System

Abstract

An object of a system according to an embodiment is to enable a user to easily generate an optimal travel plan.SOLUTION: The system according to the embodiment includes a travel plan generation section a business hours consideration section and an addition desire reflection section. The travel plan generation portion generates an optimal travel plan only by the user inputting "when and where to go". The business hours consideration section considers a place that can be visited within business hours based on the travel plan generated by the travel plan generation section. The addition request reflection unit reflects the user's addition request based on the location considered by the business hours consideration 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 requires detailed advance research when planning a trip, which is time-consuming for users.

[0005] The system according to the embodiment aims to enable a user to easily generate an optimal travel plan. [Means for solving the problem]

[0006] The system according to the embodiment includes a travel plan generation unit, a business hours consideration unit, and an additional request reflection unit. The travel plan generation unit generates an optimal travel plan simply by the user inputting "when and where they want to go." The business hours consideration unit considers places that can be visited within business hours based on the travel plan generated by the travel plan generation unit. The additional request reflection unit reflects additional requests from the user based on the places considered by the business hours consideration unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to easily generate an optimal travel plan. [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 personal travel plan generation system according to an embodiment of the present invention is a system that generates and proposes optimal travel plans simply by the user inputting "when and where they want to go." As a result, the personal travel plan generation system automatically creates travel plans based on the user's wishes and also takes into account places that can be visited during business hours, eliminating the need for detailed advance research.

[0029] A personal travel plan generation system according to an embodiment includes a travel plan generation unit, a business hours consideration unit, and an additional request reflection unit. The travel plan generation unit generates an optimal travel plan based on a user's input of "when and where they want to go." For example, if a user inputs "I want to go to Tokyo next Saturday," the generation AI collects information on tourist spots and events in Tokyo based on the schedule and proposes an optimal route. The generation AI receives inputs from the user, including prompts containing instructions on what the user wants the generation AI to do. The generation AI generates a travel plan based on the prompts. The business hours consideration unit considers places that can be visited within business hours based on the travel plan generated by the travel plan generation unit. For example, if a user inputs "I want to eat a famous ramen," the generation AI checks the business hours of the ramen restaurant and adjusts the plan so that the visit is within business hours. This eliminates the need to worry about the restaurant actually being closed. The additional request reflection unit reflects the user's additional requests based on the places considered by the business hours consideration unit. For example, if a user inputs "I want to stop by Tokyo Tower," the generation AI considers the request and proposes an optimal route that includes Tokyo Tower. The plan can also be adjusted to suit the user's preferences. This allows the personal travel plan generation system to allow the user to easily create the most suitable travel plan.

[0030] The travel plan generation unit can learn the user's past travel history and preferences and generate individually customized travel plans. For example, the generation AI analyzes the user's past travel history and generates a travel plan that suits the user's preferences based on data such as places visited, length of stay, and ratings. For example, if a tourist spot visited in the past has a high rating, similar tourist spots can be included in the new plan. This makes it possible to provide a customized travel plan based on the user's past travel history and preferences.

[0031] The travel plan generation unit can analyze a user's social media posts and photos and propose travel plans that reflect the user's interests and concerns. For example, the generation AI in the travel plan generation unit analyzes a user's social media posts and extracts frequently used keywords and hashtags. For example, if a user frequently uses tags such as "#cafehopping" and "#artgallery," the unit will propose travel plans that include cafes and art galleries. This makes it possible to provide travel plans that reflect the user's interests and concerns based on the user's social media posts and photos.

[0032] The business hours consideration unit checks store hours and congestion status in real time and can suggest the optimal time to visit. For example, the generation AI checks store hours in real time and includes in the plan only those locations that are open during the time the user visits. For example, it automatically excludes stores that are closed. This allows the unit to check store hours and congestion status in real time and suggest the optimal time to visit.

[0033] The business hours consideration unit can propose an efficient route by taking into account the user's means of transportation and traffic conditions. For example, the generation AI considers the user's means of transportation (walking, car, public transportation, etc.) and proposes the optimal route. For example, if traveling by foot, it proposes a short route. This makes it possible to propose an efficient route by taking into account the user's means of transportation and traffic conditions.

[0034] The business hours consideration unit automatically collects business hours information from different cities and countries and can propose global travel plans. For example, the generation AI automatically collects business hours information from different cities and countries and includes in the plan only places that are open during the time the user visits. For example, differences in business hours between countries are taken into account. This makes it possible to propose global travel plans based on business hours information from different cities and countries.

[0035] The business hours consideration unit can propose a reasonable plan by taking into account the user's health condition and physical condition. For example, the generation AI in the business hours consideration unit proposes a reasonable travel plan by taking into account the user's health condition and physical condition. For example, for a user who is not confident in their physical strength, a plan with short travel distances and plenty of rest time is proposed. This makes it possible to propose a reasonable plan by taking into account the user's health condition and physical condition.

[0036] The additional request reflection unit can analyze the user's additional requests in detail and generate a customized plan that meets the requests. For example, the additional request reflection unit uses a generation AI to analyze the user's additional requests in detail and generate a customized plan that meets the requests. For example, in response to a request such as "I want to eat the local specialty XX," a plan that includes a restaurant that serves that specialty is proposed. This makes it possible to provide a customized plan that meets the user's additional requests.

[0037] The additional request reflection unit learns from the user's past feedback and can propose more accurate plans. For example, the generation AI learns from the user's past feedback and uses it to generate the next travel plan. For example, places that have been highly rated in the past can be included in the new plan. This makes it possible to provide more accurate plans based on the user's past feedback.

[0038] The additional request reflection unit can propose the optimal plan by taking into consideration the additional requests and feedback of other users. For example, the generation AI analyzes the additional requests and feedback of other users and proposes a plan that reflects particularly highly rated requests. For example, tourist spots that many users have given high ratings can be included in the new plan. This makes it possible to provide the optimal plan by taking into consideration the additional requests and feedback of other users.

[0039] The additional request reflection unit can automatically reflect additional requests according to different seasons and events and propose them to the user. For example, the generation AI collects seasonal specialty products and event information, and the additional request reflection unit automatically reflects additional requests based on that information. For example, in spring, a plan including famous cherry blossom viewing spots is proposed. This makes it possible to automatically reflect and propose additional requests according to different seasons and events.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The personal travel plan generation system may further include a health management unit that monitors the user's health condition. The health management unit collects data such as the user's heart rate, number of steps, and sleep time, and monitors the user's health condition in real time during the trip. For example, if the user feels tired, it may suggest taking a break. The health management unit may also automatically adjust a reasonable travel plan according to the user's health condition. This allows the user to enjoy their trip while maintaining their health.

[0042] The personal travel plan generation system can also include a diet management unit that takes into account the user's dietary restrictions and allergy information. The diet management unit suggests appropriate restaurants and meal plans based on the user's dietary restrictions and allergy information. For example, if the user requests gluten-free meals, the unit suggests plans that include restaurants that offer gluten-free menus. The diet management unit can also customize meal plans to suit the user's preferences. This allows the user to enjoy their meals with peace of mind.

[0043] The personal travel plan generation system may further include an interest reflection unit that reflects the user's hobbies and interests. The interest reflection unit suggests activities and events related to the travel plan based on the user's hobbies and interests. For example, if the user is a music lover, the interest reflection unit may suggest a plan that includes local live concerts and music festivals. The interest reflection unit may also provide special experiences tailored to the user's interests. This allows the user to enjoy a trip that suits their hobbies and interests.

[0044] The personal travel plan generation system may further include a pet companion unit that takes into account the user's pet. When a user travels with a pet, the pet companion unit suggests pet-friendly accommodations, restaurants, parks, etc. For example, when a user travels with a dog, the pet companion unit suggests a plan that includes a dog run where the dog can play and a pet-friendly cafe. The pet companion unit may also provide activities that take into consideration the health and safety of the pet. This allows the user to enjoy traveling with their pet.

[0045] The personal travel plan generation system may further include a budget management unit that takes the user's budget into consideration. The budget management unit proposes the optimal travel plan according to the user's budget. For example, if the user is traveling on a limited budget, the budget management unit may propose a plan that includes cost-effective accommodations and restaurants. The budget management unit may also provide activities that can be enjoyed to the maximum extent possible within the user's budget. This allows the user to enjoy their trip without worrying about the budget.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The travel plan generation unit generates the optimal travel plan simply by the user inputting "when and where they want to go." For example, if the user inputs "I want to go to Tokyo next Saturday," the generation AI will collect information on tourist spots and events in Tokyo based on that date and suggest the optimal route. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates a travel plan based on that prompt. Step 2: The business hours consideration unit considers places that can be visited within business hours based on the travel plan generated by the travel plan generation unit. For example, if a user inputs "I want to eat a famous ramen," the generation AI checks the business hours of the ramen shop and adjusts the plan so that the visit can be made within business hours. This eliminates the need to worry about "actually, the shop is closed." Step 3: The additional request reflection unit reflects the user's additional requests based on the locations considered by the business hours consideration unit. For example, if the user inputs "I want to stop by Tokyo Tower," the generation AI will take that request into account and propose the optimal route that includes Tokyo Tower. It can also be adjusted to suit the user's preferences.

[0048] (Example 2) The personal travel plan generation system according to an embodiment of the present invention is a system that generates and proposes optimal travel plans simply by the user inputting "when and where they want to go." As a result, the personal travel plan generation system automatically creates travel plans based on the user's wishes and also takes into account places that can be visited during business hours, eliminating the need for detailed advance research.

[0049] A personal travel plan generation system according to an embodiment includes a travel plan generation unit, a business hours consideration unit, and an additional request reflection unit. The travel plan generation unit generates an optimal travel plan based on a user's input of "when and where they want to go." For example, if a user inputs "I want to go to Tokyo next Saturday," the generation AI collects information on tourist spots and events in Tokyo based on the schedule and proposes an optimal route. The generation AI receives inputs from the user, including prompts containing instructions on what the user wants the generation AI to do. The generation AI generates a travel plan based on the prompts. The business hours consideration unit considers places that can be visited within business hours based on the travel plan generated by the travel plan generation unit. For example, if a user inputs "I want to eat a famous ramen," the generation AI checks the business hours of the ramen restaurant and adjusts the plan so that the visit is within business hours. This eliminates the need to worry about the restaurant actually being closed. The additional request reflection unit reflects the user's additional requests based on the places considered by the business hours consideration unit. For example, if a user inputs "I want to stop by Tokyo Tower," the generation AI considers the request and proposes an optimal route that includes Tokyo Tower. The plan can also be adjusted to suit the user's preferences. This allows the personal travel plan generation system to allow the user to easily create the most suitable travel plan.

[0050] The travel plan generation unit can learn the user's past travel history and preferences and generate individually customized travel plans. For example, the generation AI analyzes the user's past travel history and generates a travel plan that suits the user's preferences based on data such as places visited, length of stay, and ratings. For example, if a tourist spot visited in the past has a high rating, similar tourist spots can be included in the new plan. This makes it possible to provide a customized travel plan based on the user's past travel history and preferences.

[0051] The travel plan generation unit can analyze a user's social media posts and photos and propose travel plans that reflect the user's interests and concerns. For example, the generation AI in the travel plan generation unit analyzes a user's social media posts and extracts frequently used keywords and hashtags. For example, if a user frequently uses tags such as "#cafehopping" and "#artgallery," the unit will propose travel plans that include cafes and art galleries. This makes it possible to provide travel plans that reflect the user's interests and concerns based on the user's social media posts and photos.

[0052] The travel plan generation unit uses the emotion estimation function to analyze the emotions of the user when entering their travel plan and generate a plan that elicits positive emotions. For example, the generation AI of the travel plan generation unit analyzes the user's facial expressions and voice when entering their travel plan and calculates an emotion score. For example, if the user is smiling when entering their travel plan, the unit generates a travel plan that includes many positive elements based on that emotion. This makes it possible to provide a travel plan that elicits positive emotions based on the user's emotions.

[0053] The business hours consideration unit checks store hours and congestion status in real time and can suggest the optimal time to visit. For example, the generation AI checks store hours in real time and includes in the plan only those locations that are open during the time the user visits. For example, it automatically excludes stores that are closed. This allows the unit to check store hours and congestion status in real time and suggest the optimal time to visit.

[0054] The business hours consideration unit can propose an efficient route by taking into account the user's means of transportation and traffic conditions. For example, the generation AI considers the user's means of transportation (walking, car, public transportation, etc.) and proposes the optimal route. For example, if traveling by foot, it proposes a short route. This makes it possible to propose an efficient route by taking into account the user's means of transportation and traffic conditions.

[0055] The business hours consideration unit uses the emotion estimation function to analyze the atmosphere of places visited by the user and their emotions toward the services, and can suggest places that will provide a positive experience. For example, the business hours consideration unit uses a generation AI to analyze the user's past visit history and reviews to identify places that elicit positive emotions. For example, it prioritizes highly rated places to include in the plan. This makes it possible to suggest places that will provide a positive experience based on the user's emotions.

[0056] The business hours consideration unit automatically collects business hours information from different cities and countries and can propose global travel plans. For example, the generation AI automatically collects business hours information from different cities and countries and includes in the plan only places that are open during the time the user visits. For example, differences in business hours between countries are taken into account. This makes it possible to propose global travel plans based on business hours information from different cities and countries.

[0057] The business hours consideration unit can propose a reasonable plan by taking into account the user's health condition and physical condition. For example, the generation AI in the business hours consideration unit proposes a reasonable travel plan by taking into account the user's health condition and physical condition. For example, for a user who is not confident in their physical strength, a plan with short travel distances and plenty of rest time is proposed. This makes it possible to propose a reasonable plan by taking into account the user's health condition and physical condition.

[0058] The business hours consideration unit can use the emotion estimation function to analyze reviews and ratings of places the user visits and suggest places that are emotionally satisfying. For example, the business hours consideration unit uses a generation AI to analyze the user's past reviews and ratings and identify places that are emotionally satisfying. For example, highly rated places are prioritized and included in the plan. This makes it possible to suggest places that are highly satisfying based on the user's emotions.

[0059] The additional request reflection unit can analyze the user's additional requests in detail and generate a customized plan that meets the requests. For example, the additional request reflection unit uses a generation AI to analyze the user's additional requests in detail and generate a customized plan that meets the requests. For example, in response to a request such as "I want to eat the local specialty XX," a plan that includes a restaurant that serves that specialty is proposed. This makes it possible to provide a customized plan that meets the user's additional requests.

[0060] The additional request reflection unit learns from the user's past feedback and can propose more accurate plans. For example, the generation AI learns from the user's past feedback and uses it to generate the next travel plan. For example, places that have been highly rated in the past can be included in the new plan. This makes it possible to provide more accurate plans based on the user's past feedback.

[0061] The additional request reflection unit can use the emotion estimation function to analyze the user's emotions regarding the additional request and generate a plan that elicits positive emotions. For example, the additional request reflection unit analyzes the emotions expressed when the generation AI inputs the user's additional request and generates a plan that elicits positive emotions. For example, if the user is excited, the additional request reflection unit proposes a plan that includes active activities based on that emotion. This makes it possible to provide a plan that elicits positive emotions based on the user's emotions.

[0062] The additional request reflection unit can propose the optimal plan by taking into consideration the additional requests and feedback of other users. For example, the generation AI analyzes the additional requests and feedback of other users and proposes a plan that reflects particularly highly rated requests. For example, tourist spots that many users have given high ratings can be included in the new plan. This makes it possible to provide the optimal plan by taking into consideration the additional requests and feedback of other users.

[0063] The additional request reflection unit can automatically reflect additional requests according to different seasons and events and propose them to the user. For example, the generation AI collects seasonal specialty products and event information, and the additional request reflection unit automatically reflects additional requests based on that information. For example, in spring, a plan including famous cherry blossom viewing spots is proposed. This makes it possible to automatically reflect and propose additional requests according to different seasons and events.

[0064] The additional request reflection unit can use the emotion estimation function to analyze the user's emotions regarding the additional request in real time and propose the optimal plan. For example, the additional request reflection unit can analyze the emotions in real time when the generation AI inputs the user's additional request and propose a plan that elicits positive emotions. For example, if the user is excited, the additional request reflection unit can propose a plan that includes active activities based on that emotion. This makes it possible to propose the optimal plan in real time based on the user's emotions.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The personal travel plan generation system may further include a health management unit that monitors the user's health condition. The health management unit collects data such as the user's heart rate, number of steps, and sleep time, and monitors the user's health condition in real time during the trip. For example, if the user feels tired, it may suggest taking a break. The health management unit may also automatically adjust a reasonable travel plan according to the user's health condition. This allows the user to enjoy their trip while maintaining their health.

[0067] The personal travel plan generation system can also include a diet management unit that takes into account the user's dietary restrictions and allergy information. The diet management unit suggests appropriate restaurants and meal plans based on the user's dietary restrictions and allergy information. For example, if the user requests gluten-free meals, the unit suggests plans that include restaurants that offer gluten-free menus. The diet management unit can also customize meal plans to suit the user's preferences. This allows the user to enjoy their meals with peace of mind.

[0068] The personal travel plan generation system may further include an interest reflection unit that reflects the user's hobbies and interests. The interest reflection unit suggests activities and events related to the travel plan based on the user's hobbies and interests. For example, if the user is a music lover, the interest reflection unit may suggest a plan that includes local live concerts and music festivals. The interest reflection unit may also provide special experiences tailored to the user's interests. This allows the user to enjoy a trip that suits their hobbies and interests.

[0069] The personal travel plan generation system may further include a relaxation suggestion unit that estimates the user's emotions and reduces stress during the trip. The relaxation suggestion unit analyzes the user's emotions and suggests places and activities that will help the user relax if they are feeling stressed. For example, if the user is tired, the relaxation suggestion unit may suggest a plan that includes a spa, hot spring, or relaxation massage. The relaxation suggestion unit may also provide relaxing music or a meditation app based on the user's emotions. This allows the user to relax and reduce stress during the trip.

[0070] The personal travel plan generation system may further include an emotional experience suggestion unit that estimates the user's emotions and provides emotional experiences during the trip. The emotional experience suggestion unit analyzes the user's emotions and suggests places and activities that will elicit emotions. For example, if the user is looking for an emotional experience, it will suggest a plan that includes beautiful scenery and historical landmarks. The emotional experience suggestion unit may also provide emotional stories and episodes based on the user's emotions. This allows the user to enjoy emotional experiences during the trip.

[0071] The personal travel plan generation system may further include a social experience suggestion unit that estimates the user's emotions and provides social experiences during the trip. The social experience suggestion unit analyzes the user's emotions and suggests events and interactions with local people if the user is looking for a social experience. For example, if the user is looking for a social experience, the social experience suggestion unit may suggest a plan that includes a local cultural exchange event or party. The social experience suggestion unit may also provide activities that promote interactions with local people based on the user's emotions. This allows the user to meet new people and enjoy social experiences during the trip.

[0072] The personal travel plan generation system may further include an adventure experience suggestion unit that estimates the user's emotions and provides adventurous experiences during the trip. The adventure experience suggestion unit analyzes the user's emotions and suggests thrilling activities and extreme sports if the user is seeking adventure. For example, if the user is seeking adventure, it may suggest plans including skydiving and rafting. The adventure experience suggestion unit may also provide special experiences that stimulate the user's sense of adventure based on the user's emotions. This allows the user to enjoy adventurous experiences during the trip.

[0073] The personal travel plan generation system may further include a learning experience suggestion unit that estimates the user's emotions and provides learning experiences during the trip. The learning experience suggestion unit analyzes the user's emotions and, if the user is seeking learning, suggests activities related to local culture and history. For example, if the user is seeking learning, it suggests a plan that includes local museums and historical sites. The learning experience suggestion unit may also provide guided tours and workshops by local experts based on the user's emotions. This allows the user to enjoy learning experiences during their trip.

[0074] The personal travel plan generation system may further include a pet companion unit that takes into account the user's pet. When a user travels with a pet, the pet companion unit suggests pet-friendly accommodations, restaurants, parks, etc. For example, when a user travels with a dog, the pet companion unit suggests a plan that includes a dog run where the dog can play and a pet-friendly cafe. The pet companion unit may also provide activities that take into consideration the health and safety of the pet. This allows the user to enjoy traveling with their pet.

[0075] The personal travel plan generation system may further include a budget management unit that takes the user's budget into consideration. The budget management unit proposes the optimal travel plan according to the user's budget. For example, if the user is traveling on a limited budget, the budget management unit may propose a plan that includes cost-effective accommodations and restaurants. The budget management unit may also provide activities that can be enjoyed to the maximum extent possible within the user's budget. This allows the user to enjoy their trip without worrying about the budget.

[0076] The processing flow of the second embodiment will be briefly explained below.

[0077] Step 1: The travel plan generation unit generates the optimal travel plan simply by the user inputting "when and where they want to go." For example, if the user inputs "I want to go to Tokyo next Saturday," the generation AI will collect information on tourist spots and events in Tokyo based on that date and suggest the optimal route. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates a travel plan based on that prompt. Step 2: The business hours consideration unit considers places that can be visited within business hours based on the travel plan generated by the travel plan generation unit. For example, if a user inputs "I want to eat a famous ramen," the generation AI checks the business hours of the ramen shop and adjusts the plan so that the visit can be made within business hours. This eliminates the need to worry about "actually, the shop is closed." Step 3: The additional request reflection unit reflects the user's additional requests based on the locations considered by the business hours consideration unit. For example, if the user inputs "I want to stop by Tokyo Tower," the generation AI will take that request into account and propose the optimal route that includes Tokyo Tower. It can also be adjusted to suit the user's preferences.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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).

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0112] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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."

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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]

[0145] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A travel plan generation unit that generates an optimal travel plan by simply inputting "when and where the user wants to go"; an operating hours consideration unit that considers places that can be visited within operating hours based on the travel plan generated by the travel plan generation unit; an additional request reflection unit that reflects an additional request from the user based on the location considered by the business hours consideration unit. A system characterized by:

2. The travel plan generation unit Learn the user's past travel history and preferences to generate personalized travel plans 2. The system of claim 1.

3. The business hours consideration unit Check store hours and congestion status in real time and suggest visiting times 2. The system of claim 1.

4. The additional request reflection unit Analyze the additional requests of the user in detail and generate a customized plan according to the requests.

2. The system of claim 1.

5. The travel plan generation unit Analyzing the emotions of the user when inputting a travel plan and generating a plan that elicits positive emotions 2. The system of claim 1.

6. The business hours consideration unit Analyzing the user's feelings toward the atmosphere and services of the places they visit and suggesting places that will provide a positive experience 2. The system of claim 1.

7. The additional request reflection unit Analyzing the user's feelings regarding the additional request and generating a plan to elicit positive feelings 2. The system of claim 1.

8. The additional request reflection unit Analyze the user's feelings regarding the additional request in real time and propose the optimal plan 2. The system of claim 1.

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