System

Abstract

An object of the system according to the embodiment is to propose a qualification, a hobby, and a special skill optimal for a user and to support learning and community formation based on the proposed qualification, hobby, and special skill.SOLUTION: A system includes an information analysis unit, a proposal unit, a learning support unit, and a community support unit. The information analysis unit analyzes an information asset. The suggestion unit suggests a qualification, a hobby, or a special skill optimal for the user on the basis of the information analyzed by the information analysis unit. The learning support unit provides the learning resource for qualification acquisition proposed by the proposal unit. A community support part supports community formation in an area or in old age on the basis of the hobby or special skill proposed by the proposal part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of not providing sufficient systems that suggest the most suitable qualifications, hobbies, and special skills to users and support learning and community formation based on them.

[0005] The system according to the embodiment aims to suggest the most suitable qualifications, hobbies, and special skills to the user and to support learning and community formation based on them. [Means for solving the problem]

[0006] The system according to the embodiment includes an information analysis unit, a proposal unit, a learning support unit, and a community support unit. The information analysis unit analyzes information assets. The proposal unit proposes the most suitable qualifications, hobbies, or special skills for the user based on the information analyzed by the information analysis unit. The learning support unit provides learning resources for acquiring the qualifications proposed by the proposal unit. The community support unit supports the formation of local or retirement communities based on the hobbies or special skills proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the most suitable qualifications, hobbies, and special skills to the user, and support learning and community formation based on them. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The assistance system according to an embodiment of the present invention uses generative AI to analyze information assets and assist users in acquiring qualifications, hobbies, and special skills. This enables the assistance system to maximize the user's abilities and provide skills that will be useful in a variety of situations over the long term.

[0029] The support system according to the embodiment includes an information analysis unit, a proposal unit, a learning support unit, and a community support unit. The information analysis unit analyzes information assets. For example, the information analysis unit can analyze information assets such as digital data, documents, images, and audio data. The information analysis unit also uses a generation AI to analyze the information assets and propose optimal qualifications, hobbies, and special skills for the user. The proposal unit proposes optimal qualifications, hobbies, and special skills for the user based on the information analyzed by the information analysis unit. For example, the proposal unit can propose an appropriate study plan for obtaining qualifications or how to start a hobby based on the user's interests and past experience. The proposal unit can also analyze the user's emotional data and propose optimal qualifications, hobbies, and special skills based on the user's emotions. The learning support unit provides learning resources for obtaining the qualifications proposed by the proposal unit. For example, the learning support unit can generate past qualification exam questions and mock exams and provide them to the user. The learning support unit can also provide a customized study plan tailored to the user's learning style and pace. The community support unit supports the formation of local and retirement communities based on the hobbies and special skills suggested by the suggestion unit. For example, the community support unit can provide information about local events and activities, creating an environment in which users can easily participate. The community support unit can also analyze the user's emotional data and suggest optimal community activities based on the emotions. As a result, the support system according to the embodiment can maximize the user's abilities and provide skills that will be useful in a variety of situations over the long term.

[0030] The suggestion unit can suggest an appropriate study plan for obtaining a qualification or how to start a hobby based on the user's interests and past experience. The suggestion unit, for example, suggests an appropriate study plan for obtaining a qualification based on the user's interests and past experience. For example, it suggests an optimal study plan based on fields or activities that the user has been interested in in the past. The suggestion unit also suggests how the user can start a hobby. For example, it provides a beginner's guide for the user to start a new hobby or a list of necessary tools and materials. This makes it possible to suggest an optimal study plan or how to start a hobby based on the user's interests and past experience.

[0031] The learning support unit can generate past questions from qualification exams and mock exams and provide them to users. For example, the learning support unit generates past questions from qualification exams and provides them to users. For example, it provides them in the form of an online quiz based on a collection of past exam questions. The learning support unit also generates mock exams and provides them to users. For example, it provides online mock exams and paper-based mock exams. This allows users to efficiently aim to obtain qualifications.

[0032] The community support section provides information about local events and activities, creating an environment that makes it easy for users to participate. The community support section, for example, provides information about local events and activities. For example, it provides information about local festivals, workshops, seminars, etc. The community support section also creates an environment that makes it easy for users to participate. For example, it provides an event calendar, detailed explanations of activities, and how to participate. This makes it easier for users to participate in local events and activities.

[0033] The information analysis unit analyzes past successes and failures and can propose solutions to current issues based on the results. The information analysis unit, for example, analyzes past successes and failures. For example, it collects and analyzes successes and failures from past projects. The information analysis unit also proposes solutions to current issues based on the results. For example, it analyzes the factors behind past successes and failures and applies them to current projects. This makes it possible to solve current issues while utilizing wisdom from the past.

[0034] The information analysis unit can analyze a user's past behavioral history and social media posts to discover potential interests and skills. For example, the information analysis unit analyzes the content of a user's social media posts to identify areas of interest and activities. For example, the information analysis unit can discover a user's potential interests based on frequently posted keywords and hashtags. The information analysis unit can also analyze a user's past behavioral history to discover potential skills. For example, the information analysis unit can identify a user's potential skills based on website browsing history and purchase history. This makes it possible to discover a user's potential interests and skills.

[0035] The information analysis unit can analyze the user's lifestyle patterns and suggest optimal activities for each time period and day of the week. The information analysis unit, for example, analyzes the user's lifestyle patterns and suggests optimal activities for specific time periods and days of the week. For example, it suggests relaxing hobbies for weekday evenings and active activities for weekends. The information analysis unit also suggests optimal activities for each time period and day of the week based on the user's lifestyle patterns. For example, it analyzes the daily schedule, frequency of activities, and time periods to suggest optimal activities. This makes it possible to suggest optimal activities based on the user's lifestyle patterns.

[0036] The suggestion unit can also analyze the interests and skills of the user's family and friends to suggest common hobbies and activities. For example, the suggestion unit analyzes social media posts of the user's family and friends to identify common interests and skills. For example, the suggestion unit suggests hobbies that the whole family can enjoy or activities that can be done together with friends. The suggestion unit also suggests common hobbies and activities based on the interests and skills of the user's family and friends. For example, the suggestion unit suggests hobbies that the whole family can enjoy or activities that can be done together with friends. This allows the user to enjoy common hobbies and activities with their family and friends.

[0037] The information analysis unit can analyze information assets from different cultural spheres and regions and make suggestions from a global perspective. The information analysis unit, for example, analyzes information assets from different cultural spheres and regions and makes suggestions to the user from a global perspective. For example, it introduces overseas qualifications, hobbies, and special skills to broaden the user's perspective. The information analysis unit also makes suggestions from a global perspective based on information assets from different cultural spheres and regions. For example, it collects information from different cultural spheres and regions and makes optimal suggestions to the user. This makes it possible to make suggestions to the user from a global perspective.

[0038] The learning support unit can provide a customized learning plan that matches the user's learning style and pace. For example, the learning support unit analyzes the user's learning style and pace and provides a customized learning plan based on the results. For example, it provides visual learning materials to a user who is good at visual learning, and audio learning materials to a user who is good at auditory learning. The learning support unit also provides a learning plan that matches the user's learning pace. For example, it provides an optimal learning plan based on the user's learning progress and individual level of understanding. This makes it possible to provide a customized learning plan that matches the user's learning style and pace.

[0039] The learning support unit can support practical learning by generating not only past questions from qualification exams but also related work experience and case studies. For example, the learning support unit generates past questions from qualification exams and provides them to users. It also generates related work experience and case studies to support practical learning. For example, it simulates problems faced in actual work. The learning support unit also supports practical learning based on work experience and case studies. For example, it provides scenarios and problem-solving processes based on actual cases. This allows users to aim to obtain qualifications through practical learning.

[0040] The learning support unit can also suggest career paths and paths after obtaining qualifications, and support long-term goal setting. For example, the learning support unit uses generation AI to make suggestions about career paths and paths after obtaining qualifications. For example, it can introduce occupations and industries where the acquired qualifications can be utilized. The learning support unit also supports the user in setting long-term goals. For example, it can suggest steps for career advancement and the selection of schools to go to. This can support the user in setting long-term goals.

[0041] The learning support unit can introduce online communities and forums related to obtaining qualifications and promote interaction with fellow learners. For example, the learning support unit uses a generation AI to introduce online communities and forums related to obtaining qualifications and provide an environment in which the user can interact with fellow learners. For example, it introduces forums where users can exchange information with peers who are aiming for the same qualification. The learning support unit also encourages users to interact with fellow learners through online communities and forums. For example, it deepens interactions with fellow learners through discussions and joint projects. This allows users to interact with fellow learners and maintain their motivation.

[0042] The suggestion unit can analyze the user's past history of hobbies and special skills and suggest new related activities. The suggestion unit, for example, analyzes the user's past history of hobbies and special skills and suggests new related activities. For example, it suggests new activities related to hobbies that the user enjoyed in the past or new fields in which special skills can be utilized. The suggestion unit also suggests new activities based on the user's history of hobbies and special skills. For example, it analyzes past activity records and suggests new related activities. This makes it possible to suggest new activities based on the user's past history of hobbies and special skills.

[0043] The suggestion unit can suggest events and workshops related to the user's hobbies and special skills, and promote actual experiences. For example, the suggestion unit uses a generation AI to suggest events and workshops related to the user's hobbies and special skills, and promote actual experiences. For example, the suggestion unit introduces events and workshops in areas that interest the user. The suggestion unit also suggests events and workshops related to the user's hobbies and special skills. For example, it suggests practical training courses and discussion-style workshops. This makes it easier for the user to participate in events and workshops related to their hobbies and special skills.

[0044] The suggestion unit can suggest new activities that combine different hobbies and special skills, broadening the user's interests. For example, the suggestion unit uses a generation AI to suggest new activities that combine different hobbies and special skills, broadening the user's interests. For example, it can suggest food photography, which combines cooking and photography. The suggestion unit also suggests new activities based on different hobbies and special skills. For example, it can suggest activities in new fields based on a combination of different hobbies and special skills. This makes it possible to suggest new activities that broaden the user's interests.

[0045] The community support unit can analyze the user's past community participation history and suggest new related activities. The community support unit, for example, analyzes the user's past community participation history and suggests new activities related to it. For example, it introduces new community activities related to events and activities that the user has participated in in the past. The community support unit also suggests new activities based on the user's community participation history. For example, it analyzes past events and activity records and suggests new related community activities. This makes it possible to suggest new activities based on the user's past community participation history.

[0046] The community support unit can analyze community activities in different regions and cultural spheres and make suggestions from a global perspective. The community support unit, for example, analyzes community activities in different regions and cultural spheres and makes suggestions to the user from a global perspective. For example, it introduces community activities and events overseas to broaden the user's perspective. The community support unit also makes suggestions from a global perspective based on community activities in different regions and cultural spheres. For example, it collects information on different regions and cultural spheres and makes optimal suggestions to the user. This makes it possible to suggest community activities to the user from a global perspective.

[0047] The community support department can make hybrid proposals that combine online and offline community activities. The community support department can make hybrid proposals that combine online and offline community activities. For example, preparatory learning and interaction can be conducted online, followed by participation in actual offline events and activities. The community support department can also make hybrid proposals based on online and offline community activities. For example, discussions and information sharing can be conducted in online forums, followed by participation in offline hands-on experiences and workshops. This makes it possible to make proposals to users that combine online and offline community activities.

[0048] The information analysis unit analyzes past successes and failures and can propose solutions to current issues based on the results. The information analysis unit, for example, analyzes past successes and failures. For example, it collects and analyzes successes and failures from past projects. The information analysis unit also proposes solutions to current issues based on the results. For example, it analyzes the factors behind past successes and failures and applies them to current projects. This makes it possible to solve current issues while utilizing wisdom from the past.

[0049] The information analysis unit can automatically generate planning proposals specialized for different markets or regions. The information analysis unit, for example, uses generation AI to automatically generate planning proposals specialized for different markets or regions. For example, it makes proposals according to market needs and regional characteristics. The information analysis unit also automatically generates specialized planning proposals based on information on different markets or regions. For example, it proposes marketing plans or product development plans suited to specific industries or regional markets. This makes it possible to automatically generate planning proposals specialized for different markets or regions.

[0050] The information analysis unit can automatically generate a visual prototype of the proposed plan, making it easier to understand visually. The information analysis unit can automatically generate a visual prototype of the proposed plan, for example, using generative AI. For example, it can visually display a product design or service flow. The information analysis unit can also promote visual understanding of the proposal based on the visual prototype. For example, it can create a 3D model, wireframe, or mockup and provide it to the user. This allows the automatic generation of a visual prototype of the proposed plan, making it easier to understand visually.

[0051] The information analysis unit can combine ideas from different industries and applications to discover new market needs. For example, the information analysis unit combines ideas from different industries to discover new market needs. For example, it generates ideas that combine the technology field with the consumer market. The information analysis unit also discovers new market needs based on ideas from different applications. For example, it combines ideas for home and commercial use to propose new products and services. This makes it possible to combine ideas from different industries and applications to discover new market needs.

[0052] The information analysis unit can implement an agile methodology in which the combined ideas are implemented as a prototype and improved based on feedback. The information analysis unit, for example, implements an agile methodology in which the combined ideas are implemented as a prototype and improved based on feedback from users. For example, a prototype is developed in a short period of time and user opinions are reflected. The information analysis unit also uses an agile methodology to continuously improve the prototype. For example, the prototype is improved using methods such as sprints, scrum, and kanban. This makes it possible to implement an agile methodology in which the combined ideas are implemented as a prototype and improved based on feedback.

[0053] The information analysis unit can use the generation AI to analyze information assets and support the acquisition of qualifications, hobbies, and special skills. The information analysis unit, for example, uses the generation AI to analyze information assets and support the acquisition of qualifications, hobbies, and special skills. For example, when the generation AI proposes a new plan, it learns from past successes and failures and generates a proposal with the highest probability of success. The information analysis unit also uses the generation AI to suggest the qualifications, hobbies, and special skills that are best suited to the user. For example, it suggests a study plan for obtaining the best qualifications or how to start a hobby based on the user's interests and past experience. In this way, the generation AI can be used to analyze information assets and support the acquisition of qualifications, hobbies, and special skills.

[0054] The information analysis unit collects user feedback on proposed plans in real time and can make improvements immediately. For example, the information analysis unit collects user feedback on proposed plans in real time and makes improvements immediately based on the results. For example, it uses online surveys and comment functions. The information analysis unit also improves proposals based on user feedback. For example, it improves the quality of proposals through user tests and surveys. This allows feedback to be collected on proposed plans in real time and can make improvements immediately.

[0055] The information analysis unit can automatically generate planning proposals specialized for different markets or regions. The information analysis unit, for example, uses generation AI to automatically generate planning proposals specialized for different markets or regions. For example, it makes proposals according to market needs and regional characteristics. The information analysis unit also automatically generates specialized planning proposals based on information on different markets or regions. For example, it proposes marketing plans or product development plans suited to specific industries or regional markets. This makes it possible to automatically generate planning proposals specialized for different markets or regions.

[0056] The information analysis unit can automatically generate a visual prototype of the proposed plan, making it easier to understand visually. The information analysis unit can automatically generate a visual prototype of the proposed plan, for example, using generative AI. For example, it can visually display a product design or service flow. The information analysis unit can also promote visual understanding of the proposal based on the visual prototype. For example, it can create a 3D model, wireframe, or mockup and provide it to the user. This allows the automatic generation of a visual prototype of the proposed plan, making it easier to understand visually.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The suggestion unit can analyze the user's health data and suggest optimal qualifications, hobbies, and special skills based on the user's health condition. For example, the suggestion unit can analyze the user's exercise habits and dietary habits and suggest hobbies and special skills that are useful for maintaining health. The suggestion unit can also suggest activities that are useful for stress reduction and relaxation based on the user's health data. This makes it possible to suggest optimal qualifications, hobbies, and special skills based on the user's health condition.

[0059] The suggestion unit can also analyze the user's occupational data and suggest qualifications and special skills that will be useful for career advancement. For example, it can analyze the user's current occupation and industry trends and suggest skills that will be in high demand in the future. The suggestion unit can also suggest qualifications and special skills that will be useful for changing jobs or careers based on the user's occupational data. This makes it possible to suggest the most suitable qualifications and special skills based on the user's occupational data.

[0060] The learning support unit can also analyze the user's learning history and provide optimal learning resources based on past learning results. For example, it can propose a new learning plan based on the learning methods and materials that have produced good results for the user in the past. The learning support unit can also monitor the user's learning progress in real time based on the user's learning history and adjust the learning plan as necessary. This makes it possible to provide optimal learning resources based on the user's learning history.

[0061] The community support unit can also analyze the user's geographic data to suggest local events and activities. For example, it can suggest events that are tailored to the culture or season of the area where the user lives. The community support unit can also suggest nearby community activities and volunteer opportunities based on the user's geographic data. This allows it to suggest optimal community activities based on the user's geographic data.

[0062] The information analysis unit can also analyze the user's purchasing history and suggest new qualifications, hobbies, and special skills based on their interests. For example, it can suggest related qualifications and hobbies based on books and products the user has purchased in the past. The information analysis unit can also suggest new fields that the user may be interested in in the future based on the user's purchasing history. This makes it possible to suggest optimal qualifications, hobbies, and special skills based on the user's purchasing history.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The information analysis unit analyzes information assets. For example, it can analyze information assets such as digital data, documents, images, and audio data. It also uses generative AI to analyze information assets and suggest the most suitable qualifications, hobbies, and special skills for the user. Step 2: The suggestion unit suggests the most suitable qualifications, hobbies, and special skills to the user based on the information analyzed by the information analysis unit. For example, based on the user's interests and past experiences, it suggests a suitable study plan for obtaining qualifications or how to start a hobby. It can also analyze the user's emotional data and suggest the most suitable qualifications, hobbies, and special skills based on their emotions. Step 3: The learning support unit provides the user with the learning resources suggested by the suggestion unit. For example, it generates past exam questions and practice tests and provides them to the user. It can also provide a customized learning plan tailored to the user's learning style and pace. Step 4: The community support unit supports the formation of local and retirement communities based on the hobbies and special skills proposed by the proposal unit. For example, it provides information about local events and activities, creating an environment that makes it easy for users to participate. It can also analyze users' emotional data and suggest optimal community activities based on their emotions.

[0065] (Example 2) The assistance system according to an embodiment of the present invention uses generative AI to analyze information assets and assist users in acquiring qualifications, hobbies, and special skills. This enables the assistance system to maximize the user's abilities and provide skills that will be useful in a variety of situations over the long term.

[0066] The support system according to the embodiment includes an information analysis unit, a proposal unit, a learning support unit, and a community support unit. The information analysis unit analyzes information assets. For example, the information analysis unit can analyze information assets such as digital data, documents, images, and audio data. The information analysis unit also uses a generation AI to analyze the information assets and propose optimal qualifications, hobbies, and special skills for the user. The proposal unit proposes optimal qualifications, hobbies, and special skills for the user based on the information analyzed by the information analysis unit. For example, the proposal unit can propose an appropriate study plan for obtaining qualifications or how to start a hobby based on the user's interests and past experience. The proposal unit can also analyze the user's emotional data and propose optimal qualifications, hobbies, and special skills based on the user's emotions. The learning support unit provides learning resources for obtaining the qualifications proposed by the proposal unit. For example, the learning support unit can generate past qualification exam questions and mock exams and provide them to the user. The learning support unit can also provide a customized study plan tailored to the user's learning style and pace. The community support unit supports the formation of local and retirement communities based on the hobbies and special skills suggested by the suggestion unit. For example, the community support unit can provide information about local events and activities, creating an environment in which users can easily participate. The community support unit can also analyze the user's emotional data and suggest optimal community activities based on the emotions. As a result, the support system according to the embodiment can maximize the user's abilities and provide skills that will be useful in a variety of situations over the long term.

[0067] The suggestion unit can suggest an appropriate study plan for obtaining a qualification or how to start a hobby based on the user's interests and past experience. The suggestion unit, for example, suggests an appropriate study plan for obtaining a qualification based on the user's interests and past experience. For example, it suggests an optimal study plan based on fields or activities that the user has been interested in in the past. The suggestion unit also suggests how the user can start a hobby. For example, it provides a beginner's guide for the user to start a new hobby or a list of necessary tools and materials. This makes it possible to suggest an optimal study plan or how to start a hobby based on the user's interests and past experience.

[0068] The learning support unit can generate past questions from qualification exams and mock exams and provide them to users. For example, the learning support unit generates past questions from qualification exams and provides them to users. For example, it provides them in the form of an online quiz based on a collection of past exam questions. The learning support unit also generates mock exams and provides them to users. For example, it provides online mock exams and paper-based mock exams. This allows users to efficiently aim to obtain qualifications.

[0069] The community support section provides information about local events and activities, creating an environment that makes it easy for users to participate. The community support section, for example, provides information about local events and activities. For example, it provides information about local festivals, workshops, seminars, etc. The community support section also creates an environment that makes it easy for users to participate. For example, it provides an event calendar, detailed explanations of activities, and how to participate. This makes it easier for users to participate in local events and activities.

[0070] The information analysis unit analyzes past successes and failures and can propose solutions to current issues based on the results. The information analysis unit, for example, analyzes past successes and failures. For example, it collects and analyzes successes and failures from past projects. The information analysis unit also proposes solutions to current issues based on the results. For example, it analyzes the factors behind past successes and failures and applies them to current projects. This makes it possible to solve current issues while utilizing wisdom from the past.

[0071] The suggestion unit can analyze the user's emotional data and suggest the most suitable qualifications, hobbies, and special skills based on the user's emotions. For example, the suggestion unit collects the user's emotional data, and the generation AI analyzes the data to suggest the qualifications, hobbies, and special skills that the user feels most positive about. For example, suggestions are made based on activities that the user has enjoyed in the past and areas in which they have been interested. This makes it possible to suggest the most suitable qualifications, hobbies, and special skills based on the user's emotions.

[0072] The information analysis unit can analyze a user's past behavioral history and social media posts to discover potential interests and skills. For example, the information analysis unit analyzes the content of a user's social media posts to identify areas of interest and activities. For example, the information analysis unit can discover a user's potential interests based on frequently posted keywords and hashtags. The information analysis unit can also analyze a user's past behavioral history to discover potential skills. For example, the information analysis unit can identify a user's potential skills based on website browsing history and purchase history. This makes it possible to discover a user's potential interests and skills.

[0073] The information analysis unit can analyze the user's lifestyle patterns and suggest optimal activities for each time period and day of the week. The information analysis unit, for example, analyzes the user's lifestyle patterns and suggests optimal activities for specific time periods and days of the week. For example, it suggests relaxing hobbies for weekday evenings and active activities for weekends. The information analysis unit also suggests optimal activities for each time period and day of the week based on the user's lifestyle patterns. For example, it analyzes the daily schedule, frequency of activities, and time periods to suggest optimal activities. This makes it possible to suggest optimal activities based on the user's lifestyle patterns.

[0074] The suggestion unit can also analyze the interests and skills of the user's family and friends to suggest common hobbies and activities. For example, the suggestion unit analyzes social media posts of the user's family and friends to identify common interests and skills. For example, the suggestion unit suggests hobbies that the whole family can enjoy or activities that can be done together with friends. The suggestion unit also suggests common hobbies and activities based on the interests and skills of the user's family and friends. For example, the suggestion unit suggests hobbies that the whole family can enjoy or activities that can be done together with friends. This allows the user to enjoy common hobbies and activities with their family and friends.

[0075] The information analysis unit can analyze information assets from different cultural spheres and regions and make suggestions from a global perspective. The information analysis unit, for example, analyzes information assets from different cultural spheres and regions and makes suggestions to the user from a global perspective. For example, it introduces overseas qualifications, hobbies, and special skills to broaden the user's perspective. The information analysis unit also makes suggestions from a global perspective based on information assets from different cultural spheres and regions. For example, it collects information from different cultural spheres and regions and makes optimal suggestions to the user. This makes it possible to make suggestions to the user from a global perspective.

[0076] The suggestion unit can use the emotion estimation function to preferentially suggest information assets that evoke the most positive emotions in the user. For example, the suggestion unit uses the emotion estimation function to identify information assets that evoke the most positive emotions in the user and preferentially suggest them. For example, the suggestion unit makes suggestions based on activities that the user has enjoyed in the past or areas of interest to the user. The suggestion unit also uses the emotion estimation function to preferentially suggest information assets that evoke the most positive emotions in the user. For example, the suggestion unit analyzes the user's emotion data and suggests the most suitable information assets. This allows the suggestion unit to preferentially suggest information assets that evoke the most positive emotions in the user.

[0077] The learning support unit can provide a customized learning plan that matches the user's learning style and pace. For example, the learning support unit analyzes the user's learning style and pace and provides a customized learning plan based on the results. For example, it provides visual learning materials to a user who is good at visual learning, and audio learning materials to a user who is good at auditory learning. The learning support unit also provides a learning plan that matches the user's learning pace. For example, it provides an optimal learning plan based on the user's learning progress and individual level of understanding. This makes it possible to provide a customized learning plan that matches the user's learning style and pace.

[0078] The learning support unit can support practical learning by generating not only past questions from qualification exams but also related work experience and case studies. For example, the learning support unit generates past questions from qualification exams and provides them to users. It also generates related work experience and case studies to support practical learning. For example, it simulates problems faced in actual work. The learning support unit also supports practical learning based on work experience and case studies. For example, it provides scenarios and problem-solving processes based on actual cases. This allows users to aim to obtain qualifications through practical learning.

[0079] The learning support unit can use the emotion estimation function to monitor the stress level of the user while they are studying and suggest appropriate breaks and ways to refresh themselves. The learning support unit, for example, uses the emotion estimation function to monitor the stress level of the user while they are studying in real time. For example, it analyzes the user's facial expressions and voice and suggests taking a break if stress is increasing. The learning support unit also suggests appropriate ways to refresh themselves depending on the user's stress level. For example, it suggests short breaks or relaxation exercises. This makes it possible to monitor the user's stress level and suggest appropriate breaks and ways to refresh themselves.

[0080] The learning support unit can also suggest career paths and paths after obtaining qualifications, and support long-term goal setting. For example, the learning support unit uses generation AI to make suggestions about career paths and paths after obtaining qualifications. For example, it can introduce occupations and industries where the acquired qualifications can be utilized. The learning support unit also supports the user in setting long-term goals. For example, it can suggest steps for career advancement and the selection of schools to go to. This can support the user in setting long-term goals.

[0081] The learning support unit can introduce online communities and forums related to obtaining qualifications and promote interaction with fellow learners. For example, the learning support unit uses a generation AI to introduce online communities and forums related to obtaining qualifications and provide an environment in which the user can interact with fellow learners. For example, it introduces forums where users can exchange information with peers who are aiming for the same qualification. The learning support unit also encourages users to interact with fellow learners through online communities and forums. For example, it deepens interactions with fellow learners through discussions and joint projects. This allows users to interact with fellow learners and maintain their motivation.

[0082] The learning support unit can use the emotion estimation function to provide preferentially the learning resources that the user finds most motivating. For example, the learning support unit uses the emotion estimation function to identify the learning resources that the user finds most motivating and provide them preferentially. For example, suggestions are made based on learning materials and learning methods that the user found highly motivating in the past. The learning support unit also analyzes the user's emotion data and provides optimal learning resources. For example, the learning resources that the user finds most motivating are suggested based on the user's emotion score. This allows the learning resources that the user finds most motivating to be provided preferentially.

[0083] The suggestion unit can analyze the emotion data and suggest new hobbies and special skills based on the emotion. For example, the suggestion unit collects the user's emotion data, and the generation AI analyzes the data to suggest new hobbies and special skills that the user will feel the most positive emotion about. For example, suggestions are made based on activities that the user has enjoyed in the past and areas of interest. The suggestion unit also suggests new hobbies and special skills based on the emotion data. For example, it suggests new hobbies and special skills that will evoke the most positive emotion based on the user's emotion score. This makes it possible to suggest new hobbies and special skills based on the user's emotion.

[0084] The suggestion unit can analyze the user's past history of hobbies and special skills and suggest new related activities. The suggestion unit, for example, analyzes the user's past history of hobbies and special skills and suggests new related activities. For example, it suggests new activities related to hobbies that the user enjoyed in the past or new fields in which special skills can be utilized. The suggestion unit also suggests new activities based on the user's history of hobbies and special skills. For example, it analyzes past activity records and suggests new related activities. This makes it possible to suggest new activities based on the user's past history of hobbies and special skills.

[0085] The suggestion unit can use the emotion estimation function to identify the moment the user is enjoying the most and provide resources to enhance that activity. For example, the suggestion unit can use the emotion estimation function to identify the moment the user is enjoying the most and provide resources to enhance that activity. For example, it can provide educational materials and tools related to a hobby the user enjoys. The suggestion unit can also analyze the user's emotion data to identify the moment the user is enjoying the most. For example, it can analyze the user's facial expressions and voice and calculate an emotion score. This can identify the moment the user is enjoying the most and provide resources to enhance that activity.

[0086] The suggestion unit can suggest events and workshops related to the user's hobbies and special skills, and promote actual experiences. For example, the suggestion unit uses a generation AI to suggest events and workshops related to the user's hobbies and special skills, and promote actual experiences. For example, the suggestion unit introduces events and workshops in areas that interest the user. The suggestion unit also suggests events and workshops related to the user's hobbies and special skills. For example, it suggests practical training courses and discussion-style workshops. This makes it easier for the user to participate in events and workshops related to their hobbies and special skills.

[0087] The suggestion unit can suggest new activities that combine different hobbies and special skills, broadening the user's interests. For example, the suggestion unit uses a generation AI to suggest new activities that combine different hobbies and special skills, broadening the user's interests. For example, it can suggest food photography, which combines cooking and photography. The suggestion unit also suggests new activities based on different hobbies and special skills. For example, it can suggest activities in new fields based on a combination of different hobbies and special skills. This makes it possible to suggest new activities that broaden the user's interests.

[0088] The suggestion unit can use the emotion estimation function to preferentially suggest hobbies and special skills that the user feels most positive about. For example, the suggestion unit uses the emotion estimation function to identify hobbies and special skills that the user feels most positive about and preferentially suggest them. For example, suggestions are made based on activities that the user has enjoyed in the past or areas of interest to the user. The suggestion unit also uses the emotion estimation function to preferentially suggest hobbies and special skills that the user feels most positive about. For example, the suggestion unit analyzes the user's emotion data and suggests the most suitable hobbies and special skills. This allows the suggestion unit to preferentially suggest hobbies and special skills that the user feels most positive about.

[0089] The community support unit can analyze the user's emotional data and suggest optimal community activities based on the emotions. For example, the community support unit collects the user's emotional data, and the generation AI analyzes the data to suggest community activities that will evoke the most positive emotions in the user. For example, suggestions are made based on activities that the user has enjoyed in the past and areas of interest. The community support unit also suggests optimal community activities based on the emotional data. For example, it suggests community activities that will evoke the most positive emotions based on the user's emotional score. This makes it possible to suggest optimal community activities based on the user's emotions.

[0090] The community support unit can analyze the user's past community participation history and suggest new related activities. The community support unit, for example, analyzes the user's past community participation history and suggests new activities related to it. For example, it introduces new community activities related to events and activities that the user has participated in in the past. The community support unit also suggests new activities based on the user's community participation history. For example, it analyzes past events and activity records and suggests new related community activities. This makes it possible to suggest new activities based on the user's past community participation history.

[0091] The community support unit can use the emotion estimation function to preferentially suggest community activities that evoke the most positive emotions in the user. For example, the community support unit uses the emotion estimation function to identify community activities that evoke the most positive emotions in the user and preferentially suggest them. For example, suggestions are made based on activities that the user has enjoyed in the past or areas of interest to the user. The community support unit also uses the emotion estimation function to preferentially suggest community activities that evoke the most positive emotions in the user. For example, the emotion estimation function analyzes the user's emotion data and suggests the most suitable community activities. This allows preferentially suggesting community activities that evoke the most positive emotions in the user.

[0092] The community support unit can analyze community activities in different regions and cultural spheres and make suggestions from a global perspective. The community support unit, for example, analyzes community activities in different regions and cultural spheres and makes suggestions to the user from a global perspective. For example, it introduces community activities and events overseas to broaden the user's perspective. The community support unit also makes suggestions from a global perspective based on community activities in different regions and cultural spheres. For example, it collects information on different regions and cultural spheres and makes optimal suggestions to the user. This makes it possible to suggest community activities to the user from a global perspective.

[0093] The community support department can make hybrid proposals that combine online and offline community activities. The community support department can make hybrid proposals that combine online and offline community activities. For example, preparatory learning and interaction can be conducted online, followed by participation in actual offline events and activities. The community support department can also make hybrid proposals based on online and offline community activities. For example, discussions and information sharing can be conducted in online forums, followed by participation in offline hands-on experiences and workshops. This makes it possible to make proposals to users that combine online and offline community activities.

[0094] The community support unit can use the emotion estimation function to preferentially suggest community activities that evoke the most positive emotions in the user. For example, the community support unit uses the emotion estimation function to identify community activities that evoke the most positive emotions in the user and preferentially suggest them. For example, suggestions are made based on activities that the user has enjoyed in the past or areas of interest to the user. The community support unit also uses the emotion estimation function to preferentially suggest community activities that evoke the most positive emotions in the user. For example, the emotion estimation function analyzes the user's emotion data and suggests the most suitable community activities. This allows preferentially suggesting community activities that evoke the most positive emotions in the user.

[0095] The information analysis unit analyzes past successes and failures and can propose solutions to current issues based on the results. The information analysis unit, for example, analyzes past successes and failures. For example, it collects and analyzes successes and failures from past projects. The information analysis unit also proposes solutions to current issues based on the results. For example, it analyzes the factors behind past successes and failures and applies them to current projects. This makes it possible to solve current issues while utilizing wisdom from the past.

[0096] The information analysis unit can automatically generate planning proposals specialized for different markets or regions. The information analysis unit, for example, uses generation AI to automatically generate planning proposals specialized for different markets or regions. For example, it makes proposals according to market needs and regional characteristics. The information analysis unit also automatically generates specialized planning proposals based on information on different markets or regions. For example, it proposes marketing plans or product development plans suited to specific industries or regional markets. This makes it possible to automatically generate planning proposals specialized for different markets or regions.

[0097] The information analysis unit can automatically generate a visual prototype of the proposed plan, making it easier to understand visually. The information analysis unit can automatically generate a visual prototype of the proposed plan, for example, using generative AI. For example, it can visually display a product design or service flow. The information analysis unit can also promote visual understanding of the proposal based on the visual prototype. For example, it can create a 3D model, wireframe, or mockup and provide it to the user. This allows the automatic generation of a visual prototype of the proposed plan, making it easier to understand visually.

[0098] The information analysis unit can combine ideas from different industries and applications to discover new market needs. For example, the information analysis unit combines ideas from different industries to discover new market needs. For example, it generates ideas that combine the technology field with the consumer market. The information analysis unit also discovers new market needs based on ideas from different applications. For example, it combines ideas for home and commercial use to propose new products and services. This makes it possible to combine ideas from different industries and applications to discover new market needs.

[0099] The information analysis unit can implement an agile methodology in which the combined ideas are implemented as a prototype and improved based on feedback. The information analysis unit, for example, implements an agile methodology in which the combined ideas are implemented as a prototype and improved based on feedback from users. For example, a prototype is developed in a short period of time and user opinions are reflected. The information analysis unit also uses an agile methodology to continuously improve the prototype. For example, the prototype is improved using methods such as sprints, scrum, and kanban. This makes it possible to implement an agile methodology in which the combined ideas are implemented as a prototype and improved based on feedback.

[0100] The information analysis unit uses the emotion estimation function to monitor the user's emotional response to the combined ideas in real time, and is able to continuously search for the optimal combination. The information analysis unit, for example, uses the emotion estimation function to monitor the user's emotional response to the combined ideas in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The information analysis unit also continuously searches for the optimal combination of ideas based on the user's emotional response. For example, it analyzes the user's emotional data and suggests the combination of ideas that elicits the most positive response. This makes it possible to monitor the user's emotional response to the combined ideas in real time and continuously search for the optimal combination.

[0101] The information analysis unit can use the generation AI to analyze information assets and support the acquisition of qualifications, hobbies, and special skills. The information analysis unit, for example, uses the generation AI to analyze information assets and support the acquisition of qualifications, hobbies, and special skills. For example, when the generation AI proposes a new plan, it learns from past successes and failures and generates a proposal with the highest probability of success. The information analysis unit also uses the generation AI to suggest the qualifications, hobbies, and special skills that are best suited to the user. For example, it suggests a study plan for obtaining the best qualifications or how to start a hobby based on the user's interests and past experience. In this way, the generation AI can be used to analyze information assets and support the acquisition of qualifications, hobbies, and special skills.

[0102] The information analysis unit collects user feedback on proposed plans in real time and can make improvements immediately. For example, the information analysis unit collects user feedback on proposed plans in real time and makes improvements immediately based on the results. For example, it uses online surveys and comment functions. The information analysis unit also improves proposals based on user feedback. For example, it improves the quality of proposals through user tests and surveys. This allows feedback to be collected on proposed plans in real time and can make improvements immediately.

[0103] The information analysis unit can use the emotion estimation function to make personalized proposals based on the user's emotions when proposing new plans. The information analysis unit, for example, uses the emotion estimation function to make personalized proposals based on the user's emotions. For example, it proposes optimal plans based on the user's emotion score. The information analysis unit also analyzes the user's emotion data to make personalized proposals. For example, it provides proposals and customized content based on the user's preferences. This makes it possible to make personalized proposals based on the user's emotions when proposing new plans.

[0104] The information analysis unit can automatically generate planning proposals specialized for different markets or regions. The information analysis unit, for example, uses generation AI to automatically generate planning proposals specialized for different markets or regions. For example, it makes proposals according to market needs and regional characteristics. The information analysis unit also automatically generates specialized planning proposals based on information on different markets or regions. For example, it proposes marketing plans or product development plans suited to specific industries or regional markets. This makes it possible to automatically generate planning proposals specialized for different markets or regions.

[0105] The information analysis unit can automatically generate a visual prototype of the proposed plan, making it easier to understand visually. The information analysis unit can automatically generate a visual prototype of the proposed plan, for example, using generative AI. For example, it can visually display a product design or service flow. The information analysis unit can also promote visual understanding of the proposal based on the visual prototype. For example, it can create a 3D model, wireframe, or mockup and provide it to the user. This allows the automatic generation of a visual prototype of the proposed plan, making it easier to understand visually.

[0106] The information analysis unit uses the emotion estimation function to monitor the user's emotional response to new project proposals, thereby enabling the quality of proposals to be continuously improved. The information analysis unit, for example, uses the emotion estimation function to monitor the user's emotional response to new project proposals in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The information analysis unit also continuously improves the quality of proposals based on the user's emotional response. For example, it analyzes the user's emotional data and makes proposals that show the most positive response. This makes it possible to monitor the user's emotional response to new project proposals and continuously improve the quality of proposals.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The suggestion unit can analyze the user's health data and suggest optimal qualifications, hobbies, and special skills based on the user's health condition. For example, the suggestion unit can analyze the user's exercise habits and dietary habits and suggest hobbies and special skills that are useful for maintaining health. The suggestion unit can also suggest activities that are useful for stress reduction and relaxation based on the user's health data. This makes it possible to suggest optimal qualifications, hobbies, and special skills based on the user's health condition.

[0109] The suggestion unit can also analyze the user's occupational data and suggest qualifications and special skills that will be useful for career advancement. For example, it can analyze the user's current occupation and industry trends and suggest skills that will be in high demand in the future. The suggestion unit can also suggest qualifications and special skills that will be useful for changing jobs or careers based on the user's occupational data. This makes it possible to suggest the most suitable qualifications and special skills based on the user's occupational data.

[0110] The learning support unit can also analyze the user's learning history and provide optimal learning resources based on past learning results. For example, it can propose a new learning plan based on the learning methods and materials that have produced good results for the user in the past. The learning support unit can also monitor the user's learning progress in real time based on the user's learning history and adjust the learning plan as necessary. This makes it possible to provide optimal learning resources based on the user's learning history.

[0111] The community support unit can also analyze the user's geographic data to suggest local events and activities. For example, it can suggest events that are tailored to the culture or season of the area where the user lives. The community support unit can also suggest nearby community activities and volunteer opportunities based on the user's geographic data. This allows it to suggest optimal community activities based on the user's geographic data.

[0112] The information analysis unit can also analyze the user's purchasing history and suggest new qualifications, hobbies, and special skills based on their interests. For example, it can suggest related qualifications and hobbies based on books and products the user has purchased in the past. The information analysis unit can also suggest new fields that the user may be interested in in the future based on the user's purchasing history. This makes it possible to suggest optimal qualifications, hobbies, and special skills based on the user's purchasing history.

[0113] The suggestion unit can analyze the user's emotional data and suggest optimal relaxation and stress relief methods based on the user's emotions. For example, suggestions can be made based on activities or places where the user has found relaxation in the past. The suggestion unit can also suggest relaxation methods that correspond to emotional fluctuations based on the user's emotional data. This makes it possible to suggest optimal relaxation and stress relief methods based on the user's emotions.

[0114] The suggestion unit can analyze the user's emotional data and suggest optimal travel destinations and tourist spots based on the user's emotions. For example, it can suggest new travel destinations based on emotional data from places the user has visited in the past. The suggestion unit can also suggest optimal travel destinations according to the season and climate based on the user's emotional data. This makes it possible to suggest optimal travel destinations and tourist spots based on the user's emotions.

[0115] The learning support unit can use the emotion estimation function to monitor the user's motivation in real time while studying and provide appropriate encouragement and feedback. For example, if the user's motivation drops while studying, it can present encouraging messages or success stories. The learning support unit can also provide feedback according to the user's learning progress based on the user's emotion data. This makes it easier for the user to maintain their motivation while studying.

[0116] The community support unit can use the emotion estimation function to preferentially suggest community activities that evoke the most positive emotions in the user. For example, suggestions are made based on activities the user has enjoyed in the past or areas of interest to the user. The community support unit can also analyze the user's emotion data to suggest optimal community activities. This allows preferentially suggesting community activities that evoke the most positive emotions in the user.

[0117] The suggestion unit can use the emotion estimation function to preferentially suggest hobbies and special skills that the user feels the most positive about. For example, suggestions are made based on activities the user has enjoyed in the past or areas of interest to the user. The suggestion unit can also analyze the user's emotion data to suggest optimal hobbies and special skills. This allows preferentially suggesting hobbies and special skills that the user feels the most positive about.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The information analysis unit analyzes information assets. For example, it can analyze information assets such as digital data, documents, images, and audio data. It also uses generative AI to analyze information assets and suggest the most suitable qualifications, hobbies, and special skills for the user. Step 2: The suggestion unit suggests the most suitable qualifications, hobbies, and special skills to the user based on the information analyzed by the information analysis unit. For example, based on the user's interests and past experiences, it suggests a suitable study plan for obtaining qualifications or how to start a hobby. It can also analyze the user's emotional data and suggest the most suitable qualifications, hobbies, and special skills based on their emotions. Step 3: The learning support unit provides the user with the learning resources suggested by the suggestion unit. For example, it generates past exam questions and practice tests and provides them to the user. It can also provide a customized learning plan tailored to the user's learning style and pace. Step 4: The community support unit supports the formation of local and retirement communities based on the hobbies and special skills proposed by the proposal unit. For example, it provides information about local events and activities, creating an environment that makes it easy for users to participate. It can also analyze users' emotional data and suggest optimal community activities based on their emotions.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0122] 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.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0164] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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. [Explanation of symbols]

[0187] 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. an information analysis department that analyzes information assets; a suggestion unit that suggests the most suitable qualifications, hobbies, or special skills to the user based on the information analyzed by the information analysis unit; a learning support unit that provides learning resources for acquiring the qualifications proposed by the proposal unit; and a community support unit that supports the formation of a local or elderly community based on the hobbies or special skills proposed by the proposal unit. A system characterized by:

2. The information analysis unit Analyze past successes and failures and use them to propose solutions to current challenges 2. The system of claim 1.

3. The learning support unit Providing a customized learning plan tailored to the user's learning style or pace 2. The system of claim 1.

4. The community support department: Analyzes emotional data and suggests optimal community activities based on emotions 2. The system of claim 1.

5. The proposal unit Analyzing the emotional data of the user and suggesting optimal qualifications, hobbies, or special skills based on the emotions 2. The system of claim 1.

6. The learning support unit Using emotion estimation, the system monitors the user's stress level while studying and suggests appropriate breaks or ways to refresh.

2. The system of claim 1.

7. The proposal unit Analyzing emotion data and suggesting new hobbies or special skills based on emotion 2. The system of claim 1.

8. The information analysis unit Using an emotion estimation function, personalized proposals are made based on the user's emotions when proposing new projects.

2. The system of claim 1.

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