System

Abstract

An object of a system according to an embodiment is to enable a high school student to specifically imagine a future job or future based on his / her own interest or favorite content.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. An acceptance part inputs the interest and the favorite contents of the high school student. The analysis unit analyzes the information input by the reception unit. The generation unit generates a future occupation or a specific future based on the information analyzed by the analysis unit. The providing unit provides the plan generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult for high school students to concretely imagine their future careers and futures based on their interests and strengths.

[0005] The system according to the embodiment aims to enable high school students to concretely imagine their future careers and futures based on their interests and strengths. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs the high school student's interests and strengths. The analysis unit analyzes the information input by the reception unit. The generation unit generates a future career and specific future based on the information analyzed by the analysis unit. The provision unit provides the plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable high school students to concretely imagine their future careers and futures based on their own interests and strengths. [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) In an embodiment of the present invention, a future career generation system uses a generative AI to generate a future career and a dream future for a high school student based on their interests and strengths. In the future career generation system, high school students input their interests and strengths, and the generative AI analyzes the input information to generate a future career and a dream future that is optimal for the student. The generated future career and a dream future are provided as a detailed plan including specific steps, necessary skills, related academic fields, and more. For example, in the future career generation system, high school students input their interests and strengths. Then, the generative AI analyzes the input information to generate a future career and a dream future that is optimal for the student. The generated future career and a dream future are provided as a detailed plan including specific steps, necessary skills, related academic fields, and more. This allows high school students to have a concrete image of their future and motivate them to work hard toward their goals. Furthermore, by learning how the generative AI works and how to use it, students can also acquire skills related to the generative AI. For example, by learning how the generative AI works and how to use it, it is possible to work in a career that utilizes generative AI in the future. In this way, the future career generation system not only broadens the future possibilities for high school students, but also broadens the range of future career choices by allowing them to acquire skills related to generative AI. In this way, the future career generation system not only broadens the future possibilities for high school students, but also broadens the range of future career choices by allowing them to acquire skills related to generative AI. For example, possible careers that utilize generative AI include data scientist and AI engineer. In this way, generative AI can help greatly expand the future possibilities by generating future careers and dream futures based on high school students' own interests and strengths.

[0029] A future career generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs a high school student's interests and special skills. For example, the reception unit can receive information such as a subject, hobbies, and special skills input by a user. The reception unit can also support multiple input methods, such as voice input and text input. The analysis unit analyzes the information input by the reception unit using a generation AI. For example, the analysis unit can analyze the input information using techniques such as data mining, statistical analysis, and machine learning algorithms. The analysis unit can also use the generation AI to extract patterns from the input information and predict future careers and specific future outcomes. The generation unit generates future careers and specific future outcomes based on the information analyzed by the analysis unit. For example, the generation unit can generate future careers and specific future outcomes using techniques such as simulation, predictive modeling, and scenario planning. The generation unit can also generate detailed plans using the generation AI. The provision unit provides the plan generated by the generation unit. For example, the provision unit can provide the generated plan to a user using a user interface, a report format, a notification method, and the like. The providing unit can also use the generation AI to evaluate the reliability and accuracy of the generated plan. As a result, the future career generation system according to the embodiment can generate future careers and dream futures based on the interests and strengths of high school students, and provide specific plans, thereby expanding future possibilities.

[0030] The future career generation system includes a collection unit that collects data used by the generation AI for analysis. The collection unit collects data used by the generation AI for analysis. For example, the collection unit can collect data in various formats, such as text data, numerical data, and image data. The collection unit can also collect data from public databases on the Internet and academic papers. Furthermore, the collection unit can collect user input data and past data and use them for analysis. In this way, by collecting data used by the generation AI for analysis, the accuracy of the analysis can be improved.

[0031] The future career generation system includes a learning unit that teaches how generative AI works and how to use it. The learning unit provides educational materials and training programs for learning how generative AI works and how to use it. For example, the learning unit can provide educational materials and training programs in the form of online courses, workshops, hands-on sessions, etc. The learning unit can also provide opportunities to learn how generative AI works and how to use it through video lectures, practical assignments, tests, etc. In this way, by learning how generative AI works and how to use it, people can acquire skills related to generative AI.

[0032] The future career generation system includes an evaluation unit that evaluates the reliability and accuracy of information provided by the generation AI. The evaluation unit provides standards and methods for evaluating the reliability and accuracy of information provided by the generation AI. For example, the evaluation unit can set evaluation standards such as error rate, accuracy, and reproducibility to evaluate the reliability and accuracy of information provided by the generation AI. The evaluation unit can also provide specific evaluation methods such as evaluation items, evaluation scales, and evaluation procedures. This makes it possible to improve the quality of the information provided by the generation AI by evaluating its reliability and accuracy.

[0033] The collection unit can collect data related to the interests and strengths of high school students. The collection unit collects data related to the interests and strengths of high school students. For example, the collection unit can collect data related to academic performance, extracurricular activities, hobbies, etc. The collection unit can also collect related data from public databases on the Internet, academic papers, etc. Furthermore, the collection unit can collect user input data and past data and use them for analysis. In this way, collecting data related to the interests and strengths of high school students enables more appropriate analysis.

[0034] The learning department may provide educational materials and training programs for learning how generative AI works and how to use it. The learning department may provide educational materials and training programs for learning how generative AI works and how to use it. For example, the learning department may provide opportunities to learn how generative AI works and how to use it through video lectures, practical assignments, tests, etc. The learning department may also provide educational materials and training programs in the form of online courses, workshops, hands-on sessions, etc. This may enhance the learning effect by providing educational materials and training programs for learning how generative AI works and how to use it.

[0035] The evaluation unit can provide standards and methods for evaluating the reliability and accuracy of information provided by the generation AI. The evaluation unit provides standards and methods for evaluating the reliability and accuracy of information provided by the generation AI. For example, the evaluation unit can set evaluation criteria such as error rate, accuracy, and reproducibility to evaluate the reliability and accuracy of information provided by the generation AI. The evaluation unit can also provide specific evaluation methods such as evaluation items, evaluation scales, and evaluation procedures. This makes it possible to ensure the consistency and reliability of evaluations by providing standards and methods for evaluating the reliability and accuracy of information provided by the generation AI.

[0036] The reception unit can analyze the high school student's past input history and suggest an appropriate input method. The reception unit analyzes the high school student's past input history and suggest the optimal input method. For example, the reception unit automatically displays the interests and skills that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the interests and skills that will be used during a specific time period from the user's past input history. In this way, by analyzing the past input history, the optimal input method can be suggested to the user, improving input efficiency.

[0037] The reception unit can filter the input content based on the user's current learning situation and areas of interest at the time of input. The reception unit filters the input content based on the user's current learning situation and areas of interest at the time of input. For example, the reception unit can preferentially display content that the user is good at or that is related to a subject that the user is currently studying. The reception unit can also suggest related input options based on the user's areas of interest. The reception unit can also filter appropriate input content taking into account the user's learning progress. In this way, by filtering the input content based on the user's learning situation and areas of interest, more appropriate input content can be provided.

[0038] The reception unit can select the optimum input means depending on the user's input method at the time of input. The reception unit selects the optimum input means depending on the user's input method (voice, text, image, etc.) at the time of input. For example, if the user desires voice input, the reception unit can provide a voice recognition function with priority. Also, if the user desires text input, the reception unit can provide a keyboard input with priority. Also, if the user desires image input, the reception unit can provide an image recognition function with priority. In this way, by selecting the optimum input means depending on the user's input method, it is possible to improve the convenience of input.

[0039] The reception unit can prioritize input of highly relevant information by taking into account the user's geographical location information when inputting information. The reception unit can prioritize input of highly relevant information by taking into account the user's geographical location information when inputting information. For example, if the user is in a specific area, the reception unit can prioritize displaying interests and skills related to that area. Furthermore, if the user is traveling, the reception unit can also prioritize displaying information related to the travel destination. Furthermore, if the user is at school, the reception unit can also prioritize displaying information related to the school. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0040] The reception unit can analyze the user's social media activity at the time of input and cause the user to input related information. The reception unit can analyze the user's social media activity at the time of input and cause the user to input related information. For example, the reception unit can automatically display the interests and skills the user has shared on social media as input candidates. The reception unit can also analyze the user's social media activity and suggest related information as input candidates. The reception unit can also suggest related information as input candidates by referring to the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and the user can input related information efficiently.

[0041] The reception unit can customize the input method by reflecting the user's past feedback when inputting data. The reception unit customizes the input method by reflecting the user's past feedback when inputting data. For example, the reception unit suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and customize the input interface. The reception unit can also optimize the input procedure by referring to the user's past feedback. In this way, the input method can be optimized by reflecting the user's past feedback, and user satisfaction can be improved.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the input information during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the input information during analysis. For example, the analysis unit performs a detailed analysis on information of high importance. The analysis unit can also perform a simplified analysis on information of low importance. The analysis unit can also perform an analysis at an appropriate level of detail on information of medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the input information.

[0043] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. The analysis unit applies different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies an academic analysis algorithm to information related to academic fields. The analysis unit can also apply an occupational aptitude analysis algorithm to information related to occupations. The analysis unit can also apply an hobby aptitude analysis algorithm to information related to hobbies and interests. In this way, by applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided.

[0044] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0045] The analysis unit can determine the priority of analysis based on the time of submission of information during analysis. The analysis unit determines the priority of analysis based on the time of submission of information during analysis. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also postpone information that was submitted recently. The analysis unit can also appropriately prioritize information that was submitted recently. In this way, by determining the priority of analysis based on the time of submission of information, it is possible to prioritize analysis of the most recent information.

[0046] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit adjusts the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of information with high relevance. The analysis unit can also postpone analysis of information with low relevance. The analysis unit can also moderately prioritize information with medium relevance. In this way, adjusting the order of analysis based on the relevance of information enables efficient analysis.

[0047] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can provide analysis results that use a lot of technical terms to a user with high level of expertise. The analysis unit can also provide analysis results that are explained in simple terms to a user with low level of expertise. The analysis unit can also provide analysis results that use a moderate amount of technical terms to a user with medium level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0048] The generation unit can adjust the level of detail of the generation based on the importance of the future occupation or dream at the time of generation. The generation unit adjusts the level of detail of the generation based on the importance of the future occupation or dream at the time of generation. For example, the generation unit generates a detailed plan for an occupation or dream with high importance. The generation unit can also generate a simplified plan for an occupation or dream with low importance. The generation unit can also generate a plan with an appropriate level of detail for an occupation or dream with medium importance. In this way, by adjusting the level of detail of the generation based on the importance of the future occupation or dream, it is possible to prioritize the provision of information that is important to the user.

[0049] The generation unit can apply different generation algorithms depending on the occupation or dream category during generation. The generation unit applies different generation algorithms depending on the occupation or dream category during generation. For example, the generation unit applies an academic generation algorithm to occupations and dreams related to academic fields. The generation unit can also apply a technical generation algorithm to occupations and dreams related to technical fields. The generation unit can also apply an artistic generation algorithm to occupations and dreams related to the arts field. In this way, by applying different generation algorithms depending on the occupation or dream category, it is possible to provide a more appropriate plan.

[0050] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit optimizes the generation algorithm based on the user's past generation results. The generation unit can also analyze the user's past generation results and improve the accuracy of generation. The generation unit can also adjust the level of detail of generation by referring to the user's past generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results.

[0051] The generation unit can determine the priority of generation based on the submission date of the occupation or dream at the time of generation. The generation unit determines the priority of generation based on the submission date of the occupation or dream at the time of generation. For example, the generation unit generates plans with priority for occupations or dreams whose submission date is close. The generation unit can also generate plans with a later date for occupations or dreams whose submission date is far away. The generation unit can also generate plans with a moderate priority for occupations or dreams whose submission date is medium. In this way, by determining the priority of generation based on the submission date of the occupation or dream, it is possible to provide an appropriate plan according to the submission date.

[0052] The generation unit can adjust the order of generation based on the relevance of occupations and dreams during generation. The generation unit adjusts the order of generation based on the relevance of occupations and dreams during generation. For example, the generation unit generates plans with priority for occupations and dreams with high relevance. The generation unit can also generate plans later for occupations and dreams with low relevance. The generation unit can also generate plans with moderate priority for occupations and dreams with medium relevance. In this way, by adjusting the order of generation based on the relevance of occupations and dreams, highly relevant information can be provided preferentially.

[0053] The generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise during generation. The generation unit adjusts the use of technical terminology in the generation according to the user's level of expertise during generation. For example, the generation unit generates a plan that uses a lot of technical terminology for a user with high level of expertise. The generation unit can also generate a plan that is explained in simple terms for a user with low level of expertise. The generation unit can also generate a plan that uses a moderate amount of technical terminology for a user with medium level of expertise. In this way, by adjusting the use of technical terminology in the generation according to the user's level of expertise, it is possible to provide a plan that is easy for the user to understand.

[0054] The providing unit can select an appropriate delivery method based on the user's past feedback at the time of delivery. The providing unit selects the optimal delivery method by referring to the user's past feedback at the time of delivery. For example, the providing unit suggests the optimal delivery method based on feedback provided by the user in the past. The providing unit can also analyze the user's past feedback and customize the delivery interface. The providing unit can also optimize the delivery procedure by referring to the user's past feedback. In this way, the optimal delivery method can be selected by referring to the user's past feedback, and user satisfaction can be improved.

[0055] The providing unit can customize the content to be provided according to the user's current learning situation when providing the information. The providing unit customizes the content to be provided according to the user's current learning situation when providing the information. For example, the providing unit prioritizes providing information related to the subject the user is currently studying. The providing unit can also customize appropriate content to be provided taking into account the user's learning progress. The providing unit can also suggest a related plan based on the user's learning situation. In this way, by customizing the content to be provided according to the user's current learning situation, more appropriate information can be provided.

[0056] The providing unit can improve the providing method by reflecting user feedback at the time of providing. The providing unit improves the providing method by reflecting user feedback at the time of providing. For example, the providing unit improves the providing method based on feedback provided by the user. The providing unit can also analyze user feedback and optimize the providing interface. The providing unit can also improve the providing procedure by referring to user feedback. In this way, the providing method can be optimized by reflecting user feedback, and user satisfaction can be improved.

[0057] The providing unit can select an appropriate method of providing information based on the user's geographical location information at the time of providing the information. The providing unit selects the optimal method of providing information by taking the user's geographical location information into consideration at the time of providing the information. For example, if the user is in a specific area, the providing unit can provide information related to that area preferentially. Furthermore, if the user is traveling, the providing unit can also provide information related to the travel destination preferentially. Furthermore, if the user is at school, the providing unit can provide information related to the school preferentially. In this way, highly relevant information can be provided by taking the user's geographical location information into consideration.

[0058] The providing unit can analyze the user's social media activity at the time of providing and suggest content to be provided. The providing unit analyzes the user's social media activity at the time of providing and suggest content to be provided. For example, the providing unit automatically displays the interests and skills shared by the user on social media as suggestion candidates. The providing unit can also analyze the user's social media activity and suggest related information as suggestion candidates. The providing unit can also refer to the activity of the user's friends on social media and suggest related information as suggestion candidates. In this way, related information can be efficiently provided by analyzing the user's social media activity.

[0059] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the information. The providing unit customizes the delivery method by reflecting the user's past feedback when providing the information. For example, the providing unit suggests an optimal delivery method based on feedback provided by the user in the past. The providing unit can also analyze the user's past feedback and customize the delivery interface. The providing unit can also optimize the delivery procedure by referring to the user's past feedback. In this way, the delivery method can be optimized by reflecting the user's past feedback, and user satisfaction can be improved.

[0060] The collection unit can select the optimal collection method by referring to the user's past data collection history when collecting data. The collection unit selects the optimal collection method by referring to the user's past data collection history when collecting data. For example, the collection unit can propose the optimal collection method based on data collected by the user in the past. The collection unit can also analyze the user's past data collection history and optimize the collection interface. The collection unit can also optimize the collection procedure by referring to the user's past data collection history. In this way, by referring to the user's past data collection history, the optimal collection method can be selected and the efficiency of data collection can be improved.

[0061] The collection unit can filter the collected content based on the user's current interests and areas of expertise at the time of collection. The collection unit filters the collected content based on the user's current interests and areas of expertise at the time of collection. For example, the collection unit preferentially collects data related to areas in which the user is currently interested. The collection unit can also preferentially collect related data based on the user's areas of expertise. The collection unit can also filter appropriate collected content taking the user's interests and areas of expertise into consideration. In this way, highly relevant data can be collected by filtering the collected content based on the user's current interests and areas of expertise.

[0062] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information when collecting data. The collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, if the user is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, if the user is traveling, the collection unit can also prioritize collecting data related to the travel destination. Furthermore, if the user is at school, the collection unit can prioritize collecting data related to the school. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information.

[0063] The collection unit can analyze the user's social media activities and collect related data at the time of collection. The collection unit analyzes the user's social media activities and collects related data at the time of collection. For example, the collection unit collects data related to the interests and skills the user has shared on social media. The collection unit can also analyze the user's social media activities and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. In this way, related data can be efficiently collected by analyzing the user's social media activities.

[0064] The learning unit can optimize the learning algorithm by referring to past learning data during learning. The learning unit optimizes the learning algorithm by referring to past learning data during learning. For example, the learning unit optimizes the learning algorithm based on the user's past learning data. The learning unit can also analyze the user's past learning data to improve the accuracy of learning. The learning unit can also adjust the level of detail of learning by referring to the user's past learning data. In this way, by referring to the past learning data, the learning algorithm can be optimized and the accuracy of learning can be improved.

[0065] The learning unit can update the learning materials by reflecting user feedback during learning. The learning unit updates the learning materials by reflecting user feedback during learning. For example, the learning unit updates the learning materials based on feedback provided by the user. The learning unit can also analyze the user feedback and optimize the content of the learning materials. The learning unit can also improve the composition of the learning materials by referring to the user feedback. In this way, by reflecting the user feedback, the learning materials can be optimized and the learning effect can be improved.

[0066] The learning unit can weight the learning data during learning based on the submission time of the learning content. The learning unit weights the learning data during learning based on the submission time of the learning content. For example, the learning unit can assign a higher weight to learning content that is submitted soon. The learning unit can also assign a lower weight to learning content that is submitted farther back. The learning unit can also assign a moderate weight to learning content that is submitted at a medium time. In this way, by weighting the learning data based on the submission time of the learning content, it is possible to provide appropriate learning data according to the submission time.

[0067] The learning unit can integrate information from different data sources to enrich the learning data during learning. The learning unit integrates information from different data sources to enrich the learning data during learning. For example, the learning unit can integrate school teaching material data and data from an online learning platform to enrich the learning data. The learning unit can also integrate the user's past learning history and current learning progress to enrich the learning data. The learning unit can also integrate external data related to the user's interests and areas of expertise to enrich the learning data. In this way, by integrating information from different data sources, the learning data can be enriched and the quality of learning can be improved.

[0068] The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data during evaluation. The evaluation unit optimizes the evaluation algorithm by referring to past evaluation data during evaluation. For example, the evaluation unit optimizes the evaluation algorithm based on the user's past evaluation data. The evaluation unit can also analyze the user's past evaluation data to improve the accuracy of the evaluation. The evaluation unit can also adjust the level of detail of the evaluation by referring to the user's past evaluation data. In this way, by referring to the past evaluation data, the evaluation algorithm can be optimized and the accuracy of the evaluation can be improved.

[0069] The evaluation unit can update the evaluation criteria by reflecting user feedback during evaluation. The evaluation unit updates the evaluation criteria by reflecting user feedback during evaluation. For example, the evaluation unit updates the evaluation criteria based on feedback provided by the user. The evaluation unit can also analyze the user feedback and optimize the content of the evaluation criteria. The evaluation unit can also improve the configuration of the evaluation criteria by referring to the user feedback. In this way, the evaluation criteria can be optimized by reflecting user feedback, and the quality of the evaluation can be improved.

[0070] The evaluation unit can weight the evaluation data based on the submission time of the evaluation content during evaluation. The evaluation unit weights the evaluation data based on the submission time of the evaluation content during evaluation. For example, the evaluation unit can assign a higher weight to evaluation content that was submitted recently. The evaluation unit can also assign a lower weight to evaluation content that was submitted recently. The evaluation unit can also assign a moderate weight to evaluation content that was submitted at a medium time. In this way, by weighting the evaluation data based on the submission time of the evaluation content, an appropriate evaluation can be performed according to the submission time.

[0071] The evaluation unit can enrich the evaluation data by integrating information from different data sources during evaluation. The evaluation unit enriches the evaluation data by integrating information from different data sources during evaluation. For example, the evaluation unit enriches the evaluation data by integrating school evaluation data with evaluation data from an online learning platform. The evaluation unit can also enrich the evaluation data by integrating the user's past evaluation history with their current learning progress. The evaluation unit can also enrich the evaluation data by integrating external data related to the user's interests and areas of expertise. In this way, by integrating information from different data sources, the evaluation data can be enriched and the quality of the evaluation can be improved.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The reception unit can analyze the high school student's past input history and suggest an appropriate input method. For example, the reception unit automatically displays as candidates the interests and skills that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the interests and skills that the user will use during a specific time period based on the user's past input history. In this way, by analyzing the past input history, the optimal input method can be suggested to the user, improving input efficiency.

[0074] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input information. For example, the analysis unit performs a detailed analysis on information of high importance. The analysis unit can also perform a simplified analysis on information of low importance. The analysis unit can also perform an analysis with an appropriate level of detail on information of medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the input information.

[0075] The generation unit can adjust the level of detail of the generation based on the importance of the future occupation or dream during generation. For example, the generation unit generates a detailed plan for an occupation or dream with high importance. The generation unit can also generate a simplified plan for an occupation or dream with low importance. The generation unit can also generate a plan with an appropriate level of detail for an occupation or dream with medium importance. In this way, by adjusting the level of detail of the generation based on the importance of the future occupation or dream, it is possible to provide information that is important to the user preferentially.

[0076] The providing unit can select an appropriate delivery method based on the user's past feedback when providing the service. For example, the providing unit can suggest an optimal delivery method based on feedback provided by the user in the past. The providing unit can also analyze the user's past feedback and customize the delivery interface. The providing unit can also optimize the delivery procedure by referring to the user's past feedback. In this way, the optimal delivery method can be selected by referring to the user's past feedback, and user satisfaction can be improved.

[0077] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. For example, the learning unit optimizes the learning algorithm based on the user's past learning data. The learning unit can also analyze the user's past learning data to improve the accuracy of learning. The learning unit can also adjust the level of detail of learning by referring to the user's past learning data. In this way, by referring to the past learning data, the learning algorithm can be optimized and the accuracy of learning can be improved.

[0078] The processing flow of the first embodiment will be briefly explained below.

[0079] Step 1: The reception unit inputs the high school student's interests and strengths. For example, the reception unit can accept information such as subjects, hobbies, and special skills entered by the user. The reception unit can also support multiple input methods, such as voice input and text input. Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit. For example, the analysis unit can analyze the entered information using techniques such as data mining, statistical analysis, and machine learning algorithms. The analysis unit can also use the generation AI to extract patterns from the entered information and predict future careers and specific future outcomes. Step 3: The generation unit generates future occupations and specific futures based on the information analyzed by the analysis unit. For example, the generation unit can generate future occupations and specific futures using techniques such as simulation, predictive modeling, and scenario planning. The generation unit can also generate detailed plans using generation AI. Step 4: The providing unit provides the plan generated by the generating unit. For example, the providing unit can provide the generated plan to the user using a user interface, a report format, a notification method, etc. The providing unit can also use the generating AI to evaluate the reliability and accuracy of the generated plan.

[0080] (Example 2) In an embodiment of the present invention, a future career generation system uses a generative AI to generate a future career and a dream future for a high school student based on their interests and strengths. In the future career generation system, high school students input their interests and strengths, and the generative AI analyzes the input information to generate a future career and a dream future that is optimal for the student. The generated future career and a dream future are provided as a detailed plan including specific steps, necessary skills, related academic fields, and more. For example, in the future career generation system, high school students input their interests and strengths. Then, the generative AI analyzes the input information to generate a future career and a dream future that is optimal for the student. The generated future career and a dream future are provided as a detailed plan including specific steps, necessary skills, related academic fields, and more. This allows high school students to have a concrete image of their future and motivate them to work hard toward their goals. Furthermore, by learning how the generative AI works and how to use it, students can also acquire skills related to the generative AI. For example, by learning how the generative AI works and how to use it, it is possible to work in a career that utilizes generative AI in the future. In this way, the future career generation system not only broadens the future possibilities for high school students, but also broadens the range of future career choices by allowing them to acquire skills related to generative AI. In this way, the future career generation system not only broadens the future possibilities for high school students, but also broadens the range of future career choices by allowing them to acquire skills related to generative AI. For example, possible careers that utilize generative AI include data scientist and AI engineer. In this way, generative AI can help greatly expand the future possibilities by generating future careers and dream futures based on high school students' own interests and strengths.

[0081] A future career generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs a high school student's interests and special skills. For example, the reception unit can receive information such as a subject, hobbies, and special skills input by a user. The reception unit can also support multiple input methods, such as voice input and text input. The analysis unit analyzes the information input by the reception unit using a generation AI. For example, the analysis unit can analyze the input information using techniques such as data mining, statistical analysis, and machine learning algorithms. The analysis unit can also use the generation AI to extract patterns from the input information and predict future careers and specific future outcomes. The generation unit generates future careers and specific future outcomes based on the information analyzed by the analysis unit. For example, the generation unit can generate future careers and specific future outcomes using techniques such as simulation, predictive modeling, and scenario planning. The generation unit can also generate detailed plans using the generation AI. The provision unit provides the plan generated by the generation unit. For example, the provision unit can provide the generated plan to a user using a user interface, a report format, a notification method, and the like. The providing unit can also use the generation AI to evaluate the reliability and accuracy of the generated plan. As a result, the future career generation system according to the embodiment can generate future careers and dream futures based on the interests and strengths of high school students, and provide specific plans, thereby expanding future possibilities.

[0082] The future career generation system includes a collection unit that collects data used by the generation AI for analysis. The collection unit collects data used by the generation AI for analysis. For example, the collection unit can collect data in various formats, such as text data, numerical data, and image data. The collection unit can also collect data from public databases on the Internet and academic papers. Furthermore, the collection unit can collect user input data and past data and use them for analysis. In this way, by collecting data used by the generation AI for analysis, the accuracy of the analysis can be improved.

[0083] The future career generation system includes a learning unit that teaches how generative AI works and how to use it. The learning unit provides educational materials and training programs for learning how generative AI works and how to use it. For example, the learning unit can provide educational materials and training programs in the form of online courses, workshops, hands-on sessions, etc. The learning unit can also provide opportunities to learn how generative AI works and how to use it through video lectures, practical assignments, tests, etc. In this way, by learning how generative AI works and how to use it, people can acquire skills related to generative AI.

[0084] The future career generation system includes an evaluation unit that evaluates the reliability and accuracy of information provided by the generation AI. The evaluation unit provides standards and methods for evaluating the reliability and accuracy of information provided by the generation AI. For example, the evaluation unit can set evaluation standards such as error rate, accuracy, and reproducibility to evaluate the reliability and accuracy of information provided by the generation AI. The evaluation unit can also provide specific evaluation methods such as evaluation items, evaluation scales, and evaluation procedures. This makes it possible to improve the quality of the information provided by the generation AI by evaluating its reliability and accuracy.

[0085] The collection unit can collect data related to the interests and strengths of high school students. The collection unit collects data related to the interests and strengths of high school students. For example, the collection unit can collect data related to academic performance, extracurricular activities, hobbies, etc. The collection unit can also collect related data from public databases on the Internet, academic papers, etc. Furthermore, the collection unit can collect user input data and past data and use them for analysis. In this way, collecting data related to the interests and strengths of high school students enables more appropriate analysis.

[0086] The learning department may provide educational materials and training programs for learning how generative AI works and how to use it. The learning department may provide educational materials and training programs for learning how generative AI works and how to use it. For example, the learning department may provide opportunities to learn how generative AI works and how to use it through video lectures, practical assignments, tests, etc. The learning department may also provide educational materials and training programs in the form of online courses, workshops, hands-on sessions, etc. This may enhance the learning effect by providing educational materials and training programs for learning how generative AI works and how to use it.

[0087] The evaluation unit can provide standards and methods for evaluating the reliability and accuracy of information provided by the generation AI. The evaluation unit provides standards and methods for evaluating the reliability and accuracy of information provided by the generation AI. For example, the evaluation unit can set evaluation criteria such as error rate, accuracy, and reproducibility to evaluate the reliability and accuracy of information provided by the generation AI. The evaluation unit can also provide specific evaluation methods such as evaluation items, evaluation scales, and evaluation procedures. This makes it possible to ensure the consistency and reliability of evaluations by providing standards and methods for evaluating the reliability and accuracy of information provided by the generation AI.

[0088] The reception unit can estimate the user's emotion and change the settings of the input interface based on the estimated user emotion. The reception unit can estimate the user's emotion and adjust the design of the input interface based on the estimated user emotion. For example, if the user is nervous, the reception unit can provide an interface with subdued colors to reduce visual stress. If the user is having fun, the reception unit can provide an interface with bright colors to make input work more enjoyable. If the user is tired, the reception unit can provide a simple, highly visible interface to make input work easier. This can improve the user's input experience by adjusting the design of the input interface according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The reception unit can analyze the high school student's past input history and suggest an appropriate input method. The reception unit analyzes the high school student's past input history and suggest the optimal input method. For example, the reception unit automatically displays the interests and skills that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the interests and skills that will be used during a specific time period from the user's past input history. In this way, by analyzing the past input history, the optimal input method can be suggested to the user, improving input efficiency.

[0090] The reception unit can filter the input content based on the user's current learning situation and areas of interest at the time of input. The reception unit filters the input content based on the user's current learning situation and areas of interest at the time of input. For example, the reception unit can preferentially display content that the user is good at or that is related to a subject that the user is currently studying. The reception unit can also suggest related input options based on the user's areas of interest. The reception unit can also filter appropriate input content taking into account the user's learning progress. In this way, by filtering the input content based on the user's learning situation and areas of interest, more appropriate input content can be provided.

[0091] The reception unit can select the optimum input means depending on the user's input method at the time of input. The reception unit selects the optimum input means depending on the user's input method (voice, text, image, etc.) at the time of input. For example, if the user desires voice input, the reception unit can provide a voice recognition function with priority. Also, if the user desires text input, the reception unit can provide a keyboard input with priority. Also, if the user desires image input, the reception unit can provide an image recognition function with priority. In this way, by selecting the optimum input means depending on the user's input method, it is possible to improve the convenience of input.

[0092] The reception unit can estimate the user's emotions and determine the order of input contents based on the estimated user emotions. The reception unit can estimate the user's emotions and determine the priority of input contents based on the estimated user emotions. For example, if the user is stressed, the reception unit can prioritize displaying simple input contents. Also, if the user is relaxed, the reception unit can prioritize displaying detailed input contents. Also, if the user is in a hurry, the reception unit can prioritize displaying contents that can be quickly entered. In this way, by prioritizing input contents based on the user's emotions, it is possible to provide appropriate input contents according to the user's situation. Emotion estimation is realized using an emotion estimation function using, 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.

[0093] The reception unit can prioritize input of highly relevant information by taking into account the user's geographical location information when inputting information. The reception unit can prioritize input of highly relevant information by taking into account the user's geographical location information when inputting information. For example, if the user is in a specific area, the reception unit can prioritize displaying interests and skills related to that area. Furthermore, if the user is traveling, the reception unit can also prioritize displaying information related to the travel destination. Furthermore, if the user is at school, the reception unit can also prioritize displaying information related to the school. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0094] The reception unit can analyze the user's social media activity at the time of input and cause the user to input related information. The reception unit can analyze the user's social media activity at the time of input and cause the user to input related information. For example, the reception unit can automatically display the interests and skills the user has shared on social media as input candidates. The reception unit can also analyze the user's social media activity and suggest related information as input candidates. The reception unit can also suggest related information as input candidates by referring to the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and the user can input related information efficiently.

[0095] The reception unit can customize the input method by reflecting the user's past feedback when inputting data. The reception unit customizes the input method by reflecting the user's past feedback when inputting data. For example, the reception unit suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and customize the input interface. The reception unit can also optimize the input procedure by referring to the user's past feedback. In this way, the input method can be optimized by reflecting the user's past feedback, and user satisfaction can be improved.

[0096] The analysis unit can estimate the user's emotions and change the parameters of the analysis algorithm based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, the analysis unit can apply an algorithm that performs a detailed analysis when the user is relaxed. The analysis unit can also apply an algorithm that performs a quick analysis when the user is in a hurry. The analysis unit can also apply an algorithm that provides visually stimulating analysis results when the user is excited. By adjusting the analysis algorithm based on the user's emotions, the accuracy of the analysis results can be improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] The analysis unit can adjust the level of detail of the analysis based on the importance of the input information during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the input information during analysis. For example, the analysis unit performs a detailed analysis on information of high importance. The analysis unit can also perform a simplified analysis on information of low importance. The analysis unit can also perform an analysis at an appropriate level of detail on information of medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the input information.

[0098] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. The analysis unit applies different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies an academic analysis algorithm to information related to academic fields. The analysis unit can also apply an occupational aptitude analysis algorithm to information related to occupations. The analysis unit can also apply an hobby aptitude analysis algorithm to information related to hobbies and interests. In this way, by applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided.

[0099] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0100] The analysis unit can estimate the user's emotions and change the display format of the analysis results based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. By adjusting the display method of the analysis results based on the user's emotions, it is possible to provide a display that is easy for the user to view. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] The analysis unit can determine the priority of analysis based on the time of submission of information during analysis. The analysis unit determines the priority of analysis based on the time of submission of information during analysis. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also postpone information that was submitted recently. The analysis unit can also appropriately prioritize information that was submitted recently. In this way, by determining the priority of analysis based on the time of submission of information, it is possible to prioritize analysis of the most recent information.

[0102] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit adjusts the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of information with high relevance. The analysis unit can also postpone analysis of information with low relevance. The analysis unit can also moderately prioritize information with medium relevance. In this way, adjusting the order of analysis based on the relevance of information enables efficient analysis.

[0103] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can provide analysis results that use a lot of technical terms to a user with high level of expertise. The analysis unit can also provide analysis results that are explained in simple terms to a user with low level of expertise. The analysis unit can also provide analysis results that use a moderate amount of technical terms to a user with medium level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0104] The generation unit can estimate the user's emotions and change the expression format of the generated plan based on the estimated user emotions. The generation unit can estimate the user's emotions and adjust the expression format of the generated plan based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can generate a plan that emphasizes the shortest route. If the user is excited, the generation unit can generate a plan that adds visually stimulating effects. In this way, by adjusting the expression format of the plan based on the user's emotions, it is possible to provide a plan that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] The generation unit can adjust the level of detail of the generation based on the importance of the future occupation or dream at the time of generation. The generation unit adjusts the level of detail of the generation based on the importance of the future occupation or dream at the time of generation. For example, the generation unit generates a detailed plan for an occupation or dream with high importance. The generation unit can also generate a simplified plan for an occupation or dream with low importance. The generation unit can also generate a plan with an appropriate level of detail for an occupation or dream with medium importance. In this way, by adjusting the level of detail of the generation based on the importance of the future occupation or dream, it is possible to prioritize the provision of information that is important to the user.

[0106] The generation unit can apply different generation algorithms depending on the occupation or dream category during generation. The generation unit applies different generation algorithms depending on the occupation or dream category during generation. For example, the generation unit applies an academic generation algorithm to occupations and dreams related to academic fields. The generation unit can also apply a technical generation algorithm to occupations and dreams related to technical fields. The generation unit can also apply an artistic generation algorithm to occupations and dreams related to the arts field. In this way, by applying different generation algorithms depending on the occupation or dream category, it is possible to provide a more appropriate plan.

[0107] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit optimizes the generation algorithm based on the user's past generation results. The generation unit can also analyze the user's past generation results and improve the accuracy of generation. The generation unit can also adjust the level of detail of generation by referring to the user's past generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results.

[0108] The generation unit can estimate the user's emotions and change the level of detail of the plan to be generated based on the estimated user emotions. The generation unit can estimate the user's emotions and adjust the length of the plan to be generated based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point plan. If the user is relaxed, the generation unit can generate a longer plan with detailed explanations. If the user is excited, the generation unit can generate a plan with visually stimulating effects. By adjusting the length of the plan based on the user's emotions, it is possible to provide a plan of an appropriate length for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0109] The generation unit can determine the priority of generation based on the submission date of the occupation or dream at the time of generation. The generation unit determines the priority of generation based on the submission date of the occupation or dream at the time of generation. For example, the generation unit generates plans with priority for occupations or dreams whose submission date is close. The generation unit can also generate plans with a later date for occupations or dreams whose submission date is far away. The generation unit can also generate plans with a moderate priority for occupations or dreams whose submission date is medium. In this way, by determining the priority of generation based on the submission date of the occupation or dream, it is possible to provide an appropriate plan according to the submission date.

[0110] The generation unit can adjust the order of generation based on the relevance of occupations and dreams during generation. The generation unit adjusts the order of generation based on the relevance of occupations and dreams during generation. For example, the generation unit generates plans with priority for occupations and dreams with high relevance. The generation unit can also generate plans later for occupations and dreams with low relevance. The generation unit can also generate plans with moderate priority for occupations and dreams with medium relevance. In this way, by adjusting the order of generation based on the relevance of occupations and dreams, highly relevant information can be provided preferentially.

[0111] The generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise during generation. The generation unit adjusts the use of technical terminology in the generation according to the user's level of expertise during generation. For example, the generation unit generates a plan that uses a lot of technical terminology for a user with high level of expertise. The generation unit can also generate a plan that is explained in simple terms for a user with low level of expertise. The generation unit can also generate a plan that uses a moderate amount of technical terminology for a user with medium level of expertise. In this way, by adjusting the use of technical terminology in the generation according to the user's level of expertise, it is possible to provide a plan that is easy for the user to understand.

[0112] The providing unit can estimate the user's emotions and change the display format of the plan to be provided based on the estimated user emotions. The providing unit can estimate the user's emotions and adjust the display method of the plan to be provided based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. If the user is relaxed, the providing unit can also provide a display method including detailed information. If the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method of the plan based on the user's emotions, it is possible to provide a display that is easy for the user to view. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0113] The providing unit can select an appropriate delivery method based on the user's past feedback at the time of delivery. The providing unit selects the optimal delivery method by referring to the user's past feedback at the time of delivery. For example, the providing unit suggests the optimal delivery method based on feedback provided by the user in the past. The providing unit can also analyze the user's past feedback and customize the delivery interface. The providing unit can also optimize the delivery procedure by referring to the user's past feedback. In this way, the optimal delivery method can be selected by referring to the user's past feedback, and user satisfaction can be improved.

[0114] The providing unit can customize the content to be provided according to the user's current learning situation when providing the information. The providing unit customizes the content to be provided according to the user's current learning situation when providing the information. For example, the providing unit prioritizes providing information related to the subject the user is currently studying. The providing unit can also customize appropriate content to be provided taking into account the user's learning progress. The providing unit can also suggest a related plan based on the user's learning situation. In this way, by customizing the content to be provided according to the user's current learning situation, more appropriate information can be provided.

[0115] The providing unit can improve the providing method by reflecting user feedback at the time of providing. The providing unit improves the providing method by reflecting user feedback at the time of providing. For example, the providing unit improves the providing method based on feedback provided by the user. The providing unit can also analyze user feedback and optimize the providing interface. The providing unit can also improve the providing procedure by referring to user feedback. In this way, the providing method can be optimized by reflecting user feedback, and user satisfaction can be improved.

[0116] The providing unit can estimate the user's emotions and determine the order of plans to provide based on the estimated user emotions. The providing unit can estimate the user's emotions and determine the priority of plans to provide based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing simple plans. Also, if the user is relaxed, the providing unit can prioritize providing detailed plans. Also, if the user is in a hurry, the providing unit can prioritize providing plans that can be provided quickly. In this way, by prioritizing plans based on the user's emotions, it is possible to provide an appropriate plan according to the user's situation. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0117] The providing unit can select an appropriate method of providing information based on the user's geographical location information at the time of providing the information. The providing unit selects the optimal method of providing information by taking the user's geographical location information into consideration at the time of providing the information. For example, if the user is in a specific area, the providing unit can provide information related to that area preferentially. Furthermore, if the user is traveling, the providing unit can also provide information related to the travel destination preferentially. Furthermore, if the user is at school, the providing unit can provide information related to the school preferentially. In this way, highly relevant information can be provided by taking the user's geographical location information into consideration.

[0118] The providing unit can analyze the user's social media activity at the time of providing and suggest content to be provided. The providing unit analyzes the user's social media activity at the time of providing and suggest content to be provided. For example, the providing unit automatically displays the interests and skills shared by the user on social media as suggestion candidates. The providing unit can also analyze the user's social media activity and suggest related information as suggestion candidates. The providing unit can also refer to the activity of the user's friends on social media and suggest related information as suggestion candidates. In this way, related information can be efficiently provided by analyzing the user's social media activity.

[0119] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the information. The providing unit customizes the delivery method by reflecting the user's past feedback when providing the information. For example, the providing unit suggests an optimal delivery method based on feedback provided by the user in the past. The providing unit can also analyze the user's past feedback and customize the delivery interface. The providing unit can also optimize the delivery procedure by referring to the user's past feedback. In this way, the delivery method can be optimized by reflecting the user's past feedback, and user satisfaction can be improved.

[0120] The collection unit can estimate the user's emotions and change the category of data to be collected based on the estimated user emotions. The collection unit can estimate the user's emotions and adjust the type of data to be collected based on the estimated user emotions. For example, the collection unit can collect detailed data when the user is relaxed. The collection unit can also collect simple data when the user is in a hurry. The collection unit can also collect visually stimulating data when the user is excited. In this way, by adjusting the type of data to be collected based on the user's emotions, appropriate data can be collected according to the user's situation. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0121] The collection unit can select the optimal collection method by referring to the user's past data collection history when collecting data. The collection unit selects the optimal collection method by referring to the user's past data collection history when collecting data. For example, the collection unit can propose the optimal collection method based on data collected by the user in the past. The collection unit can also analyze the user's past data collection history and optimize the collection interface. The collection unit can also optimize the collection procedure by referring to the user's past data collection history. In this way, by referring to the user's past data collection history, the optimal collection method can be selected and the efficiency of data collection can be improved.

[0122] The collection unit can filter the collected content based on the user's current interests and areas of expertise at the time of collection. The collection unit filters the collected content based on the user's current interests and areas of expertise at the time of collection. For example, the collection unit preferentially collects data related to areas in which the user is currently interested. The collection unit can also preferentially collect related data based on the user's areas of expertise. The collection unit can also filter appropriate collected content taking the user's interests and areas of expertise into consideration. In this way, highly relevant data can be collected by filtering the collected content based on the user's current interests and areas of expertise.

[0123] The collection unit can estimate the user's emotions and determine the order of data to be collected based on the estimated user emotions. The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting simple data. Also, if the user is relaxed, the collection unit can prioritize collecting detailed data. Also, if the user is in a hurry, the collection unit can prioritize collecting data that can be collected quickly. In this way, by determining the priority of data to be collected based on the user's emotions, it is possible to prioritize collecting appropriate data according to the user's situation. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0124] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information when collecting data. The collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, if the user is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, if the user is traveling, the collection unit can also prioritize collecting data related to the travel destination. Furthermore, if the user is at school, the collection unit can prioritize collecting data related to the school. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information.

[0125] The collection unit can analyze the user's social media activities and collect related data at the time of collection. The collection unit analyzes the user's social media activities and collects related data at the time of collection. For example, the collection unit collects data related to the interests and skills the user has shared on social media. The collection unit can also analyze the user's social media activities and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. In this way, related data can be efficiently collected by analyzing the user's social media activities.

[0126] The learning unit can estimate the user's emotions and change the content of the learning materials based on the estimated user emotions. The learning unit can estimate the user's emotions and adjust the content of the learning materials based on the estimated user emotions. For example, the learning unit can provide detailed learning materials when the user is relaxed. The learning unit can also provide simple learning materials when the user is in a hurry. The learning unit can also provide visually stimulating learning materials when the user is excited. In this way, by adjusting the content of the learning materials based on the user's emotions, it is possible to provide appropriate learning materials according to the user's situation. 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 these examples.

[0127] The learning unit can optimize the learning algorithm by referring to past learning data during learning. The learning unit optimizes the learning algorithm by referring to past learning data during learning. For example, the learning unit optimizes the learning algorithm based on the user's past learning data. The learning unit can also analyze the user's past learning data to improve the accuracy of learning. The learning unit can also adjust the level of detail of learning by referring to the user's past learning data. In this way, by referring to the past learning data, the learning algorithm can be optimized and the accuracy of learning can be improved.

[0128] The learning unit can update the learning materials by reflecting user feedback during learning. The learning unit updates the learning materials by reflecting user feedback during learning. For example, the learning unit updates the learning materials based on feedback provided by the user. The learning unit can also analyze the user feedback and optimize the content of the learning materials. The learning unit can also improve the composition of the learning materials by referring to the user feedback. In this way, by reflecting the user feedback, the learning materials can be optimized and the learning effect can be improved.

[0129] The learning unit can estimate the user's emotions and change the frequency of learning based on the estimated user emotions. The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, the learning unit can reduce the frequency of learning when the user is stressed. The learning unit can also increase the frequency of learning when the user is relaxed. The learning unit can also adjust the frequency of learning when the user is in a hurry to enable efficient learning. In this way, by adjusting the frequency of learning based on the user's emotions, it is possible to provide an appropriate learning frequency according to the user's situation. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0130] The learning unit can weight the learning data during learning based on the submission time of the learning content. The learning unit weights the learning data during learning based on the submission time of the learning content. For example, the learning unit can assign a higher weight to learning content that is submitted soon. The learning unit can also assign a lower weight to learning content that is submitted farther back. The learning unit can also assign a moderate weight to learning content that is submitted at a medium time. In this way, by weighting the learning data based on the submission time of the learning content, it is possible to provide appropriate learning data according to the submission time.

[0131] The learning unit can integrate information from different data sources to enrich the learning data during learning. The learning unit integrates information from different data sources to enrich the learning data during learning. For example, the learning unit can integrate school teaching material data and data from an online learning platform to enrich the learning data. The learning unit can also integrate the user's past learning history and current learning progress to enrich the learning data. The learning unit can also integrate external data related to the user's interests and areas of expertise to enrich the learning data. In this way, by integrating information from different data sources, the learning data can be enriched and the quality of learning can be improved.

[0132] The evaluation unit can estimate the user's emotions and change the evaluation criteria based on the estimated user emotions. The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. For example, the evaluation unit can apply detailed evaluation criteria when the user is relaxed. The evaluation unit can also apply simple evaluation criteria when the user is in a hurry. The evaluation unit can also apply visually stimulating evaluation criteria when the user is excited. In this way, by adjusting the evaluation criteria based on the user's emotions, an appropriate evaluation can be performed according to the user's situation. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0133] The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data during evaluation. The evaluation unit optimizes the evaluation algorithm by referring to past evaluation data during evaluation. For example, the evaluation unit optimizes the evaluation algorithm based on the user's past evaluation data. The evaluation unit can also analyze the user's past evaluation data to improve the accuracy of the evaluation. The evaluation unit can also adjust the level of detail of the evaluation by referring to the user's past evaluation data. In this way, by referring to the past evaluation data, the evaluation algorithm can be optimized and the accuracy of the evaluation can be improved.

[0134] The evaluation unit can update the evaluation criteria by reflecting user feedback during evaluation. The evaluation unit updates the evaluation criteria by reflecting user feedback during evaluation. For example, the evaluation unit updates the evaluation criteria based on feedback provided by the user. The evaluation unit can also analyze the user feedback and optimize the content of the evaluation criteria. The evaluation unit can also improve the configuration of the evaluation criteria by referring to the user feedback. In this way, the evaluation criteria can be optimized by reflecting user feedback, and the quality of the evaluation can be improved.

[0135] The evaluation unit can estimate the user's emotions and change the frequency of evaluations based on the estimated user emotions. The evaluation unit can estimate the user's emotions and adjust the frequency of evaluations based on the estimated user emotions. For example, the evaluation unit can reduce the frequency of evaluations when the user is stressed. The evaluation unit can also increase the frequency of evaluations when the user is relaxed. The evaluation unit can also adjust the frequency of evaluations when the user is in a hurry to enable efficient evaluations. In this way, by adjusting the frequency of evaluations based on the user's emotions, it is possible to provide an appropriate evaluation frequency according to the user's situation. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0136] The evaluation unit can weight the evaluation data based on the submission time of the evaluation content during evaluation. The evaluation unit weights the evaluation data based on the submission time of the evaluation content during evaluation. For example, the evaluation unit can assign a higher weight to evaluation content that was submitted recently. The evaluation unit can also assign a lower weight to evaluation content that was submitted recently. The evaluation unit can also assign a moderate weight to evaluation content that was submitted at a medium time. In this way, by weighting the evaluation data based on the submission time of the evaluation content, an appropriate evaluation can be performed according to the submission time.

[0137] The evaluation unit can enrich the evaluation data by integrating information from different data sources during evaluation. The evaluation unit enriches the evaluation data by integrating information from different data sources during evaluation. For example, the evaluation unit enriches the evaluation data by integrating school evaluation data with evaluation data from an online learning platform. The evaluation unit can also enrich the evaluation data by integrating the user's past evaluation history with their current learning progress. The evaluation unit can also enrich the evaluation data by integrating external data related to the user's interests and areas of expertise. In this way, by integrating information from different data sources, the evaluation data can be enriched and the quality of the evaluation can be improved. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, generation unit, provision unit, collection unit, learning unit, and evaluation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives information input by a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using the generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a future career and a specific future based on the analysis results. The provision unit is realized by the control unit 46A of the smart device 14 and provides the generated plan to the user. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects data used by the generation AI for analysis. The learning unit is realized by the control unit 46A of the smart device 14 and provides educational materials and training programs for learning how the generation AI works and how to use it. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the reliability and accuracy of the information provided by the generation AI. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, provision unit, collection unit, learning unit, and evaluation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives information input by the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using the generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a future career and a specific future based on the analysis results. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the generated plan to the user. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects data used by the generation AI for analysis. The learning unit is realized by the control unit 46A of the smart glasses 214 and provides educational materials and training programs for learning how the generation AI works and how to use it. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12, and evaluates the reliability and accuracy of the information provided by the generation AI. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, provision unit, collection unit, learning unit, and evaluation unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives information input by a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using the generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a future career and a specific future based on the analysis results. The provision unit is realized by the control unit 46A of the headset-type terminal 314 and provides the generated plan to the user. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects data used by the generation AI for analysis. The learning unit is realized by the control unit 46A of the headset-type terminal 314 and provides educational materials and training programs for learning how the generation AI works and how to use it. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12, and evaluates the reliability and accuracy of the information provided by the generation AI. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, generation unit, provision unit, collection unit, learning unit, and evaluation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives information input by a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using the generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a future career and a specific future based on the analysis results. The provision unit is realized by the control unit 46A of the robot 414 and provides the generated plan to the user. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects data used by the generation AI for analysis. The learning unit is realized by the control unit 46A of the robot 414 and provides educational materials and training programs for learning how the generation AI works and how to use it. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the reliability and accuracy of the information provided by the generation AI.

[0138] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0139] The reception unit can estimate the user's emotions and change the settings of the input interface based on the estimated user's emotions. For example, if the user is nervous, the reception unit can provide an interface with calm colors to reduce visual stress. Alternatively, if the user is having fun, the reception unit can provide an interface with bright colors to make input work more enjoyable. Alternatively, if the user is tired, the reception unit can provide a simple, highly visible interface to make input work easier. In this way, the user's input experience can be improved by adjusting the design of the input interface according to the user's emotions.

[0140] The analysis unit can estimate the user's emotions and change the parameters of the analysis algorithm based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can apply an algorithm that performs a detailed analysis. If the user is in a hurry, the analysis unit can also apply an algorithm that performs a quick analysis. If the user is excited, the analysis unit can also apply an algorithm that provides visually stimulating analysis results. In this way, by adjusting the analysis algorithm based on the user's emotions, the accuracy of the analysis results can be improved.

[0141] The generation unit can estimate the user's emotions and change the representation format of the generated plan based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate a plan that emphasizes the shortest route. If the user is excited, the generation unit can also generate a plan that adds visually stimulating effects. In this way, by adjusting the representation format of the plan based on the user's emotions, it is possible to provide a plan that is easy for the user to understand.

[0142] The providing unit can estimate the user's emotions and change the display format of the plan to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. If the user is relaxed, the providing unit can also provide a display method including detailed information. If the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method of the plan based on the user's emotions, it is possible to provide a display that is easy for the user to view.

[0143] The evaluation unit can estimate the user's emotions and change the evaluation criteria based on the estimated user's emotions. For example, the evaluation unit can apply detailed evaluation criteria when the user is relaxed. Alternatively, the evaluation unit can apply simple evaluation criteria when the user is in a hurry. Alternatively, the evaluation unit can apply visually stimulating evaluation criteria when the user is excited. In this way, by adjusting the evaluation criteria based on the user's emotions, it is possible to perform an appropriate evaluation according to the user's situation.

[0144] The reception unit can analyze the high school student's past input history and suggest an appropriate input method. For example, the reception unit automatically displays as candidates the interests and skills that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the interests and skills that the user will use during a specific time period based on the user's past input history. In this way, by analyzing the past input history, the optimal input method can be suggested to the user, improving input efficiency.

[0145] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input information. For example, the analysis unit performs a detailed analysis on information of high importance. The analysis unit can also perform a simplified analysis on information of low importance. The analysis unit can also perform an analysis with an appropriate level of detail on information of medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the input information.

[0146] The generation unit can adjust the level of detail of the generation based on the importance of the future occupation or dream during generation. For example, the generation unit generates a detailed plan for an occupation or dream with high importance. The generation unit can also generate a simplified plan for an occupation or dream with low importance. The generation unit can also generate a plan with an appropriate level of detail for an occupation or dream with medium importance. In this way, by adjusting the level of detail of the generation based on the importance of the future occupation or dream, it is possible to provide information that is important to the user preferentially.

[0147] The providing unit can select an appropriate delivery method based on the user's past feedback when providing the service. For example, the providing unit can suggest an optimal delivery method based on feedback provided by the user in the past. The providing unit can also analyze the user's past feedback and customize the delivery interface. The providing unit can also optimize the delivery procedure by referring to the user's past feedback. In this way, the optimal delivery method can be selected by referring to the user's past feedback, and user satisfaction can be improved.

[0148] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. For example, the learning unit optimizes the learning algorithm based on the user's past learning data. The learning unit can also analyze the user's past learning data to improve the accuracy of learning. The learning unit can also adjust the level of detail of learning by referring to the user's past learning data. In this way, by referring to the past learning data, the learning algorithm can be optimized and the accuracy of learning can be improved.

[0149] The processing flow of the second embodiment will be briefly explained below.

[0150] Step 1: The reception unit inputs the high school student's interests and strengths. For example, the reception unit can accept information such as subjects, hobbies, and special skills entered by the user. The reception unit can also support multiple input methods, such as voice input and text input. Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit. For example, the analysis unit can analyze the entered information using techniques such as data mining, statistical analysis, and machine learning algorithms. The analysis unit can also use the generation AI to extract patterns from the entered information and predict future careers and specific future outcomes. Step 3: The generation unit generates future occupations and specific futures based on the information analyzed by the analysis unit. For example, the generation unit can generate future occupations and specific futures using techniques such as simulation, predictive modeling, and scenario planning. The generation unit can also generate detailed plans using generation AI. Step 4: The providing unit provides the plan generated by the generating unit. For example, the providing unit can provide the generated plan to the user using a user interface, a report format, a notification method, etc. The providing unit can also use the generating AI to evaluate the reliability and accuracy of the generated plan.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0172] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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).

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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."

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] [Explanation of symbols]

[0223] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk where high school students can input their interests and strengths, an analysis unit that analyzes the information input by the reception unit; a generation unit that generates a future occupation or a specific future based on the information analyzed by the analysis unit; a providing unit that provides the plan generated by the generating unit; Equipped with A system characterized by:

2. Equipped with a collection unit that collects data used by the generation AI for analysis 2. The system of claim 1.

3. Equipped with a learning section to learn how generative AI works and how to use it 2. The system of claim 1.

4. Equipped with an evaluation unit that evaluates the reliability and accuracy of the information provided by the generation AI 2. The system of claim 1.

5. The collecting unit Collect data related to high school students' interests and strengths 3. The system of claim 2.

6. The learning unit Providing educational materials and training programs to learn how generative AI works and how to use it 4. The system of claim 3.

7. The evaluation unit Providing standards and methods for evaluating the reliability and accuracy of information provided by generative AI 5. The system of claim 4.

8. The reception unit Estimating user emotions and changing input interface settings based on the estimated user emotions 2. The system of claim 1.

9. The reception unit Analyzing high school students' past input history and suggesting appropriate input methods 2. The system of claim 1.

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