System

Abstract

To provide a system that estimates the risk of suicide based on the user's facial expressions and movements and offers proper care.SOLUTION: A system comprises a capturing unit, an analysis unit, and a care providing unit. The capturing unit captures the user's facial expressions and movements with a camera. The analysis unit analyzes the video captured by the capturing unit and estimates the risk of suicide from the user's facial expressions and movements. The care providing unit provides care based on the estimation result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to accurately estimate a user's suicide risk from their facial expressions and movements and provide prompt care.

[0005] The system of the embodiment aims to estimate the risk of suicide from a user's facial expressions and movements and provide appropriate care. [Means for solving the problem]

[0006] The system according to the embodiment includes an imaging unit, an analysis unit, and a care provision unit. The imaging unit captures the facial expressions and movements of a user with a camera. The analysis unit analyzes the video captured by the imaging unit and estimates the risk of suicide from the facial expressions and movements of the user. The care provision unit provides care based on the estimation results obtained by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can estimate the risk of suicide from a user's facial expressions and movements and provide appropriate care. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 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. The smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The suicide prevention system according to the embodiment of the present invention is a system that estimates the possibility of suicide from facial expressions and movements captured by a camera and provides appropriate care. In this system, a camera captures the user's facial expressions and movements, and an AI analyzes the captured image to estimate the possibility of suicide, and provides appropriate care based on the estimation result. For example, the camera captures the user's facial expressions and body movements in detail, and the AI ​​performs analysis based on data learned in advance. Based on the estimation result, a response is taken, such as displaying a message encouraging consultation with a specialist or sending a notification to an emergency contact. It is also possible to provide the user with advice on how to relax and a method of stress management. This system makes it possible to detect signs of suicide early and take appropriate measures. For example, by introducing this system in schools and workplaces, it is possible to detect signs of suicide without people around you noticing and respond early. This aims to reduce the number of suicides in Japan to zero. This allows the suicide prevention system to estimate the possibility of suicide from the user's facial expressions and movements and provide appropriate care.

[0029] The suicide prevention system according to the embodiment includes a photographing unit, an analysis unit, and a care providing unit. The photographing unit photographs the user's facial expressions and movements with a camera. The user's facial expressions and movements include, but are not limited to, smiles, anger, sadness, hand movements, and body movements. The photographing unit, for example, uses a high-resolution camera to capture the user's facial expressions in detail. The photographing unit can also use a wide-angle camera to capture the user's overall body movements. Furthermore, the photographing unit can use multiple cameras to track the user's movements in real time. The analysis unit analyzes the video captured by the photographing unit and estimates the risk of suicide from the user's facial expressions and movements. The analysis unit performs analysis based on, for example, data learned in advance. The data learned in advance includes, for example, past user data and general facial expression data sets. The analysis unit, for example, reads emotions from facial expressions and detects signs of stress and anxiety from hand movements. The analysis unit can estimate the risk of suicide from the user's facial expressions and movements using AI. The care providing unit provides care based on the estimation results obtained by the analysis unit. The care providing unit provides, for example, advice on relaxation methods and stress management. Examples of relaxation methods include deep breathing, meditation, yoga, and the like. Examples of stress management advice include time management, exercise, and hobby recommendations. The care providing unit can also display a message encouraging the user to consult with a specialist. Examples of messages encouraging the user to consult with a specialist include text messages, voice messages, and notification methods. The care providing unit can also send a notification to an emergency contact. Examples of emergency contacts include family, friends, and medical institutions. This allows the suicide prevention system according to the embodiment to estimate the possibility of suicide from the user's facial expressions and movements and provide appropriate care.

[0030] The photographing unit photographs the user's facial expressions and movements with a camera. The user's facial expressions and movements include, but are not limited to, smiles, anger, sadness, hand movements, and body movements. The photographing unit, for example, uses a high-resolution camera to capture the user's facial expressions in detail. A high-resolution camera can capture minute changes in facial expressions and skin tension, which is important for more accurately grasping the user's emotional state. The photographing unit can also use a wide-angle camera to capture the user's entire body movements. A wide-angle camera can capture the user's entire body movements and changes in posture, and is particularly effective in detecting signs of stress and anxiety, such as hand movements and body shaking. Furthermore, the photographing unit can use multiple cameras to track the user's movements in real time. By using multiple cameras, it is possible to obtain images from different angles and analyze the user's movements in three dimensions. This makes it possible to capture the user's movements and changes in facial expressions in more detail, and improve the accuracy of the data provided to the analysis unit. The photographing unit is required to appropriately arrange these cameras and install them so as not to interfere with the user's movements. For example, the cameras are installed in the corners of a room or on the ceiling to provide an environment in which the user can act naturally. In addition, to protect privacy, it is important that the imaging unit obtains the user's consent before taking the image and ensures transparency regarding the handling of the data. This allows the imaging unit to capture the user's facial expressions and movements with high accuracy, improving the quality of the data provided to the analysis unit.

[0031] The analysis unit analyzes the video captured by the shooting unit and estimates the risk of suicide from the user's facial expressions and movements. The analysis unit performs analysis based on, for example, pre-learned data. The pre-learned data includes, for example, past user data and general facial expression datasets. These datasets include various emotional states and behavioral patterns, and serve as the basis for AI to analyze the user's facial expressions and movements. The analysis unit, for example, reads emotions from facial expressions and detects signs of stress and anxiety from hand movements. AI can analyze facial expressions using image recognition technology to capture subtle changes in facial expressions and muscle movements. For example, it extracts features that indicate emotions, such as eyebrow movements and the rising and falling of the corners of the mouth, and estimates the user's emotional state. It also analyzes changes in hand movements and body posture to detect signs of stress and anxiety. For example, it identifies behavioral patterns that indicate stress and anxiety, such as trembling hands, body shaking, and frequent movements. The analysis unit can use AI to estimate the risk of suicide from the user's facial expressions and movements. AI compares past data with real-time data to detect abnormal patterns and signs of risk. For example, if a user shows expressions of sadness or anger for a long period of time, the AI ​​will recognize this as a sign of risk and notify the care delivery department. Furthermore, the analysis unit can take into account the user's behavioral history and environmental data to perform more accurate risk estimation. This allows the analysis unit to estimate the risk of suicide with high accuracy from the user's expressions and movements, and provide the care delivery department with information to provide appropriate care.

[0032] The care providing unit provides care based on the estimation result obtained by the analysis unit. The care providing unit provides, for example, advice on relaxation methods and stress management. Examples of relaxation methods include deep breathing, meditation, yoga, and the like. These methods help the user to release mental and physical tension and relax. For example, the method of deep breathing is specifically explained and the user is guided to practice it. In addition, meditation and yoga sessions are provided to create an environment in which the user can relax. Advice on stress management includes, for example, time management, exercise, and hobby recommendations. The time management advice suggests a method for the user to use time efficiently and reduce stress. The exercise recommendation introduces a method for the user to perform moderate exercise and release stress. The hobby recommendation suggests a method for the user to find and enjoy an activity that interests the user and reduce stress. The care providing unit can also display a message encouraging the user to consult with a specialist. The message encouraging the user to consult with a specialist can include, for example, a text message, a voice message, and a notification method. This allows the user to receive support from a specialist when necessary. The care providing unit can also send a notification to an emergency contact. Emergency contacts include, for example, family, friends, medical institutions, etc. This allows the user to receive support quickly if he or she is in a dangerous condition. The care provision unit plays an important role in providing appropriate care according to the user's condition and protecting the user's safety and health. Furthermore, the care provision unit can collect user feedback and continuously improve the quality of the care provided. For example, the user can evaluate the satisfaction and effectiveness of the care provided to them, and review the content of the care based on the results. This allows the care provision unit to provide the most appropriate care to the user and reduce the user's suicide risk.

[0033] The analysis unit may include a learning data unit that analyzes the user's facial expressions and movements based on previously learned data. The learning data unit performs learning based on, for example, past user data. The past user data includes, for example, data on the user's facial expressions and movements. The learning data unit may also perform learning based on a general facial expression data set. The general facial expression data set includes, for example, facial expression data such as smile, anger, and sadness. The learning data unit can use AI to learn a model for analyzing the user's facial expressions and movements. As a result, the analysis is performed based on previously learned data, improving the accuracy of the analysis. A part or all of the above-mentioned processing in the learning data unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning data unit may input past user data to the generation AI and cause the generation AI to select the learning data.

[0034] The care providing unit may include an advice unit that provides advice on a relaxation method or stress management. The advice unit provides, for example, a relaxation method. The relaxation method includes, for example, deep breathing, meditation, yoga, and the like. The advice unit may also provide advice on stress management. The stress management advice includes, for example, time management, exercise, and hobby recommendations. The advice unit can provide the user with appropriate relaxation methods and stress management advice using AI. As a result, the user's stress can be reduced by providing advice on relaxation methods and stress management. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the user's state to the generation AI and cause the generation AI to provide advice on relaxation methods and stress management.

[0035] The care providing unit can display a message encouraging the user to consult with an expert. The care providing unit, for example, displays a message encouraging the user to consult with an expert. The content of the message includes, for example, the contact information of the expert and the consultation method. The display method of the message includes, for example, a text message, a voice message, a notification method, and the like. The care providing unit can use AI to display a message encouraging the user to consult with an expert at an appropriate timing. This allows the user to receive appropriate support by encouraging the user to consult with an expert. Some or all of the above-mentioned processing in the care providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the care providing unit can input the user's condition into the generation AI and cause the generation AI to execute the display of a message encouraging the user to consult with an expert.

[0036] The care providing unit can send a notification to the emergency contact. For example, when the user's condition is determined to be an emergency, the care providing unit sends a notification to the emergency contact. The emergency contact includes, for example, family, friends, medical institutions, etc. Notification methods include, for example, text messages, voice messages, and telephone calls. The care providing unit can use AI to monitor the user's condition in real time and send a notification to an appropriate contact in the event of an emergency. This allows for a quick response by sending a notification to the emergency contact. Some or all of the above-mentioned processing in the care providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the care providing unit can input the user's condition to the generation AI and cause the generation AI to execute a notification to the emergency contact.

[0037] The photographing unit can use multiple cameras to capture not only the facial expression of the user but also the movement of the entire body in detail when photographing. For example, the photographing unit can install multiple cameras and simultaneously photograph the face and the entire body of the user from different angles. In addition, the arrangement of the cameras can be adjusted to photograph from an optimal viewpoint according to the movement of the user. Furthermore, a high-resolution camera can be used to capture the subtle facial expressions and movements of the user in detail. As a result, by using multiple cameras, the facial expressions and movements of the user can be captured in detail. A part or all of the above-mentioned processing in the photographing unit may be performed, for example, using AI or may be performed without using AI. For example, the photographing unit can input data from multiple cameras to the generation AI and cause the generation AI to execute an analysis of the facial expressions and movements of the user.

[0038] The photographing unit can simultaneously record the user's environmental sound during photographing and use it for analysis. The photographing unit, for example, has a built-in microphone in the camera and simultaneously records the environmental sound around the user. The environmental sound can also be analyzed and used as data for estimating the user's stress level and emotional state. Furthermore, it is also possible to detect changes in the environmental sound and grasp changes in the user's behavior and situation. In this way, by recording the environmental sound, the user's stress level and emotional state can be more accurately estimated. A part or all of the above-mentioned processing in the photographing unit may be performed, for example, using AI, or may be performed without using AI. For example, the photographing unit can input the environmental sound data to the generation AI and cause the generation AI to execute the analysis of the environmental sound.

[0039] The imaging unit can detect abnormalities by comparing the past video data of the user at the time of imaging. For example, the imaging unit compares the past video data with the current video data to detect changes in facial expressions and movements. In addition, when abnormal movements or facial expressions are detected, an alert can be issued. Furthermore, based on past data, the imaging unit can learn the user's normal behavioral patterns and detect abnormalities. This allows for early detection of abnormalities by comparing with past video data. Some or all of the above-mentioned processing in the imaging unit may be performed, for example, using AI, or may be performed without using AI. For example, the imaging unit can input past video data to a generation AI and cause the generation AI to detect abnormalities.

[0040] The photographing unit can capture the movement of objects around the user during photographing and use it for analysis. For example, the photographing unit uses a camera to simultaneously photograph the movement of objects around the user and use it for analysis. The photographing unit can also estimate the user's behavior and situation from the movement of the object. Furthermore, it can detect changes in the movement of the object and grasp changes in the user's behavior and situation. As a result, the user's behavior and situation can be grasped more accurately by capturing the movement of the surrounding objects. Part or all of the above-mentioned processing in the photographing unit may be performed, for example, using AI or may be performed without using AI. For example, the photographing unit can input object movement data to the generation AI and cause the generation AI to execute analysis of the object movement.

[0041] During the analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral data. The analysis unit, for example, analyzes the current behavior based on the user's past behavioral data. Also, the analysis unit can detect abnormal behavior by referring to past data. Furthermore, the analysis unit can learn past behavioral patterns and improve the accuracy of the analysis. As a result, the accuracy of the analysis is improved by referring to the past behavioral data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the past behavioral data to the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0042] The analysis unit can perform the analysis by using the user's biometric information in addition to the analysis. The analysis unit, for example, measures the user's heart rate and body temperature and uses them in the analysis. The analysis unit can also estimate the user's stress level and emotional state based on the biometric information. Furthermore, the accuracy of the analysis can be improved by combining the biometric information with the video data. As a result, the accuracy of the analysis is improved by using the biometric information in addition to the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the biometric information data to the generation AI and have the generation AI execute the analysis.

[0043] The analysis unit can analyze the user's social media activity during the analysis and reflect it in the analysis result. The analysis unit can, for example, analyze the user's social media posts and estimate the emotional state. The analysis unit can also analyze the user's behavior based on the social media activity pattern. Furthermore, the accuracy of the analysis result can be improved by combining social media data. In this way, the accuracy of the analysis result is improved by analyzing the social media activity. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or can be performed without using AI. For example, the analysis unit can input the social media data to the generation AI and have the generation AI perform the analysis.

[0044] The analysis unit can perform the analysis while taking into consideration the user's life rhythm. The analysis unit, for example, analyzes a behavior pattern based on the user's life rhythm. It can also detect changes in the life rhythm and analyze abnormalities. Furthermore, it can also improve the accuracy of the analysis result by taking the life rhythm into consideration. As a result, the accuracy of the analysis result is improved by taking the life rhythm into consideration. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the life rhythm data to the generation AI and have the generation AI execute the analysis.

[0045] The care providing unit can provide optimal care by referring to the user's past care history when providing care. The care providing unit can, for example, suggest an optimal care method based on the user's past care history. Also, the care providing unit can select an effective care method by referring to the past care history. Furthermore, a care method suited to the user can be provided based on the care history. In this way, optimal care can be provided by referring to the past care history. Some or all of the above-mentioned processing in the care providing unit can be performed, for example, using AI or can be performed without using AI. For example, the care providing unit can input past care history data to the generation AI and cause the generation AI to select the optimal care method.

[0046] The care providing unit can customize the means of care based on the user's current living situation when providing care. The care providing unit, for example, suggests an optimal care method based on the user's current living situation. The care providing unit can also adjust the care method in consideration of changes in the living situation. Furthermore, the care providing unit can customize the means of care according to the current living situation. As a result, more appropriate care can be provided by customizing the means of care based on the current living situation. Some or all of the above-mentioned processing in the care providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the care providing unit can input current living situation data to the generation AI and cause the generation AI to customize the means of care.

[0047] The care providing unit can provide care by taking into consideration the geographical location information of the user when providing care. The care providing unit can, for example, suggest an optimal care method based on the user's current location. It can also adjust the care method by taking into consideration the geographical location information. Furthermore, it can customize the means of care according to the current location. In this way, more appropriate care can be provided by taking into consideration the geographical location information. Some or all of the above-mentioned processing in the care providing unit can be performed, for example, using AI or without using AI. For example, the care providing unit can input geographical location information data to the generation AI and cause the generation AI to provide care.

[0048] The care providing unit can analyze the user's social media activity and propose a means of care when providing care. The care providing unit can, for example, analyze the user's social media posts and propose an optimal care method. The care providing unit can also adjust the care method based on social media activity patterns. Furthermore, it can combine social media data to propose a means of care. In this way, more appropriate means of care can be proposed by analyzing social media activity. Some or all of the above-mentioned processing in the care providing unit can be performed, for example, using AI or without using AI. For example, the care providing unit can input social media data to a generation AI and have the generation AI execute a proposal of a means of care.

[0049] The learning data unit can optimize the learning algorithm by referring to past learning data during learning. The learning data unit optimizes the current learning algorithm, for example, based on past learning data. Also, the learning accuracy can be improved by referring to past data. Furthermore, the optimal learning algorithm can be selected based on the learning data. As a result, the accuracy of the learning algorithm is improved by referring to the past learning data. Some or all of the above-mentioned processing in the learning data unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning data unit can input past learning data to the generation AI and cause the generation AI to optimize the learning algorithm.

[0050] The learning data unit can weight the learning data based on the user's life rhythm during learning. The learning data unit weights the learning data based on the user's life rhythm, for example. The learning data unit can also adjust the weighting of the learning data in consideration of changes in the life rhythm. Furthermore, the weighting of the learning data can be customized according to the life rhythm. As a result, the accuracy of learning is improved by weighting the learning data based on the life rhythm. A part or all of the above-mentioned processing in the learning data unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning data unit can input life rhythm data to the generation AI and cause the generation AI to perform weighting of the learning data.

[0051] When providing advice, the advice unit can provide optimal advice by referring to the user's past advice history. The advice unit can provide optimal advice, for example, based on the user's past advice history. Also, the advice unit can select effective advice by referring to the past advice history. Furthermore, the advice unit can provide advice suited to the user based on the advice history. In this way, optimal advice can be provided by referring to the past advice history. Some or all of the above-mentioned processing in the advice unit can be performed, for example, using AI, or can be performed without using AI. For example, the advice unit can input past advice history data to a generation AI and cause the generation AI to provide optimal advice.

[0052] When providing advice, the advice unit can provide the advice taking into consideration the geographical location information of the user. The advice unit can provide optimal advice based on, for example, the user's current location. The advice unit can also adjust the advice content taking into consideration the geographical location information. Furthermore, the means of advice can be customized according to the current location. In this way, more appropriate advice can be provided by taking the geographical location information into consideration. Some or all of the above-mentioned processing in the advice unit can be performed, for example, using AI or can be performed without using AI. For example, the advice unit can input geographical location information data to the generation AI and cause the generation AI to provide the advice.

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

[0054] The analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral data. For example, the analysis unit analyzes the current behavior based on the user's past behavioral data. Also, the analysis unit can detect abnormal behavior by referring to past data. Furthermore, the analysis unit can learn past behavioral patterns and improve the accuracy of the analysis. As a result, the analysis accuracy is improved by referring to the past behavioral data. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input past behavioral data to the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0055] When providing care, the care providing unit can provide optimal care by referring to the user's past care history. For example, the optimal care method can be suggested based on the user's past care history. Also, an effective care method can be selected by referring to the past care history. Furthermore, a care method suited to the user can be provided based on the care history. In this way, optimal care can be provided by referring to the past care history. Some or all of the above-mentioned processing in the care providing unit may be performed using AI, or may be performed without using AI. For example, the care providing unit can input past care history data to the generation AI and cause the generation AI to select the optimal care method.

[0056] The shooting unit can simultaneously record the user's environmental sound during shooting and use it for analysis. For example, a microphone is built into the camera to simultaneously record the environmental sound around the user. The environmental sound can also be analyzed and used as data for estimating the user's stress level and emotional state. Furthermore, it is possible to detect changes in the environmental sound and grasp changes in the user's behavior and situation. In this way, by recording the environmental sound, the user's stress level and emotional state can be more accurately estimated. Some or all of the above-mentioned processing in the shooting unit may be performed using AI or may be performed without using AI. For example, the shooting unit can input the environmental sound data to the generation AI and cause the generation AI to perform analysis of the environmental sound.

[0057] The analysis unit can perform the analysis by using the user's biometric information in addition to the analysis. For example, the user's heart rate and body temperature are measured and used for the analysis. The user's stress level and emotional state can also be estimated based on the biometric information. Furthermore, the accuracy of the analysis can be improved by combining the biometric information with the video data. As a result, the accuracy of the analysis is improved by using the biometric information in addition to the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI or may be performed without using AI. For example, the analysis unit can input the biometric information data to the generation AI and have the generation AI perform the analysis.

[0058] The care providing unit can customize the means of care based on the user's current living situation when providing care. For example, the optimal care method can be suggested based on the user's current living situation. The care method can also be adjusted taking into account changes in the living situation. Furthermore, the means of care can be customized according to the current living situation. As a result, more appropriate care can be provided by customizing the means of care based on the current living situation. Some or all of the above-mentioned processing in the care providing unit may be performed using AI or may be performed without using AI. For example, the care providing unit can input current living situation data to the generation AI and cause the generation AI to customize the means of care.

[0059] The process flow of the first embodiment will be briefly described below.

[0060] Step 1: The camera captures the user's facial expressions and movements with a camera. The user's facial expressions and movements include, for example, smiling, anger, sadness, hand movements, and body movements. The camera uses a high-resolution camera to capture the user's facial expressions in detail, and can also use a wide-angle camera to capture the user's entire body movements. It can also use multiple cameras to track the user's movements in real time. Step 2: The analysis unit analyzes the video captured by the camera unit and estimates the risk of suicide from the user's facial expressions and movements. The analysis unit performs analysis based on pre-learned data, reading emotions from facial expressions and detecting signs of stress and anxiety from hand movements. Using AI, the analysis unit can estimate the risk of suicide from the user's facial expressions and movements. Step 3: The care provider provides care based on the results of the analysis. The care provider can provide advice on relaxation and stress management, display a message encouraging the patient to consult a specialist, or send a notification to an emergency contact.

[0061] (Example 2) The suicide prevention system according to the embodiment of the present invention is a system that estimates the possibility of suicide from facial expressions and movements captured by a camera and provides appropriate care. In this system, a camera captures the user's facial expressions and movements, and an AI analyzes the captured image to estimate the possibility of suicide, and provides appropriate care based on the estimation result. For example, the camera captures the user's facial expressions and body movements in detail, and the AI ​​performs analysis based on data learned in advance. Based on the estimation result, a response is taken, such as displaying a message encouraging consultation with a specialist or sending a notification to an emergency contact. It is also possible to provide the user with advice on how to relax and a method of stress management. This system makes it possible to detect signs of suicide early and take appropriate measures. For example, by introducing this system in schools and workplaces, it is possible to detect signs of suicide without people around you noticing and respond early. This aims to reduce the number of suicides in Japan to zero. This allows the suicide prevention system to estimate the possibility of suicide from the user's facial expressions and movements and provide appropriate care.

[0062] The suicide prevention system according to the embodiment includes a photographing unit, an analysis unit, and a care providing unit. The photographing unit photographs the user's facial expressions and movements with a camera. The user's facial expressions and movements include, but are not limited to, smiles, anger, sadness, hand movements, and body movements. The photographing unit, for example, uses a high-resolution camera to capture the user's facial expressions in detail. The photographing unit can also use a wide-angle camera to capture the user's overall body movements. Furthermore, the photographing unit can use multiple cameras to track the user's movements in real time. The analysis unit analyzes the video captured by the photographing unit and estimates the risk of suicide from the user's facial expressions and movements. The analysis unit performs analysis based on, for example, data learned in advance. The data learned in advance includes, for example, past user data and general facial expression data sets. The analysis unit, for example, reads emotions from facial expressions and detects signs of stress and anxiety from hand movements. The analysis unit can estimate the risk of suicide from the user's facial expressions and movements using AI. The care providing unit provides care based on the estimation results obtained by the analysis unit. The care providing unit provides, for example, advice on relaxation methods and stress management. Examples of relaxation methods include deep breathing, meditation, yoga, and the like. Examples of stress management advice include time management, exercise, and hobby recommendations. The care providing unit can also display a message encouraging the user to consult with a specialist. Examples of messages encouraging the user to consult with a specialist include text messages, voice messages, and notification methods. The care providing unit can also send a notification to an emergency contact. Examples of emergency contacts include family, friends, and medical institutions. This allows the suicide prevention system according to the embodiment to estimate the possibility of suicide from the user's facial expressions and movements and provide appropriate care.

[0063] The photographing unit photographs the user's facial expressions and movements with a camera. The user's facial expressions and movements include, but are not limited to, smiles, anger, sadness, hand movements, and body movements. The photographing unit, for example, uses a high-resolution camera to capture the user's facial expressions in detail. A high-resolution camera can capture minute changes in facial expressions and skin tension, which is important for more accurately grasping the user's emotional state. The photographing unit can also use a wide-angle camera to capture the user's entire body movements. A wide-angle camera can capture the user's entire body movements and changes in posture, and is particularly effective in detecting signs of stress and anxiety, such as hand movements and body shaking. Furthermore, the photographing unit can use multiple cameras to track the user's movements in real time. By using multiple cameras, it is possible to obtain images from different angles and analyze the user's movements in three dimensions. This makes it possible to capture the user's movements and changes in facial expressions in more detail, and improve the accuracy of the data provided to the analysis unit. The photographing unit is required to appropriately arrange these cameras and install them so as not to interfere with the user's movements. For example, the cameras are installed in the corners of a room or on the ceiling to provide an environment in which the user can act naturally. In addition, to protect privacy, it is important that the imaging unit obtains the user's consent before taking the image and ensures transparency regarding the handling of the data. This allows the imaging unit to capture the user's facial expressions and movements with high accuracy, improving the quality of the data provided to the analysis unit.

[0064] The analysis unit analyzes the video captured by the shooting unit and estimates the risk of suicide from the user's facial expressions and movements. The analysis unit performs analysis based on, for example, pre-learned data. The pre-learned data includes, for example, past user data and general facial expression datasets. These datasets include various emotional states and behavioral patterns, and serve as the basis for AI to analyze the user's facial expressions and movements. The analysis unit, for example, reads emotions from facial expressions and detects signs of stress and anxiety from hand movements. AI can analyze facial expressions using image recognition technology to capture subtle changes in facial expressions and muscle movements. For example, it extracts features that indicate emotions, such as eyebrow movements and the rising and falling of the corners of the mouth, and estimates the user's emotional state. It also analyzes changes in hand movements and body posture to detect signs of stress and anxiety. For example, it identifies behavioral patterns that indicate stress and anxiety, such as trembling hands, body shaking, and frequent movements. The analysis unit can use AI to estimate the risk of suicide from the user's facial expressions and movements. AI compares past data with real-time data to detect abnormal patterns and signs of risk. For example, if a user shows expressions of sadness or anger for a long period of time, the AI ​​will recognize this as a sign of risk and notify the care delivery department. Furthermore, the analysis unit can take into account the user's behavioral history and environmental data to perform more accurate risk estimation. This allows the analysis unit to estimate the risk of suicide with high accuracy from the user's expressions and movements, and provide the care delivery department with information to provide appropriate care.

[0065] The care providing unit provides care based on the estimation result obtained by the analysis unit. The care providing unit provides, for example, advice on relaxation methods and stress management. Examples of relaxation methods include deep breathing, meditation, yoga, and the like. These methods help the user to release mental and physical tension and relax. For example, the method of deep breathing is specifically explained and the user is guided to practice it. In addition, meditation and yoga sessions are provided to create an environment in which the user can relax. Advice on stress management includes, for example, time management, exercise, and hobby recommendations. The time management advice suggests a method for the user to use time efficiently and reduce stress. The exercise recommendation introduces a method for the user to perform moderate exercise and release stress. The hobby recommendation suggests a method for the user to find and enjoy an activity that interests the user and reduce stress. The care providing unit can also display a message encouraging the user to consult with a specialist. The message encouraging the user to consult with a specialist can include, for example, a text message, a voice message, and a notification method. This allows the user to receive support from a specialist when necessary. The care providing unit can also send a notification to an emergency contact. Emergency contacts include, for example, family, friends, medical institutions, etc. This allows the user to receive support quickly if he or she is in a dangerous condition. The care provision unit plays an important role in providing appropriate care according to the user's condition and protecting the user's safety and health. Furthermore, the care provision unit can collect user feedback and continuously improve the quality of the care provided. For example, the user can evaluate the satisfaction and effectiveness of the care provided to them, and review the content of the care based on the results. This allows the care provision unit to provide the most appropriate care to the user and reduce the user's suicide risk.

[0066] The analysis unit may include a learning data unit that analyzes the user's facial expressions and movements based on previously learned data. The learning data unit performs learning based on, for example, past user data. The past user data includes, for example, data on the user's facial expressions and movements. The learning data unit may also perform learning based on a general facial expression data set. The general facial expression data set includes, for example, facial expression data such as smile, anger, and sadness. The learning data unit can use AI to learn a model for analyzing the user's facial expressions and movements. As a result, the analysis is performed based on previously learned data, improving the accuracy of the analysis. A part or all of the above-mentioned processing in the learning data unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning data unit may input past user data to the generation AI and cause the generation AI to select the learning data.

[0067] The care providing unit may include an advice unit that provides advice on a relaxation method or stress management. The advice unit provides, for example, a relaxation method. The relaxation method includes, for example, deep breathing, meditation, yoga, and the like. The advice unit may also provide advice on stress management. The stress management advice includes, for example, time management, exercise, and hobby recommendations. The advice unit can provide the user with appropriate relaxation methods and stress management advice using AI. As a result, the user's stress can be reduced by providing advice on relaxation methods and stress management. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the user's state to the generation AI and cause the generation AI to provide advice on relaxation methods and stress management.

[0068] The care providing unit can display a message encouraging the user to consult with an expert. The care providing unit, for example, displays a message encouraging the user to consult with an expert. The content of the message includes, for example, the contact information of the expert and the consultation method. The display method of the message includes, for example, a text message, a voice message, a notification method, and the like. The care providing unit can use AI to display a message encouraging the user to consult with an expert at an appropriate timing. This allows the user to receive appropriate support by encouraging the user to consult with an expert. Some or all of the above-mentioned processing in the care providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the care providing unit can input the user's condition into the generation AI and cause the generation AI to execute the display of a message encouraging the user to consult with an expert.

[0069] The care providing unit can send a notification to the emergency contact. For example, when the user's condition is determined to be an emergency, the care providing unit sends a notification to the emergency contact. The emergency contact includes, for example, family, friends, medical institutions, etc. Notification methods include, for example, text messages, voice messages, and telephone calls. The care providing unit can use AI to monitor the user's condition in real time and send a notification to an appropriate contact in the event of an emergency. This allows for a quick response by sending a notification to the emergency contact. Some or all of the above-mentioned processing in the care providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the care providing unit can input the user's condition to the generation AI and cause the generation AI to execute a notification to the emergency contact.

[0070] The photographing unit can estimate the user's emotion and adjust the timing of photographing based on the estimated user's emotion. For example, when the user is stressed, the camera automatically photographs frequently to collect detailed data. Also, when the user is relaxed, the camera can periodically photograph and collect the minimum necessary data. Furthermore, when the user is in a hurry, the camera can photograph multiple times in a short time to quickly collect data. This allows more appropriate data to be collected by adjusting the timing of photographing according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the photographing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the photographing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the timing of photographing.

[0071] The photographing unit can use multiple cameras to capture not only the facial expression of the user but also the movement of the entire body in detail when photographing. For example, the photographing unit can install multiple cameras and simultaneously photograph the face and the entire body of the user from different angles. In addition, the arrangement of the cameras can be adjusted to photograph from an optimal viewpoint according to the movement of the user. Furthermore, a high-resolution camera can be used to capture the subtle facial expressions and movements of the user in detail. As a result, by using multiple cameras, the facial expressions and movements of the user can be captured in detail. A part or all of the above-mentioned processing in the photographing unit may be performed, for example, using AI or may be performed without using AI. For example, the photographing unit can input data from multiple cameras to the generation AI and cause the generation AI to execute an analysis of the facial expressions and movements of the user.

[0072] The photographing unit can simultaneously record the user's environmental sound during photographing and use it for analysis. The photographing unit, for example, has a built-in microphone in the camera and simultaneously records the environmental sound around the user. The environmental sound can also be analyzed and used as data for estimating the user's stress level and emotional state. Furthermore, it is also possible to detect changes in the environmental sound and grasp changes in the user's behavior and situation. In this way, by recording the environmental sound, the user's stress level and emotional state can be more accurately estimated. A part or all of the above-mentioned processing in the photographing unit may be performed, for example, using AI, or may be performed without using AI. For example, the photographing unit can input the environmental sound data to the generation AI and cause the generation AI to execute the analysis of the environmental sound.

[0073] The photographing unit can estimate the user's emotion and select the part to be photographed based on the estimated user's emotion. For example, when the user is feeling stressed, the photographing unit focuses on the facial expression. Also, when the user is relaxed, the photographing unit can photograph the movement of the entire body. Furthermore, when the user is in a hurry, the photographing unit can photograph the movement of the hands and the movement of the feet. In this way, by selecting the part to be photographed according to the user's emotion, more appropriate data can be collected. The estimation of the emotion is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the photographing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the photographing unit can input the user's emotion data to the generation AI and cause the generation AI to select the part to be photographed.

[0074] The imaging unit can detect abnormalities by comparing the past video data of the user at the time of imaging. For example, the imaging unit compares the past video data with the current video data to detect changes in facial expressions and movements. In addition, when abnormal movements or facial expressions are detected, an alert can be issued. Furthermore, based on past data, the imaging unit can learn the user's normal behavioral patterns and detect abnormalities. This allows for early detection of abnormalities by comparing with past video data. Some or all of the above-mentioned processing in the imaging unit may be performed, for example, using AI, or may be performed without using AI. For example, the imaging unit can input past video data to a generation AI and cause the generation AI to detect abnormalities.

[0075] The photographing unit can capture the movement of objects around the user during photographing and use it for analysis. For example, the photographing unit uses a camera to simultaneously photograph the movement of objects around the user and use it for analysis. The photographing unit can also estimate the user's behavior and situation from the movement of the object. Furthermore, it can detect changes in the movement of the object and grasp changes in the user's behavior and situation. As a result, the user's behavior and situation can be grasped more accurately by capturing the movement of the surrounding objects. Part or all of the above-mentioned processing in the photographing unit may be performed, for example, using AI or may be performed without using AI. For example, the photographing unit can input object movement data to the generation AI and cause the generation AI to execute analysis of the object movement.

[0076] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. For example, when the user is feeling stressed, the analysis unit can focus on analyzing stress-related data. Also, when the user is relaxed, the analysis unit can focus on analyzing relaxation-related data. Furthermore, when the user is in a hurry, the analysis can be performed quickly and the results can be provided. This improves the accuracy of the analysis by adjusting the analysis algorithm according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the analysis algorithm.

[0077] During the analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral data. The analysis unit, for example, analyzes the current behavior based on the user's past behavioral data. Also, the analysis unit can detect abnormal behavior by referring to past data. Furthermore, the analysis unit can learn past behavioral patterns and improve the accuracy of the analysis. As a result, the accuracy of the analysis is improved by referring to the past behavioral data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the past behavioral data to the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0078] The analysis unit can perform the analysis by using the user's biometric information in addition to the analysis. The analysis unit, for example, measures the user's heart rate and body temperature and uses them in the analysis. The analysis unit can also estimate the user's stress level and emotional state based on the biometric information. Furthermore, the accuracy of the analysis can be improved by combining the biometric information with the video data. As a result, the accuracy of the analysis is improved by using the biometric information in addition to the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the biometric information data to the generation AI and have the generation AI execute the analysis.

[0079] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible display method. Also, when the user is relaxed, the analysis unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows more appropriate information to be provided by adjusting the display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method.

[0080] The analysis unit can analyze the user's social media activity during the analysis and reflect it in the analysis result. The analysis unit can, for example, analyze the user's social media posts and estimate the emotional state. The analysis unit can also analyze the user's behavior based on the social media activity pattern. Furthermore, the accuracy of the analysis result can be improved by combining social media data. In this way, the accuracy of the analysis result is improved by analyzing the social media activity. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or can be performed without using AI. For example, the analysis unit can input the social media data to the generation AI and have the generation AI perform the analysis.

[0081] The analysis unit can perform the analysis while taking into consideration the user's life rhythm. The analysis unit, for example, analyzes a behavior pattern based on the user's life rhythm. It can also detect changes in the life rhythm and analyze abnormalities. Furthermore, it can also improve the accuracy of the analysis result by taking the life rhythm into consideration. As a result, the accuracy of the analysis result is improved by taking the life rhythm into consideration. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the life rhythm data to the generation AI and have the generation AI execute the analysis.

[0082] The care providing unit can estimate the user's emotion and adjust the content of care based on the estimated user's emotion. For example, when the user is feeling stressed, the care providing unit can suggest a relaxation method. Also, when the user is relaxed, the care providing unit can suggest a stress management method. Furthermore, when the user is in a hurry, the care providing unit can suggest a care method that can be quickly performed. This allows more appropriate care to be provided by adjusting the content of care according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the care providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the care providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the content of care.

[0083] The care providing unit can provide optimal care by referring to the user's past care history when providing care. The care providing unit can, for example, suggest an optimal care method based on the user's past care history. Also, the care providing unit can select an effective care method by referring to the past care history. Furthermore, a care method suited to the user can be provided based on the care history. In this way, optimal care can be provided by referring to the past care history. Some or all of the above-mentioned processing in the care providing unit can be performed, for example, using AI or can be performed without using AI. For example, the care providing unit can input past care history data to the generation AI and cause the generation AI to select the optimal care method.

[0084] The care providing unit can customize the means of care based on the user's current living situation when providing care. The care providing unit, for example, suggests an optimal care method based on the user's current living situation. The care providing unit can also adjust the care method in consideration of changes in the living situation. Furthermore, the care providing unit can customize the means of care according to the current living situation. As a result, more appropriate care can be provided by customizing the means of care based on the current living situation. Some or all of the above-mentioned processing in the care providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the care providing unit can input current living situation data to the generation AI and cause the generation AI to customize the means of care.

[0085] The care providing unit can estimate the user's emotion and determine the priority of care based on the estimated user's emotion. For example, when the user is feeling stressed, the care providing unit preferentially suggests a relaxation method. Also, when the user is relaxed, the care providing unit can preferentially suggest a stress management method. Furthermore, when the user is in a hurry, the care providing unit can preferentially suggest a care method that can be quickly performed. In this way, by determining the priority of care according to the user's emotion, more effective care can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the care providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the care providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of care.

[0086] The care providing unit can provide care by taking into consideration the geographical location information of the user when providing care. The care providing unit can, for example, suggest an optimal care method based on the user's current location. It can also adjust the care method by taking into consideration the geographical location information. Furthermore, it can customize the means of care according to the current location. In this way, more appropriate care can be provided by taking into consideration the geographical location information. Some or all of the above-mentioned processing in the care providing unit can be performed, for example, using AI or without using AI. For example, the care providing unit can input geographical location information data to the generation AI and cause the generation AI to provide care.

[0087] The care providing unit can analyze the user's social media activity and propose a means of care when providing care. The care providing unit can, for example, analyze the user's social media posts and propose an optimal care method. The care providing unit can also adjust the care method based on social media activity patterns. Furthermore, it can combine social media data to propose a means of care. In this way, more appropriate means of care can be proposed by analyzing social media activity. Some or all of the above-mentioned processing in the care providing unit can be performed, for example, using AI or without using AI. For example, the care providing unit can input social media data to a generation AI and have the generation AI execute a proposal of a means of care.

[0088] The learning data unit can estimate the user's emotion and select learning data based on the estimated user's emotion. For example, when the user is feeling stressed, the learning data unit can focus on learning stress-related data. Also, when the user is relaxed, the learning data unit can focus on learning relaxation-related data. Furthermore, when the user is in a hurry, data that can be quickly learned can be selected. This improves the accuracy of learning by selecting learning data based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the learning data unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning data unit can input the user's emotion data to the generation AI and cause the generation AI to select the learning data.

[0089] The learning data unit can optimize the learning algorithm by referring to past learning data during learning. The learning data unit optimizes the current learning algorithm, for example, based on past learning data. Also, the learning accuracy can be improved by referring to past data. Furthermore, the optimal learning algorithm can be selected based on the learning data. As a result, the accuracy of the learning algorithm is improved by referring to the past learning data. Some or all of the above-mentioned processing in the learning data unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning data unit can input past learning data to the generation AI and cause the generation AI to optimize the learning algorithm.

[0090] The learning data unit can estimate the user's emotion and adjust the frequency of learning based on the estimated user's emotion. For example, the learning data unit can increase the frequency of learning when the user is stressed. Also, when the user is relaxed, the learning can be performed in a short time when the user is in a hurry. This improves the efficiency of learning by adjusting the frequency of learning based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the learning data unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning data unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the frequency of learning.

[0091] The learning data unit can weight the learning data based on the user's life rhythm during learning. The learning data unit weights the learning data based on the user's life rhythm, for example. The learning data unit can also adjust the weighting of the learning data in consideration of changes in the life rhythm. Furthermore, the weighting of the learning data can be customized according to the life rhythm. As a result, the accuracy of learning is improved by weighting the learning data based on the life rhythm. A part or all of the above-mentioned processing in the learning data unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning data unit can input life rhythm data to the generation AI and cause the generation AI to perform weighting of the learning data.

[0092] The advice unit can estimate the user's emotion and adjust the content of the advice based on the estimated user's emotion. For example, when the user is feeling stressed, the advice unit can suggest a relaxation method. Also, when the user is relaxed, the advice unit can suggest a method of stress management. Furthermore, when the user is in a hurry, the advice unit can provide advice that can be quickly implemented. This allows more appropriate advice to be provided by adjusting the content of the advice based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the content of the advice.

[0093] When providing advice, the advice unit can provide optimal advice by referring to the user's past advice history. The advice unit can provide optimal advice, for example, based on the user's past advice history. Also, the advice unit can select effective advice by referring to the past advice history. Furthermore, the advice unit can provide advice suited to the user based on the advice history. In this way, optimal advice can be provided by referring to the past advice history. Some or all of the above-mentioned processing in the advice unit can be performed, for example, using AI, or can be performed without using AI. For example, the advice unit can input past advice history data to a generation AI and cause the generation AI to provide optimal advice.

[0094] The advice unit can estimate the user's emotion and determine the priority of advice based on the estimated user's emotion. For example, when the user is feeling stressed, the advice unit can preferentially suggest a relaxation method. Also, when the user is relaxed, the advice unit can preferentially suggest a stress management method. Furthermore, when the user is in a hurry, the advice unit can preferentially suggest advice that can be quickly implemented. In this way, by determining the priority of advice based on the user's emotion, more effective advice can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of advice.

[0095] When providing advice, the advice unit can provide the advice taking into consideration the geographical location information of the user. The advice unit can provide optimal advice based on, for example, the user's current location. The advice unit can also adjust the advice content taking into consideration the geographical location information. Furthermore, the means of advice can be customized according to the current location. In this way, more appropriate advice can be provided by taking the geographical location information into consideration. Some or all of the above-mentioned processing in the advice unit can be performed, for example, using AI or can be performed without using AI. For example, the advice unit can input geographical location information data to the generation AI and cause the generation AI to provide the advice.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on the estimated emotions. For example, if the user is feeling strong stress, stress-related data is analyzed with priority. Also, if the user is relaxed, relaxation-related data can be analyzed with priority. Furthermore, if the user is in a hurry, analysis can be performed quickly and the results can be provided. This improves the accuracy of the analysis by adjusting the priority of the analysis according to the user's emotions. The estimation of emotions is realized using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to determine the priority of the analysis.

[0098] The care providing unit can estimate the user's emotions and adjust the frequency of care based on the estimated emotions. For example, if the user feels strong stress, a relaxation method is frequently suggested. Also, if the user is relaxed, the frequency of care can be reduced. Furthermore, if the user is in a hurry, a care method that can be performed in a short time can be suggested. In this way, more appropriate care can be provided by adjusting the frequency of care based on the user's emotions. The estimation of emotions is realized using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the care providing unit may be performed using an AI, or may be performed without using an AI. For example, the care providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the frequency of care.

[0099] The photographing unit can estimate the user's emotion and adjust the camera settings based on the estimated emotion. For example, if the user is stressed, the camera resolution is increased to collect detailed data. Also, if the user is relaxed, the camera resolution can be reduced to collect the minimum necessary data. Furthermore, if the user is in a hurry, the camera frame rate can be increased to collect data quickly. This allows more appropriate data to be collected by adjusting the camera settings according to the user's emotion. The emotion estimation is realized using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the photographing unit may be performed using an AI or may be performed without using an AI. For example, the photographing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the camera settings.

[0100] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. This allows more appropriate information to be provided by adjusting the display method according to the user's emotions. The estimation of emotions is realized using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method.

[0101] The care providing unit can estimate the user's emotions and determine the priority of care based on the estimated emotions. For example, if the user is feeling stressed, a relaxation method is preferentially suggested. Also, if the user is relaxed, a stress management method can be preferentially suggested. Furthermore, if the user is in a hurry, a care method that can be quickly performed can be preferentially suggested. In this way, by determining the priority of care based on the user's emotions, more effective care can be provided. The estimation of emotions is realized using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the care providing unit may be performed using an AI, or may be performed without using an AI. For example, the care providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of care.

[0102] The analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral data. For example, the analysis unit analyzes the current behavior based on the user's past behavioral data. Also, the analysis unit can detect abnormal behavior by referring to past data. Furthermore, the analysis unit can learn past behavioral patterns and improve the accuracy of the analysis. As a result, the analysis accuracy is improved by referring to the past behavioral data. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input past behavioral data to the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0103] When providing care, the care providing unit can provide optimal care by referring to the user's past care history. For example, the optimal care method can be suggested based on the user's past care history. Also, an effective care method can be selected by referring to the past care history. Furthermore, a care method suited to the user can be provided based on the care history. In this way, optimal care can be provided by referring to the past care history. Some or all of the above-mentioned processing in the care providing unit may be performed using AI, or may be performed without using AI. For example, the care providing unit can input past care history data to the generation AI and cause the generation AI to select the optimal care method.

[0104] The shooting unit can simultaneously record the user's environmental sound during shooting and use it for analysis. For example, a microphone is built into the camera to simultaneously record the environmental sound around the user. The environmental sound can also be analyzed and used as data for estimating the user's stress level and emotional state. Furthermore, it is possible to detect changes in the environmental sound and grasp changes in the user's behavior and situation. In this way, by recording the environmental sound, the user's stress level and emotional state can be more accurately estimated. Some or all of the above-mentioned processing in the shooting unit may be performed using AI or may be performed without using AI. For example, the shooting unit can input the environmental sound data to the generation AI and cause the generation AI to perform analysis of the environmental sound.

[0105] The analysis unit can perform the analysis by using the user's biometric information in addition to the analysis. For example, the user's heart rate and body temperature are measured and used for the analysis. The user's stress level and emotional state can also be estimated based on the biometric information. Furthermore, the accuracy of the analysis can be improved by combining the biometric information with the video data. As a result, the accuracy of the analysis is improved by using the biometric information in addition to the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI or may be performed without using AI. For example, the analysis unit can input the biometric information data to the generation AI and have the generation AI perform the analysis.

[0106] The care providing unit can customize the means of care based on the user's current living situation when providing care. For example, the optimal care method can be suggested based on the user's current living situation. The care method can also be adjusted taking into account changes in the living situation. Furthermore, the means of care can be customized according to the current living situation. As a result, more appropriate care can be provided by customizing the means of care based on the current living situation. Some or all of the above-mentioned processing in the care providing unit may be performed using AI or may be performed without using AI. For example, the care providing unit can input current living situation data to the generation AI and cause the generation AI to customize the means of care.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The camera captures the user's facial expressions and movements with a camera. The user's facial expressions and movements include, for example, smiling, anger, sadness, hand movements, and body movements. The camera uses a high-resolution camera to capture the user's facial expressions in detail, and can also use a wide-angle camera to capture the user's entire body movements. It can also use multiple cameras to track the user's movements in real time. Step 2: The analysis unit analyzes the video captured by the camera unit and estimates the risk of suicide from the user's facial expressions and movements. The analysis unit performs analysis based on pre-learned data, reading emotions from facial expressions and detecting signs of stress and anxiety from hand movements. Using AI, the analysis unit can estimate the risk of suicide from the user's facial expressions and movements. Step 3: The care provider provides care based on the results of the analysis. The care provider can provide advice on relaxation and stress management, display a message encouraging the patient to consult a specialist, or send a notification to an emergency contact.

[0109] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0111] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements including the above-mentioned photographing unit, analysis unit, and care providing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the smart device 14, and captures the user's facial expressions and movements in detail. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's facial expressions and movements based on previously learned data, and estimates the risk of suicide. The care providing unit is realized, for example, by the control unit 46A of the smart device 14, and provides advice on relaxation methods and stress management. In addition, the care providing unit is also realized by the specific processing unit 290 of the data processing device 12, and provides a message encouraging consultation with a specialist and a notification to an emergency contact. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0116] 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.

[0117] 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 the voice according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0119] 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 performed in a secure state.

[0120] Fig. 4 shows an example of 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.

[0121] 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.

[0122] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0125] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0126] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0127] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements including the above-mentioned photographing unit, analysis unit, and care providing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the smart glasses 214, and captures the user's facial expressions and movements in detail. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's facial expressions and movements based on previously learned data, and estimates the risk of suicide. The care providing unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides advice on relaxation methods and stress management. In addition, the care providing unit is also realized by the specific processing unit 290 of the data processing device 12, and provides a message encouraging consultation with a specialist and a notification to an emergency contact. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0132] 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.

[0133] 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 the voice according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0135] 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 performed in a secure state.

[0136] 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.

[0137] 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.

[0138] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0139] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0141] 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 voice indicating a user input for 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.

[0142] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0143] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements including the above-mentioned photographing unit, analysis unit, and care providing unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the headset type terminal 314, and captures the user's facial expressions and movements in detail. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's facial expressions and movements based on previously learned data, and estimates the risk of suicide. The care providing unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides advice on relaxation methods and stress management. In addition, the care providing unit is also realized by the specific processing unit 290 of the data processing device 12, and provides a message encouraging consultation with a specialist and a notification to an emergency contact. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0148] 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.

[0149] 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 the voice according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0151] 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 performed in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0153] 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.

[0154] 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.

[0155] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0156] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0157] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0158] 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 a voice indicating a user input for 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.

[0159] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0160] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0161] Each of the multiple elements including the above-mentioned photographing unit, analysis unit, and care providing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the robot 414, and captures the user's facial expressions and movements in detail. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's facial expressions and movements based on previously learned data, and estimates the risk of suicide. The care providing unit is realized, for example, by the control unit 46A of the robot 414, and provides advice on relaxation methods and stress management. The care providing unit is also realized by the specific processing unit 290 of the data processing device 12, and provides a message encouraging consultation with a specialist and a notification to an emergency contact. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0163] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0165] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0167] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0169] 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.

[0170] 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 Universal Serial Bus (USB) 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.

[0171] In addition, 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 upon request from the data processing device 12.

[0172] 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.

[0173] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0174] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0175] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0176] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0178] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0180] (Appendix 1) A camera unit that captures the user's facial expressions and movements; An analysis unit that analyzes the video captured by the imaging unit and estimates the risk of suicide from the user's facial expressions and movements; A care providing unit that provides care based on the estimation result obtained by the analysis unit. A system characterized by: (Appendix 2) The analysis unit is Equipped with a learning data section that analyzes the user's facial expressions and movements based on pre-trained data 2. The system of claim 1, (Appendix 3) The care providing department includes: Equipped with an advice section that provides advice on relaxation methods or stress management 2. The system of claim 1, (Appendix 4) The care providing department includes: Display a message encouraging you to consult with an expert 2. The system of claim 1, (Appendix 5) The care providing department includes: Send a notification to your emergency contacts 2. The system of claim 1, (Appendix 6) The imaging unit includes: Estimate the user's emotions and adjust the timing of shooting based on the estimated user's emotions 2. The system of claim 1, (Appendix 7) The imaging unit includes: When shooting, multiple cameras are used to capture not only the user's facial expressions but also the movements of the entire body in detail. 2. The system of claim 1, (Appendix 8) The imaging unit includes: When taking a picture, the user's surrounding sounds are simultaneously recorded and used for analysis. 2. The system of claim 1, (Appendix 9) The imaging unit includes: The user's emotions are estimated, and the parts of the body to be photographed are selected based on the estimated user's emotions. 2. The system of claim 1, (Appendix 10) The imaging unit includes: When shooting, the system compares the image data with the user's past image data to detect abnormalities. 2. The system of claim 1, (Appendix 11) The imaging unit includes: When taking a photo, the movement of objects around the user is also captured and used for analysis. 2. The system of claim 1, (Appendix 12) The analysis unit is Inferring user emotions and adjusting analysis algorithms based on the inferred user emotions 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, the accuracy of the analysis is improved by referring to the user's past behavior data. 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, the user's biometric information is also used. 2. The system of claim 1, (Appendix 15) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, the user's social media activity is analyzed and reflected in the analysis results. 2. The system of claim 1, (Appendix 17) The analysis unit is When analyzing, the user's daily rhythm is taken into account 2. The system of claim 1, (Appendix 18) The care providing department includes: Estimating a user's emotions and adjusting care content based on the estimated user's emotions 2. The system of claim 1, (Appendix 19) The care providing department includes: When providing care, provide care by referring to the user's past care history 2. The system of claim 1, (Appendix 20) The care providing department includes: At the time of care delivery, customize care delivery based on the user's current living situation 2. The system of claim 1, (Appendix 21) The care providing department includes: To estimate a user's emotion and determine a priority of care based on the estimated user's emotion. 2. The system of claim 1, (Appendix 22) The care providing department includes: When providing care, the geographic location of the user is taken into consideration. 2. The system of claim 1, (Appendix 23) The care providing department includes: Analyzing users' social media activity to suggest care measures when providing care 2. The system of claim 1, (Appendix 24) The learning data unit includes: Estimate the user's emotions and select learning data based on the estimated user emotions. 3. The system of claim 2, (Appendix 25) The learning data unit includes: During training, the learning algorithm is optimized by referencing past training data. 3. The system of claim 2, (Appendix 26) The learning data unit includes: Infer user emotions and adjust the frequency of learning based on the estimated user emotions. 3. The system of claim 2, (Appendix 27) The learning data unit includes: During learning, the learning data is weighted based on the user's daily rhythm 3. The system of claim 2, (Appendix 28) The advice unit, Estimate the user's emotions and adjust the advice content based on the estimated user emotions 4. The system of claim 3. (Appendix 29) The advice unit, When providing advice, the advice is provided by referring to the user's past advice history. 4. The system of claim 3. (Appendix 30) The advice unit, Estimate the user's emotions and prioritize advice based on the estimated user emotions. 4. The system of claim 3. (Appendix 31) The advice unit, When providing advice, the advice is provided taking into account the user's geographic location information. 4. The system of claim 3. [Explanation of symbols]

[0181] 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 camera unit that captures the user's facial expressions and movements; An analysis unit that analyzes the video captured by the imaging unit and estimates the risk of suicide from the user's facial expressions and movements; A care providing unit that provides care based on the estimation result obtained by the analysis unit. A system characterized by:

2. The analysis unit is Equipped with a learning data section that analyzes the user's facial expressions and movements based on pre-trained data 2. The system of claim 1 .

3. The care providing department includes: Equipped with an advice section that provides advice on relaxation methods or stress management 2. The system of claim 1 .

4. The care providing department includes: Display a message encouraging you to consult with an expert 2. The system of claim 1 .

5. The care providing department includes: Send a notification to your emergency contacts 2. The system of claim 1 .

6. The imaging unit includes: Estimate the user's emotions and adjust the timing of shooting based on the estimated user's emotions 2. The system of claim 1 .

7. The imaging unit includes: When shooting, multiple cameras are used to capture not only the user's facial expressions but also the movements of the entire body in detail.

2. The system of claim 1 .

8. The imaging unit includes: When taking a picture, the user's surrounding sounds are simultaneously recorded and used for analysis.

2. The system of claim 1 .

9. The imaging unit includes: The user's emotions are estimated, and the parts of the body to be photographed are selected based on the estimated user's emotions.

2. The system of claim 1 .

10. The imaging unit includes: When shooting, the system compares the image data with the user's past image data to detect abnormalities.

2. The system of claim 1 .

Images