System

Abstract

An object of the system according to the embodiment is to evaluate the mental state of the user in real time and to immediately intervene when an abnormality is detected.SOLUTION: A system includes a monitoring unit, an analysis unit, an evaluation unit, and an intervention unit. The monitoring unit automatically monitors the daily digital communication with the consent of the user. The analysis unit analyzes the content of the digital communication monitored by the monitoring unit. The evaluation unit evaluates the mental state of the user on the basis of the content analyzed by the analysis unit. The intervention unit performs real-time intervention on the user when an abnormality is detected in the mental state evaluated by the evaluation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to monitor a user's mental state in real time and respond immediately if an abnormality is detected.

[0005] The system according to the embodiment aims to assess the mental state of the user in real time and intervene immediately if an abnormality is detected. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, an analysis unit, an evaluation unit, and an intervention unit. The monitoring unit automatically monitors daily digital communications with the user's consent. The analysis unit analyzes the content of the digital communications monitored by the monitoring unit. The evaluation unit evaluates the user's mental state based on the content analyzed by the analysis unit. The intervention unit performs real-time intervention on the user when an abnormality is detected in the mental state evaluated by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can assess the user's mental state in real time and intervene immediately if an abnormality is detected. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AutoMind Tracker system according to an embodiment of the present invention is installed on a user's digital device and automatically monitors daily digital communications with the user's consent. Generative AI performs language analysis to evaluate the user's mental state, and if an abnormality is detected, it intervenes in real time. This allows the AutoMind Tracker system to prevent the user from developing mental disorders.

[0029] The AutoMind Tracker system according to an embodiment includes a monitoring unit, an analysis unit, an evaluation unit, and an intervention unit. The monitoring unit automatically monitors daily digital communications with the user's consent. For example, the monitoring unit monitors the user's emails, messaging apps, and social media posts. The monitoring unit can be installed on the user's digital device and run in the background. The monitoring unit can collect the content of the user's statements and messages in real time. For example, the monitoring unit monitors the user's messages to friends and social media posts and analyzes the tone and keywords of the language used. The analysis unit analyzes the content of the digital communications monitored by the monitoring unit. For example, the analysis unit uses generative AI to analyze the emotion and tone of the user's statements and messages. The analysis unit can also use natural language processing technology to analyze the content of the user's statements and messages. The analysis unit can extract keywords from the user's statements and messages and calculate an emotion score. For example, if the user frequently uses negative words such as "tired" or "painful," the analysis unit evaluates this as a sign of stress or anxiety. The evaluation unit evaluates the user's mental state based on the content analyzed by the analysis unit. For example, the evaluation unit evaluates the user's stress level and emotional stability based on the emotion score calculated by the analysis unit. The evaluation unit can also evaluate the user's mental state based on the tone and keywords in the user's utterances and messages. The evaluation unit can also track fluctuations in the user's mental state in real time. For example, the evaluation unit evaluates fluctuations in the user's mental state based on the content of the user's utterances and messages. The intervention unit performs real-time intervention on the user when an abnormality is detected in the mental state evaluated by the evaluation unit. For example, the intervention unit provides the user with appropriate advice and support. The intervention unit can also suggest ways for the user to relax. If a serious mental disorder is detected, the intervention unit can encourage the user to contact a professional counselor or medical institution.For example, if the intervention unit determines that the user is feeling stressed, it provides advice such as "Try some relaxation techniques." This allows the AutoMind Tracker system according to the embodiment to prevent the user from developing mental disorders. For example, the system can constantly monitor the user's mental state through daily digital communication and respond quickly if an abnormality is detected. Furthermore, the user can use digital devices with peace of mind.

[0030] The monitoring unit can detect fluctuations in mental state by analyzing speech patterns during specific time periods and situations in digital communications. For example, the monitoring unit uses the generation AI to monitor a user's digital communications 24 hours a day and analyze speech patterns during specific time periods (e.g., late at night or early in the morning). This detects whether the user is feeling stressed during specific time periods. The monitoring unit also uses the generation AI to analyze the user's speech patterns during specific situations (e.g., at work or on vacation) to detect fluctuations in mental state. For example, if negative speech increases during work hours, it is determined that stress is increasing. The monitoring unit also uses the generation AI to analyze speech patterns before and after specific events (e.g., meetings or presentations) in the user's digital communications to detect fluctuations in mental state. This makes it possible to detect fluctuations in mental state by analyzing speech patterns during specific time periods and situations.

[0031] The monitoring unit can compare a user's past communication history with their current comments to identify abnormal changes. For example, the monitoring unit uses the generation AI to analyze the user's communication history from the past year and compare it with their current comments. For example, if there is an increase in negative comments compared to the past, this is identified as an abnormal change. The monitoring unit also uses the generation AI to learn normal speech patterns based on the user's past communication history and compare them with their current comments. For example, if there is a sudden increase in emotional comments, this is identified as an abnormal change. The monitoring unit also uses the generation AI to compare the user's past communication history with their current comments in real time to identify abnormal changes. For example, if there is a sudden change in the tone of the comments compared to the past, this is identified as an abnormal change. In this way, abnormal changes can be identified by comparing past communication history with current comments.

[0032] The monitoring unit can expand the scope of digital communication monitoring to include not only text but also the content of voice and video calls, enabling more multifaceted analysis. For example, the monitoring unit uses a generative AI to monitor and analyze not only a user's text messages but also the content of voice and video calls. For example, it uses voice recognition technology to convert the content of voice calls into text and analyzes it. The monitoring unit also monitors the content of a user's video calls and analyzes emotions using facial recognition technology. For example, it evaluates the user's emotional state from facial expressions during video calls. The monitoring unit also builds a system that monitors the content of voice and video calls and integrates it with text messages for analysis. For example, it compares the content of voice calls with the content of text messages and evaluates consistency. This enables more multifaceted analysis by monitoring not only text but also the content of voice and video calls.

[0033] The monitoring unit can comprehensively monitor communication between different devices and evaluate the user's overall mental state. For example, the monitoring unit uses a generation AI to comprehensively monitor and analyze communication between different devices, such as a user's smartphone, tablet, and PC. For example, it centralizes and analyzes data collected from each device. The monitoring unit also comprehensively monitors communication between different devices and builds a system to evaluate the user's overall mental state. For example, it integrates and analyzes smartphone messages and PC emails. The monitoring unit also monitors communication between the user's different devices in real time and evaluates the user's overall mental state. For example, it analyzes data from a smartwatch as well. This makes it possible to comprehensively monitor communication between different devices and evaluate the user's overall mental state.

[0034] The analysis unit analyzes the subtle nuances and context contained in the user's speech, enabling a more accurate assessment of the user's mental state. For example, the analysis unit allows the generation AI to analyze the subtle nuances and context contained in the user's speech to evaluate the user's mental state. For example, if the same word has different meanings depending on the context, the analysis unit analyzes the differences. The analysis unit also analyzes the subtle nuances contained in the user's speech to build a system that evaluates the user's mental state more accurately. For example, it accurately understands sarcasm and jokes. The analysis unit also allows the generation AI to analyze the context contained in the user's speech to evaluate the user's mental state. For example, it evaluates the speech taking into account the relevance to past speech. This allows for a more accurate assessment of the user's mental state by analyzing the subtle nuances and context contained in speech.

[0035] The analysis unit can provide a customized assessment by taking into account the user's cultural background and personal language habits. For example, the analysis unit allows the generation AI to provide a customized assessment of the user's mental state by taking into account the user's cultural background and personal language habits. For example, it understands the meanings and nuances of words in a particular culture. The analysis unit also analyzes the user's personal language habits and builds a system that assesses the user's mental state based on that analysis. For example, it takes into account specific wording and methods of expression. The analysis unit also allows the generation AI to provide a customized assessment of the user's mental state by taking into account the user's cultural background. For example, it understands the differences in emotional expression in different cultural spheres. This makes it possible to provide a customized assessment by taking into account the user's cultural background and personal language habits.

[0036] The analysis unit analyzes the content of websites visited by the user and articles read by the user, and can use this information to evaluate their mental state. For example, the analysis unit allows the generation AI to analyze the content of websites visited by the user and articles read by the user, and can use this information to evaluate their mental state. For example, the analysis unit analyzes the user's interests and emotions from the content of news articles. The analysis unit also analyzes the content of websites visited by the user and builds a system that can help evaluate their mental state. For example, if a user frequently visits articles on a particular topic, the analysis unit evaluates the user's emotions toward that topic. The analysis unit also allows the generation AI to analyze the content of articles the user is reading and can use this information to help evaluate their mental state. For example, if a user reads many articles with positive content, the analysis unit evaluates the user's mental state as being good. In this way, analyzing the content of websites and articles can be useful in evaluating their mental state.

[0037] The analysis unit can automatically translate utterances in different languages ​​and perform multilingual mental state evaluations. For example, the analysis unit uses a generation AI to automatically translate utterances in different languages ​​and perform multilingual mental state evaluations. For example, it analyzes utterances in multiple languages, such as English and French. The analysis unit also automatically translates user utterances and builds a system for multilingual mental state evaluations. For example, it centrally analyzes utterances in different languages. The analysis unit also uses a generation AI to translate utterances in different languages ​​in real time to help with mental state evaluations. For example, it evaluates mental states including utterances in foreign languages. This makes it possible to automatically translate utterances in different languages ​​and perform multilingual mental state evaluations.

[0038] The intervention unit can intervene more accurately by referring to the user's past behavioral patterns and history when an abnormality is detected. For example, the generation AI analyzes the user's past behavioral patterns and history, and when an abnormality is detected, it refers to that data to intervene. For example, it provides appropriate advice based on situations in which the user felt stress in the past. The intervention unit also builds a system that suggests the optimal intervention method when an abnormality is detected based on the user's past behavioral history. For example, it suggests relaxation methods that have been effective in the past. The generation AI can also intervene more accurately when an abnormality is detected by referring to the user's past behavioral patterns. For example, it provides individualized advice based on past data. This makes it possible to intervene more accurately by referring to past behavioral patterns and history.

[0039] The intervention unit can propose the optimal intervention method based on the user's preferences and past responses after an abnormality is detected. For example, the generation AI of the intervention unit analyzes the user's preferences and past responses and proposes the optimal intervention method when an abnormality is detected. For example, it proposes a relaxation method that the user prefers. The intervention unit also builds a system that proposes the optimal intervention method when an abnormality is detected based on the user's past responses. For example, it provides advice that has been effective in the past. The generation AI of the intervention unit also refers to the user's preferences and past responses and proposes the optimal intervention method when an abnormality is detected. For example, it suggests listening to music that the user prefers. This makes it possible to propose the optimal intervention method based on the user's preferences and past responses.

[0040] The intervention unit can also notify the user's family and friends of the anomaly detection and intervention process, thereby utilizing the support network. For example, the intervention unit builds a system in which, when the generation AI detects an anomaly, it notifies the user's family and friends, thereby utilizing the support network. For example, it contacts family members in an emergency. The intervention unit also sends a notification of the anomaly detection to the user's family and friends, thereby utilizing the support network. For example, it notifies family members if the user is feeling stressed. The intervention unit also notifies the user's family and friends of the anomaly detection and intervention process, thereby utilizing the support network. For example, it contacts friends if the user is experiencing serious mental distress. In this way, the support network can be utilized by notifying family members and friends of the anomaly detection and intervention process.

[0041] The intervention unit collects user feedback after detecting an anomaly, and can evaluate and improve the effectiveness of the intervention method. For example, the intervention unit builds a system in which the generation AI collects user feedback after detecting an anomaly and evaluates the effectiveness of the intervention method. For example, it collects how the user felt about the advice provided. The intervention unit also evaluates and improves the effectiveness of the intervention method based on the user feedback. For example, it analyzes how the user felt about the relaxation method. The intervention unit also collects user feedback after the generation AI detects an anomaly, and evaluates and improves the effectiveness of the intervention method. For example, it collects how the user felt about the support provided. This allows feedback to be collected and the effectiveness of the intervention method to be evaluated and improved.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The AutoMind Tracker system can also collect a user's physical health data to help evaluate their mental state. For example, by analyzing heart rate and sleep patterns obtained from a smartwatch or fitness tracker, the system can assess a user's stress level and fatigue. It can also collect a user's exercise and diet records and use them to predict changes in their mental state. For example, it can determine that a lack of exercise or irregular eating habits may indicate a deterioration in their mental state. Furthermore, the system can integrate and analyze a user's physical health data and the content of their digital communications to provide a more accurate assessment of their mental state. This makes the use of physical health data a more comprehensive assessment of mental state.

[0044] The AutoMind Tracker system can also suggest personalized relaxation methods based on the user's hobbies and interests. For example, if the user likes music, it can suggest relaxing music. If the user likes reading, it can recommend relaxing books. Furthermore, if the user likes outdoor activities, it can suggest ways to relax in nature. This allows the system to provide more effective relaxation methods based on the user's hobbies and interests.

[0045] The AutoMind Tracker system can also evaluate a user's social connections and provide support to reduce feelings of isolation. For example, it can analyze a user's social networking site friendships and message exchanges to evaluate the strength of their social connections. If a user feels isolated, it can send a message encouraging communication with friends and family. It can also introduce events and communities where the user can make new friends. This can provide support to strengthen social connections and reduce feelings of isolation.

[0046] The AutoMind Tracker system can also evaluate a user's work environment and work stressors to support mental health in the workplace. For example, it can analyze a user's work schedule and task progress to identify sources of stress. It can also provide advice to help the user reduce stress at work. It can also suggest relaxation breaks and stress management techniques to support mental health in the workplace. This allows the system to evaluate a user's work environment and work stressors to support mental health in the workplace.

[0047] The AutoMind Tracker system can also evaluate the user's lifestyle habits and provide advice to promote a healthy lifestyle. For example, it can analyze the user's sleep patterns and food records and provide advice to maintain a healthy lifestyle. If the user is not getting enough exercise, it can suggest an appropriate exercise plan. It can also introduce relaxation techniques and mindfulness exercises to reduce the user's stress level. This allows the system to evaluate the user's lifestyle habits and provide advice to promote a healthy lifestyle.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The monitoring unit automatically monitors daily digital communications with the user's consent. For example, the monitoring unit may monitor the user's emails, messaging apps, and social media posts, and may be installed on the user's digital device and operate in the background. The monitoring unit may also collect the content of the user's statements and messages in real time. Step 2: The analysis unit analyzes the content of the digital communications monitored by the monitoring unit. For example, the analysis unit uses generative AI to analyze the emotion and tone of the user's statements and messages, and analyzes the content using natural language processing technology. The analysis unit can also extract keywords from the user's statements and messages and calculate an emotion score. Step 3: The evaluation unit evaluates the user's mental state based on the content analyzed by the analysis unit. For example, the evaluation unit can evaluate the user's stress level and emotional stability based on the emotion score calculated by the analysis unit and track fluctuations in the user's mental state in real time. Step 4: The intervention unit performs real-time intervention on the user if an abnormality is detected in the mental state assessed by the evaluation unit. For example, the intervention unit can provide the user with appropriate advice and support and suggest ways to relax. Furthermore, if a serious mental disorder is detected, the intervention unit can encourage the user to contact a professional counselor or medical institution.

[0050] (Example 2) The AutoMind Tracker system according to an embodiment of the present invention is installed on a user's digital device and automatically monitors daily digital communications with the user's consent. Generative AI performs language analysis to evaluate the user's mental state, and if an abnormality is detected, it intervenes in real time. This allows the AutoMind Tracker system to prevent the user from developing mental disorders.

[0051] The AutoMind Tracker system according to an embodiment includes a monitoring unit, an analysis unit, an evaluation unit, and an intervention unit. The monitoring unit automatically monitors daily digital communications with the user's consent. For example, the monitoring unit monitors the user's emails, messaging apps, and social media posts. The monitoring unit can be installed on the user's digital device and run in the background. The monitoring unit can collect the content of the user's statements and messages in real time. For example, the monitoring unit monitors the user's messages to friends and social media posts and analyzes the tone and keywords of the language used. The analysis unit analyzes the content of the digital communications monitored by the monitoring unit. For example, the analysis unit uses generative AI to analyze the emotion and tone of the user's statements and messages. The analysis unit can also use natural language processing technology to analyze the content of the user's statements and messages. The analysis unit can extract keywords from the user's statements and messages and calculate an emotion score. For example, if the user frequently uses negative words such as "tired" or "painful," the analysis unit evaluates this as a sign of stress or anxiety. The evaluation unit evaluates the user's mental state based on the content analyzed by the analysis unit. For example, the evaluation unit evaluates the user's stress level and emotional stability based on the emotion score calculated by the analysis unit. The evaluation unit can also evaluate the user's mental state based on the tone and keywords in the user's utterances and messages. The evaluation unit can also track fluctuations in the user's mental state in real time. For example, the evaluation unit evaluates fluctuations in the user's mental state based on the content of the user's utterances and messages. The intervention unit performs real-time intervention on the user when an abnormality is detected in the mental state evaluated by the evaluation unit. For example, the intervention unit provides the user with appropriate advice and support. The intervention unit can also suggest ways for the user to relax. If a serious mental disorder is detected, the intervention unit can encourage the user to contact a professional counselor or medical institution.For example, if the intervention unit determines that the user is feeling stressed, it provides advice such as "Try some relaxation techniques." This allows the AutoMind Tracker system according to the embodiment to prevent the user from developing mental disorders. For example, the system can constantly monitor the user's mental state through daily digital communication and respond quickly if an abnormality is detected. Furthermore, the user can use digital devices with peace of mind.

[0052] The monitoring unit can detect fluctuations in mental state by analyzing speech patterns during specific time periods and situations in digital communications. For example, the monitoring unit uses the generation AI to monitor a user's digital communications 24 hours a day and analyze speech patterns during specific time periods (e.g., late at night or early in the morning). This detects whether the user is feeling stressed during specific time periods. The monitoring unit also uses the generation AI to analyze the user's speech patterns during specific situations (e.g., at work or on vacation) to detect fluctuations in mental state. For example, if negative speech increases during work hours, it is determined that stress is increasing. The monitoring unit also uses the generation AI to analyze speech patterns before and after specific events (e.g., meetings or presentations) in the user's digital communications to detect fluctuations in mental state. This makes it possible to detect fluctuations in mental state by analyzing speech patterns during specific time periods and situations.

[0053] The monitoring unit can compare a user's past communication history with their current comments to identify abnormal changes. For example, the monitoring unit uses the generation AI to analyze the user's communication history from the past year and compare it with their current comments. For example, if there is an increase in negative comments compared to the past, this is identified as an abnormal change. The monitoring unit also uses the generation AI to learn normal speech patterns based on the user's past communication history and compare them with their current comments. For example, if there is a sudden increase in emotional comments, this is identified as an abnormal change. The monitoring unit also uses the generation AI to compare the user's past communication history with their current comments in real time to identify abnormal changes. For example, if there is a sudden change in the tone of the comments compared to the past, this is identified as an abnormal change. In this way, abnormal changes can be identified by comparing past communication history with current comments.

[0054] The monitoring unit uses the emotion estimation function to monitor changes in emotions contained in user comments in real time and can immediately notify if an abnormality is detected. For example, the monitoring unit uses the emotion estimation function to analyze emotions contained in user comments in real time and detect abnormal changes in emotions. For example, it notifies if there is a sudden increase in emotions such as anger or sadness. The monitoring unit also builds a system in which the generation AI monitors changes in emotions contained in user comments in real time and immediately notifies if an abnormality is detected. For example, it issues an alert if there is a sudden increase in negative emotions. The monitoring unit also provides a function to monitor changes in emotions contained in user comments in real time and notify if an abnormality is detected. For example, it notifies if the emotion score exceeds a certain threshold. This makes it possible to monitor changes in emotions in real time and immediately notify if an abnormality is detected.

[0055] The monitoring unit can expand the scope of digital communication monitoring to include not only text but also the content of voice and video calls, enabling more multifaceted analysis. For example, the monitoring unit uses a generative AI to monitor and analyze not only a user's text messages but also the content of voice and video calls. For example, it uses voice recognition technology to convert the content of voice calls into text and analyzes it. The monitoring unit also monitors the content of a user's video calls and analyzes emotions using facial recognition technology. For example, it evaluates the user's emotional state from facial expressions during video calls. The monitoring unit also builds a system that monitors the content of voice and video calls and integrates it with text messages for analysis. For example, it compares the content of voice calls with the content of text messages and evaluates consistency. This enables more multifaceted analysis by monitoring not only text but also the content of voice and video calls.

[0056] The monitoring unit can comprehensively monitor communication between different devices and evaluate the user's overall mental state. For example, the monitoring unit uses a generation AI to comprehensively monitor and analyze communication between different devices, such as a user's smartphone, tablet, and PC. For example, it centralizes and analyzes data collected from each device. The monitoring unit also comprehensively monitors communication between different devices and builds a system to evaluate the user's overall mental state. For example, it integrates and analyzes smartphone messages and PC emails. The monitoring unit also monitors communication between the user's different devices in real time and evaluates the user's overall mental state. For example, it analyzes data from a smartwatch as well. This makes it possible to comprehensively monitor communication between different devices and evaluate the user's overall mental state.

[0057] The monitoring unit can use the emotion estimation function to analyze the emotions of a user when using a specific application and evaluate the impact of each application. For example, the monitoring unit uses the emotion estimation function to analyze the emotions of a user when using a specific application (e.g., a social networking site or a game). For example, it monitors changes in emotions while using a social networking site in real time. The monitoring unit also builds a system in which a generation AI analyzes the emotions of a user when using a specific application and evaluates the impact. For example, it evaluates stress levels while playing a game. The monitoring unit also analyzes the emotions of a user when using a specific application and evaluates the impact of each application. For example, it monitors changes in emotions while using a work app. This makes it possible to analyze emotions when using a specific application and evaluate the impact of each application.

[0058] The analysis unit analyzes the subtle nuances and context contained in the user's speech, enabling a more accurate assessment of the user's mental state. For example, the analysis unit allows the generation AI to analyze the subtle nuances and context contained in the user's speech to evaluate the user's mental state. For example, if the same word has different meanings depending on the context, the analysis unit analyzes the differences. The analysis unit also analyzes the subtle nuances contained in the user's speech to build a system that evaluates the user's mental state more accurately. For example, it accurately understands sarcasm and jokes. The analysis unit also allows the generation AI to analyze the context contained in the user's speech to evaluate the user's mental state. For example, it evaluates the speech taking into account the relevance to past speech. This allows for a more accurate assessment of the user's mental state by analyzing the subtle nuances and context contained in speech.

[0059] The analysis unit can provide a customized assessment by taking into account the user's cultural background and personal language habits. For example, the analysis unit allows the generation AI to provide a customized assessment of the user's mental state by taking into account the user's cultural background and personal language habits. For example, it understands the meanings and nuances of words in a particular culture. The analysis unit also analyzes the user's personal language habits and builds a system that assesses the user's mental state based on that analysis. For example, it takes into account specific wording and methods of expression. The analysis unit also allows the generation AI to provide a customized assessment of the user's mental state by taking into account the user's cultural background. For example, it understands the differences in emotional expression in different cultural spheres. This makes it possible to provide a customized assessment by taking into account the user's cultural background and personal language habits.

[0060] The analysis unit can use the emotion estimation function to evaluate the intensity of emotions contained in the user's statements and track fluctuations in mental state in detail. For example, the analysis unit uses the emotion estimation function to evaluate the intensity of emotions contained in the user's statements and track fluctuations in mental state in detail. For example, it records daily fluctuations based on emotion scores. The analysis unit also builds a system in which the generation AI analyzes the intensity of emotions contained in the user's statements in real time and tracks fluctuations in mental state. For example, it issues an alert if there is a sudden change in emotion intensity. The analysis unit also evaluates the intensity of emotions contained in the user's statements and tracks fluctuations in mental state in detail. For example, it analyzes the balance between positive and negative emotions. This makes it possible to evaluate the intensity of emotions and track fluctuations in mental state in detail.

[0061] The analysis unit analyzes the content of websites visited by the user and articles read by the user, and can use this information to evaluate their mental state. For example, the analysis unit allows the generation AI to analyze the content of websites visited by the user and articles read by the user, and can use this information to evaluate their mental state. For example, the analysis unit analyzes the user's interests and emotions from the content of news articles. The analysis unit also analyzes the content of websites visited by the user and builds a system that can help evaluate their mental state. For example, if a user frequently visits articles on a particular topic, the analysis unit evaluates the user's emotions toward that topic. The analysis unit also allows the generation AI to analyze the content of articles the user is reading and can use this information to help evaluate their mental state. For example, if a user reads many articles with positive content, the analysis unit evaluates the user's mental state as being good. In this way, analyzing the content of websites and articles can be useful in evaluating their mental state.

[0062] The analysis unit can automatically translate utterances in different languages ​​and perform multilingual mental state evaluations. For example, the analysis unit uses a generation AI to automatically translate utterances in different languages ​​and perform multilingual mental state evaluations. For example, it analyzes utterances in multiple languages, such as English and French. The analysis unit also automatically translates user utterances and builds a system for multilingual mental state evaluations. For example, it centrally analyzes utterances in different languages. The analysis unit also uses a generation AI to translate utterances in different languages ​​in real time to help with mental state evaluations. For example, it evaluates mental states including utterances in foreign languages. This makes it possible to automatically translate utterances in different languages ​​and perform multilingual mental state evaluations.

[0063] The analysis unit can use the emotion estimation function to analyze the content of videos watched by a user or music listened to by a user, and evaluate the impact they have on the user's mental state. For example, the analysis unit uses the emotion estimation function to analyze the content of videos watched by a user and evaluate the impact they have on the user's mental state. For example, it analyzes emotional changes after watching an emotional video. The analysis unit also constructs a system in which a generative AI analyzes the content of music listened to by a user and evaluates the impact it has on the user's mental state. For example, it analyzes emotional changes after listening to music with a relaxing effect. The analysis unit also analyzes the content of videos watched by a user or music listened to by a user, and evaluates the impact it has on the user's mental state. For example, it analyzes emotional changes after watching videos or music with positive content. In this way, by analyzing the content of videos and music, it is possible to evaluate the impact they have on the user's mental state.

[0064] The intervention unit can intervene more accurately by referring to the user's past behavioral patterns and history when an abnormality is detected. For example, the generation AI analyzes the user's past behavioral patterns and history, and when an abnormality is detected, it refers to that data to intervene. For example, it provides appropriate advice based on situations in which the user felt stress in the past. The intervention unit also builds a system that suggests the optimal intervention method when an abnormality is detected based on the user's past behavioral history. For example, it suggests relaxation methods that have been effective in the past. The generation AI can also intervene more accurately when an abnormality is detected by referring to the user's past behavioral patterns. For example, it provides individualized advice based on past data. This makes it possible to intervene more accurately by referring to past behavioral patterns and history.

[0065] The intervention unit can propose the optimal intervention method based on the user's preferences and past responses after an abnormality is detected. For example, the generation AI of the intervention unit analyzes the user's preferences and past responses and proposes the optimal intervention method when an abnormality is detected. For example, it proposes a relaxation method that the user prefers. The intervention unit also builds a system that proposes the optimal intervention method when an abnormality is detected based on the user's past responses. For example, it provides advice that has been effective in the past. The generation AI of the intervention unit also refers to the user's preferences and past responses and proposes the optimal intervention method when an abnormality is detected. For example, it suggests listening to music that the user prefers. This makes it possible to propose the optimal intervention method based on the user's preferences and past responses.

[0066] The intervention unit can use the emotion estimation function to generate a personalized intervention message according to the emotional state of the user. The intervention unit, for example, uses the emotion estimation function to generate a personalized intervention message according to the emotional state of the user. For example, if the user is feeling stressed, a message encouraging relaxation is provided. The intervention unit also builds a system in which a generation AI analyzes the user's emotional state in real time and generates a personalized intervention message. For example, if the user is feeling anxious, a message of reassurance is provided. The intervention unit also generates a personalized intervention message according to the user's emotional state. For example, if the user is feeling sad, an encouraging message is provided. This makes it possible to generate a personalized intervention message according to the emotional state.

[0067] The intervention unit can also notify the user's family and friends of the anomaly detection and intervention process, thereby utilizing the support network. For example, the intervention unit builds a system in which, when the generation AI detects an anomaly, it notifies the user's family and friends, thereby utilizing the support network. For example, it contacts family members in an emergency. The intervention unit also sends a notification of the anomaly detection to the user's family and friends, thereby utilizing the support network. For example, it notifies family members if the user is feeling stressed. The intervention unit also notifies the user's family and friends of the anomaly detection and intervention process, thereby utilizing the support network. For example, it contacts friends if the user is experiencing serious mental distress. In this way, the support network can be utilized by notifying family members and friends of the anomaly detection and intervention process.

[0068] The intervention unit collects user feedback after detecting an anomaly, and can evaluate and improve the effectiveness of the intervention method. For example, the intervention unit builds a system in which the generation AI collects user feedback after detecting an anomaly and evaluates the effectiveness of the intervention method. For example, it collects how the user felt about the advice provided. The intervention unit also evaluates and improves the effectiveness of the intervention method based on the user feedback. For example, it analyzes how the user felt about the relaxation method. The intervention unit also collects user feedback after the generation AI detects an anomaly, and evaluates and improves the effectiveness of the intervention method. For example, it collects how the user felt about the support provided. This allows feedback to be collected and the effectiveness of the intervention method to be evaluated and improved.

[0069] The intervention unit can use the emotion estimation function to evaluate and optimize the emotional impact of an intervention message that a user receives after an anomaly is detected. The intervention unit, for example, uses the emotion estimation function to evaluate the emotional impact of an intervention message that a user receives after an anomaly is detected. For example, it analyzes how the message affected the user's emotions. The intervention unit also constructs a system in which the generation AI evaluates and optimizes the emotional impact of an intervention message that a user receives. For example, it adjusts the content of the message to match the user's emotional state. The intervention unit also evaluates and optimizes the emotional impact of an intervention message that a user receives after an anomaly is detected. For example, it provides a message that elicits positive emotions. This makes it possible to evaluate and optimize the emotional impact of the intervention message.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The AutoMind Tracker system can also collect a user's physical health data to help evaluate their mental state. For example, by analyzing heart rate and sleep patterns obtained from a smartwatch or fitness tracker, the system can assess a user's stress level and fatigue. It can also collect a user's exercise and diet records and use them to predict changes in their mental state. For example, it can determine that a lack of exercise or irregular eating habits may indicate a deterioration in their mental state. Furthermore, the system can integrate and analyze a user's physical health data and the content of their digital communications to provide a more accurate assessment of their mental state. This makes the use of physical health data a more comprehensive assessment of mental state.

[0072] The AutoMind Tracker system can also suggest personalized relaxation methods based on the user's hobbies and interests. For example, if the user likes music, it can suggest relaxing music. If the user likes reading, it can recommend relaxing books. Furthermore, if the user likes outdoor activities, it can suggest ways to relax in nature. This allows the system to provide more effective relaxation methods based on the user's hobbies and interests.

[0073] The AutoMind Tracker system can also evaluate a user's social connections and provide support to reduce feelings of isolation. For example, it can analyze a user's social networking site friendships and message exchanges to evaluate the strength of their social connections. If a user feels isolated, it can send a message encouraging communication with friends and family. It can also introduce events and communities where the user can make new friends. This can provide support to strengthen social connections and reduce feelings of isolation.

[0074] The AutoMind Tracker system can also evaluate a user's work environment and work stressors to support mental health in the workplace. For example, it can analyze a user's work schedule and task progress to identify sources of stress. It can also provide advice to help the user reduce stress at work. It can also suggest relaxation breaks and stress management techniques to support mental health in the workplace. This allows the system to evaluate a user's work environment and work stressors to support mental health in the workplace.

[0075] The AutoMind Tracker system can also evaluate the user's lifestyle habits and provide advice to promote a healthy lifestyle. For example, it can analyze the user's sleep patterns and food records and provide advice to maintain a healthy lifestyle. If the user is not getting enough exercise, it can suggest an appropriate exercise plan. It can also introduce relaxation techniques and mindfulness exercises to reduce the user's stress level. This allows the system to evaluate the user's lifestyle habits and provide advice to promote a healthy lifestyle.

[0076] The AutoMind Tracker system also uses a user's emotion estimation function to analyze the user's emotions when they are in a specific location and evaluate the impact of each location. For example, it monitors the user's emotions in real time when they are at work, at home, or in public places. It can also analyze changes in emotions when the user is in a specific location and evaluate the impact that location has on the user's mental state. It can also identify places where the user finds relaxation or stress and provide appropriate advice. This makes it possible to analyze the user's emotions when they are in a specific location and evaluate the impact of each location.

[0077] The AutoMind Tracker system also uses a user's emotion estimation function to analyze the emotions a user feels at specific times of the day and evaluate the impact of each time period. For example, it can monitor the emotions a user feels at specific times of the day, such as mornings, evenings, or weekends, in real time. It can also analyze the emotional changes a user feels at specific times of the day and evaluate the impact of those times on the user's mental state. It can also identify times when a user feels relaxed or stressed and provide appropriate advice. This makes it possible to analyze the emotions felt at specific times of the day and evaluate the impact of each time period.

[0078] The AutoMind Tracker system also uses a user's emotion estimation function to analyze the emotions a user feels when communicating with specific people and evaluate the impact of each relationship. For example, it monitors the user's emotions in real time when communicating with family, friends, and colleagues. It can also analyze emotional changes when the user communicates with specific people and evaluate the impact that relationship has on the user's mental state. It can also identify relationships that the user finds relaxing or stressful and provide appropriate advice. This allows it to analyze the emotions a user feels when communicating with specific people and evaluate the impact that each relationship has.

[0079] The AutoMind Tracker system also uses a user's emotion estimation function to analyze the user's emotions when performing specific activities and evaluate the impact of each activity. For example, it monitors the user's emotions in real time when performing specific activities such as exercise, reading, or cooking. It can also analyze emotional changes when the user performs specific activities and evaluate the impact of those activities on the user's mental state. It can also identify activities that the user finds relaxing or stressful and provide appropriate advice. This allows it to analyze the user's emotions when performing specific activities and evaluate the impact of each activity.

[0080] The AutoMind Tracker system also uses a user's emotion estimation function to analyze the emotions a user feels when participating in specific events and evaluate the impact of each event. For example, it monitors the user's emotions in real time when participating in specific events such as meetings, parties, and trips. It can also analyze emotional changes when a user participates in a specific event and evaluate the impact of the event on the user's mental state. It can also identify events that the user finds relaxing or stressful and provide appropriate advice. This makes it possible to analyze the emotions a user feels when participating in specific events and evaluate the impact of each event.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The monitoring unit automatically monitors daily digital communications with the user's consent. For example, the monitoring unit may monitor the user's emails, messaging apps, and social media posts, and may be installed on the user's digital device and operate in the background. The monitoring unit may also collect the content of the user's statements and messages in real time. Step 2: The analysis unit analyzes the content of the digital communications monitored by the monitoring unit. For example, the analysis unit uses generative AI to analyze the emotion and tone of the user's statements and messages, and analyzes the content using natural language processing technology. The analysis unit can also extract keywords from the user's statements and messages and calculate an emotion score. Step 3: The evaluation unit evaluates the user's mental state based on the content analyzed by the analysis unit. For example, the evaluation unit can evaluate the user's stress level and emotional stability based on the emotion score calculated by the analysis unit and track fluctuations in the user's mental state in real time. Step 4: The intervention unit performs real-time intervention on the user if an abnormality is detected in the mental state assessed by the evaluation unit. For example, the intervention unit can provide the user with appropriate advice and support and suggest ways to relax. Furthermore, if a serious mental disorder is detected, the intervention unit can encourage the user to contact a professional counselor or medical institution.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] 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.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An application installed on a user's digital device, comprising: a monitoring unit that automatically monitors daily digital communications with the user's consent; an analysis unit that analyzes the content of the digital communication monitored by the monitoring unit; an evaluation unit that evaluates the mental state of the user based on the content analyzed by the analysis unit; an intervention unit that performs real-time intervention on the user when an abnormality is detected in the mental state evaluated by the evaluation unit. A system characterized by:

2. The monitoring unit Analyzing speech patterns in specific time periods and situations in the digital communication to detect fluctuations in the mental state 2. The system of claim 1.

3. The monitoring unit The monitoring of digital communications will be expanded to include not only text but also voice and video calls, allowing for more comprehensive analysis.

2. The system of claim 1.

4. The analysis unit Analyzing the subtle nuances and context contained in the user's statements to more accurately assess the user's mental state 2. The system of claim 1.

5. The intervention unit is When detecting an anomaly, the system references the user's past behavioral patterns and history to provide more accurate intervention.

2. The system of claim 1.

6. The monitoring unit Monitor changes in emotions contained in the user's statements in real time and immediately notify if an abnormality is detected.

2. The system of claim 1.

7. The analysis unit Evaluating the intensity of emotions contained in the user's statements and tracking fluctuations in the user's mental state in detail 2. The system of claim 1.

8. The intervention unit is Generating a personalized intervention message according to the emotional state of the user 2. The system of claim 1.

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