System

Abstract

An object of a system according to an embodiment is to enable a person who is not confident in communication to learn a dialogue technique according to a situation.SOLUTION: In one embodiment, a system comprises a personality analyzer, an emotion analyzer, a context analyzer, a hint provider, and a training module provider. The personality analysis unit analyzes the personality of the user. The emotion analysis unit analyzes the user's current emotion. The context analyzer analyzes the context of the dialog. The hint providing unit provides a hint and a phrase for communication in real time. The training module providing unit provides a training module tailored to a specific scenario.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult for people who are not confident in their communication skills to learn conversation techniques appropriate to the situation.

[0005] The system according to the embodiment aims to enable people who lack confidence in their communication skills to learn conversation techniques suited to the situation. [Means for solving the problem]

[0006] The system according to the embodiment includes a personality analysis unit, a sentiment analysis unit, a context analysis unit, a hint provision unit, and a training module provision unit. The personality analysis unit analyzes the user's personality. The sentiment analysis unit analyzes the user's current emotions. The context analysis unit analyzes the context of the dialogue. The hint provision unit provides communication hints and phrases in real time. The training module provision unit provides training modules tailored to specific scenarios. [Effects of the Invention]

[0007] The system according to the embodiment can enable people who are not confident in their communication skills to learn conversation techniques suited to the situation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The Chameleon Communicator AI according to an embodiment of the present invention is a system that analyzes a user's personality, current emotions, and conversation context, and provides communication hints and phrases in real time, allowing the user to communicate with confidence.

[0029] The Chameleon Communicator AI according to the embodiment includes a personality analysis unit, a sentiment analysis unit, a context analysis unit, a hint provision unit, and a training module provision unit. The personality analysis unit analyzes the user's personality. For example, the personality analysis unit classifies the user's personality based on the Big Five personality traits. The personality analysis unit can also analyze the user's personality using MBTI. The personality analysis unit can also estimate the user's personality based on the user's past behavioral data. The sentiment analysis unit analyzes the user's current emotions. For example, the sentiment analysis unit analyzes the user's facial expressions to estimate emotions. The sentiment analysis unit can also analyze the user's tone of voice to estimate emotions. The sentiment analysis unit can also analyze the user's text input to estimate emotions. The context analysis unit analyzes the context of the conversation. For example, the context analysis unit analyzes the person, location, and time of the conversation and suggests appropriate conversation techniques. The context analysis unit can also analyze the conversation history to understand the flow of the topic. The context analysis unit can also analyze the conversation partner's past statements and predict the conversation partner's personality and preferences. The hint provision unit provides communication hints and phrases in real time. For example, the hint provision unit provides appropriate advice when the user is struggling during a conversation. The hint provision unit can also analyze the timing and pauses of the user's statements and suggest optimal timing for speaking. The hint provision unit can also analyze the user's tone and speed of voice and suggest optimal tone and speed. The training module provision unit provides training modules tailored to specific scenarios. For example, the training module provision unit provides training modules for presentations and sales. The training module provision unit can also analyze the user's past training history and provide feedback on progress and areas for improvement. The training module provision unit can also monitor the user's performance during training in real time and immediately suggest areas for improvement. As a result, the chameleon communicator AI according to the embodiment allows users to communicate with confidence.For example, if a user feels anxious about interacting in a new environment, the Chameleon Communicator AI can alleviate that anxiety and suggest appropriate conversation techniques, allowing the user to communicate with confidence.

[0030] The personality analysis unit can analyze a user's past SNS posts and message history to track changes in personality and emotions over the long term. For example, the personality analysis unit collects a user's past SNS posts and performs emotion analysis using natural language processing technology. For example, it classifies the content of posts into positive, negative, and neutral emotions and tracks changes in emotions over time. The personality analysis unit can also analyze a user's message history to track changes in emotions over the long term. For example, it analyzes the content and frequency of messages to understand changes in the user's emotions. This allows for long-term tracking of changes in a user's personality and emotions, making it possible to provide more accurate dialogue techniques.

[0031] The emotion analysis unit monitors the user's biometric information in real time, allowing for a more accurate understanding of changes in emotions. The emotion analysis unit, for example, monitors the user's heart rate in real time to detect changes in emotions. For example, if an increase in heart rate indicates tension or excitement, a dialogue technique is suggested based on that information. The emotion analysis unit can also monitor the user's electrodermal activity to understand changes in emotions. For example, if changes in electrodermal activity indicate stress or relaxation, a dialogue technique is suggested based on that information. The emotion analysis unit can also monitor the user's breathing patterns to understand changes in emotions. For example, shallow and rapid breathing may indicate tension or anxiety, and a dialogue technique is suggested based on that information. In this way, by monitoring the user's biometric information in real time, changes in emotions can be more accurately understood.

[0032] The context analysis unit can predict the personality and preferences of a conversation partner by analyzing the conversation partner's past statements and behavioral history. The context analysis unit, for example, collects the conversation partner's past statements and analyzes the personality and preferences using natural language processing technology. For example, the conversation partner's personality can be predicted based on frequently used words and phrases. The context analysis unit can also analyze the conversation partner's behavioral history to predict preferences. For example, it analyzes past behavioral patterns to understand the conversation partner's interests and concerns. The context analysis unit can also analyze the conversation partner's past preferences to predict personality and preferences. For example, it makes predictions based on options and actions chosen in the past. In this way, the conversation partner's personality and preferences can be predicted by analyzing the conversation partner's past statements and behavioral history.

[0033] The context analysis unit can utilize data related to geographic information and time of day to suggest appropriate topics and phrases based on the location and time of the conversation. For example, the context analysis unit can suggest appropriate topics based on the location of the conversation. For example, it can suggest topics or tourist spots related to a specific area. The context analysis unit can also suggest appropriate phrases based on the time of the conversation. For example, it can suggest phrases according to the time of day, such as morning greetings and evening greetings. The context analysis unit can also utilize geographic information to provide information related to the location of the conversation. For example, it can suggest information about restaurants and events near the location of the conversation. This makes it possible to suggest appropriate topics and phrases based on the location and time of the conversation.

[0034] The hint providing unit can analyze the timing of a user's speech and how to pause, and suggest the optimal timing for speaking. The hint providing unit, for example, analyzes the timing of a user's speech and suggests the optimal timing for speaking. For example, it suggests the timing to speak immediately after the other person has finished speaking. The hint providing unit can also analyze the intervals between a user's speech and suggest appropriate timing. For example, it adjusts the timing of speech according to the flow of the dialogue. The hint providing unit can also analyze the rhythm of a user's speech and suggest the optimal timing. For example, speaking in time with the rhythm can help the dialogue progress smoothly. In this way, it is possible to analyze the timing of a user's speech and how to pause, and suggest the optimal timing for speaking.

[0035] The hint providing unit can analyze the tone and speed of the user's voice and suggest the optimal tone and speed. The hint providing unit, for example, analyzes the tone of the user's voice and suggests the optimal tone. For example, if the user is nervous, it suggests a calmer tone. The hint providing unit can also analyze the speed of the user's voice and suggest the optimal speed. For example, if the user tends to speak quickly, it suggests speaking more slowly. The hint providing unit can also analyze the strength of the user's voice and suggest the optimal tone. For example, it suggests a stronger voice in a situation where a stronger tone is needed. In this way, the tone and speed of the user's voice can be analyzed and the optimal tone and speed can be suggested.

[0036] The training module providing unit can analyze the user's past training history and provide feedback on progress and areas for improvement. The training module providing unit, for example, stores the user's past training history in a database and analyzes progress. For example, it provides feedback based on past training content and results. The training module providing unit can also analyze the user's training history and identify areas for improvement. For example, it can analyze past training data and identify areas that need improvement. The training module providing unit can also monitor the user's progress in real time and provide immediate feedback. This allows the user's past training history to be analyzed and feedback on progress and areas for improvement to be provided.

[0037] The training module providing unit can monitor the user's performance during training in real time and immediately suggest areas for improvement. The training module providing unit, for example, monitors the user's performance during training in real time and provides immediate feedback. For example, it can point out areas for improvement in pronunciation or speaking style in real time. The training module providing unit can also analyze the user's movements during training and suggest areas for improvement. For example, it can point out areas for improvement in gestures or posture while practicing a presentation. The training module providing unit can also monitor the user's emotions during training and provide feedback according to the emotions. This makes it possible to monitor the user's performance during training in real time and immediately suggest areas for improvement.

[0038] The training module providing unit can customize the training modules according to different industries and occupations. The training module providing unit provides, for example, training modules according to different industries. For example, training to enhance presentation skills and sales skills for the IT industry is provided. The training module providing unit can also provide training modules according to different occupations. For example, training to enhance communication skills for marketing positions is provided. The training module providing unit can also customize the training content according to the industry and occupation. This allows the training modules to be customized according to different industries and occupations.

[0039] The training module providing unit can make the training module compatible with group training or pair training. The training module providing unit, for example, provides a training module compatible with group training. For example, it provides training to strengthen team building or group discussion skills. The training module providing unit can also provide a training module compatible with pair training. For example, it provides training to improve communication skills through pair role-playing. The training module providing unit can also customize the training content for groups or pairs. This makes it possible to make the training module compatible with group training or pair training.

[0040] The training module providing unit can analyze the user's past successful experiences and provide feedback based on those successful experiences. The training module providing unit, for example, stores the user's past successful experiences in a database and analyzes those experiences. For example, it provides feedback based on successful presentations or sales cases. The training module providing unit can also provide feedback based on the user's successful experiences. For example, it can look back on past successful experiences and analyze the factors that led to their success and provide feedback. The training module providing unit can also suggest training content based on the user's successful experiences. This makes it possible to analyze the user's past successful experiences and provide feedback based on those successful experiences.

[0041] The training module providing unit can analyze the user's self-evaluation and evaluations from others and suggest specific areas for improvement. The training module providing unit, for example, collects the user's self-evaluation and suggests specific areas for improvement based on that evaluation. For example, if the user evaluates that they are not confident in their speaking ability, the training module providing unit can provide training to strengthen that area. The training module providing unit can also analyze evaluations from others and suggest specific areas for improvement. For example, based on feedback from others, it can identify areas for improvement in the user's communication skills. The training module providing unit can also comprehensively analyze the self-evaluation and evaluations from others and suggest areas for improvement. In this way, the user's self-evaluation and evaluations from others can be analyzed and specific areas for improvement can be suggested.

[0042] The training module providing unit can adapt the support for developing confident speaking to different cultural and linguistic areas. The training module providing unit provides, for example, support for developing confident speaking that is adapted to different cultural areas. For example, it provides dialogue techniques and feedback that are adapted to the cultural background. The training module providing unit can also provide support that is adapted to different linguistic areas. For example, it provides training to strengthen communication skills that are adapted to language differences. The training module providing unit can also customize the support content according to the cultural and linguistic areas. This allows the support for developing confident speaking to be adapted to different cultural and linguistic areas.

[0043] The training module providing unit can provide support for becoming a confident speaker as visual or audio feedback. The training module providing unit provides support for becoming a confident speaker, for example, as visual feedback. For example, it may display positive messages or success stories on a screen. The training module providing unit can also provide support as audio feedback. For example, it may provide encouraging messages via audio. The training module providing unit can also provide feedback that combines both visual and audio feedback. In this way, support for becoming a confident speaker can be provided as visual or audio feedback.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] Chameleon Communicator AI can predict the personality and preferences of a conversation partner by analyzing their past statements and behavioral history. For example, it can collect the conversation partner's past statements and use natural language processing technology to analyze their personality and preferences. It can predict the conversation partner's personality based on frequently used words and phrases. It can also analyze the conversation partner's behavioral history to predict their preferences. This makes it possible to predict the conversation partner's personality and preferences by analyzing their past statements and behavioral history.

[0046] Chameleon Communicator AI can utilize geographical and time-of-day data to suggest appropriate topics and phrases based on the location and time of the conversation. For example, it can suggest appropriate topics based on the location of the conversation, suggest topics and tourist spots related to a specific area, and suggest appropriate phrases based on the time of the conversation. This allows it to suggest appropriate topics and phrases based on the location and time of the conversation.

[0047] Chameleon Communicator AI can analyze the timing of a user's speech and how they pause, and suggest the optimal time to speak. For example, it can analyze the timing of a user's speech and suggest the optimal time to speak. It can also analyze the intervals between users' speech and suggest the optimal time to speak. This allows it to analyze the timing of a user's speech and how they pause, and suggest the optimal time to speak.

[0048] Chameleon Communicator AI can analyze the tone and speed of a user's voice and suggest the optimal tone and speed. For example, it can analyze the tone of a user's voice and suggest the optimal tone. If the user is nervous, it can suggest a calm tone. It can also analyze the speed of a user's voice and suggest the optimal speed. This allows it to analyze the tone and speed of a user's voice and suggest the optimal tone and speed.

[0049] Chameleon Communicator AI can analyze a user's past successful experiences and provide feedback based on those experiences. For example, it can store a user's past successful experiences in a database and analyze those experiences. It can provide feedback based on examples of successful presentations or sales. It can also provide feedback based on the user's past successful experiences.

[0050] Chameleon Communicator AI can analyze the user's self-evaluation and evaluations from others, and suggest specific areas for improvement. For example, it can collect the user's self-evaluation and suggest specific areas for improvement based on that evaluation. If the user evaluates themselves as not confident in their speaking style, it can provide training to strengthen that area. It can also analyze evaluations from others and suggest specific areas for improvement. This allows it to analyze the user's self-evaluation and evaluations from others, and suggest specific areas for improvement.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The personality analysis unit analyzes the user's personality. For example, it classifies and analyzes the user's personality using the Big Five personality traits or MBTI. It can also estimate the user's personality based on their past behavioral data. Step 2: The emotion analysis unit analyzes the user's current emotion, for example, by analyzing the user's facial expression, tone of voice, and text input to estimate the emotion. Step 3: The context analysis unit analyzes the context of the conversation. For example, it analyzes the person, location, and time of the conversation and suggests appropriate conversation techniques. It can also analyze the conversation history and the other person's past comments to predict the flow of the conversation, the other person's personality, and preferences. Step 4: The hint provider provides communication hints and phrases in real time. For example, if the user has difficulty during a conversation, it can provide appropriate advice and make optimal suggestions by analyzing the timing of speech, pauses, tone of voice, and speed. Step 5: The training module provider provides training modules tailored to specific scenarios. For example, it provides training modules for presentations or sales, analyzes the user's past training history, and provides feedback on progress and areas for improvement. It can also monitor performance during training in real time and immediately suggest areas for improvement.

[0053] (Example 2) The Chameleon Communicator AI according to an embodiment of the present invention is a system that analyzes a user's personality, current emotions, and conversation context, and provides communication hints and phrases in real time, allowing the user to communicate with confidence.

[0054] The Chameleon Communicator AI according to the embodiment includes a personality analysis unit, a sentiment analysis unit, a context analysis unit, a hint provision unit, and a training module provision unit. The personality analysis unit analyzes the user's personality. For example, the personality analysis unit classifies the user's personality based on the Big Five personality traits. The personality analysis unit can also analyze the user's personality using MBTI. The personality analysis unit can also estimate the user's personality based on the user's past behavioral data. The sentiment analysis unit analyzes the user's current emotions. For example, the sentiment analysis unit analyzes the user's facial expressions to estimate emotions. The sentiment analysis unit can also analyze the user's tone of voice to estimate emotions. The sentiment analysis unit can also analyze the user's text input to estimate emotions. The context analysis unit analyzes the context of the conversation. For example, the context analysis unit analyzes the person, location, and time of the conversation and suggests appropriate conversation techniques. The context analysis unit can also analyze the conversation history to understand the flow of the topic. The context analysis unit can also analyze the conversation partner's past statements and predict the conversation partner's personality and preferences. The hint provision unit provides communication hints and phrases in real time. For example, the hint provision unit provides appropriate advice when the user is struggling during a conversation. The hint provision unit can also analyze the timing and pauses of the user's statements and suggest optimal timing for speaking. The hint provision unit can also analyze the user's tone and speed of voice and suggest optimal tone and speed. The training module provision unit provides training modules tailored to specific scenarios. For example, the training module provision unit provides training modules for presentations and sales. The training module provision unit can also analyze the user's past training history and provide feedback on progress and areas for improvement. The training module provision unit can also monitor the user's performance during training in real time and immediately suggest areas for improvement. As a result, the chameleon communicator AI according to the embodiment allows users to communicate with confidence.For example, if a user feels anxious about interacting in a new environment, the Chameleon Communicator AI can alleviate that anxiety and suggest appropriate conversation techniques, allowing the user to communicate with confidence.

[0055] The personality analysis unit can analyze a user's past SNS posts and message history to track changes in personality and emotions over the long term. For example, the personality analysis unit collects a user's past SNS posts and performs emotion analysis using natural language processing technology. For example, it classifies the content of posts into positive, negative, and neutral emotions and tracks changes in emotions over time. The personality analysis unit can also analyze a user's message history to track changes in emotions over the long term. For example, it analyzes the content and frequency of messages to understand changes in the user's emotions. This allows for long-term tracking of changes in a user's personality and emotions, making it possible to provide more accurate dialogue techniques.

[0056] The emotion analysis unit monitors the user's biometric information in real time, allowing for a more accurate understanding of changes in emotions. The emotion analysis unit, for example, monitors the user's heart rate in real time to detect changes in emotions. For example, if an increase in heart rate indicates tension or excitement, a dialogue technique is suggested based on that information. The emotion analysis unit can also monitor the user's electrodermal activity to understand changes in emotions. For example, if changes in electrodermal activity indicate stress or relaxation, a dialogue technique is suggested based on that information. The emotion analysis unit can also monitor the user's breathing patterns to understand changes in emotions. For example, shallow and rapid breathing may indicate tension or anxiety, and a dialogue technique is suggested based on that information. In this way, by monitoring the user's biometric information in real time, changes in emotions can be more accurately understood.

[0057] The emotion analysis unit uses the emotion estimation function to analyze the emotions in the text entered by the user in real time and classify the intensity and type of emotions in detail. The emotion analysis unit, for example, analyzes the text entered by the user using natural language processing technology and quantifies the intensity of the emotions. For example, a high score is assigned to a strong positive emotion. The emotion analysis unit can also classify the type of emotion from the user's text. For example, it can classify into emotion categories such as joy, sadness, and anger. The emotion analysis unit can also analyze changes in emotions from the user's text in real time. For example, it can detect changes in emotions by detecting changes in the content of the text. This allows the emotion in the text entered by the user to be analyzed in real time and the intensity and type of emotions to be classified in detail.

[0058] The context analysis unit can predict the personality and preferences of a conversation partner by analyzing the conversation partner's past statements and behavioral history. The context analysis unit, for example, collects the conversation partner's past statements and analyzes the personality and preferences using natural language processing technology. For example, the conversation partner's personality can be predicted based on frequently used words and phrases. The context analysis unit can also analyze the conversation partner's behavioral history to predict preferences. For example, it analyzes past behavioral patterns to understand the conversation partner's interests and concerns. The context analysis unit can also analyze the conversation partner's past preferences to predict personality and preferences. For example, it makes predictions based on options and actions chosen in the past. In this way, the conversation partner's personality and preferences can be predicted by analyzing the conversation partner's past statements and behavioral history.

[0059] The context analysis unit can utilize data related to geographic information and time of day to suggest appropriate topics and phrases based on the location and time of the conversation. For example, the context analysis unit can suggest appropriate topics based on the location of the conversation. For example, it can suggest topics or tourist spots related to a specific area. The context analysis unit can also suggest appropriate phrases based on the time of the conversation. For example, it can suggest phrases according to the time of day, such as morning greetings and evening greetings. The context analysis unit can also utilize geographic information to provide information related to the location of the conversation. For example, it can suggest information about restaurants and events near the location of the conversation. This makes it possible to suggest appropriate topics and phrases based on the location and time of the conversation.

[0060] The context analysis unit can use the emotion estimation function to analyze the emotions of the conversation partner in real time and suggest dialogue techniques according to those emotions. The context analysis unit, for example, analyzes the conversation partner's facial expressions and tone of voice to estimate emotions in real time. For example, if the conversation partner is nervous, it can suggest phrases to relax them. The context analysis unit can also analyze the conversation partner's text input to estimate emotions. For example, it can estimate emotions from the content and expressions of the text and suggest appropriate dialogue techniques. The context analysis unit can also analyze the conversation partner's biometric information to estimate emotions. For example, it can estimate emotions based on heart rate and electrodermal activity and suggest dialogue techniques. This makes it possible to analyze the conversation partner's emotions in real time and suggest dialogue techniques according to those emotions.

[0061] The hint providing unit can analyze the timing of a user's speech and how to pause, and suggest the optimal timing for speaking. The hint providing unit, for example, analyzes the timing of a user's speech and suggests the optimal timing for speaking. For example, it suggests the timing to speak immediately after the other person has finished speaking. The hint providing unit can also analyze the intervals between a user's speech and suggest appropriate timing. For example, it adjusts the timing of speech according to the flow of the dialogue. The hint providing unit can also analyze the rhythm of a user's speech and suggest the optimal timing. For example, speaking in time with the rhythm can help the dialogue progress smoothly. In this way, it is possible to analyze the timing of a user's speech and how to pause, and suggest the optimal timing for speaking.

[0062] The hint providing unit can analyze the tone and speed of the user's voice and suggest the optimal tone and speed. The hint providing unit, for example, analyzes the tone of the user's voice and suggests the optimal tone. For example, if the user is nervous, it suggests a calmer tone. The hint providing unit can also analyze the speed of the user's voice and suggest the optimal speed. For example, if the user tends to speak quickly, it suggests speaking more slowly. The hint providing unit can also analyze the strength of the user's voice and suggest the optimal tone. For example, it suggests a stronger voice in a situation where a stronger tone is needed. In this way, the tone and speed of the user's voice can be analyzed and the optimal tone and speed can be suggested.

[0063] The hint providing unit can use the emotion estimation function to suggest specific phrases and topics in real time according to the user's emotions. For example, the hint providing unit analyzes the user's emotions in real time and suggests specific phrases in accordance with the emotions. For example, if the user is nervous, it suggests phrases to help the user relax. The hint providing unit can also suggest topics in accordance with the user's emotions. For example, if the user is feeling down, it suggests topics to lift the user's spirits. The hint providing unit can also update phrases and topics in real time according to changes in the user's emotions. For example, if the user's emotions change, it suggests new phrases and topics in accordance with the changes. This makes it possible to suggest specific phrases and topics in real time according to the user's emotions.

[0064] The training module providing unit can analyze the user's past training history and provide feedback on progress and areas for improvement. The training module providing unit, for example, stores the user's past training history in a database and analyzes progress. For example, it provides feedback based on past training content and results. The training module providing unit can also analyze the user's training history and identify areas for improvement. For example, it can analyze past training data and identify areas that need improvement. The training module providing unit can also monitor the user's progress in real time and provide immediate feedback. This allows the user's past training history to be analyzed and feedback on progress and areas for improvement to be provided.

[0065] The training module providing unit can monitor the user's performance during training in real time and immediately suggest areas for improvement. The training module providing unit, for example, monitors the user's performance during training in real time and provides immediate feedback. For example, it can point out areas for improvement in pronunciation or speaking style in real time. The training module providing unit can also analyze the user's movements during training and suggest areas for improvement. For example, it can point out areas for improvement in gestures or posture while practicing a presentation. The training module providing unit can also monitor the user's emotions during training and provide feedback according to the emotions. This makes it possible to monitor the user's performance during training in real time and immediately suggest areas for improvement.

[0066] The training module providing unit can customize training content according to the user's emotions using the emotion estimation function. The training module providing unit, for example, analyzes the user's emotions in real time and suggests training content according to those emotions. For example, if the user is nervous, it provides training to help the user relax. The training module providing unit can also adjust the difficulty of the training according to the user's emotions. For example, if the user is feeling stressed, it provides training with a lower level of difficulty. The training module providing unit can also update the training content in real time according to changes in the user's emotions. This makes it possible to customize training content according to the user's emotions.

[0067] The training module providing unit can customize the training modules according to different industries and occupations. The training module providing unit provides, for example, training modules according to different industries. For example, training to enhance presentation skills and sales skills for the IT industry is provided. The training module providing unit can also provide training modules according to different occupations. For example, training to enhance communication skills for marketing positions is provided. The training module providing unit can also customize the training content according to the industry and occupation. This allows the training modules to be customized according to different industries and occupations.

[0068] The training module providing unit can make the training module compatible with group training or pair training. The training module providing unit, for example, provides a training module compatible with group training. For example, it provides training to strengthen team building or group discussion skills. The training module providing unit can also provide a training module compatible with pair training. For example, it provides training to improve communication skills through pair role-playing. The training module providing unit can also customize the training content for groups or pairs. This makes it possible to make the training module compatible with group training or pair training.

[0069] The training module providing unit can use the emotion estimation function to provide motivational messages and music according to the user's emotions. The training module providing unit, for example, analyzes the user's emotions in real time and provides motivational messages according to the emotions. For example, if the user is feeling down, it provides an encouraging message. The training module providing unit can also provide music according to the user's emotions. For example, if the user wants to relax, it provides relaxing music. The training module providing unit can also update the motivational messages and music in real time according to changes in the user's emotions. This makes it possible to provide motivational messages and music according to the user's emotions.

[0070] The training module providing unit can analyze the user's past successful experiences and provide feedback based on those successful experiences. The training module providing unit, for example, stores the user's past successful experiences in a database and analyzes those experiences. For example, it provides feedback based on successful presentations or sales cases. The training module providing unit can also provide feedback based on the user's successful experiences. For example, it can look back on past successful experiences and analyze the factors that led to their success and provide feedback. The training module providing unit can also suggest training content based on the user's successful experiences. This makes it possible to analyze the user's past successful experiences and provide feedback based on those successful experiences.

[0071] The training module providing unit can analyze the user's self-evaluation and evaluations from others and suggest specific areas for improvement. The training module providing unit, for example, collects the user's self-evaluation and suggests specific areas for improvement based on that evaluation. For example, if the user evaluates that they are not confident in their speaking ability, the training module providing unit can provide training to strengthen that area. The training module providing unit can also analyze evaluations from others and suggest specific areas for improvement. For example, based on feedback from others, it can identify areas for improvement in the user's communication skills. The training module providing unit can also comprehensively analyze the self-evaluation and evaluations from others and suggest areas for improvement. In this way, the user's self-evaluation and evaluations from others can be analyzed and specific areas for improvement can be suggested.

[0072] The training module providing unit can use the emotion estimation function to provide positive feedback according to the user's emotions in real time. The training module providing unit, for example, analyzes the user's emotions in real time and provides positive feedback according to the emotions. For example, if the user is nervous, it provides words of encouragement to help the user relax. The training module providing unit can also update the positive feedback according to the user's emotions in real time. For example, if the user's emotions change, it provides new feedback according to the changes. The training module providing unit can also adjust the content of the feedback according to changes in the user's emotions. This makes it possible to provide positive feedback according to the user's emotions in real time.

[0073] The training module providing unit can adapt the support for developing confident speaking to different cultural and linguistic areas. The training module providing unit provides, for example, support for developing confident speaking that is adapted to different cultural areas. For example, it provides dialogue techniques and feedback that are adapted to the cultural background. The training module providing unit can also provide support that is adapted to different linguistic areas. For example, it provides training to strengthen communication skills that are adapted to language differences. The training module providing unit can also customize the support content according to the cultural and linguistic areas. This allows the support for developing confident speaking to be adapted to different cultural and linguistic areas.

[0074] The training module providing unit can provide support for becoming a confident speaker as visual or audio feedback. The training module providing unit provides support for becoming a confident speaker, for example, as visual feedback. For example, it may display positive messages or success stories on a screen. The training module providing unit can also provide support as audio feedback. For example, it may provide encouraging messages via audio. The training module providing unit can also provide feedback that combines both visual and audio feedback. In this way, support for becoming a confident speaker can be provided as visual or audio feedback.

[0075] The training module providing unit can use the emotion estimation function to suggest relaxation methods and mental health care that correspond to the user's emotions. The training module providing unit, for example, analyzes the user's emotions in real time and suggests relaxation methods that correspond to those emotions. For example, if the user is feeling high in stress, it can suggest deep breathing or meditation. The training module providing unit can also suggest mental health care that corresponds to the user's emotions. For example, if the user is feeling anxious, it can provide relaxation music or mental health care advice. The training module providing unit can also update the relaxation methods and mental health care in real time according to changes in the user's emotions. This makes it possible to suggest relaxation methods and mental health care that correspond to the user's emotions.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] Chameleon Communicator AI can estimate the user's emotions and suggest appropriate conversation techniques based on those emotions. For example, if the user is nervous, it can suggest phrases to help them relax. If the user is excited, it can also provide advice to help them stay calm. Furthermore, if the user is sad, it can suggest words of comfort. This allows it to suggest conversation techniques in real time that correspond to the user's emotions.

[0078] Chameleon Communicator AI can analyze a user's past social media posts and message history to track changes in personality and emotions over the long term. For example, it can collect a user's past social media posts and perform sentiment analysis using natural language processing technology. It can classify the content of posts into positive, negative, and neutral emotions and track changes in emotions over time. It can also analyze a user's message history and track changes in emotions over the long term. This allows for more accurate communication techniques by tracking changes in a user's personality and emotions over the long term.

[0079] Chameleon Communicator AI monitors the user's biometric information in real time, allowing it to more accurately grasp changes in emotions. For example, it can monitor the user's heart rate in real time to detect changes in emotions. If an increase in heart rate indicates tension or excitement, it can suggest dialogue techniques based on that information. It can also monitor the user's electrodermal activity to grasp changes in emotions. This allows it to more accurately grasp changes in emotions by monitoring the user's biometric information in real time.

[0080] Chameleon Communicator AI can analyze the emotions in the text entered by the user in real time and classify the intensity and type of emotion in detail. For example, it can analyze the text entered by the user using natural language processing technology and quantify the intensity of the emotion. A high score is assigned if the emotion is strong positively. It can also classify the type of emotion from the user's text. This allows it to analyze the emotions in the text entered by the user in real time and classify the intensity and type of emotion in detail.

[0081] Chameleon Communicator AI can predict the personality and preferences of a conversation partner by analyzing their past statements and behavioral history. For example, it can collect the conversation partner's past statements and use natural language processing technology to analyze their personality and preferences. It can predict the conversation partner's personality based on frequently used words and phrases. It can also analyze the conversation partner's behavioral history to predict their preferences. This makes it possible to predict the conversation partner's personality and preferences by analyzing their past statements and behavioral history.

[0082] Chameleon Communicator AI can utilize geographical and time-of-day data to suggest appropriate topics and phrases based on the location and time of the conversation. For example, it can suggest appropriate topics based on the location of the conversation, suggest topics and tourist spots related to a specific area, and suggest appropriate phrases based on the time of the conversation. This allows it to suggest appropriate topics and phrases based on the location and time of the conversation.

[0083] Chameleon Communicator AI can analyze the timing of a user's speech and how they pause, and suggest the optimal time to speak. For example, it can analyze the timing of a user's speech and suggest the optimal time to speak. It can also analyze the intervals between users' speech and suggest the optimal time to speak. This allows it to analyze the timing of a user's speech and how they pause, and suggest the optimal time to speak.

[0084] Chameleon Communicator AI can analyze the tone and speed of a user's voice and suggest the optimal tone and speed. For example, it can analyze the tone of a user's voice and suggest the optimal tone. If the user is nervous, it can suggest a calm tone. It can also analyze the speed of a user's voice and suggest the optimal speed. This allows it to analyze the tone and speed of a user's voice and suggest the optimal tone and speed.

[0085] Chameleon Communicator AI can analyze a user's past successful experiences and provide feedback based on those experiences. For example, it can store a user's past successful experiences in a database and analyze those experiences. It can provide feedback based on examples of successful presentations or sales. It can also provide feedback based on the user's past successful experiences.

[0086] Chameleon Communicator AI can analyze the user's self-evaluation and evaluations from others, and suggest specific areas for improvement. For example, it can collect the user's self-evaluation and suggest specific areas for improvement based on that evaluation. If the user evaluates themselves as not confident in their speaking style, it can provide training to strengthen that area. It can also analyze evaluations from others and suggest specific areas for improvement. This allows it to analyze the user's self-evaluation and evaluations from others, and suggest specific areas for improvement.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The personality analysis unit analyzes the user's personality. For example, it classifies and analyzes the user's personality using the Big Five personality traits or MBTI. It can also estimate the user's personality based on their past behavioral data. Step 2: The emotion analysis unit analyzes the user's current emotion, for example, by analyzing the user's facial expression, tone of voice, and text input to estimate the emotion. Step 3: The context analysis unit analyzes the context of the conversation. For example, it analyzes the person, location, and time of the conversation and suggests appropriate conversation techniques. It can also analyze the conversation history and the other person's past comments to predict the flow of the conversation, the other person's personality, and preferences. Step 4: The hint provider provides communication hints and phrases in real time. For example, if the user has difficulty during a conversation, it can provide appropriate advice and make optimal suggestions by analyzing the timing of speech, pauses, tone of voice, and speed. Step 5: The training module provider provides training modules tailored to specific scenarios. For example, it provides training modules for presentations or sales, analyzes the user's past training history, and provides feedback on progress and areas for improvement. It can also monitor performance during training in real time and immediately suggest areas for improvement.

[0089] 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.

[0090] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0102] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0103] 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.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 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.

[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 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.

[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. 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.

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

[0117] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

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

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0156] 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 personality analysis unit that analyzes the user's personality; a sentiment analysis unit for analyzing a user's current sentiment; a context analysis unit that analyzes the context of the dialogue; A hint section that provides communication hints and phrases in real time; a training module providing unit that provides a training module tailored to a specific scenario; A system characterized by:

2. The emotion analysis unit Monitor the user's biological information in real time to more accurately grasp changes in emotions.

2. The system of claim 1.

3. The context analysis unit Analyze the past statements and behavioral history of the person you are talking to and predict their personality and preferences 2. The system of claim 1.

4. The hint providing unit Analyze the timing and pauses of the user's speech and suggest the best time to speak.

2. The system of claim 1.

5. The training module providing unit Analyze the user's past training history and provide feedback on progress and areas for improvement 2. The system of claim 1.

6. The emotion analysis unit Using the emotion estimation function, the emotion of the text entered by the user is analyzed in real time, and the intensity and type of emotion are classified in detail.

2. The system of claim 1.

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