System

Abstract

An object of the system according to the embodiment is to create an efficient travel plan and make a reservation collectively.SOLUTION: A system according to an embodiment includes a reception unit, a generation unit, and a reservation unit. The reception unit receives a travel condition of a user. The generation unit generates an efficient travel plan in consideration of the timetable and the business hours of the facility based on the information received by the reception unit. The reservation unit reserves a restaurant or an activity on the basis of the travel plan generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult to efficiently create travel plans and make reservations all at once.

[0005] The system according to the embodiment aims to create an efficient travel plan and make reservations in one go. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a reservation unit. The reception unit receives travel conditions from a user. The generation unit creates an efficient travel plan based on the information received by the reception unit, taking into account timetables and facility opening hours. The reservation unit makes reservations for restaurants and activities based on the travel plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can create an efficient travel plan and make reservations all at once. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A travel plan creation system according to an embodiment of the present invention accepts a user's travel requirements, and a generation AI creates an optimal travel plan and makes reservations for restaurants and activities. In the travel plan creation system, a user inputs desired travel requirements, and a generation AI creates an optimal travel plan based on those requirements. The generated plan takes into account timetables and facility opening hours, enabling efficient travel. Furthermore, restaurant and activity reservations can be made all at once. For example, if a user inputs "I want to do some sightseeing in Tokyo," the generation AI creates an efficient travel plan taking into account the opening hours and access methods of Tokyo's tourist spots. Furthermore, restaurant and activity reservations can be made simultaneously, allowing the user to complete travel preparations hassle-free. This allows the travel plan creation system to efficiently accept a user's travel requirements, generate an optimal travel plan, and make reservations. For example, a user inputs desired travel requirements, and a generation AI creates an optimal travel plan based on those requirements. The generated plan takes into account timetables and facility opening hours, enabling efficient travel. Furthermore, restaurant and activity reservations can be made all at once. This allows the user to complete travel preparations hassle-free.

[0029] The travel plan creation system according to the embodiment includes a reception unit, a generation unit, and a reservation unit. The reception unit receives a user's travel conditions. The user's travel conditions include, but are not limited to, a departure point, a destination, a travel itinerary, and desired tourist spots. The reception unit receives information such as the user's desired departure point, destination, travel itinerary, and desired tourist spots. The generation unit uses a generation AI to create an efficient travel plan based on the information received by the reception unit, taking into account timetables and facility opening hours. The generation unit calculates an optimal route based on, for example, map data, timetable data, and facility opening hours data, and generates a travel plan along that route. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and generates an optimal travel plan based on information input by the user. The reservation unit makes reservations for restaurants and activities based on the travel plan generated by the generation unit. The reservation unit automatically makes reservations for restaurants and activities included in the travel plan, for example. This allows the travel plan creation system according to the embodiment to efficiently receive a user's travel conditions, generate an optimal travel plan, and make reservations.

[0030] The generation unit can calculate an efficient route based on map data, timetable data, and facility operating hours data, and generate a travel plan that follows that route. The generation unit can calculate an efficient route based on, for example, map data, timetable data, and facility operating hours data, and generate a travel plan that follows that route. For example, the generation unit can calculate an optimal travel route based on map data and minimize travel time based on timetable data. The generation unit can also determine an optimal order of tourist spots based on facility operating hours data. This makes it possible to calculate an optimal route based on map data, timetable data, and facility operating hours data, and generate an efficient travel plan. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input map data, timetable data, and facility operating hours data into the generation AI and have the generation AI calculate the optimal route.

[0031] The reservation unit can automatically make reservations for restaurants and activities included in the travel plan. The reservation unit, for example, automatically makes reservations for restaurants and activities included in the travel plan. For example, the reservation unit makes restaurant reservations and confirms the reservations based on the travel plan. The reservation unit can also make activity reservations and confirm the reservations. Furthermore, the reservation unit can also automate the reservation confirmation procedure in cooperation with a reservation system. This allows restaurants and activities to be automatically booked based on the travel plan. Some or all of the above-described processing in the reservation unit may be performed using, or without, the generation AI. For example, the reservation unit can input information about restaurants and activities included in the travel plan into the generation AI and have the generation AI make the reservations.

[0032] The reception unit can accept the user's desired departure point and destination, travel itinerary, desired tourist spots, and related information. The reception unit, for example, accepts the user's desired departure point and destination, travel itinerary, desired tourist spots, and related information. For example, the reception unit provides an interface through which the user inputs the desired departure point and destination. The reception unit can also provide an interface through which the user inputs the desired travel itinerary. Furthermore, the reception unit can also provide an interface through which the user inputs the desired tourist spots. This allows the user to accept detailed travel conditions desired by the user. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the information entered by the user to a generation AI, which can analyze the information.

[0033] The reception unit can analyze the user's past travel history and suggest an efficient method for inputting travel parameters. The reception unit, for example, analyzes the user's past travel history and suggests an efficient method for inputting travel parameters. For example, the reception unit automatically suggests related travel parameters based on places the user has visited and services the user has used in the past. The reception unit can also prioritize suggestions based on input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest travel parameters related to specific seasons or events based on the user's past travel history. This can reduce the effort required for input by suggesting the optimal input method based on the user's past travel history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past travel history data into the generation AI and have the generation AI suggest the optimal input method.

[0034] The reception unit can automatically acquire the user's current location information and set it as the departure point when accepting travel conditions. For example, the reception unit can automatically acquire the user's current location information and set it as the departure point when accepting travel conditions. For example, when the user opens the app, the reception unit can automatically acquire the user's current location and set it as the departure point. Furthermore, when the user inputs a destination, the reception unit can suggest optimal candidate destinations taking into account the distance from the current location. Furthermore, when the user uses the app while traveling, the reception unit can update the user's current location in real time and reflect it as the departure point. This reduces the effort required for input by automatically acquiring the user's current location information and setting it as the departure point. Some or all of the above-described processing in the reception unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's current location information into the generation AI and have the generation AI set the departure point.

[0035] The reception unit can adjust input items to reflect the user's past feedback when accepting travel conditions. For example, the reception unit can adjust input items to reflect the user's past feedback when accepting travel conditions. For example, the reception unit can delete unnecessary input items and display only necessary items based on feedback previously provided by the user. The reception unit can also set priorities for specific conditions and optimize input items based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and adjust the design and layout of the input interface. This allows for optimizing input items to reflect the user's past feedback, thereby providing a more user-friendly system. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI adjust the input items.

[0036] The reception unit can analyze the user's social media activity and suggest related travel conditions when receiving travel conditions. For example, the reception unit can analyze the user's social media activity and suggest related travel conditions when receiving travel conditions. For example, the reception unit can suggest related travel conditions based on the locations where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest tourist spots and activities that the user may be interested in. Furthermore, the reception unit can suggest related travel conditions based on the activity of the user's friends on social media. This allows for the provision of more appropriate travel plans by suggesting related travel conditions based on the user's social media activity. Some or all of the above-described processing in the reception unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's social media data into the generation AI and have the generation AI suggest related travel conditions.

[0037] The reception unit can provide an efficient input interface by taking into account the user's device information when accepting travel conditions. For example, the reception unit can provide an efficient input interface by taking into account the user's device information when accepting travel conditions. For example, if the user is using a smartphone, the reception unit can provide an input interface tailored to the screen size. Furthermore, if the user is using a tablet, the reception unit can provide an input interface optimized for a large screen. Furthermore, if the user is using a smartwatch, the reception unit can provide a simple and highly visible input interface. This allows for the provision of an optimal input interface according to the user's device information, thereby providing a more user-friendly system. Some or all of the above-described processing in the reception unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's device information into the generation AI and have the generation AI provide the optimal input interface.

[0038] The reception unit can automatically suggest recommended travel destinations based on the user's past travel history when receiving travel conditions. For example, the reception unit can automatically suggest recommended travel destinations based on the user's past travel history when receiving travel conditions. For example, the reception unit can automatically suggest related travel destinations based on places the user has visited or services they have used. The reception unit can also predict and suggest travel destinations related to specific seasons or events based on the user's past travel history. Furthermore, the reception unit can analyze the user's past travel history and suggest the most popular travel destinations. This allows for more appropriate travel plans to be provided by suggesting recommended travel destinations based on the user's past travel history. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's past travel history data into the generation AI and have the generation AI suggest recommended travel destinations.

[0039] When generating a travel plan, the generation unit can refer to the user's past travel history to enhance the level of detail of the plan. For example, when generating a travel plan, the generation unit can refer to the user's past travel history to enhance the level of detail of the plan. For example, the generation unit can suggest related travel plans based on places the user has visited and services they have used in the past. The generation unit can also predict and suggest travel plans related to specific seasons or events based on the user's past travel history. Furthermore, the generation unit can analyze the user's past travel history and suggest the most popular travel plans. This can improve the accuracy of the plan by referring to the user's past travel history. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input the user's past travel history data into the generation AI and have the generation AI make suggestions to enhance the level of detail of the plan.

[0040] The generation unit can create an efficient travel plan by taking into account the user's current health condition when generating the travel plan. For example, the generation unit can create an efficient plan by taking into account the user's current health condition when generating the travel plan. For example, if the user is tired, the generation unit can create a plan that includes relaxing places and activities. Also, if the user is seeking healthy exercise, the generation unit can create a plan that includes active activities. Furthermore, if the user is feeling unwell, the generation unit can create a plan that includes rest points. This makes it possible to provide a more appropriate travel plan by creating an optimal plan based on the user's health condition. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's health data into the generation AI and have the generation AI create an optimal plan.

[0041] The generation unit can customize tourist spots based on the user's interests when generating a travel plan. The generation unit customizes tourist spots based on the user's interests when generating a travel plan, for example. For example, the generation unit incorporates tourist spots that the user is interested in into the plan. The generation unit can also incorporate stores and restaurants that the user frequently visits into the plan. Furthermore, the generation unit can incorporate places that the user is likely to be interested in into the plan based on the user's past search history. This allows for customizing tourist spots based on the user's interests, making it possible to provide a more appropriate travel plan. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's interest data into the generation AI and have the generation AI customize the tourist spots.

[0042] The generation unit can propose an efficient route by taking into account the user's geographical location information when generating a travel plan. For example, the generation unit can propose an efficient route by taking into account the user's geographical location information when generating a travel plan. For example, the generation unit can prioritize proposing tourist spots closest to the user's current location. The generation unit can also propose an efficient travel route based on the user's geographical location information. Furthermore, the generation unit can propose a route that avoids congestion by taking into account the user's geographical location information. This makes it possible to provide a more efficient travel plan by proposing an optimal route by taking into account the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI propose an efficient route.

[0043] The generation unit can analyze the user's social media activities and suggest related tourist spots when generating a travel plan. For example, the generation unit can analyze the user's social media activities and suggest related tourist spots when generating a travel plan. For example, the generation unit can suggest related tourist spots based on locations where the user has checked in on social media. The generation unit can also analyze the content of the user's social media posts and suggest tourist spots that the user may be interested in. Furthermore, the generation unit can also suggest related tourist spots based on the activities of the user's friends on social media. This allows for a more appropriate travel plan to be provided by suggesting related tourist spots based on the user's social media activities. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input the user's social media data into the generation AI and have the generation AI suggest related tourist spots.

[0044] The generation unit can adjust the contents of the plan by reflecting the user's past feedback when generating the travel plan. For example, the generation unit can adjust the contents of the plan by reflecting the user's past feedback when generating the travel plan. For example, the generation unit can delete unnecessary tourist spots and activities and display only necessary items based on feedback provided by the user in the past. The generation unit can also set priorities for specific conditions based on the user's past feedback and optimize the contents of the plan. Furthermore, the generation unit can analyze the user's past feedback and adjust the design and layout of the plan. This allows the user's past feedback to be reflected in optimizing the contents of the plan, thereby providing a more appropriate travel plan. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input the user's past feedback data into the generation AI and have the generation AI adjust the contents of the plan.

[0045] The reservation unit can suggest the optimal reservation method by referring to the user's past reservation history when making a reservation. For example, the reservation unit can suggest the optimal reservation method by referring to the user's past reservation history when making a reservation. For example, the reservation unit can suggest a relevant reservation method based on restaurants and activities the user has used in the past. The reservation unit can also predict and suggest reservation methods related to specific seasons or events from the user's past reservation history. Furthermore, the reservation unit can analyze the user's past reservation history and suggest the most popular reservation method. In this way, the optimal reservation method can be suggested by referring to the user's past reservation history. Some or all of the above-mentioned processing in the reservation unit may be performed using, or without, a generation AI. For example, the reservation unit can input the user's past reservation history data into the generation AI and have the generation AI suggest the optimal reservation method.

[0046] The reservation unit can propose an efficient reservation time by taking into account the user's current schedule when making a reservation. For example, the reservation unit can propose an efficient reservation time by taking into account the user's current schedule when making a reservation. For example, the reservation unit can refer to the user's calendar information and propose a reservation during an available time slot. The reservation unit can also propose an optimal reservation time by taking travel time into account, based on the user's schedule. Furthermore, the reservation unit can adjust and propose the reservation time in real time if the user's schedule changes. This makes it possible to propose an optimal reservation time by taking into account the user's current schedule. Some or all of the above-described processing in the reservation unit may be performed using, or without, a generation AI. For example, the reservation unit can input the user's calendar information into the generation AI and have the generation AI propose an efficient reservation time.

[0047] The reservation unit can improve the accuracy of reservations by reflecting user feedback at the time of reservation. For example, the reservation unit can improve the accuracy of reservations by reflecting user feedback at the time of reservation. For example, the reservation unit can delete unnecessary reservation options and display only necessary items based on feedback previously provided by the user. The reservation unit can also set priorities for specific conditions and optimize the reservation content based on the user's past feedback. Furthermore, the reservation unit can analyze the user's past feedback and adjust the design and layout of the reservation interface. This allows the accuracy of reservations to be improved by reflecting user feedback. Some or all of the above-described processing in the reservation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reservation unit can input the user's past feedback data into the generation AI and have the generation AI improve the accuracy of reservations.

[0048] The reservation unit can suggest an efficient reservation destination by taking into account the user's geographical location information when making a reservation. The reservation unit, for example, can suggest an efficient reservation destination by taking into account the user's geographical location information when making a reservation. For example, the reservation unit prioritizes suggesting restaurants or activities closest to the user's current location. The reservation unit can also suggest a reservation destination that takes into account an efficient travel route based on the user's geographical location information. Furthermore, the reservation unit can suggest a reservation destination that avoids congestion by taking into account the user's geographical location information. In this way, the optimal reservation destination can be suggested by taking into account the user's geographical location information. Some or all of the above-described processing in the reservation unit may be performed using, or without, a generation AI. For example, the reservation unit can input the user's geographical location information into the generation AI and have the generation AI suggest an efficient reservation destination.

[0049] The reservation unit can analyze the user's social media activity and suggest related reservation destinations at the time of reservation. For example, the reservation unit can analyze the user's social media activity and suggest related reservation destinations at the time of reservation. For example, the reservation unit can suggest related reservation destinations based on the location where the user checked in on social media. The reservation unit can also analyze the user's social media posts to suggest restaurants and activities that the user may be interested in. Furthermore, the reservation unit can suggest related reservation destinations based on the activity of the user's friends on social media. This allows for more appropriate reservations by suggesting related reservation destinations based on the user's social media activity. Some or all of the above-described processing in the reservation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reservation unit can input the user's social media data into the generation AI and have the generation AI suggest related reservation destinations.

[0050] The reservation unit can optimize the reservation content by reflecting the user's past feedback at the time of reservation. For example, the reservation unit can optimize the reservation content by reflecting the user's past feedback at the time of reservation. For example, the reservation unit can delete unnecessary reservation options and display only necessary items based on feedback provided by the user in the past. The reservation unit can also set priorities for specific conditions based on the user's past feedback and optimize the reservation content. Furthermore, the reservation unit can analyze the user's past feedback and adjust the design and layout of the reservation interface. In this way, the reservation content can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the reservation unit can be performed using, or without, a generation AI. For example, the reservation unit can input the user's past feedback data into the generation AI and have the generation AI optimize the reservation content.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] When accepting the user's travel conditions, the reception unit can analyze the user's past travel history and automatically suggest similar travel conditions. For example, it can suggest related travel conditions based on tourist spots the user has visited or services the user has used in the past. The reception unit can also prioritize suggestions based on input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest travel conditions related to specific seasons or events based on the user's past travel history. This reduces the effort required for input by suggesting the optimal input method based on the user's past travel history.

[0053] The generation unit can adjust the travel plan taking into account the user's current health condition. For example, if the user is tired, the generation unit can create a plan that includes places and activities where the user can relax. Also, if the user is seeking healthy exercise, the generation unit can create a plan that includes active activities. Furthermore, if the user is feeling unwell, the generation unit can create a plan that includes rest points. In this way, the generation unit can provide a more appropriate travel plan by creating an optimal plan according to the user's health condition.

[0054] The reception unit can analyze the user's social media activity and suggest relevant travel conditions. For example, it can suggest relevant travel conditions based on the locations where the user has checked in on social media. It can also analyze the content of the user's social media posts to suggest tourist spots and activities that the user may be interested in. It can also suggest relevant travel conditions based on the activities of the user's friends on social media. This makes it possible to provide more appropriate travel plans by suggesting relevant travel conditions based on the user's social media activity.

[0055] The reception unit can provide an efficient input interface by taking into account the user's device information. For example, if the user is using a smartphone, an input interface that matches the screen size can be provided. Also, if the user is using a tablet, an input interface optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, an input interface that is simple and highly visible can be provided. This makes it possible to provide a system that is easier to use by providing an optimal input interface according to the user's device information.

[0056] The generation unit can increase the level of detail of the plan by referring to the user's past travel history. For example, it can suggest related travel plans based on places the user has visited and services they have used in the past. It can also predict and suggest travel plans related to specific seasons or events based on the user's past travel history. It can also analyze the user's past travel history and suggest the most popular travel plans. This allows the accuracy of the plan to be improved by referring to the user's past travel history.

[0057] The reception unit can adjust input items by reflecting the user's past feedback. For example, it can delete unnecessary input items and display only necessary items based on the user's past feedback. It can also set priorities for specific conditions based on the user's past feedback and optimize the input items. Furthermore, it can analyze the user's past feedback and adjust the design and layout of the input interface. In this way, it is possible to provide a system that is easier to use by optimizing the input items by reflecting the user's past feedback.

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

[0059] Step 1: The reception unit receives the user's travel conditions. The user's travel conditions include, for example, the departure point, destination, travel dates, and desired tourist spots. By receiving this information, the reception unit can create a travel plan based on the user's wishes. Step 2: The generation unit creates an efficient travel plan based on the information received by the reception unit, taking into account timetables and facility opening hours. The generation unit calculates the optimal route based on map data, timetable data, and facility opening hours data, and generates a travel plan that follows that route. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to generate the optimal travel plan based on the information input by the user. Step 3: The reservation unit makes reservations for restaurants and activities based on the travel plan generated by the generation unit. The reservation unit automatically makes reservations for restaurants and activities included in the travel plan.

[0060] (Example 2) A travel plan creation system according to an embodiment of the present invention accepts a user's travel requirements, and a generation AI creates an optimal travel plan and makes reservations for restaurants and activities. In the travel plan creation system, a user inputs desired travel requirements, and a generation AI creates an optimal travel plan based on those requirements. The generated plan takes into account timetables and facility opening hours, enabling efficient travel. Furthermore, restaurant and activity reservations can be made all at once. For example, if a user inputs "I want to do some sightseeing in Tokyo," the generation AI creates an efficient travel plan taking into account the opening hours and access methods of Tokyo's tourist spots. Furthermore, restaurant and activity reservations can be made simultaneously, allowing the user to complete travel preparations hassle-free. This allows the travel plan creation system to efficiently accept a user's travel requirements, generate an optimal travel plan, and make reservations. For example, a user inputs desired travel requirements, and a generation AI creates an optimal travel plan based on those requirements. The generated plan takes into account timetables and facility opening hours, enabling efficient travel. Furthermore, restaurant and activity reservations can be made all at once. This allows the user to complete travel preparations hassle-free.

[0061] The travel plan creation system according to the embodiment includes a reception unit, a generation unit, and a reservation unit. The reception unit receives a user's travel conditions. The user's travel conditions include, but are not limited to, a departure point, a destination, a travel itinerary, and desired tourist spots. The reception unit receives information such as the user's desired departure point, destination, travel itinerary, and desired tourist spots. The generation unit uses a generation AI to create an efficient travel plan based on the information received by the reception unit, taking into account timetables and facility opening hours. The generation unit calculates an optimal route based on, for example, map data, timetable data, and facility opening hours data, and generates a travel plan along that route. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and generates an optimal travel plan based on information input by the user. The reservation unit makes reservations for restaurants and activities based on the travel plan generated by the generation unit. The reservation unit automatically makes reservations for restaurants and activities included in the travel plan, for example. This allows the travel plan creation system according to the embodiment to efficiently receive a user's travel conditions, generate an optimal travel plan, and make reservations.

[0062] The generation unit can calculate an efficient route based on map data, timetable data, and facility operating hours data, and generate a travel plan that follows that route. The generation unit can calculate an efficient route based on, for example, map data, timetable data, and facility operating hours data, and generate a travel plan that follows that route. For example, the generation unit can calculate an optimal travel route based on map data and minimize travel time based on timetable data. The generation unit can also determine an optimal order of tourist spots based on facility operating hours data. This makes it possible to calculate an optimal route based on map data, timetable data, and facility operating hours data, and generate an efficient travel plan. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input map data, timetable data, and facility operating hours data into the generation AI and have the generation AI calculate the optimal route.

[0063] The reservation unit can automatically make reservations for restaurants and activities included in the travel plan. The reservation unit, for example, automatically makes reservations for restaurants and activities included in the travel plan. For example, the reservation unit makes restaurant reservations and confirms the reservations based on the travel plan. The reservation unit can also make activity reservations and confirm the reservations. Furthermore, the reservation unit can also automate the reservation confirmation procedure in cooperation with a reservation system. This allows restaurants and activities to be automatically booked based on the travel plan. Some or all of the above-described processing in the reservation unit may be performed using, or without, the generation AI. For example, the reservation unit can input information about restaurants and activities included in the travel plan into the generation AI and have the generation AI make the reservations.

[0064] The reception unit can accept the user's desired departure point and destination, travel itinerary, desired tourist spots, and related information. The reception unit, for example, accepts the user's desired departure point and destination, travel itinerary, desired tourist spots, and related information. For example, the reception unit provides an interface through which the user inputs the desired departure point and destination. The reception unit can also provide an interface through which the user inputs the desired travel itinerary. Furthermore, the reception unit can also provide an interface through which the user inputs the desired tourist spots. This allows the user to accept detailed travel conditions desired by the user. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the information entered by the user to a generation AI, which can analyze the information.

[0065] The reception unit can estimate the user's emotions and customize the input interface for travel conditions based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and customizes the input interface for travel conditions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple and intuitive interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input of travel conditions. This allows the input interface to be customized according to the user's emotions, thereby providing a more user-friendly system. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0066] The reception unit can analyze the user's past travel history and suggest an efficient method for inputting travel parameters. The reception unit, for example, analyzes the user's past travel history and suggests an efficient method for inputting travel parameters. For example, the reception unit automatically suggests related travel parameters based on places the user has visited and services the user has used in the past. The reception unit can also prioritize suggestions based on input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest travel parameters related to specific seasons or events based on the user's past travel history. This can reduce the effort required for input by suggesting the optimal input method based on the user's past travel history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past travel history data into the generation AI and have the generation AI suggest the optimal input method.

[0067] The reception unit can automatically acquire the user's current location information and set it as the departure point when accepting travel conditions. For example, the reception unit can automatically acquire the user's current location information and set it as the departure point when accepting travel conditions. For example, when the user opens the app, the reception unit can automatically acquire the user's current location and set it as the departure point. Furthermore, when the user inputs a destination, the reception unit can suggest optimal candidate destinations taking into account the distance from the current location. Furthermore, when the user uses the app while traveling, the reception unit can update the user's current location in real time and reflect it as the departure point. This reduces the effort required for input by automatically acquiring the user's current location information and setting it as the departure point. Some or all of the above-described processing in the reception unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's current location information into the generation AI and have the generation AI set the departure point.

[0068] The reception unit can adjust input items to reflect the user's past feedback when accepting travel conditions. For example, the reception unit can adjust input items to reflect the user's past feedback when accepting travel conditions. For example, the reception unit can delete unnecessary input items and display only necessary items based on feedback previously provided by the user. The reception unit can also set priorities for specific conditions and optimize input items based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and adjust the design and layout of the input interface. This allows for optimizing input items to reflect the user's past feedback, thereby providing a more user-friendly system. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI adjust the input items.

[0069] The reception unit can estimate the user's emotions and prioritize the travel conditions based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and prioritizes the travel conditions based on the estimated user emotions. For example, if the user is relaxed, the reception unit can prioritize suggesting tourist spots and activities. Furthermore, if the user is feeling stressed, the reception unit can prioritize suggesting relaxing places and activities. Furthermore, if the user is in a hurry, the reception unit can prioritize suggesting routes and means of transportation that can shorten travel time. This allows the user to prioritize the travel conditions based on the user's emotions, thereby providing a more appropriate travel plan. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0070] The reception unit can analyze the user's social media activity and suggest related travel conditions when receiving travel conditions. For example, the reception unit can analyze the user's social media activity and suggest related travel conditions when receiving travel conditions. For example, the reception unit can suggest related travel conditions based on the locations where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest tourist spots and activities that the user may be interested in. Furthermore, the reception unit can suggest related travel conditions based on the activity of the user's friends on social media. This allows for the provision of more appropriate travel plans by suggesting related travel conditions based on the user's social media activity. Some or all of the above-described processing in the reception unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's social media data into the generation AI and have the generation AI suggest related travel conditions.

[0071] The reception unit can provide an efficient input interface by taking into account the user's device information when accepting travel conditions. For example, the reception unit can provide an efficient input interface by taking into account the user's device information when accepting travel conditions. For example, if the user is using a smartphone, the reception unit can provide an input interface tailored to the screen size. Furthermore, if the user is using a tablet, the reception unit can provide an input interface optimized for a large screen. Furthermore, if the user is using a smartwatch, the reception unit can provide a simple and highly visible input interface. This allows for the provision of an optimal input interface according to the user's device information, thereby providing a more user-friendly system. Some or all of the above-described processing in the reception unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's device information into the generation AI and have the generation AI provide the optimal input interface.

[0072] The reception unit can automatically suggest recommended travel destinations based on the user's past travel history when receiving travel conditions. For example, the reception unit can automatically suggest recommended travel destinations based on the user's past travel history when receiving travel conditions. For example, the reception unit can automatically suggest related travel destinations based on places the user has visited or services they have used. The reception unit can also predict and suggest travel destinations related to specific seasons or events based on the user's past travel history. Furthermore, the reception unit can analyze the user's past travel history and suggest the most popular travel destinations. This allows for more appropriate travel plans to be provided by suggesting recommended travel destinations based on the user's past travel history. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's past travel history data into the generation AI and have the generation AI suggest recommended travel destinations.

[0073] The generation unit can estimate the user's emotions and adjust the representation of the travel plan based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the representation of the travel plan based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a travel plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate a travel plan that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can also generate a travel plan that adds visually stimulating effects. This allows for providing a more appropriate travel plan by adjusting the representation of the travel plan according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0074] When generating a travel plan, the generation unit can refer to the user's past travel history to enhance the level of detail of the plan. For example, when generating a travel plan, the generation unit can refer to the user's past travel history to enhance the level of detail of the plan. For example, the generation unit can suggest related travel plans based on places the user has visited and services they have used in the past. The generation unit can also predict and suggest travel plans related to specific seasons or events based on the user's past travel history. Furthermore, the generation unit can analyze the user's past travel history and suggest the most popular travel plans. This can improve the accuracy of the plan by referring to the user's past travel history. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input the user's past travel history data into the generation AI and have the generation AI make suggestions to enhance the level of detail of the plan.

[0075] The generation unit can create an efficient travel plan by taking into account the user's current health condition when generating the travel plan. For example, the generation unit can create an efficient plan by taking into account the user's current health condition when generating the travel plan. For example, if the user is tired, the generation unit can create a plan that includes relaxing places and activities. Also, if the user is seeking healthy exercise, the generation unit can create a plan that includes active activities. Furthermore, if the user is feeling unwell, the generation unit can create a plan that includes rest points. This makes it possible to provide a more appropriate travel plan by creating an optimal plan based on the user's health condition. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's health data into the generation AI and have the generation AI create an optimal plan.

[0076] The generation unit can customize tourist spots based on the user's interests when generating a travel plan. The generation unit customizes tourist spots based on the user's interests when generating a travel plan, for example. For example, the generation unit incorporates tourist spots that the user is interested in into the plan. The generation unit can also incorporate stores and restaurants that the user frequently visits into the plan. Furthermore, the generation unit can incorporate places that the user is likely to be interested in into the plan based on the user's past search history. This allows for customizing tourist spots based on the user's interests, making it possible to provide a more appropriate travel plan. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's interest data into the generation AI and have the generation AI customize the tourist spots.

[0077] The generation unit can estimate the user's emotions and adjust the length of the travel plan based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the length of the travel plan based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short and concise travel plan. If the user is relaxed, the generation unit can generate a longer travel plan with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a travel plan with visually stimulating effects. This allows the length of the travel plan to be adjusted according to the user's emotions, thereby providing a more appropriate travel plan. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0078] The generation unit can propose an efficient route by taking into account the user's geographical location information when generating a travel plan. For example, the generation unit can propose an efficient route by taking into account the user's geographical location information when generating a travel plan. For example, the generation unit can prioritize proposing tourist spots closest to the user's current location. The generation unit can also propose an efficient travel route based on the user's geographical location information. Furthermore, the generation unit can propose a route that avoids congestion by taking into account the user's geographical location information. This makes it possible to provide a more efficient travel plan by proposing an optimal route by taking into account the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI propose an efficient route.

[0079] The generation unit can analyze the user's social media activities and suggest related tourist spots when generating a travel plan. For example, the generation unit can analyze the user's social media activities and suggest related tourist spots when generating a travel plan. For example, the generation unit can suggest related tourist spots based on locations where the user has checked in on social media. The generation unit can also analyze the content of the user's social media posts and suggest tourist spots that the user may be interested in. Furthermore, the generation unit can also suggest related tourist spots based on the activities of the user's friends on social media. This allows for a more appropriate travel plan to be provided by suggesting related tourist spots based on the user's social media activities. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input the user's social media data into the generation AI and have the generation AI suggest related tourist spots.

[0080] The generation unit can adjust the contents of the plan by reflecting the user's past feedback when generating the travel plan. For example, the generation unit can adjust the contents of the plan by reflecting the user's past feedback when generating the travel plan. For example, the generation unit can delete unnecessary tourist spots and activities and display only necessary items based on feedback provided by the user in the past. The generation unit can also set priorities for specific conditions based on the user's past feedback and optimize the contents of the plan. Furthermore, the generation unit can analyze the user's past feedback and adjust the design and layout of the plan. This allows the user's past feedback to be reflected in optimizing the contents of the plan, thereby providing a more appropriate travel plan. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input the user's past feedback data into the generation AI and have the generation AI adjust the contents of the plan.

[0081] The reservation unit can estimate the user's emotions and prioritize reservations based on the estimated user emotions. The reservation unit, for example, estimates the user's emotions and prioritizes reservations based on the estimated user emotions. For example, if the user is relaxed, the reservation unit can prioritize reservations for tourist spots and activities. Furthermore, if the user is feeling stressed, the reservation unit can prioritize reservations for relaxing places and activities. Furthermore, if the user is in a hurry, the reservation unit can prioritize reservations for routes and means of transportation that shorten travel time. This allows for more appropriate reservations by prioritizing reservations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reservation unit can be performed using, for example, the generation AI. For example, the reservation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0082] The reservation unit can suggest the optimal reservation method by referring to the user's past reservation history when making a reservation. For example, the reservation unit can suggest the optimal reservation method by referring to the user's past reservation history when making a reservation. For example, the reservation unit can suggest a relevant reservation method based on restaurants and activities the user has used in the past. The reservation unit can also predict and suggest reservation methods related to specific seasons or events from the user's past reservation history. Furthermore, the reservation unit can analyze the user's past reservation history and suggest the most popular reservation method. In this way, the optimal reservation method can be suggested by referring to the user's past reservation history. Some or all of the above-mentioned processing in the reservation unit may be performed using, or without, a generation AI. For example, the reservation unit can input the user's past reservation history data into the generation AI and have the generation AI suggest the optimal reservation method.

[0083] The reservation unit can propose an efficient reservation time by taking into account the user's current schedule when making a reservation. For example, the reservation unit can propose an efficient reservation time by taking into account the user's current schedule when making a reservation. For example, the reservation unit can refer to the user's calendar information and propose a reservation during an available time slot. The reservation unit can also propose an optimal reservation time by taking travel time into account, based on the user's schedule. Furthermore, the reservation unit can adjust and propose the reservation time in real time if the user's schedule changes. This makes it possible to propose an optimal reservation time by taking into account the user's current schedule. Some or all of the above-described processing in the reservation unit may be performed using, or without, a generation AI. For example, the reservation unit can input the user's calendar information into the generation AI and have the generation AI propose an efficient reservation time.

[0084] The reservation unit can improve the accuracy of reservations by reflecting user feedback at the time of reservation. For example, the reservation unit can improve the accuracy of reservations by reflecting user feedback at the time of reservation. For example, the reservation unit can delete unnecessary reservation options and display only necessary items based on feedback previously provided by the user. The reservation unit can also set priorities for specific conditions and optimize the reservation content based on the user's past feedback. Furthermore, the reservation unit can analyze the user's past feedback and adjust the design and layout of the reservation interface. This allows the accuracy of reservations to be improved by reflecting user feedback. Some or all of the above-described processing in the reservation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reservation unit can input the user's past feedback data into the generation AI and have the generation AI improve the accuracy of reservations.

[0085] The reservation unit can estimate a user's emotions and adjust the display method of the reservation based on the estimated user emotions. For example, the reservation unit can estimate a user's emotions and adjust the display method of the reservation based on the estimated user emotions. For example, if the user is nervous, the reservation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the reservation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the reservation unit can provide a display method that focuses on the main points. This allows the reservation display method to be adjusted according to the user's emotions, thereby providing more appropriate reservation information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reservation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reservation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0086] The reservation unit can suggest an efficient reservation destination by taking into account the user's geographical location information when making a reservation. The reservation unit, for example, can suggest an efficient reservation destination by taking into account the user's geographical location information when making a reservation. For example, the reservation unit prioritizes suggesting restaurants or activities closest to the user's current location. The reservation unit can also suggest a reservation destination that takes into account an efficient travel route based on the user's geographical location information. Furthermore, the reservation unit can suggest a reservation destination that avoids congestion by taking into account the user's geographical location information. In this way, the optimal reservation destination can be suggested by taking into account the user's geographical location information. Some or all of the above-described processing in the reservation unit may be performed using, or without, a generation AI. For example, the reservation unit can input the user's geographical location information into the generation AI and have the generation AI suggest an efficient reservation destination.

[0087] The reservation unit can analyze the user's social media activity and suggest related reservation destinations at the time of reservation. For example, the reservation unit can analyze the user's social media activity and suggest related reservation destinations at the time of reservation. For example, the reservation unit can suggest related reservation destinations based on the location where the user checked in on social media. The reservation unit can also analyze the user's social media posts to suggest restaurants and activities that the user may be interested in. Furthermore, the reservation unit can suggest related reservation destinations based on the activity of the user's friends on social media. This allows for more appropriate reservations by suggesting related reservation destinations based on the user's social media activity. Some or all of the above-described processing in the reservation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reservation unit can input the user's social media data into the generation AI and have the generation AI suggest related reservation destinations.

[0088] The reservation unit can optimize the reservation content by reflecting the user's past feedback at the time of reservation. For example, the reservation unit can optimize the reservation content by reflecting the user's past feedback at the time of reservation. For example, the reservation unit can delete unnecessary reservation options and display only necessary items based on feedback provided by the user in the past. The reservation unit can also set priorities for specific conditions based on the user's past feedback and optimize the reservation content. Furthermore, the reservation unit can analyze the user's past feedback and adjust the design and layout of the reservation interface. In this way, the reservation content can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the reservation unit can be performed using, or without, a generation AI. For example, the reservation unit can input the user's past feedback data into the generation AI and have the generation AI optimize the reservation content. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and reservation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives the user's travel conditions. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates an optimal travel plan using a generation AI. The reservation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes reservations for restaurants and activities based on the generated travel plan. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and reservation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives the user's travel conditions. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates an optimal travel plan using a generation AI. The reservation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes restaurant and activity reservations based on the generated travel plan. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and reservation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives the user's travel conditions. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates an optimal travel plan using a generation AI. The reservation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes reservations for restaurants and activities based on the generated travel plan. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and reservation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives the user's travel conditions. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates an optimal travel plan using a generation AI. The reservation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes reservations for restaurants and activities based on the generated travel plan.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] When accepting the user's travel conditions, the reception unit can analyze the user's past travel history and automatically suggest similar travel conditions. For example, it can suggest related travel conditions based on tourist spots the user has visited or services the user has used in the past. The reception unit can also prioritize suggestions based on input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest travel conditions related to specific seasons or events based on the user's past travel history. This reduces the effort required for input by suggesting the optimal input method based on the user's past travel history.

[0091] The generation unit can adjust the travel plan taking into account the user's current health condition. For example, if the user is tired, the generation unit can create a plan that includes places and activities where the user can relax. Also, if the user is seeking healthy exercise, the generation unit can create a plan that includes active activities. Furthermore, if the user is feeling unwell, the generation unit can create a plan that includes rest points. In this way, the generation unit can provide a more appropriate travel plan by creating an optimal plan according to the user's health condition.

[0092] The reservation unit can estimate the user's emotions and determine the priority of reservations based on the estimated user's emotions. For example, if the user is relaxed, reservations for tourist spots and activities can be prioritized. Also, if the user is feeling stressed, reservations for places and activities that allow relaxation can be prioritized. Furthermore, if the user is in a hurry, reservations for routes and means of transportation that shorten travel time can be prioritized. In this way, by prioritizing reservations according to the user's emotions, more appropriate reservations can be provided.

[0093] The reception unit can analyze the user's social media activity and suggest relevant travel conditions. For example, it can suggest relevant travel conditions based on the locations where the user has checked in on social media. It can also analyze the content of the user's social media posts to suggest tourist spots and activities that the user may be interested in. It can also suggest relevant travel conditions based on the activities of the user's friends on social media. This makes it possible to provide more appropriate travel plans by suggesting relevant travel conditions based on the user's social media activity.

[0094] The generation unit can estimate the user's emotions and adjust the way the travel plan is presented based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a travel plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can generate a travel plan that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a travel plan that adds visually stimulating effects. In this way, by adjusting the way the travel plan is presented according to the user's emotions, a more appropriate travel plan can be provided.

[0095] The reception unit can provide an efficient input interface by taking into account the user's device information. For example, if the user is using a smartphone, an input interface that matches the screen size can be provided. Also, if the user is using a tablet, an input interface optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, an input interface that is simple and highly visible can be provided. This makes it possible to provide a system that is easier to use by providing an optimal input interface according to the user's device information.

[0096] The generation unit can increase the level of detail of the plan by referring to the user's past travel history. For example, it can suggest related travel plans based on places the user has visited and services they have used in the past. It can also predict and suggest travel plans related to specific seasons or events based on the user's past travel history. It can also analyze the user's past travel history and suggest the most popular travel plans. This allows the accuracy of the plan to be improved by referring to the user's past travel history.

[0097] The reservation unit can estimate the user's emotions and adjust the display method of reservations based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of reservations according to the user's emotions, more appropriate reservation information can be provided.

[0098] The generation unit can estimate the user's emotions and adjust the length of the travel plan based on the estimated user emotions. For example, if the user is in a hurry, a short and to-the-point travel plan can be generated. If the user is relaxed, a longer travel plan with detailed explanations can be generated. Furthermore, if the user is excited, a travel plan with visually stimulating effects can be generated. In this way, by adjusting the length of the travel plan according to the user's emotions, a more appropriate travel plan can be provided.

[0099] The reception unit can adjust input items by reflecting the user's past feedback. For example, it can delete unnecessary input items and display only necessary items based on the user's past feedback. It can also set priorities for specific conditions based on the user's past feedback and optimize the input items. Furthermore, it can analyze the user's past feedback and adjust the design and layout of the input interface. In this way, it is possible to provide a system that is easier to use by optimizing the input items by reflecting the user's past feedback.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The reception unit receives the user's travel conditions. The user's travel conditions include, for example, the departure point, destination, travel dates, and desired tourist spots. By receiving this information, the reception unit can create a travel plan based on the user's wishes. Step 2: The generation unit creates an efficient travel plan based on the information received by the reception unit, taking into account timetables and facility opening hours. The generation unit calculates the optimal route based on map data, timetable data, and facility opening hours data, and generates a travel plan that follows that route. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to generate the optimal travel plan based on the information input by the user. Step 3: The reservation unit makes reservations for restaurants and activities based on the travel plan generated by the generation unit. The reservation unit automatically makes reservations for restaurants and activities included in the travel plan.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0157] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0158] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0163] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0164] 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.

[0165] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0166] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0167] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0168] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0170] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0171] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0172] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0173] [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives travel conditions from a user; a generation unit that generates an efficient travel plan based on the information received by the reception unit, taking into account timetables and facility opening hours; a reservation unit that makes reservations for restaurants and activities based on the travel plan generated by the generation unit. A system characterized by:

2. The generation unit Calculates efficient routes based on map data, timetable data, and facility opening hours data, and generates travel plans along those routes 2. The system of claim 1.

3. The reservation unit Automatically book restaurant and activity reservations for your trip 2. The system of claim 1.

4. The reception unit Accepts the user's desired departure and destination, travel dates, desired tourist spots, and related information 2. The system of claim 1.

5. The reception unit The user's emotions are estimated, and the input interface for travel conditions is customized based on the estimated user's emotions.

2. The system of claim 1.

6. The reception unit Analyzes the user's past travel history and suggests efficient ways to input travel conditions 2. The system of claim 1.

7. The reception unit When accepting travel conditions, the user's current location information is automatically acquired and set as the departure point.

2. The system of claim 1.

8. The reception unit When accepting travel conditions, adjust the input fields based on the user's past feedback.

2. The system of claim 1.

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