System

Abstract

The system according to the embodiment aims to discover and cultivate the personality and talents of a user.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects information of a user. The analysis unit analyzes the information collected by the collection unit and specifies a characteristic of the user. The suggestion unit suggests a growth plan based on the characteristics identified by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have not been sufficient in efficiently discovering and cultivating users' individuality and talents, and there is room for improvement.

[0005] The system according to the embodiment aims to discover and develop the individuality and talents of users. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects information about a user. The analysis unit analyzes the information collected by the collection unit and identifies characteristics of the user. The proposal unit proposes a development plan based on the characteristics identified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can discover and develop the individuality and talents of the user. [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) An application according to an embodiment of the present invention is a system for discovering and developing a user's undiscovered individuality and talents. In this system, a user registers for an app and inputs information such as a self-introduction, interests, and past experiences. AI then analyzes this information to identify the user's individuality and talents. Furthermore, a development plan appropriate for the user is proposed based on the identified individuality and talents. This development plan includes learning content, training programs, and practical opportunities. It is also possible to solicit sponsors for monetization. For example, a user registers for an app and inputs information such as a self-introduction, interests, and past experiences. The user then provides a detailed profile and data for the AI ​​to analyze. For example, the user inputs information such as hobbies, special skills, past work experience, and educational background. The AI ​​then analyzes the input information and identifies the user's individuality and talents. The AI ​​finds patterns based on the user's information and discovers hidden talents and characteristics that the user may not be aware of. For example, if a user has successfully completed many projects in the past, it can identify the user as having talent in leadership or project management. Furthermore, a development plan appropriate for the user is proposed based on the identified individuality and talents. This development plan includes learning content, training programs, and practical opportunities. For example, if a user's leadership talent is identified, they can be offered online courses and workshops to develop their leadership skills, as well as opportunities to demonstrate their leadership skills in actual projects. It is also possible to recruit sponsors and generate revenue. Sponsors can fund the user's development plan and support the user's growth process. This allows the user to maximize their individuality and talents and achieve self-realization. This allows the application to discover the user's individuality and talents and propose development plans to support the user's self-realization. For example, the user can discover their own value and purpose in life, and build a career and lifestyle that utilizes their strengths.

[0029] The development support system according to the embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects user information. The user information includes, but is not limited to, personal information, behavioral history, and interests. For example, the collection unit collects information by a user registering for an app and inputting information such as a self-introduction, interests, and past experiences. The collection unit can also track the user's behavioral history to identify the user's interests. For example, the collection unit can collect a history of websites visited by the user and apps used by the user. The collection unit can also analyze the user's social media activity to identify the user's interests. For example, the collection unit can identify the user's interests based on information shared by the user on social media. The analysis unit analyzes the information collected by the collection unit to identify the user's personality and talents. The analysis can be performed using, for example, data mining, statistical analysis, machine learning algorithms, or the like, but is not limited to, these examples. For example, the analysis unit can use data mining technology to find patterns in the user's information and identify the user's personality and talents. The analysis unit can also analyze the user's information and identify characteristics using statistical analysis. Furthermore, the analysis unit can use a machine learning algorithm to analyze user information and identify characteristics. For example, the analysis unit can use a machine learning algorithm to identify characteristics from the user's past behavioral history. The suggestion unit proposes a development plan based on the personality and talents identified by the analysis unit. The development plan can include, but is not limited to, a learning program, a training schedule, and goal setting. For example, the suggestion unit can propose an online course or workshop for developing leadership skills to a user for whom leadership talent has been identified. The suggestion unit can also provide opportunities for the user to demonstrate leadership in actual projects based on the user's characteristics. Furthermore, the suggestion unit can propose a learning program or a training schedule based on the user's characteristics. For example, the suggestion unit can set goals for developing leadership skills based on the user's characteristics.As a result, the development support system according to the embodiment can discover the individuality and talents of the user and propose a development plan, thereby supporting the user's self-realization.

[0030] The development support system includes a sponsor unit that solicits sponsors. The sponsor unit solicits sponsors. Methods of soliciting sponsors include, but are not limited to, advertising campaigns, crowdfunding, corporate partnerships, and the like. For example, the sponsor unit can solicit sponsors through advertising campaigns. The sponsor unit can also solicit sponsors using a crowdfunding platform. Furthermore, the sponsor unit can solicit sponsors by partnering with companies. For example, the sponsor unit partners with companies to have the companies provide funding for the user's development plan. This allows sponsors to be solicited, thereby making it possible to fund the user's development plan. Some or all of the above-described processing in the sponsor unit may be performed, for example, using AI, or may be performed without using AI. For example, the sponsor unit may have AI optimize an advertising campaign to solicit sponsors.

[0031] The training support system includes a revenue management unit that manages revenue. The revenue management unit manages revenue. Revenue management methods include, but are not limited to, revenue calculation methods, revenue distribution methods, and revenue tracking methods. For example, the revenue management unit can provide a revenue calculation method. The revenue management unit can also provide a revenue distribution method. The revenue management unit can also provide a revenue tracking method. For example, the revenue calculation method provides a method for calculating the total revenue. The revenue distribution method provides a method for distributing revenue to users and sponsors. The revenue tracking method provides a method for tracking the revenue flow. This allows the system to be monetized by managing revenue. Some or all of the above-described processing in the revenue management unit may be performed using AI, for example, or may be performed without using AI. For example, the revenue management unit may have AI perform revenue calculation and optimization of distribution.

[0032] The training support system includes a learning unit that provides learning content. The learning unit provides the learning content. Examples of learning content include, but are not limited to, online courses, teaching materials, and workshops. For example, the learning unit can provide online courses. The learning unit can also provide teaching materials. The learning unit can also provide workshops. For example, the learning unit provides online courses for developing leadership. The learning unit provides teaching materials for developing leadership. The learning unit provides workshops for developing leadership. In this way, by providing the learning content, the individuality and talents of the user can be developed. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can cause AI to optimize the learning content.

[0033] The training support system includes a training unit that provides a training program. The training unit provides the training program. Examples of training programs include, but are not limited to, fitness programs, skill training, and mental training. For example, the training unit can provide a fitness program. The training unit can also provide skill training. The training unit can also provide mental training. For example, the training unit provides a fitness program for developing leadership. The training unit provides skill training for developing leadership. The training unit provides mental training for developing leadership. By providing the training program, the user's skills can be improved. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can cause AI to optimize the training program.

[0034] The training support system includes a practice unit that provides practice opportunities. The practice unit provides the practice opportunities. Examples of practice opportunities include, but are not limited to, internships, project participation, and on-the-job training. For example, the practice unit can provide internships. The practice unit can also provide project participation. The practice unit can also provide on-the-job training. For example, the practice unit provides internships to develop leadership skills. The practice unit provides project participation to develop leadership skills. The practice unit provides on-the-job training to develop leadership skills. By providing practice opportunities, it is possible to provide a place for users to actually demonstrate their skills. Some or all of the above-described processing in the practice unit may be performed using, for example, AI, or may be performed without using AI. For example, the practice unit can cause AI to optimize the practice opportunities.

[0035] The collection unit can analyze the user's past behavioral history and select an appropriate information collection method. For example, the collection unit prioritizes selecting information collection methods (such as questionnaires and interviews) that the user has frequently used in the past. The collection unit can also identify the most effective information collection method from the user's past behavioral history, for example. The collection unit can also customize the timing and means of information collection based on the user's past behavioral history, for example. This enables efficient information collection by selecting the optimal information collection method based on the user's past behavioral history. Some or all of the above-described processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the user's past behavioral history data into a generation AI and cause the generation AI to select the optimal information collection method.

[0036] When collecting information, the collection unit can filter the information based on the user's current living situation and areas of interest. For example, the collection unit preferentially collects information related to areas in which the user is currently interested. For example, the collection unit can also filter appropriate information according to the user's living situation (work, home, etc.). For example, the collection unit can also collect related information based on the user's current areas of interest. In this way, highly relevant information can be collected by filtering information based on the user's living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's living situation and areas of interest into the generation AI and have the generation AI perform information filtering.

[0037] When collecting information, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can conduct a voice interview. For example, if the user prefers text input, the collection unit can also collect information in the form of a questionnaire. For example, if the user prefers image input, the collection unit can also collect information using images. This enables efficient information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's input method data into the generation AI and cause the generation AI to select the optimal collection means.

[0038] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting information related to the user's current location. For example, the collection unit can also filter highly relevant information based on the user's geographical location information. For example, the collection unit can also select an optimal information collection means by taking into account the user's geographical location information. This makes it possible to efficiently collect highly relevant information by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data to the generation AI and cause the generation AI to filter the information.

[0039] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit collects related information based on information shared by the user on social media. For example, the collection unit can analyze the user's social media activities and collect information based on the user's interests. For example, the collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, highly relevant information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect information.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. The collection unit can improve the information collection method, for example, based on feedback provided by the user in the past. The collection unit can also adjust the type of information to be collected, for example, by reflecting the user's past feedback. The collection unit can also select the optimal information collection means, for example, by referring to the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on important information. For example, the analysis unit can also perform a simplified analysis on less important information. For example, the analysis unit can also adjust the depth and scope of the analysis according to the importance of the information. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input information importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a natural language processing algorithm to text information. For example, the analysis unit can also apply an image analysis algorithm to image information. For example, the analysis unit can also apply a voice analysis algorithm to voice information. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also improve the analysis method by reflecting the user's past analysis results. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. The analysis unit, for example, prioritizes analysis of the most recent information. The analysis unit can also postpone analysis of information that was submitted earlier, for example. The analysis unit can also adjust the order of analysis based on the time of submission, for example. This enables efficient analysis by determining the priority of analysis based on the time of submission of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of information to the generation AI and have the generation AI determine the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. For example, the analysis unit can also postpone analysis of less relevant information. For example, the analysis unit can also adjust the order of analysis based on the relevance of the information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. For example, if the user does not have technical expertise, the analysis unit can also provide analysis results in simple language. For example, the analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide analysis results that are easier to understand by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terminology in the analysis.

[0047] The proposal unit can adjust the level of detail of the proposal based on the importance of the development plan when making a proposal. The proposal unit, for example, makes a detailed proposal for an important development plan. The proposal unit can also make a simplified proposal for a less important development plan. The proposal unit can also adjust the depth and scope of the proposal, for example, according to the importance of the development plan. This enables efficient proposals by adjusting the level of detail of the proposal according to the importance of the development plan. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the importance data of the development plan to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0048] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the development plan. For example, the proposal unit applies an educational algorithm to learning content. For example, the proposal unit can also apply a training algorithm to a training program. For example, the proposal unit can also apply a practice algorithm to a practice opportunity. This improves the accuracy of the proposal by applying an appropriate proposal algorithm depending on the category of the development plan. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input category data of the development plan into the generation AI and cause the generation AI to apply the proposal algorithm.

[0049] 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, optimizes the proposal algorithm based on the user's past proposal results. The suggestion unit can also improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit can also improve the proposal method by reflecting the user's past proposal results. This improves the accuracy of the proposal by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0050] When making a proposal, the proposal unit can determine the priority of the proposals based on the submission date of the development plans. For example, the proposal unit prioritizes the most recent development plans. For example, the proposal unit can also propose development plans that have been submitted earlier later. For example, the proposal unit can also adjust the order of proposals based on the submission date. This enables efficient proposals by determining the priority of proposals based on the submission date of the development plans. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input data on the submission date of the development plans into the generation AI and have the generation AI determine the priority of the proposals.

[0051] The proposal unit can adjust the order of proposals based on the relevance of the development plans when making a proposal. The proposal unit, for example, prioritizes the proposal of highly relevant development plans. The proposal unit can also postpone the proposal of less relevant development plans, for example. The proposal unit can also adjust the order of proposals based on the relevance of the development plans. This enables efficient proposals by adjusting the order of proposals based on the relevance of the development plans. Some or all of the above-described processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input relevance data of the development plans into the generation AI and cause the generation AI to adjust the order of proposals.

[0052] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can provide a proposal that uses a lot of technical terminology. For example, if the user does not have technical expertise, the suggestion unit can also provide a proposal in simple language. For example, the suggestion unit can also adjust the way the proposal is expressed according to the user's level of expertise. This makes it possible to provide a proposal that is easier to understand by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into a generation AI and cause the generation AI to use technical terminology in the proposal.

[0053] When recruiting sponsors, the sponsor unit can analyze the user's past activity history and select the most suitable sponsor. The sponsor unit selects the most suitable sponsor, for example, based on projects in which the user has been involved in the past. The sponsor unit can also analyze the user's past activity history and select a highly relevant sponsor, for example. The sponsor unit can also narrow down the target of sponsor recruitment by referring to the user's past activity history, for example. This enables effective sponsor recruitment by selecting the most suitable sponsor based on the user's past activity history. Some or all of the above-mentioned processing in the sponsor unit may be performed, for example, using AI, or may be performed without using AI. For example, the sponsor unit can input the user's past activity history data into the generation AI and have the generation AI select a sponsor.

[0054] When recruiting sponsors, the sponsor unit can filter based on the user's current project or area of ​​interest. For example, the sponsor unit prioritizes recruiting sponsors related to the user's current area of ​​interest. For example, the sponsor unit can also filter sponsors related to the user's current project. For example, the sponsor unit can also select the optimal sponsor based on the user's area of ​​interest. In this way, by filtering sponsors based on the user's current project or area of ​​interest, highly relevant sponsors can be recruited. Some or all of the above-described processing in the sponsor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sponsor unit can input the user's current project or area of ​​interest data into the generation AI and have the generation AI perform sponsor filtering.

[0055] When recruiting sponsors, the sponsor unit can prioritize recruiting highly relevant sponsors by taking into account the user's geographical location information. For example, the sponsor unit prioritizes recruiting sponsors related to the user's current location. For example, the sponsor unit can also filter highly relevant sponsors based on the user's geographical location information. For example, the sponsor unit can also select the most suitable sponsor by taking into account the user's geographical location information. This makes it possible to efficiently recruit highly relevant sponsors by taking into account the user's geographical location information. Some or all of the above-described processing in the sponsor unit may be performed using AI, for example, or may be performed without using AI. For example, the sponsor unit can input the user's geographical location information data into the generation AI and have the generation AI perform sponsor filtering.

[0056] When recruiting sponsors, the sponsor unit can analyze the user's social media activity and recruit relevant sponsors. The sponsor unit, for example, recruits relevant sponsors based on information shared by the user on social media. The sponsor unit, for example, can analyze the user's social media activity and recruit sponsors based on their interests. The sponsor unit, for example, can also recruit relevant sponsors by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to recruit highly relevant sponsors. Some or all of the above-described processing in the sponsor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sponsor unit can input the user's social media activity data into the generation AI and have the generation AI execute the sponsor recruitment.

[0057] During revenue management, the revenue management unit can analyze the user's past revenue history and select the optimal management method. The revenue management unit, for example, selects the optimal revenue management method based on the user's past revenue history. The revenue management unit can also analyze the user's past revenue history to improve the accuracy of revenue management. The revenue management unit can also improve the revenue management method by referring to the user's past revenue history. This enables effective revenue management by selecting the optimal management method based on the user's past revenue history. Some or all of the above-mentioned processing in the revenue management unit can be performed, for example, using AI or without AI. For example, the revenue management unit can input the user's past revenue history data into the generation AI and have the generation AI select the revenue management method.

[0058] During revenue management, the revenue management unit can customize the management means based on the user's current revenue situation. The revenue management unit, for example, provides an optimal revenue management means based on the user's current revenue situation. The revenue management unit can also adjust the revenue management method, for example, according to the user's current revenue situation. The revenue management unit can also customize the revenue management method, for example, by reflecting the user's current revenue situation. This enables more appropriate revenue management by customizing the management means based on the user's current revenue situation. Some or all of the above-described processing in the revenue management unit may be performed, for example, using AI or without AI. For example, the revenue management unit can input the user's current revenue situation data into the generation AI and have the generation AI customize the revenue management means.

[0059] The revenue management unit can select the optimal management method during revenue management by taking into account the user's geographic location information. The revenue management unit, for example, provides a revenue management method related to the user's current location. The revenue management unit can also select the optimal revenue management method based on the user's geographic location information. The revenue management unit can also customize the revenue management method by taking into account the user's geographic location information. This makes it possible to provide the optimal revenue management method by taking into account the user's geographic location information. Some or all of the above-mentioned processing in the revenue management unit can be performed using, for example, AI, or can be performed without using AI. For example, the revenue management unit can input the user's geographic location information data into the generation AI and have the generation AI select the revenue management method.

[0060] During revenue management, the revenue management unit can analyze the user's social media activity and suggest management measures. The revenue management unit can suggest revenue management measures, for example, based on information shared by the user on social media. The revenue management unit can also analyze the user's social media activity and suggest optimal revenue management measures. The revenue management unit can also suggest revenue management measures, for example, by referring to the activity of the user's friends on social media. In this way, optimal revenue management measures can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the revenue management unit may be performed, for example, using AI, or may be performed without using AI. For example, the revenue management unit can input the user's social media activity data into a generation AI and have the generation AI execute the suggestion of revenue management measures.

[0061] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit optimizes the learning algorithm based on, for example, the user's past learning data. The learning unit can also improve the accuracy of learning by referring to, for example, the user's past learning data. The learning unit can also improve the learning method by reflecting, for example, the user's past learning data. By referring to the past learning data, the accuracy of the learning algorithm is improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's past learning data into the generation AI and cause the generation AI to optimize the learning algorithm.

[0062] During learning, the learning unit can update the learning data by reflecting user feedback. The learning unit updates the learning data based on, for example, feedback provided by the user. The learning unit can also improve the accuracy of learning by reflecting user feedback, for example. The learning unit can also improve the learning method by referring to, for example, user feedback. In this way, the accuracy of the learning data is improved by reflecting user feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user feedback data into the generation AI and cause the generation AI to update the learning data.

[0063] During learning, the learning unit can weight the learning data based on the submission date of the learning content. For example, the learning unit can assign a higher weight to the most recent learning content. For example, the learning unit can also assign a lower weight to learning content that was submitted earlier. The learning unit can also adjust the weighting of the learning data based on the submission date, for example. By weighting based on the submission date of the learning content, the accuracy of the learning data is improved. Some or all of the above-described processing in the learning unit can be performed using, for example, AI, or without AI. For example, the learning unit can input data on the submission date of the learning content to a generation AI and have the generation AI weight the learning data.

[0064] During learning, the learning unit can integrate information from different data sources to enrich the learning data. For example, the learning unit integrates information from different data sources to enrich the learning data. For example, the learning unit can also improve the accuracy of learning based on information from different data sources. For example, the learning unit can also improve the learning method by reflecting information from different data sources. In this way, the accuracy of the learning data is improved by integrating information from different data sources. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input information from different data sources into the generation AI and cause the generation AI to integrate the information.

[0065] The training unit can provide an optimal program by referring to the user's past training history during training. The training unit provides an optimal training program based on, for example, the user's past training history. The training unit can also improve the accuracy of training by referring to, for example, the user's past training history. The training unit can also improve a training technique by reflecting, for example, the user's past training history. In this way, an optimal training program can be provided by referring to the user's past training history. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input the user's past training history data into a generation AI and cause the generation AI to provide an optimal training program.

[0066] The training unit can customize a program based on the user's current skill level during training. The training unit provides an optimal training program based on, for example, the user's current skill level. The training unit can also adjust the training method according to, for example, the user's current skill level. The training unit can also customize the training method by reflecting, for example, the user's current skill level. This enables more effective training by customizing the program based on the user's current skill level. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input the user's current skill level data into the generation AI and cause the generation AI to customize the training program.

[0067] The training unit can provide an optimal program during training by taking into account the user's geographical location information. For example, the training unit can provide a training program related to the user's current location. For example, the training unit can also select an optimal training program based on the user's geographical location information. For example, the training unit can also customize a training method by taking into account the user's geographical location information. In this way, an optimal training program can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the training unit can be performed using AI, for example, or can be performed without using AI. For example, the training unit can input the user's geographical location information data into a generation AI and cause the generation AI to select a training program.

[0068] The training unit can analyze the user's social media activities during training and suggest a relevant program. The training unit can suggest a relevant training program based on, for example, information shared by the user on social media. The training unit can also analyze the user's social media activities and suggest a training program based on their interests. The training unit can also suggest a relevant training program based on, for example, the activities of the user's friends on social media. In this way, by analyzing the user's social media activities, it is possible to suggest a highly relevant training program. Some or all of the above-described processing in the training unit can be performed using, for example, AI, or can be performed without using AI. For example, the training unit can input the user's social media activity data into a generation AI and have the generation AI suggest a training program.

[0069] When providing a practice opportunity, the practice unit can provide the optimal opportunity by referring to the user's past practice history. The practice unit, for example, provides the optimal practice opportunity based on the user's past practice history. The practice unit can also, for example, improve the accuracy of practice by referring to the user's past practice history. The practice unit can, for example, improve the practice technique by reflecting the user's past practice history. In this way, the optimal practice opportunity can be provided by referring to the user's past practice history. Some or all of the above-described processing in the practice unit may be performed using, for example, AI, or may be performed without using AI. For example, the practice unit can input the user's past practice history data into the generation AI and cause the generation AI to provide the optimal practice opportunity.

[0070] When providing a practice opportunity, the practice unit can customize the opportunity based on the user's current skill level. The practice unit provides an optimal practice opportunity based on, for example, the user's current skill level. The practice unit can also adjust the practice method according to, for example, the user's current skill level. The practice unit can also customize the practice method by reflecting, for example, the user's current skill level. This allows for more effective practice opportunities to be provided by customizing the opportunity based on the user's current skill level. Some or all of the above-described processing in the practice unit may be performed using, for example, AI, or may be performed without using AI. For example, the practice unit can input the user's current skill level data into the generation AI and cause the generation AI to customize the practice opportunity.

[0071] When providing practice opportunities, the practice unit can provide the optimal opportunity by taking into account the user's geographical location information. For example, the practice unit can provide practice opportunities related to the user's current location. For example, the practice unit can also select the optimal practice opportunity based on the user's geographical location information. For example, the practice unit can also customize the practice method by taking into account the user's geographical location information. In this way, the optimal practice opportunity can be provided by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the practice unit may be performed using, for example, AI, or may be performed without using AI. For example, the practice unit can input the user's geographical location information data into the generation AI and cause the generation AI to select a practice opportunity.

[0072] When providing practice opportunities, the practice unit can analyze the user's social media activities and suggest relevant opportunities. The practice unit can, for example, suggest relevant practice opportunities based on information shared by the user on social media. The practice unit can, for example, analyze the user's social media activities and suggest practice opportunities based on the user's interests. The practice unit can, for example, suggest relevant practice opportunities based on the user's friends' activities on social media. In this way, by analyzing the user's social media activities, highly relevant practice opportunities can be suggested. Some or all of the above-described processing in the practice unit may be performed using, for example, AI, or may be performed without using AI. For example, the practice unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest practice opportunities.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The analysis unit can analyze the user's past behavioral history and improve the accuracy of the analysis. For example, the analysis algorithm can be optimized based on the user's past behavioral patterns. The analysis accuracy can also be improved by referring to the user's past behavioral history. The analysis method can also be improved by reflecting the user's past behavioral history. In this way, the accuracy of the analysis can be improved by referring to the user's past behavioral history. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past behavioral history data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0075] The suggestion unit can analyze the user's past suggestion results and improve the accuracy of the suggestions. For example, the suggestion algorithm can be optimized based on the results of suggestions the user has received in the past. The accuracy of the suggestions can also be improved by referring to the user's past suggestion results. The suggestion method can also be improved by reflecting the user's past suggestion results. In this way, the accuracy of the suggestions can be improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0076] The collection unit can select a means of collecting information based on the user's current living situation and areas of interest. For example, it can prioritize collection of information related to areas in which the user is currently interested. It can also select an appropriate means of collecting information depending on the user's living situation (work, home, etc.). It can also collect related information based on the user's current areas of interest. This enables efficient information collection by selecting a means of collecting information based on the user's living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's living situation and areas of interest into the generation AI and have the generation AI select an information collection means.

[0077] The revenue management unit can analyze the user's past revenue history and improve the accuracy of revenue management. For example, the revenue management algorithm can be optimized based on the user's past revenue patterns. The accuracy of revenue management can also be improved by referring to the user's past revenue history. The revenue management method can also be improved by reflecting the user's past revenue history. In this way, the accuracy of revenue management can be improved by referring to the user's past revenue history. Some or all of the above-mentioned processing in the revenue management unit can be performed, for example, using AI or without AI. For example, the revenue management unit can input the user's past revenue history data into the generation AI and have the generation AI improve the accuracy of revenue management.

[0078] The training unit can customize the training program based on the user's current skill level. For example, it can provide an optimal training program based on the user's current skill level. It can also adjust the training method according to the user's current skill level. It can also customize the training method to reflect the user's current skill level. This enables more effective training by customizing the training program based on the user's current skill level. Some or all of the above-mentioned processing in the training unit may be performed using AI, for example, or may be performed without using AI. For example, the training unit can input the user's current skill level data into the generation AI and have the generation AI customize the training program.

[0079] The processing flow of the first embodiment will be briefly explained below.

[0080] Step 1: The collection unit collects user information. User information includes personal information, behavioral history, interests, etc. The collection unit collects information when the user registers with the app and enters information such as a self-introduction, interests, and past experiences. The collection unit can also track the user's behavioral history to understand the user's interests. For example, the collection unit can collect the history of websites the user has visited and the history of apps the user has used. Furthermore, the collection unit can analyze the user's social media activity to identify the user's interests. For example, the collection unit can understand the user's interests based on information the user has shared on social media. Step 2: The analysis unit analyzes the information collected by the collection unit and identifies the user's personality and talents. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit uses data mining technology to find patterns from the user's information and identify the user's personality and talents. The analysis unit can also use statistical analysis to analyze the user's information and identify characteristics. Furthermore, the analysis unit can also use machine learning algorithms to analyze the user's information and identify characteristics. For example, the analysis unit uses machine learning algorithms to identify characteristics from the user's past behavioral history. Step 3: The suggestion unit proposes a development plan based on the personality and talents identified by the analysis unit. The development plan includes a learning program, a training schedule, goal setting, and the like. For example, the suggestion unit proposes an online course or workshop for developing leadership to a user for whom leadership talent has been identified. The suggestion unit can also provide opportunities for the user to demonstrate leadership in actual projects based on the user's characteristics. Furthermore, the suggestion unit can propose a learning program or a training schedule based on the user's characteristics. For example, the suggestion unit sets goals for developing leadership based on the user's characteristics.

[0081] (Example 2) An application according to an embodiment of the present invention is a system for discovering and developing a user's undiscovered individuality and talents. In this system, a user registers for an app and inputs information such as a self-introduction, interests, and past experiences. AI then analyzes this information to identify the user's individuality and talents. Furthermore, a development plan appropriate for the user is proposed based on the identified individuality and talents. This development plan includes learning content, training programs, and practical opportunities. It is also possible to solicit sponsors for monetization. For example, a user registers for an app and inputs information such as a self-introduction, interests, and past experiences. The user then provides a detailed profile and data for the AI ​​to analyze. For example, the user inputs information such as hobbies, special skills, past work experience, and educational background. The AI ​​then analyzes the input information and identifies the user's individuality and talents. The AI ​​finds patterns based on the user's information and discovers hidden talents and characteristics that the user may not be aware of. For example, if a user has successfully completed many projects in the past, it can identify the user as having talent in leadership or project management. Furthermore, a development plan appropriate for the user is proposed based on the identified individuality and talents. This development plan includes learning content, training programs, and practical opportunities. For example, if a user's leadership talent is identified, they can be offered online courses and workshops to develop their leadership skills, as well as opportunities to demonstrate their leadership skills in actual projects. It is also possible to recruit sponsors and generate revenue. Sponsors can fund the user's development plan and support the user's growth process. This allows the user to maximize their individuality and talents and achieve self-realization. This allows the application to discover the user's individuality and talents and propose development plans to support the user's self-realization. For example, the user can discover their own value and purpose in life, and build a career and lifestyle that utilizes their strengths.

[0082] The development support system according to the embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects user information. The user information includes, but is not limited to, personal information, behavioral history, and interests. For example, the collection unit collects information by a user registering for an app and inputting information such as a self-introduction, interests, and past experiences. The collection unit can also track the user's behavioral history to identify the user's interests. For example, the collection unit can collect a history of websites visited by the user and apps used by the user. The collection unit can also analyze the user's social media activity to identify the user's interests. For example, the collection unit can identify the user's interests based on information shared by the user on social media. The analysis unit analyzes the information collected by the collection unit to identify the user's personality and talents. The analysis can be performed using, for example, data mining, statistical analysis, machine learning algorithms, or the like, but is not limited to, these examples. For example, the analysis unit can use data mining technology to find patterns in the user's information and identify the user's personality and talents. The analysis unit can also analyze the user's information and identify characteristics using statistical analysis. Furthermore, the analysis unit can use a machine learning algorithm to analyze user information and identify characteristics. For example, the analysis unit can use a machine learning algorithm to identify characteristics from the user's past behavioral history. The suggestion unit proposes a development plan based on the personality and talents identified by the analysis unit. The development plan can include, but is not limited to, a learning program, a training schedule, and goal setting. For example, the suggestion unit can propose an online course or workshop for developing leadership skills to a user for whom leadership talent has been identified. The suggestion unit can also provide opportunities for the user to demonstrate leadership in actual projects based on the user's characteristics. Furthermore, the suggestion unit can propose a learning program or a training schedule based on the user's characteristics. For example, the suggestion unit can set goals for developing leadership skills based on the user's characteristics.As a result, the development support system according to the embodiment can discover the individuality and talents of the user and propose a development plan, thereby supporting the user's self-realization.

[0083] The development support system includes a sponsor unit that solicits sponsors. The sponsor unit solicits sponsors. Methods of soliciting sponsors include, but are not limited to, advertising campaigns, crowdfunding, corporate partnerships, and the like. For example, the sponsor unit can solicit sponsors through advertising campaigns. The sponsor unit can also solicit sponsors using a crowdfunding platform. Furthermore, the sponsor unit can solicit sponsors by partnering with companies. For example, the sponsor unit partners with companies to have the companies provide funding for the user's development plan. This allows sponsors to be solicited, thereby making it possible to fund the user's development plan. Some or all of the above-described processing in the sponsor unit may be performed, for example, using AI, or may be performed without using AI. For example, the sponsor unit may have AI optimize an advertising campaign to solicit sponsors.

[0084] The training support system includes a revenue management unit that manages revenue. The revenue management unit manages revenue. Revenue management methods include, but are not limited to, revenue calculation methods, revenue distribution methods, and revenue tracking methods. For example, the revenue management unit can provide a revenue calculation method. The revenue management unit can also provide a revenue distribution method. The revenue management unit can also provide a revenue tracking method. For example, the revenue calculation method provides a method for calculating the total revenue. The revenue distribution method provides a method for distributing revenue to users and sponsors. The revenue tracking method provides a method for tracking the revenue flow. This allows the system to be monetized by managing revenue. Some or all of the above-described processing in the revenue management unit may be performed using AI, for example, or may be performed without using AI. For example, the revenue management unit may have AI perform revenue calculation and optimization of distribution.

[0085] The training support system includes a learning unit that provides learning content. The learning unit provides the learning content. Examples of learning content include, but are not limited to, online courses, teaching materials, and workshops. For example, the learning unit can provide online courses. The learning unit can also provide teaching materials. The learning unit can also provide workshops. For example, the learning unit provides online courses for developing leadership. The learning unit provides teaching materials for developing leadership. The learning unit provides workshops for developing leadership. In this way, by providing the learning content, the individuality and talents of the user can be developed. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can cause AI to optimize the learning content.

[0086] The training support system includes a training unit that provides a training program. The training unit provides the training program. Examples of training programs include, but are not limited to, fitness programs, skill training, and mental training. For example, the training unit can provide a fitness program. The training unit can also provide skill training. The training unit can also provide mental training. For example, the training unit provides a fitness program for developing leadership. The training unit provides skill training for developing leadership. The training unit provides mental training for developing leadership. By providing the training program, the user's skills can be improved. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can cause AI to optimize the training program.

[0087] The training support system includes a practice unit that provides practice opportunities. The practice unit provides the practice opportunities. Examples of practice opportunities include, but are not limited to, internships, project participation, and on-the-job training. For example, the practice unit can provide internships. The practice unit can also provide project participation. The practice unit can also provide on-the-job training. For example, the practice unit provides internships to develop leadership skills. The practice unit provides project participation to develop leadership skills. The practice unit provides on-the-job training to develop leadership skills. By providing practice opportunities, it is possible to provide a place for users to actually demonstrate their skills. Some or all of the above-described processing in the practice unit may be performed using, for example, AI, or may be performed without using AI. For example, the practice unit can cause AI to optimize the practice opportunities.

[0088] The collection unit can analyze the user's emotions and adjust the timing of information collection based on the analyzed user's emotions. For example, if the user is feeling stressed, the collection unit can collect information during a time when the user is able to relax. For example, if the user is relaxed, the collection unit can conduct a long interview to collect detailed information. For example, if the user is in a hurry, the collection unit can collect necessary information in a short period of time. This enables more appropriate information collection by adjusting the timing of information collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a 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. Some or all of the above-mentioned processing in the collection unit can be performed using an AI, for example, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0089] The collection unit can analyze the user's past behavioral history and select an appropriate information collection method. For example, the collection unit prioritizes selecting information collection methods (such as questionnaires and interviews) that the user has frequently used in the past. The collection unit can also identify the most effective information collection method from the user's past behavioral history, for example. The collection unit can also customize the timing and means of information collection based on the user's past behavioral history, for example. This enables efficient information collection by selecting the optimal information collection method based on the user's past behavioral history. Some or all of the above-described processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the user's past behavioral history data into a generation AI and cause the generation AI to select the optimal information collection method.

[0090] When collecting information, the collection unit can filter the information based on the user's current living situation and areas of interest. For example, the collection unit preferentially collects information related to areas in which the user is currently interested. For example, the collection unit can also filter appropriate information according to the user's living situation (work, home, etc.). For example, the collection unit can also collect related information based on the user's current areas of interest. In this way, highly relevant information can be collected by filtering information based on the user's living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's living situation and areas of interest into the generation AI and have the generation AI perform information filtering.

[0091] When collecting information, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can conduct a voice interview. For example, if the user prefers text input, the collection unit can also collect information in the form of a questionnaire. For example, if the user prefers image input, the collection unit can also collect information using images. This enables efficient information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's input method data into the generation AI and cause the generation AI to select the optimal collection means.

[0092] The collection unit can analyze the user's emotions and determine the priority of information to be collected based on the analyzed user's emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting information that helps the user relax. For example, if the user is relaxed, the collection unit can also prioritize collecting detailed information. For example, if the user is in a hurry, the collection unit can also prioritize collecting important information. This allows important information to be collected preferentially by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information.

[0093] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting information related to the user's current location. For example, the collection unit can also filter highly relevant information based on the user's geographical location information. For example, the collection unit can also select an optimal information collection means by taking into account the user's geographical location information. This makes it possible to efficiently collect highly relevant information by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data to the generation AI and cause the generation AI to filter the information.

[0094] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit collects related information based on information shared by the user on social media. For example, the collection unit can analyze the user's social media activities and collect information based on the user's interests. For example, the collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, highly relevant information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect information.

[0095] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. The collection unit can improve the information collection method, for example, based on feedback provided by the user in the past. The collection unit can also adjust the type of information to be collected, for example, by reflecting the user's past feedback. The collection unit can also select the optimal information collection means, for example, by referring to the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0096] The analysis unit can analyze the user's emotions and adjust the way the analysis is presented based on the analyzed user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can provide a summary analysis result. This allows for adjusting the way the analysis is presented according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0097] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on important information. For example, the analysis unit can also perform a simplified analysis on less important information. For example, the analysis unit can also adjust the depth and scope of the analysis according to the importance of the information. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input information importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0098] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a natural language processing algorithm to text information. For example, the analysis unit can also apply an image analysis algorithm to image information. For example, the analysis unit can also apply a voice analysis algorithm to voice information. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0099] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also improve the analysis method by reflecting the user's past analysis results. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0100] The analysis unit can analyze the user's emotions and adjust the length of the analysis based on the analyzed user's emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, 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. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0101] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. The analysis unit, for example, prioritizes analysis of the most recent information. The analysis unit can also postpone analysis of information that was submitted earlier, for example. The analysis unit can also adjust the order of analysis based on the time of submission, for example. This enables efficient analysis by determining the priority of analysis based on the time of submission of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of information to the generation AI and have the generation AI determine the priority of analysis.

[0102] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. For example, the analysis unit can also postpone analysis of less relevant information. For example, the analysis unit can also adjust the order of analysis based on the relevance of the information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0103] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. For example, if the user does not have technical expertise, the analysis unit can also provide analysis results in simple language. For example, the analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide analysis results that are easier to understand by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terminology in the analysis.

[0104] The suggestion unit can analyze the user's emotions and adjust the way the suggestions are expressed based on the analyzed user's emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. For example, if the user is relaxed, the suggestion unit can also provide detailed suggestions. For example, if the user is in a hurry, the suggestion unit can also provide suggestions that focus on the main points. This allows for adjusting the way the suggestions are expressed according to the user's emotions, thereby providing more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a 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. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0105] The proposal unit can adjust the level of detail of the proposal based on the importance of the development plan when making a proposal. The proposal unit, for example, makes a detailed proposal for an important development plan. The proposal unit can also make a simplified proposal for a less important development plan. The proposal unit can also adjust the depth and scope of the proposal, for example, according to the importance of the development plan. This enables efficient proposals by adjusting the level of detail of the proposal according to the importance of the development plan. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the importance data of the development plan to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0106] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the development plan. For example, the proposal unit applies an educational algorithm to learning content. For example, the proposal unit can also apply a training algorithm to a training program. For example, the proposal unit can also apply a practice algorithm to a practice opportunity. This improves the accuracy of the proposal by applying an appropriate proposal algorithm depending on the category of the development plan. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input category data of the development plan into the generation AI and cause the generation AI to apply the proposal algorithm.

[0107] 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, optimizes the proposal algorithm based on the user's past proposal results. The suggestion unit can also improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit can also improve the proposal method by reflecting the user's past proposal results. This improves the accuracy of the proposal by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0108] The suggestion unit can analyze the user's emotions and adjust the length of the suggestions based on the analyzed user's emotions. For example, if the user is in a hurry, the suggestion unit can provide short and to-the-point suggestions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. For example, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This allows for adjusting the length of the suggestions according to the user's emotions to provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, 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. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0109] When making a proposal, the proposal unit can determine the priority of the proposals based on the submission date of the development plans. For example, the proposal unit prioritizes the most recent development plans. For example, the proposal unit can also propose development plans that have been submitted earlier later. For example, the proposal unit can also adjust the order of proposals based on the submission date. This enables efficient proposals by determining the priority of proposals based on the submission date of the development plans. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input data on the submission date of the development plans into the generation AI and have the generation AI determine the priority of the proposals.

[0110] The proposal unit can adjust the order of proposals based on the relevance of the development plans when making a proposal. The proposal unit, for example, prioritizes the proposal of highly relevant development plans. The proposal unit can also postpone the proposal of less relevant development plans, for example. The proposal unit can also adjust the order of proposals based on the relevance of the development plans. This enables efficient proposals by adjusting the order of proposals based on the relevance of the development plans. Some or all of the above-described processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input relevance data of the development plans into the generation AI and cause the generation AI to adjust the order of proposals.

[0111] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can provide a proposal that uses a lot of technical terminology. For example, if the user does not have technical expertise, the suggestion unit can also provide a proposal in simple language. For example, the suggestion unit can also adjust the way the proposal is expressed according to the user's level of expertise. This makes it possible to provide a proposal that is easier to understand by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into a generation AI and cause the generation AI to use technical terminology in the proposal.

[0112] The sponsor unit can analyze the user's emotions and adjust the timing of sponsor solicitation based on the analyzed user's emotions. For example, the sponsor unit adjusts the timing of sponsor solicitation when the user is relaxed. For example, the sponsor unit can delay the timing of sponsor solicitation when the user is stressed. For example, the sponsor unit can advance the timing of sponsor solicitation when the user is excited. This enables more effective sponsor solicitation by adjusting the timing of sponsor solicitation according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the sponsor unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sponsor unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of sponsor solicitation.

[0113] When recruiting sponsors, the sponsor unit can analyze the user's past activity history and select the most suitable sponsor. The sponsor unit selects the most suitable sponsor, for example, based on projects in which the user has been involved in the past. The sponsor unit can also analyze the user's past activity history and select a highly relevant sponsor, for example. The sponsor unit can also narrow down the target of sponsor recruitment by referring to the user's past activity history, for example. This enables effective sponsor recruitment by selecting the most suitable sponsor based on the user's past activity history. Some or all of the above-mentioned processing in the sponsor unit may be performed, for example, using AI, or may be performed without using AI. For example, the sponsor unit can input the user's past activity history data into the generation AI and have the generation AI select a sponsor.

[0114] When recruiting sponsors, the sponsor unit can filter based on the user's current project or area of ​​interest. For example, the sponsor unit prioritizes recruiting sponsors related to the user's current area of ​​interest. For example, the sponsor unit can also filter sponsors related to the user's current project. For example, the sponsor unit can also select the optimal sponsor based on the user's area of ​​interest. In this way, by filtering sponsors based on the user's current project or area of ​​interest, highly relevant sponsors can be recruited. Some or all of the above-described processing in the sponsor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sponsor unit can input the user's current project or area of ​​interest data into the generation AI and have the generation AI perform sponsor filtering.

[0115] The sponsor unit can analyze the user's emotions and determine the priority of sponsor solicitation based on the analyzed user's emotions. For example, the sponsor unit can increase the priority of sponsor solicitation when the user is relaxed. For example, the sponsor unit can decrease the priority of sponsor solicitation when the user is stressed. For example, the sponsor unit can increase the priority of sponsor solicitation when the user is excited. This enables effective sponsor solicitation by determining the priority of sponsor solicitation according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the sponsor unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sponsor unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of sponsor solicitation.

[0116] When recruiting sponsors, the sponsor unit can prioritize recruiting highly relevant sponsors by taking into account the user's geographical location information. For example, the sponsor unit prioritizes recruiting sponsors related to the user's current location. For example, the sponsor unit can also filter highly relevant sponsors based on the user's geographical location information. For example, the sponsor unit can also select the most suitable sponsor by taking into account the user's geographical location information. This makes it possible to efficiently recruit highly relevant sponsors by taking into account the user's geographical location information. Some or all of the above-described processing in the sponsor unit may be performed using AI, for example, or may be performed without using AI. For example, the sponsor unit can input the user's geographical location information data into the generation AI and have the generation AI perform sponsor filtering.

[0117] When recruiting sponsors, the sponsor unit can analyze the user's social media activity and recruit relevant sponsors. The sponsor unit, for example, recruits relevant sponsors based on information shared by the user on social media. The sponsor unit, for example, can analyze the user's social media activity and recruit sponsors based on their interests. The sponsor unit, for example, can also recruit relevant sponsors by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to recruit highly relevant sponsors. Some or all of the above-described processing in the sponsor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sponsor unit can input the user's social media activity data into the generation AI and have the generation AI execute the sponsor recruitment.

[0118] The revenue management unit can analyze the user's emotions and adjust the revenue management method based on the analyzed user's emotions. For example, if the user is relaxed, the revenue management unit can provide a detailed revenue management method. For example, if the user is stressed, the revenue management unit can provide a simplified revenue management method. For example, if the user is excited, the revenue management unit can provide a visually stimulating revenue management method. This enables more appropriate revenue management by adjusting the revenue management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-mentioned processing in the revenue management unit can be performed using AI, for example, or without AI. For example, the revenue management unit can input user emotion data into the generation AI and have the generation AI adjust the revenue management method.

[0119] During revenue management, the revenue management unit can analyze the user's past revenue history and select the optimal management method. The revenue management unit, for example, selects the optimal revenue management method based on the user's past revenue history. The revenue management unit can also analyze the user's past revenue history to improve the accuracy of revenue management. The revenue management unit can also improve the revenue management method by referring to the user's past revenue history. This enables effective revenue management by selecting the optimal management method based on the user's past revenue history. Some or all of the above-mentioned processing in the revenue management unit can be performed, for example, using AI or without AI. For example, the revenue management unit can input the user's past revenue history data into the generation AI and have the generation AI select the revenue management method.

[0120] During revenue management, the revenue management unit can customize the management means based on the user's current revenue situation. The revenue management unit, for example, provides an optimal revenue management means based on the user's current revenue situation. The revenue management unit can also adjust the revenue management method, for example, according to the user's current revenue situation. The revenue management unit can also customize the revenue management method, for example, by reflecting the user's current revenue situation. This enables more appropriate revenue management by customizing the management means based on the user's current revenue situation. Some or all of the above-described processing in the revenue management unit may be performed, for example, using AI or without AI. For example, the revenue management unit can input the user's current revenue situation data into the generation AI and have the generation AI customize the revenue management means.

[0121] The revenue management unit can analyze the user's emotions and determine revenue management priorities based on the analyzed user emotions. For example, the revenue management unit can increase the priority of revenue management when the user is relaxed. For example, the revenue management unit can decrease the priority of revenue management when the user is stressed. For example, the revenue management unit can increase the priority of revenue management when the user is excited. This enables effective revenue management by determining revenue management priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-described processing in the revenue management unit can be performed using AI, for example, or without AI. For example, the revenue management unit can input user emotion data into the generation AI and have the generation AI determine the revenue management priorities.

[0122] The revenue management unit can select the optimal management method during revenue management by taking into account the user's geographic location information. The revenue management unit, for example, provides a revenue management method related to the user's current location. The revenue management unit can also select the optimal revenue management method based on the user's geographic location information. The revenue management unit can also customize the revenue management method by taking into account the user's geographic location information. This makes it possible to provide the optimal revenue management method by taking into account the user's geographic location information. Some or all of the above-mentioned processing in the revenue management unit can be performed using, for example, AI, or can be performed without using AI. For example, the revenue management unit can input the user's geographic location information data into the generation AI and have the generation AI select the revenue management method.

[0123] During revenue management, the revenue management unit can analyze the user's social media activity and suggest management measures. The revenue management unit can suggest revenue management measures, for example, based on information shared by the user on social media. The revenue management unit can also analyze the user's social media activity and suggest optimal revenue management measures. The revenue management unit can also suggest revenue management measures, for example, by referring to the activity of the user's friends on social media. In this way, optimal revenue management measures can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the revenue management unit may be performed, for example, using AI, or may be performed without using AI. For example, the revenue management unit can input the user's social media activity data into a generation AI and have the generation AI execute the suggestion of revenue management measures.

[0124] The learning unit can analyze the user's emotions and select learning content based on the analyzed user's emotions. For example, if the user is relaxed, the learning unit can provide detailed learning content. For example, if the user is feeling stressed, the learning unit can also provide simplified learning content. For example, if the user is feeling excited, the learning unit can also provide visually stimulating learning content. This enables more effective learning by selecting learning content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-mentioned processing in the learning unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the learning unit can input the user's emotion data into the generation AI and have the generation AI select learning content.

[0125] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit optimizes the learning algorithm based on, for example, the user's past learning data. The learning unit can also improve the accuracy of learning by referring to, for example, the user's past learning data. The learning unit can also improve the learning method by reflecting, for example, the user's past learning data. By referring to the past learning data, the accuracy of the learning algorithm is improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's past learning data into the generation AI and cause the generation AI to optimize the learning algorithm.

[0126] During learning, the learning unit can update the learning data by reflecting user feedback. The learning unit updates the learning data based on, for example, feedback provided by the user. The learning unit can also improve the accuracy of learning by reflecting user feedback, for example. The learning unit can also improve the learning method by referring to, for example, user feedback. In this way, the accuracy of the learning data is improved by reflecting user feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user feedback data into the generation AI and cause the generation AI to update the learning data.

[0127] The learning unit can analyze the user's emotions and adjust the frequency of learning based on the analyzed user's emotions. For example, the learning unit can increase the frequency of learning when the user is relaxed. For example, the learning unit can decrease the frequency of learning when the user is stressed. For example, the learning unit can increase the frequency of learning when the user is excited. This allows for more effective learning by adjusting the frequency of learning according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-mentioned processing in the learning unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the learning unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the frequency of learning.

[0128] During learning, the learning unit can weight the learning data based on the submission date of the learning content. For example, the learning unit can assign a higher weight to the most recent learning content. For example, the learning unit can also assign a lower weight to learning content that was submitted earlier. The learning unit can also adjust the weighting of the learning data based on the submission date, for example. By weighting based on the submission date of the learning content, the accuracy of the learning data is improved. Some or all of the above-described processing in the learning unit can be performed using, for example, AI, or without AI. For example, the learning unit can input data on the submission date of the learning content to a generation AI and have the generation AI weight the learning data.

[0129] During learning, the learning unit can integrate information from different data sources to enrich the learning data. For example, the learning unit integrates information from different data sources to enrich the learning data. For example, the learning unit can also improve the accuracy of learning based on information from different data sources. For example, the learning unit can also improve the learning method by reflecting information from different data sources. In this way, the accuracy of the learning data is improved by integrating information from different data sources. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input information from different data sources into the generation AI and cause the generation AI to integrate the information.

[0130] The training unit can analyze the user's emotions and adjust the content of the training program based on the analyzed user's emotions. For example, if the user is relaxed, the training unit can provide a detailed training program. For example, if the user is feeling stressed, the training unit can also provide a simplified training program. For example, if the user is excited, the training unit can also provide a visually stimulating training program. This enables more effective training by adjusting the content of the training program according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-mentioned processing in the training unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the training unit can input the user's emotion data into the generation AI and have the generation AI adjust the content of the training program.

[0131] The training unit can provide an optimal program by referring to the user's past training history during training. The training unit provides an optimal training program based on, for example, the user's past training history. The training unit can also improve the accuracy of training by referring to, for example, the user's past training history. The training unit can also improve a training technique by reflecting, for example, the user's past training history. In this way, an optimal training program can be provided by referring to the user's past training history. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input the user's past training history data into a generation AI and cause the generation AI to provide an optimal training program.

[0132] The training unit can customize a program based on the user's current skill level during training. The training unit provides an optimal training program based on, for example, the user's current skill level. The training unit can also adjust the training method according to, for example, the user's current skill level. The training unit can also customize the training method by reflecting, for example, the user's current skill level. This enables more effective training by customizing the program based on the user's current skill level. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input the user's current skill level data into the generation AI and cause the generation AI to customize the training program.

[0133] The training unit can analyze the user's emotions and determine training priorities based on the analyzed user's emotions. For example, the training unit can increase the training priority if the user is relaxed. For example, the training unit can also decrease the training priority if the user is stressed. For example, the training unit can also increase the training priority if the user is excited. This enables more effective training by determining the training priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-mentioned processing in the training unit can be performed using an AI, for example, or without an AI. For example, the training unit can input the user's emotion data into the generation AI and have the generation AI determine the training priorities.

[0134] The training unit can provide an optimal program during training by taking into account the user's geographical location information. For example, the training unit can provide a training program related to the user's current location. For example, the training unit can also select an optimal training program based on the user's geographical location information. For example, the training unit can also customize a training method by taking into account the user's geographical location information. In this way, an optimal training program can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the training unit can be performed using AI, for example, or can be performed without using AI. For example, the training unit can input the user's geographical location information data into a generation AI and cause the generation AI to select a training program.

[0135] The training unit can analyze the user's social media activities during training and suggest a relevant program. The training unit can suggest a relevant training program based on, for example, information shared by the user on social media. The training unit can also analyze the user's social media activities and suggest a training program based on their interests. The training unit can also suggest a relevant training program based on, for example, the activities of the user's friends on social media. In this way, by analyzing the user's social media activities, it is possible to suggest a highly relevant training program. Some or all of the above-described processing in the training unit can be performed using, for example, AI, or can be performed without using AI. For example, the training unit can input the user's social media activity data into a generation AI and have the generation AI suggest a training program.

[0136] The practice unit can analyze the user's emotions and adjust the method of providing practice opportunities based on the analyzed user's emotions. For example, if the user is relaxed, the practice unit can provide detailed practice opportunities. For example, if the user is feeling stressed, the practice unit can provide simplified practice opportunities. For example, if the user is feeling excited, the practice unit can provide visually stimulating practice opportunities. This allows for more effective practice opportunities to be provided by adjusting the method of providing practice opportunities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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. Some or all of the above-described processing in the practice unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the practice unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the method of providing practice opportunities.

[0137] When providing a practice opportunity, the practice unit can provide the optimal opportunity by referring to the user's past practice history. The practice unit, for example, provides the optimal practice opportunity based on the user's past practice history. The practice unit can also, for example, improve the accuracy of practice by referring to the user's past practice history. The practice unit can, for example, improve the practice technique by reflecting the user's past practice history. In this way, the optimal practice opportunity can be provided by referring to the user's past practice history. Some or all of the above-described processing in the practice unit may be performed using, for example, AI, or may be performed without using AI. For example, the practice unit can input the user's past practice history data into the generation AI and cause the generation AI to provide the optimal practice opportunity.

[0138] When providing a practice opportunity, the practice unit can customize the opportunity based on the user's current skill level. The practice unit provides an optimal practice opportunity based on, for example, the user's current skill level. The practice unit can also adjust the practice method according to, for example, the user's current skill level. The practice unit can also customize the practice method by reflecting, for example, the user's current skill level. This allows for more effective practice opportunities to be provided by customizing the opportunity based on the user's current skill level. Some or all of the above-described processing in the practice unit may be performed using, for example, AI, or may be performed without using AI. For example, the practice unit can input the user's current skill level data into the generation AI and cause the generation AI to customize the practice opportunity.

[0139] The practice unit can analyze the user's emotions and determine the priority of practice opportunities based on the analyzed user's emotions. For example, the practice unit can increase the priority of practice opportunities when the user is relaxed. For example, the practice unit can also decrease the priority of practice opportunities when the user is stressed. For example, the practice unit can also increase the priority of practice opportunities when the user is excited. This allows for more effective practice opportunities to be provided by determining the priority of practice opportunities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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. Some or all of the above-described processing in the practice unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the practice unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of practice opportunities.

[0140] When providing practice opportunities, the practice unit can provide the optimal opportunity by taking into account the user's geographical location information. For example, the practice unit can provide practice opportunities related to the user's current location. For example, the practice unit can also select the optimal practice opportunity based on the user's geographical location information. For example, the practice unit can also customize the practice method by taking into account the user's geographical location information. In this way, the optimal practice opportunity can be provided by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the practice unit may be performed using, for example, AI, or may be performed without using AI. For example, the practice unit can input the user's geographical location information data into the generation AI and cause the generation AI to select a practice opportunity.

[0141] When providing practice opportunities, the practice unit can analyze the user's social media activities and suggest relevant opportunities. The practice unit can, for example, suggest relevant practice opportunities based on information shared by the user on social media. The practice unit can, for example, analyze the user's social media activities and suggest practice opportunities based on the user's interests. The practice unit can, for example, suggest relevant practice opportunities based on the user's friends' activities on social media. In this way, by analyzing the user's social media activities, highly relevant practice opportunities can be suggested. Some or all of the above-described processing in the practice unit may be performed using, for example, AI, or may be performed without using AI. For example, the practice unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest practice opportunities. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, sponsor unit, revenue management unit, learning unit, training unit, and practice unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user information via the control unit 46A of the smart device 14, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and identifies the user's personality and talents. The proposal unit proposes a development plan via the specific processing unit 290 of the data processing device 12. The sponsor unit solicits sponsors via the specific processing unit 290 of the data processing device 12. The revenue management unit manages revenue via the specific processing unit 290 of the data processing device 12. The learning unit provides learning content via the control unit 46A of the smart device 14. The training unit provides a training program via the control unit 46A of the smart device 14. The practice unit provides practice opportunities via the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, sponsor unit, revenue management unit, learning unit, training unit, and practice unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user information via the control unit 46A of the smart glasses 214 and analyzes the information via the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and identifies the user's personality and talents. The proposal unit proposes a development plan via the specific processing unit 290 of the data processing device 12. The sponsor unit solicits sponsors via the specific processing unit 290 of the data processing device 12. The revenue management unit manages revenue via the specific processing unit 290 of the data processing device 12. The learning unit provides learning content via the control unit 46A of the smart glasses 214. The training unit provides a training program via the control unit 46A of the smart glasses 214. The practice unit provides practice opportunities via the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, sponsor unit, revenue management unit, learning unit, training unit, and practice unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects user information via the control unit 46A of the headset type terminal 314, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and identifies the user's personality and talents. The proposal unit proposes a development plan via the specific processing unit 290 of the data processing device 12. The sponsor unit solicits sponsors via the specific processing unit 290 of the data processing device 12. The revenue management unit manages revenue via the specific processing unit 290 of the data processing device 12. The learning unit provides learning content via the control unit 46A of the headset type terminal 314. The training unit provides a training program via the control unit 46A of the headset type terminal 314. The practice unit provides practice opportunities via the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, sponsor unit, revenue management unit, learning unit, training unit, and practice unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user information via the control unit 46A of the robot 414, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and identifies the user's personality and talents. The proposal unit proposes a development plan via the specific processing unit 290 of the data processing device 12. The sponsor unit solicits sponsors via the specific processing unit 290 of the data processing device 12. The revenue management unit manages revenue via the specific processing unit 290 of the data processing device 12. The learning unit provides learning content via the control unit 46A of the robot 414. The training unit provides a training program via the control unit 46A of the robot 414. The practice unit provides practice opportunities via the control unit 46A of the robot 414.

[0142] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0143] The analysis unit can analyze the user's emotions and determine the analysis priority based on the analyzed user's emotions. For example, if the user is relaxed, detailed analysis can be prioritized. If the user is stressed, simplified analysis can be prioritized. If the user is in a hurry, important information can be prioritized for analysis. This allows for more appropriate analysis results to be provided by determining the analysis priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the analysis priority.

[0144] The suggestion unit can analyze the user's emotions and adjust the timing of suggestions based on the analyzed user emotions. For example, if the user is relaxed, the suggestion unit can select the timing to provide detailed suggestions. If the user is stressed, the suggestion unit can select the timing to provide simplified suggestions. If the user is in a hurry, the suggestion unit can select the timing to provide suggestions that focus on the main points. This allows the suggestion unit to provide more appropriate suggestions by adjusting the timing of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of suggestions.

[0145] The collection unit can analyze the user's emotions and determine the type of information to collect based on the analyzed user's emotions. For example, if the user is relaxed, detailed information can be collected. If the user is stressed, simplified information can be collected. If the user is in a hurry, important information can be collected first. This enables more appropriate information collection by determining the type of information to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the type of information.

[0146] The revenue management unit can analyze the user's emotions and adjust the revenue management method based on the analyzed user's emotions. For example, if the user is relaxed, a detailed revenue management method can be provided. If the user is stressed, a simplified revenue management method can be provided. If the user is in a hurry, a revenue management method that focuses on the key points can be provided. This enables more appropriate revenue management by adjusting the revenue management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-mentioned processing in the revenue management unit can be performed using AI, for example, or without AI. For example, the revenue management unit can input user emotion data into the generation AI and have the generation AI adjust the revenue management method.

[0147] The training unit can analyze the user's emotions and adjust the difficulty of the training program based on the analyzed user's emotions. For example, if the user is relaxed, a more difficult training program can be provided. If the user is stressed, a less difficult training program can be provided. If the user is in a hurry, a short, effective training program can be provided. This allows for more effective training by adjusting the difficulty of the training program according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-described processing in the training unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the training unit can input the user's emotion data into the generation AI and have the generation AI adjust the difficulty of the training program.

[0148] The analysis unit can analyze the user's past behavioral history and improve the accuracy of the analysis. For example, the analysis algorithm can be optimized based on the user's past behavioral patterns. The analysis accuracy can also be improved by referring to the user's past behavioral history. The analysis method can also be improved by reflecting the user's past behavioral history. In this way, the accuracy of the analysis can be improved by referring to the user's past behavioral history. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past behavioral history data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0149] The suggestion unit can analyze the user's past suggestion results and improve the accuracy of the suggestions. For example, the suggestion algorithm can be optimized based on the results of suggestions the user has received in the past. The accuracy of the suggestions can also be improved by referring to the user's past suggestion results. The suggestion method can also be improved by reflecting the user's past suggestion results. In this way, the accuracy of the suggestions can be improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0150] The collection unit can select a means of collecting information based on the user's current living situation and areas of interest. For example, it can prioritize collection of information related to areas in which the user is currently interested. It can also select an appropriate means of collecting information depending on the user's living situation (work, home, etc.). It can also collect related information based on the user's current areas of interest. This enables efficient information collection by selecting a means of collecting information based on the user's living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's living situation and areas of interest into the generation AI and have the generation AI select an information collection means.

[0151] The revenue management unit can analyze the user's past revenue history and improve the accuracy of revenue management. For example, the revenue management algorithm can be optimized based on the user's past revenue patterns. The accuracy of revenue management can also be improved by referring to the user's past revenue history. The revenue management method can also be improved by reflecting the user's past revenue history. In this way, the accuracy of revenue management can be improved by referring to the user's past revenue history. Some or all of the above-mentioned processing in the revenue management unit can be performed, for example, using AI or without AI. For example, the revenue management unit can input the user's past revenue history data into the generation AI and have the generation AI improve the accuracy of revenue management.

[0152] The training unit can customize the training program based on the user's current skill level. For example, it can provide an optimal training program based on the user's current skill level. It can also adjust the training method according to the user's current skill level. It can also customize the training method to reflect the user's current skill level. This enables more effective training by customizing the training program based on the user's current skill level. Some or all of the above-mentioned processing in the training unit may be performed using AI, for example, or may be performed without using AI. For example, the training unit can input the user's current skill level data into the generation AI and have the generation AI customize the training program.

[0153] The processing flow of the second embodiment will be briefly explained below.

[0154] Step 1: The collection unit collects user information. User information includes personal information, behavioral history, interests, etc. The collection unit collects information when the user registers with the app and enters information such as a self-introduction, interests, and past experiences. The collection unit can also track the user's behavioral history to understand the user's interests. For example, the collection unit can collect the history of websites the user has visited and the history of apps the user has used. Furthermore, the collection unit can analyze the user's social media activity to identify the user's interests. For example, the collection unit can understand the user's interests based on information the user has shared on social media. Step 2: The analysis unit analyzes the information collected by the collection unit and identifies the user's personality and talents. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit uses data mining technology to find patterns from the user's information and identify the user's personality and talents. The analysis unit can also use statistical analysis to analyze the user's information and identify characteristics. Furthermore, the analysis unit can also use machine learning algorithms to analyze the user's information and identify characteristics. For example, the analysis unit uses machine learning algorithms to identify characteristics from the user's past behavioral history. Step 3: The suggestion unit proposes a development plan based on the personality and talents identified by the analysis unit. The development plan includes a learning program, a training schedule, goal setting, and the like. For example, the suggestion unit proposes an online course or workshop for developing leadership to a user for whom leadership talent has been identified. The suggestion unit can also provide opportunities for the user to demonstrate leadership in actual projects based on the user's characteristics. Furthermore, the suggestion unit can propose a learning program or a training schedule based on the user's characteristics. For example, the suggestion unit sets goals for developing leadership based on the user's characteristics.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0191] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0192] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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).

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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."

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] [Explanation of symbols]

[0227] 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 collection unit that collects user information; an analysis unit that analyzes the information collected by the collection unit and identifies user characteristics; a proposal unit that proposes a development plan based on the characteristics identified by the analysis unit; Equipped with A system characterized by:

2. Have a sponsorship department to solicit sponsors 2. The system of claim 1.

3. Equipped with a revenue management department to manage revenue 2. The system of claim 1.

4. Equipped with a learning department that provides learning content 2. The system of claim 1.

5. Have a training department that provides training programs 2. The system of claim 1.

6. Have a practical department that provides practical opportunities 2. The system of claim 1.

7. The collecting unit Analyze user emotions and adjust the timing of information collection based on the analyzed user emotions.

2. The system of claim 1.

8. The collecting unit Analyze users' past behavioral history and select the appropriate information collection method 2. The system of claim 1.

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