system

Abstract

The system according to this embodiment aims to provide customized communication training tailored to individual backgrounds and situations. [Solution] The system according to the embodiment comprises a registration unit, a selection unit, an analysis unit, and a provision unit. The registration unit registers profile information. The selection unit selects the situation or theme to be practiced. The analysis unit performs analysis based on the situation or theme selected by the selection unit. The provision unit provides feedback based on the analysis results obtained by the analysis unit.

Description

Technical Field

[0004] ,

[0006] , , ,

[0005] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, customized communication training according to individual backbones or situations is not provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide customized communication training according to individual backbones or situations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a registration unit, a selection unit, an analysis unit, and a provision unit. The registration unit registers profile information. The selection unit selects the situation or theme to be practiced. The analysis unit performs analysis based on the situation or theme selected by the selection unit. The provision unit provides feedback based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide customized communication training tailored to individual backgrounds and situations. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The communication skills improvement app according to an embodiment of the present invention is a system that uses generative AI to improve the user's communication skills. In this system, the user registers profile information in the app settings, selects a situation or theme they want to practice, and the AI ​​speaks to them verbally. By responding, the system analyzes both verbal (words) and nonverbal (facial expressions and reactions) aspects. Next, a Good / Bad analysis is performed based on visual (facial expressions, reactions), auditory (tone, speed), and content (word choice), and a summary of countermeasures is provided. Furthermore, users can review past data and track their progress to confirm their growth and receive more realistic advice. This app allows users to select scenarios such as 1-on-1 meetings, presentations, interviews, blind dates, and conversations with friends, and addresses themes such as career and work-related concerns. In addition, by inputting a 360-degree evaluation of the company, the AI ​​can provide additional advice that combines this with past data. This generative AI app targets anyone who needs communication skills for B2C users, and companies that want their employees to acquire 1-on-1 and presentation skills for B2B users. As an app, it offers flexibility in terms of time and location, unlike traditional training sessions, and is available at an affordable monthly rate, unlike expensive seminars. For example, users register their profile information in the app's settings and select situations and themes they want to practice. Next, the AI ​​speaks to the user, who responds. Both verbal (words) and nonverbal (facial expressions and reactions) aspects are analyzed. A Good / Bad analysis is then performed based on visual (facial expressions, reactions), auditory (tone, speed), and content (word choice), and a summary of suggested solutions is provided. Furthermore, users can review past data and track their progress to receive more realistic advice. This app allows users to select scenarios such as 1-on-1 meetings, presentations, interviews, blind dates, and conversations with friends, addressing themes such as career and work-related concerns. Additionally, by inputting a 360-degree evaluation of their company, users can receive additional advice from the AI, combined with past data.This AI-generated app targets anyone who needs to communicate (for B2C) and companies that want to equip their employees with 1-on-1 and presentation skills (for B2B). As an app, it offers flexibility in terms of time and location (unlike training sessions) and affordability (unlike expensive seminars), with a low monthly subscription fee. This allows the app to improve users' communication skills.

[0029] The communication skills improvement application according to this embodiment comprises a registration unit, a selection unit, an analysis unit, and a provision unit. The registration unit allows the user to register profile information. Profile information includes, but is not limited to, name, age, occupation, and hobbies. For example, the user enters profile information on the application's settings screen. The registration unit also allows the user to register profile information using voice input. For example, the user enters profile information by voice using a microphone. Furthermore, the registration unit can automatically read profile information previously entered by the user. For example, it retrieves previously entered profile information from a database and reuses it. The selection unit allows the user to select situations or themes they want to practice. Situations and themes include, but are not limited to, business meetings, presentations, interviews, blind dates, and conversations with friends. The selection unit allows the user to select situations or themes on the application's menu screen. The selection unit also allows the user to select situations or themes using voice input. For example, the user selects situations or themes by voice using a microphone. Furthermore, the selection unit can suggest optimal situations and themes based on the user's past selection history. For example, it can display relevant options based on situations and themes previously selected by the user. The analysis unit performs analysis based on the situations and themes selected by the selection unit. Analysis includes, but is not limited to, voice analysis, facial expression analysis, and text analysis. For example, the analysis unit can analyze the user's voice to evaluate tone and speed. The analysis unit can also analyze the user's facial expressions to estimate emotions. For example, it can use a camera to capture the user's facial expressions and use a facial expression analysis algorithm to estimate emotions. Furthermore, the analysis unit can analyze the user's text to evaluate the logic and accuracy of the information. For example, it can analyze the text entered by the user using natural language processing technology and evaluate the content. The provision unit provides feedback based on the analysis results obtained by the analysis unit.Feedback includes, but is not limited to, audio feedback, text feedback, and visual feedback. For example, the service provider can communicate the analysis results to the user by voice. The service provider can also display the analysis results as text. For example, it can display the analysis results as text on the screen. Furthermore, the service provider can display the analysis results visually. For example, it can visually display the analysis results using graphs or charts. In this way, the communication skills improvement application according to the embodiment can improve the user's communication skills.

[0030] The registration section allows users to register their profile information. This profile information includes, but is not limited to, name, age, occupation, and hobbies. For example, users can enter their profile information on the app's settings screen. Users can access the app's settings screen and enter information such as their name, age, occupation, and hobbies. This allows the app to understand the user's basic information and provide individually optimized feedback. The registration section also allows users to register their profile information using voice input. For example, users can use a microphone to input their profile information by voice. Voice input is highly convenient because it allows users to enter information without using their hands. Furthermore, the registration section can automatically read profile information previously entered by the user. For example, it can retrieve and reuse previously entered profile information from the database. This saves users the trouble of entering the same information every time. The registration section also implements security measures to safely manage user profile information and protect privacy. For example, data is encrypted and protected from access by third parties. This allows users to register their profile information with peace of mind.

[0031] The selection function allows users to choose situations or themes they want to practice. These situations and themes include, but are not limited to, business meetings, presentations, interviews, blind dates, and conversations with friends. For example, users can select situations or themes from the app's menu screen. Users can then select a situation or theme from the menu screen and begin practicing based on that selection. The selection function also allows users to select situations or themes using voice input. For example, users can use a microphone to select situations or themes by voice. Voice input is highly convenient because it allows users to make selections hands-free. Furthermore, the selection function can suggest optimal situations or themes based on the user's past selection history. For example, it can display related options based on situations or themes the user has previously selected. This makes it easy for users to find practice content that suits them. The selection function is equipped with an algorithm to analyze the user's selection history and provide individually optimized suggestions. This ensures that users can always select the most suitable practice content, efficiently improving their communication skills.

[0032] The analysis unit performs analysis based on the situation or theme selected by the selection unit. Analysis includes, but is not limited to, voice analysis, facial expression analysis, and text analysis. For example, the analysis unit can analyze the user's voice to evaluate tone and speed. Voice analysis evaluates the tone, speed, volume, and clarity of the user's speech to identify areas for improvement. The analysis unit can also analyze the user's facial expressions to estimate emotions. For example, it can use a camera to capture the user's facial expressions and use a facial expression analysis algorithm to estimate emotions. Facial expression analysis captures changes in the user's facial expressions in real time and evaluates whether the emotional expression is appropriate. Furthermore, the analysis unit can analyze the user's text to evaluate the logic and accuracy of the information. For example, it can analyze the text entered by the user using natural language processing technology to evaluate its content. Text analysis evaluates the structure, logic, accuracy of information, and grammatical correctness of the user's writing to identify areas for improvement. The analysis unit integrates these analysis results to perform a comprehensive evaluation. For example, the results of voice analysis, facial expression analysis, and text analysis are combined to perform a comprehensive evaluation of the user's communication skills. This allows the analysis unit to clearly identify the user's strengths and weaknesses and suggest specific areas for improvement.

[0033] The service provider provides feedback based on the analysis results obtained by the analysis unit. This feedback includes, but is not limited to, audio feedback, text feedback, and visual feedback. For example, the service provider can communicate analysis results to the user verbally. Audio feedback is provided in a format that is easy for the user to hear and understand. The service provider can also display analysis results in text. For example, it can display the analysis results as text on the screen. Text feedback allows users to visually confirm the results, making it easier to grasp specific areas for improvement. Furthermore, the service provider can display analysis results visually. For example, it can use graphs or charts to visually display the results. Visual feedback allows users to easily understand the analysis results at a glance and intuitively grasp areas for improvement. The service provider can collect user feedback and continuously improve the accuracy and effectiveness of the feedback. For example, it can analyze the user's reactions and actions after receiving feedback to identify areas for improvement. The service provider can also save the user's feedback history and provide individually optimized feedback based on past feedback. This allows the service provider to provide effective feedback to users and support the improvement of their communication skills.

[0034] The analysis unit can analyze both verbal and nonverbal aspects. For example, the analysis unit can analyze verbal elements such as the user's language choice, pronunciation, and grammar. It can also analyze nonverbal elements such as the user's gestures, facial expressions, and posture. For example, it can use a camera to capture the user's gestures and facial expressions and analyze the nonverbal elements. Furthermore, the analysis unit can combine both verbal and nonverbal aspects in its analysis. For example, it can simultaneously analyze the user's pronunciation and facial expressions to perform a comprehensive evaluation. By analyzing both verbal and nonverbal aspects, it becomes possible to improve communication skills in a more comprehensive way. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's voice data and video data into a generative AI and have the generative AI perform the verbal and nonverbal analysis.

[0035] The service provider can perform Good / Bad analysis on visual, auditory, and content elements. For example, the service provider can analyze visual elements such as the user's eye contact, facial expressions, and gestures. It can also analyze auditory elements such as the user's voice tone, speed, and volume. Furthermore, it can analyze content elements such as the structure, logic, and accuracy of the user's speech. For example, the service provider can analyze the user's utterances using natural language processing technology and evaluate the content. This allows for detailed feedback through Good / Bad analysis on visual, auditory, and content elements. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's audio and video data into a generating AI and have the generating AI perform analysis on the visual, auditory, and content elements.

[0036] The service provider may include a data storage unit for accumulating past data and confirming growth. The service provider can store data such as the user's past feedback, practice history, and evaluation results. The service provider can also confirm the user's growth based on the accumulated data. For example, the service provider can display past data in chronological order to visually show the user's progress. This makes it easier to grasp the user's progress by accumulating past data and confirming growth. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past data into a generating AI and have the generating AI perform the growth confirmation.

[0037] The selection function allows users to choose scenarios such as one-on-one meetings, presentations, interviews, blind dates, and conversations with friends. For example, the user can select a scenario from the app's menu screen. The selection function also allows users to select scenarios using voice input, for example, by using a microphone to select a scenario by voice. Furthermore, the selection function can suggest optimal scenarios based on the user's past selection history, for example, by displaying relevant options based on scenarios the user has previously selected. This allows users to practice various communication skills by selecting diverse scenarios. Some or all of the above-described processes in the selection function may be performed using AI or not. For example, the selection function can input the user's past selection history into a generating AI, which can then suggest optimal scenarios.

[0038] The service provider can provide additional advice by inputting a 360-degree evaluation of a company, combined with past data. For example, the user inputs a 360-degree evaluation of the company into the app. The service provider can also provide additional advice based on the inputted 360-degree evaluation. For example, the service provider can combine the user's past data with the 360-degree evaluation to provide more realistic feedback. This allows for more realistic feedback by providing additional advice based on the company's 360-degree evaluation. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's 360-degree evaluation data into a generating AI and have the generating AI generate additional advice.

[0039] The registration unit can analyze the user's past profile information and suggest the most suitable input fields. For example, the registration unit can automatically display frequently used fields as candidates based on the profile information the user has previously entered. The registration unit can also prioritize suggesting input methods (voice, text, etc.) the user has used in the past. Furthermore, the registration unit can predict and suggest fields that the user will use at specific times of day based on their past profile information. This enables efficient information registration by suggesting the most suitable input fields based on past profile information. Some or all of the above processes in the registration unit may be performed using AI or not. For example, the registration unit can input the user's past profile information into a generating AI and have the generating AI suggest the most suitable input fields.

[0040] The registration unit can filter user profile information based on their current lifestyle and areas of interest. For example, the registration unit can prioritize displaying relevant profile items based on the user's current occupation or hobbies. It can also suggest appropriate profile items based on the user's current lifestyle (e.g., student, working professional). Furthermore, the registration unit can prompt users to enter relevant profile information based on their areas of interest. This allows for the registration of more appropriate profile information by filtering based on the user's lifestyle and areas of interest. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The registration unit can prioritize inputting highly relevant information when registering profile information, taking into account the user's geographical location. For example, if a user lives in a specific region, the registration unit can prioritize inputting profile information related to that region. Furthermore, if a user is traveling, the registration unit can prompt them to input relevant information based on their current location. In addition, the registration unit can suggest region-specific profile items based on the user's geographical location. This allows for the registration of more relevant information by considering geographical location. Some or all of the above processing in the registration unit may be performed using AI, or not. For example, the registration unit can input the user's geographical location information into a generating AI and have the generating AI suggest highly relevant information.

[0042] The registration unit can analyze a user's social media activity and input relevant information when registering profile information. For example, the registration unit can analyze the content of a user's social media posts and automatically input relevant profile information. The registration unit can also suggest relevant information based on the user's social media friendships. Furthermore, the registration unit can analyze the user's social media activity history and prompt them to input profile information based on their interests. This allows for the registration of more relevant information by analyzing social media activity. Some or all of the above processes in the registration unit may be performed using AI or not. For example, the registration unit can input the user's social media activity data into a generating AI and have the generating AI input relevant information.

[0043] The selection unit can analyze the user's past selection history to suggest the most suitable situations and themes when a selection is made. For example, the selection unit can suggest related options based on situations and themes the user has previously selected. The selection unit can also prioritize displaying situations and themes that are frequently selected based on the user's past selection history. Furthermore, the selection unit can analyze the user's past selection history to suggest the most effective situations and themes. This enables efficient selection by suggesting the most suitable situations and themes based on past selection history. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's past selection history into a generating AI and have the generating AI suggest the most suitable situations and themes.

[0044] The selection unit can filter the results based on the user's current lifestyle and areas of interest during the selection process. For example, the selection unit can prioritize displaying relevant situations and themes based on the user's current occupation or hobbies. It can also suggest appropriate situations and themes based on the user's current lifestyle (e.g., student, working adult). Furthermore, the selection unit can prompt the user to select relevant situations and themes based on their areas of interest. This allows for the selection of more appropriate situations and themes by filtering based on lifestyle and areas of interest. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0045] The selection unit can prioritize selecting situations and themes that are highly relevant, taking into account the user's geographical location when selecting situations and themes. For example, if the user lives in a specific region, the selection unit will prioritize situations and themes related to that region. Furthermore, if the user is traveling, the selection unit can prompt them to select situations and themes relevant to their current location. In addition, the selection unit can suggest region-specific situations and themes based on the user's geographical location. This allows for the selection of more relevant situations and themes by considering geographical location. Some or all of the above processing in the selection unit may be performed using AI, or not. For example, the selection unit can input the user's geographical location into a generating AI, which can then suggest highly relevant situations and themes.

[0046] The selection unit can analyze the user's social media activity and select relevant situations and themes when selecting situations and themes. For example, the selection unit can analyze the content of the user's social media posts and automatically select relevant situations and themes. The selection unit can also suggest relevant situations and themes based on the user's social media friendships. Furthermore, the selection unit can analyze the user's social media activity history and prompt them to select situations and themes based on their interests. This allows for the selection of more relevant situations and themes by analyzing social media activity. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's social media activity data into a generating AI and have the generating AI perform the selection of relevant situations and themes.

[0047] The analysis unit can analyze the user's past communication history during analysis and select the optimal analysis method. For example, the analysis unit can use a generating AI to select the optimal analysis method based on the user's past communication history. The analysis unit can also use a generating AI to select frequently used analysis methods from the user's past communication history. Furthermore, the analysis unit can analyze the user's past communication history and use a generating AI to select the most effective analysis method. This enables efficient analysis by selecting the optimal analysis method based on past communication history. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit can input the user's past communication history into a generating AI and have the generating AI select the optimal analysis method.

[0048] The analysis unit can improve the accuracy of its analysis based on the user's current lifestyle and areas of interest. For example, the analysis unit can use the generating AI to select relevant analysis methods based on the user's current occupation or hobbies. The analysis unit can also use the generating AI to select appropriate analysis methods based on the user's current lifestyle (e.g., student, working adult). Furthermore, the analysis unit can use the generating AI to select relevant analysis methods based on the user's areas of interest. This allows for more accurate analysis by performing analysis based on lifestyle and areas of interest. Some or all of the above-described processes in the analysis unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the analysis unit can input data on the user's lifestyle and areas of interest into the generating AI and have the generating AI perform the analysis accuracy improvement.

[0049] The analysis unit can improve the accuracy of its analysis by considering the user's geographical location information during the analysis process. For example, if the user lives in a specific region, the generating AI can select an analysis method relevant to that region. Furthermore, if the user is traveling, the generating AI can select a relevant analysis method based on their current location. In addition, the generating AI can select a region-specific analysis method based on the user's geographical location information. This allows for more accurate analysis by considering geographical location information. Some or all of the above-described processes in the analysis unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the analysis unit can input the user's geographical location information into the generating AI and have the generating AI perform the analysis accuracy improvement.

[0050] The analysis unit can improve the accuracy of its analysis by analyzing the user's social media activity during the analysis process. For example, the analysis unit can analyze the content of the user's social media posts, and the generating AI can select relevant analysis methods. The analysis unit can also have the generating AI select relevant analysis methods based on the user's social media friendships. Furthermore, the analysis unit can analyze the user's social media activity history, and the generating AI can select analysis methods based on their interests. This allows for more accurate analysis by analyzing social media activity. Some or all of the above-described processes in the analysis unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the analysis unit can input the user's social media activity data into the generating AI and have the generating AI perform the analysis accuracy improvement.

[0051] The feedback provider can analyze the user's past feedback history to provide optimal feedback. For example, the provider can provide relevant feedback based on feedback the user has received in the past. The provider can also prioritize feedback on frequently raised points from the user's past feedback history. Furthermore, the provider can analyze the user's past feedback history to provide the most effective feedback. This enables efficient feedback by providing optimal feedback based on past feedback history. Some or all of the above processing in the feedback provider may be performed using AI or not. For example, the provider can input the user's past feedback history into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0052] The service provider can customize the content of feedback based on the user's current lifestyle and areas of interest when providing feedback. For example, the service provider can provide relevant feedback based on the user's current occupation or hobbies. It can also provide appropriate feedback according to the user's current lifestyle (e.g., student, working adult). Furthermore, the service provider can provide relevant feedback based on the user's areas of interest. This allows for more appropriate feedback by providing feedback based on lifestyle and areas of interest. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the customization of the feedback.

[0053] The service provider can provide optimal feedback by considering the user's geographical location when providing feedback. For example, if the user lives in a specific region, the service provider can provide feedback relevant to that region. Furthermore, if the user is traveling, the service provider can provide relevant feedback based on their current location. In addition, the service provider can provide region-specific feedback based on the user's geographical location. This allows for more appropriate feedback by considering geographical location. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0054] The service provider can analyze the user's social media activity and customize the content of the feedback when providing it. For example, the service provider can analyze the content of the user's social media posts and provide relevant feedback. It can also provide relevant feedback based on the user's social media friendships. Furthermore, the service provider can analyze the user's social media activity history and provide feedback based on their interests. This allows for more appropriate feedback by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the customization of the feedback.

[0055] The data storage unit can analyze the user's past data history and select the optimal storage method when storing data. For example, the storage unit can provide relevant data storage methods based on data the user has stored in the past. The storage unit can also provide frequently used data storage methods based on the user's past data history. Furthermore, the storage unit can analyze the user's past data history and provide the most effective data storage method. This enables efficient data storage by providing the optimal storage method based on past data history. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input the user's past data history into a generating AI and have the generating AI select the optimal storage method.

[0056] The data storage unit can select the optimal data storage method when storing data, taking into account the user's geographical location information. For example, if the user lives in a specific region, the storage unit can provide a data storage method relevant to that region. Furthermore, if the user is traveling, the storage unit can provide a relevant data storage method based on their current location. In addition, the storage unit can provide region-specific data storage methods based on the user's geographical location information. This allows for more appropriate data storage by considering geographical location information. Some or all of the above processing in the storage unit may be performed using AI, or without AI. For example, the storage unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal data storage method.

[0057] The data storage unit can analyze the user's social media activity and select the optimal data storage method when storing data. For example, the storage unit can analyze the content of the user's social media posts and provide a relevant data storage method. It can also provide a relevant data storage method based on the user's social media friendships. Furthermore, the storage unit can analyze the user's social media activity history and provide a data storage method based on their interests. This makes it possible to store data more appropriately by analyzing social media activity. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input the user's social media activity data into a generating AI and have the generating AI select the optimal data storage method.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The analysis unit can analyze the user's past communication history during analysis to select the optimal analysis method. For example, the analysis unit can select the optimal analysis method based on the user's past communication history. The analysis unit can also select frequently used analysis methods from the user's past communication history. Furthermore, the analysis unit can analyze the user's past communication history and select the most effective analysis method. This enables efficient analysis by selecting the optimal analysis method based on past communication history. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's past communication history into a generative AI and have the generative AI select the optimal analysis method.

[0060] The feedback provider can analyze the user's past feedback history to provide optimal feedback. For example, the provider can provide relevant feedback based on feedback the user has received in the past. The provider can also prioritize feedback on frequently raised points from the user's past feedback history. Furthermore, the provider can analyze the user's past feedback history to provide the most effective feedback. This enables efficient feedback by providing optimal feedback based on past feedback history. Some or all of the above processing in the feedback provider may be performed using AI or not. For example, the provider can input the user's past feedback history into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0061] The selection unit can analyze the user's past selection history to suggest the most suitable situations and themes when a selection is made. For example, the selection unit can suggest related options based on situations and themes the user has previously selected. The selection unit can also prioritize displaying situations and themes that are frequently selected based on the user's past selection history. Furthermore, the selection unit can analyze the user's past selection history to suggest the most effective situations and themes. This enables efficient selection by suggesting the most suitable situations and themes based on past selection history. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's past selection history into a generating AI and have the generating AI suggest the most suitable situations and themes.

[0062] The registration unit can analyze the user's past profile information and suggest the most suitable input fields. For example, the registration unit can automatically display frequently used fields as candidates based on the profile information the user has previously entered. The registration unit can also prioritize suggesting input methods (voice, text, etc.) the user has used in the past. Furthermore, the registration unit can predict and suggest fields that the user will use at specific times of day based on their past profile information. This enables efficient information registration by suggesting the most suitable input fields based on past profile information. Some or all of the above processes in the registration unit may be performed using AI or not. For example, the registration unit can input the user's past profile information into a generating AI and have the generating AI suggest the most suitable input fields.

[0063] The service provider can provide optimal feedback by considering the user's geographical location when providing feedback. For example, if the user lives in a specific region, the service provider can provide feedback relevant to that region. Furthermore, if the user is traveling, the service provider can provide relevant feedback based on their current location. In addition, the service provider can provide region-specific feedback based on the user's geographical location. This allows for more appropriate feedback by considering geographical location. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The registration section allows users to register their profile information. This profile information includes, for example, name, age, occupation, and hobbies. Users can enter their profile information in the app's settings screen, or they can register it using voice input via the microphone. Furthermore, it is possible to retrieve and reuse previously entered profile information from the database. Step 2: The selection section allows the user to choose a situation or theme they want to practice. Situations and themes include, for example, business meetings, presentations, interviews, blind dates, and conversations with friends. Users can select situations and themes from the app's menu screen, or they can select them using voice input via the microphone. Furthermore, it is possible to suggest the most suitable situations and themes based on past selection history. Step 3: The analysis unit performs analysis based on the situation or theme selected by the selection unit. Analysis includes, for example, voice analysis, facial expression analysis, and text analysis. It can analyze the user's voice to evaluate tone and speed, and analyze facial expressions to estimate emotions. Furthermore, it can analyze text to evaluate the logic of the content and the accuracy of the information. Step 4: The service provider provides feedback based on the analysis results obtained by the analysis unit. This feedback may include, for example, audio feedback, text feedback, or visual feedback. The analysis results can be communicated to the user via audio, displayed in text, or visually represented using graphs and charts.

[0066] (Example of form 2) The communication skills improvement app according to an embodiment of the present invention is a system that uses generative AI to improve the user's communication skills. In this system, the user registers profile information in the app settings, selects a situation or theme they want to practice, and the AI ​​speaks to them verbally. By responding, the system analyzes both verbal (words) and nonverbal (facial expressions and reactions) aspects. Next, a Good / Bad analysis is performed based on visual (facial expressions, reactions), auditory (tone, speed), and content (word choice), and a summary of countermeasures is provided. Furthermore, users can review past data and track their progress to confirm their growth and receive more realistic advice. This app allows users to select scenarios such as 1-on-1 meetings, presentations, interviews, blind dates, and conversations with friends, and addresses themes such as career and work-related concerns. In addition, by inputting a 360-degree evaluation of the company, the AI ​​can provide additional advice that combines this with past data. This generative AI app targets anyone who needs communication skills for B2C users, and companies that want their employees to acquire 1-on-1 and presentation skills for B2B users. As an app, it offers flexibility in terms of time and location, unlike traditional training sessions, and is available at an affordable monthly rate, unlike expensive seminars. For example, users register their profile information in the app's settings and select situations and themes they want to practice. Next, the AI ​​speaks to the user, who responds. Both verbal (words) and nonverbal (facial expressions and reactions) aspects are analyzed. A Good / Bad analysis is then performed based on visual (facial expressions, reactions), auditory (tone, speed), and content (word choice), and a summary of suggested solutions is provided. Furthermore, users can review past data and track their progress to receive more realistic advice. This app allows users to select scenarios such as 1-on-1 meetings, presentations, interviews, blind dates, and conversations with friends, addressing themes such as career and work-related concerns. Additionally, by inputting a 360-degree evaluation of their company, users can receive additional advice from the AI, combined with past data.This AI-generated app targets anyone who needs to communicate (for B2C) and companies that want to equip their employees with 1-on-1 and presentation skills (for B2B). As an app, it offers flexibility in terms of time and location (unlike training sessions) and affordability (unlike expensive seminars), with a low monthly subscription fee. This allows the app to improve users' communication skills.

[0067] The communication skills improvement application according to this embodiment comprises a registration unit, a selection unit, an analysis unit, and a provision unit. The registration unit allows the user to register profile information. Profile information includes, but is not limited to, name, age, occupation, and hobbies. For example, the user enters profile information on the application's settings screen. The registration unit also allows the user to register profile information using voice input. For example, the user enters profile information by voice using a microphone. Furthermore, the registration unit can automatically read profile information previously entered by the user. For example, it retrieves previously entered profile information from a database and reuses it. The selection unit allows the user to select situations or themes they want to practice. Situations and themes include, but are not limited to, business meetings, presentations, interviews, blind dates, and conversations with friends. The selection unit allows the user to select situations or themes on the application's menu screen. The selection unit also allows the user to select situations or themes using voice input. For example, the user selects situations or themes by voice using a microphone. Furthermore, the selection unit can suggest optimal situations and themes based on the user's past selection history. For example, it can display relevant options based on situations and themes previously selected by the user. The analysis unit performs analysis based on the situations and themes selected by the selection unit. Analysis includes, but is not limited to, voice analysis, facial expression analysis, and text analysis. For example, the analysis unit can analyze the user's voice to evaluate tone and speed. The analysis unit can also analyze the user's facial expressions to estimate emotions. For example, it can use a camera to capture the user's facial expressions and use a facial expression analysis algorithm to estimate emotions. Furthermore, the analysis unit can analyze the user's text to evaluate the logic and accuracy of the information. For example, it can analyze the text entered by the user using natural language processing technology and evaluate the content. The provision unit provides feedback based on the analysis results obtained by the analysis unit.Feedback includes, but is not limited to, audio feedback, text feedback, and visual feedback. For example, the service provider can communicate the analysis results to the user by voice. The service provider can also display the analysis results as text. For example, it can display the analysis results as text on the screen. Furthermore, the service provider can display the analysis results visually. For example, it can visually display the analysis results using graphs or charts. In this way, the communication skills improvement application according to the embodiment can improve the user's communication skills.

[0068] The registration section allows users to register their profile information. This profile information includes, but is not limited to, name, age, occupation, and hobbies. For example, users can enter their profile information on the app's settings screen. Users can access the app's settings screen and enter information such as their name, age, occupation, and hobbies. This allows the app to understand the user's basic information and provide individually optimized feedback. The registration section also allows users to register their profile information using voice input. For example, users can use a microphone to input their profile information by voice. Voice input is highly convenient because it allows users to enter information without using their hands. Furthermore, the registration section can automatically read profile information previously entered by the user. For example, it can retrieve and reuse previously entered profile information from the database. This saves users the trouble of entering the same information every time. The registration section also implements security measures to safely manage user profile information and protect privacy. For example, data is encrypted and protected from access by third parties. This allows users to register their profile information with peace of mind.

[0069] The selection function allows users to choose situations or themes they want to practice. These situations and themes include, but are not limited to, business meetings, presentations, interviews, blind dates, and conversations with friends. For example, users can select situations or themes from the app's menu screen. Users can then select a situation or theme from the menu screen and begin practicing based on that selection. The selection function also allows users to select situations or themes using voice input. For example, users can use a microphone to select situations or themes by voice. Voice input is highly convenient because it allows users to make selections hands-free. Furthermore, the selection function can suggest optimal situations or themes based on the user's past selection history. For example, it can display related options based on situations or themes the user has previously selected. This makes it easy for users to find practice content that suits them. The selection function is equipped with an algorithm to analyze the user's selection history and provide individually optimized suggestions. This ensures that users can always select the most suitable practice content, efficiently improving their communication skills.

[0070] The analysis unit performs analysis based on the situation or theme selected by the selection unit. Analysis includes, but is not limited to, voice analysis, facial expression analysis, and text analysis. For example, the analysis unit can analyze the user's voice to evaluate tone and speed. Voice analysis evaluates the tone, speed, volume, and clarity of the user's speech to identify areas for improvement. The analysis unit can also analyze the user's facial expressions to estimate emotions. For example, it can use a camera to capture the user's facial expressions and use a facial expression analysis algorithm to estimate emotions. Facial expression analysis captures changes in the user's facial expressions in real time and evaluates whether the emotional expression is appropriate. Furthermore, the analysis unit can analyze the user's text to evaluate the logic and accuracy of the information. For example, it can analyze the text entered by the user using natural language processing technology to evaluate its content. Text analysis evaluates the structure, logic, accuracy of information, and grammatical correctness of the user's writing to identify areas for improvement. The analysis unit integrates these analysis results to perform a comprehensive evaluation. For example, the results of voice analysis, facial expression analysis, and text analysis are combined to perform a comprehensive evaluation of the user's communication skills. This allows the analysis unit to clearly identify the user's strengths and weaknesses and suggest specific areas for improvement.

[0071] The service provider provides feedback based on the analysis results obtained by the analysis unit. This feedback includes, but is not limited to, audio feedback, text feedback, and visual feedback. For example, the service provider can communicate analysis results to the user verbally. Audio feedback is provided in a format that is easy for the user to hear and understand. The service provider can also display analysis results in text. For example, it can display the analysis results as text on the screen. Text feedback allows users to visually confirm the results, making it easier to grasp specific areas for improvement. Furthermore, the service provider can display analysis results visually. For example, it can use graphs or charts to visually display the results. Visual feedback allows users to easily understand the analysis results at a glance and intuitively grasp areas for improvement. The service provider can collect user feedback and continuously improve the accuracy and effectiveness of the feedback. For example, it can analyze the user's reactions and actions after receiving feedback to identify areas for improvement. The service provider can also save the user's feedback history and provide individually optimized feedback based on past feedback. This allows the service provider to provide effective feedback to users and support the improvement of their communication skills.

[0072] The analysis unit can analyze both verbal and nonverbal aspects. For example, the analysis unit can analyze verbal elements such as the user's language choice, pronunciation, and grammar. It can also analyze nonverbal elements such as the user's gestures, facial expressions, and posture. For example, it can use a camera to capture the user's gestures and facial expressions and analyze the nonverbal elements. Furthermore, the analysis unit can combine both verbal and nonverbal aspects in its analysis. For example, it can simultaneously analyze the user's pronunciation and facial expressions to perform a comprehensive evaluation. By analyzing both verbal and nonverbal aspects, it becomes possible to improve communication skills in a more comprehensive way. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's voice data and video data into a generative AI and have the generative AI perform the verbal and nonverbal analysis.

[0073] The service provider can perform Good / Bad analysis on visual, auditory, and content elements. For example, the service provider can analyze visual elements such as the user's eye contact, facial expressions, and gestures. It can also analyze auditory elements such as the user's voice tone, speed, and volume. Furthermore, it can analyze content elements such as the structure, logic, and accuracy of the user's speech. For example, the service provider can analyze the user's utterances using natural language processing technology and evaluate the content. This allows for detailed feedback through Good / Bad analysis on visual, auditory, and content elements. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's audio and video data into a generating AI and have the generating AI perform analysis on the visual, auditory, and content elements.

[0074] The service provider may include a data storage unit for accumulating past data and confirming growth. The service provider can store data such as the user's past feedback, practice history, and evaluation results. The service provider can also confirm the user's growth based on the accumulated data. For example, the service provider can display past data in chronological order to visually show the user's progress. This makes it easier to grasp the user's progress by accumulating past data and confirming growth. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past data into a generating AI and have the generating AI perform the growth confirmation.

[0075] The selection function allows users to choose scenarios such as one-on-one meetings, presentations, interviews, blind dates, and conversations with friends. For example, the user can select a scenario from the app's menu screen. The selection function also allows users to select scenarios using voice input, for example, by using a microphone to select a scenario by voice. Furthermore, the selection function can suggest optimal scenarios based on the user's past selection history, for example, by displaying relevant options based on scenarios the user has previously selected. This allows users to practice various communication skills by selecting diverse scenarios. Some or all of the above-described processes in the selection function may be performed using AI or not. For example, the selection function can input the user's past selection history into a generating AI, which can then suggest optimal scenarios.

[0076] The service provider can provide additional advice by inputting a 360-degree evaluation of a company, combined with past data. For example, the user inputs a 360-degree evaluation of the company into the app. The service provider can also provide additional advice based on the inputted 360-degree evaluation. For example, the service provider can combine the user's past data with the 360-degree evaluation to provide more realistic feedback. This allows for more realistic feedback by providing additional advice based on the company's 360-degree evaluation. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's 360-degree evaluation data into a generating AI and have the generating AI generate additional advice.

[0077] The registration unit can estimate the user's emotions and adjust the method of inputting profile information based on the estimated emotions. For example, if the user is nervous, the registration unit can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, the registration unit can also provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the registration unit can prioritize voice input to allow for quick profile information entry. This makes it possible to register profile information more comfortably by providing an input method that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0078] The registration unit can analyze the user's past profile information and suggest the most suitable input fields. For example, the registration unit can automatically display frequently used fields as candidates based on the profile information the user has previously entered. The registration unit can also prioritize suggesting input methods (voice, text, etc.) the user has used in the past. Furthermore, the registration unit can predict and suggest fields that the user will use at specific times of day based on their past profile information. This enables efficient information registration by suggesting the most suitable input fields based on past profile information. Some or all of the above processes in the registration unit may be performed using AI or not. For example, the registration unit can input the user's past profile information into a generating AI and have the generating AI suggest the most suitable input fields.

[0079] The registration unit can filter user profile information based on their current lifestyle and areas of interest. For example, the registration unit can prioritize displaying relevant profile items based on the user's current occupation or hobbies. It can also suggest appropriate profile items based on the user's current lifestyle (e.g., student, working professional). Furthermore, the registration unit can prompt users to enter relevant profile information based on their areas of interest. This allows for the registration of more appropriate profile information by filtering based on the user's lifestyle and areas of interest. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0080] The registration unit can estimate the user's emotions and determine the priority of profile information to be entered based on the estimated emotions. For example, if the user is stressed, the registration unit can prioritize entering only the most important profile information. If the user is relaxed, the registration unit can also prompt them to enter detailed profile information. Furthermore, if the user is in a hurry, the registration unit can prioritize entering only the most necessary information. This enables efficient information registration by determining priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0081] The registration unit can prioritize inputting highly relevant information when registering profile information, taking into account the user's geographical location. For example, if a user lives in a specific region, the registration unit can prioritize inputting profile information related to that region. Furthermore, if a user is traveling, the registration unit can prompt them to input relevant information based on their current location. In addition, the registration unit can suggest region-specific profile items based on the user's geographical location. This allows for the registration of more relevant information by considering geographical location. Some or all of the above processing in the registration unit may be performed using AI, or not. For example, the registration unit can input the user's geographical location information into a generating AI and have the generating AI suggest highly relevant information.

[0082] The registration unit can analyze a user's social media activity and input relevant information when registering profile information. For example, the registration unit can analyze the content of a user's social media posts and automatically input relevant profile information. The registration unit can also suggest relevant information based on the user's social media friendships. Furthermore, the registration unit can analyze the user's social media activity history and prompt them to input profile information based on their interests. This allows for the registration of more relevant information by analyzing social media activity. Some or all of the above processes in the registration unit may be performed using AI or not. For example, the registration unit can input the user's social media activity data into a generating AI and have the generating AI input relevant information.

[0083] The selection unit can estimate the user's emotions and adjust the selection method for situations and themes based on the estimated emotions. For example, if the user is nervous, the selection unit will prioritize suggesting relaxing situations and themes. Conversely, if the user is relaxed, the selection unit can also suggest challenging situations and themes. Furthermore, if the user is in a hurry, the selection unit can suggest situations and themes that can be completed quickly. This allows for the selection of more appropriate situations and themes by providing a selection method that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0084] The selection unit can analyze the user's past selection history to suggest the most suitable situations and themes when a selection is made. For example, the selection unit can suggest related options based on situations and themes the user has previously selected. The selection unit can also prioritize displaying situations and themes that are frequently selected based on the user's past selection history. Furthermore, the selection unit can analyze the user's past selection history to suggest the most effective situations and themes. This enables efficient selection by suggesting the most suitable situations and themes based on past selection history. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's past selection history into a generating AI and have the generating AI suggest the most suitable situations and themes.

[0085] The selection unit can filter the results based on the user's current lifestyle and areas of interest during the selection process. For example, the selection unit can prioritize displaying relevant situations and themes based on the user's current occupation or hobbies. It can also suggest appropriate situations and themes based on the user's current lifestyle (e.g., student, working adult). Furthermore, the selection unit can prompt the user to select relevant situations and themes based on their areas of interest. This allows for the selection of more appropriate situations and themes by filtering based on lifestyle and areas of interest. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0086] The selection unit can estimate the user's emotions and determine the priority of situations and themes to select based on the estimated emotions. For example, if the user is stressed, the selection unit will prioritize relaxing situations and themes. Conversely, if the user is relaxed, the selection unit can encourage them to select challenging situations and themes. Furthermore, if the user is in a hurry, the selection unit can prioritize situations and themes that can be completed quickly. This allows for efficient selection of situations and themes by determining priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0087] The selection unit can prioritize selecting situations and themes that are highly relevant, taking into account the user's geographical location when selecting situations and themes. For example, if the user lives in a specific region, the selection unit will prioritize situations and themes related to that region. Furthermore, if the user is traveling, the selection unit can prompt them to select situations and themes relevant to their current location. In addition, the selection unit can suggest region-specific situations and themes based on the user's geographical location. This allows for the selection of more relevant situations and themes by considering geographical location. Some or all of the above processing in the selection unit may be performed using AI, or not. For example, the selection unit can input the user's geographical location into a generating AI, which can then suggest highly relevant situations and themes.

[0088] The selection unit can analyze the user's social media activity and select relevant situations and themes when selecting situations and themes. For example, the selection unit can analyze the content of the user's social media posts and automatically select relevant situations and themes. The selection unit can also suggest relevant situations and themes based on the user's social media friendships. Furthermore, the selection unit can analyze the user's social media activity history and prompt them to select situations and themes based on their interests. This allows for the selection of more relevant situations and themes by analyzing social media activity. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's social media activity data into a generating AI and have the generating AI perform the selection of relevant situations and themes.

[0089] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is tense, the generation AI can select a relaxing analysis method. If the user is relaxed, the generation AI can also select a more detailed analysis method. Furthermore, if the user is in a hurry, the generation AI can select a method for rapid analysis. This allows for more appropriate analysis by providing an analysis method tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using the generation AI or not. For example, the analysis unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0090] The analysis unit can analyze the user's past communication history during analysis and select the optimal analysis method. For example, the analysis unit can use a generating AI to select the optimal analysis method based on the user's past communication history. The analysis unit can also use a generating AI to select frequently used analysis methods from the user's past communication history. Furthermore, the analysis unit can analyze the user's past communication history and use a generating AI to select the most effective analysis method. This enables efficient analysis by selecting the optimal analysis method based on past communication history. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit can input the user's past communication history into a generating AI and have the generating AI select the optimal analysis method.

[0091] The analysis unit can improve the accuracy of its analysis based on the user's current lifestyle and areas of interest. For example, the analysis unit can use the generating AI to select relevant analysis methods based on the user's current occupation or hobbies. The analysis unit can also use the generating AI to select appropriate analysis methods based on the user's current lifestyle (e.g., student, working adult). Furthermore, the analysis unit can use the generating AI to select relevant analysis methods based on the user's areas of interest. This allows for more accurate analysis by performing analysis based on lifestyle and areas of interest. Some or all of the above-described processes in the analysis unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the analysis unit can input data on the user's lifestyle and areas of interest into the generating AI and have the generating AI perform the analysis accuracy improvement.

[0092] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the generation AI can provide a simple and highly visible display method. If the user is relaxed, the generation AI can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the generation AI can provide a concise display method. By providing a display method that matches the user's emotions, it becomes possible to display more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using the generation AI or not. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0093] The analysis unit can improve the accuracy of its analysis by considering the user's geographical location information during the analysis process. For example, if the user lives in a specific region, the generating AI can select an analysis method relevant to that region. Furthermore, if the user is traveling, the generating AI can select a relevant analysis method based on their current location. In addition, the generating AI can select a region-specific analysis method based on the user's geographical location information. This allows for more accurate analysis by considering geographical location information. Some or all of the above-described processes in the analysis unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the analysis unit can input the user's geographical location information into the generating AI and have the generating AI perform the analysis accuracy improvement.

[0094] The analysis unit can improve the accuracy of its analysis by analyzing the user's social media activity during the analysis process. For example, the analysis unit can analyze the content of the user's social media posts, and the generating AI can select relevant analysis methods. The analysis unit can also have the generating AI select relevant analysis methods based on the user's social media friendships. Furthermore, the analysis unit can analyze the user's social media activity history, and the generating AI can select analysis methods based on their interests. This allows for more accurate analysis by analyzing social media activity. Some or all of the above-described processes in the analysis unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the analysis unit can input the user's social media activity data into the generating AI and have the generating AI perform the analysis accuracy improvement.

[0095] The service provider can estimate the user's emotions and adjust the method of providing feedback based on the estimated emotions. For example, if the user is nervous, the service provider can provide feedback in a gentle tone. If the user is relaxed, the service provider can also provide detailed feedback. Furthermore, if the user is in a hurry, the service provider can provide concise and to-the-point feedback. This allows for more appropriate feedback by providing feedback methods tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0096] The feedback provider can analyze the user's past feedback history to provide optimal feedback. For example, the provider can provide relevant feedback based on feedback the user has received in the past. The provider can also prioritize feedback on frequently raised points from the user's past feedback history. Furthermore, the provider can analyze the user's past feedback history to provide the most effective feedback. This enables efficient feedback by providing optimal feedback based on past feedback history. Some or all of the above processing in the feedback provider may be performed using AI or not. For example, the provider can input the user's past feedback history into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0097] The service provider can customize the content of feedback based on the user's current lifestyle and areas of interest when providing feedback. For example, the service provider can provide relevant feedback based on the user's current occupation or hobbies. It can also provide appropriate feedback according to the user's current lifestyle (e.g., student, working adult). Furthermore, the service provider can provide relevant feedback based on the user's areas of interest. This allows for more appropriate feedback by providing feedback based on lifestyle and areas of interest. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the customization of the feedback.

[0098] The service provider can estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is stressed, the service provider will prioritize providing only the most important feedback. If the user is relaxed, the service provider can also provide detailed feedback. Furthermore, if the user is in a hurry, the service provider can prioritize providing only the most necessary feedback. This enables efficient feedback by prioritizing feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0099] The service provider can provide optimal feedback by considering the user's geographical location when providing feedback. For example, if the user lives in a specific region, the service provider can provide feedback relevant to that region. Furthermore, if the user is traveling, the service provider can provide relevant feedback based on their current location. In addition, the service provider can provide region-specific feedback based on the user's geographical location. This allows for more appropriate feedback by considering geographical location. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0100] The service provider can analyze the user's social media activity and customize the content of the feedback when providing it. For example, the service provider can analyze the content of the user's social media posts and provide relevant feedback. It can also provide relevant feedback based on the user's social media friendships. Furthermore, the service provider can analyze the user's social media activity history and provide feedback based on their interests. This allows for more appropriate feedback by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the customization of the feedback.

[0101] The data storage unit can estimate the user's emotions and adjust the data storage method based on the estimated emotions. For example, if the user is tense, the storage unit can provide a simple and intuitive data storage method. If the user is relaxed, the storage unit can also provide a detailed data storage method. Furthermore, if the user is in a hurry, the storage unit can provide a method for quickly storing data. By providing a data storage method that is tailored to the user's emotions, more appropriate data storage becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0102] The data storage unit can analyze the user's past data history and select the optimal storage method when storing data. For example, the storage unit can provide relevant data storage methods based on data the user has stored in the past. The storage unit can also provide frequently used data storage methods based on the user's past data history. Furthermore, the storage unit can analyze the user's past data history and provide the most effective data storage method. This enables efficient data storage by providing the optimal storage method based on past data history. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input the user's past data history into a generating AI and have the generating AI select the optimal storage method.

[0103] The data storage unit can estimate the user's emotions and adjust the data storage frequency based on the estimated emotions. For example, if the user is stressed, the data storage unit can set a lower data storage frequency. Conversely, if the user is relaxed, the data storage unit can set a higher data storage frequency. Furthermore, if the user is in a hurry, the data storage unit can store only the minimum necessary data. This allows for more appropriate data storage by providing a storage frequency that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data storage unit may be performed using AI or not. For example, the data storage unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0104] The data storage unit can select the optimal data storage method when storing data, taking into account the user's geographical location information. For example, if the user lives in a specific region, the storage unit can provide a data storage method relevant to that region. Furthermore, if the user is traveling, the storage unit can provide a relevant data storage method based on their current location. In addition, the storage unit can provide region-specific data storage methods based on the user's geographical location information. This allows for more appropriate data storage by considering geographical location information. Some or all of the above processing in the storage unit may be performed using AI, or without AI. For example, the storage unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal data storage method.

[0105] The data storage unit can analyze the user's social media activity and select the optimal data storage method when storing data. For example, the storage unit can analyze the content of the user's social media posts and provide a relevant data storage method. It can also provide a relevant data storage method based on the user's social media friendships. Furthermore, the storage unit can analyze the user's social media activity history and provide a data storage method based on their interests. This makes it possible to store data more appropriately by analyzing social media activity. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input the user's social media activity data into a generating AI and have the generating AI select the optimal data storage method.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is tense, the analysis unit can select a relaxing analysis method. If the user is relaxed, the analysis unit can also select a more detailed analysis method. Furthermore, if the user is in a hurry, the analysis unit can select a method for rapid analysis. By providing an analysis method that matches the user's emotions, more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0108] The service provider can estimate the user's emotions and adjust the method of providing feedback based on the estimated emotions. For example, if the user is nervous, the service provider can provide feedback in a gentle tone. If the user is relaxed, the service provider can also provide detailed feedback. Furthermore, if the user is in a hurry, the service provider can provide concise and to-the-point feedback. This allows for more appropriate feedback by providing feedback methods tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0109] The selection unit can estimate the user's emotions and adjust the selection method for situations and themes based on the estimated emotions. For example, if the user is nervous, the selection unit will prioritize suggesting relaxing situations and themes. Conversely, if the user is relaxed, the selection unit can also suggest challenging situations and themes. Furthermore, if the user is in a hurry, the selection unit can suggest situations and themes that can be completed quickly. This allows for the selection of more appropriate situations and themes by providing a selection method that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0110] The registration unit can estimate the user's emotions and adjust the method of inputting profile information based on the estimated emotions. For example, if the user is nervous, the registration unit can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, the registration unit can also provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the registration unit can prioritize voice input to allow for quick profile information entry. This makes it possible to register profile information more comfortably by providing an input method that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0111] The data storage unit can estimate the user's emotions and adjust the data storage method based on the estimated emotions. For example, if the user is tense, the storage unit can provide a simple and intuitive data storage method. If the user is relaxed, the storage unit can also provide a detailed data storage method. Furthermore, if the user is in a hurry, the storage unit can provide a method for quickly storing data. By providing a data storage method that is tailored to the user's emotions, more appropriate data storage becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0112] The analysis unit can analyze the user's past communication history during analysis to select the optimal analysis method. For example, the analysis unit can select the optimal analysis method based on the user's past communication history. The analysis unit can also select frequently used analysis methods from the user's past communication history. Furthermore, the analysis unit can analyze the user's past communication history and select the most effective analysis method. This enables efficient analysis by selecting the optimal analysis method based on past communication history. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's past communication history into a generative AI and have the generative AI select the optimal analysis method.

[0113] The feedback provider can analyze the user's past feedback history to provide optimal feedback. For example, the provider can provide relevant feedback based on feedback the user has received in the past. The provider can also prioritize feedback on frequently raised points from the user's past feedback history. Furthermore, the provider can analyze the user's past feedback history to provide the most effective feedback. This enables efficient feedback by providing optimal feedback based on past feedback history. Some or all of the above processing in the feedback provider may be performed using AI or not. For example, the provider can input the user's past feedback history into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0114] The selection unit can analyze the user's past selection history to suggest the most suitable situations and themes when a selection is made. For example, the selection unit can suggest related options based on situations and themes the user has previously selected. The selection unit can also prioritize displaying situations and themes that are frequently selected based on the user's past selection history. Furthermore, the selection unit can analyze the user's past selection history to suggest the most effective situations and themes. This enables efficient selection by suggesting the most suitable situations and themes based on past selection history. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's past selection history into a generating AI and have the generating AI suggest the most suitable situations and themes.

[0115] The registration unit can analyze the user's past profile information and suggest the most suitable input fields. For example, the registration unit can automatically display frequently used fields as candidates based on the profile information the user has previously entered. The registration unit can also prioritize suggesting input methods (voice, text, etc.) the user has used in the past. Furthermore, the registration unit can predict and suggest fields that the user will use at specific times of day based on their past profile information. This enables efficient information registration by suggesting the most suitable input fields based on past profile information. Some or all of the above processes in the registration unit may be performed using AI or not. For example, the registration unit can input the user's past profile information into a generating AI and have the generating AI suggest the most suitable input fields.

[0116] The service provider can provide optimal feedback by considering the user's geographical location when providing feedback. For example, if the user lives in a specific region, the service provider can provide feedback relevant to that region. Furthermore, if the user is traveling, the service provider can provide relevant feedback based on their current location. In addition, the service provider can provide region-specific feedback based on the user's geographical location. This allows for more appropriate feedback by considering geographical location. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The registration section allows users to register their profile information. This profile information includes, for example, name, age, occupation, and hobbies. Users can enter their profile information in the app's settings screen, or they can register it using voice input via the microphone. Furthermore, it is possible to retrieve and reuse previously entered profile information from the database. Step 2: The selection section allows the user to choose a situation or theme they want to practice. Situations and themes include, for example, business meetings, presentations, interviews, blind dates, and conversations with friends. Users can select situations and themes from the app's menu screen, or they can select them using voice input via the microphone. Furthermore, it is possible to suggest the most suitable situations and themes based on past selection history. Step 3: The analysis unit performs analysis based on the situation or theme selected by the selection unit. Analysis includes, for example, voice analysis, facial expression analysis, and text analysis. It can analyze the user's voice to evaluate tone and speed, and analyze facial expressions to estimate emotions. Furthermore, it can analyze text to evaluate the logic of the content and the accuracy of the information. Step 4: The service provider provides feedback based on the analysis results obtained by the analysis unit. This feedback may include, for example, audio feedback, text feedback, or visual feedback. The analysis results can be communicated to the user via audio, displayed in text, or visually represented using graphs and charts.

[0119] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0120] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0121] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0122] Each of the multiple elements described above, including the registration unit, selection unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart device 14, where the user registers profile information. The selection unit is implemented by the control unit 46A of the smart device 14, where the user selects a situation or theme they want to practice. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, where analysis is performed based on the selected situation or theme. The provision unit is implemented by the control unit 46A of the smart device 14, where feedback is provided based on the analysis results. Furthermore, the registration unit can estimate the user's emotions and adjust the method of inputting profile information based on the estimated emotions. Emotion estimation is implemented, for example, using an emotion engine or generative AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the registration unit, selection unit, analysis unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart glasses 214, where the user registers profile information. The selection unit is implemented by the control unit 46A of the smart glasses 214, where the user selects a situation or theme they want to practice. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, where analysis is performed based on the selected situation or theme. The provision unit is implemented by the control unit 46A of the smart glasses 214, where feedback is provided based on the analysis results. Furthermore, the registration unit can estimate the user's emotions and adjust the method of inputting profile information based on the estimated emotions. Emotion estimation is implemented, for example, using an emotion engine or generative AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the registration unit, selection unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the headset terminal 314, where the user registers profile information. The selection unit is implemented by the control unit 46A of the headset terminal 314, where the user selects a situation or theme they want to practice. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, where analysis is performed based on the selected situation or theme. The provision unit is implemented by the control unit 46A of the headset terminal 314, where feedback is provided based on the analysis results. Furthermore, the registration unit can estimate the user's emotions and adjust the method of inputting profile information based on the estimated emotions. Emotion estimation is implemented, for example, using an emotion engine or generative AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] As shown in Figure 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.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0163] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0164] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0166] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0168] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0169] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0170] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0171] Each of the multiple elements described above, including the registration unit, selection unit, analysis unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the robot 414, where the user registers profile information. The selection unit is implemented by the control unit 46A of the robot 414, where the user selects a situation or theme they want to practice. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, where analysis is performed based on the selected situation or theme. The provision unit is implemented by the control unit 46A of the robot 414, where feedback is provided based on the analysis results. Furthermore, the registration unit can estimate the user's emotions and adjust the method of inputting profile information based on the estimated emotions. Emotion estimation is implemented, for example, using an emotion engine or generative AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0172] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0174] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0175] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0176] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0180] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0181] 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.

[0182] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0183] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0184] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0185] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0187] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0188] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0189] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0190] (Note 1) The registration section for registering profile information, There is a selection section where you can choose the situation or theme you want to practice, An analysis unit that performs analysis based on the situation or theme selected by the selection unit, The system includes a providing unit that provides feedback based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze both verbal and nonverbal aspects. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, A Good / Bad analysis is performed based on visual, auditory, and content elements. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, It includes a data storage unit to accumulate past data and confirm growth. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned selection unit is Select scenarios such as 1-on-1 meetings, presentations, interviews, blind dates, and conversations with friends. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, By inputting a 360-degree evaluation of your company, we can provide additional advice based on past data. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned registration unit is It estimates the user's emotions and adjusts how profile information is entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned registration unit is It analyzes the user's past profile information and suggests the most suitable input fields. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned registration unit is When registering profile information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned registration unit is It estimates the user's emotions and determines the priority of profile information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned registration unit is When registering profile information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned registration unit is When registering profile information, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned selection unit is It estimates the user's emotions and adjusts the selection of situations and themes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned selection unit is When you make a selection, the system analyzes your past selection history to suggest the most suitable situations and themes. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned selection unit is When making a selection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned selection unit is It estimates the user's emotions and determines the priority of situations and themes to select based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned selection unit is When selecting situations and themes, the system prioritizes highly relevant situations and themes by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned selection unit is When selecting situations and themes, we analyze users' social media activity and choose relevant situations and themes. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the system analyzes the user's past communication history to select the most suitable analysis method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by taking into account the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, During analysis, we analyze users' social media activity to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how feedback is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing feedback, we analyze the user's past feedback history to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing feedback, the content of the feedback will be customized based on the user's current life situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing feedback, we take the user's geographical location into consideration to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing feedback, the system analyzes the user's social media activity to customize the content of the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 31) The storage unit is We estimate the user's emotions and adjust the data collection method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The storage unit is When accumulating data, the system analyzes the user's past data history to select the optimal storage method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The storage unit is It estimates the user's emotions and adjusts the frequency of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The storage unit is When accumulating data, the optimal data storage method is selected, taking into account the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 35) The storage unit is When accumulating data, the system analyzes users' social media activity to select the optimal data storage method. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The registration section for registering profile information, There is a selection section where you can choose the situation or theme you want to practice, An analysis unit that performs analysis based on the situation or theme selected by the selection unit, The system includes a providing unit that provides feedback based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze both verbal and nonverbal aspects. The system according to feature 1.

3. The aforementioned supply unit is, A Good / Bad analysis is performed based on visual, auditory, and content elements. The system according to feature 1.

4. The aforementioned supply unit is, It includes a data storage unit to accumulate past data and confirm growth. The system according to feature 1.

5. The aforementioned selection unit is Select scenarios such as 1-on-1 meetings, presentations, interviews, blind dates, and conversations with friends. The system according to feature 1.

6. The aforementioned supply unit is, By inputting a 360-degree evaluation of your company, we can provide additional advice based on past data. The system according to feature 1.

7. The aforementioned registration unit is It estimates the user's emotions and adjusts how profile information is entered based on those estimated emotions. The system according to feature 1.

8. The aforementioned registration unit is It analyzes the user's past profile information and suggests the most suitable input fields. The system according to feature 1.

9. The aforementioned registration unit is When registering profile information, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

10. The aforementioned registration unit is It estimates the user's emotions and determines the priority of profile information to be entered based on the estimated user emotions. The system according to feature 1.

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