system

Abstract

The system according to this embodiment aims to automatically generate an optimal travel plan, taking into account the user's preferences, budget, and local information, and to handle the booking process. [Solution] The system according to the embodiment comprises a learning unit, an information gathering unit, a generation unit, and a reservation unit. The learning unit learns the user's preferences and budget. The information gathering unit collects local weather forecasts and event information. The generation unit automatically generates an optimal travel plan based on the information obtained by the learning unit and the information gathering unit. The reservation unit makes reservations for transportation and accommodation based on the travel plan generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is no sufficient system that automatically generates an optimal travel plan considering the user's preferences, budget, and local information and even makes a reservation, leaving room for improvement.

[0005] The system according to the embodiment aims to automatically generate an optimal travel plan considering the user's preferences, budget, and local information and even make a reservation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a learning unit, an information gathering unit, a generation unit, and a reservation unit. The learning unit learns the user's preferences and budget. The information gathering unit collects local weather forecasts and event information. The generation unit automatically generates an optimal travel plan based on the information obtained by the learning unit and the information gathering unit. The reservation unit makes reservations for transportation and accommodation based on the travel plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate an optimal travel plan considering the user's preferences, budget, and local information, and can even handle the booking process. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

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

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The automated travel plan generation system according to an embodiment of the present invention is a system that learns the user's preferences and budget, and automatically generates an optimal travel plan considering local weather forecasts and event information. This automated travel plan generation system also makes reservations for transportation and accommodation, allowing the user to complete all travel-related procedures in one place. Furthermore, in the case of overseas travel, the automated travel plan generation system provides a language translation function to assist with communication. In addition, the automated travel plan generation system provides safety information and emergency support to ensure a safe and comfortable trip. For example, the automated travel plan generation system learns the user's preferences and budget. For example, the automated travel plan generation system collects data such as places the user has visited in the past, preferred modes of transportation, and types of accommodation, and the AI ​​analyzes this data. As a result, the automated travel plan generation system can propose a travel plan that suits the user's preferences. Next, the automated travel plan generation system considers local weather forecasts and event information. For example, the automated travel plan generation system checks the weather forecast for the travel destination and automatically generates a plan that adapts to the weather, such as suggesting indoor tourist spots on rainy days. Furthermore, the automated travel plan generation system can collect information on events held locally and suggest events to participate in based on the user's interests. In addition, the automated travel plan generation system can make reservations for transportation and accommodation. For example, it can search for and book the most suitable transportation and accommodation based on the user's desired departure and return dates and budget. This allows the automated travel plan generation system to complete all travel arrangements in a single process. For international travel, the automated travel plan generation system provides a language translation function. For example, if communication in the local language is difficult, the AI ​​can translate in real time, enabling users to communicate smoothly. The automated travel plan generation system also provides safety information and emergency support. For example, it provides local security information and emergency contact information, supporting users to enjoy their trip with peace of mind.This allows the automated travel plan generation system to automatically generate the optimal travel plan tailored to the user's preferences and budget, and to handle all travel-related procedures in one place, including booking transportation and accommodation, language translation, and safety information. This ensures a safe and comfortable travel experience. The automated travel plan generation system automatically generates the optimal travel plan based on the user's preferences and budget, and to handle all travel-related procedures in one place, including booking transportation and accommodation, language translation, and safety information.

[0029] The automated travel plan generation system according to this embodiment comprises a learning unit, an information gathering unit, a generation unit, and a booking unit. The learning unit learns the user's preferences and budget. The learning unit collects data such as places the user has visited in the past, preferred modes of transportation, and types of accommodation, and the AI ​​analyzes this data. The learning unit can, for example, propose a travel plan that suits the user's preferences. The learning unit can also propose an optimal travel plan based on the user's budget. For example, the learning unit can propose a cost-effective travel plan according to the user's budget. The information gathering unit collects local weather forecasts and event information. The information gathering unit can, for example, check the weather forecast for the travel destination and automatically generate a plan that adapts to the weather, such as suggesting indoor tourist spots on rainy days. The information gathering unit can also collect information on events held locally and suggest event participation according to the user's interests. For example, the information gathering unit can collect local event information and suggest event participation according to the user's interests. The generation unit automatically generates an optimal travel plan based on the information obtained by the learning unit and the information gathering unit. The generation unit automatically generates an optimal travel plan based on the user's preferences and budget. The generation unit can automatically generate a cost-effective travel plan based on the user's preferences and budget. The generation unit can automatically generate a travel plan that enhances user satisfaction based on the user's preferences and budget. The booking unit makes reservations for transportation and accommodation based on the travel plan generated by the generation unit. The booking unit searches for and makes reservations for the most suitable transportation and accommodation according to the user's desired departure and return dates and budget. The booking unit can search for and make reservations for cost-effective transportation and accommodation according to the user's desired departure and return dates and budget. The booking unit can search for and make reservations for transportation and accommodation that enhance user satisfaction according to the user's desired departure and return dates and budget. As a result, the automatic travel plan generation system according to this embodiment can learn the user's preferences and budget, automatically generate an optimal travel plan considering local weather forecasts and event information, and make reservations for transportation and accommodation.

[0030] The learning unit learns the user's preferences and budget. Specifically, it collects data such as places the user has visited in the past, preferred modes of transportation, types of accommodation, food preferences, and activity preferences, and the AI ​​analyzes this data. For example, it analyzes the history of tourist destinations the user has visited in the past and suggests new tourist destinations with similar characteristics. It also learns the user's preferred modes of transportation (e.g., airplane, train, bus, etc.) and types of accommodation (e.g., hotel, guesthouse, resort, etc.) and suggests the optimal travel plan based on this information. Furthermore, it can suggest cost-effective travel plans based on the user's budget. For example, it adjusts the rank of accommodation and transportation options according to the user's budget to optimize the overall travel cost. The learning unit continuously collects and updates data on the user's preferences and budget, providing customized travel plans that meet the user's needs. This makes it easy for users to find travel plans that suit their preferences and budget, making travel planning smoother and more satisfying.

[0031] The information gathering department collects local weather forecasts and event information. Specifically, it checks the weather forecast for the travel destination and automatically generates weather-appropriate plans, such as suggesting indoor tourist spots on rainy days. For example, it obtains real-time weather forecasts for the travel destination and, if rain is expected, suggests a plan that includes indoor facilities such as art museums, museums, and shopping malls. The information gathering department can also collect information on events held locally and suggest event participation based on the user's interests. For example, it collects information on local festivals, concerts, and sporting events and suggests travel plans that include these events based on the user's interests and preferences. Furthermore, the information gathering department also collects local traffic information and congestion levels to ensure that users can travel comfortably. In this way, the information gathering department can provide the latest and most accurate information to offer users the optimal travel plan, making travel planning more fulfilling.

[0032] The generation unit automatically generates optimal travel plans based on information obtained by the learning unit and information gathering unit. Specifically, the AI ​​automatically generates optimal travel plans based on the user's preferences and budget. For example, based on the user's past travel history and preference data, it selects tourist destinations and activities that the user is likely to be interested in and creates a plan that combines accommodations and transportation methods according to the budget. The generation unit can automatically generate cost-effective travel plans based on the user's preferences and budget. For example, to provide the best experience within the user's budget, it optimizes the rank of accommodations and transportation options, proposing a highly satisfying plan while keeping overall travel costs down. Furthermore, the generation unit can automatically generate travel plans that enhance user satisfaction based on the user's preferences and budget. For example, if the user is interested in specific activities or events, it will propose a plan that includes them, enriching the user's travel experience. The generation unit integrates this information and uses advanced algorithms to provide the optimal travel plan for the user. This allows the generation unit to quickly and accurately provide customized travel plans that meet the user's needs.

[0033] The booking department makes reservations for transportation and accommodation based on the travel plans generated by the generation department. Specifically, it searches for and reserves the most suitable transportation and accommodation options according to the user's desired departure and return dates and budget. For example, it can search for and reserve cost-effective transportation and accommodation options according to the user's desired departure and return dates and budget. The booking department can search for and reserve transportation and accommodation options that will increase user satisfaction according to the user's desired departure and return dates and budget. The booking department can, for example, link with the reservation systems of airlines, railway companies, and hotels to check availability in real time and provide the best options. The booking department can also apply benefits and discounts for repeat customers, taking into account the user's reservation history and preferences. Furthermore, the booking department can flexibly handle changes and cancellations of reservations, providing support that meets the user's needs. In this way, the booking department plays a crucial role in realizing the optimal travel plan for the user, enabling smooth and efficient travel planning.

[0034] The translation unit provides language translation functionality for overseas travel. For example, if communication in the local language is difficult, the translation unit uses AI to translate in real time, enabling users to communicate smoothly. The translation unit can perform text translation, for example. The translation unit can also perform voice translation, for example. The translation unit can also perform real-time translation. This allows the translation unit to provide language translation functionality for overseas travel, enabling users to communicate smoothly. Some or all of the above-described processes in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input user-generated text into a generating AI, which can then perform the translation.

[0035] The support department provides safety information and emergency support. For example, the support department provides local security information and emergency contact information to help users enjoy their trip with peace of mind. The support department can also provide information on local medical facilities. The support department can also provide emergency contact services. The support department can also establish local support systems. In this way, the support department can help users enjoy their trip with peace of mind by providing safety information and emergency support. Some or all of the above processes performed by the support department may be carried out using AI, for example, or not. For example, the support department can input local security information into a generating AI, and the generating AI can provide the information.

[0036] The learning unit analyzes the user's past travel history and selects the optimal learning algorithm. For example, the learning unit selects the optimal learning algorithm based on places the user has visited and modes of transportation used in the past. For example, the learning unit analyzes the user's past travel history to determine their preferred type of accommodation and adjusts the learning algorithm accordingly. For example, the learning unit analyzes the user's past travel history and selects an algorithm that learns their preferences for specific seasons or events. In this way, the learning unit can select the optimal learning algorithm by analyzing the user's past travel history. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's past travel history data into a generating AI, which can then select the optimal learning algorithm.

[0037] The learning unit filters data during learning based on the user's current lifestyle and areas of interest. For example, the learning unit filters relevant travel data based on the user's current occupation and lifestyle. For example, the learning unit filters travel data based on the user's current areas of interest (e.g., outdoor activities or cultural events). For example, the learning unit filters appropriate travel data based on the user's current health status and fitness level. This allows the learning unit to learn more relevant data by filtering data based on the user's current lifestyle and areas of interest. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's current lifestyle data into a generating AI, which can then filter the data.

[0038] The learning unit prioritizes learning highly relevant data, taking into account the user's geographical location information during the learning process. For example, the learning unit prioritizes learning data about nearby travel destinations and activities based on the user's current location. For example, the learning unit prioritizes learning relevant data based on the user's past travel destinations. For example, the learning unit prioritizes learning relevant data based on the user's future travel plans. This allows the learning unit to learn more appropriate data by prioritizing highly relevant data while considering the user's geographical location information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's geographical location information data into a generating AI, which can then prioritize learning highly relevant data.

[0039] The learning unit analyzes the user's social media activity and learns relevant data during the learning process. For example, the learning unit learns relevant data based on travel destinations and activities shared by the user on social media. For example, the learning unit learns relevant data based on the user's interests and accounts followed on social media. For example, the learning unit learns relevant data based on the content and comments posted by the user on social media. In this way, the learning unit can learn relevant data by analyzing the user's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's social media data into a generating AI, which can then learn relevant data.

[0040] The information gathering unit improves prediction accuracy by referring to past local weather data and event history during information gathering. For example, the information gathering unit improves the accuracy of weather forecasts by referring to past local weather data. For example, the information gathering unit predicts the timing and frequency of events by referring to past local event history. For example, the information gathering unit improves the accuracy of travel plan predictions by combining past local weather data and event history. In this way, the information gathering unit can improve prediction accuracy by referring to past local weather data and event history. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input past local weather data into a generating AI, which can then improve prediction accuracy.

[0041] The information gathering unit customizes the types of information it collects based on the user's areas of interest. For example, if the user is interested in outdoor activities, the information gathering unit prioritizes collecting relevant information. For example, if the user is interested in cultural events, the information gathering unit prioritizes collecting relevant information. For example, if the user is interested in gourmet food, the information gathering unit prioritizes collecting relevant information. In this way, the information gathering unit can collect more relevant information by customizing the types of information it collects based on the user's areas of interest. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input user area of ​​interest data into a generating AI and customize the types of information the generating AI collects.

[0042] The information gathering unit prioritizes collecting highly relevant information, taking into account the local geographical characteristics. For example, the information gathering unit prioritizes collecting information on tourist attractions and activities based on the local geographical characteristics. For example, the information gathering unit prioritizes collecting information on transportation and access based on the local geographical characteristics. For example, the information gathering unit prioritizes collecting information on climate and weather based on the local geographical characteristics. By doing so, the information gathering unit can collect more appropriate information by prioritizing the collection of highly relevant information, taking into account the local geographical characteristics. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input local geographical characteristics data into a generating AI, which can then prioritize the collection of highly relevant information.

[0043] The information gathering department analyzes local social media activity and collects relevant information during the information gathering process. For example, the information gathering department collects information on events and activities that are trending on local social media. For example, the information gathering department collects information on tourist attractions and restaurants that are being shared on local social media. For example, the information gathering department analyzes word-of-mouth and reviews on local social media and collects relevant information. In this way, the information gathering department can collect relevant information by analyzing local social media activity. Some or all of the above processing in the information gathering department may be performed using AI, for example, or without AI. For example, the information gathering department can input local social media data into a generating AI, and the generating AI can collect relevant information.

[0044] The generation unit selects the optimal generation algorithm by referring to the user's past travel plans during generation. For example, the generation unit selects the optimal generation algorithm based on travel plans the user has used in the past. For example, the generation unit analyzes the user's past travel plans to identify preferred tourist spots and activities and adjusts the generation algorithm accordingly. For example, the generation unit analyzes the user's past travel plans and selects a generation algorithm that learns preferences for specific seasons and events. This allows the generation unit to select the optimal generation algorithm by referring to the user's past travel plans. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past travel plan data into a generation AI, which can then select the optimal generation algorithm.

[0045] The generation unit customizes the plan based on the user's current living situation and areas of interest during the generation process. For example, the generation unit customizes relevant travel plans based on the user's current occupation and lifestyle. For example, the generation unit customizes travel plans based on the user's current areas of interest (e.g., outdoor activities or cultural events). For example, the generation unit customizes appropriate travel plans based on the user's current health status and fitness level. This allows the generation unit to generate more appropriate travel plans by customizing them based on the user's current living situation and areas of interest. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's current living situation data into a generation AI, which can then customize the plan.

[0046] The generation unit prioritizes generating highly relevant plans by considering the user's geographical location information during the generation process. For example, the generation unit generates plans that prioritize nearby travel destinations and activities based on the user's current location. For example, the generation unit generates relevant plans based on the user's past travel destinations. For example, the generation unit generates relevant plans based on the user's future travel plans. In this way, the generation unit can generate more appropriate travel plans by prioritizing highly relevant plans while considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location data into a generation AI, which can then prioritize generating highly relevant plans.

[0047] The generation unit analyzes the user's social media activity during generation and generates relevant plans. For example, the generation unit generates relevant plans based on travel destinations and activities shared by the user on social media. For example, the generation unit generates relevant plans based on the user's interests and accounts followed on social media. For example, the generation unit generates relevant plans based on the user's posts and comments on social media. In this way, the generation unit can generate relevant plans by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media data into a generation AI, which can then generate relevant plans.

[0048] The reservation department selects the optimal reservation method by referring to the user's past reservation history when a reservation is made. For example, the reservation department selects the optimal reservation method based on the reservation method the user has used in the past. For example, the reservation department analyzes the user's past reservation history to determine their preferred accommodations and modes of transportation and adjusts the reservation method accordingly. For example, the reservation department analyzes the user's past reservation history and selects a reservation method that learns their preferences for specific seasons or events. In this way, the reservation department can select the optimal reservation method by referring to the user's past reservation history. Some or all of the above processes in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department can input the user's past reservation history data into a generating AI, which can then select the optimal reservation method.

[0049] The reservation unit customizes reservation details based on the user's current lifestyle and areas of interest at the time of reservation. For example, the reservation unit customizes relevant reservation details based on the user's current occupation and lifestyle. For example, the reservation unit customizes reservation details based on the user's current areas of interest (e.g., outdoor activities or cultural events). For example, the reservation unit customizes appropriate reservation details based on the user's current health status and fitness level. This allows the reservation unit to make more appropriate reservations by customizing reservation details based on the user's current lifestyle and areas of interest. Some or all of the above processing in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input the user's current lifestyle data into a generating AI, which can then customize the reservation details.

[0050] The reservation department prioritizes highly relevant reservations by considering the user's geographical location information during the reservation process. For example, the reservation department prioritizes reservations for nearby accommodations and transportation based on the user's current location. For example, the reservation department makes relevant reservations based on the user's past travel destinations. For example, the reservation department makes relevant reservations based on the user's future travel plans. In this way, the reservation department can make more appropriate reservations by prioritizing highly relevant reservations by considering the user's geographical location information. Some or all of the above processing in the reservation department may be performed using AI, for example, or without AI. For example, the reservation department can input the user's geographical location data into a generating AI, which can then prioritize highly relevant reservations.

[0051] The reservation unit analyzes the user's social media activity when a reservation is made and makes a relevant reservation. For example, the reservation unit makes a relevant reservation based on travel destinations and activities shared by the user on social media. For example, the reservation unit makes a relevant reservation based on the user's interests and accounts followed on social media. For example, the reservation unit makes a relevant reservation based on the content and comments posted by the user on social media. In this way, the reservation unit can make relevant reservations by analyzing the user's social media activity. Some or all of the above processing in the reservation unit may be performed using AI, for example, or not using AI. For example, the reservation unit can input the user's social media data into a generating AI, and the generating AI can make a relevant reservation.

[0052] The translation unit selects the optimal translation algorithm by referring to the user's past translation history during translation. For example, the translation unit selects the optimal translation algorithm based on translation methods the user has used in the past. For example, the translation unit analyzes the user's past translation history to identify preferred expressions and phrases and adjusts the translation algorithm accordingly. For example, the translation unit analyzes the user's past translation history and selects a translation algorithm that learns preferences for specific situations and contexts. This allows the translation unit to select the optimal translation algorithm by referring to the user's past translation history. Some or all of the above processes in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input the user's past translation history data into a generating AI, which can then select the optimal translation algorithm.

[0053] The translation unit customizes the translation content based on the user's current lifestyle and areas of interest during the translation process. For example, the translation unit customizes relevant translation content based on the user's current occupation and lifestyle. For example, the translation unit customizes translation content based on the user's current areas of interest (e.g., outdoor activities or cultural events). For example, the translation unit customizes appropriate translation content based on the user's current health status and fitness level. This allows the translation unit to provide more appropriate translations by customizing the translation content based on the user's current lifestyle and areas of interest. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input the user's current lifestyle data into a generating AI, which can then customize the translation content.

[0054] The translation unit prioritizes highly relevant translations by considering the user's geographical location during the translation process. For example, the translation unit prioritizes translations about nearby tourist attractions and activities based on the user's current location. For example, the translation unit provides relevant translations based on the user's past travel destinations. For example, the translation unit provides relevant translations based on the user's future travel plans. This allows the translation unit to provide more appropriate translations by prioritizing highly relevant translations while considering the user's geographical location. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input the user's geographical location data into a generating AI, which can then prioritize highly relevant translations.

[0055] The translation unit analyzes the user's social media activity during translation and performs relevant translations. For example, the translation unit performs relevant translations based on travel destinations and activities shared by the user on social media. For example, the translation unit performs relevant translations based on the user's interests and accounts followed on social media. For example, the translation unit performs relevant translations based on the content and comments posted by the user on social media. In this way, the translation unit can provide relevant translations by analyzing the user's social media activity. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input the user's social media data into a generating AI, which can then perform relevant translations.

[0056] The support unit selects the optimal support method by referring to the user's past support history during support. For example, the support unit selects the optimal support method based on the support methods the user has used in the past. For example, the support unit analyzes the user's preferred support methods and responses from their past support history and adjusts the support method accordingly. For example, the support unit analyzes the user's past support history and selects a support method that learns their preferences for specific situations and contexts. This allows the support unit to select the optimal support method by referring to the user's past support history. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's past support history data into a generating AI, which can then select the optimal support method.

[0057] The support unit customizes the support provided based on the user's current living situation and areas of interest. For example, the support unit customizes relevant support based on the user's current occupation and lifestyle. For example, the support unit customizes support based on the user's current areas of interest (e.g., outdoor activities or cultural events). For example, the support unit customizes appropriate support based on the user's current health status and fitness level. This allows the support unit to provide more appropriate support by customizing support based on the user's current living situation and areas of interest. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's current living situation data into a generating AI, which can then customize the support content.

[0058] The support unit prioritizes providing relevant support by considering the user's geographical location. For example, the support unit prioritizes support regarding nearby tourist attractions and activities based on the user's current location. For example, the support unit provides relevant support based on the user's past travel destinations. For example, the support unit provides relevant support based on the user's future travel plans. In this way, the support unit can provide more appropriate support by prioritizing relevant support while considering the user's geographical location. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location data into a generating AI, which can then prioritize providing relevant support.

[0059] The support department analyzes the user's social media activity and provides relevant support during support sessions. For example, the support department provides relevant support based on travel destinations and activities shared by the user on social media. For example, the support department provides relevant support based on the user's interests and accounts followed on social media. For example, the support department provides relevant support based on the content and comments posted by the user on social media. In this way, the support department can provide relevant support by analyzing the user's social media activity. Some or all of the above processing in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the user's social media data into a generating AI, which can then provide relevant support.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The learning function not only learns the user's preferences and budget, but can also suggest travel plans that take into account the user's health condition and fitness level. For example, if the user provides health checkup data, the learning function can suggest a health-conscious travel plan based on that data. Furthermore, the learning function can suggest appropriate activities and sightseeing spots according to the user's fitness level. For example, if the user has a high fitness level, it can suggest activities such as hiking or cycling. Also, if the user is seeking relaxation, it can suggest relaxation facilities such as spas or hot springs. In this way, the learning function can suggest more appropriate travel plans based on the user's health condition and fitness level.

[0062] The learning unit can filter data based on the user's current lifestyle and areas of interest. For example, it can filter relevant travel data based on the user's current occupation and lifestyle. It can also filter travel data based on the user's current areas of interest (e.g., outdoor activities or cultural events). Furthermore, it can filter appropriate travel data based on the user's current health status and fitness level. In this way, the learning unit can learn more relevant data by filtering data based on the user's current lifestyle and areas of interest.

[0063] The information gathering unit can improve prediction accuracy by referring to past local weather data and event history during information gathering. For example, the information gathering unit can improve the accuracy of weather forecasts by referring to past local weather data. Furthermore, the information gathering unit can predict the timing and frequency of events by referring to past local event history. In addition, the information gathering unit can improve the accuracy of travel plan predictions by combining past local weather data and event history. Thus, the information gathering unit can improve prediction accuracy by referring to past local weather data and event history.

[0064] The generation unit can select the optimal generation algorithm by referring to the user's past travel plans during the generation process. For example, the generation unit can select the optimal generation algorithm based on travel plans the user has used in the past. Furthermore, the generation unit can analyze the user's past travel plans to identify preferred tourist spots and activities and adjust the generation algorithm accordingly. In addition, the generation unit can analyze the user's past travel plans and select a generation algorithm that learns preferences for specific seasons and events. Thus, the generation unit can select the optimal generation algorithm by referring to the user's past travel plans.

[0065] The reservation system can select the optimal reservation method by referring to the user's past reservation history. For example, it can select the optimal reservation method based on the reservation method the user has used in the past. Furthermore, the reservation system can analyze the user's past reservation history to identify preferred accommodations and transportation options and adjust the reservation method accordingly. In addition, the reservation system can analyze the user's past reservation history and select a reservation method that learns their preferences for specific seasons or events. Thus, the reservation system can select the optimal reservation method by referring to the user's past reservation history.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The learning unit learns the user's preferences and budget. For example, it collects data such as places the user has visited in the past, preferred modes of transportation, and types of accommodation, and the AI ​​analyzes this data. This allows the system to suggest travel plans that match the user's preferences and optimal travel plans based on their budget. Step 2: The information gathering unit collects local weather forecasts and event information. For example, it checks the weather forecast for the travel destination and automatically generates weather-appropriate plans, such as suggesting indoor tourist spots on rainy days. It can also collect information on events held locally and suggest event participation based on the user's interests. Step 3: The generation unit automatically generates the optimal travel plan based on the information obtained by the learning unit and the information gathering unit. For example, it can automatically generate cost-effective travel plans or travel plans that increase user satisfaction based on the user's preferences and budget. Step 4: The booking unit makes reservations for transportation and accommodation based on the travel plan generated by the generation unit. For example, it can search for and book the most suitable transportation and accommodation options based on the user's desired departure and return dates and budget.

[0068] (Example of form 2) The automated travel plan generation system according to an embodiment of the present invention is a system that learns the user's preferences and budget, and automatically generates an optimal travel plan considering local weather forecasts and event information. This automated travel plan generation system also makes reservations for transportation and accommodation, allowing the user to complete all travel-related procedures in one place. Furthermore, in the case of overseas travel, the automated travel plan generation system provides a language translation function to assist with communication. In addition, the automated travel plan generation system provides safety information and emergency support to ensure a safe and comfortable trip. For example, the automated travel plan generation system learns the user's preferences and budget. For example, the automated travel plan generation system collects data such as places the user has visited in the past, preferred modes of transportation, and types of accommodation, and the AI ​​analyzes this data. As a result, the automated travel plan generation system can propose a travel plan that suits the user's preferences. Next, the automated travel plan generation system considers local weather forecasts and event information. For example, the automated travel plan generation system checks the weather forecast for the travel destination and automatically generates a plan that adapts to the weather, such as suggesting indoor tourist spots on rainy days. Furthermore, the automated travel plan generation system can collect information on events held locally and suggest events to participate in based on the user's interests. In addition, the automated travel plan generation system can make reservations for transportation and accommodation. For example, it can search for and book the most suitable transportation and accommodation based on the user's desired departure and return dates and budget. This allows the automated travel plan generation system to complete all travel arrangements in a single process. For international travel, the automated travel plan generation system provides a language translation function. For example, if communication in the local language is difficult, the AI ​​can translate in real time, enabling users to communicate smoothly. The automated travel plan generation system also provides safety information and emergency support. For example, it provides local security information and emergency contact information, supporting users to enjoy their trip with peace of mind.This allows the automated travel plan generation system to automatically generate the optimal travel plan tailored to the user's preferences and budget, and to handle all travel-related procedures in one place, including booking transportation and accommodation, language translation, and safety information. This ensures a safe and comfortable travel experience. The automated travel plan generation system automatically generates the optimal travel plan based on the user's preferences and budget, and to handle all travel-related procedures in one place, including booking transportation and accommodation, language translation, and safety information.

[0069] The automated travel plan generation system according to this embodiment comprises a learning unit, an information gathering unit, a generation unit, and a booking unit. The learning unit learns the user's preferences and budget. The learning unit collects data such as places the user has visited in the past, preferred modes of transportation, and types of accommodation, and the AI ​​analyzes this data. The learning unit can, for example, propose a travel plan that suits the user's preferences. The learning unit can also propose an optimal travel plan based on the user's budget. For example, the learning unit can propose a cost-effective travel plan according to the user's budget. The information gathering unit collects local weather forecasts and event information. The information gathering unit can, for example, check the weather forecast for the travel destination and automatically generate a plan that adapts to the weather, such as suggesting indoor tourist spots on rainy days. The information gathering unit can also collect information on events held locally and suggest event participation according to the user's interests. For example, the information gathering unit can collect local event information and suggest event participation according to the user's interests. The generation unit automatically generates an optimal travel plan based on the information obtained by the learning unit and the information gathering unit. The generation unit automatically generates an optimal travel plan based on the user's preferences and budget. The generation unit can automatically generate a cost-effective travel plan based on the user's preferences and budget. The generation unit can automatically generate a travel plan that enhances user satisfaction based on the user's preferences and budget. The booking unit makes reservations for transportation and accommodation based on the travel plan generated by the generation unit. The booking unit searches for and makes reservations for the most suitable transportation and accommodation according to the user's desired departure and return dates and budget. The booking unit can search for and make reservations for cost-effective transportation and accommodation according to the user's desired departure and return dates and budget. The booking unit can search for and make reservations for transportation and accommodation that enhance user satisfaction according to the user's desired departure and return dates and budget. As a result, the automatic travel plan generation system according to this embodiment can learn the user's preferences and budget, automatically generate an optimal travel plan considering local weather forecasts and event information, and make reservations for transportation and accommodation.

[0070] The learning unit learns the user's preferences and budget. Specifically, it collects data such as places the user has visited in the past, preferred modes of transportation, types of accommodation, food preferences, and activity preferences, and the AI ​​analyzes this data. For example, it analyzes the history of tourist destinations the user has visited in the past and suggests new tourist destinations with similar characteristics. It also learns the user's preferred modes of transportation (e.g., airplane, train, bus, etc.) and types of accommodation (e.g., hotel, guesthouse, resort, etc.) and suggests the optimal travel plan based on this information. Furthermore, it can suggest cost-effective travel plans based on the user's budget. For example, it adjusts the rank of accommodation and transportation options according to the user's budget to optimize the overall travel cost. The learning unit continuously collects and updates data on the user's preferences and budget, providing customized travel plans that meet the user's needs. This makes it easy for users to find travel plans that suit their preferences and budget, making travel planning smoother and more satisfying.

[0071] The information gathering department collects local weather forecasts and event information. Specifically, it checks the weather forecast for the travel destination and automatically generates weather-appropriate plans, such as suggesting indoor tourist spots on rainy days. For example, it obtains real-time weather forecasts for the travel destination and, if rain is expected, suggests a plan that includes indoor facilities such as art museums, museums, and shopping malls. The information gathering department can also collect information on events held locally and suggest event participation based on the user's interests. For example, it collects information on local festivals, concerts, and sporting events and suggests travel plans that include these events based on the user's interests and preferences. Furthermore, the information gathering department also collects local traffic information and congestion levels to ensure that users can travel comfortably. In this way, the information gathering department can provide the latest and most accurate information to offer users the optimal travel plan, making travel planning more fulfilling.

[0072] The generation unit automatically generates optimal travel plans based on information obtained by the learning unit and information gathering unit. Specifically, the AI ​​automatically generates optimal travel plans based on the user's preferences and budget. For example, based on the user's past travel history and preference data, it selects tourist destinations and activities that the user is likely to be interested in and creates a plan that combines accommodations and transportation methods according to the budget. The generation unit can automatically generate cost-effective travel plans based on the user's preferences and budget. For example, to provide the best experience within the user's budget, it optimizes the rank of accommodations and transportation options, proposing a highly satisfying plan while keeping overall travel costs down. Furthermore, the generation unit can automatically generate travel plans that enhance user satisfaction based on the user's preferences and budget. For example, if the user is interested in specific activities or events, it will propose a plan that includes them, enriching the user's travel experience. The generation unit integrates this information and uses advanced algorithms to provide the optimal travel plan for the user. This allows the generation unit to quickly and accurately provide customized travel plans that meet the user's needs.

[0073] The booking department makes reservations for transportation and accommodation based on the travel plans generated by the generation department. Specifically, it searches for and reserves the most suitable transportation and accommodation options according to the user's desired departure and return dates and budget. For example, it can search for and reserve cost-effective transportation and accommodation options according to the user's desired departure and return dates and budget. The booking department can search for and reserve transportation and accommodation options that will increase user satisfaction according to the user's desired departure and return dates and budget. The booking department can, for example, link with the reservation systems of airlines, railway companies, and hotels to check availability in real time and provide the best options. The booking department can also apply benefits and discounts for repeat customers, taking into account the user's reservation history and preferences. Furthermore, the booking department can flexibly handle changes and cancellations of reservations, providing support that meets the user's needs. In this way, the booking department plays a crucial role in realizing the optimal travel plan for the user, enabling smooth and efficient travel planning.

[0074] The translation unit provides language translation functionality for overseas travel. For example, if communication in the local language is difficult, the translation unit uses AI to translate in real time, enabling users to communicate smoothly. The translation unit can perform text translation, for example. The translation unit can also perform voice translation, for example. The translation unit can also perform real-time translation. This allows the translation unit to provide language translation functionality for overseas travel, enabling users to communicate smoothly. Some or all of the above-described processes in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input user-generated text into a generating AI, which can then perform the translation.

[0075] The support department provides safety information and emergency support. For example, the support department provides local security information and emergency contact information to help users enjoy their trip with peace of mind. The support department can also provide information on local medical facilities. The support department can also provide emergency contact services. The support department can also establish local support systems. In this way, the support department can help users enjoy their trip with peace of mind by providing safety information and emergency support. Some or all of the above processes performed by the support department may be carried out using AI, for example, or not. For example, the support department can input local security information into a generating AI, and the generating AI can provide the information.

[0076] The learning unit estimates the user's emotions and adjusts its learning methods for preferences and budgets based on the estimated emotions. For example, if the user is stressed, the learning unit provides a simple interface and minimizes the steps required to input preferences and budgets. For example, if the user is relaxed, the learning unit provides detailed input options and suggests customizable input methods for preferences and budgets. For example, if the user is in a hurry, the learning unit prioritizes voice input to allow for quick input of preferences and budgets. This allows the learning unit to suggest a more appropriate travel plan by adjusting its learning methods for preferences and budgets according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not using AI. For example, the learning unit can input user facial expression data into a generative AI, which can then estimate emotions.

[0077] The learning unit analyzes the user's past travel history and selects the optimal learning algorithm. For example, the learning unit selects the optimal learning algorithm based on places the user has visited and modes of transportation used in the past. For example, the learning unit analyzes the user's past travel history to determine their preferred type of accommodation and adjusts the learning algorithm accordingly. For example, the learning unit analyzes the user's past travel history and selects an algorithm that learns their preferences for specific seasons or events. In this way, the learning unit can select the optimal learning algorithm by analyzing the user's past travel history. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's past travel history data into a generating AI, which can then select the optimal learning algorithm.

[0078] The learning unit filters data during learning based on the user's current lifestyle and areas of interest. For example, the learning unit filters relevant travel data based on the user's current occupation and lifestyle. For example, the learning unit filters travel data based on the user's current areas of interest (e.g., outdoor activities or cultural events). For example, the learning unit filters appropriate travel data based on the user's current health status and fitness level. This allows the learning unit to learn more relevant data by filtering data based on the user's current lifestyle and areas of interest. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's current lifestyle data into a generating AI, which can then filter the data.

[0079] The learning unit estimates the user's emotions and prioritizes training data based on the estimated emotions. For example, if the user is excited, the learning unit prioritizes learning data related to exciting activities and events. If the user is relaxed, the learning unit prioritizes learning data related to relaxing travel destinations and activities. If the user is stressed, the learning unit prioritizes learning data related to travel destinations and activities that help relieve stress. This allows the learning unit to learn more appropriate data by prioritizing training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not using AI. For example, the learning unit can input user facial expression data into a generative AI, which can then estimate emotions.

[0080] The learning unit prioritizes learning highly relevant data, taking into account the user's geographical location information during the learning process. For example, the learning unit prioritizes learning data about nearby travel destinations and activities based on the user's current location. For example, the learning unit prioritizes learning relevant data based on the user's past travel destinations. For example, the learning unit prioritizes learning relevant data based on the user's future travel plans. This allows the learning unit to learn more appropriate data by prioritizing highly relevant data while considering the user's geographical location information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's geographical location information data into a generating AI, which can then prioritize learning highly relevant data.

[0081] The learning unit analyzes the user's social media activity and learns relevant data during the learning process. For example, the learning unit learns relevant data based on travel destinations and activities shared by the user on social media. For example, the learning unit learns relevant data based on the user's interests and accounts followed on social media. For example, the learning unit learns relevant data based on the content and comments posted by the user on social media. In this way, the learning unit can learn relevant data by analyzing the user's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's social media data into a generating AI, which can then learn relevant data.

[0082] The information gathering unit estimates the user's emotions and adjusts the timing of information gathering based on the estimated emotions. For example, if the user is relaxed, the information gathering unit delays the timing of information gathering to make it easier for the user to receive the information. For example, if the user is in a hurry, the information gathering unit speeds up the timing of information gathering to provide the necessary information quickly. For example, if the user is stressed, the information gathering unit adjusts the timing of information gathering to reduce the user's stress. In this way, the information gathering unit can collect information at a more appropriate time by adjusting the timing of information gathering according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input the user's facial expression data into the generative AI, which can then estimate the emotions.

[0083] The information gathering unit improves prediction accuracy by referring to past local weather data and event history during information gathering. For example, the information gathering unit improves the accuracy of weather forecasts by referring to past local weather data. For example, the information gathering unit predicts the timing and frequency of events by referring to past local event history. For example, the information gathering unit improves the accuracy of travel plan predictions by combining past local weather data and event history. In this way, the information gathering unit can improve prediction accuracy by referring to past local weather data and event history. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input past local weather data into a generating AI, which can then improve prediction accuracy.

[0084] The information gathering unit customizes the types of information it collects based on the user's areas of interest. For example, if the user is interested in outdoor activities, the information gathering unit prioritizes collecting relevant information. For example, if the user is interested in cultural events, the information gathering unit prioritizes collecting relevant information. For example, if the user is interested in gourmet food, the information gathering unit prioritizes collecting relevant information. In this way, the information gathering unit can collect more relevant information by customizing the types of information it collects based on the user's areas of interest. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input user area of ​​interest data into a generating AI and customize the types of information the generating AI collects.

[0085] The information gathering unit estimates the user's emotions and determines the priority of information to collect based on the estimated emotions. For example, if the user is excited, the information gathering unit will prioritize collecting information about exciting events and activities. For example, if the user is relaxed, the information gathering unit will prioritize collecting information about relaxing tourist spots and activities. For example, if the user is stressed, the information gathering unit will prioritize collecting information that helps relieve stress. In this way, the information gathering unit can collect more appropriate information by prioritizing the information to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input the user's facial expression data into a generative AI, which can then estimate the emotions.

[0086] The information gathering unit prioritizes collecting highly relevant information, taking into account the local geographical characteristics. For example, the information gathering unit prioritizes collecting information on tourist attractions and activities based on the local geographical characteristics. For example, the information gathering unit prioritizes collecting information on transportation and access based on the local geographical characteristics. For example, the information gathering unit prioritizes collecting information on climate and weather based on the local geographical characteristics. By doing so, the information gathering unit can collect more appropriate information by prioritizing the collection of highly relevant information, taking into account the local geographical characteristics. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input local geographical characteristics data into a generating AI, which can then prioritize the collection of highly relevant information.

[0087] The information gathering department analyzes local social media activity and collects relevant information during the information gathering process. For example, the information gathering department collects information on events and activities that are trending on local social media. For example, the information gathering department collects information on tourist attractions and restaurants that are being shared on local social media. For example, the information gathering department analyzes word-of-mouth and reviews on local social media and collects relevant information. In this way, the information gathering department can collect relevant information by analyzing local social media activity. Some or all of the above processing in the information gathering department may be performed using AI, for example, or without AI. For example, the information gathering department can input local social media data into a generating AI, and the generating AI can collect relevant information.

[0088] The generation unit estimates the user's emotions and adjusts the travel plan generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a travel plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit generates a travel plan that emphasizes the shortest route. If the user is excited, the generation unit generates a travel plan with visually stimulating effects. In this way, the generation unit can generate a more appropriate travel plan by adjusting the travel plan generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input user facial expression data into the generation AI, which can estimate emotions.

[0089] The generation unit selects the optimal generation algorithm by referring to the user's past travel plans during generation. For example, the generation unit selects the optimal generation algorithm based on travel plans the user has used in the past. For example, the generation unit analyzes the user's past travel plans to identify preferred tourist spots and activities and adjusts the generation algorithm accordingly. For example, the generation unit analyzes the user's past travel plans and selects a generation algorithm that learns preferences for specific seasons and events. This allows the generation unit to select the optimal generation algorithm by referring to the user's past travel plans. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past travel plan data into a generation AI, which can then select the optimal generation algorithm.

[0090] The generation unit customizes the plan based on the user's current living situation and areas of interest during the generation process. For example, the generation unit customizes relevant travel plans based on the user's current occupation and lifestyle. For example, the generation unit customizes travel plans based on the user's current areas of interest (e.g., outdoor activities or cultural events). For example, the generation unit customizes appropriate travel plans based on the user's current health status and fitness level. This allows the generation unit to generate more appropriate travel plans by customizing them based on the user's current living situation and areas of interest. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's current living situation data into a generation AI, which can then customize the plan.

[0091] The generation unit estimates the user's emotions and determines the priority of the plan to generate based on the estimated emotions. For example, if the user is excited, the generation unit will generate a plan that prioritizes exciting activities and events. For example, if the user is relaxed, the generation unit will generate a plan that prioritizes relaxing sightseeing spots and activities. For example, if the user is stressed, the generation unit will generate a plan that prioritizes activities that help relieve stress. In this way, the generation unit can generate a more appropriate travel plan by determining the priority of the plan to generate according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user facial expression data into the generation AI, which can estimate emotions.

[0092] The generation unit prioritizes generating highly relevant plans by considering the user's geographical location information during the generation process. For example, the generation unit generates plans that prioritize nearby travel destinations and activities based on the user's current location. For example, the generation unit generates relevant plans based on the user's past travel destinations. For example, the generation unit generates relevant plans based on the user's future travel plans. In this way, the generation unit can generate more appropriate travel plans by prioritizing highly relevant plans while considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location data into a generation AI, which can then prioritize generating highly relevant plans.

[0093] The generation unit analyzes the user's social media activity during generation and generates relevant plans. For example, the generation unit generates relevant plans based on travel destinations and activities shared by the user on social media. For example, the generation unit generates relevant plans based on the user's interests and accounts followed on social media. For example, the generation unit generates relevant plans based on the user's posts and comments on social media. In this way, the generation unit can generate relevant plans by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media data into a generation AI, which can then generate relevant plans.

[0094] The reservation unit estimates the user's emotions and adjusts the timing of reservations based on the estimated emotions. For example, if the user is relaxed, the reservation unit may delay the reservation to make it easier for the user to accept the reservation. For example, if the user is in a hurry, the reservation unit may advance the reservation to make the necessary reservation quickly. For example, if the user is stressed, the reservation unit may adjust the reservation timing to reduce the user's stress. In this way, the reservation unit can make reservations at a more appropriate time by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation unit may be performed using AI, for example, or not using AI. For example, the reservation unit can input user facial expression data into a generative AI, which can estimate emotions.

[0095] The reservation department selects the optimal reservation method by referring to the user's past reservation history when a reservation is made. For example, the reservation department selects the optimal reservation method based on the reservation method the user has used in the past. For example, the reservation department analyzes the user's past reservation history to determine their preferred accommodations and modes of transportation and adjusts the reservation method accordingly. For example, the reservation department analyzes the user's past reservation history and selects a reservation method that learns their preferences for specific seasons or events. In this way, the reservation department can select the optimal reservation method by referring to the user's past reservation history. Some or all of the above processes in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department can input the user's past reservation history data into a generating AI, which can then select the optimal reservation method.

[0096] The reservation unit customizes reservation details based on the user's current lifestyle and areas of interest at the time of reservation. For example, the reservation unit customizes relevant reservation details based on the user's current occupation and lifestyle. For example, the reservation unit customizes reservation details based on the user's current areas of interest (e.g., outdoor activities or cultural events). For example, the reservation unit customizes appropriate reservation details based on the user's current health status and fitness level. This allows the reservation unit to make more appropriate reservations by customizing reservation details based on the user's current lifestyle and areas of interest. Some or all of the above processing in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input the user's current lifestyle data into a generating AI, which can then customize the reservation details.

[0097] The booking unit estimates the user's emotions and determines booking priorities based on the estimated emotions. For example, if the user is excited, the booking unit prioritizes booking exciting activities or events. For example, if the user is relaxed, the booking unit prioritizes booking relaxing accommodations or activities. For example, if the user is stressed, the booking unit prioritizes booking activities that help relieve stress. In this way, the booking unit can make more appropriate bookings by determining booking priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the booking unit may be performed using AI or not using AI. For example, the booking unit can input user facial expression data into a generative AI, which can estimate emotions.

[0098] The reservation department prioritizes highly relevant reservations by considering the user's geographical location information during the reservation process. For example, the reservation department prioritizes reservations for nearby accommodations and transportation based on the user's current location. For example, the reservation department makes relevant reservations based on the user's past travel destinations. For example, the reservation department makes relevant reservations based on the user's future travel plans. In this way, the reservation department can make more appropriate reservations by prioritizing highly relevant reservations by considering the user's geographical location information. Some or all of the above processing in the reservation department may be performed using AI, for example, or without AI. For example, the reservation department can input the user's geographical location data into a generating AI, which can then prioritize highly relevant reservations.

[0099] The reservation unit analyzes the user's social media activity when a reservation is made and makes a relevant reservation. For example, the reservation unit makes a relevant reservation based on travel destinations and activities shared by the user on social media. For example, the reservation unit makes a relevant reservation based on the user's interests and accounts followed on social media. For example, the reservation unit makes a relevant reservation based on the content and comments posted by the user on social media. In this way, the reservation unit can make relevant reservations by analyzing the user's social media activity. Some or all of the above processing in the reservation unit may be performed using AI, for example, or not using AI. For example, the reservation unit can input the user's social media data into a generating AI, and the generating AI can make a relevant reservation.

[0100] The translation unit estimates the user's emotions and adjusts the translation's expression based on the estimated emotions. For example, if the user is nervous, the translation unit will use a calm expression. If the user is relaxed, the translation unit will use a casual expression. If the user is in a hurry, the translation unit will use a concise and rapid expression. In this way, the translation unit can provide a more appropriate translation by adjusting the expression of the translation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using AI or not using AI. For example, the translation unit can input user facial expression data into a generative AI, which can then estimate emotions.

[0101] The translation unit selects the optimal translation algorithm by referring to the user's past translation history during translation. For example, the translation unit selects the optimal translation algorithm based on translation methods the user has used in the past. For example, the translation unit analyzes the user's past translation history to identify preferred expressions and phrases and adjusts the translation algorithm accordingly. For example, the translation unit analyzes the user's past translation history and selects a translation algorithm that learns preferences for specific situations and contexts. This allows the translation unit to select the optimal translation algorithm by referring to the user's past translation history. Some or all of the above processes in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input the user's past translation history data into a generating AI, which can then select the optimal translation algorithm.

[0102] The translation unit customizes the translation content based on the user's current lifestyle and areas of interest during the translation process. For example, the translation unit customizes relevant translation content based on the user's current occupation and lifestyle. For example, the translation unit customizes translation content based on the user's current areas of interest (e.g., outdoor activities or cultural events). For example, the translation unit customizes appropriate translation content based on the user's current health status and fitness level. This allows the translation unit to provide more appropriate translations by customizing the translation content based on the user's current lifestyle and areas of interest. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input the user's current lifestyle data into a generating AI, which can then customize the translation content.

[0103] The translation unit estimates the user's emotions and determines translation priorities based on the estimated emotions. For example, if the user is excited, the translation unit prioritizes translations about exciting activities and events. If the user is relaxed, the translation unit prioritizes translations about relaxing tourist spots and activities. If the user is stressed, the translation unit prioritizes translations about information that helps relieve stress. In this way, the translation unit can provide more appropriate translations by determining translation priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using AI or not using AI. For example, the translation unit can input user facial expression data into a generative AI, which can estimate emotions.

[0104] The translation unit prioritizes highly relevant translations by considering the user's geographical location during the translation process. For example, the translation unit prioritizes translations about nearby tourist attractions and activities based on the user's current location. For example, the translation unit provides relevant translations based on the user's past travel destinations. For example, the translation unit provides relevant translations based on the user's future travel plans. This allows the translation unit to provide more appropriate translations by prioritizing highly relevant translations while considering the user's geographical location. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input the user's geographical location data into a generating AI, which can then prioritize highly relevant translations.

[0105] The translation unit analyzes the user's social media activity during translation and performs relevant translations. For example, the translation unit performs relevant translations based on travel destinations and activities shared by the user on social media. For example, the translation unit performs relevant translations based on the user's interests and accounts followed on social media. For example, the translation unit performs relevant translations based on the content and comments posted by the user on social media. In this way, the translation unit can provide relevant translations by analyzing the user's social media activity. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input the user's social media data into a generating AI, which can then perform relevant translations.

[0106] The support unit estimates the user's emotions and adjusts the method of providing support based on the estimated emotions. For example, if the user is nervous, the support unit will provide support in a calm voice. If the user is relaxed, the support unit will provide support in a cheerful voice. If the user is in a hurry, the support unit will provide quick and concise support. In this way, the support unit can provide more appropriate support by adjusting the method of providing support according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not using AI. For example, the support unit can input user facial expression data into a generative AI, which can then estimate emotions.

[0107] The support unit selects the optimal support method by referring to the user's past support history during support. For example, the support unit selects the optimal support method based on the support methods the user has used in the past. For example, the support unit analyzes the user's preferred support methods and responses from their past support history and adjusts the support method accordingly. For example, the support unit analyzes the user's past support history and selects a support method that learns their preferences for specific situations and contexts. This allows the support unit to select the optimal support method by referring to the user's past support history. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's past support history data into a generating AI, which can then select the optimal support method.

[0108] The support unit customizes the support provided based on the user's current living situation and areas of interest. For example, the support unit customizes relevant support based on the user's current occupation and lifestyle. For example, the support unit customizes support based on the user's current areas of interest (e.g., outdoor activities or cultural events). For example, the support unit customizes appropriate support based on the user's current health status and fitness level. This allows the support unit to provide more appropriate support by customizing support based on the user's current living situation and areas of interest. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's current living situation data into a generating AI, which can then customize the support content.

[0109] The support unit estimates the user's emotions and prioritizes support based on the estimated emotions. For example, if the user is excited, the support unit will prioritize support related to exciting activities or events. If the user is relaxed, the support unit will prioritize support related to relaxing tourist spots or activities. If the user is stressed, the support unit will prioritize support that helps relieve stress. In this way, the support unit can provide more appropriate support by prioritizing support according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not using AI. For example, the support unit can input user facial expression data into a generative AI, which can estimate emotions.

[0110] The support unit prioritizes providing relevant support by considering the user's geographical location. For example, the support unit prioritizes support regarding nearby tourist attractions and activities based on the user's current location. For example, the support unit provides relevant support based on the user's past travel destinations. For example, the support unit provides relevant support based on the user's future travel plans. In this way, the support unit can provide more appropriate support by prioritizing relevant support while considering the user's geographical location. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location data into a generating AI, which can then prioritize providing relevant support.

[0111] The support department analyzes the user's social media activity and provides relevant support during support sessions. For example, the support department provides relevant support based on travel destinations and activities shared by the user on social media. For example, the support department provides relevant support based on the user's interests and accounts followed on social media. For example, the support department provides relevant support based on the content and comments posted by the user on social media. In this way, the support department can provide relevant support by analyzing the user's social media activity. Some or all of the above processing in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the user's social media data into a generating AI, which can then provide relevant support.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The learning function not only learns the user's preferences and budget, but can also suggest travel plans that take into account the user's health condition and fitness level. For example, if the user provides health checkup data, the learning function can suggest a health-conscious travel plan based on that data. Furthermore, the learning function can suggest appropriate activities and sightseeing spots according to the user's fitness level. For example, if the user has a high fitness level, it can suggest activities such as hiking or cycling. Also, if the user is seeking relaxation, it can suggest relaxation facilities such as spas or hot springs. In this way, the learning function can suggest more appropriate travel plans based on the user's health condition and fitness level.

[0114] The translation unit can estimate the user's emotions and adjust the translation's expression based on those emotions. For example, if the user is nervous, the translation unit can use calmer language to help the user communicate more comfortably. If the user is relaxed, the translation unit can use more casual language to allow the user to enjoy the communication more naturally. Furthermore, if the user is in a hurry, the translation unit can use concise and quick language to help the user communicate smoothly. In this way, the translation unit can provide more appropriate translations by adjusting the expression of the translation according to the user's emotions.

[0115] The support team can estimate the user's emotions and adjust the way support is provided based on those estimates. For example, if the user is nervous, the support team can provide support in a calm voice to help the user feel at ease. If the user is relaxed, the support team can provide support in a cheerful voice to help the user enjoy the support process. Furthermore, if the user is in a hurry, the support team can provide quick and concise support to ensure a smooth experience. In this way, the support team can provide more appropriate support by adjusting the way support is provided according to the user's emotions.

[0116] The learning unit can filter data based on the user's current lifestyle and areas of interest. For example, it can filter relevant travel data based on the user's current occupation and lifestyle. It can also filter travel data based on the user's current areas of interest (e.g., outdoor activities or cultural events). Furthermore, it can filter appropriate travel data based on the user's current health status and fitness level. In this way, the learning unit can learn more relevant data by filtering data based on the user's current lifestyle and areas of interest.

[0117] The learning unit can estimate the user's emotions and prioritize training data based on those emotions. For example, if the user is excited, it can prioritize learning data about exciting activities and events. If the user is relaxed, it can prioritize learning data about relaxing travel destinations and activities. Furthermore, if the user is stressed, it can prioritize learning data about travel destinations and activities that help relieve stress. In this way, the learning unit can learn more relevant data by prioritizing training data according to the user's emotions.

[0118] The information gathering unit can improve prediction accuracy by referring to past local weather data and event history during information gathering. For example, the information gathering unit can improve the accuracy of weather forecasts by referring to past local weather data. Furthermore, the information gathering unit can predict the timing and frequency of events by referring to past local event history. In addition, the information gathering unit can improve the accuracy of travel plan predictions by combining past local weather data and event history. Thus, the information gathering unit can improve prediction accuracy by referring to past local weather data and event history.

[0119] The information gathering unit can estimate the user's emotions and adjust the timing of information gathering based on those emotions. For example, if the user is relaxed, the timing of information gathering can be delayed to make the user more receptive to the information. If the user is in a hurry, the timing of information gathering can be accelerated to provide the necessary information quickly. Furthermore, if the user is stressed, the timing of information gathering can be adjusted to reduce the user's stress. In this way, the information gathering unit can collect information at a more appropriate time by adjusting the timing of information gathering according to the user's emotions.

[0120] The generation unit can select the optimal generation algorithm by referring to the user's past travel plans during the generation process. For example, the generation unit can select the optimal generation algorithm based on travel plans the user has used in the past. Furthermore, the generation unit can analyze the user's past travel plans to identify preferred tourist spots and activities and adjust the generation algorithm accordingly. In addition, the generation unit can analyze the user's past travel plans and select a generation algorithm that learns preferences for specific seasons and events. Thus, the generation unit can select the optimal generation algorithm by referring to the user's past travel plans.

[0121] The generation unit can estimate the user's emotions and determine the priority of the plan to generate based on those emotions. For example, if the user is excited, it can generate a plan that prioritizes exciting activities and events. If the user is relaxed, it can generate a plan that prioritizes relaxing sightseeing spots and activities. Furthermore, if the user is stressed, it can generate a plan that prioritizes activities that help relieve stress. In this way, the generation unit can generate a more appropriate travel plan by determining the priority of the plan to generate according to the user's emotions.

[0122] The reservation system can select the optimal reservation method by referring to the user's past reservation history. For example, it can select the optimal reservation method based on the reservation method the user has used in the past. Furthermore, the reservation system can analyze the user's past reservation history to identify preferred accommodations and transportation options and adjust the reservation method accordingly. In addition, the reservation system can analyze the user's past reservation history and select a reservation method that learns their preferences for specific seasons or events. Thus, the reservation system can select the optimal reservation method by referring to the user's past reservation history.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The learning unit learns the user's preferences and budget. For example, it collects data such as places the user has visited in the past, preferred modes of transportation, and types of accommodation, and the AI ​​analyzes this data. This allows the system to suggest travel plans that match the user's preferences and optimal travel plans based on their budget. Step 2: The information gathering unit collects local weather forecasts and event information. For example, it checks the weather forecast for the travel destination and automatically generates weather-appropriate plans, such as suggesting indoor tourist spots on rainy days. It can also collect information on events held locally and suggest event participation based on the user's interests. Step 3: The generation unit automatically generates the optimal travel plan based on the information obtained by the learning unit and the information gathering unit. For example, it can automatically generate cost-effective travel plans or travel plans that increase user satisfaction based on the user's preferences and budget. Step 4: The booking unit makes reservations for transportation and accommodation based on the travel plan generated by the generation unit. For example, it can search for and book the most suitable transportation and accommodation options based on the user's desired departure and return dates and budget.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the learning unit, information gathering unit, generation unit, reservation unit, translation unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart device 14 and learns the user's preferences and budget. The information gathering unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects local weather forecasts and event information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates an optimal travel plan. The reservation unit is implemented by the control unit 46A of the smart device 14 and makes reservations for transportation and accommodation. The translation unit is implemented by the control unit 46A of the smart device 14 and provides language translation functionality. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides safety information and emergency support. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the learning unit, information gathering unit, generation unit, reservation unit, translation unit, and support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart glasses 214 and learns the user's preferences and budget. The information gathering unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects local weather forecasts and event information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates an optimal travel plan. The reservation unit is implemented by the control unit 46A of the smart glasses 214 and makes reservations for transportation and accommodation. The translation unit is implemented by the control unit 46A of the smart glasses 214 and provides language translation functionality. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides safety information and emergency support. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the learning unit, information gathering unit, generation unit, reservation unit, translation unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the headset terminal 314 and learns the user's preferences and budget. The information gathering unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects local weather forecasts and event information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates an optimal travel plan. The reservation unit is implemented by the control unit 46A of the headset terminal 314 and makes reservations for transportation and accommodation. The translation unit is implemented by the control unit 46A of the headset terminal 314 and provides language translation functionality. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides safety information and emergency support. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the learning unit, information gathering unit, generation unit, reservation unit, translation unit, and support unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the robot 414 and learns the user's preferences and budget. The information gathering unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects local weather forecasts and event information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates an optimal travel plan. The reservation unit is implemented by the control unit 46A of the robot 414 and makes reservations for transportation and accommodation. The translation unit is implemented by the control unit 46A of the robot 414 and provides language translation functionality. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides safety information and emergency support. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0178] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] 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.

[0188] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] (Note 1) A learning unit that learns user preferences and budget, The information gathering department collects local weather forecasts and event information, A generation unit that automatically generates an optimal travel plan based on the information obtained by the learning unit and the information gathering unit, A reservation unit that makes reservations for transportation and accommodation based on the travel plan generated by the generation unit, Equipped with A system characterized by the following features. (Note 2) It includes a translation unit that provides language translation functionality for overseas travel. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a support department that provides safety information and emergency support. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning method for preferences and budget based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned learning unit, The system analyzes the user's past travel history and selects the optimal learning algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, During training, the data is filtered based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, It estimates the user's emotions and prioritizes the training data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning unit, During training, the system prioritizes learning highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning unit, During training, the system analyzes users' social media activity and learns from relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned information gathering unit, It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned information gathering unit, When gathering information, we improve forecast accuracy by referring to past local weather data and event history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned information gathering unit, When gathering information, customize the types of information collected based on the user's areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned information gathering unit, It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned information gathering unit, When gathering information, prioritize collecting highly relevant information, taking into account the local geographical characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned information gathering unit, When gathering information, analyze local social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is We estimate the user's emotions and adjust the travel plan generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During the generation process, the system references the user's past travel plans to select the optimal generation algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the plan is customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and determines the priority of the plans generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the system prioritizes generating highly relevant plans by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the system analyzes the user's social media activity and generates relevant plans. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reservation section is, It estimates the user's emotions and adjusts the timing of reservations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reservation section is, When a reservation is made, the system will refer to the user's past reservation history to select the most suitable reservation method. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reservation section is, When making a reservation, the reservation details are customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reservation section is, The system estimates the user's emotions and determines reservation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reservation section is, When making a reservation, the system prioritizes highly relevant reservations by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reservation section is, When a reservation is made, the system analyzes the user's social media activity and makes relevant reservations. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned translation department, It estimates the user's emotions and adjusts the translation's expression based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned translation department, During translation, the system selects the optimal translation algorithm by referring to the user's past translation history. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned translation department, During translation, the translation content is customized based on the user's current life situation and areas of interest. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned translation department, It estimates the user's emotions and determines translation priorities based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned translation department, During translation, the system prioritizes highly relevant translations by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned translation department, During translation, the system analyzes the user's social media activity and performs relevant translations. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned support unit is We estimate the user's emotions and adjust how support is provided based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned support unit is During support, the system will refer to the user's past support history to select the most appropriate support method. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned support unit is During support sessions, the support content is customized based on the user's current life circumstances and areas of interest. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned support unit is When providing support, we prioritize relevant support by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned support unit is During support, we analyze the user's social media activity and provide relevant support. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A learning unit that learns user preferences and budget, The information gathering department collects local weather forecasts and event information, A generation unit that automatically generates an optimal travel plan based on the information obtained by the learning unit and the information gathering unit, A reservation unit that makes reservations for transportation and accommodation based on the travel plan generated by the generation unit, Equipped with A system characterized by the following features.

2. It includes a translation unit that provides language translation functionality for overseas travel. The system according to feature 1.

3. It has a support department that provides safety information and emergency support. The system according to feature 1.

4. The aforementioned learning unit, It estimates the user's emotions and adjusts the learning method for preferences and budget based on the estimated user emotions. The system according to feature 1.

5. The aforementioned learning unit, The system analyzes the user's past travel history and selects the optimal learning algorithm. The system according to feature 1.

6. The aforementioned learning unit, During training, the data is filtered based on the user's current lifestyle and areas of interest. The system according to feature 1.

7. The aforementioned learning unit, It estimates the user's emotions and prioritizes the training data based on the estimated user emotions. The system according to feature 1.

8. The aforementioned learning unit, During training, the system prioritizes learning highly relevant data, taking into account the user's geographical location. The system according to feature 1.

9. The aforementioned learning unit, During training, the system analyzes users' social media activity and learns from relevant data. The system according to feature 1.

10. The aforementioned information gathering unit, It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

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