System

Abstract

An object of a system according to an embodiment is to accurately grasp a degree of stress and a trigger of a user and propose an appropriate coping method.SOLUTION: A system includes an interaction unit, an evaluation unit, a proposal unit, a tracking unit, and a specification unit. The dialogue unit includes a generation AI. The evaluator evaluates the degree of stress based on the information acquired by the communicator. The proposal unit proposes a handling method based on the degree of stress evaluated by the evaluation unit. The tracking unit tracks the user's daily life and activity history. The specification unit specifies a trigger or a pattern of stress based on the data collected by the tracking unit, and proposes an improvement measure.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 accurately grasp a user's level of stress and its triggers, and to suggest appropriate ways to deal with the stress.

[0005] The system according to the embodiment aims to accurately grasp the level of stress and triggers of the user and to suggest appropriate ways of dealing with the stress. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue unit, an evaluation unit, a proposal unit, a tracking unit, and an identification unit. The dialogue unit is equipped with a generation AI. The evaluation unit evaluates the level of stress based on information acquired by the dialogue unit. The proposal unit proposes ways to deal with the stress based on the level of stress evaluated by the evaluation unit. The tracking unit tracks the user's daily life and activity history. The identification unit identifies stress triggers and patterns based on the data collected by the tracking unit and proposes improvement measures. [Effects of the Invention]

[0007] The system according to the embodiment can accurately grasp the level of stress and triggers of the user and suggest appropriate ways to deal with the stress. [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) A stress management system according to an embodiment of the present invention allows users to interact with AI, share their stressors, and receive advice on the level of stress and how to cope with it, thereby enabling the system to effectively manage users' stress and improve their quality of life.

[0029] A stress management system according to an embodiment includes a dialogue unit, an evaluation unit, a proposal unit, a tracking unit, and an identification unit. The dialogue unit is equipped with a generation AI, and the user shares stress factors by interacting with the AI. For example, if the user inputs "I'm under a lot of pressure at work," the generation AI analyzes the information and provides advice on the level of stress and how to deal with it. The generation AI analyzes the user's input using a text generation AI (e.g., LLM) and generates appropriate advice. The evaluation unit evaluates the level of stress based on the information acquired by the dialogue unit. For example, if the user receives information such as "I've been having trouble sleeping lately," the generation AI analyzes the information and quantifies the level of stress. The evaluation unit evaluates the level of stress using a numerical score or categorization. The proposal unit proposes a method of dealing with stress based on the level of stress evaluated by the evaluation unit. For example, the proposal may include relaxation techniques, exercise, or counseling. The proposal unit uses the generation AI to propose a method of dealing with stress appropriate to the user. The tracking unit tracks the user's daily life and activity history. For example, the system collects and analyzes data such as diary entries, schedules, and exercise records entered by the user into the app. The tracking unit uses a generation AI to analyze the collected data and identify stress triggers and patterns. The identification unit identifies stress triggers and patterns based on the data collected by the tracking unit and proposes improvement measures. For example, if a pattern such as "stress increases every Monday" is discovered, the generation AI analyzes the cause and proposes improvement measures. As a result, the stress management system according to the embodiment can effectively manage a user's stress and improve their quality of life. For example, by identifying the causes of stress and practicing appropriate coping methods, stress reduction can be expected. Furthermore, by tracking daily life and activity history, stress triggers and patterns can be identified and preventive measures can be taken.

[0030] The dialogue unit can deeply understand the context of the text entered by the user and automatically generate questions to identify stress factors. For example, the dialogue unit analyzes the context of the text entered by the user and automatically generates questions to identify stress factors. For example, if the user enters "I'm under a lot of pressure at work," the dialogue unit generates a question such as "What kind of pressure are you under specifically?" This allows the dialogue unit to deeply understand the context of the text entered by the user and automatically generate questions to identify more specific stress factors.

[0031] The dialogue unit can introduce a multilingual generation AI to accommodate users from different languages ​​and cultural backgrounds, and provide advice that takes cultural backgrounds into consideration.The dialogue unit can introduce a multilingual generation AI to accommodate users from different languages ​​and cultural backgrounds, and provide advice that takes cultural backgrounds into consideration.For example, the dialogue unit can introduce a multilingual generation AI to accommodate users from different languages ​​and cultural backgrounds, and provide advice that takes cultural backgrounds into consideration.

[0032] The evaluation unit can collect physiological data (heart rate, electrodermal activity, etc.) of the user and reflect it in the evaluation of the stress level. For example, the evaluation unit collects the user's heart rate and electrodermal activity and reflects it in the evaluation of the stress level. For example, the evaluation unit obtains data from a smart watch or fitness tracker and quantifies the stress level. In this way, the user's physiological data can be collected and reflected in the evaluation of the stress level.

[0033] The evaluation unit can analyze the user's past stress data, identify individual stress patterns, and utilize them for evaluation. The evaluation unit, for example, analyzes the user's past stress data and identifies individual stress patterns. For example, it analyzes fluctuations in past stress levels and identifies specific triggers. In this way, the user's past stress data can be analyzed, and individual stress patterns can be utilized for evaluation.

[0034] The evaluation unit can incorporate feedback from the user's social network and evaluate the stress level. For example, the evaluation unit collects feedback from the user's social network and reflects it in the evaluation of the stress level. For example, the evaluation is made by incorporating opinions from family and friends. In this way, the stress level can be evaluated by incorporating feedback from the user's social network.

[0035] The evaluation unit can integrate the stress level evaluation result with the user's health data (e.g., sleep patterns and food records) and provide comprehensive health advice. The evaluation unit can, for example, integrate the stress level evaluation result with the user's health data and provide comprehensive health advice. For example, it can suggest stress countermeasures based on sleep patterns and food records. In this way, the stress level evaluation result can be integrated with the user's health data and provide comprehensive health advice.

[0036] The tracking unit collects data from the user's smart device and can track daily life in detail. For example, the tracking unit collects location information and exercise amount data from the user's smartphone or fitness tracker and can track daily life in detail. For example, the tracking unit analyzes the number of steps taken and the distance traveled to evaluate the activity level. This allows the tracking unit to collect data from the user's smart device and can track daily life in detail.

[0037] The tracking unit analyzes the content of the user's social media posts and can detect signs of stress at an early stage. The tracking unit, for example, analyzes the content of the user's social media posts and can detect signs of stress at an early stage. For example, the tracking unit analyzes changes in the frequency and content of posts and identifies signs of stress. This allows the content of the user's social media posts to be analyzed and signs of stress to be detected at an early stage.

[0038] The tracking unit can collect data from the user's home IoT devices and track changes in the living environment. For example, the tracking unit can collect data from the user's home IoT devices and track changes in the living environment. For example, the tracking unit can analyze usage data of smart thermostats and smart lights to evaluate living patterns. This allows the tracking unit to collect data from the user's home IoT devices and track changes in the living environment.

[0039] The tracking unit can collect data on the user's work environment and identify stress factors in the workplace. The tracking unit, for example, collects data on the user's work environment and identifies stress factors in the workplace. For example, the tracking unit analyzes the frequency of meetings and the volume of emails to identify stress triggers. In this way, the tracking unit can collect data on the user's work environment and identify stress factors in the workplace.

[0040] The identification unit can analyze the user's past stress data and identify a long-term stress pattern. The identification unit, for example, analyzes the user's past stress data and identifies a long-term stress pattern. For example, it analyzes fluctuations in stress levels over the past few months and identifies specific triggers. In this way, the user's past stress data can be analyzed and a long-term stress pattern can be identified.

[0041] The identification unit can analyze the user's lifestyle habits (eating, exercise, sleep, etc.) in detail and identify stress triggers. The identification unit, for example, analyzes the user's lifestyle habit data in detail and identifies stress triggers. For example, it analyzes the contents of meals, the amount of exercise, and sleep patterns to identify the causes of stress. This makes it possible to analyze the user's lifestyle habits in detail and identify stress triggers.

[0042] The identification unit can integrate data on the user's work environment and home environment and identify stress triggers caused by environmental factors. The identification unit, for example, integrates data on the user's work environment and home environment and identifies stress triggers caused by environmental factors. For example, the identification unit analyzes the frequency of workplace meetings and noise levels at home to identify causes of stress. In this way, data on the user's work environment and home environment can be integrated and stress triggers caused by environmental factors can be identified.

[0043] The identification unit can incorporate feedback from the user's social network and identify stress triggers from multiple angles. The identification unit, for example, collects feedback from the user's social network and identifies stress triggers from multiple angles. For example, the identification unit identifies the cause of stress by incorporating opinions from family and friends. This makes it possible to incorporate feedback from the user's social network and identify stress triggers from multiple angles.

[0044] The identification unit can analyze fluctuations in the user's stress level in detail and provide individualized feedback. The identification unit can, for example, analyze fluctuations in the user's stress level in detail and provide individualized feedback. For example, the identification unit can display fluctuations in the stress level by week in a graph and check progress of improvement. This allows fluctuations in the user's stress level to be analyzed in detail and individualized feedback to be provided.

[0045] The identification unit can evaluate the user's level of goal achievement and provide specific advice according to the level of achievement. The identification unit, for example, evaluates the user's level of goal achievement and provides specific advice according to the level of achievement. For example, the identification unit evaluates progress toward a set goal and suggests a relaxation method according to the level of achievement. In this way, the user's level of goal achievement can be evaluated and specific advice can be provided according to the level of achievement.

[0046] The identification unit can convert the user's feedback into a visual note or a mind map to make it easier to understand visually. For example, the identification unit can convert the user's feedback into a visual note to make it easier to understand visually. For example, the feedback content can be displayed with diagrams or icons to highlight important points. In this way, the user's feedback can be converted into a visual note or a mind map to make it easier to understand visually.

[0047] The specifying unit can enable the user's feedback to be received on different devices. For example, the specifying unit builds a system that enables the user's feedback to be received on different devices. For example, the specifying unit enables the user's feedback to be received on a smart watch or a smart speaker. This allows the user's feedback to be received on different devices.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The stress management system may further include a hobby suggestion unit that suggests relaxation methods based on the user's hobbies and interests. For example, if the user likes music, a music playlist for relaxation may be suggested. If the user likes reading, a list of books that are useful for relaxation may be provided. Furthermore, if the user likes outdoor activities, information on nearby parks and hiking trails may be provided. This allows relaxation methods to be suggested based on the user's individual hobbies and interests, thereby reducing stress.

[0050] The dialogue unit not only deeply understands the context of the text entered by the user, but also refers to the user's past dialogue history to automatically generate more appropriate questions. For example, if the user previously entered "I'm under a lot of pressure at work," a question such as "How is your work situation these days?" can be generated in the current dialogue. If the user previously entered "I've been suffering from a lack of sleep," a question such as "How is the quality of your sleep these days?" can be generated. If the user previously entered "My relationship with my family is not going well," a question such as "Has your relationship with my family improved?" can be generated. This allows the dialogue unit to more effectively proceed with identifying stress factors by referring to the user's past dialogue history and automatically generating more appropriate questions.

[0051] The dialogue unit can identify stress factors by analyzing the user's handwriting and touch input patterns, in addition to using voice input, gesture, and facial expression recognition. For example, if the user writes quickly or presses down hard, it can be determined that the user is under high stress. Also, if the user frequently performs touch input, it can be estimated that stress is increasing. Furthermore, if the user performs touch input slowly, it can be determined that the user is relaxed. In this way, by analyzing not only voice input, gesture, and facial expression recognition, but also handwriting and touch input patterns, stress factors can be identified from a more multifaceted perspective.

[0052] The dialogue unit not only employs multilingual generation AI, but can also provide advice that takes into account the user's cultural background and individual values. For example, if the user believes in a particular religion, it can suggest relaxation methods based on that religion. Also, if the user belongs to a particular cultural sphere, it can provide stress management solutions appropriate for that culture. Furthermore, if the user has specific values, it can provide advice based on those values. This allows for more effective stress management by providing advice that takes into account not only multilingual support but also cultural background and individual values.

[0053] The evaluation unit not only collects the user's physiological data, but also collects the user's environmental data (e.g., temperature, humidity, noise level, etc.) and can reflect this in the stress level evaluation. For example, if the temperature and humidity in the user's living environment are high, it can be determined that stress is likely to increase. Also, if the noise level in the user's work environment is high, it can be estimated that stress is likely to increase. Furthermore, the intensity of light and color temperature around the user can also be taken into account as factors that affect stress. In this way, by collecting not only the user's physiological data but also environmental data and reflecting this in the stress level evaluation, more accurate stress evaluation can be achieved.

[0054] The evaluation unit not only analyzes the user's past stress data, but also analyzes the user's lifestyle habits and behavioral patterns in detail, identifying individual stress patterns and utilizing them for evaluation. For example, if the user is prone to feeling stressed during a particular time period, the evaluation unit can suggest measures for that time period. If the user experiences increased stress when engaging in a particular activity, the evaluation unit can suggest avoiding that activity or incorporating relaxation techniques. Furthermore, if the user experiences increased stress when consuming a particular food or drink, the evaluation unit can advise the user to refrain from consuming that food or drink. In this way, more effective stress management can be achieved by analyzing the user's lifestyle habits and behavioral patterns in detail, identifying individual stress patterns, and utilizing them for evaluation.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The dialogue unit is equipped with a generation AI, and the user interacts with the AI ​​to share the causes of stress. For example, if the user inputs "I'm under a lot of pressure at work," the generation AI analyzes the information and provides advice on the level of stress and how to deal with it. The generation AI uses a text generation AI (e.g., LLM) to analyze the user's input and generate appropriate advice. Step 2: The evaluation unit evaluates the level of stress based on the information obtained by the dialogue unit. For example, if the generated AI receives information such as "I've been having trouble sleeping lately," it analyzes the information and quantifies the level of stress. The evaluation unit evaluates the level of stress using a numerical score or categorization. Step 3: The suggestion unit proposes coping methods based on the stress level assessed by the evaluation unit. For example, it proposes relaxation techniques, exercise, counseling, and other coping methods. The suggestion unit uses a generative AI to propose coping methods that are suitable for the user. Step 4: The tracking unit tracks the user's daily life and activity history. For example, it collects and analyzes data such as diary entries, schedules, and exercise records entered by the user into the app. The tracking unit then uses generative AI to analyze the collected data and identify stress triggers and patterns. Step 5: The identification unit identifies stress triggers and patterns based on the data collected by the tracking unit and proposes improvement measures. For example, if a pattern such as "stress increases every Monday" is discovered, the generation AI analyzes the cause and proposes improvement measures.

[0057] (Example 2) A stress management system according to an embodiment of the present invention allows users to interact with AI, share their stressors, and receive advice on the level of stress and how to cope with it, thereby enabling the system to effectively manage users' stress and improve their quality of life.

[0058] A stress management system according to an embodiment includes a dialogue unit, an evaluation unit, a proposal unit, a tracking unit, and an identification unit. The dialogue unit is equipped with a generation AI, and the user shares stress factors by interacting with the AI. For example, if the user inputs "I'm under a lot of pressure at work," the generation AI analyzes the information and provides advice on the level of stress and how to deal with it. The generation AI analyzes the user's input using a text generation AI (e.g., LLM) and generates appropriate advice. The evaluation unit evaluates the level of stress based on the information acquired by the dialogue unit. For example, if the user receives information such as "I've been having trouble sleeping lately," the generation AI analyzes the information and quantifies the level of stress. The evaluation unit evaluates the level of stress using a numerical score or categorization. The proposal unit proposes a method of dealing with stress based on the level of stress evaluated by the evaluation unit. For example, the proposal may include relaxation techniques, exercise, or counseling. The proposal unit uses the generation AI to propose a method of dealing with stress appropriate to the user. The tracking unit tracks the user's daily life and activity history. For example, the system collects and analyzes data such as diary entries, schedules, and exercise records entered by the user into the app. The tracking unit uses a generation AI to analyze the collected data and identify stress triggers and patterns. The identification unit identifies stress triggers and patterns based on the data collected by the tracking unit and proposes improvement measures. For example, if a pattern such as "stress increases every Monday" is discovered, the generation AI analyzes the cause and proposes improvement measures. As a result, the stress management system according to the embodiment can effectively manage a user's stress and improve their quality of life. For example, by identifying the causes of stress and practicing appropriate coping methods, stress reduction can be expected. Furthermore, by tracking daily life and activity history, stress triggers and patterns can be identified and preventive measures can be taken.

[0059] The dialogue unit can analyze the user's tone of voice and speaking style to estimate the level of stress and emotional state. For example, the dialogue unit analyzes the user's tone of voice and speaking style in real time to estimate the level of stress and emotional state. For example, it analyzes changes in voice pitch, speed, and intonation to quantify the stress level. In this way, by analyzing the user's tone of voice and speaking style, the level of stress and emotional state can be estimated more accurately.

[0060] The dialogue unit can deeply understand the context of the text entered by the user and automatically generate questions to identify stress factors. For example, the dialogue unit analyzes the context of the text entered by the user and automatically generates questions to identify stress factors. For example, if the user enters "I'm under a lot of pressure at work," the dialogue unit generates a question such as "What kind of pressure are you under specifically?" This allows the dialogue unit to deeply understand the context of the text entered by the user and automatically generate questions to identify more specific stress factors.

[0061] The dialogue unit can use the emotion estimation function to analyze the user's emotional state in real time and suggest relaxation methods at appropriate times. For example, the dialogue unit can use the emotion estimation function to analyze the user's emotional state in real time and suggest relaxation methods when stress levels rise. For example, the dialogue unit can suggest deep breathing or meditation techniques. This allows the dialogue unit to analyze the user's emotional state in real time and suggest relaxation methods at appropriate times.

[0062] The dialogue unit can identify stress factors using not only voice input but also gesture and facial expression recognition. For example, when a user is interacting, the dialogue unit can identify stress factors using not only voice input but also gesture and facial expression recognition. For example, the dialogue unit can analyze facial expressions and hand movements to detect signs of stress. This makes it possible to identify stress factors using not only voice input but also gesture and facial expression recognition.

[0063] The dialogue unit can introduce a multilingual generation AI to accommodate users from different languages ​​and cultural backgrounds, and provide advice that takes cultural backgrounds into consideration.The dialogue unit can introduce a multilingual generation AI to accommodate users from different languages ​​and cultural backgrounds, and provide advice that takes cultural backgrounds into consideration.For example, the dialogue unit can introduce a multilingual generation AI to accommodate users from different languages ​​and cultural backgrounds, and provide advice that takes cultural backgrounds into consideration.

[0064] The dialogue unit can use the emotion estimation function to monitor in real time the stress the user feels during the dialogue and adjust the progress of the dialogue. The dialogue unit can, for example, use the emotion estimation function to monitor in real time the stress the user feels during the dialogue and adjust the progress of the dialogue. For example, when stress increases, the dialogue can be temporarily interrupted and a relaxation method suggested. In this way, the stress the user feels during the dialogue can be monitored in real time and the progress of the dialogue can be adjusted.

[0065] The evaluation unit can collect physiological data (heart rate, electrodermal activity, etc.) of the user and reflect it in the evaluation of the stress level. For example, the evaluation unit collects the user's heart rate and electrodermal activity and reflects it in the evaluation of the stress level. For example, the evaluation unit obtains data from a smart watch or fitness tracker and quantifies the stress level. In this way, the user's physiological data can be collected and reflected in the evaluation of the stress level.

[0066] The evaluation unit can analyze the user's past stress data, identify individual stress patterns, and utilize them for evaluation. The evaluation unit, for example, analyzes the user's past stress data and identifies individual stress patterns. For example, it analyzes fluctuations in past stress levels and identifies specific triggers. In this way, the user's past stress data can be analyzed, and individual stress patterns can be utilized for evaluation.

[0067] The evaluation unit can use the emotion estimation function to suggest a coping method that takes into account the user's emotional state and provide advice according to the emotion. The evaluation unit, for example, uses the emotion estimation function to suggest a coping method that takes into account the user's emotional state. For example, when stress increases, the evaluation unit can suggest relaxation techniques or counseling. This makes it possible to suggest a coping method that takes into account the user's emotional state and provide advice according to the emotion.

[0068] The evaluation unit can incorporate feedback from the user's social network and evaluate the stress level. For example, the evaluation unit collects feedback from the user's social network and reflects it in the evaluation of the stress level. For example, the evaluation is made by incorporating opinions from family and friends. In this way, the stress level can be evaluated by incorporating feedback from the user's social network.

[0069] The evaluation unit can integrate the stress level evaluation result with the user's health data (e.g., sleep patterns and food records) and provide comprehensive health advice. The evaluation unit can, for example, integrate the stress level evaluation result with the user's health data and provide comprehensive health advice. For example, it can suggest stress countermeasures based on sleep patterns and food records. In this way, the stress level evaluation result can be integrated with the user's health data and provide comprehensive health advice.

[0070] The evaluation unit uses the emotion estimation function to evaluate the stress level based on the user's emotional state and can suggest a coping method according to the emotion. The evaluation unit, for example, uses the emotion estimation function to evaluate the stress level based on the user's emotional state. For example, the stress level is quantified based on the emotion score and a coping method is suggested. This makes it possible to evaluate the stress level based on the user's emotional state and suggest a coping method according to the emotion.

[0071] The tracking unit collects data from the user's smart device and can track daily life in detail. For example, the tracking unit collects location information and exercise amount data from the user's smartphone or fitness tracker and can track daily life in detail. For example, the tracking unit analyzes the number of steps taken and the distance traveled to evaluate the activity level. This allows the tracking unit to collect data from the user's smart device and can track daily life in detail.

[0072] The tracking unit analyzes the content of the user's social media posts and can detect signs of stress at an early stage. The tracking unit, for example, analyzes the content of the user's social media posts and can detect signs of stress at an early stage. For example, the tracking unit analyzes changes in the frequency and content of posts and identifies signs of stress. This allows the content of the user's social media posts to be analyzed and signs of stress to be detected at an early stage.

[0073] The tracking unit can collect data from the user's home IoT devices and track changes in the living environment. For example, the tracking unit can collect data from the user's home IoT devices and track changes in the living environment. For example, the tracking unit can analyze usage data of smart thermostats and smart lights to evaluate living patterns. This allows the tracking unit to collect data from the user's home IoT devices and track changes in the living environment.

[0074] The tracking unit can collect data on the user's work environment and identify stress factors in the workplace. The tracking unit, for example, collects data on the user's work environment and identifies stress factors in the workplace. For example, the tracking unit analyzes the frequency of meetings and the volume of emails to identify stress triggers. In this way, the tracking unit can collect data on the user's work environment and identify stress factors in the workplace.

[0075] The tracking unit can use the emotion estimation function to track emotional fluctuations in the user's daily life in real time and identify stress triggers. For example, the tracking unit can use the emotion estimation function to track emotional fluctuations in the user's daily life in real time and identify stress triggers. For example, the tracking unit can analyze fluctuations in emotion scores and identify that a specific event or situation is the cause of stress. This allows the emotion estimation function to track emotional fluctuations in the user's daily life in real time and identify stress triggers.

[0076] The identification unit can analyze the user's past stress data and identify a long-term stress pattern. The identification unit, for example, analyzes the user's past stress data and identifies a long-term stress pattern. For example, it analyzes fluctuations in stress levels over the past few months and identifies specific triggers. In this way, the user's past stress data can be analyzed and a long-term stress pattern can be identified.

[0077] The identification unit can analyze the user's lifestyle habits (eating, exercise, sleep, etc.) in detail and identify stress triggers. The identification unit, for example, analyzes the user's lifestyle habit data in detail and identifies stress triggers. For example, it analyzes the contents of meals, the amount of exercise, and sleep patterns to identify the causes of stress. This makes it possible to analyze the user's lifestyle habits in detail and identify stress triggers.

[0078] The identification unit can use the emotion estimation function to propose improvement measures that take into account the user's emotional state and provide a coping method that suits the emotion. The identification unit, for example, uses the emotion estimation function to propose improvement measures that take into account the user's emotional state. For example, the identification unit can propose relaxation methods or counseling when stress levels rise. This makes it possible to propose improvement measures that take into account the user's emotional state and provide a coping method that suits the emotion.

[0079] The identification unit can integrate data on the user's work environment and home environment and identify stress triggers caused by environmental factors. The identification unit, for example, integrates data on the user's work environment and home environment and identifies stress triggers caused by environmental factors. For example, the identification unit analyzes the frequency of workplace meetings and noise levels at home to identify causes of stress. In this way, data on the user's work environment and home environment can be integrated and stress triggers caused by environmental factors can be identified.

[0080] The identification unit can incorporate feedback from the user's social network and identify stress triggers from multiple angles. The identification unit, for example, collects feedback from the user's social network and identifies stress triggers from multiple angles. For example, the identification unit identifies the cause of stress by incorporating opinions from family and friends. This makes it possible to incorporate feedback from the user's social network and identify stress triggers from multiple angles.

[0081] The identification unit can use the emotion estimation function to identify stress triggers based on the user's emotional state and propose improvement measures according to the emotion. For example, the identification unit can use the emotion estimation function to identify stress triggers based on the user's emotional state and propose improvement measures according to the emotion. For example, the identification unit can identify the cause of stress based on the emotion score and propose a relaxation method. In this way, the emotion estimation function can be used to identify stress triggers based on the user's emotional state and propose improvement measures according to the emotion.

[0082] The identification unit can analyze fluctuations in the user's stress level in detail and provide individualized feedback. The identification unit can, for example, analyze fluctuations in the user's stress level in detail and provide individualized feedback. For example, the identification unit can display fluctuations in the stress level by week in a graph and check progress of improvement. This allows fluctuations in the user's stress level to be analyzed in detail and individualized feedback to be provided.

[0083] The identification unit can evaluate the user's level of goal achievement and provide specific advice according to the level of achievement. The identification unit, for example, evaluates the user's level of goal achievement and provides specific advice according to the level of achievement. For example, the identification unit evaluates progress toward a set goal and suggests a relaxation method according to the level of achievement. In this way, the user's level of goal achievement can be evaluated and specific advice can be provided according to the level of achievement.

[0084] The identification unit can use the emotion estimation function to provide feedback that takes into account the user's emotional state and provide support that is appropriate for the emotion. The identification unit, for example, uses the emotion estimation function to provide feedback that takes into account the user's emotional state. For example, the identification unit can suggest relaxation methods or counseling when stress levels rise. This allows the identification unit to use the emotion estimation function to provide feedback that takes into account the user's emotional state and provide support that is appropriate for the emotion.

[0085] The identification unit can convert the user's feedback into a visual note or a mind map to make it easier to understand visually. For example, the identification unit can convert the user's feedback into a visual note to make it easier to understand visually. For example, the feedback content can be displayed with diagrams or icons to highlight important points. In this way, the user's feedback can be converted into a visual note or a mind map to make it easier to understand visually.

[0086] The specifying unit can enable the user's feedback to be received on different devices. For example, the specifying unit builds a system that enables the user's feedback to be received on different devices. For example, the specifying unit enables the user's feedback to be received on a smart watch or a smart speaker. This allows the user's feedback to be received on different devices.

[0087] The identification unit uses the emotion estimation function to provide feedback based on the user's emotional state in real time, thereby enabling continuous support. The identification unit, for example, uses the emotion estimation function to provide feedback based on the user's emotional state in real time. For example, the identification unit suggests relaxation methods or counseling when stress levels rise. This allows the emotion estimation function to provide feedback based on the user's emotional state in real time, thereby enabling continuous support.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The stress management system may further include a hobby suggestion unit that suggests relaxation methods based on the user's hobbies and interests. For example, if the user likes music, a music playlist for relaxation may be suggested. If the user likes reading, a list of books that are useful for relaxation may be provided. Furthermore, if the user likes outdoor activities, information on nearby parks and hiking trails may be provided. This allows relaxation methods to be suggested based on the user's individual hobbies and interests, thereby reducing stress.

[0090] The dialogue unit not only analyzes the user's tone of voice and speaking style, but also the user's facial expressions and posture, making it possible to estimate the user's level of stress and emotional state. For example, if the user is smiling when speaking, it can be estimated that the user is under low stress. If the user is shrugging their shoulders or frowning, it can be estimated that the user is under high stress. Furthermore, if the user sighs frequently, it can be determined that the user's stress is increasing. In this way, by analyzing not only the user's tone of voice and speaking style, but also their facial expressions and posture, it is possible to more accurately estimate the user's level of stress and emotional state.

[0091] The dialogue unit not only deeply understands the context of the text entered by the user, but also refers to the user's past dialogue history to automatically generate more appropriate questions. For example, if the user previously entered "I'm under a lot of pressure at work," a question such as "How is your work situation these days?" can be generated in the current dialogue. If the user previously entered "I've been suffering from a lack of sleep," a question such as "How is the quality of your sleep these days?" can be generated. If the user previously entered "My relationship with my family is not going well," a question such as "Has your relationship with my family improved?" can be generated. This allows the dialogue unit to more effectively proceed with identifying stress factors by referring to the user's past dialogue history and automatically generating more appropriate questions.

[0092] The dialogue unit not only uses the emotion estimation function to analyze the user's emotional state in real time, but can also adjust the tone and content of the dialogue according to the user's emotional state. For example, if the user is feeling stressed, the dialogue unit can soften the tone and suggest ways to relax. If the user is excited, the dialogue unit can calm the tone and provide advice on how to stay calm. Furthermore, if the user is sad, the dialogue unit can soften the tone and offer words of comfort. This allows for more effective stress management by adjusting the tone and content of the dialogue according to the user's emotional state.

[0093] The dialogue unit can identify stress factors by analyzing the user's handwriting and touch input patterns, in addition to using voice input, gesture, and facial expression recognition. For example, if the user writes quickly or presses down hard, it can be determined that the user is under high stress. Also, if the user frequently performs touch input, it can be estimated that stress is increasing. Furthermore, if the user performs touch input slowly, it can be determined that the user is relaxed. In this way, by analyzing not only voice input, gesture, and facial expression recognition, but also handwriting and touch input patterns, stress factors can be identified from a more multifaceted perspective.

[0094] The dialogue unit not only employs multilingual generation AI, but can also provide advice that takes into account the user's cultural background and individual values. For example, if the user believes in a particular religion, it can suggest relaxation methods based on that religion. Also, if the user belongs to a particular cultural sphere, it can provide stress management solutions appropriate for that culture. Furthermore, if the user has specific values, it can provide advice based on those values. This allows for more effective stress management by providing advice that takes into account not only multilingual support but also cultural background and individual values.

[0095] The dialogue unit not only uses its emotion estimation function to monitor the stress the user feels during the dialogue in real time, but also flexibly changes the content of the dialogue according to the user's stress level. For example, if the user feels high stress, the dialogue content can be focused on relaxation methods and stress reduction advice. Alternatively, if the user feels low stress, the dialogue content can be changed to daily advice and positive topics. Furthermore, if the user feels medium stress, the dialogue content can be adjusted in a balanced manner to combine stress management with daily advice. This allows for more effective stress management by flexibly changing the content of the dialogue according to the user's stress level.

[0096] The evaluation unit not only collects the user's physiological data, but also collects the user's environmental data (e.g., temperature, humidity, noise level, etc.) and can reflect this in the stress level evaluation. For example, if the temperature and humidity in the user's living environment are high, it can be determined that stress is likely to increase. Also, if the noise level in the user's work environment is high, it can be estimated that stress is likely to increase. Furthermore, the intensity of light and color temperature around the user can also be taken into account as factors that affect stress. In this way, by collecting not only the user's physiological data but also environmental data and reflecting this in the stress level evaluation, more accurate stress evaluation can be achieved.

[0097] The evaluation unit not only analyzes the user's past stress data, but also analyzes the user's lifestyle habits and behavioral patterns in detail, identifying individual stress patterns and utilizing them for evaluation. For example, if the user is prone to feeling stressed during a particular time period, the evaluation unit can suggest measures for that time period. If the user experiences increased stress when engaging in a particular activity, the evaluation unit can suggest avoiding that activity or incorporating relaxation techniques. Furthermore, if the user experiences increased stress when consuming a particular food or drink, the evaluation unit can advise the user to refrain from consuming that food or drink. In this way, more effective stress management can be achieved by analyzing the user's lifestyle habits and behavioral patterns in detail, identifying individual stress patterns, and utilizing them for evaluation.

[0098] The evaluation unit not only uses the emotion estimation function to suggest coping methods that take the user's emotional state into consideration, but also adjusts the priority of coping methods according to the user's emotional state. For example, if the user is feeling very high stress, relaxation methods can be suggested as a top priority. If the user is feeling moderate stress, exercise or counseling can be suggested. Furthermore, if the user is feeling low stress, daily advice or positive activities can be suggested. This allows for more effective stress management by adjusting the priority of coping methods according to the user's emotional state.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The dialogue unit is equipped with a generation AI, and the user interacts with the AI ​​to share the causes of stress. For example, if the user inputs "I'm under a lot of pressure at work," the generation AI analyzes the information and provides advice on the level of stress and how to deal with it. The generation AI uses a text generation AI (e.g., LLM) to analyze the user's input and generate appropriate advice. Step 2: The evaluation unit evaluates the level of stress based on the information obtained by the dialogue unit. For example, if the generated AI receives information such as "I've been having trouble sleeping lately," it analyzes the information and quantifies the level of stress. The evaluation unit evaluates the level of stress using a numerical score or categorization. Step 3: The suggestion unit proposes coping methods based on the stress level assessed by the evaluation unit. For example, it proposes relaxation techniques, exercise, counseling, and other coping methods. The suggestion unit uses a generative AI to propose coping methods that are suitable for the user. Step 4: The tracking unit tracks the user's daily life and activity history. For example, it collects and analyzes data such as diary entries, schedules, and exercise records entered by the user into the app. The tracking unit then uses generative AI to analyze the collected data and identify stress triggers and patterns. Step 5: The identification unit identifies stress triggers and patterns based on the data collected by the tracking unit and proposes improvement measures. For example, if a pattern such as "stress increases every Monday" is discovered, the generation AI analyzes the cause and proposes improvement measures.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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, in order to avoid confusion and to 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.

[0167] 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]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A dialogue unit equipped with generative AI, an evaluation unit that evaluates a level of stress based on the information acquired by the dialogue unit; a suggestion unit that suggests a coping method based on the degree of stress evaluated by the evaluation unit; a tracking unit that tracks the user's daily life and activity history; and an identification unit that identifies triggers and patterns of the stress based on the data collected by the tracking unit and proposes improvement measures. A system characterized by:

2. The dialogue unit Analyzing the user's tone of voice and speaking style to estimate the user's stress level and emotional state 2. The system of claim 1.

3. The dialogue unit Identify stressors using not only voice input but also gesture and facial expression recognition 2. The system of claim 1.

4. The evaluation unit Collecting physiological data such as the user's heart rate and electrodermal activity and reflecting it in the evaluation of the user's stress level 2. The system of claim 1.

5. The tracking unit Collecting the data from the user's smart devices and tracking the user's daily life in detail 2. The system of claim 1.

6. The identification unit Analyzing the user's past stress data to identify long-term stress patterns 2. The system of claim 1.

7. The identification unit Analyze fluctuations in the user's stress level in detail and provide personalized feedback 2. The system of claim 1.

8. The dialogue unit Analyzing the user's emotional state in real time and suggesting relaxation methods at appropriate times 2. The system of claim 1.

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