system

Abstract

The system according to the embodiment aims to enable users to effectively improve their interpersonal skills. [Solution] The system according to the embodiment comprises a scenario selection unit, a dialogue unit, a feedback unit, and a progress recording unit. The scenario selection unit selects a scenario to be trained on. The dialogue unit initiates a dialogue based on the scenario selected by the scenario selection unit. The feedback unit provides feedback based on the dialogue provided by the dialogue unit. The progress recording unit records the user's progress based on the feedback provided by the feedback unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have a problem in that they lack effective training methods for people with poor communication skills to improve their interpersonal skills.

[0005] The system according to the embodiment aims to enable users to effectively improve their interpersonal skills. [Means for solving the problem]

[0006] The system according to the embodiment includes a scenario selection unit, a dialogue unit, a feedback unit, and a progress recording unit. The scenario selection unit selects a scenario in which a user wishes to train. The dialogue unit initiates dialogue based on the scenario selected by the scenario selection unit. The feedback unit provides feedback based on the dialogue provided by the dialogue unit. The progress recording unit records the user's progress based on the feedback provided by the feedback unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to effectively improve their interpersonal skills. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An embodiment of the present invention provides an interpersonal skills improvement system that uses AI to provide an interactive training program for improving a user's interpersonal skills. The system begins with the user accessing the training program and selecting a scenario they wish to train on. Next, the AI ​​initiates a dialogue based on the selected scenario and provides real-time feedback on the user's statements. This feedback is linked to a concrete action plan, ensuring the user clearly understands what they should do next. The AI ​​also records the user's progress, identifies individual weaknesses, and provides a customized training plan. Furthermore, the AI ​​analyzes not only the user's statements but also their tone of voice and facial expressions to provide comprehensive feedback. This allows the user to identify their weaknesses and effectively improve their interpersonal skills. For example, if the user selects an interview scenario, the AI ​​acts as the interviewer, asking questions and providing feedback on the answers. This allows the user to identify their weaknesses and effectively improve their interpersonal skills. Furthermore, the AI ​​analyzes not only the user's statements but also their tone of voice and facial expressions to provide comprehensive feedback. This enables the user to achieve more natural interpersonal communication. Thus, AI-powered interactive training programs are extremely beneficial tools for individuals who are undervalued due to poor communication skills. This allows interpersonal skills improvement systems to effectively enhance users' interpersonal abilities.

[0029] The interpersonal skills improvement system according to this embodiment comprises a scenario selection unit, a dialogue unit, a feedback unit, and a progress recording unit. The scenario selection unit selects a scenario that the user wants to train in. Scenarios include, but are not limited to, interview scenarios, business meeting scenarios, and casual conversation scenarios. The dialogue unit initiates a dialogue based on the scenario selected by the scenario selection unit. The dialogue unit engages in a dialogue with the user, for example, using AI. The dialogue unit can conduct dialogues in various formats, such as text-based dialogues and voice dialogues. The feedback unit provides feedback based on the dialogue provided by the dialogue unit. The feedback unit provides, for example, a specific action plan for the user's statements. The feedback unit can provide feedback in various formats, such as text feedback and voice feedback. The progress recording unit records the user's progress based on the feedback provided by the feedback unit. The progress recording unit records, for example, the degree of improvement and achievement of the user's skills. The progress recording unit can record the user's progress and provide a customized training plan. As a result, the interpersonal skills improvement system according to this embodiment can effectively improve the user's interpersonal skills.

[0030] The feedback unit can analyze the user's voice tone and facial expressions and provide feedback. For example, the feedback unit can analyze the user's voice tone. Voice tone includes, but is not limited to, speech frequency and volume. For example, the feedback unit can analyze speech frequency and evaluate the user's voice tone. The feedback unit can also analyze the user's facial expressions. Facial expressions include, but is not limited to, facial movements and changes in facial muscles. For example, the feedback unit can analyze facial movements and evaluate the user's facial expressions. In this way, the feedback unit can provide more comprehensive feedback by analyzing the user's voice tone and facial expressions. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not using AI. For example, the feedback unit can input data on the user's voice tone and facial expressions into a generating AI and have the generating AI perform the analysis.

[0031] The progress recording unit can record the user's progress and provide a customized training plan. For example, the progress recording unit records the user's progress. Progress includes, but is not limited to, the degree of skill improvement and achievement. For example, the progress recording unit can evaluate and record the user's skill improvement. The progress recording unit can also evaluate and record the user's achievement. The progress recording unit records the user's progress and provides a customized training plan. A customized training plan includes, but is not limited to, adjustments based on the user's past progress data. For example, the progress recording unit provides a specific training plan based on the user's past progress data. This enables more effective training by recording the user's progress and providing a customized training plan. Some or all of the above processing in the progress recording unit may be performed using, for example, AI, or not using AI. For example, the progress recording unit can input the user's progress data into a generating AI and have the generating AI perform analysis.

[0032] The dialogue unit can start a dialogue based on an interview scenario and ask questions to the user. The dialogue unit starts a dialogue based on, for example, the interview scenario. The interview scenario includes, but is not limited to, the types of questions and the flow of the interview. For example, the dialogue unit asks interview questions and receives the user's answers. The dialogue unit can also provide feedback on the user's answers. In this way, the dialogue unit can start a dialogue based on the interview scenario, allowing the user to practice for the interview. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input the interview scenario to a generation AI and cause the generation AI to execute the dialogue.

[0033] The feedback unit can provide a specific action plan for the user's comments. For example, the feedback unit analyzes the user's comments and provides a specific action plan. The action plan may include, but is not limited to, the next steps to be taken and improvements to be made. For example, the feedback unit can suggest the next steps to be taken based on the user's comments. The feedback unit can also point out specific improvements to be made based on the user's comments. In this way, the feedback unit can provide a specific action plan for the user's comments, allowing the user to clearly understand what to do next. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's comments to a generation AI and cause the generation AI to generate an action plan.

[0034] The scenario selection unit can provide a scenario for a business meeting or a casual conversation. For example, the scenario selection unit provides a business meeting scenario. The business meeting scenario includes, but is not limited to, an agenda and the roles of participants. For example, the scenario selection unit sets the agenda for the business meeting and determines the roles of participants. The scenario selection unit can also provide a casual conversation scenario. The casual conversation scenario includes, but is not limited to, everyday conversations and conversations with friends. For example, the scenario selection unit sets a theme for everyday conversations and provides a scenario for a conversation with friends. In this way, the scenario selection unit can provide various scenarios, such as business meetings and casual conversations, allowing the user to hone their interpersonal skills in various situations. Some or all of the above-described processing in the scenario selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario selection unit can input a scenario for a business meeting or a casual conversation into a generation AI and cause the generation AI to provide the scenario.

[0035] The scenario selection unit can analyze past scenario selection history and automatically select a scenario that corresponds to the user's growth. The scenario selection unit, for example, analyzes past scenario selection history. The scenario selection history includes, but is not limited to, previously selected scenarios and selection frequency. The scenario selection unit, for example, adjusts the difficulty level of the next scenario based on the difficulty level of previously selected scenarios. The scenario selection unit can also re-propose scenarios that the user found difficult in the past to encourage the user to overcome them. Furthermore, the scenario selection unit can also suggest more advanced scenarios based on scenarios in which the user is good. In this way, the scenario selection unit can provide scenarios that correspond to the user's growth by analyzing past scenario selection history. Some or all of the above-mentioned processing in the scenario selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario selection unit can input past scenario selection history into a generation AI and have the generation AI select a scenario.

[0036] The scenario selection unit can adjust the difficulty of the scenario based on the user's current skill level. The scenario selection unit, for example, evaluates the user's skill level. Skill levels include, but are not limited to, beginner, intermediate, and advanced. The scenario selection unit, for example, suggests a scenario of appropriate difficulty based on the user's skill level. For example, if the user's skill level is low, it can suggest a basic conversation scenario. Also, if the user's skill level is high, it can suggest a complex business scenario. In this way, the scenario selection unit can provide a scenario of appropriate difficulty by adjusting the difficulty of the scenario based on the user's skill level. Some or all of the above-mentioned processing in the scenario selection unit may be performed using, or without, AI. For example, the scenario selection unit can input the user's skill level data into a generation AI and cause the generation AI to adjust the difficulty of the scenario.

[0037] The scenario selection unit can prioritize displaying highly relevant scenarios based on the user's occupation and interests. The scenario selection unit, for example, identifies the user's occupation. Occupations include, but are not limited to, engineer, sales, etc. For example, if the user works in sales, the scenario selection unit prioritizes displaying sales talk scenarios. The scenario selection unit also identifies the user's interests. Interests include, but are not limited to, hobbies and areas of interest. For example, if the user works in engineering, the scenario selection unit prioritizes displaying scenarios containing technical explanations. Furthermore, the scenario selection unit can also prioritize displaying related scenarios based on themes in which the user is interested. This allows the scenario selection unit to provide highly relevant scenarios based on the user's occupation and interests, enabling more effective training. Some or all of the above-described processing in the scenario selection unit may be performed using, or without, AI. For example, the scenario selection unit may input the user's occupation and interest data into a generation AI and cause the generation AI to select a scenario.

[0038] The scenario selection unit can provide a scenario appropriate for the user's cultural background based on the user's geographical location information. The scenario selection unit, for example, acquires the user's geographical location information. The geographical location information includes, but is not limited to, GPS data and regional culture. For example, if the user is in Japan, the scenario selection unit can provide a scenario based on Japanese culture. Also, if the user is in the United States, the scenario selection unit can provide a scenario based on American culture. Furthermore, if the user is in a multicultural environment, the scenario selection unit can provide an international scenario. In this way, the scenario selection unit can provide a scenario appropriate for the user's cultural background by taking the user's geographical location information into consideration. Some or all of the above-described processing in the scenario selection unit may be performed using, or without, AI. For example, the scenario selection unit can input the user's geographical location information to a generation AI and cause the generation AI to provide a scenario.

[0039] The dialogue unit can analyze the user's utterances in real time and provide appropriate feedback immediately. The dialogue unit, for example, analyzes the user's utterances in real time. Real-time analysis includes, but is not limited to, speech recognition technology and natural language processing technology. The dialogue unit, for example, analyzes the user's utterances using speech recognition technology. The dialogue unit can also analyze the user's utterances using natural language processing technology. The dialogue unit can immediately provide appropriate feedback based on the analysis results. For example, if the user provides an inappropriate answer to a question, the dialogue unit can immediately present a correct answer. Furthermore, if the user provides an appropriate answer, the dialogue unit can immediately provide praising feedback. Furthermore, if the user is unsure, the dialogue unit can provide a hint. In this way, the dialogue unit can immediately provide appropriate feedback by analyzing the user's utterances in real time. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input the user's utterances into a generation AI and cause the generation AI to perform real-time analysis.

[0040] The dialogue unit can analyze the user's nonverbal expressions and perform a comprehensive dialogue evaluation. The dialogue unit, for example, analyzes the user's nonverbal expressions. Nonverbal expressions include, but are not limited to, gestures and posture. The dialogue unit, for example, analyzes gestures and evaluates the user's nonverbal expressions. The dialogue unit can also analyze posture and evaluate the user's nonverbal expressions. The dialogue unit performs a comprehensive dialogue evaluation based on the analysis results of the nonverbal expressions. For example, if the user is nervous, the dialogue unit can provide advice to relax. If the user is confident, the dialogue unit can provide positive feedback. Furthermore, if the user is feeling anxious, the dialogue unit can provide advice to reassure the user. In this way, the dialogue unit can perform a comprehensive dialogue evaluation by analyzing the user's nonverbal expressions. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input the user's nonverbal expression data into a generation AI and cause the generation AI to perform analysis.

[0041] The dialogue unit can provide a more personalized dialogue by referring to the user's past dialogue history. The dialogue unit, for example, refers to the user's past dialogue history. The dialogue history includes, but is not limited to, the content of past dialogues and the frequency of dialogues. For example, the dialogue unit may revisit a topic that the user was weak at in the past and encourage the user to overcome it. The dialogue unit can also provide a more advanced dialogue based on a topic that the user was good at in the past. Furthermore, the dialogue unit can select a topic that the user is interested in from the user's past dialogue history. This allows the dialogue unit to provide a more personalized dialogue by referring to the user's past dialogue history. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit may input the user's past dialogue history data into a generation AI and cause the generation AI to provide a personalized dialogue.

[0042] The dialogue unit can analyze the user's background sound and environmental sound and provide advice to improve the quality of the dialogue. The dialogue unit, for example, analyzes the user's background sound and environmental sound. Background sound and environmental sound include, but are not limited to, environmental sounds and noise. For example, if the background sound is loud, the dialogue unit can advise the user to move to a quieter place. Furthermore, if the environmental sound is inappropriate, the dialogue unit can advise the user to create an appropriate environment. Furthermore, if the background sound is appropriate, the dialogue unit can advise the user to continue the dialogue. In this way, the dialogue unit can provide advice to improve the quality of the dialogue by analyzing the user's background sound and environmental sound. Some or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI or without AI. For example, the dialogue unit can input the user's background sound and environmental sound data to a generation AI and cause the generation AI to perform analysis.

[0043] The feedback unit can provide a more effective action plan by referring to the user's past feedback history. The feedback unit, for example, refers to the user's past feedback history. The feedback history includes, but is not limited to, the content of past feedback and the frequency of feedback. The feedback unit, for example, provides a specific action plan based on feedback the user has received in the past. The feedback unit can also highlight areas where the user has improved in the past and encourage further improvement. Furthermore, the feedback unit can suggest an effective action plan based on the user's past feedback history. In this way, the feedback unit can provide a more effective action plan by referring to the user's past feedback history. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback history data into a generation AI and cause the generation AI to provide an action plan.

[0044] The feedback unit can provide comprehensive feedback by analyzing not only the content of the user's speech but also the tone of voice and facial expression. The feedback unit, for example, analyzes the content of the user's speech. The content of the speech includes, but is not limited to, text and voice. The feedback unit can evaluate the content of the user's speech using, for example, text analysis. The feedback unit can also evaluate the content of the user's speech using audio analysis. The feedback unit can also analyze the tone of the user's voice. The tone of voice includes, but is not limited to, voice frequency and volume. The feedback unit can analyze, for example, voice frequency to evaluate the tone of the user's voice. The feedback unit can also analyze the user's facial expression. The facial expression includes, but is not limited to, facial movements and changes in facial muscles. The feedback unit can analyze, for example, facial movements to evaluate the user's facial expression. In this way, the feedback unit can provide more comprehensive feedback by analyzing not only the content of the user's speech but also the tone of voice and facial expression. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without using AI. For example, the feedback unit can input data on the user's remarks, tone of voice, and facial expressions into the generation AI, allowing the generation AI to perform analysis.

[0045] The feedback unit can provide a highly relevant action plan based on the user's occupation and interests. The feedback unit, for example, identifies the user's occupation. Occupations include, but are not limited to, engineer, sales, etc. For example, if the user works in sales, the feedback unit points out areas for improvement in the user's sales pitch. The feedback unit also identifies the user's interests. Interests include, but are not limited to, hobbies and areas of interest. For example, if the user works in engineering, the feedback unit points out areas for improvement in the user's technical explanation. The feedback unit can also provide a relevant action plan based on the user's interests. This allows the feedback unit to provide more effective feedback by providing a highly relevant action plan based on the user's occupation and interests. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without AI. For example, the feedback unit can input the user's occupation and interest data into the generation AI and cause the generation AI to provide an action plan.

[0046] The feedback unit can provide feedback appropriate to a cultural background based on the user's geographical location information. The feedback unit, for example, acquires the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and regional culture. For example, if the user is in Japan, the feedback unit can provide feedback based on Japanese culture. Also, if the user is in the United States, the feedback unit can provide feedback based on American culture. Furthermore, if the user is in a multicultural environment, the feedback unit can provide international feedback. In this way, the feedback unit can provide feedback appropriate to a cultural background by taking the user's geographical location information into consideration. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's geographical location information to a generation AI and cause the generation AI to provide feedback.

[0047] The progress recording unit can provide a more effective customized training plan by referencing the user's past progress data. The progress recording unit, for example, references the user's past progress data. The progress data includes, but is not limited to, past progress content and progress frequency. The progress recording unit, for example, provides a specific training plan based on the user's past progress data. The progress recording unit can also highlight areas where the user has improved in the past and encourage further improvement. Furthermore, the progress recording unit can propose an effective training plan based on the user's past progress data. In this way, the progress recording unit can provide a more effective customized training plan by referencing the user's past progress data. Some or all of the above-described processing in the progress recording unit may be performed, for example, using AI or without AI. For example, the progress recording unit can input the user's past progress data into a generation AI and cause the generation AI to provide a training plan.

[0048] The progress recording unit can analyze the user's utterances and non-verbal expressions to perform a comprehensive progress evaluation. The progress recording unit, for example, analyzes the user's utterances. The utterances include, but are not limited to, text and voice. The progress recording unit can evaluate the user's utterances using, for example, text analysis. The progress recording unit can also evaluate the user's utterances using voice analysis. The progress recording unit can also analyze the user's non-verbal expressions. The non-verbal expressions include, but are not limited to, gestures and posture. The progress recording unit can analyze, for example, gestures to evaluate the user's non-verbal expressions. The progress recording unit can also analyze posture to evaluate the user's non-verbal expressions. This enables the progress recording unit to perform a comprehensive progress evaluation by analyzing the user's utterances and non-verbal expressions. Some or all of the above-described processing in the progress recording unit can be performed using, for example, AI, or without AI. For example, the progress recording unit can input the user's remarks and non-verbal expression data into the generation AI and have the generation AI perform analysis.

[0049] The progress recording unit can provide a highly relevant training plan based on the user's occupation and interests. The progress recording unit, for example, identifies the user's occupation. Occupations include, but are not limited to, engineer, sales, etc. For example, if the user is in sales, the progress recording unit can provide a training plan for sales talk. The progress recording unit can also identify the user's interests. Interests include, but are not limited to, hobbies and areas of interest. For example, if the user is in engineering, the progress recording unit can provide a training plan for technical explanations. Furthermore, the progress recording unit can also provide related training plans based on themes in which the user is interested. This allows the progress recording unit to provide a highly relevant training plan based on the user's occupation and interests, enabling more effective training. Some or all of the above-described processing in the progress recording unit may be performed using, for example, AI, or may be performed without AI. For example, the progress recording unit can input the user's occupation and interest data into a generation AI and cause the generation AI to provide a training plan.

[0050] The progress recording unit can provide a progress assessment appropriate for a cultural background based on the user's geographical location information. The progress recording unit, for example, acquires the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and regional culture. For example, if the user is in Japan, the progress recording unit can provide a progress assessment based on Japanese culture. Also, if the user is in the United States, the progress recording unit can provide a progress assessment based on American culture. Furthermore, if the user is in a multicultural environment, the progress recording unit can provide an international progress assessment. In this way, the progress recording unit can provide a progress assessment appropriate for a cultural background by taking the user's geographical location information into consideration. Some or all of the above-described processing in the progress recording unit may be performed using, or without, AI. For example, the progress recording unit can input the user's geographical location information to a generation AI and cause the generation AI to provide a progress assessment.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The interpersonal skill improvement system may further include a reward unit. The reward unit provides rewards such as badges and points when a user achieves a specific goal. For example, if a user achieves a high score in an interview scenario, the reward unit may award the user with an "interview master" badge. In addition, if a user performs continuous training, the reward unit may provide continuous training bonus points. Furthermore, the reward unit may provide customized rewards when a user improves a specific skill. In this way, the reward unit can increase the user's motivation and encourage continuous training.

[0053] The feedback unit can analyze not only the user's tone of voice and facial expressions, but also the user's gestures and posture. For example, the feedback unit can analyze the user's hand movements and body orientation while speaking and suggest appropriate gestures and postures. If the user feels nervous, the feedback unit can also provide advice on breathing techniques and posture to help the user relax. Furthermore, if the user feels confident, the feedback unit can provide positive feedback to further reinforce that confidence. This allows the feedback unit to provide more comprehensive feedback and improve the user's interpersonal skills in a variety of ways.

[0054] The progress recording unit not only records the user's progress but also provides advice in real time based on the user's progress. For example, if the user is stagnating in a particular skill, the progress recording unit can provide specific advice to improve that skill. The progress recording unit can also present a recommended training plan as the next step when the user achieves a goal. Furthermore, the progress recording unit can analyze the user's progress data and suggest methods that the user has used successfully in the past. In this way, the progress recording unit can support the user's progress in real time and provide more effective training.

[0055] The dialogue unit can not only initiate a dialogue based on an interview scenario, but also customize the dialogue by taking into account the user's past interview experience. For example, the dialogue unit can revisit questions that the user found difficult in the past and have the user practice answers to those questions. The dialogue unit can also highlight answers that the user has successfully answered in the past and provide advice for further refining those answers. Furthermore, the dialogue unit can add new challenging questions based on the user's past interview experience. This allows the dialogue unit to utilize the user's past experience and provide more personalized interview practice.

[0056] The feedback unit can not only provide a specific action plan for the user's comments, but also select a feedback method according to the user's learning style. For example, the feedback unit can provide feedback using graphs and charts to visual learners and audio feedback to auditory learners. The feedback unit can also provide feedback using specific scenarios to practical learners. Furthermore, the feedback unit can adjust the frequency and content of feedback according to the user's learning pace. This allows the feedback unit to provide feedback that is optimal for the user's learning style and support more effective learning.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The scenario selection unit selects a scenario that the user wants to train in. The scenario may include, but is not limited to, an interview scenario, a business meeting scenario, a casual conversation scenario, etc. Step 2: The dialogue unit starts a dialogue based on the scenario selected by the scenario selection unit. The dialogue unit may use AI to engage in dialogue with the user, and may conduct dialogue in various formats, such as text-based dialogue or voice dialogue. Step 3: The feedback unit provides feedback based on the dialogue provided by the dialogue unit. For example, the feedback unit may provide a specific action plan for the user's remarks, and may provide feedback in various forms, such as text feedback or voice feedback. Step 4: The progress recording unit records the user's progress based on the feedback provided by the feedback unit. The progress recording unit can, for example, record the user's skill improvement and achievements and provide a customized training plan.

[0059] (Example 2) An interpersonal skills improvement system according to an embodiment of the present invention provides an interactive training program using AI to improve a user's interpersonal skills. The system begins with the user accessing the training program and selecting a scenario for training. The AI ​​then initiates a dialogue based on the selected scenario and provides real-time feedback on the user's comments. The feedback is linked to a specific action plan, allowing the user to clearly understand what to do next. The AI ​​also records the user's progress, identifies individual weaknesses, and provides a customized training plan. Furthermore, the AI ​​analyzes not only the content of the user's comments but also their tone of voice and facial expressions to provide comprehensive feedback. This allows the user to identify their weaknesses and effectively improve their interpersonal skills. For example, if the user selects an interview scenario, the AI ​​acts as the interviewer, posing questions to the user and providing feedback on their answers. This allows the user to identify their weaknesses and effectively improve their interpersonal skills. Furthermore, the AI ​​analyzes not only the content of the user's comments but also their tone of voice and facial expressions to provide comprehensive feedback. This allows the user to achieve more natural interpersonal communication. In this way, interactive training programs using AI can be a very useful tool for people who are undervalued due to their poor communication skills, and the interpersonal skills improvement system can effectively improve users' interpersonal skills.

[0060] An interpersonal skill improvement system according to an embodiment includes a scenario selection unit, a dialogue unit, a feedback unit, and a progress recording unit. The scenario selection unit selects a scenario in which a user wants to train. Examples of scenarios include, but are not limited to, an interview scenario, a business meeting scenario, and a casual conversation scenario. The dialogue unit initiates a dialogue based on the scenario selected by the scenario selection unit. The dialogue unit uses, for example, AI to engage in dialogue with the user. The dialogue unit can engage in dialogue in various formats, such as text-based dialogue or voice dialogue. The feedback unit provides feedback based on the dialogue provided by the dialogue unit. The feedback unit can provide, for example, a specific action plan for the content of the user's remarks. The feedback unit can provide feedback in various formats, such as text feedback or voice feedback. The progress recording unit records the user's progress based on the feedback provided by the feedback unit. The progress recording unit records, for example, the user's skill improvement level and achievement level. The progress recording unit can record the user's progress and provide a customized training plan. This allows the interpersonal skill improvement system according to an embodiment to effectively improve the user's interpersonal skills.

[0061] The feedback unit can analyze the user's vocal tone and facial expression and provide feedback. The feedback unit, for example, analyzes the user's vocal tone. The vocal tone includes, but is not limited to, voice frequency and volume. The feedback unit, for example, analyzes the voice frequency and evaluates the user's vocal tone. The feedback unit can also analyze the user's facial expression. The facial expression includes, but is not limited to, facial movements and changes in facial muscles. The feedback unit, for example, analyzes facial movements and evaluates the user's facial expression. In this way, the feedback unit can provide more comprehensive feedback by analyzing the user's vocal tone and facial expression. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input data on the user's vocal tone and facial expression into a generation AI and cause the generation AI to perform analysis.

[0062] The progress recording unit can record the user's progress and provide a customized training plan. The progress recording unit, for example, records the user's progress. Progress includes, but is not limited to, skill improvement and achievement. The progress recording unit, for example, evaluates and records the user's skill improvement. The progress recording unit can also evaluate and record the user's achievement. The progress recording unit records the user's progress and provides a customized training plan. The customized training plan can include, but is not limited to, adjustments based on the user's past progress data. The progress recording unit, for example, provides a specific training plan based on the user's past progress data. In this way, the progress recording unit records the user's progress and provides a customized training plan, enabling more effective training. Some or all of the above-described processing in the progress recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the progress recording unit can input the user's progress data into a generation AI and have the generation AI perform analysis.

[0063] The dialogue unit can start a dialogue based on an interview scenario and ask questions to the user. The dialogue unit starts a dialogue based on, for example, the interview scenario. The interview scenario includes, but is not limited to, the types of questions and the flow of the interview. For example, the dialogue unit asks interview questions and receives the user's answers. The dialogue unit can also provide feedback on the user's answers. In this way, the dialogue unit can start a dialogue based on the interview scenario, allowing the user to practice for the interview. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input the interview scenario to a generation AI and cause the generation AI to execute the dialogue.

[0064] The feedback unit can provide a specific action plan for the user's comments. For example, the feedback unit analyzes the user's comments and provides a specific action plan. The action plan may include, but is not limited to, the next steps to be taken and improvements to be made. For example, the feedback unit can suggest the next steps to be taken based on the user's comments. The feedback unit can also point out specific improvements to be made based on the user's comments. In this way, the feedback unit can provide a specific action plan for the user's comments, allowing the user to clearly understand what to do next. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's comments to a generation AI and cause the generation AI to generate an action plan.

[0065] The scenario selection unit can provide a scenario for a business meeting or a casual conversation. For example, the scenario selection unit provides a business meeting scenario. The business meeting scenario includes, but is not limited to, an agenda and the roles of participants. For example, the scenario selection unit sets the agenda for the business meeting and determines the roles of participants. The scenario selection unit can also provide a casual conversation scenario. The casual conversation scenario includes, but is not limited to, everyday conversations and conversations with friends. For example, the scenario selection unit sets a theme for everyday conversations and provides a scenario for a conversation with friends. In this way, the scenario selection unit can provide various scenarios, such as business meetings and casual conversations, allowing the user to hone their interpersonal skills in various situations. Some or all of the above-described processing in the scenario selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario selection unit can input a scenario for a business meeting or a casual conversation into a generation AI and cause the generation AI to provide the scenario.

[0066] The scenario selection unit can estimate the user's emotions and propose an optimal scenario based on the estimated user's emotions. The scenario selection unit, for example, estimates the user's emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. The scenario selection unit, for example, estimates the user's emotions using facial expression analysis. The scenario selection unit can also estimate the user's emotions using voice analysis. The scenario selection unit proposes an optimal scenario based on the estimated user's emotions. For example, if the user is nervous, the scenario selection unit can propose a relaxing casual conversation scenario. If the user is confident, the scenario selection unit can propose a difficult business meeting scenario. Furthermore, if the user is depressed, the scenario selection unit can propose a scenario including an encouraging message. This allows the scenario selection unit to propose an optimal scenario based on the user's emotions, enabling more effective training. Some or all of the above-described processing in the scenario selection unit may be performed using, for example, AI, or may be performed without AI. For example, the scenario selection unit can input the user's emotion data into a generation AI and cause the generation AI to propose a scenario.

[0067] The scenario selection unit can analyze past scenario selection history and automatically select a scenario that corresponds to the user's growth. The scenario selection unit, for example, analyzes past scenario selection history. The scenario selection history includes, but is not limited to, previously selected scenarios and selection frequency. The scenario selection unit, for example, adjusts the difficulty level of the next scenario based on the difficulty level of previously selected scenarios. The scenario selection unit can also re-propose scenarios that the user found difficult in the past to encourage the user to overcome them. Furthermore, the scenario selection unit can also suggest more advanced scenarios based on scenarios in which the user is good. In this way, the scenario selection unit can provide scenarios that correspond to the user's growth by analyzing past scenario selection history. Some or all of the above-mentioned processing in the scenario selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario selection unit can input past scenario selection history into a generation AI and have the generation AI select a scenario.

[0068] The scenario selection unit can adjust the difficulty of the scenario based on the user's current skill level. The scenario selection unit, for example, evaluates the user's skill level. Skill levels include, but are not limited to, beginner, intermediate, and advanced. The scenario selection unit, for example, suggests a scenario of appropriate difficulty based on the user's skill level. For example, if the user's skill level is low, it can suggest a basic conversation scenario. Also, if the user's skill level is high, it can suggest a complex business scenario. In this way, the scenario selection unit can provide a scenario of appropriate difficulty by adjusting the difficulty of the scenario based on the user's skill level. Some or all of the above-mentioned processing in the scenario selection unit may be performed using, or without, AI. For example, the scenario selection unit can input the user's skill level data into a generation AI and cause the generation AI to adjust the difficulty of the scenario.

[0069] The scenario selection unit can estimate the user's emotions and adjust the order of scenarios based on the estimated user's emotions. The scenario selection unit, for example, estimates the user's emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. The scenario selection unit, for example, estimates the user's emotions using facial expression analysis. The scenario selection unit can also estimate the user's emotions using voice analysis. The scenario selection unit adjusts the order of scenarios based on the estimated user's emotions. For example, if the user is nervous, a relaxing scenario can be suggested first. If the user is confident, a more difficult scenario can be suggested first. Furthermore, if the user is tired, an easier scenario can be suggested first. This allows the scenario selection unit to adjust the order of scenarios based on the user's emotions, enabling more effective training. Some or all of the above-described processing in the scenario selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario selection unit can input the user's emotion data into a generation AI and cause the generation AI to adjust the order of scenarios.

[0070] The scenario selection unit can prioritize displaying highly relevant scenarios based on the user's occupation and interests. The scenario selection unit, for example, identifies the user's occupation. Occupations include, but are not limited to, engineer, sales, etc. For example, if the user works in sales, the scenario selection unit prioritizes displaying sales talk scenarios. The scenario selection unit also identifies the user's interests. Interests include, but are not limited to, hobbies and areas of interest. For example, if the user works in engineering, the scenario selection unit prioritizes displaying scenarios containing technical explanations. Furthermore, the scenario selection unit can also prioritize displaying related scenarios based on themes in which the user is interested. This allows the scenario selection unit to provide highly relevant scenarios based on the user's occupation and interests, enabling more effective training. Some or all of the above-described processing in the scenario selection unit may be performed using, or without, AI. For example, the scenario selection unit may input the user's occupation and interest data into a generation AI and cause the generation AI to select a scenario.

[0071] The scenario selection unit can provide a scenario appropriate for the user's cultural background based on the user's geographical location information. The scenario selection unit, for example, acquires the user's geographical location information. The geographical location information includes, but is not limited to, GPS data and regional culture. For example, if the user is in Japan, the scenario selection unit can provide a scenario based on Japanese culture. Also, if the user is in the United States, the scenario selection unit can provide a scenario based on American culture. Furthermore, if the user is in a multicultural environment, the scenario selection unit can provide an international scenario. In this way, the scenario selection unit can provide a scenario appropriate for the user's cultural background by taking the user's geographical location information into consideration. Some or all of the above-described processing in the scenario selection unit may be performed using, or without, AI. For example, the scenario selection unit can input the user's geographical location information to a generation AI and cause the generation AI to provide a scenario.

[0072] The dialogue unit can estimate the user's emotions and adjust the tone and pace of the dialogue based on the estimated user's emotions. The dialogue unit, for example, estimates the user's emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. The dialogue unit, for example, estimates the user's emotions using facial expression analysis. The dialogue unit can also estimate the user's emotions using voice analysis. The dialogue unit adjusts the tone and pace of the dialogue based on the estimated user's emotions. For example, if the user is nervous, the dialogue can proceed slowly in a calm tone. On the other hand, if the user is relaxed, the dialogue can proceed in a bright tone. Furthermore, if the user is in a hurry, the dialogue can proceed quickly. This allows the dialogue unit to adjust the tone and pace of the dialogue based on the user's emotions, thereby enabling more effective dialogue. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without AI. For example, the dialogue unit can input the user's emotion data into a generation AI and cause the generation AI to adjust the tone and pace of the dialogue.

[0073] The dialogue unit can analyze the user's utterances in real time and provide appropriate feedback immediately. The dialogue unit, for example, analyzes the user's utterances in real time. Real-time analysis includes, but is not limited to, speech recognition technology and natural language processing technology. The dialogue unit, for example, analyzes the user's utterances using speech recognition technology. The dialogue unit can also analyze the user's utterances using natural language processing technology. The dialogue unit can immediately provide appropriate feedback based on the analysis results. For example, if the user provides an inappropriate answer to a question, the dialogue unit can immediately present a correct answer. Furthermore, if the user provides an appropriate answer, the dialogue unit can immediately provide praising feedback. Furthermore, if the user is unsure, the dialogue unit can provide a hint. In this way, the dialogue unit can immediately provide appropriate feedback by analyzing the user's utterances in real time. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input the user's utterances into a generation AI and cause the generation AI to perform real-time analysis.

[0074] The dialogue unit can analyze the user's nonverbal expressions and perform a comprehensive dialogue evaluation. The dialogue unit, for example, analyzes the user's nonverbal expressions. Nonverbal expressions include, but are not limited to, gestures and posture. The dialogue unit, for example, analyzes gestures and evaluates the user's nonverbal expressions. The dialogue unit can also analyze posture and evaluate the user's nonverbal expressions. The dialogue unit performs a comprehensive dialogue evaluation based on the analysis results of the nonverbal expressions. For example, if the user is nervous, the dialogue unit can provide advice to relax. If the user is confident, the dialogue unit can provide positive feedback. Furthermore, if the user is feeling anxious, the dialogue unit can provide advice to reassure the user. In this way, the dialogue unit can perform a comprehensive dialogue evaluation by analyzing the user's nonverbal expressions. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input the user's nonverbal expression data into a generation AI and cause the generation AI to perform analysis.

[0075] The dialogue unit can estimate the user's emotions and adjust the theme of the dialogue based on the estimated user's emotions. The dialogue unit, for example, estimates the user's emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. The dialogue unit, for example, estimates the user's emotions using facial expression analysis. The dialogue unit can also estimate the user's emotions using voice analysis. The dialogue unit adjusts the theme of the dialogue based on the estimated user's emotions. For example, if the user is nervous, the dialogue unit can select a relaxing theme. If the user is confident, the dialogue unit can select a challenging theme. Furthermore, if the user is depressed, the dialogue unit can select an encouraging theme. This allows the dialogue unit to adjust the theme of the dialogue based on the user's emotions, enabling more effective dialogue. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input the user's emotion data into a generation AI and cause the generation AI to adjust the theme of the dialogue.

[0076] The dialogue unit can provide a more personalized dialogue by referring to the user's past dialogue history. The dialogue unit, for example, refers to the user's past dialogue history. The dialogue history includes, but is not limited to, the content of past dialogues and the frequency of dialogues. For example, the dialogue unit may revisit a topic that the user was weak at in the past and encourage the user to overcome it. The dialogue unit can also provide a more advanced dialogue based on a topic that the user was good at in the past. Furthermore, the dialogue unit can select a topic that the user is interested in from the user's past dialogue history. This allows the dialogue unit to provide a more personalized dialogue by referring to the user's past dialogue history. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit may input the user's past dialogue history data into a generation AI and cause the generation AI to provide a personalized dialogue.

[0077] The dialogue unit can analyze the user's background sound and environmental sound and provide advice to improve the quality of the dialogue. The dialogue unit, for example, analyzes the user's background sound and environmental sound. Background sound and environmental sound include, but are not limited to, environmental sounds and noise. For example, if the background sound is loud, the dialogue unit can advise the user to move to a quieter place. Furthermore, if the environmental sound is inappropriate, the dialogue unit can advise the user to create an appropriate environment. Furthermore, if the background sound is appropriate, the dialogue unit can advise the user to continue the dialogue. In this way, the dialogue unit can provide advice to improve the quality of the dialogue by analyzing the user's background sound and environmental sound. Some or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI or without AI. For example, the dialogue unit can input the user's background sound and environmental sound data to a generation AI and cause the generation AI to perform analysis.

[0078] The feedback unit can estimate the user's emotions and adjust the content and method of feedback based on the estimated user's emotions. The feedback unit, for example, estimates the user's emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. The feedback unit, for example, estimates the user's emotions using facial expression analysis. The feedback unit can also estimate the user's emotions using voice analysis. The feedback unit adjusts the content and method of feedback based on the estimated user's emotions. For example, if the user is nervous, the feedback unit can provide gentle feedback. If the user is confident, the feedback unit can point out specific areas for improvement. If the user is depressed, the feedback unit can provide feedback including encouraging words. In this way, the feedback unit can provide more effective feedback by adjusting the content and method of feedback based on the user's emotions. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's emotion data into a generation AI and cause the generation AI to adjust the content and method of feedback.

[0079] The feedback unit can provide a more effective action plan by referring to the user's past feedback history. The feedback unit, for example, refers to the user's past feedback history. The feedback history includes, but is not limited to, the content of past feedback and the frequency of feedback. The feedback unit, for example, provides a specific action plan based on feedback the user has received in the past. The feedback unit can also highlight areas where the user has improved in the past and encourage further improvement. Furthermore, the feedback unit can suggest an effective action plan based on the user's past feedback history. In this way, the feedback unit can provide a more effective action plan by referring to the user's past feedback history. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback history data into a generation AI and cause the generation AI to provide an action plan.

[0080] The feedback unit can provide comprehensive feedback by analyzing not only the content of the user's speech but also the tone of voice and facial expression. The feedback unit, for example, analyzes the content of the user's speech. The content of the speech includes, but is not limited to, text and voice. The feedback unit can evaluate the content of the user's speech using, for example, text analysis. The feedback unit can also evaluate the content of the user's speech using audio analysis. The feedback unit can also analyze the tone of the user's voice. The tone of voice includes, but is not limited to, voice frequency and volume. The feedback unit can analyze, for example, voice frequency to evaluate the tone of the user's voice. The feedback unit can also analyze the user's facial expression. The facial expression includes, but is not limited to, facial movements and changes in facial muscles. The feedback unit can analyze, for example, facial movements to evaluate the user's facial expression. In this way, the feedback unit can provide more comprehensive feedback by analyzing not only the content of the user's speech but also the tone of voice and facial expression. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without using AI. For example, the feedback unit can input data on the user's remarks, tone of voice, and facial expressions into the generation AI, allowing the generation AI to perform analysis.

[0081] The feedback unit can estimate the user's emotion and adjust the timing of feedback based on the estimated user's emotion. The feedback unit, for example, estimates the user's emotion. Emotions include, but are not limited to, facial expression analysis and voice analysis. The feedback unit, for example, estimates the user's emotion using facial expression analysis. The feedback unit can also estimate the user's emotion using voice analysis. The feedback unit adjusts the timing of feedback based on the estimated user's emotion. For example, if the user is nervous, the feedback can be provided midway through a dialogue. Alternatively, if the user is relaxed, the feedback can be provided after the dialogue ends. Furthermore, if the user is in a hurry, the feedback can be provided quickly. In this way, the feedback unit can provide more effective feedback by adjusting the timing of feedback based on the user's emotion. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's emotion data into a generation AI and cause the generation AI to adjust the timing of feedback.

[0082] The feedback unit can provide a highly relevant action plan based on the user's occupation and interests. The feedback unit, for example, identifies the user's occupation. Occupations include, but are not limited to, engineer, sales, etc. For example, if the user works in sales, the feedback unit points out areas for improvement in the user's sales pitch. The feedback unit also identifies the user's interests. Interests include, but are not limited to, hobbies and areas of interest. For example, if the user works in engineering, the feedback unit points out areas for improvement in the user's technical explanation. The feedback unit can also provide a relevant action plan based on the user's interests. This allows the feedback unit to provide more effective feedback by providing a highly relevant action plan based on the user's occupation and interests. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without AI. For example, the feedback unit can input the user's occupation and interest data into the generation AI and cause the generation AI to provide an action plan.

[0083] The feedback unit can provide feedback appropriate to a cultural background based on the user's geographical location information. The feedback unit, for example, acquires the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and regional culture. For example, if the user is in Japan, the feedback unit can provide feedback based on Japanese culture. Also, if the user is in the United States, the feedback unit can provide feedback based on American culture. Furthermore, if the user is in a multicultural environment, the feedback unit can provide international feedback. In this way, the feedback unit can provide feedback appropriate to a cultural background by taking the user's geographical location information into consideration. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's geographical location information to a generation AI and cause the generation AI to provide feedback.

[0084] The progress recording unit can estimate the user's emotions and adjust the progress recording method based on the estimated user's emotions. The progress recording unit, for example, estimates the user's emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. The progress recording unit, for example, estimates the user's emotions using facial expression analysis. The progress recording unit can also estimate the user's emotions using voice analysis. The progress recording unit adjusts the progress recording method based on the estimated user's emotions. For example, if the user is nervous, a concise progress record can be provided. If the user is relaxed, a detailed progress record can be provided. Furthermore, if the user is in a hurry, a quick progress record can be provided. This allows the progress recording unit to adjust the progress recording method based on the user's emotions, enabling more effective progress recording. Some or all of the above-described processing in the progress recording unit may be performed using, for example, AI, or may be performed without AI. For example, the progress recording unit can input the user's emotion data into a generation AI and cause the generation AI to adjust the progress recording method.

[0085] The progress recording unit can provide a more effective customized training plan by referencing the user's past progress data. The progress recording unit, for example, references the user's past progress data. The progress data includes, but is not limited to, past progress content and progress frequency. The progress recording unit, for example, provides a specific training plan based on the user's past progress data. The progress recording unit can also highlight areas where the user has improved in the past and encourage further improvement. Furthermore, the progress recording unit can propose an effective training plan based on the user's past progress data. In this way, the progress recording unit can provide a more effective customized training plan by referencing the user's past progress data. Some or all of the above-described processing in the progress recording unit may be performed, for example, using AI or without AI. For example, the progress recording unit can input the user's past progress data into a generation AI and cause the generation AI to provide a training plan.

[0086] The progress recording unit can analyze the user's utterances and non-verbal expressions to perform a comprehensive progress evaluation. The progress recording unit, for example, analyzes the user's utterances. The utterances include, but are not limited to, text and voice. The progress recording unit can evaluate the user's utterances using, for example, text analysis. The progress recording unit can also evaluate the user's utterances using voice analysis. The progress recording unit can also analyze the user's non-verbal expressions. The non-verbal expressions include, but are not limited to, gestures and posture. The progress recording unit can analyze, for example, gestures to evaluate the user's non-verbal expressions. The progress recording unit can also analyze posture to evaluate the user's non-verbal expressions. This enables the progress recording unit to perform a comprehensive progress evaluation by analyzing the user's utterances and non-verbal expressions. Some or all of the above-described processing in the progress recording unit can be performed using, for example, AI, or without AI. For example, the progress recording unit can input the user's remarks and non-verbal expression data into the generation AI and have the generation AI perform analysis.

[0087] The progress recording unit can estimate the user's emotions and adjust the frequency of progress recording based on the estimated user's emotions. The progress recording unit, for example, estimates the user's emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. The progress recording unit, for example, estimates the user's emotions using facial expression analysis. The progress recording unit can also estimate the user's emotions using voice analysis. The progress recording unit adjusts the frequency of progress recording based on the estimated user's emotions. For example, if the user is nervous, the frequency of progress recording can be reduced. Also, if the user is relaxed, the frequency of progress recording can be increased. Furthermore, if the user is in a hurry, the progress recording can be performed quickly. This allows the progress recording unit to adjust the frequency of progress recording based on the user's emotions, enabling more effective progress recording. Some or all of the above-described processing in the progress recording unit may be performed using, for example, AI, or without AI. For example, the progress recording unit can input the user's emotion data into a generation AI and cause the generation AI to adjust the frequency of progress recording.

[0088] The progress recording unit can provide a highly relevant training plan based on the user's occupation and interests. The progress recording unit, for example, identifies the user's occupation. Occupations include, but are not limited to, engineer, sales, etc. For example, if the user is in sales, the progress recording unit can provide a training plan for sales talk. The progress recording unit can also identify the user's interests. Interests include, but are not limited to, hobbies and areas of interest. For example, if the user is in engineering, the progress recording unit can provide a training plan for technical explanations. Furthermore, the progress recording unit can also provide related training plans based on themes in which the user is interested. This allows the progress recording unit to provide a highly relevant training plan based on the user's occupation and interests, enabling more effective training. Some or all of the above-described processing in the progress recording unit may be performed using, for example, AI, or may be performed without AI. For example, the progress recording unit can input the user's occupation and interest data into a generation AI and cause the generation AI to provide a training plan.

[0089] The progress recording unit can provide a progress assessment appropriate for a cultural background based on the user's geographical location information. The progress recording unit, for example, acquires the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and regional culture. For example, if the user is in Japan, the progress recording unit can provide a progress assessment based on Japanese culture. Also, if the user is in the United States, the progress recording unit can provide a progress assessment based on American culture. Furthermore, if the user is in a multicultural environment, the progress recording unit can provide an international progress assessment. In this way, the progress recording unit can provide a progress assessment appropriate for a cultural background by taking the user's geographical location information into consideration. Some or all of the above-described processing in the progress recording unit may be performed using, or without, AI. For example, the progress recording unit can input the user's geographical location information to a generation AI and cause the generation AI to provide a progress assessment. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned scenario selection unit, dialogue unit, feedback unit, and progress recording unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the scenario selection unit is realized by the control unit 46A of the smart device 14 and selects a scenario in which the user wants to train. The dialogue unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and engages in dialogue with the user using AI. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a specific action plan in response to the user's comments. The progress recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the user's skill improvement and achievement. === Hard Collateral 1-2 === Each of the multiple elements including the scenario selection unit, dialogue unit, feedback unit, and progress recording unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the scenario selection unit is realized by the control unit 46A of the smart glasses 214 and selects a scenario in which the user wants to train. The dialogue unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and engages in dialogue with the user using AI. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a specific action plan in response to the user's comments. The progress recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the user's skill improvement and achievement. === Hard Collateral 1-3 === Each of the multiple elements including the scenario selection unit, dialogue unit, feedback unit, and progress recording unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the scenario selection unit is realized by the control unit 46A of the headset-type terminal 314 and selects a scenario in which the user wants to train. The dialogue unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and engages in dialogue with the user using AI. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a specific action plan in response to the user's comments. The progress recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the user's skill improvement and achievement. === Hard Collateral 1-4 === Each of the multiple elements including the scenario selection unit, dialogue unit, feedback unit, and progress recording unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the scenario selection unit is realized by the control unit 46A of the robot 414 and selects a scenario in which the user wants to train. The dialogue unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and engages in dialogue with the user using AI. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a specific action plan in response to the user's comments. The progress recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the user's skill improvement and achievement.

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

[0091] The interpersonal skill improvement system may further include a reward unit. The reward unit provides rewards such as badges and points when a user achieves a specific goal. For example, if a user achieves a high score in an interview scenario, the reward unit may award the user with an "interview master" badge. In addition, if a user performs continuous training, the reward unit may provide continuous training bonus points. Furthermore, the reward unit may provide customized rewards when a user improves a specific skill. In this way, the reward unit can increase the user's motivation and encourage continuous training.

[0092] The feedback unit can analyze not only the user's tone of voice and facial expressions, but also the user's gestures and posture. For example, the feedback unit can analyze the user's hand movements and body orientation while speaking and suggest appropriate gestures and postures. If the user feels nervous, the feedback unit can also provide advice on breathing techniques and posture to help the user relax. Furthermore, if the user feels confident, the feedback unit can provide positive feedback to further reinforce that confidence. This allows the feedback unit to provide more comprehensive feedback and improve the user's interpersonal skills in a variety of ways.

[0093] The progress recording unit not only records the user's progress but also provides advice in real time based on the user's progress. For example, if the user is stagnating in a particular skill, the progress recording unit can provide specific advice to improve that skill. The progress recording unit can also present a recommended training plan as the next step when the user achieves a goal. Furthermore, the progress recording unit can analyze the user's progress data and suggest methods that the user has used successfully in the past. In this way, the progress recording unit can support the user's progress in real time and provide more effective training.

[0094] The dialogue unit can not only initiate a dialogue based on an interview scenario, but also customize the dialogue by taking into account the user's past interview experience. For example, the dialogue unit can revisit questions that the user found difficult in the past and have the user practice answers to those questions. The dialogue unit can also highlight answers that the user has successfully answered in the past and provide advice for further refining those answers. Furthermore, the dialogue unit can add new challenging questions based on the user's past interview experience. This allows the dialogue unit to utilize the user's past experience and provide more personalized interview practice.

[0095] The feedback unit can not only provide a specific action plan for the user's comments, but also select a feedback method according to the user's learning style. For example, the feedback unit can provide feedback using graphs and charts to visual learners and audio feedback to auditory learners. The feedback unit can also provide feedback using specific scenarios to practical learners. Furthermore, the feedback unit can adjust the frequency and content of feedback according to the user's learning pace. This allows the feedback unit to provide feedback that is optimal for the user's learning style and support more effective learning.

[0096] The scenario selection unit can not only provide scenarios for business meetings and casual conversations, but also suggest scenarios based on the user's current emotional state. For example, if the user is nervous, the scenario selection unit can suggest a relaxing casual conversation scenario. If the user is confident, the scenario selection unit can suggest a more challenging business meeting scenario. Furthermore, if the user is depressed, the scenario selection unit can suggest a scenario including an encouraging message. In this way, the scenario selection unit can provide an optimal scenario according to the user's emotional state and support more effective training.

[0097] The scenario selection unit not only estimates the user's emotions and suggests an optimal scenario based on the estimated user's emotions, but also monitors changes in the user's emotions in real time and dynamically adjusts the scenario. For example, if the user begins to feel nervous during a scenario, the scenario selection unit can switch to a relaxing scenario. Also, if the user begins to feel confident during a scenario, the scenario selection unit can switch to a more difficult scenario. Furthermore, if the user becomes tired during a scenario, the scenario selection unit can switch to an easier scenario. In this way, the scenario selection unit can dynamically adjust the scenario according to changes in the user's emotions and provide more effective training.

[0098] The scenario selection unit not only analyzes the past scenario selection history and automatically selects a scenario that corresponds to the user's growth, but also takes the user's emotional state into consideration when suggesting a scenario. For example, when re-proposing a scenario that the user found difficult in the past, the scenario selection unit may suggest the scenario only if the user is relaxed. Also, when re-proposing a scenario that the user was successful in the past, the scenario selection unit may suggest the scenario only if the user is confident. Furthermore, when adjusting the difficulty of a scenario that the user previously selected, the scenario selection unit may take the user's current emotional state into consideration. This allows the scenario selection unit to provide a scenario that comprehensively takes the user's growth and emotional state into consideration, thereby supporting more effective training.

[0099] The scenario selection unit can dynamically adjust the difficulty of a scenario not only based on the user's current skill level but also taking into account the user's emotional state. For example, the scenario selection unit can suggest a scenario with a lower level of difficulty if the user is nervous. Alternatively, the scenario selection unit can suggest a scenario with a higher level of difficulty if the user is relaxed. Furthermore, the scenario selection unit can suggest a challenging scenario if the user is confident. In this way, the scenario selection unit can provide a scenario that comprehensively takes into account the user's skill level and emotional state, thereby supporting more effective training.

[0100] The scenario selection unit not only estimates the user's emotions and adjusts the order of scenarios based on the estimated user emotions, but also monitors changes in the user's emotions in real time and dynamically changes the order of scenarios. For example, if the user begins to feel nervous in the middle of a scenario, the scenario selection unit can suggest a relaxing scenario next. Also, if the user begins to feel confident in the middle of a scenario, the scenario selection unit can suggest a more difficult scenario next. Furthermore, if the user becomes tired in the middle of a scenario, the scenario selection unit can suggest an easier scenario next. In this way, the scenario selection unit can dynamically change the order of scenarios in response to changes in the user's emotions and provide more effective training.

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

[0102] Step 1: The scenario selection unit selects a scenario that the user wants to train in. The scenario may include, but is not limited to, an interview scenario, a business meeting scenario, a casual conversation scenario, etc. Step 2: The dialogue unit starts a dialogue based on the scenario selected by the scenario selection unit. The dialogue unit may use AI to engage in dialogue with the user, and may conduct dialogue in various formats, such as text-based dialogue or voice dialogue. Step 3: The feedback unit provides feedback based on the dialogue provided by the dialogue unit. For example, the feedback unit may provide a specific action plan for the user's remarks, and may provide feedback in various forms, such as text feedback or voice feedback. Step 4: The progress recording unit records the user's progress based on the feedback provided by the feedback unit. The progress recording unit can, for example, record the user's skill improvement and achievements and provide a customized training plan.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] 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.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a scenario selection unit for selecting a scenario to be trained; a dialogue unit that starts a dialogue based on the scenario selected by the scenario selection unit; a feedback unit that provides feedback based on the dialogue provided by the dialogue unit; a progress recording unit that records the user's progress based on the feedback provided by the feedback unit. A system characterized by:

2. The feedback unit Analyzes the user's tone of voice and facial expressions to provide feedback The system of claim 1 .

3. The progress recording unit Track your progress and provide customized training plans The system of claim 1 .

4. The dialogue unit Start a dialogue based on an interview scenario and ask the user questions The system of claim 1 .

5. The feedback unit Provide a concrete action plan for what the user says The system of claim 1 .

6. The scenario selection unit Provides a scenario for a business meeting or casual conversation The system of claim 1 .

7. The scenario selection unit Estimates user emotions and proposes optimal scenarios based on the estimated user emotions The system of claim 1 .

8. The scenario selection unit Analyzes past scenario selection history and automatically selects a scenario that matches the user's growth The system of claim 1 .

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