System

Abstract

An object of the system according to the embodiment is to allow a user to efficiently exchange his / her skills and knowledge with other users.SOLUTION: A system includes a profile creation unit, a skill matching unit, and a skill exchange unit. The profile creation unit creates a profile in which a field and expertise that the user is good at are described. The skill matching unit matches a skill that can be provided and a skill desired to be learned based on the profile information of the user created by the profile creation unit. The skill exchange unit performs skill exchange between the users matched by the skill matching unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide a mechanism for users to efficiently exchange their skills and knowledge with other users, and there is room for improvement.

[0005] The system according to the embodiment aims to enable users to efficiently exchange their skills and knowledge with other users. [Means for solving the problem]

[0006] The system according to the embodiment includes a profile creation unit, a skill matching unit, and a skill exchange unit. The profile creation unit creates a profile that describes a user's areas of expertise and specialized knowledge. The skill matching unit matches skills that can be provided with skills that a user wants to learn based on the user's profile information created by the profile creation unit. The skill exchange unit allows users matched by the skill matching unit to exchange skills with each other. [Effects of the Invention]

[0007] The system according to the embodiment allows users to efficiently exchange their skills and knowledge with other users. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The skill chat service according to the embodiment of the present invention is a platform where users can offer their areas of expertise or specialized knowledge and receive other skills or knowledge of equal value from other users. This allows the skill chat service to create a community where users can improve each other's skills and learn from each other.

[0029] A skill chat service according to an embodiment includes a profile creation unit, a skill matching unit, and a skill exchange unit. The profile creation unit creates a profile describing a user's areas of expertise and specialized knowledge. For example, a user inputs their areas of expertise and specialized knowledge to create a profile. The profile creation unit can also input skills and knowledge that the user wants to learn. The skill matching unit matches skills that can be provided with skills that the user wants to learn, based on the user's profile information created by the profile creation unit. For example, it matches a user who wants to teach programming with a user who wants to learn English conversation. The skill matching unit can also perform automatic matching using AI. The skill exchange unit allows users matched by the skill matching unit to exchange skills. For example, a user who teaches programming provides programming lessons to a user who teaches English conversation, and vice versa. The skill chat service according to an embodiment can thereby provide a platform for users to exchange their skills and knowledge and learn from each other.

[0030] The profile creation unit can use the generation AI to automatically extract and suggest areas of expertise and interests from the user's past activity history and social media posts. For example, when a user creates a profile, the generation AI analyzes the user's past activity history and social media posts to automatically extract areas of expertise and interests. For example, the generation AI identifies areas of expertise such as programming and design from the content the user frequently posts and events they attend. The generation AI also suggests areas of expertise and interests based on the user's past activity history. For example, the generation AI analyzes data from online courses and workshops the user has previously attended to automatically extract skills such as programming and marketing. The generation AI also analyzes social media posts to automatically extract the user's interests and areas of expertise. For example, the generation AI identifies skills such as data science and English conversation from the articles and comments the user frequently shares. This allows the user's areas of expertise and interests to be automatically extracted and supports profile creation.

[0031] The profile creation unit can set detailed levels for the skills that a user provides and the skills that a user wants to learn, enabling more precise matching. The profile creation unit, for example, sets detailed levels for the skills that a user provides and the skills that a user wants to learn. For example, programming skills can be classified into "beginner," "intermediate," and "advanced" to improve matching accuracy. In addition, detailed levels can be set for skills that a user wants to learn, enabling more precise matching. For example, English conversation skills can be classified into "daily conversation," "business conversation," and "native level" to select an appropriate partner. In addition, detailed levels can be set for the skills that a user provides, improving matching accuracy. For example, design skills can be classified into "basic design," "intermediate design," and "advanced design" to achieve optimal matching. In this way, detailed skill level setting enables more precise matching.

[0032] The profile creation unit provides a function to add videos and audio messages to the profile, thereby more specifically expressing the user's personality and skills. The profile creation unit, for example, provides a function to add videos and audio messages to the profile, thereby more specifically expressing the user's personality and skills. For example, a user uploads a video demonstrating their skills. The unit also provides a function to add videos and audio messages to the profile, thereby more specifically expressing the user's personality and skills. For example, a user introduces themselves or explains their skills in an audio message. The unit also provides a function to add videos and audio messages to the profile, thereby more specifically expressing the user's personality and skills. For example, a user introduces past projects and achievements in a video. This more specifically expresses the user's personality and skills.

[0033] The profile creation unit supports the creation of profiles in different languages, thereby facilitating skill exchange between international users. The profile creation unit, for example, supports the creation of profiles in different languages, thereby facilitating skill exchange between international users. For example, it allows profiles to be created in multiple languages, such as English, French, and Chinese. It also supports different languages ​​when creating profiles, thereby facilitating skill exchange between international users. For example, it provides a function for a user to create a profile in their native language and automatically translate it into other languages. It also supports the creation of profiles in different languages, thereby facilitating skill exchange between international users. For example, a user can create a profile in multiple languages, thereby realizing international matching. This makes it possible to promote skill exchange between international users.

[0034] The skill matching unit can use the generation AI to analyze the user's past skill exchange history and feedback and propose optimal matching. The skill matching unit, for example, uses the generation AI to analyze the user's past skill exchange history and feedback and propose optimal matching. For example, similar matching is proposed based on the history of skill exchanges that have received high ratings in the past. The generation AI also analyzes the user's past skill exchange history and feedback and proposes optimal matching. For example, a compatible partner is selected based on past feedback data. The generation AI also analyzes the user's past skill exchange history and feedback and proposes optimal matching. For example, optimal matching is performed based on the success rate of past skill exchanges. This makes it possible to propose optimal matching based on past skill exchange history and feedback.

[0035] The skill matching unit can select the optimal partner by taking the user's learning style into consideration in the matching algorithm. The skill matching unit, for example, takes the user's learning style into consideration in the matching algorithm to select the optimal partner. For example, for a user with a visual learning style, a partner who can provide visual learning materials is selected. The user's learning style is also incorporated into the matching algorithm to select the optimal partner. For example, for a user with an auditory learning style, a partner who can provide audio learning materials is selected. The matching algorithm also takes the user's learning style into consideration to select the optimal partner. For example, for a user with an experiential learning style, a partner who can provide practical skills is selected. In this way, the optimal partner can be selected based on the user's learning style.

[0036] The skill matching unit can perform more precise matching by taking into account data on online courses and workshops that the user has previously participated in when matching. The skill matching unit can perform more precise matching by taking into account data on online courses and workshops that the user has previously participated in when matching. For example, the skill matching unit can select an appropriate partner based on the content of courses that the user has previously taken. Furthermore, data on online courses and workshops that the user has previously participated in can be incorporated into the matching algorithm to perform more precise matching. For example, users who have taken the same course can be matched with each other. Furthermore, during matching, the skill matching unit can perform more precise matching by taking into account data on online courses and workshops that the user has previously participated in. For example, a partner who is compatible with the user can be selected based on the user's past participation history. This allows for more precise matching by taking into account past participation data.

[0037] The skill matching department can provide opportunities to gain new perspectives and knowledge by matching experts from different industries and fields. For example, the skill matching department can match experts from different industries and fields to provide opportunities to gain new perspectives and knowledge. For example, it can match a technical expert with a design expert. It can also match experts from different industries and fields to provide opportunities to gain new perspectives and knowledge. For example, it can match a marketing expert with an engineer. It can also match experts from different industries and fields to provide opportunities to gain new perspectives and knowledge. For example, it can match an expert in the medical field with an expert in the IT field. In this way, it can provide opportunities to gain new perspectives and knowledge by matching experts from different industries and fields.

[0038] The skill exchange unit can use generative AI to suggest appropriate teaching materials and resources in real time during skill exchange. The skill exchange unit, for example, uses generative AI to suggest appropriate teaching materials and resources in real time during skill exchange. For example, during a programming lesson, relevant code samples and documentation are provided. The generative AI also suggests appropriate teaching materials and resources in real time during skill exchange. For example, during an English conversation lesson, relevant phrasebooks and pronunciation guides are provided. The generative AI also suggests appropriate teaching materials and resources in real time during skill exchange. For example, during a design lesson, relevant tutorial videos and templates are provided. This makes it possible to suggest appropriate teaching materials and resources in real time during skill exchange.

[0039] The skill exchange unit can provide a function to automatically record a skill exchange session so that it can be reviewed later. The skill exchange unit, for example, provides a function to automatically record a skill exchange session so that it can be reviewed later. For example, a programming lesson can be recorded so that a user can review the code later. Also, a function to automatically record a skill exchange session so that it can be reviewed later is provided. For example, an English conversation lesson can be recorded so that a user can review pronunciation and phrases. Also, a function to automatically record a skill exchange session so that it can be reviewed later is provided. For example, a design lesson can be recorded so that a user can review the design process. In this way, it is possible to record a skill exchange session so that it can be reviewed later.

[0040] The skill exchange unit can use virtual reality or augmented reality during skill exchange to provide a more interactive learning experience. The skill exchange unit, for example, uses virtual reality during skill exchange to provide a more interactive learning experience. For example, a programming lesson is conducted in a VR environment, and the user executes code in the virtual space. Also, augmented reality is used during skill exchange to provide a more interactive learning experience. For example, an English conversation lesson is conducted in an AR environment, and the user converses with a virtual character. Also, virtual reality or augmented reality is used during skill exchange to provide a more interactive learning experience. For example, a design lesson is conducted in a VR / AR environment, and the user creates a design in the virtual space. In this way, a more interactive learning experience can be provided using virtual reality or augmented reality.

[0041] The skill exchange unit can enhance the schedule adjustment function so that users in different time zones can smoothly exchange skills with each other. The skill exchange unit enhances the schedule adjustment function so that users in different time zones can smoothly exchange skills with each other. For example, the skill exchange unit automatically detects the user's time zone and proposes an optimal schedule. The schedule adjustment function is also enhanced to enable users in different time zones to smoothly exchange skills with each other. For example, the user inputs the time periods when they are available and an optimal schedule is automatically generated. The schedule adjustment function is also enhanced to enable users in different time zones to smoothly exchange skills with each other. For example, the skill exchange unit works in conjunction with the user's calendar and automatically adjusts available times. This enables users in different time zones to smoothly exchange skills with each other.

[0042] The community formation unit can use the generation AI to analyze the user's interests and past activity history and suggest the most suitable community or group. The community formation unit, for example, uses the generation AI to analyze the user's interests and past activity history and suggest the most suitable community or group. For example, related communities are suggested based on data on events and forums that the user has previously participated in. The generation AI also analyzes the user's interests and past activity history and suggests the most suitable community or group. For example, related groups are suggested based on topics that the user frequently posts on. The generation AI also analyzes the user's interests and past activity history and suggests the most suitable community or group. For example, related communities are suggested based on data on online courses and workshops that the user has previously participated in. This makes it possible to suggest the most suitable community or group based on the user's interests and past activity history.

[0043] The community formation unit can provide a function to automatically summarize discussions and information exchanges within a community and extract important points. The community formation unit, for example, provides a function to automatically summarize discussions and information exchanges within a community and extract important points. For example, it summarizes long discussions and displays main opinions and conclusions. It also provides a function to automatically summarize discussions and information exchanges and extract important points. For example, it summarizes forum posts and displays important questions and answers. It also provides a function to automatically summarize discussions and information exchanges within a community and extract important points. For example, it summarizes the contents of group chats and displays main topics and suggestions. In this way, it is possible to automatically summarize discussions and information exchanges within a community and extract important points.

[0044] The community formation unit can provide a function of automatically suggesting regular online events and workshops to promote interaction within the community. The community formation unit provides, for example, a function of automatically suggesting regular online events and workshops to promote interaction within the community. For example, related events are suggested based on the user's interests. In addition, a function of automatically suggesting regular online events and workshops to promote interaction within the community is provided. For example, appropriate events are suggested based on the user's skills and interests. In addition, a function of automatically suggesting regular online events and workshops to promote interaction within the community is provided. For example, related events are suggested based on the user's past participation history. In this way, regular online events and workshops can be automatically suggested to promote interaction within the community.

[0045] The community formation unit can introduce a real-time translation function so that users with different languages ​​and cultural backgrounds can interact with each other. The community formation unit, for example, introduces a real-time translation function so that users with different languages ​​and cultural backgrounds can interact with each other. For example, chat messages can be automatically translated so that users can communicate in their native languages. The real-time translation function can also be introduced so that users with different languages ​​and cultural backgrounds can interact smoothly with each other. For example, subtitles can be displayed in real time during video calls to remove language barriers. The real-time translation function can also be introduced so that users with different languages ​​and cultural backgrounds can interact with each other. For example, posts in forums and group chats can be automatically translated so that users can participate in discussions in different languages. This makes it possible for users with different languages ​​and cultural backgrounds to interact with each other.

[0046] The feedback and evaluation system unit can provide a function that uses a generation AI to automatically analyze feedback content and extract areas for improvement and strengths. The feedback and evaluation system unit, for example, uses a generation AI to provide a function that automatically analyzes feedback content and extracts areas for improvement and strengths. For example, it analyzes user feedback and lists specific areas for improvement and strengths. It also provides a function that uses a generation AI to automatically analyze feedback content and extract areas for improvement and strengths. For example, it analyzes text data of feedback and classifies positive comments and negative comments. It also provides a function that uses a generation AI to automatically analyze feedback content and extract areas for improvement and strengths. For example, it presents specific improvement suggestions and methods for strengthening strengths based on user feedback. In this way, it is possible to automatically analyze feedback content and extract areas for improvement and strengths.

[0047] The feedback and evaluation system unit can provide a function for automatically generating specific improvement suggestions and advice when providing feedback. The feedback and evaluation system unit, for example, provides a function for automatically generating specific improvement suggestions and advice when providing feedback. For example, when a user inputs feedback, the generation AI automatically generates and displays improvement suggestions. The feedback and evaluation system unit also provides a function for automatically generating specific improvement suggestions and advice to improve the quality of feedback. For example, when a user inputs feedback, the generation AI automatically generates and displays advice. The feedback and evaluation system unit also provides a function for automatically generating specific improvement suggestions and advice when providing feedback. For example, when a user inputs feedback, the generation AI automatically lists and displays improvements and advice. This makes it possible to automatically generate specific improvement suggestions and advice when providing feedback.

[0048] The feedback and evaluation system section can visualize the feedback content and display it in graphs and charts to allow users to intuitively understand. The feedback and evaluation system section, for example, can visualize the feedback content and display it in graphs and charts to allow users to intuitively understand. For example, the ratio of positive to negative feedback can be displayed in a pie chart. The feedback content can also be visualized to allow users to intuitively understand. For example, the feedback trend can be displayed in a line graph to show changes over time. The feedback content can also be visualized to allow users to intuitively understand. For example, the main areas for improvement in the feedback can be displayed in a bar graph. In this way, the feedback content can be visualized to allow users to intuitively understand.

[0049] The Feedback and Evaluation System Department provides opportunities to receive feedback from experts in different industries and fields, thereby promoting evaluation from multiple perspectives. The Feedback and Evaluation System Department, for example, provides opportunities to receive feedback from experts in different industries and fields, thereby promoting evaluation from multiple perspectives. For example, feedback can be received from technical experts and design experts at the same time. It also provides opportunities to receive feedback from experts in different industries and fields, thereby promoting evaluation from multiple perspectives. For example, feedback can be received from marketing experts and engineers at the same time. It also provides opportunities to receive feedback from experts in different industries and fields, thereby promoting evaluation from multiple perspectives. For example, feedback can be received from medical experts and IT experts at the same time. This provides opportunities to receive feedback from experts in different industries and fields, thereby promoting evaluation from multiple perspectives.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The skill chat service may further include an evaluation unit that evaluates a user's skills and knowledge. The evaluation unit collects feedback from other users on the skills and knowledge provided by the user and evaluates them. For example, a user who has taken a programming lesson may evaluate the quality and usefulness of the lesson. The evaluation unit may also provide specific improvements and advice to promote the improvement of the user's skills and knowledge. For example, a user who has taken an English conversation lesson may receive feedback on areas for improvement in pronunciation and grammar. This allows the user to objectively evaluate and improve their own skills and knowledge.

[0052] The skill chat service may further include a visualization unit for visually representing a user's skills and knowledge. The visualization unit visually displays the user's skills and knowledge using graphs and charts, allowing other users to intuitively understand. For example, the level of mastery of programming skills may be displayed using a bar graph. The visualization unit may also display the progress of a user's skills and knowledge using a timeline. For example, the progress of an English conversation lesson may be displayed using a line graph. This allows users to visually grasp the progress of their skills and knowledge and share it with other users.

[0053] The skill chat service may further include a sharing section for users to share their skills and knowledge with other users. The sharing section provides a platform for users to share their skills and knowledge with other users. For example, a user may share a programming code sample with other users. The sharing section may also provide a forum or group chat for users to share their skills and knowledge with other users. For example, a user may discuss the contents of an English conversation lesson with other users. This allows users to share their skills and knowledge with other users and learn from each other.

[0054] The skill chat service may further include a ranking unit for evaluating a user's skills and knowledge. The ranking unit compares the user's skills and knowledge with those of other users and displays a ranking. For example, it may display a ranking of programming skills. The ranking unit may also provide rewards or incentives based on the ranking to encourage users to improve their skills and knowledge. For example, it may provide special benefits to those who rank highly in English conversation lessons. This allows users to compare their skills and knowledge with those of other users and motivate them to improve.

[0055] The skill chat service may further include a collaboration section for sharing a user's skills and knowledge with other users. The collaboration section provides a platform for a user to collaborate with other users to utilize their skills and knowledge. For example, a user may collaborate with other users to develop a programming project. The collaboration section may also provide workshops and seminars for a user to collaborate with other users to utilize their skills and knowledge. For example, a user may collaborate with other users to take English conversation lessons. This allows a user to collaborate with other users to utilize their skills and knowledge and learn from each other.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The profile creation unit creates a profile that describes the user's areas of expertise and specialized knowledge. For example, the user inputs their areas of expertise and specialized knowledge to create a profile. The profile creation unit can also input skills and knowledge that the user wants to learn. Step 2: The skill matching unit matches the skills that can be provided with the skills that users want to learn based on the user's profile information created by the profile creation unit. For example, it matches a user who wants to teach programming with a user who wants to learn English conversation. The skill matching unit can also use AI to perform automatic matching. Step 3: The skill exchange unit allows users matched by the skill matching unit to exchange skills. For example, a user who teaches programming can provide programming lessons to a user who teaches English conversation, and vice versa.

[0058] (Example 2) The skill chat service according to the embodiment of the present invention is a platform where users can offer their areas of expertise or specialized knowledge and receive other skills or knowledge of equal value from other users. This allows the skill chat service to create a community where users can improve each other's skills and learn from each other.

[0059] A skill chat service according to an embodiment includes a profile creation unit, a skill matching unit, and a skill exchange unit. The profile creation unit creates a profile describing a user's areas of expertise and specialized knowledge. For example, a user inputs their areas of expertise and specialized knowledge to create a profile. The profile creation unit can also input skills and knowledge that the user wants to learn. The skill matching unit matches skills that can be provided with skills that the user wants to learn, based on the user's profile information created by the profile creation unit. For example, it matches a user who wants to teach programming with a user who wants to learn English conversation. The skill matching unit can also perform automatic matching using AI. The skill exchange unit allows users matched by the skill matching unit to exchange skills. For example, a user who teaches programming provides programming lessons to a user who teaches English conversation, and vice versa. The skill chat service according to an embodiment can thereby provide a platform for users to exchange their skills and knowledge and learn from each other.

[0060] The profile creation unit can use the generation AI to automatically extract and suggest areas of expertise and interests from the user's past activity history and social media posts. For example, when a user creates a profile, the generation AI analyzes the user's past activity history and social media posts to automatically extract areas of expertise and interests. For example, the generation AI identifies areas of expertise such as programming and design from the content the user frequently posts and events they attend. The generation AI also suggests areas of expertise and interests based on the user's past activity history. For example, the generation AI analyzes data from online courses and workshops the user has previously attended to automatically extract skills such as programming and marketing. The generation AI also analyzes social media posts to automatically extract the user's interests and areas of expertise. For example, the generation AI identifies skills such as data science and English conversation from the articles and comments the user frequently shares. This allows the user's areas of expertise and interests to be automatically extracted and supports profile creation.

[0061] The profile creation unit can set detailed levels for the skills that a user provides and the skills that a user wants to learn, enabling more precise matching. The profile creation unit, for example, sets detailed levels for the skills that a user provides and the skills that a user wants to learn. For example, programming skills can be classified into "beginner," "intermediate," and "advanced" to improve matching accuracy. In addition, detailed levels can be set for skills that a user wants to learn, enabling more precise matching. For example, English conversation skills can be classified into "daily conversation," "business conversation," and "native level" to select an appropriate partner. In addition, detailed levels can be set for the skills that a user provides, improving matching accuracy. For example, design skills can be classified into "basic design," "intermediate design," and "advanced design" to achieve optimal matching. In this way, detailed skill level setting enables more precise matching.

[0062] The profile creation unit can use the emotion estimation function to analyze the emotions of a user when creating a profile and provide advice to draw out positive emotions. The profile creation unit, for example, uses the emotion estimation function to analyze the emotions of a user when creating a profile in real time and provide advice to draw out positive emotions. For example, if the user is nervous, a message to relax is displayed. The emotion estimation function can also be used to analyze the user's emotions when creating a profile and provide advice to draw out positive emotions. For example, an encouraging message can be displayed to help the user gain confidence. The emotion estimation function can also be used to analyze the emotions of a user when creating a profile and provide advice to draw out positive emotions. For example, a humorous message can be displayed to help the user enjoy creating a profile. This can help the user create a profile with positive emotions.

[0063] The profile creation unit provides a function to add videos and audio messages to the profile, thereby more specifically expressing the user's personality and skills. The profile creation unit, for example, provides a function to add videos and audio messages to the profile, thereby more specifically expressing the user's personality and skills. For example, a user uploads a video demonstrating their skills. The unit also provides a function to add videos and audio messages to the profile, thereby more specifically expressing the user's personality and skills. For example, a user introduces themselves or explains their skills in an audio message. The unit also provides a function to add videos and audio messages to the profile, thereby more specifically expressing the user's personality and skills. For example, a user introduces past projects and achievements in a video. This more specifically expresses the user's personality and skills.

[0064] The profile creation unit supports the creation of profiles in different languages, thereby facilitating skill exchange between international users. The profile creation unit, for example, supports the creation of profiles in different languages, thereby facilitating skill exchange between international users. For example, it allows profiles to be created in multiple languages, such as English, French, and Chinese. It also supports different languages ​​when creating profiles, thereby facilitating skill exchange between international users. For example, it provides a function for a user to create a profile in their native language and automatically translate it into other languages. It also supports the creation of profiles in different languages, thereby facilitating skill exchange between international users. For example, a user can create a profile in multiple languages, thereby realizing international matching. This makes it possible to promote skill exchange between international users.

[0065] The profile creation unit can use the emotion estimation function to display the emotions of the user when creating a profile in real time and highlight the skills in which the user is most confident. The profile creation unit, for example, uses the emotion estimation function to display the emotions of the user when creating a profile in real time and highlight the skills in which the user is most confident. For example, the profile creation unit highlights the skills in which the user is confident. Furthermore, the emotion estimation function can be used to display the user's emotions in real time when creating a profile and highlight the skills in which the user is most confident. For example, positive feedback can be displayed for the skills in which the user is confident. Furthermore, the emotion estimation function can be used to display the emotions of the user when creating a profile in real time and highlight the skills in which the user is most confident. For example, an animation can be displayed to highlight the skills in which the user is confident. This can help the user to create a profile with confidence.

[0066] The skill matching unit can use the generation AI to analyze the user's past skill exchange history and feedback and propose optimal matching. The skill matching unit, for example, uses the generation AI to analyze the user's past skill exchange history and feedback and propose optimal matching. For example, similar matching is proposed based on the history of skill exchanges that have received high ratings in the past. The generation AI also analyzes the user's past skill exchange history and feedback and proposes optimal matching. For example, a compatible partner is selected based on past feedback data. The generation AI also analyzes the user's past skill exchange history and feedback and proposes optimal matching. For example, optimal matching is performed based on the success rate of past skill exchanges. This makes it possible to propose optimal matching based on past skill exchange history and feedback.

[0067] The skill matching unit can select the optimal partner by taking the user's learning style into consideration in the matching algorithm. The skill matching unit, for example, takes the user's learning style into consideration in the matching algorithm to select the optimal partner. For example, for a user with a visual learning style, a partner who can provide visual learning materials is selected. The user's learning style is also incorporated into the matching algorithm to select the optimal partner. For example, for a user with an auditory learning style, a partner who can provide audio learning materials is selected. The matching algorithm also takes the user's learning style into consideration to select the optimal partner. For example, for a user with an experiential learning style, a partner who can provide practical skills is selected. In this way, the optimal partner can be selected based on the user's learning style.

[0068] The skill matching unit can use the emotion estimation function to analyze the emotional state of the user and match partners who are emotionally compatible. The skill matching unit, for example, uses the emotion estimation function to analyze the emotional state of the user and match partners who are emotionally compatible. For example, users who have positive emotions are matched preferentially. The emotion estimation function can also analyze the emotional state of the user and match partners who are emotionally compatible. For example, users who have the same emotional state are matched. The emotion estimation function can also analyze the emotional state of the user and match partners who are emotionally compatible. For example, users with high emotion scores are matched preferentially. This makes it possible to match partners who are emotionally compatible.

[0069] The skill matching unit can perform more precise matching by taking into account data on online courses and workshops that the user has previously participated in when matching. The skill matching unit can perform more precise matching by taking into account data on online courses and workshops that the user has previously participated in when matching. For example, the skill matching unit can select an appropriate partner based on the content of courses that the user has previously taken. Furthermore, data on online courses and workshops that the user has previously participated in can be incorporated into the matching algorithm to perform more precise matching. For example, users who have taken the same course can be matched with each other. Furthermore, during matching, the skill matching unit can perform more precise matching by taking into account data on online courses and workshops that the user has previously participated in. For example, a partner who is compatible with the user can be selected based on the user's past participation history. This allows for more precise matching by taking into account past participation data.

[0070] The skill matching department can provide opportunities to gain new perspectives and knowledge by matching experts from different industries and fields. For example, the skill matching department can match experts from different industries and fields to provide opportunities to gain new perspectives and knowledge. For example, it can match a technical expert with a design expert. It can also match experts from different industries and fields to provide opportunities to gain new perspectives and knowledge. For example, it can match a marketing expert with an engineer. It can also match experts from different industries and fields to provide opportunities to gain new perspectives and knowledge. For example, it can match an expert in the medical field with an expert in the IT field. In this way, it can provide opportunities to gain new perspectives and knowledge by matching experts from different industries and fields.

[0071] The skill matching unit can use the emotion estimation function to monitor the emotional reactions of users when they are matched in real time and continuously adjust the optimal matching. The skill matching unit, for example, uses the emotion estimation function to monitor the emotional reactions of users when they are matched in real time and continuously adjust the optimal matching. For example, if a user shows positive emotions, the matching is maintained. The emotion estimation function also monitors the emotional reactions of users when they are matched in real time and continuously adjusts the optimal matching. For example, if a user shows negative emotions, a different partner is suggested. The emotion estimation function also monitors the emotional reactions of users when they are matched in real time and continuously adjusts the optimal matching. For example, if the emotion score is low, the matching is reevaluated. In this way, the emotional reactions can be monitored and the optimal matching can be continuously adjusted.

[0072] The skill exchange unit can use generative AI to suggest appropriate teaching materials and resources in real time during skill exchange. The skill exchange unit, for example, uses generative AI to suggest appropriate teaching materials and resources in real time during skill exchange. For example, during a programming lesson, relevant code samples and documentation are provided. The generative AI also suggests appropriate teaching materials and resources in real time during skill exchange. For example, during an English conversation lesson, relevant phrasebooks and pronunciation guides are provided. The generative AI also suggests appropriate teaching materials and resources in real time during skill exchange. For example, during a design lesson, relevant tutorial videos and templates are provided. This makes it possible to suggest appropriate teaching materials and resources in real time during skill exchange.

[0073] The skill exchange unit can provide a function to automatically record a skill exchange session so that it can be reviewed later. The skill exchange unit, for example, provides a function to automatically record a skill exchange session so that it can be reviewed later. For example, a programming lesson can be recorded so that a user can review the code later. Also, a function to automatically record a skill exchange session so that it can be reviewed later is provided. For example, an English conversation lesson can be recorded so that a user can review pronunciation and phrases. Also, a function to automatically record a skill exchange session so that it can be reviewed later is provided. For example, a design lesson can be recorded so that a user can review the design process. In this way, it is possible to record a skill exchange session so that it can be reviewed later.

[0074] The skill exchange unit can use the emotion estimation function to analyze the user's emotions during the skill exchange and provide advice for maintaining a positive learning environment. The skill exchange unit, for example, uses the emotion estimation function to analyze the user's emotions during the skill exchange in real time and provide advice for maintaining a positive learning environment. For example, if the user is feeling stressed, advice to relax is displayed. The emotion estimation function can also be used to analyze the user's emotions during the skill exchange and provide advice for maintaining a positive learning environment. For example, if the user is lacking concentration, advice to improve concentration is displayed. The emotion estimation function can also be used to analyze the user's emotions during the skill exchange and provide advice for maintaining a positive learning environment. For example, a motivating message can be displayed so that the user can enjoy learning. In this way, the user's emotions during the skill exchange can be analyzed and advice for maintaining a positive learning environment can be provided.

[0075] The skill exchange unit can use virtual reality or augmented reality during skill exchange to provide a more interactive learning experience. The skill exchange unit, for example, uses virtual reality during skill exchange to provide a more interactive learning experience. For example, a programming lesson is conducted in a VR environment, and the user executes code in the virtual space. Also, augmented reality is used during skill exchange to provide a more interactive learning experience. For example, an English conversation lesson is conducted in an AR environment, and the user converses with a virtual character. Also, virtual reality or augmented reality is used during skill exchange to provide a more interactive learning experience. For example, a design lesson is conducted in a VR / AR environment, and the user creates a design in the virtual space. In this way, a more interactive learning experience can be provided using virtual reality or augmented reality.

[0076] The skill exchange unit can enhance the schedule adjustment function so that users in different time zones can smoothly exchange skills with each other. The skill exchange unit enhances the schedule adjustment function so that users in different time zones can smoothly exchange skills with each other. For example, the skill exchange unit automatically detects the user's time zone and proposes an optimal schedule. The schedule adjustment function is also enhanced to enable users in different time zones to smoothly exchange skills with each other. For example, the user inputs the time periods when they are available and an optimal schedule is automatically generated. The schedule adjustment function is also enhanced to enable users in different time zones to smoothly exchange skills with each other. For example, the skill exchange unit works in conjunction with the user's calendar and automatically adjusts available times. This enables users in different time zones to smoothly exchange skills with each other.

[0077] The skill exchange unit can use the emotion estimation function to monitor the user's emotions during skill exchange in real time and suggest an optimal learning pace. The skill exchange unit, for example, uses the emotion estimation function to monitor the user's emotions during skill exchange in real time and suggest an optimal learning pace. For example, if the user is tired, it suggests taking a break. The emotion estimation function can also be used to monitor the user's emotions during skill exchange and suggest an optimal learning pace. For example, if the user is lacking concentration, it can adjust the pace. The emotion estimation function can also be used to monitor the user's emotions during skill exchange in real time and suggest an optimal learning pace. For example, it can adjust the pace so that the user can enjoy learning. In this way, it is possible to monitor the user's emotions during skill exchange and suggest an optimal learning pace.

[0078] The community formation unit can use the generation AI to analyze the user's interests and past activity history and suggest the most suitable community or group. The community formation unit, for example, uses the generation AI to analyze the user's interests and past activity history and suggest the most suitable community or group. For example, related communities are suggested based on data on events and forums that the user has previously participated in. The generation AI also analyzes the user's interests and past activity history and suggests the most suitable community or group. For example, related groups are suggested based on topics that the user frequently posts on. The generation AI also analyzes the user's interests and past activity history and suggests the most suitable community or group. For example, related communities are suggested based on data on online courses and workshops that the user has previously participated in. This makes it possible to suggest the most suitable community or group based on the user's interests and past activity history.

[0079] The community formation unit can provide a function to automatically summarize discussions and information exchanges within a community and extract important points. The community formation unit, for example, provides a function to automatically summarize discussions and information exchanges within a community and extract important points. For example, it summarizes long discussions and displays main opinions and conclusions. It also provides a function to automatically summarize discussions and information exchanges and extract important points. For example, it summarizes forum posts and displays important questions and answers. It also provides a function to automatically summarize discussions and information exchanges within a community and extract important points. For example, it summarizes the contents of group chats and displays main topics and suggestions. In this way, it is possible to automatically summarize discussions and information exchanges within a community and extract important points.

[0080] The community formation unit can use the emotion estimation function to analyze the emotional states of users in the community and provide advice to promote positive interactions. The community formation unit, for example, uses the emotion estimation function to analyze the emotional states of users in the community in real time and provide advice to promote positive interactions. For example, if a user shows negative emotions, an encouraging message is displayed. The emotion estimation function can also be used to analyze the emotional states of users in the community and provide advice to promote positive interactions. For example, if a user is feeling stressed, advice to relax is displayed. The emotion estimation function can also be used to analyze the emotional states of users in the community and provide advice to promote positive interactions. For example, a motivating message is displayed so that users can enjoy interacting. In this way, the emotional states of users in the community can be analyzed and advice to promote positive interactions can be provided.

[0081] The community formation unit can provide a function of automatically suggesting regular online events and workshops to promote interaction within the community. The community formation unit provides, for example, a function of automatically suggesting regular online events and workshops to promote interaction within the community. For example, related events are suggested based on the user's interests. In addition, a function of automatically suggesting regular online events and workshops to promote interaction within the community is provided. For example, appropriate events are suggested based on the user's skills and interests. In addition, a function of automatically suggesting regular online events and workshops to promote interaction within the community is provided. For example, related events are suggested based on the user's past participation history. In this way, regular online events and workshops can be automatically suggested to promote interaction within the community.

[0082] The community formation unit can introduce a real-time translation function so that users with different languages ​​and cultural backgrounds can interact with each other. The community formation unit, for example, introduces a real-time translation function so that users with different languages ​​and cultural backgrounds can interact with each other. For example, chat messages can be automatically translated so that users can communicate in their native languages. The real-time translation function can also be introduced so that users with different languages ​​and cultural backgrounds can interact smoothly with each other. For example, subtitles can be displayed in real time during video calls to remove language barriers. The real-time translation function can also be introduced so that users with different languages ​​and cultural backgrounds can interact with each other. For example, posts in forums and group chats can be automatically translated so that users can participate in discussions in different languages. This makes it possible for users with different languages ​​and cultural backgrounds to interact with each other.

[0083] The community formation unit can use the emotion estimation function to monitor the emotional reactions of users in the community in real time and suggest an optimal method of interaction. The community formation unit, for example, uses the emotion estimation function to monitor the emotional reactions of users in the community in real time and suggest an optimal method of interaction. For example, if a user shows negative emotions, the community formation unit suggests an activity to promote positive interaction. The emotion estimation function also monitors the emotional reactions of users in the community in real time and suggests an optimal method of interaction. For example, if a user is feeling stressed, the community formation unit suggests an activity that will help them relax. The emotion estimation function also monitors the emotional reactions of users in the community in real time and suggests an optimal method of interaction. For example, the community formation unit suggests an activity that will increase motivation so that users can enjoy interacting. In this way, the community formation unit can monitor the emotional reactions of users in the community and suggest an optimal method of interaction.

[0084] The feedback and evaluation system unit can provide a function that uses a generation AI to automatically analyze feedback content and extract areas for improvement and strengths. The feedback and evaluation system unit, for example, uses a generation AI to provide a function that automatically analyzes feedback content and extracts areas for improvement and strengths. For example, it analyzes user feedback and lists specific areas for improvement and strengths. It also provides a function that uses a generation AI to automatically analyze feedback content and extract areas for improvement and strengths. For example, it analyzes text data of feedback and classifies positive comments and negative comments. It also provides a function that uses a generation AI to automatically analyze feedback content and extract areas for improvement and strengths. For example, it presents specific improvement suggestions and methods for strengthening strengths based on user feedback. In this way, it is possible to automatically analyze feedback content and extract areas for improvement and strengths.

[0085] The feedback and evaluation system unit can provide a function for automatically generating specific improvement suggestions and advice when providing feedback. The feedback and evaluation system unit, for example, provides a function for automatically generating specific improvement suggestions and advice when providing feedback. For example, when a user inputs feedback, the generation AI automatically generates and displays improvement suggestions. The feedback and evaluation system unit also provides a function for automatically generating specific improvement suggestions and advice to improve the quality of feedback. For example, when a user inputs feedback, the generation AI automatically generates and displays advice. The feedback and evaluation system unit also provides a function for automatically generating specific improvement suggestions and advice when providing feedback. For example, when a user inputs feedback, the generation AI automatically lists and displays improvements and advice. This makes it possible to automatically generate specific improvement suggestions and advice when providing feedback.

[0086] The feedback and evaluation system unit can use the emotion estimation function to analyze the emotions of a user who has received feedback and provide advice to encourage positive feedback. The feedback and evaluation system unit, for example, uses the emotion estimation function to analyze the emotions of a user who has received feedback in real time and provide advice to encourage positive feedback. For example, if the user shows negative emotions, an encouraging message is displayed. The emotion estimation function can also be used to analyze the emotions of a user who has received feedback and provide advice to encourage positive feedback. For example, if the user is feeling stressed, advice to relax is displayed. The emotion estimation function can also be used to analyze the emotions of a user who has received feedback and provide advice to encourage positive feedback. For example, a motivating message is displayed so that the user can enjoy receiving feedback. In this way, the emotion estimation system unit can analyze the emotions of a user who has received feedback and provide advice to encourage positive feedback.

[0087] The feedback and evaluation system section can visualize the feedback content and display it in graphs and charts to allow users to intuitively understand. The feedback and evaluation system section, for example, can visualize the feedback content and display it in graphs and charts to allow users to intuitively understand. For example, the ratio of positive to negative feedback can be displayed in a pie chart. The feedback content can also be visualized to allow users to intuitively understand. For example, the feedback trend can be displayed in a line graph to show changes over time. The feedback content can also be visualized to allow users to intuitively understand. For example, the main areas for improvement in the feedback can be displayed in a bar graph. In this way, the feedback content can be visualized to allow users to intuitively understand.

[0088] The Feedback and Evaluation System Department provides opportunities to receive feedback from experts in different industries and fields, thereby promoting evaluation from multiple perspectives. The Feedback and Evaluation System Department, for example, provides opportunities to receive feedback from experts in different industries and fields, thereby promoting evaluation from multiple perspectives. For example, feedback can be received from technical experts and design experts at the same time. It also provides opportunities to receive feedback from experts in different industries and fields, thereby promoting evaluation from multiple perspectives. For example, feedback can be received from marketing experts and engineers at the same time. It also provides opportunities to receive feedback from experts in different industries and fields, thereby promoting evaluation from multiple perspectives. For example, feedback can be received from medical experts and IT experts at the same time. This provides opportunities to receive feedback from experts in different industries and fields, thereby promoting evaluation from multiple perspectives.

[0089] The feedback and evaluation system unit can use the emotion estimation function to monitor the emotional reactions of users who receive feedback in real time and suggest the optimal feedback method. The feedback and evaluation system unit, for example, uses the emotion estimation function to monitor the emotional reactions of users who receive feedback in real time and suggest the optimal feedback method. For example, if the user shows negative emotions, it prioritizes positive feedback. The emotion estimation function also monitors the emotional reactions of users who receive feedback in real time and suggests the optimal feedback method. For example, if the user is feeling stressed, it suggests feedback that will help them relax. The emotion estimation function also monitors the emotional reactions of users who receive feedback in real time and suggests the optimal feedback method. For example, it suggests feedback that will increase the user's motivation so that the user can enjoy receiving feedback. In this way, it is possible to monitor the emotional reactions of users who receive feedback and suggest the optimal feedback method.

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

[0091] The skill chat service may further include an evaluation unit that evaluates a user's skills and knowledge. The evaluation unit collects feedback from other users on the skills and knowledge provided by the user and evaluates them. For example, a user who has taken a programming lesson may evaluate the quality and usefulness of the lesson. The evaluation unit may also provide specific improvements and advice to promote the improvement of the user's skills and knowledge. For example, a user who has taken an English conversation lesson may receive feedback on areas for improvement in pronunciation and grammar. This allows the user to objectively evaluate and improve their own skills and knowledge.

[0092] The skill chat service may further include a visualization unit for visually representing a user's skills and knowledge. The visualization unit visually displays the user's skills and knowledge using graphs and charts, allowing other users to intuitively understand. For example, the level of mastery of programming skills may be displayed using a bar graph. The visualization unit may also display the progress of a user's skills and knowledge using a timeline. For example, the progress of an English conversation lesson may be displayed using a line graph. This allows users to visually grasp the progress of their skills and knowledge and share it with other users.

[0093] The skill chat service may further include a sharing section for users to share their skills and knowledge with other users. The sharing section provides a platform for users to share their skills and knowledge with other users. For example, a user may share a programming code sample with other users. The sharing section may also provide a forum or group chat for users to share their skills and knowledge with other users. For example, a user may discuss the contents of an English conversation lesson with other users. This allows users to share their skills and knowledge with other users and learn from each other.

[0094] The skill chat service may further include a ranking unit for evaluating a user's skills and knowledge. The ranking unit compares the user's skills and knowledge with those of other users and displays a ranking. For example, it may display a ranking of programming skills. The ranking unit may also provide rewards or incentives based on the ranking to encourage users to improve their skills and knowledge. For example, it may provide special benefits to those who rank highly in English conversation lessons. This allows users to compare their skills and knowledge with those of other users and motivate them to improve.

[0095] The skill chat service may further include a collaboration section for sharing a user's skills and knowledge with other users. The collaboration section provides a platform for a user to collaborate with other users to utilize their skills and knowledge. For example, a user may collaborate with other users to develop a programming project. The collaboration section may also provide workshops and seminars for a user to collaborate with other users to utilize their skills and knowledge. For example, a user may collaborate with other users to take English conversation lessons. This allows a user to collaborate with other users to utilize their skills and knowledge and learn from each other.

[0096] The skill chat service may further include a timing adjustment unit that estimates the user's emotions and adjusts the timing of skill exchange based on the estimated emotions. The timing adjustment unit analyzes the user's emotions in real time and suggests the optimal timing for skill exchange. For example, it may suggest skill exchange when the user is relaxed. The timing adjustment unit may also analyze the user's emotions and provide advice for adjusting the timing of skill exchange. For example, if the user is feeling stressed, it may display advice for relaxation. This allows the user to exchange skills at the optimal timing and learn effectively.

[0097] The skill chat service may further include a content adjustment unit that estimates the user's emotions and adjusts the content of the skill exchange based on the estimated emotions. The content adjustment unit analyzes the user's emotions in real time and suggests optimal skill exchange content. For example, if the user is showing positive emotions, it suggests a more difficult skill. The content adjustment unit may also analyze the user's emotions and provide advice for adjusting the content of the skill exchange. For example, if the user is showing negative emotions, it suggests an easier skill. This allows the user to exchange skills optimally according to their emotions and learn effectively.

[0098] The skill chat service may further include a partner selection unit that estimates the user's emotions and selects a partner for skill exchange based on the estimated emotions. The partner selection unit analyzes the user's emotions in real time and suggests partners who are emotionally compatible. For example, if the user is expressing positive emotions, the partner selection unit suggests partners who also have positive emotions. The partner selection unit may also analyze the user's emotions and provide advice for selecting a partner for skill exchange. For example, if the user is expressing negative emotions, the partner selection unit suggests a partner with whom the user can relax. This allows the user to exchange skills with a partner who is emotionally compatible and learn effectively.

[0099] The skill chat service may further include a progress adjustment unit that estimates the user's emotions and adjusts the progress of the skill exchange based on the estimated emotions. The progress adjustment unit analyzes the user's emotions in real time and suggests an optimal progress of the skill exchange. For example, if the user is lacking concentration, it may suggest taking a break. The progress adjustment unit may also analyze the user's emotions and provide advice for adjusting the progress of the skill exchange. For example, if the user is tired, it may display advice to slow down the pace. This allows the user to optimally progress the skill exchange according to their emotions and learn effectively.

[0100] The skill chat service may further include a feedback adjustment unit that estimates the user's emotions and adjusts the skill exchange feedback based on the estimated emotions. The feedback adjustment unit analyzes the user's emotions in real time and suggests optimal feedback. For example, if the user is expressing positive emotions, it suggests specific areas for improvement. The feedback adjustment unit may also analyze the user's emotions and provide advice for adjusting the skill exchange feedback. For example, if the user is expressing negative emotions, it displays an encouraging message. This allows the user to receive optimal feedback according to their emotions and learn effectively.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The profile creation unit creates a profile that describes the user's areas of expertise and specialized knowledge. For example, the user inputs their areas of expertise and specialized knowledge to create a profile. The profile creation unit can also input skills and knowledge that the user wants to learn. Step 2: The skill matching unit matches the skills that can be provided with the skills that users want to learn based on the user's profile information created by the profile creation unit. For example, it matches a user who wants to teach programming with a user who wants to learn English conversation. The skill matching unit can also use AI to perform automatic matching. Step 3: The skill exchange unit allows users matched by the skill matching unit to exchange skills. For example, a user who teaches programming can provide programming lessons to a user who teaches English conversation, and vice versa.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system that provides a mechanism by which a user can provide his / her own specialty or expertise and receive other skills or knowledge of equivalent value from other users, a profile creation unit that creates a profile that describes the user's areas of expertise and expertise; a skill matching unit that matches skills that can be provided with skills that the user wants to learn based on the profile information of the user created by the profile creation unit; a skill exchange unit for exchanging skills between users matched by the skill matching unit; A system characterized by:

2. The profile creation unit Using generative AI, the system automatically extracts and suggests areas of expertise and interests from the user's past activity history and social media posts.

2. The system of claim 1.

3. The profile creation unit Allows for more precise matching by setting detailed levels for the skills the user provides and the skills they wish to learn.

2. The system of claim 1.

4. The profile creation unit Analyzing the emotions of the user when creating a profile and providing advice to elicit positive emotions 2. The system of claim 1.

5. The profile creation unit Providing a function to add video and audio messages to the profile to more specifically express the user's personality and skills 2. The system of claim 1.

Images