System

Abstract

An object of a system according to an embodiment is to propose a practice that meets a user's desire.SOLUTION: A system includes a reception unit, a collection unit, an analysis unit, a proposal unit, and a feedback unit. The reception unit inputs a condition of a habit desired by a user. The collection unit collects data on the basis of the condition input by the reception unit. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes a habit suitable for the user on the basis of the analysis result obtained by the analysis unit. The feedback unit collects the evaluation of the user for the practice proposed by the proposal unit and uses the evaluation to improve the accuracy of the system.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to find lessons that meet users' needs and not being able to make appropriate suggestions.

[0005] The system according to the embodiment aims to suggest lessons that meet the user's wishes. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, an analysis unit, a suggestion unit, and a feedback unit. The reception unit inputs the conditions for lessons desired by the user. The collection unit collects data based on the conditions input by the reception unit. The analysis unit analyzes the data collected by the collection unit. The suggestion unit suggests lessons suitable for the user based on the analysis results obtained by the analysis unit. The feedback unit collects user evaluations of the lessons suggested by the suggestion unit and uses them to improve the accuracy of the system. [Effects of the Invention]

[0007] The system according to the embodiment can suggest lessons that meet the user's wishes. [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 lesson suggestion system according to an embodiment of the present invention is a system that suggests lessons that meet a user's requirements. In this system, a user inputs the requirements for a lesson they want, and a generation AI analyzes the requirements and suggests the most suitable lesson for the user. For example, if a user inputs requirements such as "a lesson that can be attended once a week on a weekday evening" or "a sports class for children," the generation AI suggests the most suitable lesson based on local lesson information and past user reviews. These suggestions are customized based on the user's preferences, allowing users to find lessons that meet their individual needs. This allows users to easily find lessons that meet their preferences. This allows the lesson suggestion system to efficiently suggest lessons that meet the user's preferences. For example, a busy businessman can find lessons that he can attend after work, or a suitable sports class for his child. Furthermore, because the generation AI takes past user reviews into account, it is possible to suggest high-quality lessons.

[0029] The extracurricular activity suggestion system according to the embodiment includes a reception unit, a collection unit, an analysis unit, a suggestion unit, and a feedback unit. The reception unit inputs the user's desired extracurricular activity conditions. The user's desired extracurricular activity conditions include, but are not limited to, location, time, cost, and content. The reception unit allows the user to input conditions such as "an extracurricular activity that can be attended once a week on a weekday evening" or "a sports class for children." The collection unit collects data based on the conditions input by the reception unit. The collected data includes, but is not limited to, local extracurricular activity information, online databases, and questionnaire surveys. The collection unit can collect data that meets the user's requirements, for example, by collecting local extracurricular activity information. The analysis unit analyzes the data collected by the collection unit. Specific analysis methods include, but are not limited to, statistical analysis and machine learning algorithms. The analysis unit can analyze the collected data to find extracurricular activities that meet the user's requirements. The suggestion unit suggests lessons suitable for the user based on the analysis results obtained by the analysis unit. Specific criteria for suggestion include, but are not limited to, a matching algorithm based on the user's conditions. The suggestion unit can suggest, for example, lessons that are optimal for the user based on the analysis results. The feedback unit collects user evaluations of the lessons suggested by the suggestion unit and uses them to improve the accuracy of the system. User evaluations include, but are not limited to, satisfaction surveys, feedback comments, and the like. The feedback unit can collect user evaluations and use them to improve the accuracy of the system. As a result, the lesson suggestion system according to the embodiment can efficiently suggest lessons that meet the user's wishes and improve the accuracy of the system.

[0030] The collection unit can collect information about local extracurricular activities. Local extracurricular activity information includes, but is not limited to, local classes, courses, and events. For example, the collection unit collects local extracurricular activity information from an online database. The collection unit can also collect local extracurricular activity information through a questionnaire survey. For example, the collection unit collects information about local classes and courses, and collects data that meets the user's requirements. The collection unit can also collect local event information and collect data for suggesting the most suitable extracurricular activity to the user. In this way, by collecting local extracurricular activity information, the most suitable extracurricular activity can be suggested to the user.

[0031] The analysis unit can analyze the collected data and find lessons that meet the user's requirements. Lessons that meet the user's requirements include, but are not limited to, age, interests, and skill level. The analysis unit can analyze the collected data using, for example, statistical analysis. The analysis unit can also analyze the data using a machine learning algorithm. For example, the analysis unit can find the most suitable lesson based on the user's age and interests. The analysis unit can also suggest lessons based on the user's skill level. This allows the analysis unit to find lessons that meet the user's requirements and make the most suitable suggestions to the user.

[0032] The suggestion unit can suggest lessons suitable for the user based on the analysis results. Examples of suggestions based on the analysis results include, but are not limited to, a scoring system, a ranking algorithm, and the like. For example, the suggestion unit can suggest the most suitable lessons for the user using a scoring system. The suggestion unit can also suggest lessons using a ranking algorithm. For example, the suggestion unit can suggest the most suitable lessons for the user in a ranking format based on the analysis results. The suggestion unit can also make customized suggestions based on the user's conditions. This allows the suggestion unit to suggest the most suitable lessons for the user based on the analysis results, thereby making suggestions that meet the user's needs.

[0033] The feedback unit can be used to collect user evaluations and improve the accuracy of the system. Methods for improving the accuracy of the system include, but are not limited to, feedback loops and algorithm improvements. For example, the feedback unit conducts a user satisfaction survey and collects evaluations. The feedback unit can also collect user feedback comments and use them to improve the system. For example, the feedback unit improves the algorithm based on the user evaluations and improves the accuracy of suggestions. The feedback unit can also reflect the user evaluations in the system as a feedback loop. In this way, collecting user evaluations and improving the accuracy of the system can result in better suggestions.

[0034] The reception unit can analyze the user's past input history and suggest an appropriate input method. For example, the reception unit automatically displays as candidates the conditions for lessons that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests the conditions for lessons to be used during a specific time period from the user's past input history. In this way, by analyzing the user's past input history, the optimal input method can be suggested, improving input efficiency.

[0035] The reception unit can customize input items based on the user's current living situation and areas of interest. For example, if the user is a busy businessman, the reception unit can prioritize displaying conditions for extracurricular activities that the user can attend after work. Furthermore, if the user is a parent, the reception unit can also prioritize displaying conditions for extracurricular activities for children. For example, if the user has a specific hobby, the reception unit can prioritize displaying conditions for extracurricular activities related to that hobby. In this way, customizing input items based on the user's living situation and areas of interest provides the user with an optimal input experience.

[0036] The reception unit can select an appropriate input means depending on the user's input method (voice, text, image, etc.). For example, the reception unit can automatically set conditions when the user simply inputs by voice, "Lessons that can be taken once a week on a weekday evening." In addition, when the user inputs detailed conditions in text, the reception unit can provide an input assistance function to enable efficient condition setting. For example, when the user sets conditions by uploading an image, the reception unit can automatically extract related conditions using image analysis technology. This improves input efficiency by selecting the optimal input means depending on the user's input method.

[0037] When collecting local extracurricular activity information, the collection unit can adjust the collection range taking into account the user's past preferences. For example, the collection unit prioritizes collecting extracurricular activity information for areas in which the user has shown interest in the past. The collection unit can also adjust the collection range based on area information for extracurricular activities the user has participated in in the past. For example, the collection unit analyzes the user's past preferences and collects extracurricular activity information for the most suitable area. In this way, by adjusting the collection range taking into account the user's past preferences, more relevant information can be collected.

[0038] When collecting data, the collection unit can perform filtering based on the user's current lifestyle and areas of interest. For example, if the user is a busy businessman, the collection unit can prioritize collecting information about extracurricular activities that can be attended after work. Furthermore, if the user is a parent, the collection unit can also prioritize collecting information about extracurricular activities for children. For example, if the user has a specific hobby, the collection unit can prioritize collecting information about extracurricular activities related to that hobby. In this way, by filtering based on the user's lifestyle and areas of interest, more relevant information can be collected.

[0039] When collecting data, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user inputs conditions by voice, the collection unit analyzes the voice data to collect information on related lessons. Also, if the user inputs detailed conditions in text, the collection unit can analyze the text data to collect information on related lessons. For example, if the user sets conditions by uploading an image, the collection unit collects information on related lessons using image analysis technology. This improves the efficiency of data collection by selecting the optimal collection means depending on the user's input method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on general data. For example, the analysis unit performs a detailed analysis on data that is of great interest to the user. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a sports-specific analysis algorithm to sports-related data. The analysis unit can also apply an art-specific analysis algorithm to art-related data. For example, the analysis unit applies a music-specific analysis algorithm to music-related data. In this way, by applying different analysis algorithms depending on the category of data, the accuracy of the analysis is improved.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit adjusts the analysis algorithm, for example, based on feedback provided by the user in the past. The analysis unit can also analyze the user's past analysis results to improve the accuracy of the analysis. For example, the analysis unit can suggest an optimal analysis method by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0043] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the extracurricular activity when making the suggestion. For example, the suggestion unit makes a detailed suggestion for an important extracurricular activity. The suggestion unit can also make a simplified suggestion for a general extracurricular activity. For example, the suggestion unit makes a detailed suggestion for an extracurricular activity in which the user is highly interested. In this way, by adjusting the level of detail of the suggestion based on the importance of the extracurricular activity, the most suitable suggestion for the user can be made.

[0044] When making suggestions, the suggestion unit can apply different suggestion algorithms depending on the category of the lesson. For example, the suggestion unit can apply a sports-specific suggestion algorithm to a sports-related lesson. The suggestion unit can also apply an art-specific suggestion algorithm to an art-related lesson. For example, the suggestion unit can apply a music-specific suggestion algorithm to a music-related lesson. In this way, by applying different suggestion algorithms depending on the category of the lesson, the accuracy of suggestions can be improved.

[0045] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, adjusts the proposal algorithm based on feedback provided by the user in the past. The suggestion unit can also analyze the user's past proposal results to improve the accuracy of the proposal. For example, the suggestion unit suggests an optimal proposal method by referring to the user's past proposal results. In this way, the accuracy of the proposal is improved by referring to the user's past proposal results.

[0046] When making a suggestion, the suggestion unit can determine the priority of the suggestion based on the time when the lesson is offered. For example, the suggestion unit gives priority to suggestions for lessons that will be held soon. The suggestion unit can also postpone suggestions for lessons that will be held in the distant future. For example, the suggestion unit determines the priority of the suggestion based on the time when the lesson is offered, so that suggestions can be made at the optimal time for the user.

[0047] The suggestion unit can adjust the order of suggestions based on the relevance of the lessons when making suggestions. For example, the suggestion unit prioritizes suggestions for highly relevant lessons. The suggestion unit can also postpone suggestions for less relevant lessons. For example, the suggestion unit adjusts the order of suggestions based on the relevance specified by the user. In this way, by adjusting the order of suggestions based on the relevance of the lessons, optimal suggestions can be made for the user.

[0048] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can provide a suggestion that uses a lot of technical terminology. Also, if the user does not have technical expertise, the suggestion unit can provide a suggestion that is explained in simple language. For example, the suggestion unit can adjust the way the suggestion is expressed according to the user's level of expertise. This allows the suggestion to be more easily understood by adjusting the use of technical terminology in the suggestion according to the user's level of expertise.

[0049] When collecting feedback, the feedback unit can select an appropriate collection method by referring to the user's past rating history. For example, the feedback unit can suggest an optimal collection method based on feedback provided by the user in the past. The feedback unit can also preferentially suggest a specific collection method (audio, text, etc.) based on the user's past rating history. For example, the feedback unit analyzes the user's past rating history and customizes the collection method. In this way, the optimal feedback collection method can be selected by referring to the user's past rating history, improving collection efficiency.

[0050] When collecting feedback, the feedback unit can filter the feedback based on the user's current living situation and areas of interest. For example, if the user is a busy businessman, the feedback unit can collect simplified feedback. Also, if the user is a parent, the feedback unit can preferentially collect feedback for children. For example, if the user has a specific hobby, the feedback unit preferentially collects feedback related to that hobby. In this way, by filtering based on the user's living situation and areas of interest, more relevant feedback can be collected.

[0051] When collecting feedback, the feedback unit can select an optimal collection means depending on the user's input method. For example, if the user provides feedback by voice, the feedback unit analyzes the voice data to collect the feedback. Also, if the user provides feedback by text, the feedback unit can analyze the text data to collect the feedback. For example, if the user provides feedback by uploading an image, the feedback unit collects the feedback using image analysis technology. In this way, by selecting an optimal collection means depending on the user's input method, the efficiency of feedback collection is improved.

[0052] When collecting feedback, the feedback unit can prioritize collecting highly relevant feedback by taking into account the user's geographical location information. For example, if the user lives in a specific area, the feedback unit can prioritize collecting feedback in that area. Also, if the user is traveling, the feedback unit can prioritize collecting feedback in the vicinity of the user's current location. For example, if the user is interested in a specific area, the feedback unit can prioritize collecting feedback related to that area. In this way, highly relevant feedback can be prioritized by taking into account the user's geographical location information.

[0053] When collecting feedback, the feedback unit can analyze the user's social media activity and collect relevant feedback. For example, the feedback unit prioritizes collecting feedback related to lessons in which the user has shown interest on social media. The feedback unit can also analyze the content of the user's social media posts and collect relevant feedback. For example, the feedback unit collects relevant feedback by referring to the activities of the user's friends on social media. This allows for efficient collection of relevant feedback by analyzing the user's social media activity.

[0054] The feedback unit can customize the collection method by reflecting the user's past feedback when collecting feedback. For example, the feedback unit can suggest an optimal collection method based on feedback provided by the user in the past. The feedback unit can also preferentially suggest a specific collection method (audio, text, etc.) based on the user's past feedback. For example, the feedback unit analyzes the user's past feedback and customizes the collection method. In this way, the optimal collection method is suggested by reflecting the user's past feedback, improving the efficiency of feedback collection.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The collection unit can collect optimal lesson information based on the user's current location information. For example, if the user is in a specific area, it will prioritize collecting lesson information held in that area. Also, if the user is traveling, it can collect lesson information around the user's current location. Furthermore, if the user is interested in a specific area, it can collect lesson information related to that area. This allows for more relevant suggestions by collecting optimal lesson information based on the user's location information.

[0057] The suggestion unit can select the optimal suggestion method based on the user's past suggestion history. For example, it can prioritize the use of suggestion formats that the user has previously preferred. It can also avoid suggestion formats that the user has previously rejected. Furthermore, it can analyze the user's past suggestion history and determine the optimal suggestion timing. In this way, by selecting the optimal suggestion method based on the user's past suggestion history, it is possible to improve the likelihood of suggestion acceptance.

[0058] The collection unit can analyze the user's social media activity and collect related extracurricular activity information. For example, it can prioritize collection of information about extracurricular activities that the user has shown interest in on social media. It can also analyze the content posted by the user on social media to collect related extracurricular activity information. Furthermore, it can collect related extracurricular activity information by referring to the activities of the user's friends on social media. In this way, it is possible to efficiently collect related extracurricular activity information by analyzing the user's social media activity.

[0059] The analysis unit can customize the analysis algorithm based on the user's current lifestyle and areas of interest. For example, if the user is a busy businessman, an algorithm that can provide results in a short time can be used. Alternatively, if the user is a parent, an algorithm specialized in extracurricular activities for children can be used. Furthermore, if the user has a particular hobby, an algorithm specialized in extracurricular activities related to that hobby can be used. In this way, by customizing the analysis algorithm based on the user's lifestyle and areas of interest, more accurate analysis results can be provided.

[0060] The feedback unit can select the optimal feedback collection method based on the user's past feedback history. For example, it can prioritize collection methods that the user has previously preferred. It can also avoid collection methods that the user has previously rejected. Furthermore, it can analyze the user's past feedback history and determine the optimal collection timing. In this way, the quality of feedback can be improved by selecting the optimal collection method based on the user's past feedback history.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit inputs the user's desired lesson conditions. The user's desired lesson conditions include, for example, location, time, cost, and content. The user can input conditions such as "a lesson that can be attended once a week on a weekday evening" or "a sports class for children." Step 2: The collection unit collects data based on the conditions entered by the reception unit. The collected data includes, for example, local extracurricular activity information, online databases, and questionnaire surveys. The collection unit collects local extracurricular activity information and can collect data that meets the user's conditions. Step 3: The analysis unit analyzes the data collected by the collection unit. Specific analysis methods include, for example, statistical analysis and machine learning algorithms. The analysis unit analyzes the collected data and can find lessons that meet the user's requirements. Step 4: The suggestion unit suggests lessons suitable for the user based on the analysis results obtained by the analysis unit. Specific criteria for suggestions include, for example, a matching algorithm based on the user's conditions. The suggestion unit can suggest lessons that are optimal for the user based on the analysis results. Step 5: The feedback unit collects user evaluations of the lessons suggested by the suggestion unit and uses them to improve the accuracy of the system. User evaluations include, for example, satisfaction surveys, feedback comments, etc. The feedback unit collects user evaluations and can use them to improve the accuracy of the system.

[0063] (Example 2) A lesson suggestion system according to an embodiment of the present invention is a system that suggests lessons that meet a user's requirements. In this system, a user inputs the requirements for a lesson they want, and a generation AI analyzes the requirements and suggests the most suitable lesson for the user. For example, if a user inputs requirements such as "a lesson that can be attended once a week on a weekday evening" or "a sports class for children," the generation AI suggests the most suitable lesson based on local lesson information and past user reviews. These suggestions are customized based on the user's preferences, allowing users to find lessons that meet their individual needs. This allows users to easily find lessons that meet their preferences. This allows the lesson suggestion system to efficiently suggest lessons that meet the user's preferences. For example, a busy businessman can find lessons that he can attend after work, or a suitable sports class for his child. Furthermore, because the generation AI takes past user reviews into account, it is possible to suggest high-quality lessons.

[0064] The extracurricular activity suggestion system according to the embodiment includes a reception unit, a collection unit, an analysis unit, a suggestion unit, and a feedback unit. The reception unit inputs the user's desired extracurricular activity conditions. The user's desired extracurricular activity conditions include, but are not limited to, location, time, cost, and content. The reception unit allows the user to input conditions such as "an extracurricular activity that can be attended once a week on a weekday evening" or "a sports class for children." The collection unit collects data based on the conditions input by the reception unit. The collected data includes, but is not limited to, local extracurricular activity information, online databases, and questionnaire surveys. The collection unit can collect data that meets the user's requirements, for example, by collecting local extracurricular activity information. The analysis unit analyzes the data collected by the collection unit. Specific analysis methods include, but are not limited to, statistical analysis and machine learning algorithms. The analysis unit can analyze the collected data to find extracurricular activities that meet the user's requirements. The suggestion unit suggests lessons suitable for the user based on the analysis results obtained by the analysis unit. Specific criteria for suggestion include, but are not limited to, a matching algorithm based on the user's conditions. The suggestion unit can suggest, for example, lessons that are optimal for the user based on the analysis results. The feedback unit collects user evaluations of the lessons suggested by the suggestion unit and uses them to improve the accuracy of the system. User evaluations include, but are not limited to, satisfaction surveys, feedback comments, and the like. The feedback unit can collect user evaluations and use them to improve the accuracy of the system. As a result, the lesson suggestion system according to the embodiment can efficiently suggest lessons that meet the user's wishes and improve the accuracy of the system.

[0065] The collection unit can collect information about local extracurricular activities. Local extracurricular activity information includes, but is not limited to, local classes, courses, and events. For example, the collection unit collects local extracurricular activity information from an online database. The collection unit can also collect local extracurricular activity information through a questionnaire survey. For example, the collection unit collects information about local classes and courses, and collects data that meets the user's requirements. The collection unit can also collect local event information and collect data for suggesting the most suitable extracurricular activity to the user. In this way, by collecting local extracurricular activity information, the most suitable extracurricular activity can be suggested to the user.

[0066] The analysis unit can analyze the collected data and find lessons that meet the user's requirements. Lessons that meet the user's requirements include, but are not limited to, age, interests, and skill level. The analysis unit can analyze the collected data using, for example, statistical analysis. The analysis unit can also analyze the data using a machine learning algorithm. For example, the analysis unit can find the most suitable lesson based on the user's age and interests. The analysis unit can also suggest lessons based on the user's skill level. This allows the analysis unit to find lessons that meet the user's requirements and make the most suitable suggestions to the user.

[0067] The suggestion unit can suggest lessons suitable for the user based on the analysis results. Examples of suggestions based on the analysis results include, but are not limited to, a scoring system, a ranking algorithm, and the like. For example, the suggestion unit can suggest the most suitable lessons for the user using a scoring system. The suggestion unit can also suggest lessons using a ranking algorithm. For example, the suggestion unit can suggest the most suitable lessons for the user in a ranking format based on the analysis results. The suggestion unit can also make customized suggestions based on the user's conditions. This allows the suggestion unit to suggest the most suitable lessons for the user based on the analysis results, thereby making suggestions that meet the user's needs.

[0068] The feedback unit can be used to collect user evaluations and improve the accuracy of the system. Methods for improving the accuracy of the system include, but are not limited to, feedback loops and algorithm improvements. For example, the feedback unit conducts a user satisfaction survey and collects evaluations. The feedback unit can also collect user feedback comments and use them to improve the system. For example, the feedback unit improves the algorithm based on the user evaluations and improves the accuracy of suggestions. The feedback unit can also reflect the user evaluations in the system as a feedback loop. In this way, collecting user evaluations and improving the accuracy of the system can result in better suggestions.

[0069] The reception unit can estimate the user's emotions and adjust the display method of the input interface based on the estimated user emotions. 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 customizable input methods. For example, if the user is in a hurry, the reception unit can prioritize voice input, allowing the user to quickly enter the desired lesson conditions. This adjusts the input interface according to the user's emotions, reducing the user's stress and improving input efficiency. Emotion estimation is achieved 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.

[0070] The reception unit can analyze the user's past input history and suggest an appropriate input method. For example, the reception unit automatically displays as candidates the conditions for lessons that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests the conditions for lessons to be used during a specific time period from the user's past input history. In this way, by analyzing the user's past input history, the optimal input method can be suggested, improving input efficiency.

[0071] The reception unit can customize input items based on the user's current living situation and areas of interest. For example, if the user is a busy businessman, the reception unit can prioritize displaying conditions for extracurricular activities that the user can attend after work. Furthermore, if the user is a parent, the reception unit can also prioritize displaying conditions for extracurricular activities for children. For example, if the user has a specific hobby, the reception unit can prioritize displaying conditions for extracurricular activities related to that hobby. In this way, customizing input items based on the user's living situation and areas of interest provides the user with an optimal input experience.

[0072] The reception unit can select an appropriate input means depending on the user's input method (voice, text, image, etc.). For example, the reception unit can automatically set conditions when the user simply inputs by voice, "Lessons that can be taken once a week on a weekday evening." In addition, when the user inputs detailed conditions in text, the reception unit can provide an input assistance function to enable efficient condition setting. For example, when the user sets conditions by uploading an image, the reception unit can automatically extract related conditions using image analysis technology. This improves input efficiency by selecting the optimal input means depending on the user's input method.

[0073] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is relaxed, the collection unit immediately collects data. The collection unit can also postpone data collection if the user is feeling stressed. For example, if the user is in a hurry, the collection unit quickly collects data. This improves the efficiency of data collection by adjusting the timing of data collection according to 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0074] When collecting local extracurricular activity information, the collection unit can adjust the collection range taking into account the user's past preferences. For example, the collection unit prioritizes collecting extracurricular activity information for areas in which the user has shown interest in the past. The collection unit can also adjust the collection range based on area information for extracurricular activities the user has participated in in the past. For example, the collection unit analyzes the user's past preferences and collects extracurricular activity information for the most suitable area. In this way, by adjusting the collection range taking into account the user's past preferences, more relevant information can be collected.

[0075] When collecting data, the collection unit can perform filtering based on the user's current lifestyle and areas of interest. For example, if the user is a busy businessman, the collection unit can prioritize collecting information about extracurricular activities that can be attended after work. Furthermore, if the user is a parent, the collection unit can also prioritize collecting information about extracurricular activities for children. For example, if the user has a specific hobby, the collection unit can prioritize collecting information about extracurricular activities related to that hobby. In this way, by filtering based on the user's lifestyle and areas of interest, more relevant information can be collected.

[0076] When collecting data, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user inputs conditions by voice, the collection unit analyzes the voice data to collect information on related lessons. Also, if the user inputs detailed conditions in text, the collection unit can analyze the text data to collect information on related lessons. For example, if the user sets conditions by uploading an image, the collection unit collects information on related lessons using image analysis technology. This improves the efficiency of data collection by selecting the optimal collection means depending on the user's input method.

[0077] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, if the user is in a hurry, the analysis unit provides analysis results that focus on the main points. This allows the analysis results to be better understood by adjusting the way the analysis is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on general data. For example, the analysis unit performs a detailed analysis on data that is of great interest to the user. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0079] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a sports-specific analysis algorithm to sports-related data. The analysis unit can also apply an art-specific analysis algorithm to art-related data. For example, the analysis unit applies a music-specific analysis algorithm to music-related data. In this way, by applying different analysis algorithms depending on the category of data, the accuracy of the analysis is improved.

[0080] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit adjusts the analysis algorithm, for example, based on feedback provided by the user in the past. The analysis unit can also analyze the user's past analysis results to improve the accuracy of the analysis. For example, the analysis unit can suggest an optimal analysis method by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0081] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the suggestion unit provides simple, highly visible suggestions. The suggestion unit can also provide detailed suggestions if the user is relaxed. For example, if the user is in a hurry, the suggestion unit provides suggestions that focus on the main points. This adjusts the way suggestions are expressed according to the user's emotions, thereby improving the likelihood of the suggestions being accepted. Emotion estimation is achieved 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.

[0082] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the extracurricular activity when making the suggestion. For example, the suggestion unit makes a detailed suggestion for an important extracurricular activity. The suggestion unit can also make a simplified suggestion for a general extracurricular activity. For example, the suggestion unit makes a detailed suggestion for an extracurricular activity in which the user is highly interested. In this way, by adjusting the level of detail of the suggestion based on the importance of the extracurricular activity, the most suitable suggestion for the user can be made.

[0083] When making suggestions, the suggestion unit can apply different suggestion algorithms depending on the category of the lesson. For example, the suggestion unit can apply a sports-specific suggestion algorithm to a sports-related lesson. The suggestion unit can also apply an art-specific suggestion algorithm to an art-related lesson. For example, the suggestion unit can apply a music-specific suggestion algorithm to a music-related lesson. In this way, by applying different suggestion algorithms depending on the category of the lesson, the accuracy of suggestions can be improved.

[0084] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, adjusts the proposal algorithm based on feedback provided by the user in the past. The suggestion unit can also analyze the user's past proposal results to improve the accuracy of the proposal. For example, the suggestion unit suggests an optimal proposal method by referring to the user's past proposal results. In this way, the accuracy of the proposal is improved by referring to the user's past proposal results.

[0085] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated user's emotions. For example, if the user is in a hurry, the suggestion unit can provide a short, to-the-point suggestion. Alternatively, if the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. For example, if the user is excited, the suggestion unit can provide a suggestion with a visually stimulating effect. This adjusts the length of the suggestion according to the user's emotions, thereby improving the acceptability of the suggestion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] When making a suggestion, the suggestion unit can determine the priority of the suggestion based on the time when the lesson is offered. For example, the suggestion unit gives priority to suggestions for lessons that will be held soon. The suggestion unit can also postpone suggestions for lessons that will be held in the distant future. For example, the suggestion unit determines the priority of the suggestion based on the time when the lesson is offered, so that suggestions can be made at the optimal time for the user.

[0087] The suggestion unit can adjust the order of suggestions based on the relevance of the lessons when making suggestions. For example, the suggestion unit prioritizes suggestions for highly relevant lessons. The suggestion unit can also postpone suggestions for less relevant lessons. For example, the suggestion unit adjusts the order of suggestions based on the relevance specified by the user. In this way, by adjusting the order of suggestions based on the relevance of the lessons, optimal suggestions can be made for the user.

[0088] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can provide a suggestion that uses a lot of technical terminology. Also, if the user does not have technical expertise, the suggestion unit can provide a suggestion that is explained in simple language. For example, the suggestion unit can adjust the way the suggestion is expressed according to the user's level of expertise. This allows the suggestion to be more easily understood by adjusting the use of technical terminology in the suggestion according to the user's level of expertise.

[0089] The feedback unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user's emotions. For example, the feedback unit collects detailed feedback when the user is relaxed. The feedback unit can also collect simplified feedback when the user is stressed. For example, the feedback unit collects quick feedback when the user is in a hurry. This improves the quality of the feedback by adjusting the feedback collection method according to 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0090] When collecting feedback, the feedback unit can select an appropriate collection method by referring to the user's past rating history. For example, the feedback unit can suggest an optimal collection method based on feedback provided by the user in the past. The feedback unit can also preferentially suggest a specific collection method (audio, text, etc.) based on the user's past rating history. For example, the feedback unit analyzes the user's past rating history and customizes the collection method. In this way, the optimal feedback collection method can be selected by referring to the user's past rating history, improving collection efficiency.

[0091] When collecting feedback, the feedback unit can filter the feedback based on the user's current living situation and areas of interest. For example, if the user is a busy businessman, the feedback unit can collect simplified feedback. Also, if the user is a parent, the feedback unit can preferentially collect feedback for children. For example, if the user has a specific hobby, the feedback unit preferentially collects feedback related to that hobby. In this way, by filtering based on the user's living situation and areas of interest, more relevant feedback can be collected.

[0092] When collecting feedback, the feedback unit can select an optimal collection means depending on the user's input method. For example, if the user provides feedback by voice, the feedback unit analyzes the voice data to collect the feedback. Also, if the user provides feedback by text, the feedback unit can analyze the text data to collect the feedback. For example, if the user provides feedback by uploading an image, the feedback unit collects the feedback using image analysis technology. In this way, by selecting an optimal collection means depending on the user's input method, the efficiency of feedback collection is improved.

[0093] The feedback unit can estimate the user's emotions and determine the priority of the feedback to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the feedback unit prioritizes collecting important feedback. The feedback unit can also collect detailed feedback when the user is relaxed. For example, when the user is in a hurry, the feedback unit prioritizes collecting the most important feedback. In this way, by determining the priority of feedback according to the user's emotions, important feedback can be collected preferentially. The emotion estimation is realized 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.

[0094] When collecting feedback, the feedback unit can prioritize collecting highly relevant feedback by taking into account the user's geographical location information. For example, if the user lives in a specific area, the feedback unit can prioritize collecting feedback in that area. Also, if the user is traveling, the feedback unit can prioritize collecting feedback in the vicinity of the user's current location. For example, if the user is interested in a specific area, the feedback unit can prioritize collecting feedback related to that area. In this way, highly relevant feedback can be prioritized by taking into account the user's geographical location information.

[0095] When collecting feedback, the feedback unit can analyze the user's social media activity and collect relevant feedback. For example, the feedback unit prioritizes collecting feedback related to lessons in which the user has shown interest on social media. The feedback unit can also analyze the content of the user's social media posts and collect relevant feedback. For example, the feedback unit collects relevant feedback by referring to the activities of the user's friends on social media. This allows for efficient collection of relevant feedback by analyzing the user's social media activity.

[0096] The feedback unit can customize the collection method by reflecting the user's past feedback when collecting feedback. For example, the feedback unit can suggest an optimal collection method based on feedback provided by the user in the past. The feedback unit can also preferentially suggest a specific collection method (audio, text, etc.) based on the user's past feedback. For example, the feedback unit analyzes the user's past feedback and customizes the collection method. In this way, the optimal collection method is suggested by reflecting the user's past feedback, improving the efficiency of feedback collection. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, collection unit, analysis unit, suggestion unit, and feedback unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, and the user inputs the desired lesson conditions. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and collects data from local lesson information and online databases. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and suggests the most suitable lesson to the user based on the analysis results. The feedback unit is implemented, for example, by the control unit 46A of the smart device 14, and collects user evaluations and uses them to improve the accuracy of the system. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, collection unit, analysis unit, suggestion unit, and feedback unit, described above, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, and the user inputs the conditions for the lessons they wish to take. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and collects data from local lesson information and online databases. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and suggests the most suitable lesson to the user based on the analysis results. The feedback unit is implemented, for example, by the control unit 46A of the smart glasses 214, and collects user evaluations and uses them to improve the accuracy of the system. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, collection unit, analysis unit, suggestion unit, and feedback unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is implemented by the microphone 238 of the headset-type terminal 314, and the user inputs the desired lesson conditions. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and collects data from local lesson information and online databases. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and suggests the most suitable lesson to the user based on the analysis results. The feedback unit is implemented, for example, by the control unit 46A of the headset-type terminal 314, and collects user evaluations and uses them to improve the accuracy of the system. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, collection unit, analysis unit, suggestion unit, and feedback unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, and the user inputs the desired lesson information. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and collects data from local lesson information and online databases. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and suggests the most suitable lesson to the user based on the analysis results. The feedback unit is implemented, for example, by the control unit 46A of the robot 414, and collects user evaluations and uses them to improve the accuracy of the system.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The suggestion unit can estimate the user's emotions and adjust the timing of the suggestions based on the estimated user emotions. For example, if the user is relaxed, the suggestion can be made immediately. If the user is stressed, the suggestion can be postponed. Furthermore, if the user is in a hurry, the suggestion can be made quickly. In this way, adjusting the timing of the suggestions according to the user's emotions improves the likelihood of the suggestions being accepted.

[0099] The collection unit can collect optimal lesson information based on the user's current location information. For example, if the user is in a specific area, it will prioritize collecting lesson information held in that area. Also, if the user is traveling, it can collect lesson information around the user's current location. Furthermore, if the user is interested in a specific area, it can collect lesson information related to that area. This allows for more relevant suggestions by collecting optimal lesson information based on the user's location information.

[0100] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated user emotions. For example, if the user is relaxed, a detailed analysis can be prioritized. If the user is stressed, a simplified analysis can be prioritized. Furthermore, if the user is in a hurry, the analysis can be performed quickly. In this way, adjusting the analysis priority according to the user's emotions improves the ease of acceptance of the analysis results.

[0101] The suggestion unit can select the optimal suggestion method based on the user's past suggestion history. For example, it can prioritize the use of suggestion formats that the user has previously preferred. It can also avoid suggestion formats that the user has previously rejected. Furthermore, it can analyze the user's past suggestion history and determine the optimal suggestion timing. In this way, by selecting the optimal suggestion method based on the user's past suggestion history, it is possible to improve the likelihood of suggestion acceptance.

[0102] The feedback unit can estimate the user's emotions and adjust the timing of collecting feedback based on the estimated user's emotions. For example, if the user is relaxed, detailed feedback can be collected. If the user is stressed, simplified feedback can be collected. Furthermore, if the user is in a hurry, feedback can be collected quickly. In this way, the quality of feedback can be improved by adjusting the timing of collecting feedback according to the user's emotions.

[0103] The collection unit can analyze the user's social media activity and collect related extracurricular activity information. For example, it can prioritize collection of information about extracurricular activities that the user has shown interest in on social media. It can also analyze the content posted by the user on social media to collect related extracurricular activity information. Furthermore, it can collect related extracurricular activity information by referring to the activities of the user's friends on social media. In this way, it is possible to efficiently collect related extracurricular activity information by analyzing the user's social media activity.

[0104] The analysis unit can customize the analysis algorithm based on the user's current lifestyle and areas of interest. For example, if the user is a busy businessman, an algorithm that can provide results in a short time can be used. Alternatively, if the user is a parent, an algorithm specialized in extracurricular activities for children can be used. Furthermore, if the user has a particular hobby, an algorithm specialized in extracurricular activities related to that hobby can be used. In this way, by customizing the analysis algorithm based on the user's lifestyle and areas of interest, more accurate analysis results can be provided.

[0105] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible suggestion can be provided. If the user is relaxed, a detailed suggestion can be provided. Furthermore, if the user is in a hurry, a suggestion that focuses on the main points can be provided. In this way, by adjusting the way suggestions are expressed according to the user's emotions, the likelihood of the suggestions being accepted can be improved.

[0106] The feedback unit can select the optimal feedback collection method based on the user's past feedback history. For example, it can prioritize collection methods that the user has previously preferred. It can also avoid collection methods that the user has previously rejected. Furthermore, it can analyze the user's past feedback history and determine the optimal collection timing. In this way, the quality of feedback can be improved by selecting the optimal collection method based on the user's past feedback history.

[0107] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, a short and to-the-point suggestion can be provided. If the user is relaxed, a longer suggestion with detailed explanations can be provided. Furthermore, if the user is excited, a suggestion with a visually stimulating effect can be provided. In this way, adjusting the length of the suggestion according to the user's emotions increases the likelihood of the suggestion being accepted.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The reception unit inputs the user's desired lesson conditions. The user's desired lesson conditions include, for example, location, time, cost, and content. The user can input conditions such as "a lesson that can be attended once a week on a weekday evening" or "a sports class for children." Step 2: The collection unit collects data based on the conditions entered by the reception unit. The collected data includes, for example, local extracurricular activity information, online databases, and questionnaire surveys. The collection unit collects local extracurricular activity information and can collect data that meets the user's conditions. Step 3: The analysis unit analyzes the data collected by the collection unit. Specific analysis methods include, for example, statistical analysis and machine learning algorithms. The analysis unit analyzes the collected data and can find lessons that meet the user's requirements. Step 4: The suggestion unit suggests lessons suitable for the user based on the analysis results obtained by the analysis unit. Specific criteria for suggestions include, for example, a matching algorithm based on the user's conditions. The suggestion unit can suggest lessons that are optimal for the user based on the analysis results. Step 5: The feedback unit collects user evaluations of the lessons suggested by the suggestion unit and uses them to improve the accuracy of the system. User evaluations include, for example, satisfaction surveys, feedback comments, etc. The feedback unit collects user evaluations and can use them to improve the accuracy of the system.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] [Explanation of symbols]

[0182] 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 conditions for lessons desired by the user; a collection unit that collects data based on the conditions input by the reception unit; an analysis unit that analyzes the data collected by the collection unit; a suggestion unit that suggests lessons suitable for the user based on the analysis results obtained by the analysis unit; a feedback unit that collects user evaluations of the lessons suggested by the suggestion unit and uses the evaluations to improve the accuracy of the system. A system characterized by:

2. The collecting unit Collect information about local extracurricular activities The system of claim 1 .

3. The analysis unit Analyze the collected data and find lessons that meet the user's requirements The system of claim 1 .

4. The proposal unit Suggesting suitable lessons to users based on analysis results The system of claim 1 .

5. The feedback unit Collect user ratings and use them to improve the accuracy of the system The system of claim 1 .

6. The reception unit The system estimates the user's emotions and adjusts the display method of the input interface based on the estimated user emotions. The system of claim 1 .

7. The reception unit Analyzes the user's input history and suggests the most appropriate input method The system of claim 1 .

8. The reception unit Customize input fields based on the user's current life situation and interests The system of claim 1 .

9. The reception unit Select the appropriate input method depending on the user's input method The system of claim 1 .

10. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions The system of claim 1 .

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