System

Abstract

An object of a system according to an embodiment is to enable a user to easily search for and reserve an appropriate combined-vacation license program.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a search unit, a display unit, and a reservation unit. The reception unit receives a condition of a user. The analysis unit analyzes the condition input by the reception unit. The retrieval part retrieves the program based on the condition analyzed by the analysis part. The display part displays the program retrieved by the retrieval part. The reservation unit reserves the program displayed by the display unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, selecting a driving school camp plan was a hassle, making it difficult for users to find a plan that suited them.

[0005] The system according to the embodiment aims to enable users to easily search for and reserve a driving license training camp program that suits them. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a search unit, a display unit, and a reservation unit. The reception unit inputs user conditions. The analysis unit analyzes the conditions input by the reception unit. The search unit searches for programs based on the conditions analyzed by the analysis unit. The display unit displays the programs searched for by the search unit. The reservation unit reserves the programs displayed by the display unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily search for and reserve a suitable driving license training camp program. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A driving school camp plan selection system according to an embodiment of the present invention allows users to search for and reserve driving school camp programs that meet their requirements via chat. In the driving school camp plan selection system, users input their desired requirements via chat, and AI responds immediately to suggest driving school camp programs tailored to their needs. From the proposed programs, users can select the most suitable plan and complete their reservation. For example, in the driving school camp plan selection system, users input requirements such as the type of license they desire, the length of the camp, their budget, and the location. For example, they input "I want to obtain a standard driver's license," "A plan that can be completed within two weeks," "A budget of less than 100,000 yen," and "A driving school camp in the Kanto region." This information is then input into the AI. The AI ​​then analyzes the input information and searches for driving school camp programs that meet the requirements. The AI ​​then extracts the plan that best suits the user's requirements from multiple programs registered in a database. For example, the system searches for plans in the Kanto region that allow users to obtain a standard driver's license within two weeks and suggests the best plan within their budget. The driving school camp plan selection system then allows users to select the most suitable plan from the proposed programs. The AI ​​then provides detailed information about each program, allowing users to compare and consider options. For example, it displays program content, fees, accommodation information, and past user reviews. Users can select the most suitable plan based on this information and complete their reservation. This allows the driving licence training camp plan selection system to easily select the most suitable driving licence training camp plan for users. The AI ​​responds instantly and makes suggestions based on the user's needs, allowing for efficient plan selection. Furthermore, chat-style interactions allow users to operate intuitively and are easily accessible from smartphones or PCs. This allows the driving licence training camp plan selection system to easily select the most suitable driving licence training camp plan for users. For example, the AI ​​responds instantly and makes suggestions based on the user's needs, allowing for efficient plan selection. Furthermore, chat-style interactions allow users to operate intuitively and are easily accessible from smartphones or PCs.

[0029] The system for selecting a driving school license plan according to the embodiment includes a reception unit, an analysis unit, a search unit, a display unit, and a reservation unit. The reception unit inputs user requirements. The user requirements include, but are not limited to, dates, location, and budget. The reception unit inputs requirements such as the type of license desired by the user, the duration of the driving school license, budget, and location. The analysis unit analyzes the requirements input by the reception unit. The analysis is performed, for example, by prioritizing the requirements and checking the consistency of the requirements, but is not limited to, examples. The analysis unit prioritizes the requirements and checks the consistency of the requirements, for example. The search unit searches for a program based on the requirements analyzed by the analysis unit. The search is performed, for example, by using a search algorithm or a database of search targets, but is not limited to, examples. The search unit uses, for example, a search algorithm to extract a plan that best suits the user's requirements from multiple programs registered in a database. The display unit displays the programs searched by the search unit. The display is performed, for example, by using a display format or a display order, but is not limited to, examples. The display unit displays, for example, the program content, fees, accommodation information, past user reviews, etc. The reservation unit reserves the program displayed by the display unit. The reservation is made, for example, through a reservation procedure or a reservation confirmation method, but is not limited to these examples. The reservation unit reserves, for example, a plan selected by the user and confirms the reservation. As a result, the driving school camp plan selection system according to the embodiment can automatically search, display, and reserve driving school camp programs based on the user's conditions.

[0030] The driving school camp plan selection system includes a detailed analysis unit that analyzes the user's conditions in detail. The detailed analysis unit analyzes the user's conditions in detail. The detailed analysis is performed, for example, by subdividing the conditions or by evaluating the detailed conditions, but is not limited to these examples. The detailed analysis unit, for example, subdivides the conditions and evaluates the detailed conditions. The detailed analysis unit can also prioritize the conditions and check the consistency of the conditions. For example, the detailed analysis unit prioritizes the conditions and checks the consistency of the conditions. As a result, the detailed analysis unit can propose a more appropriate program by analyzing the user's conditions in detail.

[0031] The driving school camp plan selection system includes an evaluation collection unit that collects evaluations from past users. The evaluation collection unit collects evaluations from past users. The evaluations are performed, for example, using evaluation items and evaluation collection means, but are not limited to these examples. For example, the evaluation collection unit collects evaluations from past users and performs evaluations based on the evaluation items. The evaluation collection unit can also collect evaluations using evaluation collection means. For example, the evaluation collection unit collects evaluations using means such as questionnaires and reviews. In this way, the evaluation collection unit can provide highly reliable information to users by collecting evaluations from past users.

[0032] The driving school camp plan selection system includes an information providing unit that provides detailed program information. The information providing unit provides detailed program information. The detailed program information is, for example, information such as the program content, fees, and schedule, but is not limited to these examples. The information providing unit provides detailed program information such as the program content, fees, and schedule. The information providing unit can also provide information such as accommodation information and past user reviews. For example, the information providing unit provides accommodation information and past user reviews. In this way, the information providing unit provides detailed program information, making it easier for users to compare and consider options.

[0033] The driving school camp plan selection system includes a comparison unit that allows the user to compare and consider. The comparison unit allows the user to compare and consider. The comparison is performed, for example, by using comparison items and a comparison method, but is not limited to these examples. The comparison unit provides comparison items such as program content, fees, accommodation information, and past user reviews. The comparison unit can also use a comparison method to allow the user to compare and consider. For example, the comparison unit provides comparison items such as program content, fees, accommodation information, and past user reviews, allowing the user to compare and consider. This allows the comparison unit to compare and consider multiple programs.

[0034] The driving school camp plan selection system includes a maintenance unit that performs database maintenance. The maintenance unit performs database maintenance. Maintenance is performed, for example, by using a data update frequency or a maintenance procedure, but is not limited to these examples. For example, the maintenance unit sets a data update frequency and updates the data periodically. The maintenance unit can also perform database maintenance using a maintenance procedure. For example, the maintenance unit performs maintenance using procedures such as data backup and data consistency check. In this way, the maintenance unit can maintain the reliability of the system by performing database maintenance.

[0035] The reception unit can analyze the user's past condition input history and suggest the optimal input method. For example, the reception unit automatically displays conditions that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest conditions to be used in a specific time period from the user's past input history. For example, the reception unit analyzes the user's past condition input history and suggests the optimal input method. In this way, the reception unit can suggest the optimal input method to the user by analyzing the past condition input history.

[0036] The reception unit can customize input items based on the user's current situation and areas of interest when entering conditions. For example, when the user enters their current situation, the reception unit automatically displays related input items based on the user's areas of interest. Furthermore, if the user has a specific area of ​​interest, the reception unit can preferentially display input items related to that area. Furthermore, the reception unit can suggest optimal input items based on the user's current situation. For example, the reception unit customizes input items based on the user's current situation and areas of interest when entering conditions. This allows the reception unit to customize input items based on the user's current situation and areas of interest, thereby enabling more appropriate condition entry.

[0037] The reception unit can select the optimal input means depending on the user's input method when inputting conditions. For example, if the user selects voice input, the reception unit inputs the conditions using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface optimized for text input. Furthermore, if the user selects image input, the reception unit can also input the conditions using image recognition technology. For example, the reception unit selects the optimal input means depending on the user's input method when inputting conditions. This allows the reception unit to select the optimal input means depending on the user's input method, thereby improving input efficiency.

[0038] The reception unit can prioritize input of highly relevant conditions in consideration of the user's geographical location information when inputting conditions. For example, when the user is in a specific area, the reception unit can prompt the user to prioritize input of conditions related to that area. Furthermore, when the user is traveling, the reception unit can also prompt the user to prioritize input of conditions related to the travel destination. Furthermore, when the user is at home, the reception unit can also prompt the user to prioritize input of conditions related to the user's home. For example, when inputting conditions, the reception unit prioritizes input of highly relevant conditions in consideration of the user's geographical location information. In this way, the reception unit can prioritize input of highly relevant conditions by considering the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity when the conditions are input and suggest related conditions. For example, the reception unit can suggest conditions related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's posts on social media and suggest related conditions. Furthermore, the reception unit can also suggest related conditions by referring to the activities of the user's friends on social media. For example, the reception unit can analyze the user's social media activity when the conditions are input and suggest related conditions. In this way, the reception unit can suggest related conditions by analyzing the user's social media activity.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when entering conditions. The reception unit, for example, suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the input method. For example, the reception unit customizes the input method by reflecting the user's past feedback when entering conditions. In this way, the reception unit can provide the optimal input method by reflecting the user's past feedback.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the conditions during analysis. For example, the analysis unit performs a detailed analysis for important conditions. The analysis unit can also perform a simplified analysis for less important conditions. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the conditions. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the conditions during analysis. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the conditions, thereby enabling efficient analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of conditions during analysis. The analysis unit selects the optimal analysis algorithm depending on, for example, the type of license. The analysis unit can also apply different analysis algorithms depending on the length of the training camp. Furthermore, the analysis unit can also select the optimal analysis algorithm depending on the budget. For example, the analysis unit applies different analysis algorithms depending on the category of conditions during analysis. In this way, the analysis unit can apply the optimal analysis algorithm depending on the category of conditions, thereby improving the accuracy of the analysis.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit, for example, proposes an optimal analysis method based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis from the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and optimize the analysis method. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results during analysis. In this way, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results.

[0044] The analysis unit can determine the analysis priority based on the time of submission of the conditions during analysis. For example, the analysis unit prioritizes analysis of the most recently submitted conditions. The analysis unit can also postpone analysis of conditions that were submitted earlier. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the time of submission. For example, the analysis unit determines the analysis priority based on the time of submission of the conditions during analysis. This allows the analysis unit to determine the analysis priority based on the time of submission of the conditions, thereby enabling efficient analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of conditions during analysis. For example, the analysis unit prioritizes analysis of highly relevant conditions. The analysis unit can also postpone analysis of less relevant conditions. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of conditions. For example, the analysis unit adjusts the order of analysis based on the relevance of conditions during analysis. This allows the analysis unit to adjust the order of analysis based on the relevance of conditions, thereby enabling efficient analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. Also, if the user does not have technical expertise, the analysis unit can avoid technical terms. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0047] The search unit can improve search accuracy by taking into account interrelationships between conditions during a search. For example, the search unit analyzes interrelationships between conditions and provides optimal search results. The search unit can also filter search results based on interrelationships between conditions. Furthermore, the search unit can improve search accuracy by taking into account interrelationships between conditions during a search. For example, the search unit improves search accuracy by taking into account interrelationships between conditions during a search. In this way, the search unit improves search accuracy by taking into account interrelationships between conditions.

[0048] The search unit can perform a search while taking into account the attribute information of the person who submitted the conditions. The search unit provides optimal search results based on, for example, the age and gender of the person who submitted the conditions. The search unit can also filter search results based on the occupation and hobbies of the person who submitted the conditions. Furthermore, the search unit can improve the accuracy of the search by taking into account the attribute information of the person who submitted the conditions. For example, the search unit can perform a search while taking into account the attribute information of the person who submitted the conditions. In this way, the search unit can provide more appropriate search results by taking into account the attribute information of the person who submitted the conditions.

[0049] The search unit can weight the search based on the frequency of submission of conditions during a search. For example, the search unit prioritizes searches for conditions that are submitted more frequently. The search unit can also postpone searches for conditions that are submitted less frequently. Furthermore, the search unit can dynamically adjust the weighting of the search based on the frequency of submission of conditions. For example, the search unit weights the search based on the frequency of submission of conditions during a search. This enables efficient searches by weighting the search based on the frequency of submission of conditions.

[0050] The search unit can perform a search taking into account the geographic distribution of the conditions. For example, the search unit analyzes the geographic distribution of the conditions and provides optimal search results. The search unit can also filter search results based on the geographic distribution of the conditions. Furthermore, the search unit can improve search accuracy by taking into account the geographic distribution of the conditions. For example, the search unit performs a search taking into account the geographic distribution of the conditions. In this way, the search unit can provide more appropriate search results by taking into account the geographic distribution of the conditions.

[0051] The search unit can improve the accuracy of the search by referring to literature related to the conditions during the search. For example, the search unit refers to literature related to the conditions and provides optimal search results. The search unit can also filter the search results based on literature related to the conditions. Furthermore, the search unit can improve the accuracy of the search by taking into account literature related to the conditions. For example, the search unit improves the accuracy of the search by referring to literature related to the conditions during the search. As a result, the search unit improves the accuracy of the search by referring to literature related to the conditions.

[0052] The search unit can perform a search taking into account the market value of the conditions. For example, the search unit analyzes the market value of the conditions and provides optimal search results. The search unit can also filter search results based on the market value of the conditions. Furthermore, the search unit can improve the accuracy of the search by taking into account the market value of the conditions. For example, the search unit performs a search taking into account the market value of the conditions. In this way, the search unit can provide more appropriate search results by taking into account the market value of the conditions.

[0053] The display unit can select the optimal display method by referring to the user's past operation history when displaying. The display unit, for example, suggests the optimal display method based on the user's past operation history. The display unit can also preferentially suggest a specific display method based on the user's past operation history. Furthermore, the display unit can analyze the user's past operation history and optimize the display method. For example, the display unit selects the optimal display method by referring to the user's past operation history when displaying. In this way, the display unit can provide the optimal display method by referring to the user's past operation history.

[0054] The display unit can customize the display content according to the user's current task when displaying the information. For example, when the user inputs the current task, the display unit automatically displays related display content. Furthermore, if the user has a specific task, the display unit can preferentially display display content related to that task. Furthermore, the display unit can suggest optimal display content based on the user's current task. For example, the display unit customizes the display content according to the user's current task when displaying the information. This allows the display unit to provide information efficiently by customizing the display content according to the user's current task.

[0055] The display unit can improve the display method by reflecting user feedback during display. The display unit can, for example, suggest an optimal display method based on feedback provided by the user. The display unit can also preferentially suggest a specific display method based on user feedback. Furthermore, the display unit can analyze user feedback and improve the display method. For example, the display unit can improve the display method by reflecting user feedback during display. In this way, the display unit can optimize the display method by reflecting user feedback.

[0056] The display unit can select the optimal display method in consideration of the user's device information when displaying. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. For example, the display unit selects the optimal display method in consideration of the user's device information when displaying. As a result, the display unit can provide the optimal display method by taking the user's device information into consideration.

[0057] The display unit can make the display content multilingual when displaying it according to the user's language setting. The display unit automatically sets the display content based on, for example, the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the display unit can provide the display content in that language. For example, the display unit can make the display content multilingual when displaying it according to the user's language setting. As a result, the display unit can provide information that is easy for the user to understand by making the display content multilingual according to the user's language setting.

[0058] The display unit can customize the display method according to the visual and auditory characteristics of the user when displaying information. For example, if the user is visually impaired, the display unit can provide audio guidance. Furthermore, if the user is hearing impaired, the display unit can also provide visual guidance. Furthermore, the display unit can customize the display method according to the visual and auditory characteristics of the user when displaying information. For example, the display unit customizes the display method according to the visual and auditory characteristics of the user when displaying information. In this way, the display unit can provide optimal information to the user by customizing the display method according to the visual and auditory characteristics of the user.

[0059] The reservation unit can suggest the optimal reservation method by referring to the user's past reservation history when making a reservation. The reservation unit can suggest the optimal reservation method, for example, based on the user's past reservation history. The reservation unit can also preferentially suggest a specific reservation method based on the user's past reservation history. Furthermore, the reservation unit can analyze the user's past reservation history and optimize the reservation method. For example, the reservation unit can suggest the optimal reservation method by referring to the user's past reservation history when making a reservation. In this way, the reservation unit can provide the optimal reservation method by referring to the user's past reservation history.

[0060] The reservation unit can customize the reservation procedure based on the user's current situation when making a reservation. For example, the reservation unit automatically displays a related reservation procedure when the user inputs their current situation. Furthermore, when the user is in a specific situation, the reservation unit can preferentially display reservation procedures related to that situation. Furthermore, the reservation unit can suggest an optimal reservation procedure based on the user's current situation. For example, the reservation unit customizes the reservation procedure based on the user's current situation when making a reservation. This allows the reservation unit to customize the reservation procedure based on the user's current situation, thereby enabling efficient reservations.

[0061] The reservation unit can improve the reservation method by reflecting user feedback at the time of reservation. The reservation unit, for example, suggests the optimal reservation method based on feedback provided by the user. The reservation unit can also preferentially suggest a specific reservation method based on user feedback. Furthermore, the reservation unit can analyze user feedback and improve the reservation method. For example, the reservation unit improves the reservation method by reflecting user feedback at the time of reservation. In this way, the reservation unit can optimize the reservation method by reflecting user feedback.

[0062] The reservation unit can select the optimal reservation method by taking into consideration the user's geographical location information when making a reservation. For example, if the user is in a specific area, the reservation unit can preferentially suggest a reservation method related to that area. Furthermore, if the user is traveling, the reservation unit can also preferentially suggest a reservation method related to the travel destination. Furthermore, if the user is at home, the reservation unit can also preferentially suggest a reservation method related to the user's home. For example, the reservation unit selects the optimal reservation method by taking into consideration the user's geographical location information when making a reservation. In this way, the reservation unit can provide the optimal reservation method by taking into consideration the user's geographical location information.

[0063] The reservation unit can analyze the user's social media activity to suggest a reservation procedure at the time of reservation. For example, the reservation unit can suggest a reservation procedure related to a location where the user has checked in on social media. The reservation unit can also analyze the content of the user's social media posts to suggest a related reservation procedure. Furthermore, the reservation unit can also refer to the activities of the user's friends on social media to suggest a related reservation procedure. For example, the reservation unit can analyze the user's social media activity to suggest a reservation procedure at the time of reservation. In this way, the reservation unit can suggest a related reservation procedure by analyzing the user's social media activity.

[0064] The reservation unit can customize the reservation method by reflecting the user's past feedback when making a reservation. The reservation unit can, for example, suggest the optimal reservation method based on feedback provided by the user in the past. The reservation unit can also preferentially suggest a specific reservation method based on the user's past feedback. Furthermore, the reservation unit can analyze the user's past feedback and customize the reservation method. For example, the reservation unit customizes the reservation method by reflecting the user's past feedback when making a reservation. In this way, the reservation unit can provide the optimal reservation method by reflecting the user's past feedback.

[0065] The detail analysis unit can adjust the level of detail of the analysis based on the importance of the condition during the detailed analysis. For example, the detail analysis unit performs a detailed analysis for an important condition. The detail analysis unit can also perform a simplified analysis for a less important condition. Furthermore, the detail analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the condition. For example, the detail analysis unit adjusts the level of detail of the analysis based on the importance of the condition during the detailed analysis. This allows the detail analysis unit to adjust the level of detail of the analysis based on the importance of the condition, thereby enabling efficient analysis.

[0066] The detailed analysis unit can apply different analysis algorithms depending on the category of conditions during the detailed analysis. The detailed analysis unit selects the optimal analysis algorithm depending on, for example, the type of license. The detailed analysis unit can also apply different analysis algorithms depending on the length of the training camp. Furthermore, the detailed analysis unit can also select the optimal analysis algorithm depending on the budget. For example, the detailed analysis unit applies different analysis algorithms depending on the category of conditions during the detailed analysis. As a result, the detailed analysis unit applies the optimal analysis algorithm depending on the category of conditions, thereby improving the accuracy of the analysis.

[0067] The detailed analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during the detailed analysis. The detailed analysis unit, for example, proposes an optimal analysis method based on the user's past analysis results. The detailed analysis unit can also improve the accuracy of the analysis from the user's past analysis results. Furthermore, the detailed analysis unit can analyze the user's past analysis results and optimize the analysis method. For example, the detailed analysis unit improves the accuracy of the analysis by referring to the user's past analysis results during the detailed analysis. In this way, the detailed analysis unit improves the accuracy of the analysis by referring to the user's past analysis results.

[0068] The detailed analysis unit can determine the priority of analysis based on the submission time of the conditions during the detailed analysis. For example, the detailed analysis unit prioritizes analysis of the most recently submitted conditions. The detailed analysis unit can also postpone analysis of conditions that were submitted earlier. Furthermore, the detailed analysis unit can dynamically adjust the priority of analysis based on the submission time. For example, the detailed analysis unit determines the priority of analysis based on the submission time of the conditions during the detailed analysis. This allows the detailed analysis unit to determine the priority of analysis based on the submission time of the conditions, thereby enabling efficient analysis.

[0069] The detailed analysis unit can adjust the order of analysis based on the relevance of conditions during detailed analysis. For example, the detailed analysis unit prioritizes analysis of highly relevant conditions. The detailed analysis unit can also postpone analysis of less relevant conditions. Furthermore, the detailed analysis unit can dynamically adjust the order of analysis based on the relevance of conditions. For example, the detailed analysis unit adjusts the order of analysis based on the relevance of conditions during detailed analysis. In this way, the detailed analysis unit can perform efficient analysis by adjusting the order of analysis based on the relevance of conditions.

[0070] The detailed analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during the detailed analysis. For example, if the user has technical expertise, the detailed analysis unit uses a lot of technical terms. Also, if the user does not have technical expertise, the detailed analysis unit can avoid technical terms. Furthermore, the detailed analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the detailed analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during the detailed analysis. In this way, the detailed analysis unit can provide analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0071] The evaluation collection unit can optimize the collection algorithm by referring to past evaluation data when collecting evaluations. The evaluation collection unit, for example, proposes an optimal collection algorithm based on past evaluation data. The evaluation collection unit can also optimize the collection algorithm from past evaluation data. Furthermore, the evaluation collection unit can analyze past evaluation data and improve the collection algorithm. For example, the evaluation collection unit optimizes the collection algorithm by referring to past evaluation data when collecting evaluations. In this way, the evaluation collection unit can optimize the collection algorithm by referring to past evaluation data.

[0072] The evaluation collection unit can improve the collection method by reflecting user feedback when collecting evaluations. The evaluation collection unit, for example, suggests an optimal collection method based on feedback provided by the user. The evaluation collection unit can also preferentially suggest a specific collection method based on user feedback. Furthermore, the evaluation collection unit can analyze user feedback and improve the collection method. For example, the evaluation collection unit improves the collection method by reflecting user feedback when collecting evaluations. In this way, the evaluation collection unit can optimize the collection method by reflecting user feedback.

[0073] The evaluation collection unit can weight the collected data based on the submission time of the evaluations when collecting the evaluations. For example, the evaluation collection unit prioritizes collection of recently submitted evaluation data. The evaluation collection unit can also postpone the collection of older submitted evaluation data. Furthermore, the evaluation collection unit can dynamically adjust the weighting of the collected data based on the submission time. For example, the evaluation collection unit weights the collected data based on the submission time of the evaluations when collecting the evaluations. In this way, the evaluation collection unit weights the collected data based on the submission time of the evaluations, thereby enabling efficient evaluation collection.

[0074] The evaluation collection unit can integrate information from different data sources to enrich the collected data during evaluation collection. For example, the evaluation collection unit integrates evaluation data from different data sources to enrich the collected data. The evaluation collection unit can also optimize the collected data based on information from the different data sources. Furthermore, the evaluation collection unit can analyze information from the different data sources to improve the collected data. For example, the evaluation collection unit integrates information from different data sources to enrich the collected data during evaluation collection. In this way, the evaluation collection unit can enrich the collected data by integrating information from the different data sources.

[0075] The information providing unit can select the optimal information providing method by referring to the user's past information providing history when providing information. The information providing unit, for example, suggests the optimal information providing method based on the user's past information providing history. The information providing unit can also preferentially suggest a specific information providing method based on the user's past information providing history. Furthermore, the information providing unit can analyze the user's past information providing history and optimize the information providing method. For example, the information providing unit selects the optimal information providing method by referring to the user's past information providing history when providing information. As a result, the information providing unit can provide the optimal information providing method by referring to the user's past information providing history.

[0076] The information providing unit can customize the content to be provided based on the user's current situation when providing information. For example, when the user inputs the current situation, the information providing unit automatically displays related content to be provided. Furthermore, when the user is in a specific situation, the information providing unit can preferentially display content to be provided that is related to that situation. Furthermore, the information providing unit can also suggest optimal content to be provided based on the user's current situation. For example, the information providing unit customizes the content to be provided based on the user's current situation when providing information. This allows the information providing unit to customize the content to be provided based on the user's current situation, thereby enabling efficient information provision.

[0077] The information providing unit can improve the information providing method by reflecting user feedback when providing information. The information providing unit, for example, suggests an optimal information providing method based on feedback provided by the user. The information providing unit can also preferentially suggest a specific information providing method based on user feedback. Furthermore, the information providing unit can analyze user feedback and improve the information providing method. For example, the information providing unit improves the information providing method by reflecting user feedback when providing information. In this way, the information providing unit can optimize the information providing method by reflecting user feedback.

[0078] The information providing unit can select the optimal information providing method by taking into consideration the user's device information when providing information. For example, if the user is using a smartphone, the information providing unit can provide a information providing method that matches the screen size. Furthermore, if the user is using a tablet, the information providing unit can also provide a information providing method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the information providing unit can also provide a simple and highly visible information providing method. For example, the information providing unit selects the optimal information providing method by taking into consideration the user's device information when providing information. As a result, the information providing unit can provide the optimal information providing method by taking into consideration the user's device information.

[0079] The information providing unit can make the provided content multilingual in accordance with the user's language setting when providing information. The information providing unit automatically sets the provided content based on, for example, the language setting of the user's device. The information providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the information providing unit can also provide the provided content in that language. For example, the information providing unit can make the provided content multilingual in accordance with the user's language setting when providing information. In this way, the information providing unit can provide information that is easy for the user to understand by making the provided content multilingual in accordance with the user's language setting.

[0080] The comparison unit can select the optimal comparison method by referring to the user's past comparison history when making a comparison. The comparison unit, for example, suggests the optimal comparison method based on the user's past comparison history. The comparison unit can also preferentially suggest a specific comparison method based on the user's past comparison history. Furthermore, the comparison unit can analyze the user's past comparison history and optimize the comparison method. For example, the comparison unit selects the optimal comparison method by referring to the user's past comparison history when making a comparison. In this way, the comparison unit can provide the optimal comparison method by referring to the user's past comparison history.

[0081] The comparison unit can customize the comparison content based on the user's current situation during comparison. For example, the comparison unit automatically displays related comparison content when the user inputs their current situation. Furthermore, when the user is in a specific situation, the comparison unit can preferentially display comparison content related to that situation. Furthermore, the comparison unit can suggest optimal comparison content based on the user's current situation. For example, the comparison unit customizes the comparison content based on the user's current situation during comparison. This allows the comparison unit to customize the comparison content based on the user's current situation, thereby enabling efficient comparison.

[0082] The comparison unit can improve the comparison method by reflecting user feedback during comparison. For example, the comparison unit can propose an optimal comparison method based on feedback provided by the user. The comparison unit can also preferentially propose a specific comparison method based on user feedback. Furthermore, the comparison unit can analyze user feedback and improve the comparison method. For example, the comparison unit improves the comparison method by reflecting user feedback during comparison. In this way, the comparison unit can optimize the comparison method by reflecting user feedback.

[0083] The comparison unit can select the optimal comparison method by taking into account the user's device information during comparison. For example, if the user is using a smartphone, the comparison unit can provide a comparison method that matches the screen size. Furthermore, if the user is using a tablet, the comparison unit can provide a comparison method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the comparison unit can provide a simple and highly visible comparison method. For example, the comparison unit can select the optimal comparison method by taking into account the user's device information during comparison. As a result, the comparison unit can provide the optimal comparison method by taking into account the user's device information.

[0084] The comparison unit can make the comparison content multilingual in accordance with the user's language setting during comparison. The comparison unit automatically sets the comparison content based on, for example, the language setting of the user's device. The comparison unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the comparison unit can provide the comparison content in that language. For example, the comparison unit can make the comparison content multilingual in accordance with the user's language setting during comparison. In this way, the comparison unit can provide information that is easy for the user to understand by making the comparison content multilingual in accordance with the user's language setting.

[0085] The comparison unit can provide related information by analyzing the user's social media activity during the comparison. For example, the comparison unit can provide information about places where the user has checked in on social media. The comparison unit can also analyze the content of the user's social media posts to provide information about related tourist spots and stores. Furthermore, the comparison unit can provide information about related places and events by referring to the activities of the user's friends on social media. For example, the comparison unit can provide related information by analyzing the user's social media activity during the comparison. This allows the comparison unit to provide related information by analyzing the user's social media activity.

[0086] The maintenance unit can optimize the maintenance algorithm by referring to past maintenance data during maintenance. The maintenance unit, for example, proposes an optimal maintenance algorithm based on past maintenance data. The maintenance unit can also optimize the maintenance algorithm from past maintenance data. Furthermore, the maintenance unit can analyze past maintenance data and improve the maintenance algorithm. For example, the maintenance unit optimizes the maintenance algorithm by referring to past maintenance data during maintenance. In this way, the maintenance unit can optimize the maintenance algorithm by referring to past maintenance data.

[0087] The maintenance unit can improve the maintenance method by reflecting user feedback during maintenance. The maintenance unit can, for example, propose an optimal maintenance method based on feedback provided by the user. The maintenance unit can also preferentially propose a specific maintenance method based on user feedback. Furthermore, the maintenance unit can analyze user feedback and improve the maintenance method. For example, the maintenance unit can improve the maintenance method by reflecting user feedback during maintenance. In this way, the maintenance unit can optimize the maintenance method by reflecting user feedback.

[0088] During maintenance, the maintenance unit can weight the maintenance data based on the time of data submission. For example, the maintenance unit prioritizes maintenance of recently submitted data. The maintenance unit can also postpone data that was submitted earlier. Furthermore, the maintenance unit can dynamically adjust the weighting of the maintenance data based on the time of submission. For example, during maintenance, the maintenance unit weights the maintenance data based on the time of data submission. This allows the maintenance unit to weight the maintenance data based on the time of data submission, thereby enabling efficient maintenance.

[0089] The maintenance department can integrate information from different data sources to enrich the maintenance data during maintenance. For example, the maintenance department integrates information from different data sources to enrich the maintenance data. The maintenance department can also optimize the maintenance data based on information from different data sources. Furthermore, the maintenance department can analyze information from different data sources to improve the maintenance data. For example, the maintenance department integrates information from different data sources to enrich the maintenance data during maintenance. This allows the maintenance department to enrich the maintenance data by integrating information from different data sources.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The driving school license plan selection system analyzes the user's past search history and can prioritize the display of plans that the user previously showed interest in. For example, if the user previously searched for a specific area or budget range, plans that match those conditions will be prioritized. It can also prioritize the display of plans that the user has previously given high ratings to or frequently viewed. It can also suggest plans similar to plans that the user has previously booked. This allows the user to quickly find a more suitable plan based on their past search history.

[0092] The driving school camp plan selection system can use the user's current geographical location information to preferentially suggest driving school camp plans in the vicinity. For example, the user can input their current location or obtain their current location using the GPS function, and driving school camp plans in the vicinity will be displayed. Also, if the user is traveling, plans related to the area they are traveling to can be suggested. Furthermore, if the user plans to stay in a specific area for an extended period of time, plans related to that area can be preferentially displayed. This allows the user to find the optimal plan based on their current geographical location information.

[0093] The driving school plan selection system can analyze a user's social media activity and suggest relevant plans. For example, it can analyze the locations where the user has checked in and the content of their posts on social media to suggest plans related to that area. It can also prioritize plans that the user's friends have participated in or highly rated. It can also suggest related plans based on the user's social media interests. This allows the user to find more appropriate plans based on their social media activity.

[0094] The driving school camp plan selection system can improve the plan suggestion method by reflecting the user's past feedback. For example, it can suggest the optimal plan suggestion method based on the user's past feedback. It can also prioritize specific suggestion methods based on the user's feedback. It can also analyze the user's feedback and improve the suggestion method. This makes it possible to provide the optimal plan suggestion method by reflecting the user's past feedback.

[0095] The driving school license plan selection system can adjust the plan explanation according to the user's level of expertise. For example, if the user has expertise, it can provide a detailed technical explanation. If the user does not have expertise, it can provide a concise and easy-to-understand explanation. Furthermore, it can dynamically adjust the plan explanation according to the user's level of expertise. This makes it possible to provide an easy-to-understand plan explanation according to the user's level of expertise.

[0096] The driving school license plan selection system can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. Also, if the user is using a tablet, it can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, it can provide a simple and highly visible display method. This allows it to provide the optimal display method by taking into account the user's device information.

[0097] The processing flow of the first embodiment will be briefly explained below.

[0098] Step 1: The reception unit inputs the user's conditions. The user's conditions include, for example, the date, location, budget, type of license desired, length of training camp, etc. The user inputs these conditions into the reception unit. Step 2: The analysis unit analyzes the conditions entered by the reception unit. The analysis is performed by methods such as prioritizing the conditions and checking the consistency of the conditions. For example, the analysis unit prioritizes the conditions and checks the consistency of the conditions. Step 3: The search unit searches for programs based on the conditions analyzed by the analysis unit. The search is performed using a search algorithm, a database of search targets, or other methods. For example, the search unit uses a search algorithm to extract the plan that best suits the user's conditions from multiple programs registered in the database. Step 4: The display unit displays the programs found by the search unit. The display is performed in a display format, a display order, etc. For example, the display unit may display the program content, price, accommodation information, past user reviews, etc. Step 5: The reservation unit reserves the program displayed by the display unit. The reservation is made by a reservation procedure, a reservation confirmation method, etc. For example, the reservation unit reserves the plan selected by the user and confirms the reservation.

[0099] (Example 2) A driving school camp plan selection system according to an embodiment of the present invention allows users to search for and reserve driving school camp programs that meet their requirements via chat. In the driving school camp plan selection system, users input their desired requirements via chat, and AI responds immediately to suggest driving school camp programs tailored to their needs. From the proposed programs, users can select the most suitable plan and complete their reservation. For example, in the driving school camp plan selection system, users input requirements such as the type of license they desire, the length of the camp, their budget, and the location. For example, they input "I want to obtain a standard driver's license," "A plan that can be completed within two weeks," "A budget of less than 100,000 yen," and "A driving school camp in the Kanto region." This information is then input into the AI. The AI ​​then analyzes the input information and searches for driving school camp programs that meet the requirements. The AI ​​then extracts the plan that best suits the user's requirements from multiple programs registered in a database. For example, the system searches for plans in the Kanto region that allow users to obtain a standard driver's license within two weeks and suggests the best plan within their budget. The driving school camp plan selection system then allows users to select the most suitable plan from the proposed programs. The AI ​​then provides detailed information about each program, allowing users to compare and consider options. For example, it displays program content, fees, accommodation information, and past user reviews. Users can select the most suitable plan based on this information and complete their reservation. This allows the driving licence training camp plan selection system to easily select the most suitable driving licence training camp plan for users. The AI ​​responds instantly and makes suggestions based on the user's needs, allowing for efficient plan selection. Furthermore, chat-style interactions allow users to operate intuitively and are easily accessible from smartphones or PCs. This allows the driving licence training camp plan selection system to easily select the most suitable driving licence training camp plan for users. For example, the AI ​​responds instantly and makes suggestions based on the user's needs, allowing for efficient plan selection. Furthermore, chat-style interactions allow users to operate intuitively and are easily accessible from smartphones or PCs.

[0100] The system for selecting a driving school license plan according to the embodiment includes a reception unit, an analysis unit, a search unit, a display unit, and a reservation unit. The reception unit inputs user requirements. The user requirements include, but are not limited to, dates, location, and budget. The reception unit inputs requirements such as the type of license desired by the user, the duration of the driving school license, budget, and location. The analysis unit analyzes the requirements input by the reception unit. The analysis is performed, for example, by prioritizing the requirements and checking the consistency of the requirements, but is not limited to, examples. The analysis unit prioritizes the requirements and checks the consistency of the requirements, for example. The search unit searches for a program based on the requirements analyzed by the analysis unit. The search is performed, for example, by using a search algorithm or a database of search targets, but is not limited to, examples. The search unit uses, for example, a search algorithm to extract a plan that best suits the user's requirements from multiple programs registered in a database. The display unit displays the programs searched by the search unit. The display is performed, for example, by using a display format or a display order, but is not limited to, examples. The display unit displays, for example, the program content, fees, accommodation information, past user reviews, etc. The reservation unit reserves the program displayed by the display unit. The reservation is made, for example, through a reservation procedure or a reservation confirmation method, but is not limited to these examples. The reservation unit reserves, for example, a plan selected by the user and confirms the reservation. As a result, the driving school camp plan selection system according to the embodiment can automatically search, display, and reserve driving school camp programs based on the user's conditions.

[0101] The driving school camp plan selection system includes a detailed analysis unit that analyzes the user's conditions in detail. The detailed analysis unit analyzes the user's conditions in detail. The detailed analysis is performed, for example, by subdividing the conditions or by evaluating the detailed conditions, but is not limited to these examples. The detailed analysis unit, for example, subdivides the conditions and evaluates the detailed conditions. The detailed analysis unit can also prioritize the conditions and check the consistency of the conditions. For example, the detailed analysis unit prioritizes the conditions and checks the consistency of the conditions. As a result, the detailed analysis unit can propose a more appropriate program by analyzing the user's conditions in detail.

[0102] The driving school camp plan selection system includes an evaluation collection unit that collects evaluations from past users. The evaluation collection unit collects evaluations from past users. The evaluations are performed, for example, using evaluation items and evaluation collection means, but are not limited to these examples. For example, the evaluation collection unit collects evaluations from past users and performs evaluations based on the evaluation items. The evaluation collection unit can also collect evaluations using evaluation collection means. For example, the evaluation collection unit collects evaluations using means such as questionnaires and reviews. In this way, the evaluation collection unit can provide highly reliable information to users by collecting evaluations from past users.

[0103] The driving school camp plan selection system includes an information providing unit that provides detailed program information. The information providing unit provides detailed program information. The detailed program information is, for example, information such as the program content, fees, and schedule, but is not limited to these examples. The information providing unit provides detailed program information such as the program content, fees, and schedule. The information providing unit can also provide information such as accommodation information and past user reviews. For example, the information providing unit provides accommodation information and past user reviews. In this way, the information providing unit provides detailed program information, making it easier for users to compare and consider options.

[0104] The driving school camp plan selection system includes a comparison unit that allows the user to compare and consider. The comparison unit allows the user to compare and consider. The comparison is performed, for example, by using comparison items and a comparison method, but is not limited to these examples. The comparison unit provides comparison items such as program content, fees, accommodation information, and past user reviews. The comparison unit can also use a comparison method to allow the user to compare and consider. For example, the comparison unit provides comparison items such as program content, fees, accommodation information, and past user reviews, allowing the user to compare and consider. This allows the comparison unit to compare and consider multiple programs.

[0105] The driving school camp plan selection system includes a maintenance unit that performs database maintenance. The maintenance unit performs database maintenance. Maintenance is performed, for example, by using a data update frequency or a maintenance procedure, but is not limited to these examples. For example, the maintenance unit sets a data update frequency and updates the data periodically. The maintenance unit can also perform database maintenance using a maintenance procedure. For example, the maintenance unit performs maintenance using procedures such as data backup and data consistency check. In this way, the maintenance unit can maintain the reliability of the system by performing database maintenance.

[0106] The reception unit can estimate the user's emotion and adjust the condition input interface based on the estimated user emotion. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick condition input. For example, the reception unit can estimate the user's emotion and adjust the condition input interface based on the estimated user emotion. This allows the reception unit to reduce user stress by adjusting the interface according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0107] The reception unit can analyze the user's past condition input history and suggest the optimal input method. For example, the reception unit automatically displays conditions that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest conditions to be used in a specific time period from the user's past input history. For example, the reception unit analyzes the user's past condition input history and suggests the optimal input method. In this way, the reception unit can suggest the optimal input method to the user by analyzing the past condition input history.

[0108] The reception unit can customize input items based on the user's current situation and areas of interest when entering conditions. For example, when the user enters their current situation, the reception unit automatically displays related input items based on the user's areas of interest. Furthermore, if the user has a specific area of ​​interest, the reception unit can preferentially display input items related to that area. Furthermore, the reception unit can suggest optimal input items based on the user's current situation. For example, the reception unit customizes input items based on the user's current situation and areas of interest when entering conditions. This allows the reception unit to customize input items based on the user's current situation and areas of interest, thereby enabling more appropriate condition entry.

[0109] The reception unit can select the optimal input means depending on the user's input method when inputting conditions. For example, if the user selects voice input, the reception unit inputs the conditions using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface optimized for text input. Furthermore, if the user selects image input, the reception unit can also input the conditions using image recognition technology. For example, the reception unit selects the optimal input means depending on the user's input method when inputting conditions. This allows the reception unit to select the optimal input means depending on the user's input method, thereby improving input efficiency.

[0110] The reception unit can estimate the user's emotions and determine the priority of conditions to be input based on the estimated user emotions. For example, if the user is nervous, the reception unit can prompt the user to input important conditions first. Furthermore, if the user is relaxed, the reception unit can prompt the user to input detailed conditions first. Furthermore, if the user is in a hurry, the reception unit can prompt the user to input the most important conditions first. For example, the reception unit can estimate the user's emotions and determine the priority of conditions to be input based on the estimated user emotions. In this way, the reception unit can prioritize the input of important conditions by determining the priority of conditions based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0111] The reception unit can prioritize input of highly relevant conditions in consideration of the user's geographical location information when inputting conditions. For example, when the user is in a specific area, the reception unit can prompt the user to prioritize input of conditions related to that area. Furthermore, when the user is traveling, the reception unit can also prompt the user to prioritize input of conditions related to the travel destination. Furthermore, when the user is at home, the reception unit can also prompt the user to prioritize input of conditions related to the user's home. For example, when inputting conditions, the reception unit prioritizes input of highly relevant conditions in consideration of the user's geographical location information. In this way, the reception unit can prioritize input of highly relevant conditions by considering the user's geographical location information.

[0112] The reception unit can analyze the user's social media activity when the conditions are input and suggest related conditions. For example, the reception unit can suggest conditions related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's posts on social media and suggest related conditions. Furthermore, the reception unit can also suggest related conditions by referring to the activities of the user's friends on social media. For example, the reception unit can analyze the user's social media activity when the conditions are input and suggest related conditions. In this way, the reception unit can suggest related conditions by analyzing the user's social media activity.

[0113] The reception unit can customize the input method by reflecting the user's past feedback when entering conditions. The reception unit, for example, suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the input method. For example, the reception unit customizes the input method by reflecting the user's past feedback when entering conditions. In this way, the reception unit can provide the optimal input method by reflecting the user's past feedback.

[0114] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user's emotions. For example, the analysis unit can provide a simple analysis method when the user is nervous. The analysis unit can also provide a detailed analysis method when the user is relaxed. Furthermore, the analysis unit can provide a quick analysis method when the user is in a hurry. For example, the analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user's emotions. In this way, the analysis unit can improve the accuracy of the analysis by adjusting the analysis method based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0115] The analysis unit can adjust the level of detail of the analysis based on the importance of the conditions during analysis. For example, the analysis unit performs a detailed analysis for important conditions. The analysis unit can also perform a simplified analysis for less important conditions. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the conditions. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the conditions during analysis. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the conditions, thereby enabling efficient analysis.

[0116] The analysis unit can apply different analysis algorithms depending on the category of conditions during analysis. The analysis unit selects the optimal analysis algorithm depending on, for example, the type of license. The analysis unit can also apply different analysis algorithms depending on the length of the training camp. Furthermore, the analysis unit can also select the optimal analysis algorithm depending on the budget. For example, the analysis unit applies different analysis algorithms depending on the category of conditions during analysis. In this way, the analysis unit can apply the optimal analysis algorithm depending on the category of conditions, thereby improving the accuracy of the analysis.

[0117] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit, for example, proposes an optimal analysis method based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis from the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and optimize the analysis method. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results during analysis. In this way, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results.

[0118] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is nervous, the analysis unit can prioritize analyzing important conditions. Also, if the user is relaxed, the analysis unit can prioritize analyzing detailed conditions. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing the most important conditions. For example, the analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. In this way, the analysis unit can prioritize analyzing important conditions by determining the analysis priority based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0119] The analysis unit can determine the analysis priority based on the time of submission of the conditions during analysis. For example, the analysis unit prioritizes analysis of the most recently submitted conditions. The analysis unit can also postpone analysis of conditions that were submitted earlier. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the time of submission. For example, the analysis unit determines the analysis priority based on the time of submission of the conditions during analysis. This allows the analysis unit to determine the analysis priority based on the time of submission of the conditions, thereby enabling efficient analysis.

[0120] The analysis unit can adjust the order of analysis based on the relevance of conditions during analysis. For example, the analysis unit prioritizes analysis of highly relevant conditions. The analysis unit can also postpone analysis of less relevant conditions. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of conditions. For example, the analysis unit adjusts the order of analysis based on the relevance of conditions during analysis. This allows the analysis unit to adjust the order of analysis based on the relevance of conditions, thereby enabling efficient analysis.

[0121] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. Also, if the user does not have technical expertise, the analysis unit can avoid technical terms. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0122] The search unit can estimate a user's emotions and adjust search criteria based on the estimated user emotions. For example, the search unit can provide simple search criteria when the user is nervous. The search unit can also provide detailed search criteria when the user is relaxed. Furthermore, the search unit can provide quick search criteria when the user is in a hurry. For example, the search unit can estimate a user's emotions and adjust search criteria based on the estimated user emotions. In this way, the search unit can provide optimal search results for the user by adjusting the search criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0123] The search unit can improve search accuracy by taking into account interrelationships between conditions during a search. For example, the search unit analyzes interrelationships between conditions and provides optimal search results. The search unit can also filter search results based on interrelationships between conditions. Furthermore, the search unit can improve search accuracy by taking into account interrelationships between conditions during a search. For example, the search unit improves search accuracy by taking into account interrelationships between conditions during a search. In this way, the search unit improves search accuracy by taking into account interrelationships between conditions.

[0124] The search unit can perform a search while taking into account the attribute information of the person who submitted the conditions. The search unit provides optimal search results based on, for example, the age and gender of the person who submitted the conditions. The search unit can also filter search results based on the occupation and hobbies of the person who submitted the conditions. Furthermore, the search unit can improve the accuracy of the search by taking into account the attribute information of the person who submitted the conditions. For example, the search unit can perform a search while taking into account the attribute information of the person who submitted the conditions. In this way, the search unit can provide more appropriate search results by taking into account the attribute information of the person who submitted the conditions.

[0125] The search unit can weight the search based on the frequency of submission of conditions during a search. For example, the search unit prioritizes searches for conditions that are submitted more frequently. The search unit can also postpone searches for conditions that are submitted less frequently. Furthermore, the search unit can dynamically adjust the weighting of the search based on the frequency of submission of conditions. For example, the search unit weights the search based on the frequency of submission of conditions during a search. This enables efficient searches by weighting the search based on the frequency of submission of conditions.

[0126] The search unit can estimate the user's emotions and adjust the display order of search results based on the estimated user emotions. For example, when the user is nervous, the search unit can prioritize displaying important search results. Furthermore, when the user is relaxed, the search unit can prioritize displaying detailed search results. Furthermore, when the user is in a hurry, the search unit can prioritize displaying the most important search results. For example, the search unit can estimate the user's emotions and adjust the display order of search results based on the estimated user emotions. In this way, the search unit can provide optimal search results for the user by adjusting the display order of search results based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0127] The search unit can perform a search taking into account the geographic distribution of the conditions. For example, the search unit analyzes the geographic distribution of the conditions and provides optimal search results. The search unit can also filter search results based on the geographic distribution of the conditions. Furthermore, the search unit can improve search accuracy by taking into account the geographic distribution of the conditions. For example, the search unit performs a search taking into account the geographic distribution of the conditions. In this way, the search unit can provide more appropriate search results by taking into account the geographic distribution of the conditions.

[0128] The search unit can improve the accuracy of the search by referring to literature related to the conditions during the search. For example, the search unit refers to literature related to the conditions and provides optimal search results. The search unit can also filter the search results based on literature related to the conditions. Furthermore, the search unit can improve the accuracy of the search by taking into account literature related to the conditions. For example, the search unit improves the accuracy of the search by referring to literature related to the conditions during the search. As a result, the search unit improves the accuracy of the search by referring to literature related to the conditions.

[0129] The search unit can perform a search taking into account the market value of the conditions. For example, the search unit analyzes the market value of the conditions and provides optimal search results. The search unit can also filter search results based on the market value of the conditions. Furthermore, the search unit can improve the accuracy of the search by taking into account the market value of the conditions. For example, the search unit performs a search taking into account the market value of the conditions. In this way, the search unit can provide more appropriate search results by taking into account the market value of the conditions.

[0130] The display unit can estimate the user's emotion and adjust the display method based on the estimated user's emotion. For example, when the user is nervous, the display unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the display unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the display unit can provide a display method that focuses on the main points. For example, the display unit can estimate the user's emotion and adjust the display method based on the estimated user's emotion. In this way, the display unit can adjust the display method based on the user's emotion, thereby enabling an optimal display for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0131] The display unit can select the optimal display method by referring to the user's past operation history when displaying. The display unit, for example, suggests the optimal display method based on the user's past operation history. The display unit can also preferentially suggest a specific display method based on the user's past operation history. Furthermore, the display unit can analyze the user's past operation history and optimize the display method. For example, the display unit selects the optimal display method by referring to the user's past operation history when displaying. In this way, the display unit can provide the optimal display method by referring to the user's past operation history.

[0132] The display unit can customize the display content according to the user's current task when displaying the information. For example, when the user inputs the current task, the display unit automatically displays related display content. Furthermore, if the user has a specific task, the display unit can preferentially display display content related to that task. Furthermore, the display unit can suggest optimal display content based on the user's current task. For example, the display unit customizes the display content according to the user's current task when displaying the information. This allows the display unit to provide information efficiently by customizing the display content according to the user's current task.

[0133] The display unit can improve the display method by reflecting user feedback during display. The display unit can, for example, suggest an optimal display method based on feedback provided by the user. The display unit can also preferentially suggest a specific display method based on user feedback. Furthermore, the display unit can analyze user feedback and improve the display method. For example, the display unit can improve the display method by reflecting user feedback during display. In this way, the display unit can optimize the display method by reflecting user feedback.

[0134] The display unit can estimate the user's emotions and prioritize the display content based on the estimated user's emotions. For example, when the user is nervous, the display unit can prioritize displaying important display content. Furthermore, when the user is relaxed, the display unit can prioritize displaying detailed display content. Furthermore, when the user is in a hurry, the display unit can prioritize displaying the most important display content. For example, the display unit can estimate the user's emotions and prioritize the display content based on the estimated user's emotions. In this way, the display unit can prioritize displaying important information. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0135] The display unit can select the optimal display method in consideration of the user's device information when displaying. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. For example, the display unit selects the optimal display method in consideration of the user's device information when displaying. As a result, the display unit can provide the optimal display method by taking the user's device information into consideration.

[0136] The display unit can make the display content multilingual when displaying it according to the user's language setting. The display unit automatically sets the display content based on, for example, the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the display unit can provide the display content in that language. For example, the display unit can make the display content multilingual when displaying it according to the user's language setting. As a result, the display unit can provide information that is easy for the user to understand by making the display content multilingual according to the user's language setting.

[0137] The display unit can customize the display method according to the visual and auditory characteristics of the user when displaying information. For example, if the user is visually impaired, the display unit can provide audio guidance. Furthermore, if the user is hearing impaired, the display unit can also provide visual guidance. Furthermore, the display unit can customize the display method according to the visual and auditory characteristics of the user when displaying information. For example, the display unit customizes the display method according to the visual and auditory characteristics of the user when displaying information. In this way, the display unit can provide optimal information to the user by customizing the display method according to the visual and auditory characteristics of the user.

[0138] The reservation unit can estimate the user's emotions and adjust the reservation procedure based on the estimated user emotions. For example, the reservation unit can provide a simple reservation procedure when the user is nervous. The reservation unit can also provide a detailed reservation procedure when the user is relaxed. Furthermore, the reservation unit can provide a quick reservation procedure when the user is in a hurry. For example, the reservation unit can estimate the user's emotions and adjust the reservation procedure based on the estimated user emotions. In this way, the reservation unit can provide the optimal reservation procedure for the user by adjusting the reservation procedure based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0139] The reservation unit can suggest the optimal reservation method by referring to the user's past reservation history when making a reservation. The reservation unit can suggest the optimal reservation method, for example, based on the user's past reservation history. The reservation unit can also preferentially suggest a specific reservation method based on the user's past reservation history. Furthermore, the reservation unit can analyze the user's past reservation history and optimize the reservation method. For example, the reservation unit can suggest the optimal reservation method by referring to the user's past reservation history when making a reservation. In this way, the reservation unit can provide the optimal reservation method by referring to the user's past reservation history.

[0140] The reservation unit can customize the reservation procedure based on the user's current situation when making a reservation. For example, the reservation unit automatically displays a related reservation procedure when the user inputs their current situation. Furthermore, when the user is in a specific situation, the reservation unit can preferentially display reservation procedures related to that situation. Furthermore, the reservation unit can suggest an optimal reservation procedure based on the user's current situation. For example, the reservation unit customizes the reservation procedure based on the user's current situation when making a reservation. This allows the reservation unit to customize the reservation procedure based on the user's current situation, thereby enabling efficient reservations.

[0141] The reservation unit can improve the reservation method by reflecting user feedback at the time of reservation. The reservation unit, for example, suggests the optimal reservation method based on feedback provided by the user. The reservation unit can also preferentially suggest a specific reservation method based on user feedback. Furthermore, the reservation unit can analyze user feedback and improve the reservation method. For example, the reservation unit improves the reservation method by reflecting user feedback at the time of reservation. In this way, the reservation unit can optimize the reservation method by reflecting user feedback.

[0142] The reservation unit can estimate the user's emotions and prioritize reservations based on the estimated user emotions. For example, if the user is nervous, the reservation unit can prioritize important reservations. Furthermore, if the user is relaxed, the reservation unit can prioritize detailed reservations. Furthermore, if the user is in a hurry, the reservation unit can prioritize the most important reservations. For example, the reservation unit can estimate the user's emotions and prioritize reservations based on the estimated user emotions. In this way, the reservation unit can prioritize important reservations by prioritizing reservations based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0143] The reservation unit can select the optimal reservation method by taking into consideration the user's geographical location information when making a reservation. For example, if the user is in a specific area, the reservation unit can preferentially suggest a reservation method related to that area. Furthermore, if the user is traveling, the reservation unit can also preferentially suggest a reservation method related to the travel destination. Furthermore, if the user is at home, the reservation unit can also preferentially suggest a reservation method related to the user's home. For example, the reservation unit selects the optimal reservation method by taking into consideration the user's geographical location information when making a reservation. In this way, the reservation unit can provide the optimal reservation method by taking into consideration the user's geographical location information.

[0144] The reservation unit can analyze the user's social media activity to suggest a reservation procedure at the time of reservation. For example, the reservation unit can suggest a reservation procedure related to a location where the user has checked in on social media. The reservation unit can also analyze the content of the user's social media posts to suggest a related reservation procedure. Furthermore, the reservation unit can also refer to the activities of the user's friends on social media to suggest a related reservation procedure. For example, the reservation unit can analyze the user's social media activity to suggest a reservation procedure at the time of reservation. In this way, the reservation unit can suggest a related reservation procedure by analyzing the user's social media activity.

[0145] The reservation unit can customize the reservation method by reflecting the user's past feedback when making a reservation. The reservation unit can, for example, suggest the optimal reservation method based on feedback provided by the user in the past. The reservation unit can also preferentially suggest a specific reservation method based on the user's past feedback. Furthermore, the reservation unit can analyze the user's past feedback and customize the reservation method. For example, the reservation unit customizes the reservation method by reflecting the user's past feedback when making a reservation. In this way, the reservation unit can provide the optimal reservation method by reflecting the user's past feedback.

[0146] The detailed analysis unit can estimate the user's emotions and adjust the method of detailed analysis based on the estimated user's emotions. For example, if the user is nervous, the detailed analysis unit can provide a simple detailed analysis method. Furthermore, if the user is relaxed, the detailed analysis unit can provide a quick detailed analysis method. For example, the detailed analysis unit can estimate the user's emotions and adjust the method of detailed analysis based on the estimated user's emotions. In this way, the detailed analysis unit can provide optimal analysis results for the user by adjusting the method of detailed analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0147] The detail analysis unit can adjust the level of detail of the analysis based on the importance of the condition during the detailed analysis. For example, the detail analysis unit performs a detailed analysis for an important condition. The detail analysis unit can also perform a simplified analysis for a less important condition. Furthermore, the detail analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the condition. For example, the detail analysis unit adjusts the level of detail of the analysis based on the importance of the condition during the detailed analysis. This allows the detail analysis unit to adjust the level of detail of the analysis based on the importance of the condition, thereby enabling efficient analysis.

[0148] The detailed analysis unit can apply different analysis algorithms depending on the category of conditions during the detailed analysis. The detailed analysis unit selects the optimal analysis algorithm depending on, for example, the type of license. The detailed analysis unit can also apply different analysis algorithms depending on the length of the training camp. Furthermore, the detailed analysis unit can also select the optimal analysis algorithm depending on the budget. For example, the detailed analysis unit applies different analysis algorithms depending on the category of conditions during the detailed analysis. As a result, the detailed analysis unit applies the optimal analysis algorithm depending on the category of conditions, thereby improving the accuracy of the analysis.

[0149] The detailed analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during the detailed analysis. The detailed analysis unit, for example, proposes an optimal analysis method based on the user's past analysis results. The detailed analysis unit can also improve the accuracy of the analysis from the user's past analysis results. Furthermore, the detailed analysis unit can analyze the user's past analysis results and optimize the analysis method. For example, the detailed analysis unit improves the accuracy of the analysis by referring to the user's past analysis results during the detailed analysis. In this way, the detailed analysis unit improves the accuracy of the analysis by referring to the user's past analysis results.

[0150] The detailed analysis unit can estimate the user's emotions and determine the priority of detailed analysis based on the estimated user's emotions. For example, if the user is nervous, the detailed analysis unit can prioritize analyzing important conditions. Furthermore, if the user is relaxed, the detailed analysis unit can prioritize analyzing detailed conditions. Furthermore, if the user is in a hurry, the detailed analysis unit can prioritize analyzing the most important conditions. For example, the detailed analysis unit can estimate the user's emotions and determine the priority of detailed analysis based on the estimated user's emotions. In this way, the detailed analysis unit can prioritize analyzing important conditions by determining the priority of detailed analysis based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0151] The detailed analysis unit can determine the priority of analysis based on the submission time of the conditions during the detailed analysis. For example, the detailed analysis unit prioritizes analysis of the most recently submitted conditions. The detailed analysis unit can also postpone analysis of conditions that were submitted earlier. Furthermore, the detailed analysis unit can dynamically adjust the priority of analysis based on the submission time. For example, the detailed analysis unit determines the priority of analysis based on the submission time of the conditions during the detailed analysis. This allows the detailed analysis unit to determine the priority of analysis based on the submission time of the conditions, thereby enabling efficient analysis.

[0152] The detailed analysis unit can adjust the order of analysis based on the relevance of conditions during detailed analysis. For example, the detailed analysis unit prioritizes analysis of highly relevant conditions. The detailed analysis unit can also postpone analysis of less relevant conditions. Furthermore, the detailed analysis unit can dynamically adjust the order of analysis based on the relevance of conditions. For example, the detailed analysis unit adjusts the order of analysis based on the relevance of conditions during detailed analysis. In this way, the detailed analysis unit can perform efficient analysis by adjusting the order of analysis based on the relevance of conditions.

[0153] The detailed analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during the detailed analysis. For example, if the user has technical expertise, the detailed analysis unit uses a lot of technical terms. Also, if the user does not have technical expertise, the detailed analysis unit can avoid technical terms. Furthermore, the detailed analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the detailed analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during the detailed analysis. In this way, the detailed analysis unit can provide analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0154] The rating collection unit can estimate the user's emotions and adjust the rating collection method based on the estimated user emotions. For example, if the user is nervous, the rating collection unit can provide a simple rating collection method. Furthermore, if the user is relaxed, the rating collection unit can provide a detailed rating collection method. Furthermore, if the user is in a hurry, the rating collection unit can provide a quick rating collection method. For example, the rating collection unit can estimate the user's emotions and adjust the rating collection method based on the estimated user emotions. In this way, the rating collection unit can adjust the rating collection method based on the user's emotions, thereby enabling optimal rating collection for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0155] The evaluation collection unit can optimize the collection algorithm by referring to past evaluation data when collecting evaluations. The evaluation collection unit, for example, proposes an optimal collection algorithm based on past evaluation data. The evaluation collection unit can also optimize the collection algorithm from past evaluation data. Furthermore, the evaluation collection unit can analyze past evaluation data and improve the collection algorithm. For example, the evaluation collection unit optimizes the collection algorithm by referring to past evaluation data when collecting evaluations. In this way, the evaluation collection unit can optimize the collection algorithm by referring to past evaluation data.

[0156] The evaluation collection unit can improve the collection method by reflecting user feedback when collecting evaluations. The evaluation collection unit, for example, suggests an optimal collection method based on feedback provided by the user. The evaluation collection unit can also preferentially suggest a specific collection method based on user feedback. Furthermore, the evaluation collection unit can analyze user feedback and improve the collection method. For example, the evaluation collection unit improves the collection method by reflecting user feedback when collecting evaluations. In this way, the evaluation collection unit can optimize the collection method by reflecting user feedback.

[0157] The evaluation collection unit can estimate the user's emotions and adjust the frequency of evaluation collection based on the estimated user emotions. For example, the evaluation collection unit can reduce the frequency of evaluation collection when the user is nervous. The evaluation collection unit can also increase the frequency of evaluation collection when the user is relaxed. Furthermore, the evaluation collection unit can also reduce the frequency of evaluation collection when the user is in a hurry. For example, the evaluation collection unit can estimate the user's emotions and adjust the frequency of evaluation collection based on the estimated user emotions. In this way, the evaluation collection unit can adjust the frequency of evaluation collection based on the user's emotions, thereby enabling optimal evaluation collection for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0158] The evaluation collection unit can weight the collected data based on the submission time of the evaluations when collecting the evaluations. For example, the evaluation collection unit prioritizes collection of recently submitted evaluation data. The evaluation collection unit can also postpone the collection of older submitted evaluation data. Furthermore, the evaluation collection unit can dynamically adjust the weighting of the collected data based on the submission time. For example, the evaluation collection unit weights the collected data based on the submission time of the evaluations when collecting the evaluations. In this way, the evaluation collection unit weights the collected data based on the submission time of the evaluations, thereby enabling efficient evaluation collection.

[0159] The evaluation collection unit can integrate information from different data sources to enrich the collected data during evaluation collection. For example, the evaluation collection unit integrates evaluation data from different data sources to enrich the collected data. The evaluation collection unit can also optimize the collected data based on information from the different data sources. Furthermore, the evaluation collection unit can analyze information from the different data sources to improve the collected data. For example, the evaluation collection unit integrates information from different data sources to enrich the collected data during evaluation collection. In this way, the evaluation collection unit can enrich the collected data by integrating information from the different data sources.

[0160] The information providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user emotions. For example, if the user is nervous, the information providing unit can provide a simple information providing method. Furthermore, if the user is relaxed, the information providing unit can provide a detailed information providing method. Furthermore, if the user is in a hurry, the information providing unit can provide a quick information providing method. For example, the information providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user emotions. In this way, the information providing unit can provide optimal information to the user by adjusting the method of providing information based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0161] The information providing unit can select the optimal information providing method by referring to the user's past information providing history when providing information. The information providing unit, for example, suggests the optimal information providing method based on the user's past information providing history. The information providing unit can also preferentially suggest a specific information providing method based on the user's past information providing history. Furthermore, the information providing unit can analyze the user's past information providing history and optimize the information providing method. For example, the information providing unit selects the optimal information providing method by referring to the user's past information providing history when providing information. As a result, the information providing unit can provide the optimal information providing method by referring to the user's past information providing history.

[0162] The information providing unit can customize the content to be provided based on the user's current situation when providing information. For example, when the user inputs the current situation, the information providing unit automatically displays related content to be provided. Furthermore, when the user is in a specific situation, the information providing unit can preferentially display content to be provided that is related to that situation. Furthermore, the information providing unit can also suggest optimal content to be provided based on the user's current situation. For example, the information providing unit customizes the content to be provided based on the user's current situation when providing information. This allows the information providing unit to customize the content to be provided based on the user's current situation, thereby enabling efficient information provision.

[0163] The information providing unit can improve the information providing method by reflecting user feedback when providing information. The information providing unit, for example, suggests an optimal information providing method based on feedback provided by the user. The information providing unit can also preferentially suggest a specific information providing method based on user feedback. Furthermore, the information providing unit can analyze user feedback and improve the information providing method. For example, the information providing unit improves the information providing method by reflecting user feedback when providing information. In this way, the information providing unit can optimize the information providing method by reflecting user feedback.

[0164] The information providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user emotions. For example, when the user is nervous, the information providing unit can prioritize providing important information. Furthermore, when the user is relaxed, the information providing unit can prioritize providing detailed information. Furthermore, when the user is in a hurry, the information providing unit can prioritize providing the most important information. For example, the information providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user emotions. In this way, the information providing unit can prioritize providing important information by determining the priority of information provision based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0165] The information providing unit can select the optimal information providing method by taking into consideration the user's device information when providing information. For example, if the user is using a smartphone, the information providing unit can provide a information providing method that matches the screen size. Furthermore, if the user is using a tablet, the information providing unit can also provide a information providing method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the information providing unit can also provide a simple and highly visible information providing method. For example, the information providing unit selects the optimal information providing method by taking into consideration the user's device information when providing information. As a result, the information providing unit can provide the optimal information providing method by taking into consideration the user's device information.

[0166] The information providing unit can make the provided content multilingual in accordance with the user's language setting when providing information. The information providing unit automatically sets the provided content based on, for example, the language setting of the user's device. The information providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the information providing unit can also provide the provided content in that language. For example, the information providing unit can make the provided content multilingual in accordance with the user's language setting when providing information. In this way, the information providing unit can provide information that is easy for the user to understand by making the provided content multilingual in accordance with the user's language setting.

[0167] The comparison unit can estimate the user's emotion and adjust the comparison method based on the estimated user's emotion. For example, the comparison unit can provide a simple comparison method when the user is nervous. The comparison unit can also provide a detailed comparison method when the user is relaxed. The comparison unit can also provide a quick comparison method when the user is in a hurry. For example, the comparison unit can estimate the user's emotion and adjust the comparison method based on the estimated user's emotion. In this way, the comparison unit can adjust the comparison method based on the user's emotion, thereby enabling an optimal comparison for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0168] The comparison unit can select the optimal comparison method by referring to the user's past comparison history when making a comparison. The comparison unit, for example, suggests the optimal comparison method based on the user's past comparison history. The comparison unit can also preferentially suggest a specific comparison method based on the user's past comparison history. Furthermore, the comparison unit can analyze the user's past comparison history and optimize the comparison method. For example, the comparison unit selects the optimal comparison method by referring to the user's past comparison history when making a comparison. In this way, the comparison unit can provide the optimal comparison method by referring to the user's past comparison history.

[0169] The comparison unit can customize the comparison content based on the user's current situation during comparison. For example, the comparison unit automatically displays related comparison content when the user inputs their current situation. Furthermore, when the user is in a specific situation, the comparison unit can preferentially display comparison content related to that situation. Furthermore, the comparison unit can suggest optimal comparison content based on the user's current situation. For example, the comparison unit customizes the comparison content based on the user's current situation during comparison. This allows the comparison unit to customize the comparison content based on the user's current situation, thereby enabling efficient comparison.

[0170] The comparison unit can improve the comparison method by reflecting user feedback during comparison. For example, the comparison unit can propose an optimal comparison method based on feedback provided by the user. The comparison unit can also preferentially propose a specific comparison method based on user feedback. Furthermore, the comparison unit can analyze user feedback and improve the comparison method. For example, the comparison unit improves the comparison method by reflecting user feedback during comparison. In this way, the comparison unit can optimize the comparison method by reflecting user feedback.

[0171] The comparison unit can estimate the user's emotions and determine the priority of comparisons based on the estimated user emotions. For example, if the user is nervous, the comparison unit can prioritize displaying important comparison content. Furthermore, if the user is relaxed, the comparison unit can prioritize displaying detailed comparison content. Furthermore, if the user is in a hurry, the comparison unit can prioritize displaying the most important comparison content. For example, the comparison unit can estimate the user's emotions and determine the priority of comparisons based on the estimated user emotions. In this way, the comparison unit can prioritize displaying important comparison content by determining the priority of comparisons based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0172] The comparison unit can select the optimal comparison method by taking into account the user's device information during comparison. For example, if the user is using a smartphone, the comparison unit can provide a comparison method that matches the screen size. Furthermore, if the user is using a tablet, the comparison unit can provide a comparison method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the comparison unit can provide a simple and highly visible comparison method. For example, the comparison unit can select the optimal comparison method by taking into account the user's device information during comparison. As a result, the comparison unit can provide the optimal comparison method by taking into account the user's device information.

[0173] The comparison unit can make the comparison content multilingual in accordance with the user's language setting during comparison. The comparison unit automatically sets the comparison content based on, for example, the language setting of the user's device. The comparison unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the comparison unit can provide the comparison content in that language. For example, the comparison unit can make the comparison content multilingual in accordance with the user's language setting during comparison. In this way, the comparison unit can provide information that is easy for the user to understand by making the comparison content multilingual in accordance with the user's language setting.

[0174] The comparison unit can provide related information by analyzing the user's social media activity during the comparison. For example, the comparison unit can provide information about places where the user has checked in on social media. The comparison unit can also analyze the content of the user's social media posts to provide information about related tourist spots and stores. Furthermore, the comparison unit can provide information about related places and events by referring to the activities of the user's friends on social media. For example, the comparison unit can provide related information by analyzing the user's social media activity during the comparison. This allows the comparison unit to provide related information by analyzing the user's social media activity.

[0175] The maintenance unit can estimate the user's emotions and adjust the maintenance method based on the estimated user emotions. For example, if the user is nervous, the maintenance unit can provide a simple maintenance method. Furthermore, if the user is relaxed, the maintenance unit can provide a detailed maintenance method. Furthermore, if the user is in a hurry, the maintenance unit can provide a quick maintenance method. For example, the maintenance unit can estimate the user's emotions and adjust the maintenance method based on the estimated user emotions. In this way, the maintenance unit can adjust the maintenance method based on the user's emotions, thereby enabling optimal maintenance for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0176] The maintenance unit can optimize the maintenance algorithm by referring to past maintenance data during maintenance. The maintenance unit, for example, proposes an optimal maintenance algorithm based on past maintenance data. The maintenance unit can also optimize the maintenance algorithm from past maintenance data. Furthermore, the maintenance unit can analyze past maintenance data and improve the maintenance algorithm. For example, the maintenance unit optimizes the maintenance algorithm by referring to past maintenance data during maintenance. In this way, the maintenance unit can optimize the maintenance algorithm by referring to past maintenance data.

[0177] The maintenance unit can improve the maintenance method by reflecting user feedback during maintenance. The maintenance unit can, for example, propose an optimal maintenance method based on feedback provided by the user. The maintenance unit can also preferentially propose a specific maintenance method based on user feedback. Furthermore, the maintenance unit can analyze user feedback and improve the maintenance method. For example, the maintenance unit can improve the maintenance method by reflecting user feedback during maintenance. In this way, the maintenance unit can optimize the maintenance method by reflecting user feedback.

[0178] The maintenance unit can estimate the user's emotions and adjust the frequency of maintenance based on the estimated user emotions. For example, the maintenance unit can reduce the frequency of maintenance when the user is nervous. The maintenance unit can also increase the frequency of maintenance when the user is relaxed. Furthermore, the maintenance unit can reduce the frequency of maintenance when the user is in a hurry. For example, the maintenance unit can estimate the user's emotions and adjust the frequency of maintenance based on the estimated user emotions. In this way, the maintenance unit can adjust the frequency of maintenance based on the user's emotions, thereby enabling optimal maintenance for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0179] During maintenance, the maintenance unit can weight the maintenance data based on the time of data submission. For example, the maintenance unit prioritizes maintenance of recently submitted data. The maintenance unit can also postpone data that was submitted earlier. Furthermore, the maintenance unit can dynamically adjust the weighting of the maintenance data based on the time of submission. For example, during maintenance, the maintenance unit weights the maintenance data based on the time of data submission. This allows the maintenance unit to weight the maintenance data based on the time of data submission, thereby enabling efficient maintenance.

[0180] The maintenance department can integrate information from different data sources to enrich the maintenance data during maintenance. For example, the maintenance department integrates information from different data sources to enrich the maintenance data. The maintenance department can also optimize the maintenance data based on information from different data sources. Furthermore, the maintenance department can analyze information from different data sources to improve the maintenance data. For example, the maintenance department integrates information from different data sources to enrich the maintenance data during maintenance. This allows the maintenance department to enrich the maintenance data by integrating information from different data sources. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, search unit, display unit, reservation unit, detailed analysis unit, evaluation collection unit, information provision unit, comparison unit, and maintenance unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives user conditions. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input conditions. The search unit is realized by the specific processing unit 290 of the data processing device 12 and searches for a program based on the analyzed conditions. The display unit is realized by the control unit 46A of the smart device 14 and displays the searched program. The reservation unit is realized by the control unit 46A of the smart device 14 and reserves the displayed program. The detailed analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user conditions in detail. The evaluation collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects past user evaluations. The information providing unit is realized by the control unit 46A of the smart device 14 and provides detailed information about the program. The comparison unit is realized by the control unit 46A of the smart device 14 and allows the user to compare and consider. The maintenance unit is realized by the specific processing unit 290 of the data processing device 12 and performs database maintenance. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, search unit, display unit, reservation unit, detail analysis unit, evaluation collection unit, information provision unit, comparison unit, and maintenance unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and inputs user conditions. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input conditions. The search unit is realized by the specific processing unit 290 of the data processing device 12 and searches for programs based on the analyzed conditions. The display unit is realized by the control unit 46A of the smart glasses 214 and displays the searched programs. The reservation unit is realized by the control unit 46A of the smart glasses 214 and reserves the displayed programs. The detail analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user conditions in detail. The evaluation collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects past user evaluations. The information providing unit is realized by the control unit 46A of the smart glasses 214 and provides detailed information about the programs. The comparison unit is realized by the control unit 46A of the smart glasses 214 and allows the user to compare and consider. The maintenance unit is realized by the specific processing unit 290 of the data processing device 12 and performs database maintenance. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, search unit, display unit, reservation unit, detailed analysis unit, evaluation collection unit, information provision unit, comparison unit, and maintenance unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and inputs user conditions. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input conditions. The search unit is realized by the specific processing unit 290 of the data processing device 12 and searches for a program based on the analyzed conditions. The display unit is realized by the control unit 46A of the headset type terminal 314 and displays the searched program. The reservation unit is realized by the control unit 46A of the headset type terminal 314 and reserves the displayed program. The detailed analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user conditions in detail. The evaluation collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects past user evaluations. The information providing unit is realized by the control unit 46A of the headset type terminal 314 and provides detailed information about the program. The comparison unit is realized by the control unit 46A of the headset type terminal 314 and allows the user to compare and consider. The maintenance unit is realized by the specific processing unit 290 of the data processing device 12 and performs maintenance of the database. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, search unit, display unit, reservation unit, detailed analysis unit, evaluation collection unit, information provision unit, comparison unit, and maintenance unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and inputs user conditions. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input conditions. The search unit is realized by the specific processing unit 290 of the data processing device 12 and searches for a program based on the analyzed conditions. The display unit is realized by the control unit 46A of the robot 414 and displays the searched program. The reservation unit is realized by the control unit 46A of the robot 414 and reserves the displayed program. The detailed analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user conditions in detail. The evaluation collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects past user evaluations. The information providing unit is realized by the control unit 46A of the robot 414 and provides detailed information about the program. The comparison unit is realized by the control unit 46A of the robot 414 and allows the user to compare and consider. The maintenance unit is realized by the specific processing unit 290 of the data processing device 12 and performs maintenance of the database.

[0181] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0182] The driving school license plan selection system analyzes the user's past search history and can prioritize the display of plans that the user previously showed interest in. For example, if the user previously searched for a specific area or budget range, plans that match those conditions will be prioritized. It can also prioritize the display of plans that the user has previously given high ratings to or frequently viewed. It can also suggest plans similar to plans that the user has previously booked. This allows the user to quickly find a more suitable plan based on their past search history.

[0183] The driving school camp plan selection system can use the user's current geographical location information to preferentially suggest driving school camp plans in the vicinity. For example, the user can input their current location or obtain their current location using the GPS function, and driving school camp plans in the vicinity will be displayed. Also, if the user is traveling, plans related to the area they are traveling to can be suggested. Furthermore, if the user plans to stay in a specific area for an extended period of time, plans related to that area can be preferentially displayed. This allows the user to find the optimal plan based on their current geographical location information.

[0184] The driving school license plan selection system can estimate the user's emotions and adjust the plan suggestion method based on the estimated emotions. For example, if the user is feeling stressed, it can suggest a simple and intuitive plan without detailed explanations. If the user is relaxed, it can provide detailed plan information and options, allowing the user to carefully consider the options. Furthermore, if the user is in a hurry, it can prioritize the most important information and allow the user to quickly select a plan. This makes it possible to suggest the optimal plan according to the user's emotions.

[0185] The driving school plan selection system can analyze a user's social media activity and suggest relevant plans. For example, it can analyze the locations where the user has checked in and the content of their posts on social media to suggest plans related to that area. It can also prioritize plans that the user's friends have participated in or highly rated. It can also suggest related plans based on the user's social media interests. This allows the user to find more appropriate plans based on their social media activity.

[0186] The driving school camp plan selection system can improve the plan suggestion method by reflecting the user's past feedback. For example, it can suggest the optimal plan suggestion method based on the user's past feedback. It can also prioritize specific suggestion methods based on the user's feedback. It can also analyze the user's feedback and improve the suggestion method. This makes it possible to provide the optimal plan suggestion method by reflecting the user's past feedback.

[0187] The driving school camp plan selection system can estimate the user's emotions and prioritize plans based on the estimated emotions. For example, if the user is nervous, important plans can be displayed with priority. Also, if the user is relaxed, detailed plans can be displayed with priority. Furthermore, if the user is in a hurry, the most important plans can be displayed with priority. In this way, by prioritizing plans based on the user's emotions, important plans can be displayed with priority.

[0188] The driving school license plan selection system can adjust the plan explanation according to the user's level of expertise. For example, if the user has expertise, it can provide a detailed technical explanation. If the user does not have expertise, it can provide a concise and easy-to-understand explanation. Furthermore, it can dynamically adjust the plan explanation according to the user's level of expertise. This makes it possible to provide an easy-to-understand plan explanation according to the user's level of expertise.

[0189] The driving school camp plan selection system can estimate the user's emotions and adjust the display method of the plan based on the estimated emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows the display method to be adjusted based on the user's emotions, making it possible to provide the most optimal display for the user.

[0190] The driving school license plan selection system can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. Also, if the user is using a tablet, it can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, it can provide a simple and highly visible display method. This allows it to provide the optimal display method by taking into account the user's device information.

[0191] The driving school camp plan selection system can estimate the user's emotions and adjust the reservation procedure based on the estimated emotions. For example, if the user is nervous, a simple reservation procedure can be provided. If the user is relaxed, a detailed reservation procedure can be provided. Furthermore, if the user is in a hurry, a quick reservation procedure can be provided. In this way, by adjusting the reservation procedure based on the user's emotions, the system can provide the optimal reservation procedure for the user.

[0192] The processing flow of the second embodiment will be briefly explained below.

[0193] Step 1: The reception unit inputs the user's conditions. The user's conditions include, for example, the date, location, budget, type of license desired, length of training camp, etc. The user inputs these conditions into the reception unit. Step 2: The analysis unit analyzes the conditions entered by the reception unit. The analysis is performed by methods such as prioritizing the conditions and checking the consistency of the conditions. For example, the analysis unit prioritizes the conditions and checks the consistency of the conditions. Step 3: The search unit searches for programs based on the conditions analyzed by the analysis unit. The search is performed using a search algorithm, a database of search targets, or other methods. For example, the search unit uses a search algorithm to extract the plan that best suits the user's conditions from multiple programs registered in the database. Step 4: The display unit displays the programs found by the search unit. The display is performed in a display format, a display order, etc. For example, the display unit may display the program content, price, accommodation information, past user reviews, etc. Step 5: The reservation unit reserves the program displayed by the display unit. The reservation is made by a reservation procedure, a reservation confirmation method, etc. For example, the reservation unit reserves the plan selected by the user and confirms the reservation.

[0194] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0195] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0196] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0197] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0198] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0199] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0200] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0201] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0202] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0203] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0204] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0205] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0206] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0207] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0208] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0209] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0210] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0211] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0212] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0213] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0214] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0215] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0216] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0217] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0218] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0219] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0220] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0221] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0222] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0223] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0224] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0225] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0226] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0227] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0228] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0229] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0230] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0231] 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.

[0232] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0233] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0234] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0235] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0236] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0237] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0238] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0239] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0240] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0241] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0242] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0243] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0244] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0245] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0246] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0247] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0248] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0249] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0250] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0251] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0252] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0253] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0254] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0255] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0256] 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.

[0257] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0258] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0259] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0260] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0261] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0262] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0263] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0264] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0265] [Explanation of symbols]

[0266] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting user conditions; an analysis unit that analyzes the conditions input by the reception unit; a search unit that searches for a program based on the conditions analyzed by the analysis unit; a display unit that displays the programs searched by the search unit; a reservation unit that reserves the program displayed by the display unit; A system characterized by:

2. A detailed analysis unit is provided to analyze the user's conditions in detail.

2. The system of claim 1.

3. Equipped with a rating collection unit that collects ratings from past users 2. The system of claim 1.

4. Equipped with an information section that provides detailed program information 2. The system of claim 1.

5. Equipped with a comparison section that allows users to compare and consider 2. The system of claim 1.

6. Equipped with a maintenance department that maintains the database 2. The system of claim 1.

7. The reception unit Estimating a user's emotion and adjusting a condition input interface based on the estimated user's emotion 2. The system of claim 1.

8. The reception unit Analyzes the user's past condition input history and suggests the optimal input method 2. The system of claim 1.

9. The reception unit Customize input fields based on the user's current situation and interests when entering conditions 2. The system of claim 1.

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