system

Abstract

The system according to this embodiment aims to objectively grasp an individual's emotional changes and psychological state, and to continuously implement and manage mental health care. [Solution] The system according to the embodiment comprises a speech recognition unit, an emotion analysis unit, a visualization unit, a warning unit, and a coordination unit. The speech recognition unit receives voice input. The emotion analysis unit analyzes the voice data received by the speech recognition unit and analyzes emotions in real time. The visualization unit visualizes the analysis results obtained by the emotion analysis unit. The warning unit issues an early warning based on the emotion trend visualized by the visualization unit. The coordination unit functions as a mental health management tool for medical professionals and companies based on the warning issued by the warning unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; 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 prior art, there is a problem that it is difficult to objectively grasp an individual's emotional changes and psychological state, and it is difficult to continuously implement and manage mental health care.

[0005] The system according to the embodiment aims to objectively grasp an individual's emotional changes and psychological state and continuously implement and manage mental health care.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a speech recognition unit, an emotion analysis unit, a visualization unit, a warning unit, and a collaboration unit. The speech recognition unit receives voice input. The emotion analysis unit analyzes the voice data received by the speech recognition unit and analyzes emotions in real time. The visualization unit visualizes the analysis results obtained by the emotion analysis unit. The warning unit issues early warnings based on the emotion trends visualized by the visualization unit. The collaboration unit functions as a mental health management tool for medical professionals and companies based on the warnings issued by the warning unit. [Effects of the Invention]

[0007] The system according to this embodiment can objectively grasp an individual's emotional changes and psychological state, and continuously implement and manage mental health care. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between 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 receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The mental health care system according to an embodiment of the present invention is a system that uses speech recognition and AI to analyze, visualize, warn, and coordinate with users' emotions in real time. This system provides easy diary input using speech recognition and real-time emotion analysis and visualization functions using AI. Specifically, the user inputs a diary by voice, and the AI ​​analyzes the voice data to analyze emotions in real time. The analysis results are visualized, allowing the user to understand their own emotional trends. Furthermore, long-term emotional trend analysis and an early warning system enable preventive care. In addition, functions for collaboration with medical professionals and mental health management tools for companies are also provided. This system enables continuous mental health care that is integrated into daily life and promotes self-understanding. It also prevents the worsening of conditions through early problem detection and intervention. For companies, the mental health status of employees can be visualized, enabling the provision of appropriate support. For example, if a user inputs "I'm a little tired today" by voice, the AI ​​analyzes the voice data and visualizes the user's emotional state as "fatigue" in real time. Furthermore, it analyzes long-term emotional trends by comparing them with past data and issues early warnings as needed. For businesses, it can visualize the emotional trends of all employees and be used as a mental health management tool. Thus, this invention is an innovative mental healthcare system that combines speech recognition and AI, and can be widely used by individuals and businesses alike. This allows the mental healthcare system to analyze, visualize, warn, and coordinate user emotions in real time, thereby enabling continuous mental healthcare.

[0029] The mental health care system according to this embodiment comprises a voice recognition unit, an emotion analysis unit, a visualization unit, a warning unit, and a coordination unit. The voice recognition unit receives voice input from the user. The voice recognition unit can input voice using, for example, a microphone. The voice recognition unit can also input voice using a device such as a smartphone or a personal computer. Furthermore, the voice recognition unit can use noise cancellation technology to improve the accuracy of voice input. The emotion analysis unit analyzes the voice data received by the voice recognition unit and analyzes emotions in real time. The emotion analysis unit estimates emotions by analyzing, for example, the tone, pitch, and speed of the voice data. The emotion analysis unit can also estimate emotions by analyzing the content of the voice data. Furthermore, the emotion analysis unit can improve the accuracy of emotion analysis by referring to past emotion data. The visualization unit visualizes the analysis results obtained by the emotion analysis unit. The visualization unit visually displays the emotion data using, for example, graphs or charts. The visualization unit can also display the temporal changes in the emotion data. Furthermore, the visualization unit can adjust the color and shape of the emotion data to provide a visually easy-to-understand display. The warning unit issues early warnings based on the emotional trends visualized by the visualization unit. For example, the warning unit detects rapid changes in emotional trends and issues an early warning. The warning unit can also adjust the timing of warnings based on the user's emotions. Furthermore, the warning unit can improve the accuracy of warnings by referring to past warning history. The collaboration unit functions as a mental health management tool for medical professionals and companies based on the warnings issued by the warning unit. For example, the collaboration unit provides the user's emotional data to medical professionals to propose optimal care. The collaboration unit can also share data with a company's mental health management tool to optimize employee care. Furthermore, the collaboration unit can collaborate with region-specific professionals, taking into account the user's geographical location. As a result, the mental healthcare system according to this embodiment can achieve continuous mental healthcare by analyzing, visualizing, warning, and collaborating with users in real time.

[0030] The speech recognition unit accepts voice input from the user. For example, the speech recognition unit can input voice using a microphone. Specifically, by using a high-sensitivity microphone, the user's voice can be captured clearly while minimizing background noise. The speech recognition unit can also input voice using devices such as smartphones and personal computers. This allows users to use the system in various locations, such as at home, at work, or while traveling. Furthermore, the speech recognition unit can use noise cancellation technology to improve the accuracy of voice input. Noise cancellation technology effectively removes ambient noise, allowing for accurate recognition of only the user's voice. For example, active noise cancellation technology can analyze ambient sounds in real time and generate sound waves with the opposite phase to cancel out the noise. This allows the speech recognition unit to recognize voice with high accuracy even in noisy environments. Additionally, the speech recognition unit can learn the user's speech patterns and accents to achieve speech recognition optimized for each individual user. This enables the speech recognition unit to accept user voice input with high accuracy and smoothly provide data to the sentiment analysis unit.

[0031] The emotion analysis unit analyzes the voice data received by the speech recognition unit and analyzes emotions in real time. For example, the emotion analysis unit estimates emotions by analyzing the tone, pitch, and speed of the voice data. Specifically, it can estimate the user's emotional state by analyzing the frequency components of the voice and detecting the pitch and fluctuations of the tone. It can also estimate emotions by analyzing the content of the voice data. For example, it can use natural language processing technology to convert the voice data to text and extract emotions from the text content. Furthermore, the emotion analysis unit can improve the accuracy of emotion analysis by referring to past emotion data. Based on past data, it can learn the user's emotional patterns and trends and estimate the current emotional state more accurately. The emotion analysis unit can automatically classify emotions from voice data using machine learning algorithms. For example, it can train models using support vector machines or deep learning to classify emotions such as joy, sadness, anger, and surprise from voice data with high accuracy. As a result, the emotion analysis unit can grasp the user's emotional state in real time and provide data to the visualization unit and warning unit quickly and accurately.

[0032] The visualization unit visualizes the analysis results obtained by the emotion analysis unit. The visualization unit visually displays emotion data using graphs and charts, for example. Specifically, it can visually show changes in emotions over time using line graphs and bar graphs. The visualization unit can also display changes in emotion data over time. For example, it can display daily, weekly, and monthly emotion trends, allowing users to grasp their own emotional fluctuations at a glance. Furthermore, the visualization unit can adjust the color and shape of the emotion data to provide a visually easy-to-understand display. For example, by using different colors for each type of emotion and representing the intensity of the emotion with shades of color, users can intuitively understand their emotional state. The visualization unit can also provide an interactive dashboard, allowing users to freely manipulate emotion data and view detailed information. For example, users can filter emotion data by selecting a specific period or display detailed analysis results for a specific emotion. This allows the visualization unit to provide users with information to understand their own emotional state in detail and take appropriate action.

[0033] The warning unit issues early warnings based on the emotional trends visualized by the visualization unit. For example, the warning unit can detect rapid changes in emotional trends and issue early warnings. Specifically, it can monitor rapid fluctuations in emotional data in real time and issue a warning if a certain threshold is exceeded. The warning unit can also adjust the timing of warnings based on the user's emotions. For example, it can issue a warning earlier if the user is stressed and delay the warning if they are relaxed. Furthermore, the warning unit can improve the accuracy of warnings by referring to past warning history. Based on past warning data, it can optimize the timing and content of warnings to provide the most effective warnings for the user. The warning unit can provide the user with visual, auditory, and tactile feedback. For example, it can communicate warnings to the user using smartphone notifications, alarm sounds, vibrations, etc. This allows the warning unit to support users in noticing rapid emotional changes early and taking appropriate measures.

[0034] The Collaboration Unit functions as a mental health management tool for healthcare professionals and companies based on warnings issued by the Warning Unit. For example, the Collaboration Unit can provide users' emotional data to healthcare professionals to suggest optimal care. Specifically, it can share users' emotional data with healthcare professionals in real time, enabling them to provide appropriate advice and treatment. The Collaboration Unit can also share data with corporate mental health management tools to optimize employee care. For example, a company's mental health officer can develop stress management and mental health care programs based on employee emotional data. Furthermore, the Collaboration Unit can collaborate with region-specific professionals, taking into account the user's geographical location. For example, if a user lives in a specific region, it can collaborate with local professionals to provide region-specific mental health care. To ensure data privacy and security, the Collaboration Unit can implement encryption technologies and access controls to ensure user data is managed securely. This allows the Collaboration Unit to effectively utilize users' emotional data and collaborate with healthcare professionals and companies to comprehensively support users' mental health care.

[0035] The speech recognition unit can analyze the user's speaking speed and tone during speech recognition and select the optimal recognition algorithm. For example, if the user speaks quickly, the speech recognition unit can switch to high-speed processing. It can also switch to high-precision processing if the user speaks slowly. Furthermore, if the user's tone changes, the speech recognition unit can select a speech recognition algorithm appropriate to that tone. This improves the accuracy of speech recognition by selecting the optimal recognition algorithm according to the user's speaking speed and tone. Some or all of the above processing in the speech recognition unit may be performed using AI, or without AI. For example, the speech recognition unit can input data on the user's speaking speed and tone into a generating AI and have the generating AI select the optimal recognition algorithm.

[0036] The speech recognition unit can have a filtering function added to automatically remove background noise during speech recognition. For example, if the user is speaking in a noisy environment, the speech recognition unit can remove background noise to improve the accuracy of speech recognition. Also, if the user is speaking in a quiet environment, the speech recognition unit can minimize the filtering function and recognize natural speech. Furthermore, if the user is speaking while moving, the speech recognition unit can remove wind noise and traffic noise to maintain the accuracy of speech recognition. In this way, removing background noise improves the accuracy of speech recognition. Some or all of the above processing in the speech recognition unit may be performed using AI, for example, or without AI. For example, the speech recognition unit can input background noise data into a generating AI and have the generating AI perform noise reduction filtering.

[0037] The emotion analysis unit can detect subtle changes in audio data during emotion analysis and analyze the emotional state in more detail. For example, the emotion analysis unit can detect subtle changes in the tone and pitch of the user's voice and analyze the emotional state in detail. It can also detect subtle changes in the user's speech rate and analyze the emotional state in detail. Furthermore, the emotion analysis unit can detect subtle changes in the volume of the user's voice and analyze the emotional state in detail. In this way, by detecting subtle changes in audio data, the emotional state can be analyzed in more detail. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input subtle changes in audio data into a generating AI and have the generating AI perform a detailed analysis of the emotional state.

[0038] The sentiment analysis unit can improve the accuracy of its analysis by referring to the user's past emotional data during sentiment analysis. For example, the sentiment analysis unit can refer to the user's past emotional data to more accurately analyze the user's current emotional state. It can also refer to the user's past emotional trends to predict the user's current emotional state. Furthermore, the sentiment analysis unit can adjust its sentiment analysis algorithm based on the user's past emotional data. This improves the accuracy of sentiment analysis by referring to past emotional data. Some or all of the above processes in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input the user's past emotional data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0039] The visualization unit can display the temporal changes in emotional data using graphs and charts during visualization. For example, the visualization unit can display a user's emotional data on a daily basis using a graph. It can also display a user's emotional data on a weekly basis using a chart. Furthermore, it can display a user's emotional data on a monthly basis using a line graph. This makes it easier to grasp emotional trends by visually displaying the temporal changes in emotional data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input emotional data into a generating AI and have the generating AI generate graphs and charts of temporal changes.

[0040] The visualization unit can add a function to compare and display the user's past emotional trends during visualization. For example, the visualization unit can compare and display the user's emotional trends over the past year. It can also compare and display the user's emotional trends over the past six months. Furthermore, it can compare and display the user's emotional trends over the past month. This allows for a more detailed understanding of emotional changes by comparing and displaying past emotional trends. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input past emotional trend data into a generating AI and have the generating AI perform the generation of the comparison display.

[0041] The warning unit can detect rapid changes in sentiment trends and issue early warnings when a warning is issued. For example, the warning unit will issue an early warning if the user's sentiment trend rapidly deteriorates. The warning unit can also issue a cautionary warning if the user's sentiment trend rapidly improves. Furthermore, if the user's sentiment trend fluctuates rapidly, the warning unit can issue a warning along with detailed analysis results. This allows for early warnings by detecting rapid changes in sentiment trends. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input sentiment trend data into a generating AI and have the generating AI perform the detection of rapid changes and the issuance of early warnings.

[0042] The warning unit can improve the accuracy of warnings by referring to the user's past warning history when issuing a warning. For example, the warning unit can refer to the user's past warning history to improve the accuracy of the current warning. The warning unit can also adjust the frequency and timing of warnings based on the user's past warning history. Furthermore, the warning unit can customize the content of warnings based on the user's past warning history. This improves the accuracy of warnings by referring to past warning history. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input past warning history data into a generating AI and have the generating AI perform the task of improving the accuracy of warnings.

[0043] The collaboration unit can provide the user's past emotional data to a specialist during the collaboration process to propose optimal care. For example, the collaboration unit can provide the user's past emotional data to a specialist to propose optimal counseling. Furthermore, the collaboration unit can also allow the specialist to propose a stress management plan based on the user's past emotional data. Additionally, the collaboration unit can refer to the user's past emotional data to allow the specialist to propose relaxation methods. In this way, providing past emotional data to a specialist ensures optimal care is proposed. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input past emotional data into a generating AI and have the generating AI propose optimal care.

[0044] The collaboration unit can optimize employee care by sharing data with the company's mental health management tools during collaboration. For example, the collaboration unit can optimize employee stress management by sharing data with the company's mental health management tools. It can also propose employee relaxation plans by sharing data with the company's mental health management tools. Furthermore, the collaboration unit can optimize employee counseling plans by sharing data with the company's mental health management tools. In this way, employee care is optimized by sharing data with the company's mental health management tools. Some or all of the above processes in the collaboration unit may be performed using AI, for example, or not using AI. For example, the collaboration unit can input data from the company's mental health management tools into a generating AI and have the generating AI perform the optimization of care.

[0045] The collaboration unit can collaborate with region-specific experts, taking into account the user's geographical location information during the collaboration process. For example, if the user is in an urban area, the collaboration unit will collaborate with urban area experts. It can also collaborate with regional experts if the user is in a rural area. Furthermore, if the user is overseas, the collaboration unit can collaborate with experts in that region. This allows for more appropriate care to be provided by collaborating with region-specific experts. Some or all of the above-described processes in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the user's geographical location information into a generating AI and have the generating AI perform the collaboration with region-specific experts.

[0046] The integration unit can, during integration, refer to the user's social media activity and connect with relevant experts and tools. For example, the integration unit can connect with experts that the user follows on social media. The integration unit can also connect with relevant tools based on the user's social media activity. Furthermore, the integration unit can connect with experts related to topics that the user has shown interest in on social media. In this way, by referring to social media activity, the integration unit can connect with experts and tools relevant to the user. Some or all of the above processing in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input the user's social media activity data into a generating AI and have the generating AI perform the integration with relevant experts and tools.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] A mental health care system can be enhanced with a feature that suggests exercise plans based on the user's activity history, without having to estimate the user's emotions. For example, if the user has jogged in the past, it can suggest continuing to jog. Similarly, if the user has practiced yoga in the past, it can suggest continuing yoga. Furthermore, if the user has walked in the past, it can suggest continuing to walk. This allows the system to support consistent exercise habits by suggesting exercise plans based on the user's activity history.

[0049] A mental health care system can be enhanced with a feature that suggests meal plans based on the user's eating history, without having to estimate the user's emotions. For example, if a user has previously consumed a balanced diet, the system can suggest continuing that diet. It can also suggest meals containing specific nutrients if the user has consumed a lot of them in the past. Furthermore, if a user has had a particular eating pattern in the past, the system can suggest maintaining that pattern. This allows the system to support healthy eating habits by suggesting meal plans based on the user's eating history.

[0050] Mental health care systems can be enhanced with a feature that suggests relaxation techniques based on the user's activity history, without having to estimate the user's emotions. For example, if the user has meditated in the past, it can suggest continuing that practice. Similarly, if the user has practiced deep breathing in the past, it can suggest continuing that practice. Furthermore, if the user has listened to relaxation music in the past, it can suggest continuing that practice. This allows for support of mental and physical refreshment by suggesting relaxation techniques based on the user's activity history.

[0051] A mental health care system can add a feature that recommends books based on a user's reading history, without having to estimate the user's emotions. For example, if a user has previously read books of a particular genre, it can recommend books of that genre. It can also recommend new works by a particular author if the user has previously read books by that author. Furthermore, if a user has previously read books on a particular theme, it can recommend books related to that theme. This allows for book recommendations based on the user's reading history, thereby supporting the enjoyment of reading.

[0052] The mental health care system can add a feature that recommends movies based on the user's movie viewing history, without having to estimate the user's emotions. For example, if a user has previously watched movies of a particular genre, it can recommend movies of that genre. It can also recommend new releases by directors who have previously watched films by that director. Furthermore, if a user has previously watched movies on a particular theme, it can recommend movies related to that theme. This allows for movie recommendations based on the user's viewing history, thereby supporting the enjoyment of watching movies.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The speech recognition unit accepts the user's voice input. The speech recognition unit can input voice using, for example, a microphone. It can also input voice using devices such as smartphones or personal computers. Furthermore, the speech recognition unit can use noise cancellation technology to improve the accuracy of voice input. Step 2: The emotion analysis unit analyzes the audio data received by the speech recognition unit and analyzes the emotions in real time. For example, the emotion analysis unit estimates emotions by analyzing the tone, pitch, and speed of the audio data. The emotion analysis unit can also estimate emotions by analyzing the content of the audio data. Furthermore, the emotion analysis unit can improve the accuracy of emotion analysis by referring to past emotion data. Step 3: The visualization unit visualizes the analysis results obtained by the sentiment analysis unit. The visualization unit visually displays the sentiment data using, for example, graphs and charts. The visualization unit can also display the changes in sentiment data over time. Furthermore, the visualization unit can adjust the color and shape of the sentiment data to provide a visually easy-to-understand display. Step 4: The warning unit issues early warnings based on the sentiment trends visualized by the visualization unit. For example, the warning unit can detect abrupt changes in sentiment trends and issue an early warning. The warning unit can also adjust the timing of warnings based on the user's emotions. Furthermore, the warning unit can improve the accuracy of warnings by referring to past warning history. Step 5: The Collaboration Unit functions as a mental health management tool for healthcare professionals and businesses based on the warnings issued by the Warning Unit. For example, the Collaboration Unit provides users' emotional data to healthcare professionals to suggest optimal care. The Collaboration Unit can also share data with corporate mental health management tools to optimize employee care. Furthermore, the Collaboration Unit can collaborate with region-specific professionals, taking into account the user's geographical location.

[0055] (Example of form 2) The mental health care system according to an embodiment of the present invention is a system that uses speech recognition and AI to analyze, visualize, warn, and coordinate with users' emotions in real time. This system provides easy diary input using speech recognition and real-time emotion analysis and visualization functions using AI. Specifically, the user inputs a diary by voice, and the AI ​​analyzes the voice data to analyze emotions in real time. The analysis results are visualized, allowing the user to understand their own emotional trends. Furthermore, long-term emotional trend analysis and an early warning system enable preventive care. In addition, functions for collaboration with medical professionals and mental health management tools for companies are also provided. This system enables continuous mental health care that is integrated into daily life and promotes self-understanding. It also prevents the worsening of conditions through early problem detection and intervention. For companies, the mental health status of employees can be visualized, enabling the provision of appropriate support. For example, if a user inputs "I'm a little tired today" by voice, the AI ​​analyzes the voice data and visualizes the user's emotional state as "fatigue" in real time. Furthermore, it analyzes long-term emotional trends by comparing them with past data and issues early warnings as needed. For businesses, it can visualize the emotional trends of all employees and be used as a mental health management tool. Thus, this invention is an innovative mental healthcare system that combines speech recognition and AI, and can be widely used by individuals and businesses alike. This allows the mental healthcare system to analyze, visualize, warn, and coordinate user emotions in real time, thereby enabling continuous mental healthcare.

[0056] The mental health care system according to this embodiment comprises a voice recognition unit, an emotion analysis unit, a visualization unit, a warning unit, and a coordination unit. The voice recognition unit receives voice input from the user. The voice recognition unit can input voice using, for example, a microphone. The voice recognition unit can also input voice using a device such as a smartphone or a personal computer. Furthermore, the voice recognition unit can use noise cancellation technology to improve the accuracy of voice input. The emotion analysis unit analyzes the voice data received by the voice recognition unit and analyzes emotions in real time. The emotion analysis unit estimates emotions by analyzing, for example, the tone, pitch, and speed of the voice data. The emotion analysis unit can also estimate emotions by analyzing the content of the voice data. Furthermore, the emotion analysis unit can improve the accuracy of emotion analysis by referring to past emotion data. The visualization unit visualizes the analysis results obtained by the emotion analysis unit. The visualization unit visually displays the emotion data using, for example, graphs or charts. The visualization unit can also display the temporal changes in the emotion data. Furthermore, the visualization unit can adjust the color and shape of the emotion data to provide a visually easy-to-understand display. The warning unit issues early warnings based on the emotional trends visualized by the visualization unit. For example, the warning unit detects rapid changes in emotional trends and issues an early warning. The warning unit can also adjust the timing of warnings based on the user's emotions. Furthermore, the warning unit can improve the accuracy of warnings by referring to past warning history. The collaboration unit functions as a mental health management tool for medical professionals and companies based on the warnings issued by the warning unit. For example, the collaboration unit provides the user's emotional data to medical professionals to propose optimal care. The collaboration unit can also share data with a company's mental health management tool to optimize employee care. Furthermore, the collaboration unit can collaborate with region-specific professionals, taking into account the user's geographical location. As a result, the mental healthcare system according to this embodiment can achieve continuous mental healthcare by analyzing, visualizing, warning, and collaborating with users in real time.

[0057] The speech recognition unit accepts voice input from the user. For example, the speech recognition unit can input voice using a microphone. Specifically, by using a high-sensitivity microphone, the user's voice can be captured clearly while minimizing background noise. The speech recognition unit can also input voice using devices such as smartphones and personal computers. This allows users to use the system in various locations, such as at home, at work, or while traveling. Furthermore, the speech recognition unit can use noise cancellation technology to improve the accuracy of voice input. Noise cancellation technology effectively removes ambient noise, allowing for accurate recognition of only the user's voice. For example, active noise cancellation technology can analyze ambient sounds in real time and generate sound waves with the opposite phase to cancel out the noise. This allows the speech recognition unit to recognize voice with high accuracy even in noisy environments. Additionally, the speech recognition unit can learn the user's speech patterns and accents to achieve speech recognition optimized for each individual user. This enables the speech recognition unit to accept user voice input with high accuracy and smoothly provide data to the sentiment analysis unit.

[0058] The emotion analysis unit analyzes the voice data received by the speech recognition unit and analyzes emotions in real time. For example, the emotion analysis unit estimates emotions by analyzing the tone, pitch, and speed of the voice data. Specifically, it can estimate the user's emotional state by analyzing the frequency components of the voice and detecting the pitch and fluctuations of the tone. It can also estimate emotions by analyzing the content of the voice data. For example, it can use natural language processing technology to convert the voice data to text and extract emotions from the text content. Furthermore, the emotion analysis unit can improve the accuracy of emotion analysis by referring to past emotion data. Based on past data, it can learn the user's emotional patterns and trends and estimate the current emotional state more accurately. The emotion analysis unit can automatically classify emotions from voice data using machine learning algorithms. For example, it can train models using support vector machines or deep learning to classify emotions such as joy, sadness, anger, and surprise from voice data with high accuracy. As a result, the emotion analysis unit can grasp the user's emotional state in real time and provide data to the visualization unit and warning unit quickly and accurately.

[0059] The visualization unit visualizes the analysis results obtained by the emotion analysis unit. The visualization unit visually displays emotion data using graphs and charts, for example. Specifically, it can visually show changes in emotions over time using line graphs and bar graphs. The visualization unit can also display changes in emotion data over time. For example, it can display daily, weekly, and monthly emotion trends, allowing users to grasp their own emotional fluctuations at a glance. Furthermore, the visualization unit can adjust the color and shape of the emotion data to provide a visually easy-to-understand display. For example, by using different colors for each type of emotion and representing the intensity of the emotion with shades of color, users can intuitively understand their emotional state. The visualization unit can also provide an interactive dashboard, allowing users to freely manipulate emotion data and view detailed information. For example, users can filter emotion data by selecting a specific period or display detailed analysis results for a specific emotion. This allows the visualization unit to provide users with information to understand their own emotional state in detail and take appropriate action.

[0060] The warning unit issues early warnings based on the emotional trends visualized by the visualization unit. For example, the warning unit can detect rapid changes in emotional trends and issue early warnings. Specifically, it can monitor rapid fluctuations in emotional data in real time and issue a warning if a certain threshold is exceeded. The warning unit can also adjust the timing of warnings based on the user's emotions. For example, it can issue a warning earlier if the user is stressed and delay the warning if they are relaxed. Furthermore, the warning unit can improve the accuracy of warnings by referring to past warning history. Based on past warning data, it can optimize the timing and content of warnings to provide the most effective warnings for the user. The warning unit can provide the user with visual, auditory, and tactile feedback. For example, it can communicate warnings to the user using smartphone notifications, alarm sounds, vibrations, etc. This allows the warning unit to support users in noticing rapid emotional changes early and taking appropriate measures.

[0061] The Collaboration Unit functions as a mental health management tool for healthcare professionals and companies based on warnings issued by the Warning Unit. For example, the Collaboration Unit can provide users' emotional data to healthcare professionals to suggest optimal care. Specifically, it can share users' emotional data with healthcare professionals in real time, enabling them to provide appropriate advice and treatment. The Collaboration Unit can also share data with corporate mental health management tools to optimize employee care. For example, a company's mental health officer can develop stress management and mental health care programs based on employee emotional data. Furthermore, the Collaboration Unit can collaborate with region-specific professionals, taking into account the user's geographical location. For example, if a user lives in a specific region, it can collaborate with local professionals to provide region-specific mental health care. To ensure data privacy and security, the Collaboration Unit can implement encryption technologies and access controls to ensure user data is managed securely. This allows the Collaboration Unit to effectively utilize users' emotional data and collaborate with healthcare professionals and companies to comprehensively support users' mental health care.

[0062] The speech recognition unit can estimate the user's emotions and adjust the accuracy of voice input based on the estimated emotions. For example, if the user is stressed, the speech recognition unit can increase the sensitivity of speech recognition to reduce input errors. Conversely, if the user is relaxed, the speech recognition unit can maintain normal speech recognition accuracy to encourage natural input. Furthermore, if the user is in a hurry, the speech recognition unit can prioritize the speed of speech recognition to enable rapid input. This allows for more accurate speech recognition by adjusting the accuracy of voice input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the speech recognition unit may be performed using AI, or not. For example, the speech recognition unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0063] The speech recognition unit can analyze the user's speaking speed and tone during speech recognition and select the optimal recognition algorithm. For example, if the user speaks quickly, the speech recognition unit can switch to high-speed processing. It can also switch to high-precision processing if the user speaks slowly. Furthermore, if the user's tone changes, the speech recognition unit can select a speech recognition algorithm appropriate to that tone. This improves the accuracy of speech recognition by selecting the optimal recognition algorithm according to the user's speaking speed and tone. Some or all of the above processing in the speech recognition unit may be performed using AI, or without AI. For example, the speech recognition unit can input data on the user's speaking speed and tone into a generating AI and have the generating AI select the optimal recognition algorithm.

[0064] The speech recognition unit can have a filtering function added to automatically remove background noise during speech recognition. For example, if the user is speaking in a noisy environment, the speech recognition unit can remove background noise to improve the accuracy of speech recognition. Also, if the user is speaking in a quiet environment, the speech recognition unit can minimize the filtering function and recognize natural speech. Furthermore, if the user is speaking while moving, the speech recognition unit can remove wind noise and traffic noise to maintain the accuracy of speech recognition. In this way, removing background noise improves the accuracy of speech recognition. Some or all of the above processing in the speech recognition unit may be performed using AI, for example, or without AI. For example, the speech recognition unit can input background noise data into a generating AI and have the generating AI perform noise reduction filtering.

[0065] The emotion analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the emotion analysis unit can apply a stress-specific analysis algorithm. It can also apply a standard analysis algorithm if the user is relaxed. Furthermore, if the user is excited, the emotion analysis unit can apply an analysis algorithm specifically for excited states. This improves the accuracy of emotion analysis by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the emotion analysis unit may be performed using AI, or not. For example, the emotion analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis algorithm.

[0066] The emotion analysis unit can detect subtle changes in audio data during emotion analysis and analyze the emotional state in more detail. For example, the emotion analysis unit can detect subtle changes in the tone and pitch of the user's voice and analyze the emotional state in detail. It can also detect subtle changes in the user's speech rate and analyze the emotional state in detail. Furthermore, the emotion analysis unit can detect subtle changes in the volume of the user's voice and analyze the emotional state in detail. In this way, by detecting subtle changes in audio data, the emotional state can be analyzed in more detail. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input subtle changes in audio data into a generating AI and have the generating AI perform a detailed analysis of the emotional state.

[0067] The sentiment analysis unit can improve the accuracy of its analysis by referring to the user's past emotional data during sentiment analysis. For example, the sentiment analysis unit can refer to the user's past emotional data to more accurately analyze the user's current emotional state. It can also refer to the user's past emotional trends to predict the user's current emotional state. Furthermore, the sentiment analysis unit can adjust its sentiment analysis algorithm based on the user's past emotional data. This improves the accuracy of sentiment analysis by referring to past emotional data. Some or all of the above processes in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input the user's past emotional data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0068] The visualization unit can estimate the user's emotions and adjust the color and shape of the visualization based on the estimated emotions. For example, if the user is tense, the visualization unit can provide a visualization with calm colors. It can also provide a visualization with bright colors if the user is relaxed. Furthermore, if the user is excited, the visualization unit can use visually stimulating shapes and colors. This allows for a visually easy-to-understand display by adjusting the color and shape of the visualization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the visualization unit may be performed using AI, or not. For example, the visualization unit can input user emotion data into the generative AI and have the generative AI adjust the color and shape of the visualization.

[0069] The visualization unit can display the temporal changes in emotional data using graphs and charts during visualization. For example, the visualization unit can display a user's emotional data on a daily basis using a graph. It can also display a user's emotional data on a weekly basis using a chart. Furthermore, it can display a user's emotional data on a monthly basis using a line graph. This makes it easier to grasp emotional trends by visually displaying the temporal changes in emotional data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input emotional data into a generating AI and have the generating AI generate graphs and charts of temporal changes.

[0070] The visualization unit can add a function to compare and display the user's past emotional trends during visualization. For example, the visualization unit can compare and display the user's emotional trends over the past year. It can also compare and display the user's emotional trends over the past six months. Furthermore, it can compare and display the user's emotional trends over the past month. This allows for a more detailed understanding of emotional changes by comparing and displaying past emotional trends. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input past emotional trend data into a generating AI and have the generating AI perform the generation of the comparison display.

[0071] The warning unit can estimate the user's emotions and adjust the timing of warnings based on the estimated emotions. For example, if the user is feeling stressed, the warning unit will issue a warning early. Conversely, if the user is relaxed, the warning unit can issue a warning at a normal time. Furthermore, if the user is in a hurry, the warning unit can prioritize issuing urgent warnings. This allows warnings to be issued at the appropriate time by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the warning unit may be performed using AI, or not. For example, the warning unit can input user emotion data into a generative AI and have the generative AI adjust the timing of warnings.

[0072] The warning unit can detect rapid changes in sentiment trends and issue early warnings when a warning is issued. For example, the warning unit will issue an early warning if the user's sentiment trend rapidly deteriorates. The warning unit can also issue a cautionary warning if the user's sentiment trend rapidly improves. Furthermore, if the user's sentiment trend fluctuates rapidly, the warning unit can issue a warning along with detailed analysis results. This allows for early warnings by detecting rapid changes in sentiment trends. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input sentiment trend data into a generating AI and have the generating AI perform the detection of rapid changes and the issuance of early warnings.

[0073] The warning unit can improve the accuracy of warnings by referring to the user's past warning history when issuing a warning. For example, the warning unit can refer to the user's past warning history to improve the accuracy of the current warning. The warning unit can also adjust the frequency and timing of warnings based on the user's past warning history. Furthermore, the warning unit can customize the content of warnings based on the user's past warning history. This improves the accuracy of warnings by referring to past warning history. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input past warning history data into a generating AI and have the generating AI perform the task of improving the accuracy of warnings.

[0074] The collaboration unit can estimate the user's emotions and select experts and tools to collaborate with based on the estimated emotions. For example, if the user is feeling stressed, the collaboration unit will collaborate with a stress management expert. It can also collaborate with relaxation tools if the user is relaxed. Furthermore, if the user is agitated, the collaboration unit can collaborate with a counseling expert. This enables effective care by selecting the most suitable experts and tools according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the collaboration unit may be performed using AI, or not. For example, the collaboration unit can input user emotion data into a generative AI and have the generative AI select experts and tools.

[0075] The collaboration unit can provide the user's past emotional data to a specialist during the collaboration process to propose optimal care. For example, the collaboration unit can provide the user's past emotional data to a specialist to propose optimal counseling. Furthermore, the collaboration unit can also allow the specialist to propose a stress management plan based on the user's past emotional data. Additionally, the collaboration unit can refer to the user's past emotional data to allow the specialist to propose relaxation methods. In this way, providing past emotional data to a specialist ensures optimal care is proposed. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input past emotional data into a generating AI and have the generating AI propose optimal care.

[0076] The collaboration unit can optimize employee care by sharing data with the company's mental health management tools during collaboration. For example, the collaboration unit can optimize employee stress management by sharing data with the company's mental health management tools. It can also propose employee relaxation plans by sharing data with the company's mental health management tools. Furthermore, the collaboration unit can optimize employee counseling plans by sharing data with the company's mental health management tools. In this way, employee care is optimized by sharing data with the company's mental health management tools. Some or all of the above processes in the collaboration unit may be performed using AI, for example, or not using AI. For example, the collaboration unit can input data from the company's mental health management tools into a generating AI and have the generating AI perform the optimization of care.

[0077] The collaboration unit can estimate the user's emotions and adjust the frequency of collaboration based on the estimated emotions. For example, if the user is stressed, the collaboration unit will collaborate with experts more frequently. Conversely, if the user is relaxed, the collaboration unit can maintain a normal frequency of collaboration. Furthermore, if the user is in a hurry, the collaboration unit can prioritize urgent collaborations. This allows for timely care by adjusting the frequency of collaboration according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the collaboration unit may be performed using AI, or not. For example, the collaboration unit can input user emotion data into a generative AI and have the generative AI adjust the frequency of collaboration.

[0078] The collaboration unit can collaborate with region-specific experts, taking into account the user's geographical location information during the collaboration process. For example, if the user is in an urban area, the collaboration unit will collaborate with urban area experts. It can also collaborate with regional experts if the user is in a rural area. Furthermore, if the user is overseas, the collaboration unit can collaborate with experts in that region. This allows for more appropriate care to be provided by collaborating with region-specific experts. Some or all of the above-described processes in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the user's geographical location information into a generating AI and have the generating AI perform the collaboration with region-specific experts.

[0079] The integration unit can, during integration, refer to the user's social media activity and connect with relevant experts and tools. For example, the integration unit can connect with experts that the user follows on social media. The integration unit can also connect with relevant tools based on the user's social media activity. Furthermore, the integration unit can connect with experts related to topics that the user has shown interest in on social media. In this way, by referring to social media activity, the integration unit can connect with experts and tools relevant to the user. Some or all of the above processing in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input the user's social media activity data into a generating AI and have the generating AI perform the integration with relevant experts and tools.

[0080] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0081] Mental health care systems can be enhanced with features that estimate a user's emotions and provide music based on those estimates. For example, if a user is stressed, relaxing music can be provided. If the user is relaxed, calming music can be provided to maintain that mood. Furthermore, if the user is excited, energetic music can be provided. By providing music that matches the user's emotions, it is possible to stabilize and improve their emotional state.

[0082] A mental health care system can be enhanced with a feature that estimates the user's emotions and suggests exercise plans based on those emotions. For example, if the user is stressed, it can suggest relaxing yoga or stretching. If the user is relaxed, it can suggest light jogging or walking. Furthermore, if the user is excited, it can suggest energetic exercises. This allows for the promotion of mental and physical health by suggesting exercise plans tailored to the user's emotions.

[0083] A mental health care system can be enhanced with a feature that estimates the user's emotions and suggests meal plans based on those emotions. For example, if the user is stressed, it can suggest relaxing herbal tea or a light snack. If the user is relaxed, it can suggest a balanced meal. Furthermore, if the user is agitated, it can suggest a high-calorie meal for energy replenishment. This allows the system to support healthy eating habits by suggesting meal plans tailored to the user's emotions.

[0084] Mental health care systems can be enhanced with features that estimate a user's emotions and suggest relaxation techniques based on those emotions. For example, if a user is stressed, it can suggest deep breathing or meditation. If the user is relaxed, it can suggest relaxation music or aromatherapy. Furthermore, if the user is agitated, it can suggest massage or a warm bath. This allows for mental and physical refreshment by suggesting relaxation techniques tailored to the user's emotions.

[0085] A mental health care system can be enhanced with a feature that estimates a user's emotions and recommends books and movies based on those estimates. For example, if a user is feeling stressed, it can recommend relaxing books or movies. If the user is relaxed, it can recommend calming books or movies to maintain that mood. Furthermore, if the user is excited, it can recommend energetic books or movies. By recommending books and movies that match the user's emotions, it can help stabilize and improve their emotional state.

[0086] A mental health care system can be enhanced with a feature that suggests exercise plans based on the user's activity history, without having to estimate the user's emotions. For example, if the user has jogged in the past, it can suggest continuing to jog. Similarly, if the user has practiced yoga in the past, it can suggest continuing yoga. Furthermore, if the user has walked in the past, it can suggest continuing to walk. This allows the system to support consistent exercise habits by suggesting exercise plans based on the user's activity history.

[0087] A mental health care system can be enhanced with a feature that suggests meal plans based on the user's eating history, without having to estimate the user's emotions. For example, if a user has previously consumed a balanced diet, the system can suggest continuing that diet. It can also suggest meals containing specific nutrients if the user has consumed a lot of them in the past. Furthermore, if a user has had a particular eating pattern in the past, the system can suggest maintaining that pattern. This allows the system to support healthy eating habits by suggesting meal plans based on the user's eating history.

[0088] Mental health care systems can be enhanced with a feature that suggests relaxation techniques based on the user's activity history, without having to estimate the user's emotions. For example, if the user has meditated in the past, it can suggest continuing that practice. Similarly, if the user has practiced deep breathing in the past, it can suggest continuing that practice. Furthermore, if the user has listened to relaxation music in the past, it can suggest continuing that practice. This allows for support of mental and physical refreshment by suggesting relaxation techniques based on the user's activity history.

[0089] A mental health care system can add a feature that recommends books based on a user's reading history, without having to estimate the user's emotions. For example, if a user has previously read books of a particular genre, it can recommend books of that genre. It can also recommend new works by a particular author if the user has previously read books by that author. Furthermore, if a user has previously read books on a particular theme, it can recommend books related to that theme. This allows for book recommendations based on the user's reading history, thereby supporting the enjoyment of reading.

[0090] The mental health care system can add a feature that recommends movies based on the user's movie viewing history, without having to estimate the user's emotions. For example, if a user has previously watched movies of a particular genre, it can recommend movies of that genre. It can also recommend new releases by directors who have previously watched films by that director. Furthermore, if a user has previously watched movies on a particular theme, it can recommend movies related to that theme. This allows for movie recommendations based on the user's viewing history, thereby supporting the enjoyment of watching movies.

[0091] The following briefly describes the processing flow for example form 2.

[0092] Step 1: The speech recognition unit accepts the user's voice input. The speech recognition unit can input voice using, for example, a microphone. It can also input voice using devices such as smartphones or personal computers. Furthermore, the speech recognition unit can use noise cancellation technology to improve the accuracy of voice input. Step 2: The emotion analysis unit analyzes the audio data received by the speech recognition unit and analyzes the emotions in real time. For example, the emotion analysis unit estimates emotions by analyzing the tone, pitch, and speed of the audio data. The emotion analysis unit can also estimate emotions by analyzing the content of the audio data. Furthermore, the emotion analysis unit can improve the accuracy of emotion analysis by referring to past emotion data. Step 3: The visualization unit visualizes the analysis results obtained by the sentiment analysis unit. The visualization unit visually displays the sentiment data using, for example, graphs and charts. The visualization unit can also display the changes in sentiment data over time. Furthermore, the visualization unit can adjust the color and shape of the sentiment data to provide a visually easy-to-understand display. Step 4: The warning unit issues early warnings based on the sentiment trends visualized by the visualization unit. For example, the warning unit can detect abrupt changes in sentiment trends and issue an early warning. The warning unit can also adjust the timing of warnings based on the user's emotions. Furthermore, the warning unit can improve the accuracy of warnings by referring to past warning history. Step 5: The Collaboration Unit functions as a mental health management tool for healthcare professionals and businesses based on the warnings issued by the Warning Unit. For example, the Collaboration Unit provides users' emotional data to healthcare professionals to suggest optimal care. The Collaboration Unit can also share data with corporate mental health management tools to optimize employee care. Furthermore, the Collaboration Unit can collaborate with region-specific professionals, taking into account the user's geographical location.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] Each of the multiple elements described above, including the voice recognition unit, emotion analysis unit, visualization unit, warning unit, and collaboration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the voice recognition unit receives voice input from the user using the microphone 38B of the smart device 14. The emotion analysis unit analyzes the voice data using the specific processing unit 290 of the data processing unit 12 and analyzes emotions in real time. The visualization unit visually displays the emotion data using the display 40A of the smart device 14. The warning unit detects rapid changes in emotion trends using the specific processing unit 290 of the data processing unit 12 and issues an early warning. The collaboration unit functions as a mental health management tool for medical professionals and companies using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0097] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the voice recognition unit, emotion analysis unit, visualization unit, warning unit, and coordination unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the voice recognition unit receives voice input from the user using the microphone 238 of the smart glasses 214. The emotion analysis unit analyzes the voice data using the specific processing unit 290 of the data processing unit 12 and analyzes emotions in real time. The visualization unit visually displays the emotion data using the display of the smart glasses 214. The warning unit detects rapid changes in emotion trends using the specific processing unit 290 of the data processing unit 12 and issues an early warning. The coordination unit functions as a mental health management tool for medical professionals and companies using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the voice recognition unit, emotion analysis unit, visualization unit, warning unit, and collaboration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the voice recognition unit receives user voice input using the microphone 238 of the headset terminal 314. The emotion analysis unit analyzes the voice data using the specific processing unit 290 of the data processing unit 12 and analyzes emotions in real time. The visualization unit visually displays the emotion data using the display 343 of the headset terminal 314. The warning unit detects rapid changes in emotion trends using the specific processing unit 290 of the data processing unit 12 and issues an early warning. The collaboration unit functions as a mental health management tool for medical professionals and companies using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the voice recognition unit, emotion analysis unit, visualization unit, warning unit, and coordination unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the voice recognition unit receives voice input from the user using the microphone 238 of the robot 414. The emotion analysis unit analyzes the voice data using the specific processing unit 290 of the data processing unit 12 and analyzes emotions in real time. The visualization unit visually displays the emotion data using the display of the robot 414. The warning unit detects rapid changes in emotion trends using the specific processing unit 290 of the data processing unit 12 and issues an early warning. The coordination unit functions as a mental health management tool for medical professionals and companies using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] (Note 1) A voice recognition unit that accepts voice input, The emotion analysis unit analyzes the voice data received by the voice recognition unit and analyzes emotions in real time. A visualization unit that visualizes the analysis results obtained by the emotion analysis unit, A warning unit that issues an early warning based on the emotional trend visualized by the aforementioned visualization unit, The system includes a linking unit that functions as a mental health management tool for medical professionals and companies based on warnings issued by the aforementioned warning unit. A system characterized by the following features. (Note 2) The aforementioned speech recognition unit, It estimates the user's emotions and adjusts the accuracy of voice input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned speech recognition unit, During speech recognition, the system analyzes the user's speaking speed and tone to select the optimal recognition algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned speech recognition unit, Add a filtering function that automatically removes background noise during speech recognition. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned emotion analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned emotion analysis unit, During emotion analysis, subtle changes in voice data are detected to analyze the emotional state in more detail. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned emotion analysis unit, During sentiment analysis, we improve the accuracy of the analysis by referencing the user's past sentiment data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned visualization unit, It estimates the user's emotions and adjusts the color and shape of the visualization based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned visualization unit, When visualizing, the temporal changes in sentiment data are displayed in graphs and charts. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned visualization unit, When visualizing data, add a feature that compares and displays the user's past sentiment trends. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned warning unit is It estimates the user's emotions and adjusts the timing of warnings based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned warning unit is During a warning, it detects rapid changes in sentiment trends and issues an early warning. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned warning unit is When issuing a warning, the system improves the accuracy of the warning by referencing the user's past warning history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned linkage unit is, It estimates the user's emotions and selects experts and tools to collaborate with based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned linkage unit is, During integration, the user's past emotional data is provided to experts to propose the most appropriate care. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned linkage unit is, During integration, companies can share mental health management tools and data to optimize employee care. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the frequency of interaction based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned linkage unit is, When collaborating, the system takes the user's geographical location into consideration and connects with local experts. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned linkage unit is, During integration, the system references the user's social media activity to connect with relevant experts and tools. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0165] 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 voice recognition unit that accepts voice input, The emotion analysis unit analyzes the voice data received by the voice recognition unit and analyzes emotions in real time. A visualization unit that visualizes the analysis results obtained by the emotion analysis unit, A warning unit that issues an early warning based on the emotional trend visualized by the aforementioned visualization unit, The system includes a linking unit that functions as a mental health management tool for medical professionals and companies based on warnings issued by the aforementioned warning unit. A system characterized by the following features.

2. The aforementioned speech recognition unit, It estimates the user's emotions and adjusts the accuracy of voice input based on the estimated emotions. The system according to feature 1.

3. The aforementioned speech recognition unit, During speech recognition, the system analyzes the user's speaking speed and tone to select the optimal recognition algorithm. The system according to feature 1.

4. The aforementioned speech recognition unit, Add a filtering function that automatically removes background noise during speech recognition. The system according to feature 1.

5. The aforementioned emotion analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system according to feature 1.

6. The aforementioned emotion analysis unit, During emotion analysis, subtle changes in voice data are detected to analyze the emotional state in more detail. The system according to feature 1.

7. The aforementioned emotion analysis unit, During sentiment analysis, we improve the accuracy of the analysis by referencing the user's past sentiment data. The system according to feature 1.

8. The aforementioned visualization unit, It estimates the user's emotions and adjusts the color and shape of the visualization based on the estimated user emotions. The system according to feature 1.

9. The aforementioned visualization unit, When visualizing, the temporal changes in sentiment data are displayed in graphs and charts. The system according to feature 1.

10. The aforementioned visualization unit, When visualizing data, add a feature that compares and displays the user's past sentiment trends. The system according to feature 1.

Images