system

Abstract

The system according to this embodiment aims to assist people who have difficulty reading the emotions of others in their ability to read emotions. [Solution] The system according to the embodiment comprises a recording unit, an analysis unit, a provision unit, and a support unit. The recording unit records conversations. The analysis unit analyzes the data recorded by the recording unit in real time. The provision unit provides appropriate advice based on the results of the analysis by the analysis unit. The support unit assists in self-understanding based on the advice provided by the provision unit.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is no sufficient means to assist the emotion recognition ability for people who have difficulty in reading the emotions of others, and there is room for improvement.

[0005] The system according to the embodiment aims to assist the emotion recognition ability for people who have difficulty in reading the emotions of others.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recording / video recording unit, an analysis unit, a provision unit, and a support unit. The recording / video recording unit records conversations. The analysis unit analyzes the data recorded by the recording / video recording unit in real time. The provision unit provides appropriate advice based on the results of the analysis by the analysis unit. The support unit assists in self-understanding based on the advice provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can assist people who have difficulty reading the emotions of others in understanding their emotions. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) 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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The emotion understanding support system according to an embodiment of the present invention is an AI tool that helps people with Asperger's syndrome or autism spectrum disorder to understand the emotions of others. This emotion understanding support system aims to improve the quality of communication by working in conjunction with a communication application, analyzing emotions in real time, and providing appropriate advice. Specifically, first, when a user has a conversation using a communication application, the conversation is recorded and videotaped. This recorded videotape data is analyzed in real time by the AI. The AI ​​analyzes facial expressions from the video and emotions from the audio. For example, if the other person is smiling while speaking, the AI ​​recognizes the facial expression and determines that the other person is happy. Also, if the other person's voice tone is low and they are speaking slowly, the AI ​​determines that the other person is calm. Next, based on the analysis results, the AI ​​displays appropriate advice on the user interface (UI). For example, if the other person is angry, the AI ​​displays advice such as, "It seems the other person is angry. Please respond calmly." This allows the user to understand the other person's emotions in real time and take appropriate action. Furthermore, after the conversation ends, the user can reconcile their self-understanding at that time with the AI's understanding. The AI ​​learns how the user interpreted the other person's emotions and gradually improves its accuracy. For example, if a user misunderstands another person's emotions, the AI ​​will point out the misunderstanding and teach the correct interpretation. This allows the user to understand their own tendencies in understanding emotions and improve their future responses. This tool brings about the following specific effects: First, the quality of communication improves through real-time feedback. Second, it can provide more personalized support by learning how to interpret others' emotions and improving accuracy. It also provides a sense of security to people who feel anxious about reading emotions and reduces stress in social situations. Furthermore, it not only assists in recognizing emotions but also functions as an educational tool for users to learn emotional cues. Thus, this invention is a useful tool for people with Asperger's syndrome or autism spectrum disorder to understand the emotions of others and improve the quality of their communication.This is expected to improve users' quality of life (QOL), including the quality of their work, relationships with friends, and relationships with family. The emotion understanding support system will enable people with Asperger's syndrome or autism spectrum disorder to understand the emotions of others and improve the quality of their communication.

[0029] The emotion understanding support system according to this embodiment comprises a recording unit, an analysis unit, a provision unit, and a support unit. The recording unit records conversations. The recording unit can record conversations in conjunction with, for example, a communication application. The recording unit can also save the recorded data and play it back later. Furthermore, the recording unit has a function to automatically control the start and stop of recording. For example, the recording unit can detect the start of a conversation and automatically start recording, and detect the end of a conversation and automatically stop recording. The analysis unit analyzes the data recorded by the recording unit in real time. For example, the analysis unit analyzes facial expressions from videos and emotions from audio. For example, the analysis unit extracts facial features from videos and analyzes facial expressions using an expression recognition algorithm. The analysis unit also extracts audio features from audio and analyzes emotions using an emotion recognition algorithm. For example, the analysis unit analyzes features such as pitch, tone, and speed of the audio and estimates emotions. The provision unit provides appropriate advice based on the results analyzed by the analysis unit. The providing unit displays advice such as, "It seems the other person is angry. Let's respond calmly," if the other person is angry. The providing unit also displays the content of the advice on the user interface (UI). For example, the providing unit can display the advice as a text message. The providing unit can also play the advice as an audio message. The support unit assists self-understanding based on the advice provided by the providing unit. For example, the support unit learns how the user interpreted the other person's emotions and improves its accuracy. For example, if the support unit misunderstands the other person's emotions, it points out the misunderstanding and teaches the correct interpretation. The support unit also grasps the user's tendency in understanding emotions and provides advice to improve future responses. As a result, the emotion understanding support system according to this embodiment is capable of recording and videotaping conversations, real-time analysis, advice provision, and self-understanding support.

[0030] The recording unit records conversations. For example, it can record conversations in conjunction with communication applications. Specifically, it uses the APIs of these applications to acquire audio and video data of conversations. The recording unit saves the acquired data in a high-quality format for later playback. Furthermore, the recording unit has a function to automatically control the start and stop of recording. For example, it uses speech recognition technology to detect the start of a conversation and automatically begins recording. When it detects the end of the conversation, it automatically stops recording. This function allows users to have natural conversations without being aware of the recording operation. The recording unit can also save the recorded data to cloud storage. This allows users to access and play back the data from any internet-connected device. In addition, the recording unit encrypts and controls access to ensure the security of the recorded data. This prevents unauthorized access and leakage of recorded data. The recording unit efficiently records conversations while protecting user privacy.

[0031] The analysis unit analyzes data recorded and filmed by the recording and filming unit in real time. For example, the analysis unit analyzes facial expressions from videos and emotions from audio. Specifically, the analysis unit extracts facial features from video data and analyzes facial expressions using an expression recognition algorithm. The expression recognition algorithm is trained using deep learning technology and can detect subtle facial movements and changes with high accuracy. For example, it analyzes facial muscle movements, eye movements, and mouth shape to identify emotions such as joy, anger, sadness, and surprise. The analysis unit also extracts audio features from audio data and analyzes emotions using an emotion recognition algorithm. Audio features include pitch, tone, speed, and intensity, and by analyzing these features, the analysis unit estimates the speaker's emotional state. For example, a high tone and fast speed indicate excitement, while a low tone and slow speed indicate calmness. Furthermore, the analysis unit can integrate audio and video data to perform more accurate emotion analysis. For example, it checks whether the speaker's facial expressions and voice tone match, and if they match, it determines that the emotion is highly reliable. This allows the analytics department to grasp changes in emotions during conversations in real time and provide users with accurate emotional information.

[0032] The service provider provides appropriate advice based on the results analyzed by the analysis unit. For example, if the other party is angry, the service provider might display advice such as, "The other party seems angry. Please respond calmly." Specifically, the service provider generates appropriate advice based on the emotion analysis results received from the analysis unit, by referring to pre-configured advice templates. The advice templates contain multiple pieces of advice corresponding to various emotional states, and the most suitable advice is selected according to the user's situation. The service provider displays the generated advice on the user interface (UI). For example, the service provider can display the advice as a text message. The text message pops up on the user's screen, visually conveying the advice. The service provider can also play the advice as an audio message. The audio message function allows users to receive advice even when they are not looking at the screen. Furthermore, the service provider can customize the content of the advice. For example, the way the advice is expressed and the level of detail can be adjusted according to the user's preferences. This allows the service provider to provide advice in the most optimal way for the user and support emotional understanding.

[0033] The support department assists with self-understanding based on the advice provided by the service provider. For example, the support department learns how users interpret others' emotions and improves its accuracy. Specifically, the support department records how users react to the advice provided and learns from that data. For example, if a user calmly responds according to the advice, the support department evaluates whether the response was successful and, if successful, confirms the effectiveness of the advice. Conversely, if a user does not follow the advice, or if the situation does not improve even after following the advice, the support department analyzes the cause and improves the content and method of providing the advice. The support department also grasps the user's tendencies in understanding emotions and provides advice to improve future responses. For example, if a user tends to misunderstand certain emotions, the support department provides information to deepen their understanding of those emotions. Furthermore, the support department has a function to visualize the user's progress in understanding emotions. For example, it displays what emotions the user has understood in the past and what advice they have received in graphs and charts, allowing them to feel the improvement in their self-understanding. In this way, the support department can continuously support the user's understanding of emotions and promote the improvement of self-understanding.

[0034] The analysis unit can analyze facial expressions from videos and emotions from audio. For example, the analysis unit can extract facial features from videos and analyze expressions using facial recognition algorithms. For example, the analysis unit can detect facial feature points and classify facial expressions. The analysis unit can also extract audio features from audio and analyze emotions using emotion recognition algorithms. For example, the analysis unit can analyze features such as pitch, tone, and speed of audio and estimate emotions. By analyzing emotions from both videos and audio, more accurate emotion recognition becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit inputs video data and audio data into a generative AI, and the generative AI analyzes emotions. The generative AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0035] The service provider can display advice such as "The other person seems angry. Please respond calmly" if the other person is angry. The service provider can, for example, detect anger from the other person's facial expressions and voice and display advice based on the results. For example, if the service provider determines that the other person is angry when their voice is high-pitched and they are speaking quickly, it will display advice such as "The other person seems angry. Please respond calmly." The service provider can also display the content of the advice on the user interface (UI). For example, the service provider can display the advice as a text message. The service provider can also play the advice as an audio message. This improves the quality of communication by providing appropriate advice according to the other person's emotions. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider inputs the other person's emotion data into a generative AI, and the generative AI generates the advice. The generative AI is, for example, a text generative AI (e.g., LLM) or a multimodal generative AI, but is not limited to such examples.

[0036] The support unit can learn how the user interprets the other person's emotions and improve its accuracy. For example, if the user misunderstands the other person's emotions, the support unit will point out the misunderstanding and teach the correct interpretation. For example, if the user misunderstood the other person's anger and was unable to respond calmly, the support unit will point out the misunderstanding and advise the user to respond calmly next time. The support unit also understands the user's tendencies in understanding emotions and provides advice to improve future responses. For example, if the support unit correctly understands the other person's emotions, it will provide feedback on that success and strengthen the user's response in similar situations. This improves the accuracy of the user's emotion interpretation, enabling the provision of more personalized support. Some or all of the above processing in the support unit may be performed using, for example, generative AI, or not using generative AI. For example, the support unit inputs the user's emotion interpretation data into a generative AI, which learns and improves its accuracy. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0037] The recording unit can select the optimal recording method by referring to the user's past conversation history during recording. For example, the recording unit can start recording at a similar time period based on the time period when the user previously recorded important conversations. For example, the recording unit can analyze the user's past recording history and select the optimal recording time. The recording unit can also automatically apply recording settings (sound quality, recording angle, etc.) that the user has used in the past. For example, the recording unit can refer to the user's past settings and record with similar settings. The recording unit can also prioritize recording conversations with specific individuals based on the user's past conversation history. For example, the recording unit prioritizes recording conversations with individuals the user frequently converses with. This allows the optimal recording method to be selected by referring to the user's past conversation history. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI.

[0038] The recording unit can automatically adjust the recording settings based on the user's current situation and environment during recording. For example, if the user is in a quiet environment, the recording unit can record in high quality. For example, the recording unit can detect the ambient noise level and record in high quality in quiet environments. The recording unit can also enable noise cancellation when the user is in a noisy environment. For example, the recording unit can detect ambient noise and enable noise cancellation. The recording unit can also enable image stabilization when the user is moving. For example, the recording unit can detect the user's movement and enable image stabilization. This enables optimal recording by automatically adjusting the recording settings according to the user's situation and environment. Some or all of the above processing in the recording unit may be performed using AI, for example, or without using AI.

[0039] The recording unit can prioritize recording conversations that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific location, the recording unit will prioritize recording conversations related to that location. For example, the recording unit will acquire the user's geographical location information and prioritize recording conversations related to that location. The recording unit can also prioritize recording conversations related to tourist destinations if the user is traveling. For example, the recording unit will acquire the user's geographical location information and prioritize recording conversations related to tourist destinations. The recording unit can also prioritize recording conversations related to work if the user is at their workplace. For example, the recording unit will acquire the user's geographical location information and prioritize recording conversations related to their workplace. In this way, by taking the user's geographical location information into consideration, highly relevant conversations can be prioritized for recording. Some or all of the above processing in the recording unit may be performed using AI, for example, or without using AI.

[0040] The recording unit can analyze the user's social media activity during recording and record relevant conversations. For example, if the user frequently posts about a particular topic on social media, the recording unit can record conversations related to that topic. For example, if the recording unit analyzes the user's social media activity and records conversations related to a particular topic. The recording unit can also prioritize recording conversations with a particular person if the user frequently interacts with that person on social media. For example, if the recording unit analyzes the user's social media activity and prioritizes recording conversations with a particular person. The recording unit can also record conversations related to an event if the user participates in a particular event on social media. For example, if the recording unit analyzes the user's social media activity and records conversations related to an event. In this way, relevant conversations can be recorded by analyzing the user's social media activity. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI.

[0041] The analysis unit can improve the accuracy of sentiment analysis by considering the context of the conversation during analysis. For example, the analysis unit can accurately capture changes in emotion by considering the context before and after the conversation. For example, the analysis unit can accurately capture changes in emotion by analyzing the content of statements before and after the conversation. The analysis unit can also evaluate the intensity of emotion based on the topic of the conversation. For example, the analysis unit can evaluate the intensity of emotion by analyzing the topic of the conversation. The analysis unit can also adjust the interpretation of emotion by considering the relationships between the participants in the conversation. For example, the analysis unit can adjust the interpretation of emotion by analyzing the relationships between the participants in the conversation. In this way, changes in emotion can be accurately captured by considering the context of the conversation. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0042] The analysis unit can perform analysis while considering the attribute information of the conversation participants. For example, the analysis unit can adjust the interpretation of emotions by considering the age and gender of the conversation participants. For example, the analysis unit can analyze the age and gender of the conversation participants and adjust the interpretation of emotions. The analysis unit can also adjust the interpretation of emotions by considering the occupation and role of the conversation participants. For example, the analysis unit can analyze the occupation and role of the conversation participants and adjust the interpretation of emotions. The analysis unit can also adjust the interpretation of emotions by considering the cultural background of the conversation participants. For example, the analysis unit can analyze the cultural background of the conversation participants and adjust the interpretation of emotions. In this way, the interpretation of emotions can be adjusted by considering the attribute information of the conversation participants. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0043] The analysis unit can perform analysis while considering the geographical distribution of conversations. For example, the analysis unit can adjust the interpretation of emotions based on the location where the conversation takes place. For example, the analysis unit can analyze the location where the conversation takes place and adjust the interpretation of emotions. The analysis unit can also evaluate the intensity of emotions while considering the geographical background. For example, the analysis unit can analyze the geographical background and evaluate the intensity of emotions. The analysis unit can also adjust the interpretation of emotions while considering geographical cultural differences. For example, the analysis unit can analyze geographical cultural differences and adjust the interpretation of emotions. In this way, the interpretation of emotions can be adjusted by considering the geographical distribution of conversations. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0044] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the conversation during the analysis. For example, the analysis unit can reinforce its interpretation of emotions by referring to literature related to the topic of the conversation. For example, the analysis unit can analyze literature related to the topic of the conversation to reinforce its interpretation of emotions. The analysis unit can also evaluate the intensity of emotions by referring to research related to the background of the conversation. For example, the analysis unit can analyze research related to the background of the conversation to evaluate the intensity of emotions. The analysis unit can also adjust its interpretation of emotions by referring to literature related to the attributes of the participants in the conversation. For example, the analysis unit can analyze literature related to the attributes of the participants in the conversation to adjust its interpretation of emotions. In this way, the interpretation of emotions can be reinforced by referring to relevant literature on the conversation. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI.

[0045] The service provider can adjust the level of detail of the advice based on the importance of the conversation when providing advice. For example, in the case of an important conversation, the service provider will provide detailed advice. For example, the service provider will analyze the content of the conversation and provide detailed advice for important conversations. The service provider can also provide concise advice for general conversations. For example, the service provider will analyze the content of the conversation and provide concise advice for general conversations. The service provider can also provide quick and to the point for urgent conversations. For example, the service provider will analyze the content of the conversation and provide quick and to the point for urgent conversations. This allows the service provider to provide appropriate advice by adjusting the level of detail of the advice according to the importance of the conversation. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI.

[0046] The service provider can apply different advice algorithms depending on the category of the conversation when providing advice. For example, in the case of a business conversation, the service provider can provide professional advice. For example, the service provider can analyze the content of the conversation and provide professional advice for business conversations. The service provider can also provide friendly advice in the case of a private conversation. For example, the service provider can analyze the content of the conversation and provide friendly advice for private conversations. The service provider can also provide quick and specific advice in the case of an urgent conversation. For example, the service provider can analyze the content of the conversation and provide quick and specific advice for an urgent conversation. This improves the quality of communication by providing appropriate advice according to the category of the conversation. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI.

[0047] The advice provider can prioritize advice based on the timing of the conversation submission when providing advice. For example, the provider will provide advice with the highest priority in the case of an urgent conversation. For example, the provider will analyze the timing of the conversation submission and provide advice with the highest priority for urgent conversations. The provider can also prioritize advice for important conversations. For example, the provider will analyze the timing of the conversation submission and provide advice for important conversations. The provider can also provide advice with the normal priority for general conversations. For example, the provider will analyze the timing of the conversation submission and provide advice with the normal priority for general conversations. This allows advice to be provided at the appropriate time by determining the priority of advice according to the timing of the conversation submission. Some or all of the above processing in the advice provider may be performed using AI, for example, or not using AI.

[0048] The advice provider can adjust the order of advice based on the relevance of the conversation when providing advice. For example, the provider can provide advice first in the case of an important conversation. For example, the provider can analyze the relevance of the conversation and provide advice first for important conversations. The provider can also provide advice in the usual order for general conversations. For example, the provider can analyze the relevance of the conversation and provide advice in the usual order for general conversations. The provider can also provide advice quickly in the case of an urgent conversation. For example, the provider can analyze the relevance of the conversation and provide advice quickly for urgent conversations. This allows important advice to be prioritized by adjusting the order of advice according to the relevance of the conversation. Some or all of the above processing in the provider may be performed using AI, for example, or not using AI.

[0049] The support unit can select the optimal support method by referring to the user's past emotion interpretation history during support. For example, the support unit can provide support methods to help the user learn correct interpretations based on past misinterpretations of emotion. For example, the support unit can analyze the user's past emotion interpretation history and provide support methods to help the user learn correct interpretations based on misinterpretations. The support unit can also provide support methods for similar situations based on the user's past successful emotion interpretations. For example, the support unit can analyze the user's past emotion interpretation history and provide support methods for similar situations based on successful interpretations. The support unit can also identify specific patterns from the user's past emotion interpretation history and propose the optimal support method. For example, the support unit can analyze the user's past emotion interpretation history, identify specific patterns, and propose the optimal support method. This allows the support unit to provide the optimal support method by referring to the user's past emotion interpretation history. Some or all of the above processing in the support unit may be performed using AI, for example, or without using AI.

[0050] The support unit can customize the means of support based on the user's current living situation when providing assistance. For example, if the user is busy, the support unit can provide effective support methods in a short amount of time. For example, the support unit can analyze the user's living situation and provide effective support methods in a short amount of time if the user is busy. The support unit can also provide detailed support methods if the user is relaxed. For example, the support unit can analyze the user's living situation and provide detailed support methods if the user is relaxed. The support unit can also provide support methods to reduce stress if the user is stressed. For example, the support unit can analyze the user's living situation and provide support methods to reduce stress if the user is stressed. By customizing the means of support according to the user's living situation, more effective support can be provided. Some or all of the above processing in the support unit may be performed using AI, for example, or without using AI.

[0051] The support unit can select the optimal support method by considering the user's geographical location information when providing support. For example, if the user is in a specific location, the support unit can provide support methods related to that location. For example, the support unit can obtain the user's geographical location information and provide support methods related to that location. The support unit can also provide support methods related to the travel destination if the user is traveling. For example, the support unit can obtain the user's geographical location information and provide support methods related to the travel destination. The support unit can also provide support methods related to work if the user is at their workplace. For example, the support unit can obtain the user's geographical location information and provide support methods related to the workplace. In this way, the optimal support method can be provided by considering the user's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without using AI.

[0052] The support unit can analyze the user's social media activity and propose support methods when providing assistance. For example, if the user frequently posts on a particular topic on social media, the support unit can provide support methods related to that topic. For example, the support unit analyzes the user's social media activity and provides support methods related to that topic. The support unit can also provide support methods related to a particular person if the user frequently interacts with that person on social media. For example, the support unit analyzes the user's social media activity and provides support methods related to that person. The support unit can also provide support methods related to an event if the user participates in a particular event on social media. For example, the support unit analyzes the user's social media activity and provides support methods related to that event. In this way, by analyzing the user's social media activity, relevant support methods can be provided. Some or all of the above processing in the support unit may be performed using AI, for example, or without using AI.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The recording unit can select the optimal recording method by referring to the user's past conversation history during recording. For example, the recording unit can start recording at a similar time period based on the time when the user previously recorded important conversations. For example, the recording unit can analyze the user's past recording history and select the optimal recording time. The recording unit can also automatically apply recording settings (sound quality, recording angle, etc.) that the user has used in the past. For example, the recording unit can refer to the user's past settings and record with similar settings. Furthermore, the recording unit can prioritize recording conversations with specific individuals based on the user's past conversation history. For example, the recording unit will prioritize recording conversations with individuals the user frequently converses with. This allows the system to select the optimal recording method by referring to the user's past conversation history.

[0055] The recording unit can automatically adjust the recording settings based on the user's current situation and environment during recording. For example, if the user is in a quiet environment, the recording unit will record in high quality. For example, the recording unit will detect the ambient noise level and record in high quality in quiet environments. The recording unit can also enable noise cancellation when the user is in a noisy environment. For example, the recording unit will detect ambient noise and enable noise cancellation. The recording unit can also enable image stabilization when the user is moving. For example, the recording unit will detect the user's movement and enable image stabilization. This allows for optimal recording by automatically adjusting the recording settings according to the user's situation and environment.

[0056] The recording unit can prioritize recording conversations that are highly relevant to the user's location, taking the user's geographical location into consideration. For example, if the user is in a specific location, the recording unit will prioritize recording conversations related to that location. For example, the recording unit will acquire the user's geographical location and prioritize recording conversations related to that location. The recording unit can also prioritize recording conversations related to tourist destinations if the user is traveling. For example, the recording unit will acquire the user's geographical location and prioritize recording conversations related to tourist destinations. Furthermore, if the user is at their workplace, the recording unit can prioritize recording conversations related to work. For example, the recording unit will acquire the user's geographical location and prioritize recording conversations related to the workplace. In this way, by considering the user's geographical location, the recording unit can prioritize recording conversations that are highly relevant.

[0057] The recording unit can analyze the user's social media activity during recording and record relevant conversations. For example, if the user frequently posts about a particular topic on social media, the recording unit will record conversations related to that topic. The recording unit can also analyze the user's social media activity and record conversations related to that topic. Furthermore, if the user frequently interacts with a particular person on social media, the recording unit can prioritize recording conversations with that person. The recording unit can also record conversations related to a particular event if the user participates in that event on social media. The recording unit can analyze the user's social media activity and record conversations related to that event. In this way, by analyzing the user's social media activity, relevant conversations can be recorded.

[0058] The analysis unit can improve the accuracy of sentiment analysis by considering the context of the conversation during analysis. For example, the analysis unit can accurately capture changes in emotion by considering the context before and after the conversation. For example, the analysis unit can analyze the content of statements before and after the conversation to accurately capture changes in emotion. The analysis unit can also evaluate the intensity of emotion based on the topic of the conversation. For example, the analysis unit can analyze the topic of the conversation to evaluate the intensity of emotion. The analysis unit can also adjust the interpretation of emotion by considering the relationships between the participants in the conversation. For example, the analysis unit can analyze the relationships between the participants in the conversation and adjust the interpretation of emotion. In this way, changes in emotion can be accurately captured by considering the context of the conversation.

[0059] The analysis unit can perform its analysis while considering the attribute information of the conversation participants. For example, the analysis unit can adjust the interpretation of emotions by considering the age and gender of the conversation participants. For example, the analysis unit can analyze the age and gender of the conversation participants and adjust the interpretation of emotions. The analysis unit can also adjust the interpretation of emotions by considering the occupation and role of the conversation participants. For example, the analysis unit can analyze the occupation and role of the conversation participants and adjust the interpretation of emotions. The analysis unit can also adjust the interpretation of emotions by considering the cultural background of the conversation participants. For example, the analysis unit can analyze the cultural background of the conversation participants and adjust the interpretation of emotions. In this way, the interpretation of emotions can be adjusted by considering the attribute information of the conversation participants.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The recording unit records the conversation. The recording unit can record conversations in conjunction with, for example, a communication app. The recording unit can also save the recorded data and play it back later. Furthermore, the recording unit has a function to automatically control the start and stop of recording. For example, the recording unit can detect the start of a conversation and automatically start recording, and detect the end of a conversation and automatically stop recording. Step 2: The analysis unit analyzes the data recorded and filmed by the recording and filming unit in real time. For example, the analysis unit analyzes facial expressions from videos and emotions from audio. For example, the analysis unit extracts facial features from videos and analyzes facial expressions using an expression recognition algorithm. The analysis unit also extracts audio features from audio and analyzes emotions using an emotion recognition algorithm. For example, the analysis unit analyzes features such as pitch, tone, and speed of the audio and estimates the emotion. Step 3: The service provider provides appropriate advice based on the results analyzed by the analysis unit. For example, if the other party is angry, the service provider might display advice such as, "The other party seems angry. Please respond calmly." The service provider also displays the content of the advice on the user interface (UI). For example, the service provider can display the advice as a text message. The service provider can also play the advice as an audio message. Step 4: The support team assists with self-understanding based on the advice provided by the service team. For example, the support team learns how the user interpreted the other person's emotions and improves accuracy. For instance, if the user misunderstood the other person's emotions, the support team points out the misunderstanding and teaches the correct interpretation. The support team also understands the user's tendencies in understanding emotions and provides advice to improve future responses.

[0062] (Example of form 2) The emotion understanding support system according to an embodiment of the present invention is an AI tool that helps people with Asperger's syndrome or autism spectrum disorder to understand the emotions of others. This emotion understanding support system aims to improve the quality of communication by working in conjunction with a communication application, analyzing emotions in real time, and providing appropriate advice. Specifically, first, when a user has a conversation using a communication application, the conversation is recorded and videotaped. This recorded videotape data is analyzed in real time by the AI. The AI ​​analyzes facial expressions from the video and emotions from the audio. For example, if the other person is smiling while speaking, the AI ​​recognizes the facial expression and determines that the other person is happy. Also, if the other person's voice tone is low and they are speaking slowly, the AI ​​determines that the other person is calm. Next, based on the analysis results, the AI ​​displays appropriate advice on the user interface (UI). For example, if the other person is angry, the AI ​​displays advice such as, "It seems the other person is angry. Please respond calmly." This allows the user to understand the other person's emotions in real time and take appropriate action. Furthermore, after the conversation ends, the user can reconcile their self-understanding at that time with the AI's understanding. The AI ​​learns how the user interpreted the other person's emotions and gradually improves its accuracy. For example, if a user misunderstands another person's emotions, the AI ​​will point out the misunderstanding and teach the correct interpretation. This allows the user to understand their own tendencies in understanding emotions and improve their future responses. This tool brings about the following specific effects: First, the quality of communication improves through real-time feedback. Second, it can provide more personalized support by learning how to interpret others' emotions and improving accuracy. It also provides a sense of security to people who feel anxious about reading emotions and reduces stress in social situations. Furthermore, it not only assists in recognizing emotions but also functions as an educational tool for users to learn emotional cues. Thus, this invention is a useful tool for people with Asperger's syndrome or autism spectrum disorder to understand the emotions of others and improve the quality of their communication.This is expected to improve users' quality of life (QOL), including the quality of their work, relationships with friends, and relationships with family. The emotion understanding support system will enable people with Asperger's syndrome or autism spectrum disorder to understand the emotions of others and improve the quality of their communication.

[0063] The emotion understanding support system according to this embodiment comprises a recording unit, an analysis unit, a provision unit, and a support unit. The recording unit records conversations. The recording unit can record conversations in conjunction with, for example, a communication application. The recording unit can also save the recorded data and play it back later. Furthermore, the recording unit has a function to automatically control the start and stop of recording. For example, the recording unit can detect the start of a conversation and automatically start recording, and detect the end of a conversation and automatically stop recording. The analysis unit analyzes the data recorded by the recording unit in real time. For example, the analysis unit analyzes facial expressions from videos and emotions from audio. For example, the analysis unit extracts facial features from videos and analyzes facial expressions using an expression recognition algorithm. The analysis unit also extracts audio features from audio and analyzes emotions using an emotion recognition algorithm. For example, the analysis unit analyzes features such as pitch, tone, and speed of the audio and estimates emotions. The provision unit provides appropriate advice based on the results analyzed by the analysis unit. The providing unit displays advice such as, "It seems the other person is angry. Let's respond calmly," if the other person is angry. The providing unit also displays the content of the advice on the user interface (UI). For example, the providing unit can display the advice as a text message. The providing unit can also play the advice as an audio message. The support unit assists self-understanding based on the advice provided by the providing unit. For example, the support unit learns how the user interpreted the other person's emotions and improves its accuracy. For example, if the support unit misunderstands the other person's emotions, it points out the misunderstanding and teaches the correct interpretation. The support unit also grasps the user's tendency in understanding emotions and provides advice to improve future responses. As a result, the emotion understanding support system according to this embodiment is capable of recording and videotaping conversations, real-time analysis, advice provision, and self-understanding support.

[0064] The recording unit records conversations. For example, it can record conversations in conjunction with communication applications. Specifically, it uses the APIs of these applications to acquire audio and video data of conversations. The recording unit saves the acquired data in a high-quality format for later playback. Furthermore, the recording unit has a function to automatically control the start and stop of recording. For example, it uses speech recognition technology to detect the start of a conversation and automatically begins recording. When it detects the end of the conversation, it automatically stops recording. This function allows users to have natural conversations without being aware of the recording operation. The recording unit can also save the recorded data to cloud storage. This allows users to access and play back the data from any internet-connected device. In addition, the recording unit encrypts and controls access to ensure the security of the recorded data. This prevents unauthorized access and leakage of recorded data. The recording unit efficiently records conversations while protecting user privacy.

[0065] The analysis unit analyzes data recorded and filmed by the recording and filming unit in real time. For example, the analysis unit analyzes facial expressions from videos and emotions from audio. Specifically, the analysis unit extracts facial features from video data and analyzes facial expressions using an expression recognition algorithm. The expression recognition algorithm is trained using deep learning technology and can detect subtle facial movements and changes with high accuracy. For example, it analyzes facial muscle movements, eye movements, and mouth shape to identify emotions such as joy, anger, sadness, and surprise. The analysis unit also extracts audio features from audio data and analyzes emotions using an emotion recognition algorithm. Audio features include pitch, tone, speed, and intensity, and by analyzing these features, the analysis unit estimates the speaker's emotional state. For example, a high tone and fast speed indicate excitement, while a low tone and slow speed indicate calmness. Furthermore, the analysis unit can integrate audio and video data to perform more accurate emotion analysis. For example, it checks whether the speaker's facial expressions and voice tone match, and if they match, it determines that the emotion is highly reliable. This allows the analytics department to grasp changes in emotions during conversations in real time and provide users with accurate emotional information.

[0066] The service provider provides appropriate advice based on the results analyzed by the analysis unit. For example, if the other party is angry, the service provider might display advice such as, "The other party seems angry. Please respond calmly." Specifically, the service provider generates appropriate advice based on the emotion analysis results received from the analysis unit, by referring to pre-configured advice templates. The advice templates contain multiple pieces of advice corresponding to various emotional states, and the most suitable advice is selected according to the user's situation. The service provider displays the generated advice on the user interface (UI). For example, the service provider can display the advice as a text message. The text message pops up on the user's screen, visually conveying the advice. The service provider can also play the advice as an audio message. The audio message function allows users to receive advice even when they are not looking at the screen. Furthermore, the service provider can customize the content of the advice. For example, the way the advice is expressed and the level of detail can be adjusted according to the user's preferences. This allows the service provider to provide advice in the most optimal way for the user and support emotional understanding.

[0067] The support department assists with self-understanding based on the advice provided by the service provider. For example, the support department learns how users interpret others' emotions and improves its accuracy. Specifically, the support department records how users react to the advice provided and learns from that data. For example, if a user calmly responds according to the advice, the support department evaluates whether the response was successful and, if successful, confirms the effectiveness of the advice. Conversely, if a user does not follow the advice, or if the situation does not improve even after following the advice, the support department analyzes the cause and improves the content and method of providing the advice. The support department also grasps the user's tendencies in understanding emotions and provides advice to improve future responses. For example, if a user tends to misunderstand certain emotions, the support department provides information to deepen their understanding of those emotions. Furthermore, the support department has a function to visualize the user's progress in understanding emotions. For example, it displays what emotions the user has understood in the past and what advice they have received in graphs and charts, allowing them to feel the improvement in their self-understanding. In this way, the support department can continuously support the user's understanding of emotions and promote the improvement of self-understanding.

[0068] The analysis unit can analyze facial expressions from videos and emotions from audio. For example, the analysis unit can extract facial features from videos and analyze expressions using facial recognition algorithms. For example, the analysis unit can detect facial feature points and classify facial expressions. The analysis unit can also extract audio features from audio and analyze emotions using emotion recognition algorithms. For example, the analysis unit can analyze features such as pitch, tone, and speed of audio and estimate emotions. By analyzing emotions from both videos and audio, more accurate emotion recognition becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit inputs video data and audio data into a generative AI, and the generative AI analyzes emotions. The generative AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0069] The service provider can display advice such as "The other person seems angry. Please respond calmly" if the other person is angry. The service provider can, for example, detect anger from the other person's facial expressions and voice and display advice based on the results. For example, if the service provider determines that the other person is angry when their voice is high-pitched and they are speaking quickly, it will display advice such as "The other person seems angry. Please respond calmly." The service provider can also display the content of the advice on the user interface (UI). For example, the service provider can display the advice as a text message. The service provider can also play the advice as an audio message. This improves the quality of communication by providing appropriate advice according to the other person's emotions. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider inputs the other person's emotion data into a generative AI, and the generative AI generates the advice. The generative AI is, for example, a text generative AI (e.g., LLM) or a multimodal generative AI, but is not limited to such examples.

[0070] The support unit can learn how the user interprets the other person's emotions and improve its accuracy. For example, if the user misunderstands the other person's emotions, the support unit will point out the misunderstanding and teach the correct interpretation. For example, if the user misunderstood the other person's anger and was unable to respond calmly, the support unit will point out the misunderstanding and advise the user to respond calmly next time. The support unit also understands the user's tendencies in understanding emotions and provides advice to improve future responses. For example, if the support unit correctly understands the other person's emotions, it will provide feedback on that success and strengthen the user's response in similar situations. This improves the accuracy of the user's emotion interpretation, enabling the provision of more personalized support. Some or all of the above processing in the support unit may be performed using, for example, generative AI, or not using generative AI. For example, the support unit inputs the user's emotion interpretation data into a generative AI, which learns and improves its accuracy. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0071] The recording unit can estimate the user's emotions and adjust the timing of recording based on the estimated emotions. For example, if the user is tense, the recording unit may not start recording until the user is relaxed. For example, the recording unit may detect tension from the user's facial expressions and voice and not start recording until the user is relaxed. The recording unit can also start recording immediately if the user is excited to ensure that important information is not missed. For example, the recording unit may detect excitement from the tone and speed of the user's voice and start recording immediately. The recording unit can also start recording if the user is calm to maintain a natural flow of conversation. For example, the recording unit may detect calmness from the user's facial expressions and voice and start recording at a natural timing. In this way, by adjusting the timing of recording according to the user's emotions, important information can be recorded without being missed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to these examples.

[0072] The recording unit can select the optimal recording method by referring to the user's past conversation history during recording. For example, the recording unit can start recording at a similar time period based on the time period when the user previously recorded important conversations. For example, the recording unit can analyze the user's past recording history and select the optimal recording time. The recording unit can also automatically apply recording settings (sound quality, recording angle, etc.) that the user has used in the past. For example, the recording unit can refer to the user's past settings and record with similar settings. The recording unit can also prioritize recording conversations with specific individuals based on the user's past conversation history. For example, the recording unit prioritizes recording conversations with individuals the user frequently converses with. This allows the optimal recording method to be selected by referring to the user's past conversation history. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI.

[0073] The recording unit can automatically adjust the recording settings based on the user's current situation and environment during recording. For example, if the user is in a quiet environment, the recording unit can record in high quality. For example, the recording unit can detect the ambient noise level and record in high quality in quiet environments. The recording unit can also enable noise cancellation when the user is in a noisy environment. For example, the recording unit can detect ambient noise and enable noise cancellation. The recording unit can also enable image stabilization when the user is moving. For example, the recording unit can detect the user's movement and enable image stabilization. This enables optimal recording by automatically adjusting the recording settings according to the user's situation and environment. Some or all of the above processing in the recording unit may be performed using AI, for example, or without using AI.

[0074] The audio recording unit can estimate the user's emotions and determine recording priorities based on those emotions. For example, if the user is stressed, the unit will prioritize recording important conversations. For instance, it can detect stress from the user's facial expressions and voice and prioritize recording important conversations. Similarly, if the user is relaxed, the unit can prioritize recording normal conversations. For example, it can detect relaxed emotions from the user's facial expressions and voice and prioritize recording normal conversations. Furthermore, if the user is in a hurry, the unit can quickly record important information. For example, it can detect urgency from the user's tone and speed of voice and quickly record important information. This allows for prioritizing recordings according to the user's emotions, ensuring that important conversations are recorded first. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0075] The recording unit can prioritize recording conversations that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific location, the recording unit will prioritize recording conversations related to that location. For example, the recording unit will acquire the user's geographical location information and prioritize recording conversations related to that location. The recording unit can also prioritize recording conversations related to tourist destinations if the user is traveling. For example, the recording unit will acquire the user's geographical location information and prioritize recording conversations related to tourist destinations. The recording unit can also prioritize recording conversations related to work if the user is at their workplace. For example, the recording unit will acquire the user's geographical location information and prioritize recording conversations related to their workplace. In this way, by taking the user's geographical location information into consideration, highly relevant conversations can be prioritized for recording. Some or all of the above processing in the recording unit may be performed using AI, for example, or without using AI.

[0076] The recording unit can analyze the user's social media activity during recording and record relevant conversations. For example, if the user frequently posts about a particular topic on social media, the recording unit can record conversations related to that topic. For example, if the recording unit analyzes the user's social media activity and records conversations related to a particular topic. The recording unit can also prioritize recording conversations with a particular person if the user frequently interacts with that person on social media. For example, if the recording unit analyzes the user's social media activity and prioritizes recording conversations with a particular person. The recording unit can also record conversations related to an event if the user participates in a particular event on social media. For example, if the recording unit analyzes the user's social media activity and records conversations related to an event. In this way, relevant conversations can be recorded by analyzing the user's social media activity. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI.

[0077] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can improve the accuracy of the emotion analysis to provide more detailed feedback. For example, the analysis unit can detect tension from the user's facial expressions and voice to improve the accuracy of the emotion analysis. The analysis unit can also maintain normal accuracy in emotion analysis and provide general feedback if the user is relaxed. For example, the analysis unit can detect relaxed emotions from the user's facial expressions and voice to maintain normal accuracy in emotion analysis. The analysis unit can also adjust the accuracy of emotion analysis to avoid overreactions if the user is excited. For example, the analysis unit can detect excitement from the tone and speed of the user's voice to adjust the accuracy of the emotion analysis. This allows for the provision of detailed feedback by adjusting the accuracy of the analysis according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The analysis unit can improve the accuracy of sentiment analysis by considering the context of the conversation during analysis. For example, the analysis unit can accurately capture changes in emotion by considering the context before and after the conversation. For example, the analysis unit can accurately capture changes in emotion by analyzing the content of statements before and after the conversation. The analysis unit can also evaluate the intensity of emotion based on the topic of the conversation. For example, the analysis unit can evaluate the intensity of emotion by analyzing the topic of the conversation. The analysis unit can also adjust the interpretation of emotion by considering the relationships between the participants in the conversation. For example, the analysis unit can adjust the interpretation of emotion by analyzing the relationships between the participants in the conversation. In this way, changes in emotion can be accurately captured by considering the context of the conversation. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0079] The analysis unit can perform analysis while considering the attribute information of the conversation participants. For example, the analysis unit can adjust the interpretation of emotions by considering the age and gender of the conversation participants. For example, the analysis unit can analyze the age and gender of the conversation participants and adjust the interpretation of emotions. The analysis unit can also adjust the interpretation of emotions by considering the occupation and role of the conversation participants. For example, the analysis unit can analyze the occupation and role of the conversation participants and adjust the interpretation of emotions. The analysis unit can also adjust the interpretation of emotions by considering the cultural background of the conversation participants. For example, the analysis unit can analyze the cultural background of the conversation participants and adjust the interpretation of emotions. In this way, the interpretation of emotions can be adjusted by considering the attribute information of the conversation participants. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, the analysis unit can detect tension from the user's facial expressions and voice and provide a simple and highly visible display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For example, the analysis unit can detect relaxation from the user's facial expressions and voice and provide a display method that includes detailed information. The analysis unit can also provide a concise display method if the user is in a hurry. For example, the analysis unit can detect urgency from the tone and speed of the user's voice and provide a concise display method. By adjusting the display method according to the user's emotions, highly visible displays are possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The analysis unit can perform analysis while considering the geographical distribution of conversations. For example, the analysis unit can adjust the interpretation of emotions based on the location where the conversation takes place. For example, the analysis unit can analyze the location where the conversation takes place and adjust the interpretation of emotions. The analysis unit can also evaluate the intensity of emotions while considering the geographical background. For example, the analysis unit can analyze the geographical background and evaluate the intensity of emotions. The analysis unit can also adjust the interpretation of emotions while considering geographical cultural differences. For example, the analysis unit can analyze geographical cultural differences and adjust the interpretation of emotions. In this way, the interpretation of emotions can be adjusted by considering the geographical distribution of conversations. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0082] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the conversation during the analysis. For example, the analysis unit can reinforce its interpretation of emotions by referring to literature related to the topic of the conversation. For example, the analysis unit can analyze literature related to the topic of the conversation to reinforce its interpretation of emotions. The analysis unit can also evaluate the intensity of emotions by referring to research related to the background of the conversation. For example, the analysis unit can analyze research related to the background of the conversation to evaluate the intensity of emotions. The analysis unit can also adjust its interpretation of emotions by referring to literature related to the attributes of the participants in the conversation. For example, the analysis unit can analyze literature related to the attributes of the participants in the conversation to adjust its interpretation of emotions. In this way, the interpretation of emotions can be reinforced by referring to relevant literature on the conversation. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI.

[0083] The service provider can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is nervous, the service provider can provide advice in a calm manner. For example, the service provider can detect nervousness from the user's facial expressions and voice and provide advice in a calm manner. The service provider can also provide advice in a cheerful manner if the user is relaxed. For example, the service provider can detect relaxed emotions from the user's facial expressions and voice and provide advice in a cheerful manner. The service provider can also provide concise and quick advice if the user is in a hurry. For example, the service provider can detect urgency from the tone and speed of the user's voice and provide concise and quick advice. In this way, by adjusting the way advice is expressed according to the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to these examples.

[0084] The service provider can adjust the level of detail of the advice based on the importance of the conversation when providing advice. For example, in the case of an important conversation, the service provider will provide detailed advice. For example, the service provider will analyze the content of the conversation and provide detailed advice for important conversations. The service provider can also provide concise advice for general conversations. For example, the service provider will analyze the content of the conversation and provide concise advice for general conversations. The service provider can also provide quick and to the point for urgent conversations. For example, the service provider will analyze the content of the conversation and provide quick and to the point for urgent conversations. This allows the service provider to provide appropriate advice by adjusting the level of detail of the advice according to the importance of the conversation. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI.

[0085] The service provider can apply different advice algorithms depending on the category of the conversation when providing advice. For example, in the case of a business conversation, the service provider can provide professional advice. For example, the service provider can analyze the content of the conversation and provide professional advice for business conversations. The service provider can also provide friendly advice in the case of a private conversation. For example, the service provider can analyze the content of the conversation and provide friendly advice for private conversations. The service provider can also provide quick and specific advice in the case of an urgent conversation. For example, the service provider can analyze the content of the conversation and provide quick and specific advice for an urgent conversation. This improves the quality of communication by providing appropriate advice according to the category of the conversation. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI.

[0086] The service provider can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is nervous, the service provider can provide short, concise advice. For example, the service provider can detect nervousness from the user's facial expressions and voice and provide short, concise advice. The service provider can also provide detailed advice if the user is relaxed. For example, the service provider can detect relaxedness from the user's facial expressions and voice and provide detailed advice. The service provider can also provide quick and concise advice if the user is in a hurry. For example, the service provider can detect urgency from the user's tone and speed of voice and provide quick and concise advice. By adjusting the length of the advice according to the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The advice provider can prioritize advice based on the timing of the conversation submission when providing advice. For example, the provider will provide advice with the highest priority in the case of an urgent conversation. For example, the provider will analyze the timing of the conversation submission and provide advice with the highest priority for urgent conversations. The provider can also prioritize advice for important conversations. For example, the provider will analyze the timing of the conversation submission and provide advice for important conversations. The provider can also provide advice with the normal priority for general conversations. For example, the provider will analyze the timing of the conversation submission and provide advice with the normal priority for general conversations. This allows advice to be provided at the appropriate time by determining the priority of advice according to the timing of the conversation submission. Some or all of the above processing in the advice provider may be performed using AI, for example, or not using AI.

[0088] The advice provider can adjust the order of advice based on the relevance of the conversation when providing advice. For example, the provider can provide advice first in the case of an important conversation. For example, the provider can analyze the relevance of the conversation and provide advice first for important conversations. The provider can also provide advice in the usual order for general conversations. For example, the provider can analyze the relevance of the conversation and provide advice in the usual order for general conversations. The provider can also provide advice quickly in the case of an urgent conversation. For example, the provider can analyze the relevance of the conversation and provide advice quickly for urgent conversations. This allows important advice to be prioritized by adjusting the order of advice according to the relevance of the conversation. Some or all of the above processing in the provider may be performed using AI, for example, or not using AI.

[0089] The support unit can estimate the user's emotions and adjust the self-understanding support methods based on the estimated emotions. For example, if the user is tense, the support unit can provide methods to help them relax. For example, the support unit can detect tension from the user's facial expressions and voice and provide methods to help them relax. The support unit can also provide detailed support methods to deepen self-understanding if the user is relaxed. For example, the support unit can detect relaxed emotions from the user's facial expressions and voice and provide detailed support methods to deepen self-understanding. The support unit can also provide methods to help the user calm down if they are excited. For example, the support unit can detect excitement from the tone and speed of the user's voice and provide methods to help them calm down. By adjusting the self-understanding support methods according to the user's emotions, more appropriate support can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0090] The support unit can select the optimal support method by referring to the user's past emotion interpretation history during support. For example, the support unit can provide support methods to help the user learn correct interpretations based on past misinterpretations of emotion. For example, the support unit can analyze the user's past emotion interpretation history and provide support methods to help the user learn correct interpretations based on misinterpretations. The support unit can also provide support methods for similar situations based on the user's past successful emotion interpretations. For example, the support unit can analyze the user's past emotion interpretation history and provide support methods for similar situations based on successful interpretations. The support unit can also identify specific patterns from the user's past emotion interpretation history and propose the optimal support method. For example, the support unit can analyze the user's past emotion interpretation history, identify specific patterns, and propose the optimal support method. This allows the support unit to provide the optimal support method by referring to the user's past emotion interpretation history. Some or all of the above processing in the support unit may be performed using AI, for example, or without using AI.

[0091] The support unit can customize the means of support based on the user's current living situation when providing assistance. For example, if the user is busy, the support unit can provide effective support methods in a short amount of time. For example, the support unit can analyze the user's living situation and provide effective support methods in a short amount of time if the user is busy. The support unit can also provide detailed support methods if the user is relaxed. For example, the support unit can analyze the user's living situation and provide detailed support methods if the user is relaxed. The support unit can also provide support methods to reduce stress if the user is stressed. For example, the support unit can analyze the user's living situation and provide support methods to reduce stress if the user is stressed. By customizing the means of support according to the user's living situation, more effective support can be provided. Some or all of the above processing in the support unit may be performed using AI, for example, or without using AI.

[0092] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is tense, the support unit will prioritize support to help them relax. For example, the support unit can detect tension from the user's facial expressions and voice and prioritize support to help them relax. Also, if the user is relaxed, the support unit can prioritize support to help them deepen their self-understanding. For example, the support unit can detect relaxation from the user's facial expressions and voice and prioritize support to help them deepen their self-understanding. Also, if the user is excited, the support unit can prioritize support to help them calm down. For example, the support unit can detect excitement from the tone and speed of the user's voice and prioritize support to help them calm down. In this way, by determining the priority of support according to the user's emotions, more appropriate support can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to these examples.

[0093] The support unit can select the optimal support method by considering the user's geographical location information when providing support. For example, if the user is in a specific location, the support unit can provide support methods related to that location. For example, the support unit can obtain the user's geographical location information and provide support methods related to that location. The support unit can also provide support methods related to the travel destination if the user is traveling. For example, the support unit can obtain the user's geographical location information and provide support methods related to the travel destination. The support unit can also provide support methods related to work if the user is at their workplace. For example, the support unit can obtain the user's geographical location information and provide support methods related to the workplace. In this way, the optimal support method can be provided by considering the user's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without using AI.

[0094] The support unit can analyze the user's social media activity and propose support methods when providing assistance. For example, if the user frequently posts on a particular topic on social media, the support unit can provide support methods related to that topic. For example, the support unit analyzes the user's social media activity and provides support methods related to that topic. The support unit can also provide support methods related to a particular person if the user frequently interacts with that person on social media. For example, the support unit analyzes the user's social media activity and provides support methods related to that person. The support unit can also provide support methods related to an event if the user participates in a particular event on social media. For example, the support unit analyzes the user's social media activity and provides support methods related to that event. In this way, by analyzing the user's social media activity, relevant support methods can be provided. Some or all of the above processing in the support unit may be performed using AI, for example, or without using AI.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The recording unit can estimate the user's emotions and adjust the timing of recording based on those emotions. For example, if the user is tense, the recording unit will not start recording until the user is relaxed. For instance, the recording unit can detect tension from the user's facial expressions and voice and not start recording until the user is relaxed. Alternatively, if the user is excited, the recording unit can start recording immediately to ensure that important information is not missed. For example, the recording unit can detect excitement from the user's tone and speed of voice and start recording immediately. Furthermore, if the user is calm, the recording unit can start recording to maintain a natural flow of conversation. For example, the recording unit can detect calmness from the user's facial expressions and voice and start recording at a natural timing. This allows for recording and recording without missing important information by adjusting the timing according to the user's emotions.

[0097] The recording unit can select the optimal recording method by referring to the user's past conversation history during recording. For example, the recording unit can start recording at a similar time period based on the time when the user previously recorded important conversations. For example, the recording unit can analyze the user's past recording history and select the optimal recording time. The recording unit can also automatically apply recording settings (sound quality, recording angle, etc.) that the user has used in the past. For example, the recording unit can refer to the user's past settings and record with similar settings. Furthermore, the recording unit can prioritize recording conversations with specific individuals based on the user's past conversation history. For example, the recording unit will prioritize recording conversations with individuals the user frequently converses with. This allows the system to select the optimal recording method by referring to the user's past conversation history.

[0098] The recording unit can automatically adjust the recording settings based on the user's current situation and environment during recording. For example, if the user is in a quiet environment, the recording unit will record in high quality. For example, the recording unit will detect the ambient noise level and record in high quality in quiet environments. The recording unit can also enable noise cancellation when the user is in a noisy environment. For example, the recording unit will detect ambient noise and enable noise cancellation. The recording unit can also enable image stabilization when the user is moving. For example, the recording unit will detect the user's movement and enable image stabilization. This allows for optimal recording by automatically adjusting the recording settings according to the user's situation and environment.

[0099] The audio recording unit can estimate the user's emotions and determine recording priorities based on those emotions. For example, if the user is stressed, the unit will prioritize recording important conversations. For instance, it can detect stress from the user's facial expressions and voice and prioritize recording important conversations. Similarly, if the user is relaxed, the unit can prioritize recording normal conversations. For example, it can detect relaxed emotions from the user's facial expressions and voice and prioritize recording normal conversations. Furthermore, if the user is in a hurry, the unit can quickly record important information. For example, it can detect urgency from the user's tone and speed of voice and quickly record important information. This allows for prioritizing recordings according to the user's emotions, ensuring that important conversations are recorded first.

[0100] The recording unit can prioritize recording conversations that are highly relevant to the user's location, taking the user's geographical location into consideration. For example, if the user is in a specific location, the recording unit will prioritize recording conversations related to that location. For example, the recording unit will acquire the user's geographical location and prioritize recording conversations related to that location. The recording unit can also prioritize recording conversations related to tourist destinations if the user is traveling. For example, the recording unit will acquire the user's geographical location and prioritize recording conversations related to tourist destinations. Furthermore, if the user is at their workplace, the recording unit can prioritize recording conversations related to work. For example, the recording unit will acquire the user's geographical location and prioritize recording conversations related to the workplace. In this way, by considering the user's geographical location, the recording unit can prioritize recording conversations that are highly relevant.

[0101] The recording unit can analyze the user's social media activity during recording and record relevant conversations. For example, if the user frequently posts about a particular topic on social media, the recording unit will record conversations related to that topic. The recording unit can also analyze the user's social media activity and record conversations related to that topic. Furthermore, if the user frequently interacts with a particular person on social media, the recording unit can prioritize recording conversations with that person. The recording unit can also record conversations related to a particular event if the user participates in that event on social media. The recording unit can analyze the user's social media activity and record conversations related to that event. In this way, by analyzing the user's social media activity, relevant conversations can be recorded.

[0102] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can improve the accuracy of the emotion analysis to provide more detailed feedback. For instance, it can detect tension from the user's facial expressions and voice to improve the accuracy of the emotion analysis. The analysis unit can also maintain normal accuracy in emotion analysis and provide general feedback if the user is relaxed. For example, it can detect relaxed emotions from the user's facial expressions and voice to maintain normal accuracy in emotion analysis. Furthermore, if the user is excited, the analysis unit can adjust the accuracy of the emotion analysis to avoid overreactions. For example, it can detect excitement from the tone and speed of the user's voice to adjust the accuracy of the emotion analysis. This allows for the provision of detailed feedback by adjusting the accuracy of the analysis according to the user's emotions.

[0103] The analysis unit can improve the accuracy of sentiment analysis by considering the context of the conversation during analysis. For example, the analysis unit can accurately capture changes in emotion by considering the context before and after the conversation. For example, the analysis unit can analyze the content of statements before and after the conversation to accurately capture changes in emotion. The analysis unit can also evaluate the intensity of emotion based on the topic of the conversation. For example, the analysis unit can analyze the topic of the conversation to evaluate the intensity of emotion. The analysis unit can also adjust the interpretation of emotion by considering the relationships between the participants in the conversation. For example, the analysis unit can analyze the relationships between the participants in the conversation and adjust the interpretation of emotion. In this way, changes in emotion can be accurately captured by considering the context of the conversation.

[0104] The analysis unit can perform its analysis while considering the attribute information of the conversation participants. For example, the analysis unit can adjust the interpretation of emotions by considering the age and gender of the conversation participants. For example, the analysis unit can analyze the age and gender of the conversation participants and adjust the interpretation of emotions. The analysis unit can also adjust the interpretation of emotions by considering the occupation and role of the conversation participants. For example, the analysis unit can analyze the occupation and role of the conversation participants and adjust the interpretation of emotions. The analysis unit can also adjust the interpretation of emotions by considering the cultural background of the conversation participants. For example, the analysis unit can analyze the cultural background of the conversation participants and adjust the interpretation of emotions. In this way, the interpretation of emotions can be adjusted by considering the attribute information of the conversation participants.

[0105] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For instance, the analysis unit can detect tension from the user's facial expressions and voice and provide a simple and highly visible display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For instance, the analysis unit can detect relaxation from the user's facial expressions and voice and provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the essentials. For example, the analysis unit can detect urgency from the user's tone and speed of voice and provide a display method that focuses on the essentials. By adjusting the display method according to the user's emotions, a highly visible display becomes possible.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The recording unit records the conversation. The recording unit can record conversations in conjunction with, for example, a communication app. The recording unit can also save the recorded data and play it back later. Furthermore, the recording unit has a function to automatically control the start and stop of recording. For example, the recording unit can detect the start of a conversation and automatically start recording, and detect the end of a conversation and automatically stop recording. Step 2: The analysis unit analyzes the data recorded and filmed by the recording and filming unit in real time. For example, the analysis unit analyzes facial expressions from videos and emotions from audio. For example, the analysis unit extracts facial features from videos and analyzes facial expressions using an expression recognition algorithm. The analysis unit also extracts audio features from audio and analyzes emotions using an emotion recognition algorithm. For example, the analysis unit analyzes features such as pitch, tone, and speed of the audio and estimates the emotion. Step 3: The service provider provides appropriate advice based on the results analyzed by the analysis unit. For example, if the other party is angry, the service provider might display advice such as, "The other party seems angry. Please respond calmly." The service provider also displays the content of the advice on the user interface (UI). For example, the service provider can display the advice as a text message. The service provider can also play the advice as an audio message. Step 4: The support team assists with self-understanding based on the advice provided by the service team. For example, the support team learns how the user interpreted the other person's emotions and improves accuracy. For instance, if the user misunderstood the other person's emotions, the support team points out the misunderstanding and teaches the correct interpretation. The support team also understands the user's tendencies in understanding emotions and provides advice to improve future responses.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0110] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0111] Each of the multiple elements described above, including the recording / video recording unit, analysis unit, provision unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the recording / video recording unit records conversations using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A automatically controls the start and stop of the recording / video recording. The analysis unit is implemented in real time by the specific processing unit 290 of the data processing unit 12. The provision unit is implemented in real time by the control unit 46A of the smart device 14, and displays appropriate advice on the user interface based on the analysis results. The support unit is implemented in real time by the specific processing unit 290 of the data processing unit 12, and learns the user's tendency to understand emotions and improves accuracy. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0115] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0116] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0118] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0119] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0120] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0121] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0122] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0126] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the recording / video recording unit, analysis unit, provision unit, and support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the recording / video recording unit records conversations using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A automatically controls the start and stop of the recording / video recording. The analysis unit is implemented in real time by the specific processing unit 290 of the data processing unit 12. The provision unit is implemented in real time by the control unit 46A of the smart glasses 214, and displays appropriate advice on the user interface based on the analysis results. The support unit is implemented in real time by the specific processing unit 290 of the data processing unit 12, and learns the user's tendency to understand emotions and improves accuracy. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the recording / video recording unit, analysis unit, provision unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the recording / video recording unit records conversations using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A automatically controls the start and stop of the recording / video recording. The analysis unit is implemented in real time by the specific processing unit 290 of the data processing unit 12. The provision unit is implemented in real time by the control unit 46A of the headset terminal 314, and displays appropriate advice on the user interface based on the analysis results. The support unit is implemented in real time by the specific processing unit 290 of the data processing unit 12, and learns the user's tendency to understand emotions and improves accuracy. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0152] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the recording / video recording unit, analysis unit, provision unit, and support unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the recording / video recording unit records conversations using the camera 42 and microphone 238 of the robot 414, and the control unit 46A automatically controls the start and stop of the recording / video recording. The analysis unit is implemented in real time by the specific processing unit 290 of the data processing unit 12. The provision unit is implemented in real time by the control unit 46A of the robot 414, and displays appropriate advice on the user interface based on the analysis results. The support unit is implemented in real time by the specific processing unit 290 of the data processing unit 12, and learns the user's tendency to understand emotions and improves accuracy. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0161] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0162] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0163] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0164] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0165] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0166] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0167] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0168] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0169] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0170] 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.

[0171] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0172] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0173] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0174] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0175] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0176] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0177] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0178] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0179] (Note 1) A recording and video recording unit that records conversations, An analysis unit that analyzes the data recorded by the aforementioned recording and video recording unit in real time, A provision unit provides appropriate advice based on the results of the analysis performed by the aforementioned analysis unit, The system includes a support unit that assists in self-understanding based on the advice provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit is Analyze facial expressions from videos, analyze emotions from audio. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, If the other party is angry, it will display advice such as, "It seems the other party is angry. Please respond calmly." The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned support unit, It learns how users interpret the other person's emotions and improves accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned recording and video recording unit is It estimates the user's emotions and adjusts the timing of audio and video recordings based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned recording and video recording unit is When recording audio or video, the system selects the optimal recording method by referring to the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned recording and video recording unit is During audio and video recording, the system automatically adjusts the recording and video settings based on the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned recording and video recording unit is It estimates the user's emotions and determines the priority of recording audio and video based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned recording and video recording unit is When recording audio or video, the system prioritizes recording and video recording of highly relevant conversations, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned recording and video recording unit is During audio and video recording, the system analyzes the user's social media activity and records relevant conversations. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During analysis, consider the context of the conversation to improve the accuracy of sentiment analysis. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During the analysis, the attribute information of the conversation participants will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During the analysis, the geographical distribution of conversations will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, we refer to relevant literature related to the conversation to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing advice, adjust the level of detail based on the importance of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the category of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing advice, prioritize the advice based on when the conversation was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing advice, adjust the order of advice based on the relevance of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned support unit, It estimates the user's emotions and adjusts the method of supporting self-understanding based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned support unit, During support, the system selects the most appropriate support method by referring to the user's past emotional interpretation history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit, During support, the means of assistance are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit, It estimates the user's emotions and determines the priority of support based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit, When providing support, the optimal support method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit, When providing support, we analyze the user's social media activity and propose ways to support them. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A recording and video recording unit that records conversations, An analysis unit that analyzes the data recorded by the aforementioned recording and video recording unit in real time, A provision unit provides appropriate advice based on the results of the analysis performed by the aforementioned analysis unit, The system includes a support unit that assists in self-understanding based on the advice provided by the aforementioned provision unit. A system characterized by the following features.

2. The aforementioned analysis unit is Analyze facial expressions from videos, analyze emotions from audio. The system according to feature 1.

3. The aforementioned support unit, It learns how users interpret the other person's emotions and improves accuracy. The system according to feature 1.

4. The aforementioned recording and video recording unit is It estimates the user's emotions and adjusts the timing of audio and video recordings based on the estimated emotions. The system according to feature 1.

5. The aforementioned recording and video recording unit is When recording audio or video, the system selects the optimal recording method by referring to the user's past conversation history. The system according to feature 1.

6. The aforementioned recording and video recording unit is During audio and video recording, the system automatically adjusts the recording and video settings based on the user's current situation and environment. The system according to feature 1.

7. The aforementioned recording and video recording unit is It estimates the user's emotions and determines the priority of recording audio and video based on the estimated user emotions. The system according to feature 1.

8. The aforementioned recording and video recording unit is When recording audio or video, the system prioritizes recording and video recording of highly relevant conversations, taking into account the user's geographical location. The system according to feature 1.

9. The aforementioned recording and video recording unit is During audio and video recording, the system analyzes the user's social media activity and records relevant conversations. The system according to feature 1.

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