furthermore, the service can suggest new destinations and activities related to those previously visited. This allows the service to leverage a traveler's past travel history to provide more personalized plans. Information on past travel history can be obtained from the traveler's input history and social media activity.

[0090] The reception desk can estimate the traveler's emotions and adjust the priority of input fields based on those estimates. For example, if the traveler is stressed, the most important input fields will be displayed first, and the process will be simplified. If the traveler is relaxed, detailed input fields can be displayed in order, and a customizable input method can be provided. Furthermore, if the traveler is in a hurry, voice input can be prioritized, allowing them to quickly enter their preferences and conditions. In this way, a more comfortable input experience can be provided by adjusting the priority of input fields according to the traveler's emotions. Emotion estimation can be performed using, for example, an emotion engine or generative AI.

[0091] The generation unit can adjust the plan in real time based on the traveler's current location and time of day. For example, it can adjust indoor and outdoor activities based on weather information at the traveler's current location. It can also suggest appropriate activities considering the traveler's current situation (e.g., fatigue level and health condition). Furthermore, it can suggest the optimal travel route considering the traveler's current mode of transportation and traffic conditions. This allows for the provision of more appropriate plans by adjusting the plan according to the traveler's current situation and weather information. Current situation and weather information include, but are not limited to, real-time weather forecasts and current location conditions.

[0092] The service provider can estimate the traveler's emotions and adjust how the plan is displayed based on those emotions. For example, if the traveler is stressed, a simple and highly visible display method can be provided. If the traveler is relaxed, a display method including detailed information can be provided. Furthermore, if the traveler is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting how the plan is displayed according to the traveler's emotions, a more comfortable viewing experience can be provided. Emotion estimation can be performed, for example, using an emotion engine or generative AI.

[0093] The reception desk can analyze travelers' social media activity and automatically input relevant travel information. For example, it can suggest relevant tourist destinations and activities based on past travel photos and posts shared by travelers on social media. It can also analyze posts from travel influencers that travelers follow and automatically input relevant travel information. Furthermore, it can automatically input relevant travel information based on events and festivals that travelers have shown interest in on social media. This allows for the automatic input of relevant travel information by analyzing travelers' social media activity, saving manual input time. Social media activity includes, but is not limited to, the content of posts, the number of likes, and follower reactions.

[0094] The following briefly describes the processing flow for example form 2.

[0095] Step 1: The reception desk enters the traveler's preferences and requirements. These preferences and requirements include, for example, budget, type of destination, and type of accommodation. The reception desk can enter the traveler's preferences and requirements via a smartphone app or website. Step 2: The generation unit analyzes the information entered by the reception unit and generates the optimal travel route and schedule. Based on the traveler's preferences and conditions, the generation unit generates the optimal plan, taking into account the order of tourist destinations to visit, means of transportation, and length of stay. The generation unit can use an AI agent to generate the optimal travel plan based on the traveler's preferences and conditions. Step 3: The provider provides the plan generated by the generator to the traveler. The provider can provide the generated plan to the traveler via a smartphone app or website.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0097] 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.

[0098] 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.

[0099] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit allows travelers to input their preferences and conditions using the reception device 38 of the smart device 14. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal travel plan based on the travelers' preferences and conditions. The provision unit can provide the generated plan to the travelers using the output device 40 of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0100] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit allows travelers to input their preferences and conditions using the microphone 238 of the smart glasses 214. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates an optimal travel plan based on the travelers' preferences and conditions. The provision unit can provide the generated plan to the travelers using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0116] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit allows travelers to input their preferences and conditions using the microphone 238 of the headset terminal 314. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal travel plan based on the travelers' preferences and conditions. The provision unit can provide the generated plan to the travelers using the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit can input the traveler's preferences and conditions using the microphone 238 of the robot 414. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates an optimal travel plan based on the traveler's preferences and conditions. The provision unit can provide the generated plan to the traveler using the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] (Note 1) A reception desk where travelers enter their preferences and requirements, A generation unit analyzes the information entered by the reception unit and generates the optimal travel route and schedule, The system includes a provisioning unit that provides the plan generated by the generation unit to travelers. A system characterized by the following features. (Note 2) The aforementioned reception unit is Travelers can input their preferences and requirements through a smartphone app or website. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Based on the traveler's preferences and circumstances, we generate the optimal plan, taking into account the order of desired tourist destinations, modes of transportation, and length of stay. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We provide travelers with plans generated through smartphone apps and websites. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the traveler's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is We analyze travelers' past travel history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Prioritize input fields based on the traveler's current location and time of day. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the traveler's emotions and adjusts the order of input fields based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze travelers' social media activity and automatically populate it with relevant travel information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Providing the optimal input interface while considering the traveler's device information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is It estimates the traveler's emotions and adjusts the level of detail in the plan generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is By referencing travelers' past travel history, we generate more personalized plans. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is We adjust the plan in real time, taking into account the traveler's current situation and weather information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the traveler's emotions and determines the priority of the plan based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is Analyze travelers' social media activity and incorporate relevant tourist destinations and activities into the itinerary. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is We generate the optimal plan by taking into account the traveler's device information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, We estimate the traveler's sentiment and adjust how the plan is displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, We optimize the content of the plans we offer by referring to past traveler feedback. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We adjust the timing of plan delivery based on the traveler's current location and time of day. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the traveler's emotions and adjusts the display order of plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, Analyze travelers' social media activity and add relevant information to your plans. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, We select the optimal plan delivery method by considering the traveler's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where travelers enter their preferences and requirements, A generation unit analyzes the information entered by the reception unit and generates the optimal travel route and schedule, The system includes a provisioning unit that provides the plan generated by the generation unit to travelers. A system characterized by the following features.

2. The aforementioned reception unit is Travelers can input their preferences and requirements through a smartphone app or website. The system according to feature 1.

3. The generating unit is Based on the traveler's preferences and circumstances, we generate the optimal plan, taking into account the order of desired tourist destinations, modes of transportation, and length of stay. The system according to feature 1.

4. The aforementioned supply unit is, We provide travelers with plans generated through smartphone apps and websites. The system according to feature 1.

5. The aforementioned reception unit is It estimates the traveler's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is We analyze travelers' past travel history and suggest the optimal input method. The system according to feature 1.

7. The aforementioned reception unit is Prioritize input fields based on the traveler's current location and time of day. The system according to feature 1.

8. The aforementioned reception unit is It estimates the traveler's emotions and adjusts the order of input fields based on the estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is Analyze travelers' social media activity and automatically populate it with relevant travel information. The system according to feature 1.

10. The aforementioned reception unit is Providing the optimal input interface while considering the traveler's device information. The system according to feature 1.

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