System

Abstract

An object of a system according to an embodiment is to enable a traveler to create his or her own original travel plan and to collectively make reservations for restaurants and activities.SOLUTION: A system includes an information collection part, an analysis part, a plan generation part, and a reservation part. The information collection part collects information based on the desire of the traveler. The analysis unit analyzes the information collected by the information collection unit. The plan generator generates an original travel plan based on the information analyzed by the analyzer. The reservation unit makes a reservation for a restaurant or an activity based on the travel plan generated by the plan 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 the drawback of making it difficult for travelers to create their own original travel plans and make reservations for restaurants and activities all at once.

[0005] The system according to the embodiment aims to enable travelers to create their own original travel plans and make reservations for restaurants and activities all at once. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, a plan creation unit, and a reservation unit. The information collection unit collects information based on the traveler's preferences. The analysis unit analyzes the information collected by the information collection unit. The plan creation unit creates an original travel plan based on the information analyzed by the analysis unit. The reservation unit makes reservations for restaurants and activities based on the travel plan created by the plan creation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows travelers to create their own original travel plans and make reservations for restaurants and activities 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The travel plan creation system according to an embodiment of the present invention is a system that allows travelers to instantly create their own original travel plans and make reservations for restaurants and activities all at once. This allows the travel plan creation system to create an optimal travel plan based on the travelers' wishes and make reservations all at once.

[0029] The travel plan creation system according to the embodiment includes an information collection unit, an analysis unit, a plan creation unit, and a reservation unit. The information collection unit collects information based on a traveler's preferences. For example, the information collection unit inputs information such as the traveler's desired travel destination, itinerary, places they want to visit, and activities they want to experience. The information collection unit can also collect the traveler's past travel history and reviews. For example, the information collection unit analyzes reviews of places the traveler has previously visited and activities they have participated in. The information collection unit can also analyze the traveler's social media posts to reflect their interests and preferences in real time. For example, the information collection unit can identify tourist spots and activities of interest from photos and comments posted by the traveler. The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit collects and analyzes information such as timetables and facility opening hours to propose efficient travel routes. The analysis unit can also dynamically adjust the plan taking into account information on transportation delays and real-time congestion conditions. For example, the analysis unit recalculates travel times based on train delay information. The analysis unit can also collect information on special events and seasonal events at facilities and reflect this information in the plan. For example, it incorporates special museum exhibitions and seasonal events into the plan. The plan generation unit generates an original travel plan based on the information analyzed by the analysis unit. For example, the generation AI generates an original travel plan that matches the traveler's preferences. The plan generation unit can also dynamically adjust the plan and suggest the optimal route based on the traveler's real-time location information. For example, it can suggest the tourist attraction closest to the current location. Furthermore, the plan generation unit can add surprise elements to the plan based on the traveler's preferences to provide unexpected enjoyment. For example, it can suggest events and activities related to the traveler's favorite themes. The reservation unit makes restaurant and activity reservations based on the travel plan generated by the plan generation unit. For example, it automatically makes reservations for restaurants and activities included in the travel plan. The reservation unit can also suggest the most suitable restaurant based on the traveler's dietary preferences and allergy information. For example, it can suggest vegetarian and gluten-free restaurants.Furthermore, the reservation unit can analyze reviews of restaurants and activities and prioritize reservations for highly rated restaurants. For example, it can suggest restaurants with many positive reviews. This allows the travel plan generation system according to the embodiment to generate an original travel plan based on the traveler's preferences and make reservations all at once. For example, the traveler can efficiently visit the tourist spots and activities they desire, making travel planning go smoothly. Also, eliminating the hassle of making reservations simplifies travel preparations.

[0030] The information collection unit analyzes a traveler's past travel history or reviews and can make optimal suggestions to each individual traveler. For example, the information collection unit retrieves the traveler's past travel history from a database and analyzes reviews of places visited and activities participated in. For example, the traveler's preferences are identified based on ratings of cities visited in the past and tours participated in. The information collection unit also analyzes reviews and ratings posted by the traveler in the past to understand the traveler's preferences and interests. For example, tourist spots and restaurants that the traveler has given high ratings are prioritized for suggestions. The information collection unit also makes new suggestions based on the traveler's past travel history and reviews of similar travelers. For example, the travel plan is customized based on reviews of other travelers with the same hobbies and interests. This makes it possible to make optimal suggestions based on the traveler's past travel history and reviews.

[0031] The information collection unit can analyze the traveler's social media posts and reflect their interests or preferences in real time. For example, the information collection unit connects the traveler's social media accounts and analyzes the content of the posts. For example, it identifies tourist spots and activities of interest from photos and comments posted by the traveler. The information collection unit also analyzes the content of the social media posts in real time to reflect the traveler's latest interests and preferences. For example, it incorporates places and events discussed in recent posts into the travel plan. The information collection unit also analyzes posts from the traveler's social media followers and friends to suggest places and activities of common interest. For example, it adds restaurants and tourist spots that friends have given high ratings to the travel plan. This makes it possible to reflect the traveler's interests and preferences in real time based on the traveler's social media posts.

[0032] The information collection unit can propose a reasonable plan taking into account the traveler's health condition or physical fitness level. The information collection unit, for example, provides a function for inputting the traveler's health condition and physical fitness level and generates a plan based on that. For example, a plan with shorter travel distances is proposed for a traveler with low physical fitness. The information collection unit also builds a system that proposes activities taking into account the traveler's health condition and physical fitness level. For example, activities that require physical fitness are avoided and relaxing activities are proposed. The information collection unit also collects the traveler's health data and generates a reasonable plan. For example, an appropriate plan is proposed based on data from a smartwatch or fitness tracker. This makes it possible to propose a reasonable plan taking into account the traveler's health condition and physical fitness level.

[0033] The analysis unit can dynamically adjust the plan by taking into account transportation delay information or real-time congestion conditions. The analysis unit, for example, collects transportation delay information in real time and builds a system that dynamically adjusts the plan. For example, it recalculates travel time based on train delay information. The analysis unit also considers real-time congestion conditions and proposes an optimal travel route to avoid congestion. For example, it proposes less crowded routes by avoiding congested routes. The analysis unit also develops a system that dynamically adjusts the travel plan based on transportation delay information and congestion conditions. For example, if a delay occurs, it adjusts the start time of the next activity. This makes it possible to dynamically adjust the plan by taking into account transportation delay information and congestion conditions.

[0034] The analysis unit can collect information about special events or seasonal events at facilities and reflect it in the plan. For example, the analysis unit builds a system that automatically collects information about special events or seasonal events at facilities and reflects it in a travel plan. For example, it incorporates special exhibitions at art museums and seasonal events into the plan. The analysis unit also collects seasonal event information and reflects it in a travel plan. For example, it suggests cherry blossom viewing spots during cherry blossom season. The analysis unit also develops a system that updates information about special events or seasonal events at facilities in real time and reflects it in a travel plan. For example, it automatically incorporates the dates and locations of events into the plan. This makes it possible to collect information about special events or seasonal events at facilities and reflect it in a plan.

[0035] The plan generation unit can add surprise elements to the plan according to the traveler's preferences, providing unexpected fun. The plan generation unit, for example, analyzes the traveler's preferences and builds a system that adds surprise elements to the plan. For example, it suggests events and activities related to themes that the traveler likes. The plan generation unit also adds surprise elements based on the traveler's past travel history and reviews. For example, it suggests new spots related to places that the traveler has given high ratings. The plan generation unit also generates a plan that provides unexpected fun according to the traveler's preferences. For example, it suggests hidden sights and local events that the traveler might be interested in. This makes it possible to add surprise elements to the plan according to the traveler's preferences, providing unexpected fun.

[0036] The reservation department can suggest the most suitable restaurant by taking into account the traveler's dietary preferences or allergy information. For example, the reservation department provides a function for inputting the traveler's dietary preferences and allergy information, and builds a system that suggests the most suitable restaurant based on that. For example, it suggests vegetarian or gluten-free restaurants. The reservation department also incorporates the most suitable restaurants into the travel plan based on the dietary preferences and allergy information. For example, it prioritizes suggesting restaurants that serve the traveler's favorite cuisine. The reservation department also develops a system that analyzes the traveler's dietary preferences and allergy information in real time and suggests the most suitable restaurant. For example, it dynamically updates the restaurant list according to the traveler's input. This makes it possible to suggest the most suitable restaurant by taking into account the traveler's dietary preferences and allergy information.

[0037] The reservation unit can analyze reviews of restaurants or activities and prioritize reservations for those with high ratings. The reservation unit, for example, analyzes reviews of restaurants and activities and builds a system that prioritizes reservations for those with high ratings. For example, it suggests restaurants with many positive reviews. The reservation unit also prioritizes reservations for highly rated restaurants and activities based on traveler preferences. For example, it suggests activities that travelers have given high ratings. The reservation unit also develops a system that incorporates the best restaurants and activities into travel plans based on reviews and ratings. For example, it prioritizes suggestions for those with high rating scores. This makes it possible to analyze reviews of restaurants and activities and prioritize reservations for those with high ratings.

[0038] The reservation department can monitor the reservation status of restaurants or activities in real time and notify users of waiting lists or availability. For example, the reservation department builds a system that monitors the reservation status of restaurants and activities in real time and notifies users of waiting lists or availability. For example, it notifies users when a reservation is canceled. The reservation department also provides a function that allows travelers to check the availability of their desired restaurants or activities in real time. For example, it notifies users immediately when a space becomes available. The reservation department also develops a system that manages a waiting list and automatically makes reservations when a space becomes available. For example, it gives priority to reservations to travelers on the waiting list. This makes it possible to monitor the reservation status of restaurants and activities in real time and notify users of waiting lists or availability.

[0039] The reservation department can provide travelers with safety or risk information for activities they will participate in, allowing them to participate with peace of mind. The reservation department, for example, builds a system that provides safety and risk information for activities they will participate in. For example, it displays safety guidelines and risk assessments for activities. The reservation department also enables travelers to participate with peace of mind based on the safety and risk information for activities. For example, it prioritizes suggesting activities with high safety. The reservation department also develops a system that provides safety and risk information taking into account the traveler's health condition and physical fitness level. For example, it displays risk information for activities that require physical fitness. This allows travelers to provide safety and risk information for activities they will participate in, allowing them to participate with peace of mind.

[0040] The plan generation unit can dynamically adjust the plan based on real-time feedback from travelers. The plan generation unit, for example, collects real-time feedback from travelers and builds a system that dynamically adjusts the plan based on that feedback. For example, it adds places that travelers want to visit. The plan generation unit also updates the plan in real time based on the travelers' feedback. For example, if a traveler wants to change the length of stay, it is immediately reflected. The plan generation unit also analyzes real-time feedback and develops a system that dynamically adjusts the travel plan. For example, it regenerates the plan based on the travelers' opinions. This makes it possible to dynamically adjust the plan based on the travelers' real-time feedback.

[0041] The plan generation unit can analyze the traveler's past customization history and make optimal customization suggestions. The plan generation unit, for example, retrieves the traveler's past customization history from a database and builds a system that makes optimal customization suggestions based on that. For example, suggestions are made based on tourist attractions and activities added in the past. The plan generation unit also analyzes the traveler's past customization history and makes new suggestions by referring to the customization history of similar travelers. For example, suggestions are made based on the customization history of other travelers with the same hobbies and interests. The plan generation unit also develops a system that automatically customizes travel plans based on the traveler's past customization history. For example, activities that have been highly rated in the past are given priority in suggestions. This makes it possible to analyze the traveler's past customization history and make optimal customization suggestions.

[0042] The plan generation unit can incorporate advice from local guides or experts when customizing the travel plan. For example, the plan generation unit builds a system that incorporates advice from local guides and experts into the travel plan. For example, it suggests recommended local spots and hidden gems. The plan generation unit also works with local guides and experts to add expert advice to the travel plan. For example, it provides historical background and cultural information. The plan generation unit also develops a system that customizes the travel plan based on reviews and ratings from local guides and experts. For example, it suggests highly rated guided tours. This makes it possible to incorporate advice from local guides and experts when customizing the travel plan.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The travel plan generation system can also include a health management unit that monitors the traveler's health condition and proposes health-conscious plans. For example, it can monitor the traveler's heart rate and blood pressure in real time and propose activities to avoid excessive exercise. The health management unit can also propose appropriate restaurants taking into account the traveler's dietary restrictions and allergy information. For example, it can propose restaurants that offer low-carb menus to a diabetic traveler. Furthermore, the health management unit can propose activities for relaxation and stress relief based on the traveler's health condition. For example, it can propose yoga or meditation sessions. This makes it possible to provide plans that take the traveler's health condition into consideration.

[0045] The travel plan generation system may further include a feedback collection unit that dynamically adjusts the plan based on real-time feedback from travelers. For example, a traveler may add a place they want to visit. The feedback collection unit also updates the plan in real time based on the traveler's feedback. For example, if a traveler wants to change the length of stay, this is immediately reflected. The feedback collection unit also analyzes the real-time feedback and develops a system that dynamically adjusts the travel plan. For example, the plan is regenerated based on the traveler's opinions. This makes it possible to dynamically adjust the plan based on real-time feedback from travelers.

[0046] The travel plan generation system can also be equipped with a customization history analysis unit that analyzes a traveler's past customization history and makes optimal customization suggestions. For example, a system is constructed that retrieves a traveler's past customization history from a database and makes optimal customization suggestions based on that. For example, suggestions are made based on tourist attractions and activities added in the past. The customization history analysis unit also analyzes a traveler's past customization history and makes new suggestions by referring to the customization history of similar travelers. For example, suggestions are made based on the customization history of other travelers with the same hobbies and interests. The customization history analysis unit also develops a system that automatically customizes travel plans based on a traveler's past customization history. For example, it prioritizes suggestions of activities that have been highly rated in the past. This makes it possible to analyze a traveler's past customization history and make optimal customization suggestions.

[0047] The travel plan generation system can also be equipped with a safety management section to ensure the safety of travelers. For example, a system is constructed that provides safety and risk information for activities in which travelers participate. For example, safety guidelines and risk assessments for activities are displayed. The safety management section also enables travelers to participate with peace of mind based on the safety and risk information for activities. For example, it prioritizes suggesting activities with high safety. The safety management section also develops a system that provides safety and risk information taking into account the traveler's health condition and physical fitness level. For example, it displays risk information for activities that require physical fitness. This makes it possible to provide safety and risk information for activities in which travelers participate, allowing them to participate with peace of mind.

[0048] The travel plan generation system can also be equipped with a guide collaboration unit that incorporates advice from local guides and experts. For example, a system can be built that incorporates advice from local guides and experts into travel plans. For example, it can suggest recommended local spots and hidden gems. The guide collaboration unit can also collaborate with local guides and experts to add specialized advice to travel plans. For example, it can provide historical background and cultural information. The guide collaboration unit can also develop a system that customizes travel plans based on reviews and ratings from local guides and experts. For example, it can suggest highly rated guided tours. This makes it possible to incorporate advice from local guides and experts into the customization of travel plans.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The information collection unit collects information based on the traveler's preferences. For example, the traveler inputs information such as the travel destination and itinerary they wish to travel to, the places they want to visit, and the activities they want to experience. The information collection unit can also collect the traveler's past travel history and reviews. For example, it analyzes reviews of places the traveler has visited and activities they have participated in in the past. Furthermore, the information collection unit can analyze the traveler's social media posts to reflect their interests and preferences in real time. For example, it can identify tourist spots and activities that the traveler is interested in from photos and comments posted by the traveler. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, it collects and analyzes information such as timetables and facility opening hours to propose efficient travel routes. The analysis unit can also dynamically adjust the plan by taking into account information about transportation delays and real-time congestion. For example, it can recalculate travel time based on train delay information. Furthermore, the analysis unit can collect information about special events and seasonal events at facilities and reflect this in the plan. For example, it can incorporate special exhibitions and seasonal events at art museums into the plan. Step 3: The plan generation unit generates an original travel plan based on the information analyzed by the analysis unit. For example, the generation AI generates an original travel plan that matches the traveler's preferences. The plan generation unit can also dynamically adjust the plan and suggest the optimal route based on the traveler's real-time location information. For example, it can suggest the tourist attraction closest to the current location. Furthermore, the plan generation unit can add surprise elements to the plan based on the traveler's preferences to provide unexpected fun. For example, it can suggest events and activities related to the traveler's favorite themes. Step 4: The reservation unit makes reservations for restaurants and activities based on the travel plan generated by the plan generation unit. For example, it automatically makes reservations for restaurants and activities included in the travel plan. The reservation unit can also suggest the most suitable restaurant taking into account the traveler's food preferences and allergy information. For example, it can suggest vegetarian or gluten-free restaurants. Furthermore, the reservation unit can analyze reviews of restaurants and activities and prioritize reservations for those with high ratings. For example, it can suggest restaurants with many positive reviews.

[0051] (Example 2) The travel plan creation system according to an embodiment of the present invention is a system that allows travelers to instantly create their own original travel plans and make reservations for restaurants and activities all at once. This allows the travel plan creation system to create an optimal travel plan based on the travelers' wishes and make reservations all at once.

[0052] The travel plan creation system according to the embodiment includes an information collection unit, an analysis unit, a plan creation unit, and a reservation unit. The information collection unit collects information based on a traveler's preferences. For example, the information collection unit inputs information such as the traveler's desired travel destination, itinerary, places they want to visit, and activities they want to experience. The information collection unit can also collect the traveler's past travel history and reviews. For example, the information collection unit analyzes reviews of places the traveler has previously visited and activities they have participated in. The information collection unit can also analyze the traveler's social media posts to reflect their interests and preferences in real time. For example, the information collection unit can identify tourist spots and activities of interest from photos and comments posted by the traveler. The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit collects and analyzes information such as timetables and facility opening hours to propose efficient travel routes. The analysis unit can also dynamically adjust the plan taking into account information on transportation delays and real-time congestion conditions. For example, the analysis unit recalculates travel times based on train delay information. The analysis unit can also collect information on special events and seasonal events at facilities and reflect this information in the plan. For example, it incorporates special museum exhibitions and seasonal events into the plan. The plan generation unit generates an original travel plan based on the information analyzed by the analysis unit. For example, the generation AI generates an original travel plan that matches the traveler's preferences. The plan generation unit can also dynamically adjust the plan and suggest the optimal route based on the traveler's real-time location information. For example, it can suggest the tourist attraction closest to the current location. Furthermore, the plan generation unit can add surprise elements to the plan based on the traveler's preferences to provide unexpected enjoyment. For example, it can suggest events and activities related to the traveler's favorite themes. The reservation unit makes restaurant and activity reservations based on the travel plan generated by the plan generation unit. For example, it automatically makes reservations for restaurants and activities included in the travel plan. The reservation unit can also suggest the most suitable restaurant based on the traveler's dietary preferences and allergy information. For example, it can suggest vegetarian and gluten-free restaurants.Furthermore, the reservation unit can analyze reviews of restaurants and activities and prioritize reservations for highly rated restaurants. For example, it can suggest restaurants with many positive reviews. This allows the travel plan generation system according to the embodiment to generate an original travel plan based on the traveler's preferences and make reservations all at once. For example, the traveler can efficiently visit the tourist spots and activities they desire, making travel planning go smoothly. Also, eliminating the hassle of making reservations simplifies travel preparations.

[0053] The information collection unit analyzes a traveler's past travel history or reviews and can make optimal suggestions to each individual traveler. For example, the information collection unit retrieves the traveler's past travel history from a database and analyzes reviews of places visited and activities participated in. For example, the traveler's preferences are identified based on ratings of cities visited in the past and tours participated in. The information collection unit also analyzes reviews and ratings posted by the traveler in the past to understand the traveler's preferences and interests. For example, tourist spots and restaurants that the traveler has given high ratings are prioritized for suggestions. The information collection unit also makes new suggestions based on the traveler's past travel history and reviews of similar travelers. For example, the travel plan is customized based on reviews of other travelers with the same hobbies and interests. This makes it possible to make optimal suggestions based on the traveler's past travel history and reviews.

[0054] The information collection unit can analyze the traveler's social media posts and reflect their interests or preferences in real time. For example, the information collection unit connects the traveler's social media accounts and analyzes the content of the posts. For example, it identifies tourist spots and activities of interest from photos and comments posted by the traveler. The information collection unit also analyzes the content of the social media posts in real time to reflect the traveler's latest interests and preferences. For example, it incorporates places and events discussed in recent posts into the travel plan. The information collection unit also analyzes posts from the traveler's social media followers and friends to suggest places and activities of common interest. For example, it adds restaurants and tourist spots that friends have given high ratings to the travel plan. This makes it possible to reflect the traveler's interests and preferences in real time based on the traveler's social media posts.

[0055] The information collection unit uses the emotion estimation function to analyze the emotional nuances of the wishes entered by the traveler and make more personalized suggestions. The information collection unit, for example, analyzes the text of the wishes entered by the traveler and identifies the emotional nuances using the emotion estimation function. For example, wishes with strong positive emotions are preferentially reflected. The information collection unit also uses the emotion estimation function to analyze the emotions entered by the traveler in real time and make suggestions based on the emotions. For example, activities that the traveler feels like enjoying are preferentially suggested. The information collection unit also analyzes the traveler's emotional state and makes suggestions that are emotionally satisfying. For example, if the traveler has a strong desire to relax, relaxation spots and spas are suggested. This allows the emotional nuances of the traveler's wishes to be analyzed and more personalized suggestions to be made.

[0056] The information collection unit can propose a reasonable plan taking into account the traveler's health condition or physical fitness level. The information collection unit, for example, provides a function for inputting the traveler's health condition and physical fitness level and generates a plan based on that. For example, a plan with shorter travel distances is proposed for a traveler with low physical fitness. The information collection unit also builds a system that proposes activities taking into account the traveler's health condition and physical fitness level. For example, activities that require physical fitness are avoided and relaxing activities are proposed. The information collection unit also collects the traveler's health data and generates a reasonable plan. For example, an appropriate plan is proposed based on data from a smartwatch or fitness tracker. This makes it possible to propose a reasonable plan taking into account the traveler's health condition and physical fitness level.

[0057] The analysis unit can dynamically adjust the plan by taking into account transportation delay information or real-time congestion conditions. The analysis unit, for example, collects transportation delay information in real time and builds a system that dynamically adjusts the plan. For example, it recalculates travel time based on train delay information. The analysis unit also considers real-time congestion conditions and proposes an optimal travel route to avoid congestion. For example, it proposes less crowded routes by avoiding congested routes. The analysis unit also develops a system that dynamically adjusts the travel plan based on transportation delay information and congestion conditions. For example, if a delay occurs, it adjusts the start time of the next activity. This makes it possible to dynamically adjust the plan by taking into account transportation delay information and congestion conditions.

[0058] The analysis unit can collect information about special events or seasonal events at facilities and reflect it in the plan. For example, the analysis unit builds a system that automatically collects information about special events or seasonal events at facilities and reflects it in a travel plan. For example, it incorporates special exhibitions at art museums and seasonal events into the plan. The analysis unit also collects seasonal event information and reflects it in a travel plan. For example, it suggests cherry blossom viewing spots during cherry blossom season. The analysis unit also develops a system that updates information about special events or seasonal events at facilities in real time and reflects it in a travel plan. For example, it automatically incorporates the dates and locations of events into the plan. This makes it possible to collect information about special events or seasonal events at facilities and reflect it in a plan.

[0059] The analysis unit uses the emotion estimation function to analyze reviews or ratings of facilities visited by travelers and can prioritize suggesting facilities that provide high emotional satisfaction. For example, the analysis unit uses the emotion estimation function to analyze reviews and ratings of facilities visited by travelers and identify facilities that provide high emotional satisfaction. For example, it prioritizes suggesting facilities with many positive reviews. The analysis unit also uses the emotion estimation function to analyze facility reviews and ratings and incorporate facilities that provide high emotional satisfaction into the travel plan. For example, it prioritizes suggesting facilities with high emotional scores. The analysis unit also analyzes the emotional state of travelers and builds a system that suggests facilities that provide high emotional satisfaction. For example, it suggests relaxation facilities to travelers who want to relax. This makes it possible to analyze reviews and ratings of facilities visited by travelers and prioritize suggesting facilities that provide high emotional satisfaction.

[0060] The plan generation unit can add surprise elements to the plan according to the traveler's preferences, providing unexpected fun. The plan generation unit, for example, analyzes the traveler's preferences and builds a system that adds surprise elements to the plan. For example, it suggests events and activities related to themes that the traveler likes. The plan generation unit also adds surprise elements based on the traveler's past travel history and reviews. For example, it suggests new spots related to places that the traveler has given high ratings. The plan generation unit also generates a plan that provides unexpected fun according to the traveler's preferences. For example, it suggests hidden sights and local events that the traveler might be interested in. This makes it possible to add surprise elements to the plan according to the traveler's preferences, providing unexpected fun.

[0061] The plan generation unit uses the emotion estimation function to monitor the emotional state of the traveler in real time and generate a plan that provides high emotional satisfaction. The plan generation unit, for example, uses the emotion estimation function to build a system that monitors the emotional state of the traveler in real time. For example, it analyzes the traveler's facial expressions and voice and calculates an emotion score. The plan generation unit also generates a plan that provides high emotional satisfaction based on the traveler's emotional state. For example, it prioritizes suggesting activities that evoke strong positive emotions. The plan generation unit also collects emotion estimation data in real time and develops a system that dynamically adjusts the travel plan. For example, it updates the plan according to changes in the traveler's emotions. This makes it possible to monitor the emotional state of the traveler in real time and generate a plan that provides high emotional satisfaction.

[0062] The reservation department can suggest the most suitable restaurant by taking into account the traveler's dietary preferences or allergy information. For example, the reservation department provides a function for inputting the traveler's dietary preferences and allergy information, and builds a system that suggests the most suitable restaurant based on that. For example, it suggests vegetarian or gluten-free restaurants. The reservation department also incorporates the most suitable restaurants into the travel plan based on the dietary preferences and allergy information. For example, it prioritizes suggesting restaurants that serve the traveler's favorite cuisine. The reservation department also develops a system that analyzes the traveler's dietary preferences and allergy information in real time and suggests the most suitable restaurant. For example, it dynamically updates the restaurant list according to the traveler's input. This makes it possible to suggest the most suitable restaurant by taking into account the traveler's dietary preferences and allergy information.

[0063] The reservation unit can analyze reviews of restaurants or activities and prioritize reservations for those with high ratings. The reservation unit, for example, analyzes reviews of restaurants and activities and builds a system that prioritizes reservations for those with high ratings. For example, it suggests restaurants with many positive reviews. The reservation unit also prioritizes reservations for highly rated restaurants and activities based on traveler preferences. For example, it suggests activities that travelers have given high ratings. The reservation unit also develops a system that incorporates the best restaurants and activities into travel plans based on reviews and ratings. For example, it prioritizes suggestions for those with high rating scores. This makes it possible to analyze reviews of restaurants and activities and prioritize reservations for those with high ratings.

[0064] The reservation unit can use the emotion estimation function to suggest restaurants or activities based on the traveler's emotions. The reservation unit, for example, uses the emotion estimation function to build a system that suggests restaurants and activities based on the traveler's emotions. For example, it makes optimal suggestions based on the traveler's emotion score. The reservation unit also analyzes the traveler's emotional state in real time and suggests restaurants and activities based on the emotions. For example, it prioritizes suggesting activities that have strong positive emotions. The reservation unit also collects emotion estimation data in real time and develops a system that dynamically adjusts the travel plan. For example, it suggests restaurants and activities according to changes in the traveler's emotions. This makes it possible to suggest restaurants and activities based on the traveler's emotions.

[0065] The reservation department can monitor the reservation status of restaurants or activities in real time and notify users of waiting lists or availability. For example, the reservation department builds a system that monitors the reservation status of restaurants and activities in real time and notifies users of waiting lists or availability. For example, it notifies users when a reservation is canceled. The reservation department also provides a function that allows travelers to check the availability of their desired restaurants or activities in real time. For example, it notifies users immediately when a space becomes available. The reservation department also develops a system that manages a waiting list and automatically makes reservations when a space becomes available. For example, it gives priority to reservations to travelers on the waiting list. This makes it possible to monitor the reservation status of restaurants and activities in real time and notify users of waiting lists or availability.

[0066] The reservation department can provide travelers with safety or risk information for activities they will participate in, allowing them to participate with peace of mind. The reservation department, for example, builds a system that provides safety and risk information for activities they will participate in. For example, it displays safety guidelines and risk assessments for activities. The reservation department also enables travelers to participate with peace of mind based on the safety and risk information for activities. For example, it prioritizes suggesting activities with high safety. The reservation department also develops a system that provides safety and risk information taking into account the traveler's health condition and physical fitness level. For example, it displays risk information for activities that require physical fitness. This allows travelers to provide safety and risk information for activities they will participate in, allowing them to participate with peace of mind.

[0067] The reservation department uses the emotion estimation function to set reservation priorities based on the emotions of travelers, allowing for reservations with a high level of satisfaction. The reservation department, for example, uses the emotion estimation function to build a system that sets reservation priorities based on the emotions of travelers. For example, it prioritizes reservations for activities with a high emotion score. The reservation department also analyzes the emotional state of travelers in real time and sets reservation priorities based on emotions. For example, it prioritizes reservations for restaurants with strong positive emotions. The reservation department also collects emotion estimation data in real time and develops a system that dynamically adjusts travel plans. For example, it sets reservation priorities according to changes in travelers' emotions. This allows for reservation priorities to be set based on travelers' emotions, allowing for reservations with a high level of satisfaction.

[0068] The plan generation unit can dynamically adjust the plan based on real-time feedback from travelers. The plan generation unit, for example, collects real-time feedback from travelers and builds a system that dynamically adjusts the plan based on that feedback. For example, it adds places that travelers want to visit. The plan generation unit also updates the plan in real time based on the travelers' feedback. For example, if a traveler wants to change the length of stay, it is immediately reflected. The plan generation unit also analyzes real-time feedback and develops a system that dynamically adjusts the travel plan. For example, it regenerates the plan based on the travelers' opinions. This makes it possible to dynamically adjust the plan based on the travelers' real-time feedback.

[0069] The plan generation unit can analyze the traveler's past customization history and make optimal customization suggestions. The plan generation unit, for example, retrieves the traveler's past customization history from a database and builds a system that makes optimal customization suggestions based on that. For example, suggestions are made based on tourist attractions and activities added in the past. The plan generation unit also analyzes the traveler's past customization history and makes new suggestions by referring to the customization history of similar travelers. For example, suggestions are made based on the customization history of other travelers with the same hobbies and interests. The plan generation unit also develops a system that automatically customizes travel plans based on the traveler's past customization history. For example, activities that have been highly rated in the past are given priority in suggestions. This makes it possible to analyze the traveler's past customization history and make optimal customization suggestions.

[0070] The plan generation unit can use the emotion estimation function to make customization suggestions based on the traveler's emotions. The plan generation unit, for example, uses the emotion estimation function to build a system that makes customization suggestions based on the traveler's emotions. For example, the plan generation unit makes optimal customization suggestions based on the traveler's emotion score. The plan generation unit also analyzes the traveler's emotional state in real time and makes customization suggestions based on emotions. For example, it prioritizes suggestions for activities that evoke strong positive emotions. The plan generation unit also collects emotion estimation data in real time and develops a system that dynamically adjusts the travel plan. For example, it makes customization suggestions according to changes in the traveler's emotions. This makes it possible to make customization suggestions based on the traveler's emotions.

[0071] The plan generation unit can incorporate advice from local guides or experts when customizing the travel plan. For example, the plan generation unit builds a system that incorporates advice from local guides and experts into the travel plan. For example, it suggests recommended local spots and hidden gems. The plan generation unit also works with local guides and experts to add expert advice to the travel plan. For example, it provides historical background and cultural information. The plan generation unit also develops a system that customizes the travel plan based on reviews and ratings from local guides and experts. For example, it suggests highly rated guided tours. This makes it possible to incorporate advice from local guides and experts when customizing the travel plan.

[0072] The plan generation unit uses the emotion estimation function to customize the plan in real time based on the traveler's emotions, thereby improving traveler satisfaction. The plan generation unit, for example, uses the emotion estimation function to build a system that customizes the plan in real time based on the traveler's emotions. For example, the plan adjusts the plan based on the traveler's emotion score. The plan generation unit also analyzes the traveler's emotional state in real time and customizes the plan based on emotions. For example, it prioritizes suggesting activities that evoke strong positive emotions. The plan generation unit also collects emotion estimation data in real time and develops a system that dynamically adjusts the travel plan. For example, it updates the plan in response to changes in the traveler's emotions. This allows customization in real time based on the traveler's emotions and improves traveler satisfaction.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The travel plan generation system can also include a health management unit that monitors the traveler's health condition and proposes health-conscious plans. For example, it can monitor the traveler's heart rate and blood pressure in real time and propose activities to avoid excessive exercise. The health management unit can also propose appropriate restaurants taking into account the traveler's dietary restrictions and allergy information. For example, it can propose restaurants that offer low-carb menus to a diabetic traveler. Furthermore, the health management unit can propose activities for relaxation and stress relief based on the traveler's health condition. For example, it can propose yoga or meditation sessions. This makes it possible to provide plans that take the traveler's health condition into consideration.

[0075] The travel plan generation system may further include an emotion analysis unit that estimates the traveler's emotions and proposes plans based on those emotions. For example, the system analyzes the text of the traveler's wishes and identifies emotional nuances using the emotion estimation function. For example, wishes with strong positive emotions are prioritized. The emotion analysis unit also analyzes the traveler's emotional state in real time and makes suggestions based on the traveler's emotions. For example, it prioritizes suggestions of activities that the traveler feels like enjoying. The emotion analysis unit also analyzes the traveler's emotional state and makes suggestions that are emotionally satisfying. For example, if the traveler has a strong desire to relax, it suggests relaxation spots and spas. This makes it possible to make personalized suggestions based on the traveler's emotions.

[0076] The travel plan generation system may further include a feedback collection unit that dynamically adjusts the plan based on real-time feedback from travelers. For example, a traveler may add a place they want to visit. The feedback collection unit also updates the plan in real time based on the traveler's feedback. For example, if a traveler wants to change the length of stay, this is immediately reflected. The feedback collection unit also analyzes the real-time feedback and develops a system that dynamically adjusts the travel plan. For example, the plan is regenerated based on the traveler's opinions. This makes it possible to dynamically adjust the plan based on real-time feedback from travelers.

[0077] The travel plan generation system can also be equipped with an emotion customization unit that estimates the traveler's emotions and makes customized suggestions based on the emotions. For example, a system that uses the emotion estimation function to make customized suggestions based on the traveler's emotions is constructed. For example, optimal customized suggestions are made based on the traveler's emotion score. The emotion customization unit also analyzes the traveler's emotional state in real time and makes customized suggestions based on the emotions. For example, it prioritizes suggesting activities that evoke strong positive emotions. The emotion customization unit also collects emotion estimation data in real time and develops a system that dynamically adjusts the travel plan. For example, it makes customized suggestions according to changes in the traveler's emotions. This makes it possible to make customized suggestions based on the traveler's emotions.

[0078] The travel plan generation system can also be equipped with a customization history analysis unit that analyzes a traveler's past customization history and makes optimal customization suggestions. For example, a system is constructed that retrieves a traveler's past customization history from a database and makes optimal customization suggestions based on that. For example, suggestions are made based on tourist attractions and activities added in the past. The customization history analysis unit also analyzes a traveler's past customization history and makes new suggestions by referring to the customization history of similar travelers. For example, suggestions are made based on the customization history of other travelers with the same hobbies and interests. The customization history analysis unit also develops a system that automatically customizes travel plans based on a traveler's past customization history. For example, it prioritizes suggestions of activities that have been highly rated in the past. This makes it possible to analyze a traveler's past customization history and make optimal customization suggestions.

[0079] The travel plan generation system can also include an emotion reservation unit that estimates the emotions of travelers and prioritizes reservations based on the emotions. For example, a system is constructed that uses the emotion estimation function to prioritize reservations based on the emotions of travelers. For example, activities with high emotion scores are given priority when booking. The emotion reservation unit also analyzes the emotional state of travelers in real time and prioritizes reservations based on the emotions. For example, restaurants with strong positive emotions are given priority when booking. The emotion reservation unit also collects emotion estimation data in real time and develops a system that dynamically adjusts travel plans. For example, reservation priorities are set according to changes in the travelers' emotions. This allows reservations to be prioritized based on the travelers' emotions, resulting in highly satisfying reservations.

[0080] The travel plan generation system can also be equipped with a safety management section to ensure the safety of travelers. For example, a system is constructed that provides safety and risk information for activities in which travelers participate. For example, safety guidelines and risk assessments for activities are displayed. The safety management section also enables travelers to participate with peace of mind based on the safety and risk information for activities. For example, it prioritizes suggesting activities with high safety. The safety management section also develops a system that provides safety and risk information taking into account the traveler's health condition and physical fitness level. For example, it displays risk information for activities that require physical fitness. This makes it possible to provide safety and risk information for activities in which travelers participate, allowing them to participate with peace of mind.

[0081] The travel plan generation system can also include an emotion suggestion unit that estimates the traveler's emotions and suggests restaurants and activities based on the emotions. For example, a system is constructed that uses the emotion estimation function to suggest restaurants and activities based on the traveler's emotions. For example, optimal suggestions are made based on the traveler's emotion score. The emotion suggestion unit also analyzes the traveler's emotional state in real time and suggests restaurants and activities based on the emotions. For example, activities with strong positive emotions are preferentially suggested. The emotion suggestion unit also collects emotion estimation data in real time and develops a system that dynamically adjusts the travel plan. For example, restaurants and activities are suggested according to changes in the traveler's emotions. This makes it possible to suggest restaurants and activities based on the traveler's emotions.

[0082] The travel plan generation system can also be equipped with a guide collaboration unit that incorporates advice from local guides and experts. For example, a system can be built that incorporates advice from local guides and experts into travel plans. For example, it can suggest recommended local spots and hidden gems. The guide collaboration unit can also collaborate with local guides and experts to add specialized advice to travel plans. For example, it can provide historical background and cultural information. The guide collaboration unit can also develop a system that customizes travel plans based on reviews and ratings from local guides and experts. For example, it can suggest highly rated guided tours. This makes it possible to incorporate advice from local guides and experts into the customization of travel plans.

[0083] The travel plan generation system can also include an emotion customization unit that estimates the traveler's emotions and performs emotion-based customization in real time. For example, a system is constructed that uses the emotion estimation function to perform real-time customization based on the traveler's emotions. For example, the plan is adjusted based on the traveler's emotion score. The emotion customization unit also analyzes the traveler's emotional state in real time and performs emotion-based customization. For example, it prioritizes suggesting activities that evoke strong positive emotions. The emotion customization unit also collects emotion estimation data in real time and develops a system that dynamically adjusts the travel plan. For example, it updates the plan according to changes in the traveler's emotions. This allows for real-time customization based on the traveler's emotions, improving traveler satisfaction.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The information collection unit collects information based on the traveler's preferences. For example, the traveler inputs information such as the travel destination and itinerary they wish to travel to, the places they want to visit, and the activities they want to experience. The information collection unit can also collect the traveler's past travel history and reviews. For example, it analyzes reviews of places the traveler has visited and activities they have participated in in the past. Furthermore, the information collection unit can analyze the traveler's social media posts to reflect their interests and preferences in real time. For example, it can identify tourist spots and activities that the traveler is interested in from photos and comments posted by the traveler. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, it collects and analyzes information such as timetables and facility opening hours to propose efficient travel routes. The analysis unit can also dynamically adjust the plan by taking into account information about transportation delays and real-time congestion. For example, it can recalculate travel time based on train delay information. Furthermore, the analysis unit can collect information about special events and seasonal events at facilities and reflect this in the plan. For example, it can incorporate special exhibitions and seasonal events at art museums into the plan. Step 3: The plan generation unit generates an original travel plan based on the information analyzed by the analysis unit. For example, the generation AI generates an original travel plan that matches the traveler's preferences. The plan generation unit can also dynamically adjust the plan and suggest the optimal route based on the traveler's real-time location information. For example, it can suggest the tourist attraction closest to the current location. Furthermore, the plan generation unit can add surprise elements to the plan based on the traveler's preferences to provide unexpected fun. For example, it can suggest events and activities related to the traveler's favorite themes. Step 4: The reservation unit makes reservations for restaurants and activities based on the travel plan generated by the plan generation unit. For example, it automatically makes reservations for restaurants and activities included in the travel plan. The reservation unit can also suggest the most suitable restaurant taking into account the traveler's food preferences and allergy information. For example, it can suggest vegetarian or gluten-free restaurants. Furthermore, the reservation unit can analyze reviews of restaurants and activities and prioritize reservations for those with high ratings. For example, it can suggest restaurants with many positive reviews.

[0086] 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.

[0087] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0088] 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0099] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 7, a 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0153] 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. an information collection department that collects information based on the traveler's wishes; an analysis unit that analyzes the information collected by the information collection unit; a plan generation unit that generates an original travel plan based on the information analyzed by the analysis unit; a reservation unit that makes reservations for restaurants and activities based on the travel plan created by the plan creation unit. A system characterized by:

2. The information collecting unit Analyzing the traveler's social media posts to reflect their interests or preferences in real time 2. The system of claim 1.

3. The analysis unit Dynamically adjust your plan to take into account transit delays or real-time congestion information 2. The system of claim 1.

4. The plan generation unit Dynamically adjust plans and suggest optimal routes based on the traveler's real-time location information 2. The system of claim 1.

5. The reservation unit Taking into account the traveler's dietary preferences or allergy information, suggest the most suitable restaurant 2. The system of claim 1.

6. The information collecting unit Analyzing the emotional nuances of the traveler's input and providing more personalized recommendations 2. The system of claim 1.

7. The analysis unit Analyzing reviews or ratings of facilities visited by the traveler and preferentially suggesting facilities that provide high emotional satisfaction 2. The system of claim 1.

8. The plan generation unit Monitor the emotional state of the traveler in real time and generate an emotionally satisfying plan.

2. The system of claim 1.

Images