System

Abstract

The system according to the embodiment aims to simplify the process of travel planning and to make personalized suggestions according to the user's preferences.SOLUTION: A system according to an embodiment includes a user preference learning unit, a personalization proposal unit, a real-time information acquisition unit, an automatic budget control unit, a collective reservation unit, and a customization unit. The user preference learning unit learns the user's preference. The personalized suggestion unit suggests tourist spots, restaurants, and activities personalized based on the user's preference. The real-time information acquisition unit acquires real-time information of a travel destination. The automated budget manager optimizes the travel plan based on the user's budget. The collective reservation unit makes a collective reservation based on the travel plan. The customization unit customizes the travel plan in accordance with a user's request.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 technologies have not done enough to simplify the complex process of travel planning and provide personalized suggestions based on user preferences, leaving room for improvement.

[0005] The system according to the embodiment aims to simplify the process of travel planning and provide personalized suggestions according to the user's preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a user preference learning unit, a personalization suggestion unit, a real-time information acquisition unit, an automatic budget management unit, a bulk booking unit, and a customization unit. The user preference learning unit learns the user's preferences. The personalization suggestion unit suggests personalized tourist spots, restaurants, and activities based on the user's preferences. The real-time information acquisition unit acquires real-time information about travel destinations. The automatic budget management unit optimizes travel plans based on the user's budget. The bulk booking unit makes bulk bookings based on the travel plans. The customization unit customizes the travel plans according to the user's requests. [Effects of the Invention]

[0007] The system according to the embodiment simplifies the process of travel planning and can provide personalized suggestions based on the user's preferences. [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 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 planning assistant system according to an embodiment of the present invention learns user preferences, uses generative AI to suggest personalized tourist spots, restaurants, and activities, obtains real-time information, performs automatic budget management, provides bulk booking functionality, and allows flexible customization, thereby simplifying and optimizing the complex process of travel planning and providing a user-centered experience.

[0029] The travel planning assistant system according to the embodiment includes a user preference learning unit, a personalization suggestion unit, a real-time information acquisition unit, an automatic budget management unit, a bulk booking unit, and a customization unit. The user preference learning unit learns the user's preferences. For example, it analyzes the user's past travel history and rating data to learn the user's preferences. The user preference learning unit can also analyze the user's social media activities to learn the user's interests. The user preference learning unit can also analyze the user's survey results to learn the user's preferences. The personalization suggestion unit suggests personalized tourist spots, restaurants, and activities based on the user's preferences. For example, it analyzes data on places the user has previously visited and restaurants they have rated to suggest recommended spots for the user's next travel destination. The personalization suggestion unit can also suggest travel plans based on specific themes based on the user's preferences. The personalization suggestion unit can also suggest specific restaurants and activities based on the user's preferences. The real-time information acquisition unit acquires real-time information about the travel destination. For example, it acquires weather information, traffic information, event information, etc. in real time and provides it to the user. The real-time information acquisition unit can also provide real-time information on nearby congestion and waiting times based on the user's location information. The real-time information acquisition unit can also analyze local news and trending information from social media to provide the latest information on tourist attractions and events. The automatic budget management unit optimizes travel plans based on the user's budget. For example, it can suggest optimal accommodations and restaurants within the user's set budget to prevent overspending. The automatic budget management unit can also analyze the user's past spending patterns to improve the accuracy of budget management. The automatic budget management unit can also obtain local exchange rates and price information in real time and reflect this in budget management. The bulk reservation unit makes bulk reservations based on the user's travel plans. For example, accommodations, transportation, and activities can be booked all at once. The bulk reservation unit can also provide comprehensive travel plans, including reservations for local guides and tours.The bulk reservation unit can also optimize reservation timing and make reservations at the most cost-effective time, taking price fluctuations into account. The customization unit flexibly customizes travel plans according to the user's requests. For example, the customization unit can accommodate requests from users to add specific tourist spots or exclude specific restaurants. The customization unit can also propose reasonable plans taking into account the user's health and physical condition. The customization unit can also dig deep into the user's hobbies and interests to propose travel plans based on specific themes. This allows the travel planning assistant system according to the embodiment to simplify and optimize the complex process of travel planning and provide a user-centered experience. For example, users can easily find tourist spots and restaurants that suit their preferences and create optimal travel plans within their budget. Furthermore, obtaining real-time information can prevent travel problems. Furthermore, using the bulk reservation function eliminates the hassle of making reservations and allows travel planning to proceed smoothly.

[0030] The user preference learning unit can analyze the user's past travel history and rating data to suggest recommended spots at the user's next travel destination. The user preference learning unit, for example, analyzes the user's past travel history and rating data to suggest recommended spots at the user's next travel destination. For example, it prioritizes the suggestions of spots that the user has previously given high ratings. The user preference learning unit also analyzes the user's social media activity to reflect the user's interests and concerns at the travel destination in real time. For example, it links the user's social media accounts and analyzes the content of posts and hashtags to identify the user's interests and concerns at the travel destination. The user preference learning unit can also analyze the results of a user survey to suggest recommended spots at the user's next travel destination. For example, it suggests recommended spots at the user's next travel destination based on the preferences the user answered in a survey. In this way, it is possible to suggest recommended spots at the user's next travel destination based on the user's past travel history and rating data.

[0031] The real-time information acquisition unit can acquire weather information, traffic information, and event information in real time and provide it to the user. For example, the real-time information acquisition unit can acquire weather information, traffic information, and event information in real time and provide it to the user. For example, weather information can be acquired from data from the Japan Meteorological Agency or weather forecast services. Traffic information can be acquired from transportation operation information and traffic congestion information. Event information can be acquired from local event calendars and social media events. The real-time information acquisition unit can also provide information on surrounding congestion and waiting times in real time based on the user's location information. For example, location information from the user's smart device can be used to provide information on congestion and waiting times at surrounding tourist spots and activities in real time. The real-time information acquisition unit can also analyze local news and trend information from social media to provide the latest tourist spot and event information. For example, the real-time information acquisition unit can analyze trend information from local news sites and social media to provide the latest tourist spot and event information. This allows the user to always have the information they need during their trip.

[0032] The automatic budget management unit can optimize travel plans based on a user's budget and prevent the user from going over budget. The automatic budget management unit, for example, can optimize travel plans based on a user's budget and prevent the user from going over budget. For example, it can suggest optimal accommodations and restaurants within the budget set by the user. The automatic budget management unit can also analyze the user's past spending patterns to improve the accuracy of budget management. For example, it can suggest optimal choices within the budget based on past spending data. The automatic budget management unit can also obtain local exchange rates and price information in real time and reflect this in budget management. For example, it can adjust the budget according to fluctuations in exchange rates. This allows the user to create an optimal travel plan within their budget.

[0033] The bulk reservation unit can make reservations for accommodation, transportation, and activities all at once. The bulk reservation unit, for example, makes reservations for accommodation, transportation, and activities all at once. For example, accommodation reservations can be made for hotels, guesthouses, resorts, etc. in one go. Transportation reservations can be made for flights, trains, buses, etc. in one go. Activity reservations can be made for sightseeing tours, sports activities, cultural experiences, etc. in one go. The bulk reservation unit can also provide comprehensive travel plans that include reservations for local guides and tours. For example, in addition to accommodation and transportation, local guides and tours can also be booked in one go. The bulk reservation unit can also optimize the timing of reservations and make reservations at the most cost-effective time, taking price fluctuations into consideration. For example, the bulk reservation unit can suggest the best time to make reservations based on price fluctuation data. This saves users the trouble of making reservations individually.

[0034] The customization unit can flexibly customize the travel plan according to the user's requests. The customization unit flexibly customizes the travel plan according to the user's requests. For example, this can accommodate cases where the user wants to add specific tourist spots or exclude specific restaurants. The customization unit can also propose a reasonable plan taking into account the user's health condition and physical condition. For example, it can propose tourist spots that suit the user's physical strength based on the user's health data. The customization unit can also dig deeper into the user's hobbies and interests and propose a travel plan based on a specific theme. For example, it can propose tourist spots and activities related to the user's hobbies. This allows for flexible customization according to the user's requests.

[0035] The real-time information acquisition unit can analyze local news and trend information from social media to provide the latest information on tourist spots and events. The real-time information acquisition unit can, for example, analyze local news and trend information from social media in real time to provide the latest information on tourist spots and events. For example, it can suggest popular local spots and events. The real-time information acquisition unit can also provide information on surrounding congestion and waiting times in real time based on the user's location information. For example, it can use location information from the user's smart device to provide information on congestion and waiting times at surrounding tourist spots and activities in real time. The real-time information acquisition unit can also simultaneously collect information in different languages ​​to provide information in multiple languages. For example, it can provide local news and social media information in multiple languages. This makes it possible to provide the latest information on local tourist spots and events.

[0036] The automatic budget management unit can analyze a user's past spending patterns and improve the accuracy of budget management. The automatic budget management unit, for example, analyzes a user's past spending patterns and improves the accuracy of budget management. For example, it suggests optimal choices within a budget based on past spending data. The automatic budget management unit can also use an emotion estimation function to optimize suggestions so that the user can make choices that will give the user high satisfaction within the budget. For example, it suggests accommodations and restaurants that will give the user high satisfaction within the budget. The automatic budget management unit can also obtain local exchange rates and price information in real time and reflect this in budget management. For example, it adjusts the budget in accordance with fluctuations in exchange rates. In this way, it is possible to analyze a user's past spending patterns and improve the accuracy of budget management.

[0037] The bulk reservation unit can propose optimal options by taking into account the user's past reservation history and ratings when making a reservation. The bulk reservation unit proposes optimal options based on, for example, the user's past reservation history and rating data. For example, it prioritizes the proposal of accommodations and restaurants that have received high ratings in the past. The bulk reservation unit can also use an emotion estimation function to provide an interface to reduce anxiety felt by the user during the reservation process. For example, it can display a reassuring message if the user feels anxious. The bulk reservation unit can also analyze the user's facial expressions and voice and detect anxiety using the emotion estimation function. For example, it can detect anxiety by analyzing changes in the user's facial expressions and tone of voice. The bulk reservation unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and detect anxiety using the emotion estimation function. For example, it can detect anxiety based on fluctuations in heart rate. This allows the bulk reservation unit to propose optimal options by taking into account the user's past reservation history and ratings.

[0038] The customization unit can analyze the user's customization history and reflect it in the next travel plan. For example, the customization unit analyzes the user's customization history and reflects it in the next travel plan. For example, the customization unit can suggest the next travel plan based on past customization data. The customization unit can also use an emotion estimation function to analyze the emotions the user feels during customization and make optimal suggestions. For example, the customization unit can make suggestions to help the user relax if they feel stressed. The customization unit can also analyze the user's facial expressions and voice and analyze their emotions using the emotion estimation function. For example, the customization unit can analyze emotions by analyzing changes in the user's facial expressions and tone of voice. The customization unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and analyze their emotions using the emotion estimation function. For example, the customization unit can analyze emotions based on fluctuations in heart rate. This allows the user's customization history to be analyzed and reflected in the next travel plan.

[0039] The real-time information acquisition unit can provide information on the congestion status and waiting times of nearby tourist spots and activities in real time based on the user's location information. For example, the real-time information acquisition unit can provide information on the congestion status and waiting times of nearby tourist spots and activities in real time based on the user's location information. For example, the real-time information acquisition unit can suggest alternative routes to avoid crowds. The real-time information acquisition unit can also use location information from the user's smart device to provide information on the congestion status and waiting times of nearby tourist spots and activities in real time. For example, the real-time information acquisition unit can use data from the user's smartwatch or fitness tracker to provide real-time information. The real-time information acquisition unit can also analyze local news and trending information on social media to provide the latest information on tourist spots and events. For example, it can suggest popular local spots and events. This makes it possible to provide information on the congestion status and waiting times of nearby tourist spots and activities in real time based on the user's location information.

[0040] The automatic budget management unit can acquire local exchange rates and price information in real time and reflect it in budget management. The automatic budget management unit, for example, acquires local exchange rates and price information in real time and reflects it in budget management. For example, it adjusts the budget according to fluctuations in exchange rates. The automatic budget management unit can also analyze the user's past spending patterns to improve the accuracy of budget management. For example, it can suggest optimal choices within the budget based on past spending data. The automatic budget management unit can also use an emotion estimation function to optimize suggestions so that the user can make highly satisfying choices within the budget. For example, it can suggest accommodations and restaurants that the user is highly satisfied with within the budget. This makes it possible to acquire local exchange rates and price information in real time and reflect it in budget management.

[0041] The customization unit can propose a reasonable plan taking into account the user's health condition and physical condition. The customization unit can, for example, propose a reasonable plan taking into account the user's health condition and physical condition. For example, it can propose tourist spots that suit the user's physical strength based on the user's health data. The customization unit can also dig deeper into the user's hobbies and interests and propose a travel plan based on a specific theme. For example, it can propose tourist spots and activities related to the user's hobbies. The customization unit can also analyze the user's customization history and reflect it in the next travel plan. For example, it can propose the next travel plan based on past customization data. This makes it possible to propose a reasonable plan taking into account the user's health condition and physical condition.

[0042] The real-time information acquisition unit can provide real-time information by utilizing data from the user's smart device. The real-time information acquisition unit can provide real-time information by utilizing data from, for example, the user's smartwatch or fitness tracker. For example, it can provide information based on the user's health condition. The real-time information acquisition unit can also provide real-time information about the congestion status and waiting times of nearby tourist spots and activities based on the user's location information. For example, it can suggest alternative routes to avoid crowds. The real-time information acquisition unit can also analyze local news and trending information on social media to provide the latest information on tourist spots and events. For example, it can suggest popular local spots and events. In this way, it is possible to provide real-time information by utilizing data from the user's smart device.

[0043] The automatic budget management unit can link data from the user's credit cards and bank accounts and track expenses in real time. The automatic budget management unit, for example, links data from the user's credit cards and bank accounts and tracks expenses in real time. For example, it optimizes budget management based on the expense data. The automatic budget management unit can also analyze the user's past expense patterns and improve the accuracy of budget management. For example, it can suggest optimal choices within the budget based on past expense data. The automatic budget management unit can also obtain local exchange rates and price information in real time and reflect this in budget management. For example, it can adjust the budget according to fluctuations in exchange rates. In this way, it can link data from the user's credit cards and bank accounts and track expenses in real time.

[0044] The bulk booking unit can provide comprehensive travel plans, including reservations for local guides and tours. The bulk booking unit can provide comprehensive travel plans, including reservations for local guides and tours. For example, in addition to accommodations and transportation, it can also make reservations for local guides and tours all at once. The bulk booking unit can also optimize the timing of reservations and make reservations at the most advantageous time, taking price fluctuations into consideration. For example, it can suggest the optimal reservation timing based on price fluctuation data. The bulk booking unit can also suggest optimal options based on the user's past reservation history and rating data. For example, it can prioritize suggestions for accommodations and restaurants that have been highly rated in the past. This makes it possible to provide comprehensive travel plans, including reservations for local guides and tours.

[0045] The customization unit can incorporate the opinions of the user's family and friends to provide the optimal plan for the entire group. The customization unit can, for example, incorporate the opinions of the user's family and friends to provide the optimal plan for the entire group. For example, it can propose a travel plan that reflects the opinions of everyone. The customization unit can also propose a reasonable plan taking into account the user's health condition and physical condition. For example, it can propose tourist spots that are appropriate for the user's physical strength based on the user's health data. The customization unit can also dig deeper into the user's hobbies and interests to propose a travel plan based on a specific theme. For example, it can propose tourist spots and activities related to the user's hobbies. In this way, the optimal plan for the entire group can be provided by incorporating the opinions of the user's family and friends.

[0046] The real-time information acquisition unit can simultaneously collect information in different languages ​​and provide information in multiple languages. The real-time information acquisition unit can simultaneously collect information in different languages ​​and provide information in multiple languages. For example, it can provide local news and social media information in multiple languages. The real-time information acquisition unit can also provide real-time information on the congestion status and waiting times of nearby tourist spots and activities based on the user's location information. For example, it can suggest alternative routes to avoid crowds. The real-time information acquisition unit can also analyze local news and trending information on social media and provide the latest information on tourist spots and events. For example, it can suggest popular local spots and events. This makes it possible to simultaneously collect information in different languages ​​and provide information in multiple languages.

[0047] The automatic budget management unit can also take into account the user's travel insurance and point programs to propose the optimal plan. The automatic budget management unit can, for example, take into account the user's travel insurance and point programs to propose the optimal plan. For example, it can use points to propose accommodations and restaurants at discounted prices. The automatic budget management unit can also analyze the user's past spending patterns to improve the accuracy of budget management. For example, it can propose the optimal choice within the budget based on past spending data. The automatic budget management unit can also obtain local exchange rates and price information in real time and reflect this in budget management. For example, it can adjust the budget according to fluctuations in exchange rates. This makes it possible to propose the optimal plan taking into account the user's travel insurance and point programs.

[0048] The bulk reservation unit can take into account a user's specific requests (e.g., allergy-friendly or special facilities) when making a reservation. The bulk reservation unit can, for example, suggest restaurants that cater to allergies or accommodations with special facilities. The bulk reservation unit can also suggest optimal options based on the user's past reservation history and rating data. For example, it can prioritize suggesting accommodations and restaurants that have received high ratings in the past. The bulk reservation unit can also use an emotion estimation function to provide an interface to reduce anxiety felt by the user during the reservation process. For example, it can display a reassuring message if the user feels anxious. The bulk reservation unit can also analyze the user's facial expressions and voice and detect anxiety using the emotion estimation function. For example, it can detect anxiety by analyzing changes in the user's facial expression and tone of voice. The bulk reservation unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and detect anxiety using the emotion estimation function. For example, it can detect anxiety based on fluctuations in heart rate. This allows the user's specific requests (e.g., allergy-friendly or special facilities) to be taken into account when making a reservation.

[0049] The customization unit can dig deep into the user's hobbies and interests and propose a travel plan that fits a specific theme. The customization unit can, for example, dig deep into the user's hobbies and interests and propose a travel plan that fits a specific theme. For example, it can propose tourist spots and activities related to the user's hobbies. The customization unit can also propose a reasonable plan that takes into account the user's health condition and physical condition. For example, it can propose tourist spots that fit the user's physical strength based on the user's health data. The customization unit can also analyze the user's customization history and reflect it in the next travel plan. For example, it can propose the next travel plan based on past customization data. This makes it possible to dig deep into the user's hobbies and interests and propose a travel plan that fits a specific theme.

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

[0051] The travel planning assistant system can also include a health management unit that collects the user's health data and supports health management during the trip. For example, it can collect heart rate, step count, and sleep data from the user's smartwatch or fitness tracker to monitor the user's health condition during the trip. The health management unit can also suggest appropriate rest times and snacks based on the user's health data. Furthermore, the health management unit can provide information on medical facilities and pharmacies at the travel destination and support emergency response. This allows the user to maintain their health while traveling and enjoy their trip with peace of mind.

[0052] The travel planning assistant system can also include a cultural experience suggestion unit that suggests cultural experiences at the user's next travel destination based on the user's past travel history and evaluation data. For example, the system can analyze data on museums and art galleries that the user has visited in the past and suggest recommended cultural facilities at the user's next travel destination. The cultural experience suggestion unit can also suggest local traditional festivals and events based on the user's interests. Furthermore, the cultural experience suggestion unit can suggest experiential activities such as local craft making and cooking classes according to the user's preferences. This allows the user to enjoy cultural experiences at their travel destination more deeply.

[0053] The automated budget management unit can further consider the user's travel insurance and points program to propose the optimal plan. For example, it can use points to propose discounted accommodation and restaurant prices. The automated budget management unit can also analyze the user's past spending patterns to improve the accuracy of budget management. For example, it can propose the optimal choice within the budget based on past spending data. The automated budget management unit can also obtain local exchange rates and price information in real time and reflect this in budget management. For example, it can adjust the budget according to fluctuations in exchange rates. This makes it possible to propose the optimal plan, taking into account the user's travel insurance and points program.

[0054] The customization unit can also incorporate the opinions of the user's family and friends to provide the optimal plan for the entire group. For example, it can propose a travel plan that reflects the opinions of everyone. The customization unit can also propose a reasonable plan taking into account the user's health condition and physical condition. For example, it can propose tourist spots that suit the user's physical strength based on the user's health data. The customization unit can also dig deeper into the user's hobbies and interests to propose a travel plan based on a specific theme. This allows the optimal plan for the entire group to be provided, incorporating the opinions of the user's family and friends.

[0055] The travel planning assistant system can also include a safety management module to ensure the user's safety during their trip. For example, it can provide information on the security situation at the travel destination and emergency contact information. The safety management module can also suggest routes to avoid dangerous areas based on the user's location information. Furthermore, the safety management module can use data from the user's smart device to automatically send alerts in the event of an emergency. This allows the user to stay safe during their trip.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The user preference learning unit learns the user's preferences. For example, it analyzes the user's past travel history and rating data to learn the user's preferences. It can also analyze the user's social media activity and survey results to learn the user's interests. Step 2: The personalized suggestion module suggests personalized tourist spots, restaurants, and activities based on the user's preferences. For example, it analyzes data on places the user has visited and restaurants they have rated in the past to suggest recommended spots for their next trip. It can also suggest themed travel plans or specific restaurants and activities. Step 3: The real-time information acquisition unit acquires real-time information about the travel destination. For example, it acquires weather information, traffic information, event information, etc. in real time and provides it to the user. It can also analyze the surrounding congestion situation, waiting times, local news, and trending information on social media based on the user's location information, and provide the latest information on tourist spots and events. Step 4: The automatic budget management unit optimizes travel plans based on the user's budget. For example, it suggests the best accommodations and restaurants within the user's set budget, preventing overspending. It can also analyze the user's past spending patterns to improve the accuracy of budget management. It can also obtain local exchange rates and price information in real time and reflect this in budget management. Step 5: The bulk booking section makes bulk bookings based on the user's travel plans. For example, you can book accommodation, transportation, and activities all at once. You can also provide comprehensive travel plans, including reservations for local guides and tours. Furthermore, the bulk booking section can optimize the timing of bookings, taking into account price fluctuations, to make reservations at the best price. Step 6: The customization unit flexibly customizes the travel plan according to the user's requests. For example, the system can accommodate requests for adding specific tourist spots or excluding specific restaurants. It can also propose a reasonable plan that takes into account the user's health and physical condition. It can also dig deeper into the user's hobbies and interests to propose travel plans based on specific themes.

[0058] (Example 2) The travel planning assistant system according to an embodiment of the present invention learns user preferences, uses generative AI to suggest personalized tourist spots, restaurants, and activities, obtains real-time information, performs automatic budget management, provides bulk booking functionality, and allows flexible customization, thereby simplifying and optimizing the complex process of travel planning and providing a user-centered experience.

[0059] The travel planning assistant system according to the embodiment includes a user preference learning unit, a personalization suggestion unit, a real-time information acquisition unit, an automatic budget management unit, a bulk booking unit, and a customization unit. The user preference learning unit learns the user's preferences. For example, it analyzes the user's past travel history and rating data to learn the user's preferences. The user preference learning unit can also analyze the user's social media activities to learn the user's interests. The user preference learning unit can also analyze the user's survey results to learn the user's preferences. The personalization suggestion unit suggests personalized tourist spots, restaurants, and activities based on the user's preferences. For example, it analyzes data on places the user has previously visited and restaurants they have rated to suggest recommended spots for the user's next travel destination. The personalization suggestion unit can also suggest travel plans based on specific themes based on the user's preferences. The personalization suggestion unit can also suggest specific restaurants and activities based on the user's preferences. The real-time information acquisition unit acquires real-time information about the travel destination. For example, it acquires weather information, traffic information, event information, etc. in real time and provides it to the user. The real-time information acquisition unit can also provide real-time information on nearby congestion and waiting times based on the user's location information. The real-time information acquisition unit can also analyze local news and trending information from social media to provide the latest information on tourist attractions and events. The automatic budget management unit optimizes travel plans based on the user's budget. For example, it can suggest optimal accommodations and restaurants within the user's set budget to prevent overspending. The automatic budget management unit can also analyze the user's past spending patterns to improve the accuracy of budget management. The automatic budget management unit can also obtain local exchange rates and price information in real time and reflect this in budget management. The bulk reservation unit makes bulk reservations based on the user's travel plans. For example, accommodations, transportation, and activities can be booked all at once. The bulk reservation unit can also provide comprehensive travel plans, including reservations for local guides and tours.The bulk reservation unit can also optimize reservation timing and make reservations at the most cost-effective time, taking price fluctuations into account. The customization unit flexibly customizes travel plans according to the user's requests. For example, the customization unit can accommodate requests from users to add specific tourist spots or exclude specific restaurants. The customization unit can also propose reasonable plans taking into account the user's health and physical condition. The customization unit can also dig deep into the user's hobbies and interests to propose travel plans based on specific themes. This allows the travel planning assistant system according to the embodiment to simplify and optimize the complex process of travel planning and provide a user-centered experience. For example, users can easily find tourist spots and restaurants that suit their preferences and create optimal travel plans within their budget. Furthermore, obtaining real-time information can prevent travel problems. Furthermore, using the bulk reservation function eliminates the hassle of making reservations and allows travel planning to proceed smoothly.

[0060] The user preference learning unit can analyze the user's past travel history and rating data to suggest recommended spots at the user's next travel destination. The user preference learning unit, for example, analyzes the user's past travel history and rating data to suggest recommended spots at the user's next travel destination. For example, it prioritizes the suggestions of spots that the user has previously given high ratings. The user preference learning unit also analyzes the user's social media activity to reflect the user's interests and concerns at the travel destination in real time. For example, it links the user's social media accounts and analyzes the content of posts and hashtags to identify the user's interests and concerns at the travel destination. The user preference learning unit can also analyze the results of a user survey to suggest recommended spots at the user's next travel destination. For example, it suggests recommended spots at the user's next travel destination based on the preferences the user answered in a survey. In this way, it is possible to suggest recommended spots at the user's next travel destination based on the user's past travel history and rating data.

[0061] The real-time information acquisition unit can acquire weather information, traffic information, and event information in real time and provide it to the user. For example, the real-time information acquisition unit can acquire weather information, traffic information, and event information in real time and provide it to the user. For example, weather information can be acquired from data from the Japan Meteorological Agency or weather forecast services. Traffic information can be acquired from transportation operation information and traffic congestion information. Event information can be acquired from local event calendars and social media events. The real-time information acquisition unit can also provide information on surrounding congestion and waiting times in real time based on the user's location information. For example, location information from the user's smart device can be used to provide information on congestion and waiting times at surrounding tourist spots and activities in real time. The real-time information acquisition unit can also analyze local news and trend information from social media to provide the latest tourist spot and event information. For example, the real-time information acquisition unit can analyze trend information from local news sites and social media to provide the latest tourist spot and event information. This allows the user to always have the information they need during their trip.

[0062] The automatic budget management unit can optimize travel plans based on a user's budget and prevent the user from going over budget. The automatic budget management unit, for example, can optimize travel plans based on a user's budget and prevent the user from going over budget. For example, it can suggest optimal accommodations and restaurants within the budget set by the user. The automatic budget management unit can also analyze the user's past spending patterns to improve the accuracy of budget management. For example, it can suggest optimal choices within the budget based on past spending data. The automatic budget management unit can also obtain local exchange rates and price information in real time and reflect this in budget management. For example, it can adjust the budget according to fluctuations in exchange rates. This allows the user to create an optimal travel plan within their budget.

[0063] The bulk reservation unit can make reservations for accommodation, transportation, and activities all at once. The bulk reservation unit, for example, makes reservations for accommodation, transportation, and activities all at once. For example, accommodation reservations can be made for hotels, guesthouses, resorts, etc. in one go. Transportation reservations can be made for flights, trains, buses, etc. in one go. Activity reservations can be made for sightseeing tours, sports activities, cultural experiences, etc. in one go. The bulk reservation unit can also provide comprehensive travel plans that include reservations for local guides and tours. For example, in addition to accommodation and transportation, local guides and tours can also be booked in one go. The bulk reservation unit can also optimize the timing of reservations and make reservations at the most cost-effective time, taking price fluctuations into consideration. For example, the bulk reservation unit can suggest the best time to make reservations based on price fluctuation data. This saves users the trouble of making reservations individually.

[0064] The customization unit can flexibly customize the travel plan according to the user's requests. The customization unit flexibly customizes the travel plan according to the user's requests. For example, this can accommodate cases where the user wants to add specific tourist spots or exclude specific restaurants. The customization unit can also propose a reasonable plan taking into account the user's health condition and physical condition. For example, it can propose tourist spots that suit the user's physical strength based on the user's health data. The customization unit can also dig deeper into the user's hobbies and interests and propose a travel plan based on a specific theme. For example, it can propose tourist spots and activities related to the user's hobbies. This allows for flexible customization according to the user's requests.

[0065] The user preference learning unit can use the emotion estimation function to suggest tourist spots and activities that provide an emotionally positive experience based on the user's past travel history and ratings. The user preference learning unit, for example, analyzes the user's past travel history and rating data and uses the emotion estimation function to identify tourist spots and activities that elicit positive emotions. For example, it prioritizes suggesting spots that the user has previously given high ratings. The user preference learning unit also analyzes the user's social media activity to reflect the user's interests and concerns at the travel destination in real time. For example, it links the user's social media accounts and analyzes the content of posts and hashtags to identify the user's interests and concerns at the travel destination. The user preference learning unit can also analyze the user's survey results to suggest recommended spots at the user's next travel destination. For example, it suggests recommended spots at the user's next travel destination based on the preferences the user answered in the survey. This makes it possible to suggest tourist spots and activities that provide a positive experience based on the user's emotions.

[0066] The automatic budget management unit can use the emotion estimation function to optimize suggestions so that the user can make highly satisfying choices within their budget. The automatic budget management unit, for example, uses the emotion estimation function to optimize suggestions so that the user can make highly satisfying choices within their budget. For example, it suggests accommodations and restaurants that the user is highly satisfied with within their budget. The automatic budget management unit can also analyze the user's past spending patterns to improve the accuracy of budget management. For example, it suggests optimal choices within a budget based on past spending data. The automatic budget management unit can also obtain local exchange rates and price information in real time and reflect this in budget management. For example, it adjusts the budget according to fluctuations in exchange rates. This allows the suggestions to be optimized so that the user can make highly satisfying choices within their budget.

[0067] The bulk reservation unit can use the emotion estimation function to provide an interface for reducing the anxiety that the user feels during the reservation process. The bulk reservation unit can, for example, use the emotion estimation function to provide an interface for reducing the anxiety that the user feels during the reservation process. For example, it can display a reassuring message if the user feels anxious. The bulk reservation unit can also analyze the user's facial expressions and voice and detect anxiety using the emotion estimation function. For example, it can detect anxiety by analyzing changes in the user's facial expressions and tone of voice. The bulk reservation unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and detect anxiety using the emotion estimation function. For example, it can detect anxiety based on fluctuations in heart rate. This makes it possible to provide an interface for reducing the anxiety that the user feels during the reservation process.

[0068] The customization unit can use the emotion estimation function to analyze the emotions felt by the user during customization and make optimal suggestions. The customization unit can, for example, use the emotion estimation function to analyze the emotions felt by the user during customization and make optimal suggestions. For example, it can make suggestions to help the user relax if they are feeling stressed. The customization unit can also analyze the user's facial expressions and voice and analyze emotions using the emotion estimation function. For example, it can analyze changes in the user's facial expressions and tone of voice to analyze emotions. The customization unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and analyze emotions using the emotion estimation function. For example, it can analyze emotions based on fluctuations in heart rate. This allows the customization unit to analyze the emotions felt by the user during customization and make optimal suggestions.

[0069] The real-time information acquisition unit can analyze local news and trend information from social media to provide the latest information on tourist spots and events. The real-time information acquisition unit can, for example, analyze local news and trend information from social media in real time to provide the latest information on tourist spots and events. For example, it can suggest popular local spots and events. The real-time information acquisition unit can also provide information on surrounding congestion and waiting times in real time based on the user's location information. For example, it can use location information from the user's smart device to provide information on congestion and waiting times at surrounding tourist spots and activities in real time. The real-time information acquisition unit can also simultaneously collect information in different languages ​​to provide information in multiple languages. For example, it can provide local news and social media information in multiple languages. This makes it possible to provide the latest information on local tourist spots and events.

[0070] The automatic budget management unit can analyze a user's past spending patterns and improve the accuracy of budget management. The automatic budget management unit, for example, analyzes a user's past spending patterns and improves the accuracy of budget management. For example, it suggests optimal choices within a budget based on past spending data. The automatic budget management unit can also use an emotion estimation function to optimize suggestions so that the user can make choices that will give the user high satisfaction within the budget. For example, it suggests accommodations and restaurants that will give the user high satisfaction within the budget. The automatic budget management unit can also obtain local exchange rates and price information in real time and reflect this in budget management. For example, it adjusts the budget in accordance with fluctuations in exchange rates. In this way, it is possible to analyze a user's past spending patterns and improve the accuracy of budget management.

[0071] The bulk reservation unit can propose optimal options by taking into account the user's past reservation history and ratings when making a reservation. The bulk reservation unit proposes optimal options based on, for example, the user's past reservation history and rating data. For example, it prioritizes the proposal of accommodations and restaurants that have received high ratings in the past. The bulk reservation unit can also use an emotion estimation function to provide an interface to reduce anxiety felt by the user during the reservation process. For example, it can display a reassuring message if the user feels anxious. The bulk reservation unit can also analyze the user's facial expressions and voice and detect anxiety using the emotion estimation function. For example, it can detect anxiety by analyzing changes in the user's facial expressions and tone of voice. The bulk reservation unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and detect anxiety using the emotion estimation function. For example, it can detect anxiety based on fluctuations in heart rate. This allows the bulk reservation unit to propose optimal options by taking into account the user's past reservation history and ratings.

[0072] The customization unit can analyze the user's customization history and reflect it in the next travel plan. For example, the customization unit analyzes the user's customization history and reflects it in the next travel plan. For example, the customization unit can suggest the next travel plan based on past customization data. The customization unit can also use an emotion estimation function to analyze the emotions the user feels during customization and make optimal suggestions. For example, the customization unit can make suggestions to help the user relax if they feel stressed. The customization unit can also analyze the user's facial expressions and voice and analyze their emotions using the emotion estimation function. For example, the customization unit can analyze emotions by analyzing changes in the user's facial expressions and tone of voice. The customization unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and analyze their emotions using the emotion estimation function. For example, the customization unit can analyze emotions based on fluctuations in heart rate. This allows the user's customization history to be analyzed and reflected in the next travel plan.

[0073] The real-time information acquisition unit can provide information on the congestion status and waiting times of nearby tourist spots and activities in real time based on the user's location information. For example, the real-time information acquisition unit can provide information on the congestion status and waiting times of nearby tourist spots and activities in real time based on the user's location information. For example, the real-time information acquisition unit can suggest alternative routes to avoid crowds. The real-time information acquisition unit can also use location information from the user's smart device to provide information on the congestion status and waiting times of nearby tourist spots and activities in real time. For example, the real-time information acquisition unit can use data from the user's smartwatch or fitness tracker to provide real-time information. The real-time information acquisition unit can also analyze local news and trending information on social media to provide the latest information on tourist spots and events. For example, it can suggest popular local spots and events. This makes it possible to provide information on the congestion status and waiting times of nearby tourist spots and activities in real time based on the user's location information.

[0074] The automatic budget management unit can acquire local exchange rates and price information in real time and reflect it in budget management. The automatic budget management unit, for example, acquires local exchange rates and price information in real time and reflects it in budget management. For example, it adjusts the budget according to fluctuations in exchange rates. The automatic budget management unit can also analyze the user's past spending patterns to improve the accuracy of budget management. For example, it can suggest optimal choices within the budget based on past spending data. The automatic budget management unit can also use an emotion estimation function to optimize suggestions so that the user can make highly satisfying choices within the budget. For example, it can suggest accommodations and restaurants that the user is highly satisfied with within the budget. This makes it possible to acquire local exchange rates and price information in real time and reflect it in budget management.

[0075] The customization unit can propose a reasonable plan taking into account the user's health condition and physical condition. The customization unit can, for example, propose a reasonable plan taking into account the user's health condition and physical condition. For example, it can propose tourist spots that suit the user's physical strength based on the user's health data. The customization unit can also dig deeper into the user's hobbies and interests and propose a travel plan based on a specific theme. For example, it can propose tourist spots and activities related to the user's hobbies. The customization unit can also analyze the user's customization history and reflect it in the next travel plan. For example, it can propose the next travel plan based on past customization data. This makes it possible to propose a reasonable plan taking into account the user's health condition and physical condition.

[0076] The real-time information acquisition unit can provide real-time information by utilizing data from the user's smart device. The real-time information acquisition unit can provide real-time information by utilizing data from, for example, the user's smartwatch or fitness tracker. For example, it can provide information based on the user's health condition. The real-time information acquisition unit can also provide real-time information about the congestion status and waiting times of nearby tourist spots and activities based on the user's location information. For example, it can suggest alternative routes to avoid crowds. The real-time information acquisition unit can also analyze local news and trending information on social media to provide the latest information on tourist spots and events. For example, it can suggest popular local spots and events. In this way, it is possible to provide real-time information by utilizing data from the user's smart device.

[0077] The automatic budget management unit can link data from the user's credit cards and bank accounts and track expenses in real time. The automatic budget management unit, for example, links data from the user's credit cards and bank accounts and tracks expenses in real time. For example, it optimizes budget management based on the expense data. The automatic budget management unit can also analyze the user's past expense patterns and improve the accuracy of budget management. For example, it can suggest optimal choices within the budget based on past expense data. The automatic budget management unit can also obtain local exchange rates and price information in real time and reflect this in budget management. For example, it can adjust the budget according to fluctuations in exchange rates. In this way, it can link data from the user's credit cards and bank accounts and track expenses in real time.

[0078] The bulk booking unit can provide comprehensive travel plans, including reservations for local guides and tours. The bulk booking unit can provide comprehensive travel plans, including reservations for local guides and tours. For example, in addition to accommodations and transportation, it can also make reservations for local guides and tours all at once. The bulk booking unit can also optimize the timing of reservations and make reservations at the most advantageous time, taking price fluctuations into consideration. For example, it can suggest the optimal reservation timing based on price fluctuation data. The bulk booking unit can also suggest optimal options based on the user's past reservation history and rating data. For example, it can prioritize suggestions for accommodations and restaurants that have been highly rated in the past. This makes it possible to provide comprehensive travel plans, including reservations for local guides and tours.

[0079] The customization unit can incorporate the opinions of the user's family and friends to provide the optimal plan for the entire group. The customization unit can, for example, incorporate the opinions of the user's family and friends to provide the optimal plan for the entire group. For example, it can propose a travel plan that reflects the opinions of everyone. The customization unit can also propose a reasonable plan taking into account the user's health condition and physical condition. For example, it can propose tourist spots that are appropriate for the user's physical strength based on the user's health data. The customization unit can also dig deeper into the user's hobbies and interests to propose a travel plan based on a specific theme. For example, it can propose tourist spots and activities related to the user's hobbies. In this way, the optimal plan for the entire group can be provided by incorporating the opinions of the user's family and friends.

[0080] The real-time information acquisition unit can simultaneously collect information in different languages ​​and provide information in multiple languages. The real-time information acquisition unit can simultaneously collect information in different languages ​​and provide information in multiple languages. For example, it can provide local news and social media information in multiple languages. The real-time information acquisition unit can also provide real-time information on the congestion status and waiting times of nearby tourist spots and activities based on the user's location information. For example, it can suggest alternative routes to avoid crowds. The real-time information acquisition unit can also analyze local news and trending information on social media and provide the latest information on tourist spots and events. For example, it can suggest popular local spots and events. This makes it possible to simultaneously collect information in different languages ​​and provide information in multiple languages.

[0081] The automatic budget management unit can also take into account the user's travel insurance and point programs to propose the optimal plan. The automatic budget management unit can, for example, take into account the user's travel insurance and point programs to propose the optimal plan. For example, it can use points to propose accommodations and restaurants at discounted prices. The automatic budget management unit can also analyze the user's past spending patterns to improve the accuracy of budget management. For example, it can propose the optimal choice within the budget based on past spending data. The automatic budget management unit can also obtain local exchange rates and price information in real time and reflect this in budget management. For example, it can adjust the budget according to fluctuations in exchange rates. This makes it possible to propose the optimal plan taking into account the user's travel insurance and point programs.

[0082] The bulk reservation unit can take into account a user's specific requests (e.g., allergy-friendly or special facilities) when making a reservation. The bulk reservation unit can, for example, suggest restaurants that cater to allergies or accommodations with special facilities. The bulk reservation unit can also suggest optimal options based on the user's past reservation history and rating data. For example, it can prioritize suggesting accommodations and restaurants that have received high ratings in the past. The bulk reservation unit can also use an emotion estimation function to provide an interface to reduce anxiety felt by the user during the reservation process. For example, it can display a reassuring message if the user feels anxious. The bulk reservation unit can also analyze the user's facial expressions and voice and detect anxiety using the emotion estimation function. For example, it can detect anxiety by analyzing changes in the user's facial expression and tone of voice. The bulk reservation unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and detect anxiety using the emotion estimation function. For example, it can detect anxiety based on fluctuations in heart rate. This allows the user's specific requests (e.g., allergy-friendly or special facilities) to be taken into account when making a reservation.

[0083] The customization unit can dig deep into the user's hobbies and interests and propose a travel plan that fits a specific theme. The customization unit can, for example, dig deep into the user's hobbies and interests and propose a travel plan that fits a specific theme. For example, it can propose tourist spots and activities related to the user's hobbies. The customization unit can also propose a reasonable plan that takes into account the user's health condition and physical condition. For example, it can propose tourist spots that fit the user's physical strength based on the user's health data. The customization unit can also analyze the user's customization history and reflect it in the next travel plan. For example, it can propose the next travel plan based on past customization data. This makes it possible to dig deep into the user's hobbies and interests and propose a travel plan that fits a specific theme.

[0084] The automatic budget management unit can use the emotion estimation function to provide advice to reduce the stress the user feels about budget management. The automatic budget management unit, for example, uses the emotion estimation function to provide advice to reduce the stress the user feels about budget management. For example, if the user feels stressed, the automatic budget management unit can provide advice to help the user relax. The automatic budget management unit can also analyze the user's facial expressions and voice and detect stress using the emotion estimation function. For example, stress can be detected by analyzing changes in the user's facial expressions and tone of voice. The automatic budget management unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and detect stress using the emotion estimation function. For example, stress can be detected based on fluctuations in heart rate. This makes it possible to provide advice to reduce the stress the user feels about budget management.

[0085] The bulk reservation unit can use the emotion estimation function to monitor the level of satisfaction felt by the user after completing a reservation in real time and improve the service based on the feedback. The bulk reservation unit can, for example, use the emotion estimation function to monitor the level of satisfaction felt by the user after completing a reservation in real time and improve the service based on the feedback. For example, the bulk reservation unit can identify areas for improvement in the service based on the user's emotion data. The bulk reservation unit can also analyze the user's facial expressions and voice and monitor the level of satisfaction using the emotion estimation function. For example, it can analyze changes in the user's facial expressions and tone of voice to monitor the level of satisfaction. The bulk reservation unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and monitor the level of satisfaction using the emotion estimation function. For example, it can monitor the level of satisfaction based on fluctuations in heart rate. This allows the bulk reservation unit to monitor the level of satisfaction felt by the user after completing a reservation in real time and improve the service based on the feedback.

[0086] The customization unit can use the emotion estimation function to collect emotional responses to a plan customized by the user in real time and provide an optimal plan. The customization unit, for example, uses the emotion estimation function to collect emotional responses to a plan customized by the user in real time and provide an optimal plan. For example, the customization unit adjusts the plan based on the user's emotion data. The customization unit can also analyze the user's facial expressions and voice and collect emotional responses using the emotion estimation function. For example, the customization unit can analyze changes in the user's facial expressions and tone of voice to collect emotional responses. The customization unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and collect emotional responses using the emotion estimation function. For example, the customization unit can collect emotional responses based on fluctuations in heart rate. In this way, the customization unit can collect emotional responses to a plan customized by the user in real time and provide an optimal plan.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The travel planning assistant system can also include a health management unit that collects the user's health data and supports health management during the trip. For example, it can collect heart rate, step count, and sleep data from the user's smartwatch or fitness tracker to monitor the user's health condition during the trip. The health management unit can also suggest appropriate rest times and snacks based on the user's health data. Furthermore, the health management unit can provide information on medical facilities and pharmacies at the travel destination and support emergency response. This allows the user to maintain their health while traveling and enjoy their trip with peace of mind.

[0089] The travel planning assistant system can also include a cultural experience suggestion unit that suggests cultural experiences at the user's next travel destination based on the user's past travel history and evaluation data. For example, the system can analyze data on museums and art galleries that the user has visited in the past and suggest recommended cultural facilities at the user's next travel destination. The cultural experience suggestion unit can also suggest local traditional festivals and events based on the user's interests. Furthermore, the cultural experience suggestion unit can suggest experiential activities such as local craft making and cooking classes according to the user's preferences. This allows the user to enjoy cultural experiences at their travel destination more deeply.

[0090] The real-time information acquisition unit can further estimate the user's emotions and provide information to reduce stress during the trip. For example, if the user feels stressed, it can suggest tourist spots or cafes where the user can relax. The real-time information acquisition unit can also analyze the user's facial expressions and voice and detect stress using the emotion estimation function. For example, it can detect stress by analyzing changes in the user's facial expressions and tone of voice. The real-time information acquisition unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and detect stress using the emotion estimation function. This allows the user to reduce stress during their trip and enjoy a more comfortable trip.

[0091] The automated budget management unit can further consider the user's travel insurance and points program to propose the optimal plan. For example, it can use points to propose discounted accommodation and restaurant prices. The automated budget management unit can also analyze the user's past spending patterns to improve the accuracy of budget management. For example, it can propose the optimal choice within the budget based on past spending data. The automated budget management unit can also obtain local exchange rates and price information in real time and reflect this in budget management. For example, it can adjust the budget according to fluctuations in exchange rates. This makes it possible to propose the optimal plan, taking into account the user's travel insurance and points program.

[0092] The bulk reservation unit can further take into account the user's specific requests (e.g., allergy-friendly or special facilities). For example, it can suggest restaurants that are allergy-friendly or accommodations with special facilities. The bulk reservation unit can also suggest optimal options based on the user's past reservation history and rating data. For example, it can prioritize suggesting accommodations and restaurants that have received high ratings in the past. The bulk reservation unit can also use an emotion estimation function to provide an interface to reduce the anxiety the user feels during the reservation process. For example, it can display a reassuring message if the user feels anxious. The bulk reservation unit can also analyze the user's facial expressions and voice and use the emotion estimation function to detect anxiety. This allows the user's specific requests (e.g., allergy-friendly or special facilities) to be taken into account when making a reservation.

[0093] The customization unit can also incorporate the opinions of the user's family and friends to provide the optimal plan for the entire group. For example, it can propose a travel plan that reflects the opinions of everyone. The customization unit can also propose a reasonable plan taking into account the user's health condition and physical condition. For example, it can propose tourist spots that suit the user's physical strength based on the user's health data. The customization unit can also dig deeper into the user's hobbies and interests to propose a travel plan based on a specific theme. This allows the optimal plan for the entire group to be provided, incorporating the opinions of the user's family and friends.

[0094] The user preference learning unit can further estimate the user's emotions and suggest tourist spots and activities that will provide a positive experience based on the emotions. For example, it can prioritize suggestions of spots that the user has previously given high ratings. The user preference learning unit can also analyze the user's social media activity and reflect their interests and concerns at the travel destination in real time. For example, it can link the user's social media accounts and analyze the content of posts and hashtags to identify their interests and concerns at the travel destination. The user preference learning unit can also analyze the results of user surveys and suggest recommended spots at the next travel destination. This makes it possible to suggest tourist spots and activities that will provide a positive experience based on the user's emotions.

[0095] The travel planning assistant system can also include a safety management module to ensure the user's safety during their trip. For example, it can provide information on the security situation at the travel destination and emergency contact information. The safety management module can also suggest routes to avoid dangerous areas based on the user's location information. Furthermore, the safety management module can use data from the user's smart device to automatically send alerts in the event of an emergency. This allows the user to stay safe during their trip.

[0096] The automatic budget management unit can further estimate the user's emotions and provide advice to reduce stress felt from budget management. For example, if the user feels stressed, it can provide advice to help the user relax. The automatic budget management unit can also analyze the user's facial expressions and voice and detect stress using the emotion estimation function. For example, it can detect stress by analyzing changes in the user's facial expressions and tone of voice. The automatic budget management unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and detect stress using the emotion estimation function. This makes it possible to provide advice to reduce stress felt by the user from budget management.

[0097] The bulk reservation unit can also monitor the level of satisfaction felt by users after completing reservations in real time and improve services based on feedback. For example, it can identify areas for improvement in services based on users' emotional data. The bulk reservation unit can also analyze users' facial expressions and voices and monitor satisfaction using an emotion estimation function. For example, it can analyze changes in the user's facial expressions and tone of voice to monitor satisfaction. The bulk reservation unit can also collect users' biometric data (heart rate and electrodermal activity) using sensors and monitor satisfaction using an emotion estimation function. This allows it to monitor users' satisfaction in real time after completing reservations and improve services based on feedback.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The user preference learning unit learns the user's preferences. For example, it analyzes the user's past travel history and rating data to learn the user's preferences. It can also analyze the user's social media activity and survey results to learn the user's interests. Step 2: The personalized suggestion module suggests personalized tourist spots, restaurants, and activities based on the user's preferences. For example, it analyzes data on places the user has visited and restaurants they have rated in the past to suggest recommended spots for their next trip. It can also suggest themed travel plans or specific restaurants and activities. Step 3: The real-time information acquisition unit acquires real-time information about the travel destination. For example, it acquires weather information, traffic information, event information, etc. in real time and provides it to the user. It can also analyze the surrounding congestion situation, waiting times, local news, and trending information on social media based on the user's location information, and provide the latest information on tourist spots and events. Step 4: The automatic budget management unit optimizes travel plans based on the user's budget. For example, it suggests the best accommodations and restaurants within the user's set budget, preventing overspending. It can also analyze the user's past spending patterns to improve the accuracy of budget management. It can also obtain local exchange rates and price information in real time and reflect this in budget management. Step 5: The bulk booking section makes bulk bookings based on the user's travel plans. For example, you can book accommodation, transportation, and activities all at once. You can also provide comprehensive travel plans, including reservations for local guides and tours. Furthermore, the bulk booking section can optimize the timing of bookings, taking into account price fluctuations, to make reservations at the best price. Step 6: The customization unit flexibly customizes the travel plan according to the user's requests. For example, the system can accommodate requests for adding specific tourist spots or excluding specific restaurants. It can also propose a reasonable plan that takes into account the user's health and physical condition. It can also dig deeper into the user's hobbies and interests to propose travel plans based on specific themes.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0159] 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.

[0160] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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]

[0167] 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 user preference learning unit that learns user preferences; a personalized suggestion unit that suggests tourist spots, restaurants, and activities personalized based on the user's preferences; a real-time information acquisition unit that acquires real-time information about a travel destination; an automatic budget management unit that optimizes travel plans based on the user's budget; a bulk booking unit that makes a bulk booking based on the travel plan; A customization unit that customizes the travel plan according to the user's requests. A system characterized by:

2. The real-time information acquisition unit Obtaining weather information, traffic information, and event information in real time and providing it to the user 2. The system of claim 1.

3. The automatic budget management unit Optimizing the travel plan based on the user's budget to prevent budget overruns 2. The system of claim 1.

4. The bulk reservation unit Book accommodation, transportation and activities all in one place 2. The system of claim 1.

5. The user preference learning unit Using an emotion estimation function, the tourist spots and activities that provide an emotionally positive experience are suggested based on the user's past travel history and ratings.

2. The system of claim 1.

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