System

Abstract

An object of a system according to an embodiment is to analyze the behavior of an elderly family member and detect symptoms of dementia and depression at an early stage.SOLUTION: A system according to an embodiment includes a behavior analysis unit, a log recording unit, a report generation unit, an alert notification unit, and a camera image confirmation unit. The behavior analysis part analyzes the behavior of the aged family by using a sensor, sound and video. The log recording unit records the behavior analyzed by the behavior analysis unit in a log. The report generation unit provides information in a report format based on the log recorded by the log recording unit. The alert notification unit detects an action log that seems to be a symptom of progress of dementia or depression and performs alert notification to the family. The camera image confirmation unit confirms the camera image when necessary.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult to remotely and appropriately monitor the behavior of elderly family members and detect symptoms of dementia or depression early.

[0005] The system according to the embodiment aims to analyze the behavior of elderly family members and detect symptoms of dementia and depression at an early stage. [Means for solving the problem]

[0006] The system according to the embodiment includes a behavior analysis unit, a log recording unit, a report generation unit, an alert notification unit, and a camera video confirmation unit. The behavior analysis unit analyzes the behavior of elderly family members using sensors, audio, and video. The log recording unit records the behavior analyzed by the behavior analysis unit in a log. The report generation unit provides information in report format based on the log recorded by the log recording unit. The alert notification unit detects behavior logs that appear to indicate the progression of dementia or symptoms of depression and sends an alert to the family. The camera video confirmation unit checks the camera video when necessary. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the behavior of elderly family members and detect symptoms of dementia and depression at an early stage. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The behavior analysis system according to an embodiment of the present invention analyzes the behavior of elderly family members, records the logs, provides information in the form of reports, detects abnormal behavior and notifies the family members, and checks camera footage when necessary. This allows the behavior analysis system to effectively monitor the safety and health of elderly family members even from a distance.

[0029] A behavior analysis system according to an embodiment includes a behavior analysis unit, a log recording unit, a report generation unit, an alert notification unit, and a camera video confirmation unit. The behavior analysis unit analyzes the behavior of elderly family members using sensors, audio, and video. For example, it monitors the frequency of walking around the room, meal times, sleep patterns, and the like. The behavior analysis unit generates a behavior log by using a generation AI that analyzes data from the sensors and cameras. The log recording unit records the behavior analyzed by the behavior analysis unit in a log. For example, the data analyzed by the generation AI is saved as time-series data. The log recording unit can also save data in text format or as an event log. The report generation unit provides information in report format based on the log saved by the log recording unit. For example, the generation AI can provide information such as, "Today's walking distance was 5,000 steps, meals were eaten three times, and sleep time was 8 hours." The report generation unit can also provide information visually using PDF format, graphs, and charts. The alert notification unit detects behavior logs that suggest progression of dementia or symptoms of depression and sends an alert to family members. For example, an alert is issued if abnormal behavior such as walking less than usual, eating less, or sleeping for an extremely short time is detected. The alert notification unit uses the generation AI to analyze behavior log data and detect abnormalities. The camera footage confirmation unit checks camera footage when necessary. For example, upon receiving an alert notification, the user can check the camera footage to confirm the actual situation. The camera footage confirmation unit also uses the generation AI to analyze the camera footage and provide necessary information. This allows the behavior analysis system according to the embodiment to effectively monitor the safety and health of elderly family members, even from a distance. For example, understanding daily behavioral patterns allows for quick response when an abnormality occurs. It can also be useful for early detection of dementia and depression.

[0030] The behavior analysis unit can incorporate an algorithm that compares data from the elderly family member's past behavior and detects abnormal behavior early. The behavior analysis unit, for example, accumulates data from the elderly family member's past behavior and compares it with their current behavior to detect abnormal behavior early. For example, it compares their walking pattern from the past year with their current walking pattern to detect abnormalities. The behavior analysis unit also incorporates an algorithm that uses a generative AI to compare data from the past behavior and detect abnormal behavior earlier. For example, it uses a machine learning algorithm to learn abnormal behavior patterns from past data and compare it with current behavior to detect abnormalities. The behavior analysis unit also uses a rule-based detection method to detect abnormal behavior by comparing it with past data. For example, it determines that a specific behavior pattern is abnormal if it continues for a certain period of time. This allows for early detection of abnormal behavior by comparing it with past behavior data.

[0031] The behavior analysis unit can analyze fluctuations in behavior patterns taking environmental factors into account. The behavior analysis unit, for example, acquires weather data and analyzes the impact on the behavior patterns of elderly family members. For example, it records in a log the differences in behavior patterns on rainy days and sunny days. The behavior analysis unit also analyzes fluctuations in behavior patterns taking seasonal changes into account. For example, it analyzes the differences in behavior patterns between summer and winter and records them in a log. The behavior analysis unit also monitors indoor temperature and analyzes fluctuations in behavior patterns. For example, it analyzes the differences in behavior patterns when the indoor temperature is high and when it is low. This makes it possible to analyze fluctuations in behavior patterns taking environmental factors into account.

[0032] The behavior analysis unit can also be applied to analyzing pet behavior, allowing for monitoring of pet health. For example, the behavior analysis unit monitors pet behavior using sensors and cameras, and the generation AI analyzes that behavior and records it in a log. For example, it records the time and amount of meals, the amount of exercise, etc. The behavior analysis unit also analyzes behavior patterns to monitor pet health. For example, it analyzes the pet's sleep time and activity level to evaluate its health. The behavior analysis unit also analyzes behavior patterns to detect abnormal behavior in pets. For example, it detects abnormal movements or a decrease in appetite. This allows for application to pet behavior analysis, allowing for monitoring of pet health.

[0033] The behavior analysis unit can be applied to analyze employee behavior in the workplace and monitor employee stress and fatigue. For example, the behavior analysis unit monitors employee behavior in the workplace using sensors and cameras, and the generation AI analyzes that behavior and records it in a log. For example, it records work hours, break times, work efficiency, etc. The behavior analysis unit also analyzes behavior patterns to monitor employee stress and fatigue. For example, it analyzes changes in heart rate and work efficiency to evaluate stress and fatigue. The behavior analysis unit also analyzes behavior patterns to detect abnormal employee behavior. For example, it detects long periods of inactivity or abnormal movements. This can be applied to analyzing employee behavior in the workplace and monitoring employee stress and fatigue.

[0034] The report generation unit can add a function that compares with past data and highlights abnormal patterns. For example, when checking logs, the report generation unit adds a function that allows the generation AI to compare with past data and highlight abnormal patterns. For example, it can detect abnormal behavior by comparing with data from the past week. The report generation unit also adds a function that allows the generation AI to compare with past data and highlight abnormal patterns. For example, it can determine that an abnormality exists when a specific behavior pattern continues for a certain period of time. The report generation unit also adds a function that allows the generation AI to compare with past data and highlight abnormal patterns. For example, it can highlight abnormalities using color coding or alert displays. This allows family members to quickly identify abnormalities by comparing with past data and highlighting abnormal patterns.

[0035] The report generation unit can add a function that predicts the behavioral patterns of elderly family members and includes future behavior in the report. For example, the report generation unit adds a function in which the generation AI analyzes the behavioral patterns of elderly family members and predicts future behavior. For example, it provides information such as, "Tomorrow's walking distance is predicted to be about 5,000 steps." The report generation unit also predicts behavioral patterns and includes future behavior in the report. For example, it uses a machine learning algorithm to predict future behavior from past data. The report generation unit also adds a function in which the generation AI predicts behavioral patterns and includes future behavior in the report. For example, it predicts future behavior based on trend analysis of past data. In this way, by predicting the behavioral patterns of elderly family members and including future behavior in the report, family members can consider countermeasures in advance.

[0036] The report generation unit can be applied to logging student behavior at school, allowing the development of a system that allows teachers and parents to monitor student behavior. For example, the report generation unit monitors students' behavior at school using sensors and cameras, and the generation AI analyzes and logs that behavior. For example, it records the student's level of concentration during class and their behavior during breaks. The report generation unit also generates reports based on the data analyzed by the generation AI and provides them to teachers and parents. For example, it can provide information such as, "Today's student concentration during class was high, and they actively interacted with their friends during breaks." The report generation unit can also provide information visually using PDF format, graphs, and charts. This can be applied to logging student behavior at school, allowing teachers and parents to monitor student behavior.

[0037] The report generation unit can be applied to athletes' training logs to develop a system that allows coaches to monitor their performance. For example, the report generation unit monitors athletes' training using sensors and cameras, and the generation AI analyzes their behavior and records the log. For example, it records the intensity and duration of training. The report generation unit also generates a report based on the data analyzed by the generation AI and provides it to the coach. For example, it provides information such as "Today's training intensity was high and lasted for one hour." The report generation unit can also provide information visually using PDF format, graphs, and charts. This can be applied to athletes' training logs to enable coaches to monitor their performance.

[0038] The alert notification unit can be added with a function to monitor the social activities of elderly family members and detect social isolation. The alert notification unit, for example, monitors the social activities of elderly family members, and the generation AI analyzes the data to detect social isolation. For example, it records the frequency of conversations with friends and the number of times people go out. The alert notification unit also analyzes social activities to detect social isolation. For example, it detects social isolation based on not going out for a certain period of time or a decrease in conversations with friends. The alert notification unit also adds a function to analyze social activities and detect social isolation. For example, it analyzes the frequency of participation in community activities to detect social isolation. This allows the social activities of elderly family members to be monitored and social isolation to be detected early.

[0039] The alert notification unit can add a function to analyze the sleep patterns of elderly family members in detail and detect sleep disorders early. The alert notification unit, for example, monitors the sleep patterns of elderly family members using a sensor, and the generation AI analyzes the data to detect sleep disorders. For example, it records sleep time and sleep quality. The alert notification unit also analyzes sleep patterns in detail and detects sleep disorders. For example, it analyzes the number of awakenings during the night and the depth of sleep to detect sleep disorders. The alert notification unit also adds a function to analyze sleep patterns in detail and detect sleep disorders. For example, it analyzes signs of insomnia and hypersomnia to detect sleep disorders. This allows for detailed analysis of the sleep patterns of elderly family members and early detection of sleep disorders.

[0040] The alert notification unit can be applied to student mental health monitoring, allowing the development of a system that detects signs of stress and depression at school early. The alert notification unit, for example, monitors students' behavior using sensors or cameras, and the generation AI analyzes the data to record their mental health status. For example, it records their concentration level during class and their behavior during breaks. The alert notification unit also analyzes behavioral patterns to detect signs of stress and depression at school early. For example, it analyzes changes in behavior and emotions to detect signs of stress and depression. The alert notification unit can also develop a system in which the generation AI analyzes behavioral patterns to detect signs of stress and depression early. For example, it detects signs of stress and depression based on changes in heart rate and behavioral patterns. This can be applied to student mental health monitoring, allowing the early detection of signs of stress and depression at school.

[0041] The alert notification unit can be applied to employee mental health monitoring in the workplace, allowing the development of a system that detects signs of stress and burnout early. For example, the alert notification unit monitors employee behavior in the workplace using sensors or cameras, and the generation AI analyzes the data to record mental health status. For example, it records work hours, break times, work efficiency, etc. The alert notification unit also analyzes behavioral patterns to detect signs of stress and burnout in the workplace early. For example, it analyzes changes in behavior and emotions to detect signs of stress and burnout. The alert notification unit can also develop a system in which the generation AI analyzes behavioral patterns to detect signs of stress and burnout early. For example, it detects signs of stress and burnout based on changes in heart rate and behavioral patterns. This can be applied to employee mental health monitoring in the workplace, allowing for early detection of signs of stress and burnout.

[0042] The camera footage confirmation unit can be equipped with a function that automatically highlights abnormal behavior, allowing family members to quickly identify any abnormalities. For example, the camera footage confirmation unit will develop a system that analyzes camera footage in real time and automatically highlights abnormal behavior. For example, it will detect falls or long periods of inactivity and highlight them in the footage. The camera footage confirmation unit will also automatically highlight abnormal behavior. For example, it will highlight abnormalities using color coding or alert displays. The camera footage confirmation unit will also add a function that uses the generation AI to automatically highlight abnormal behavior. For example, it will detect abnormal movements or stops and mark them in the footage. This will allow family members to quickly identify any abnormalities by automatically highlighting abnormal behavior.

[0043] The camera video confirmation unit can be added with a function that analyzes video and automatically extracts important events. For example, a system can be developed for the camera video confirmation unit that analyzes camera video in real time and automatically extracts important events. For example, it can detect falls or long periods of inactivity and mark them in the video. The camera video confirmation unit can also automatically extract important events. For example, it can highlight important events using color coding or alert displays. The camera video confirmation unit can also be added with a function that uses the generation AI to automatically extract important events. For example, it can detect abnormal movements or stops and mark them in the video. This allows important events to be automatically extracted, allowing family members to respond quickly.

[0044] The camera footage confirmation unit can be applied to a pet monitoring system to develop a system that detects abnormal pet behavior. For example, the camera footage confirmation unit monitors pet behavior with a camera, and a generation AI analyzes the video to develop a system that detects abnormal behavior. For example, it detects abnormal movements or long periods of inactivity. The camera footage confirmation unit also analyzes behavioral patterns to detect abnormal pet behavior. For example, it detects abnormal vocalizations or a decrease in appetite. The camera footage confirmation unit also develops a system that uses a generation AI to analyze pet behavior and detect abnormal behavior. For example, it detects abnormal movements or long periods of inactivity and marks them in the video. This allows the system to be applied to a pet monitoring system to detect abnormal pet behavior.

[0045] The camera footage confirmation unit can be applied to factory monitoring systems to develop systems that detect abnormal machine operation. For example, the camera footage confirmation unit monitors machines in a factory with a camera, and a generation AI analyzes the footage to detect abnormal operation. For example, it detects abnormal vibrations or stoppages. The camera footage confirmation unit also analyzes operation patterns to detect abnormal machine operation. For example, it detects abnormal movements or stoppages. The camera footage confirmation unit also analyzes machines in a factory with a generation AI to develop systems that detect abnormal operation. For example, it detects abnormal vibrations or stoppages and marks them in the footage. This can be applied to factory monitoring systems to detect abnormal machine operation.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] When analyzing a user's behavior, the behavior analysis unit can infer the motivation behind the behavior and record changes in motivation in the log recording unit. For example, it can analyze the reason why a user repeats a particular behavior and infer the motivation. For example, it can analyze whether changes in meal times or frequency are due to health reasons or simply a change in habit. The behavior analysis unit can also detect abnormal behavior based on changes in motivation. For example, it can detect an abnormality when behavior that differs from normal motivation is observed. This allows for more detailed behavior analysis by analyzing the motivation behind the behavior and recording changes in motivation in a log.

[0048] When analyzing a user's behavior, the behavior analysis unit can predict behavior patterns and predict future behavior. For example, it can predict the next day's behavior pattern based on past behavior data. For example, it can provide information such as, "Tomorrow's walking distance is predicted to be about 5,000 steps." The behavior analysis unit can also detect abnormal behavior based on predicted behavior patterns. For example, it can detect an abnormality when the predicted behavior pattern differs significantly from the actual behavior. This makes it possible to predict behavior patterns and future behavior, allowing countermeasures to be considered in advance.

[0049] When analyzing user behavior, the behavior analysis unit can cluster behavior patterns and identify different behavior patterns. For example, the behavior data of the user can be analyzed using a clustering algorithm to identify different behavior patterns. For example, it can distinguish between everyday behavior patterns and behavior patterns during specific events. The behavior analysis unit can also detect abnormal behavior based on the clustered behavior patterns. For example, it can detect an abnormality when behavior that differs from normal clusters is observed. In this way, clustering behavior patterns and identifying different behavior patterns enables more detailed behavior analysis.

[0050] When analyzing a user's behavior, the behavior analysis unit can evaluate the impact of the behavior and predict the outcome of the behavior. For example, it can evaluate the impact of a specific behavior on health and predict the outcome. For example, it can predict the positive impact that an increase in exercise will have on health. The behavior analysis unit can also detect abnormal behavior based on the impact of the behavior. For example, it can detect an abnormality when behavior that has a negative impact on health is observed. This allows for more detailed behavior analysis by evaluating the impact of the behavior and predicting the outcome of the behavior.

[0051] When analyzing a user's behavior, the behavior analysis unit can evaluate the risk of the behavior and identify high-risk behavior. For example, it can evaluate the risk that a specific behavior poses to health and identify that risk. For example, it can evaluate the risk that lack of exercise poses to health. The behavior analysis unit can also detect abnormal behavior based on high-risk behavior. For example, it can detect an abnormality when high-risk behavior that has a negative impact on health is observed. This allows for more detailed behavior analysis by evaluating the risk of behavior and identifying high-risk behavior.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The behavior analysis unit analyzes the behavior of elderly family members using sensors, audio, and video. For example, it monitors how often they walk around the room, meal times, and sleep patterns. The behavior analysis unit also uses a generation AI to analyze data from sensors and cameras to generate a behavior log. Step 2: The log recording unit records the behavior analyzed by the behavior analysis unit in a log. For example, the data analyzed by the generation AI is saved as time-series data. The log recording unit can also save the data in text format or as an event log. Step 3: The report generation unit provides information in the form of a report based on the logs recorded by the log recording unit. For example, the generation AI may provide information such as, "Today's walking distance was 5,000 steps, meals were 3 times, and sleep time was 8 hours." The report generation unit can also provide information visually using PDF format, graphs, and charts. Step 4: The alert notification unit detects behavioral logs that may indicate the progression of dementia or symptoms of depression and sends an alert to family members. For example, an alert is issued if abnormal behavior such as walking less than usual, eating less, or sleeping for an extremely short time is detected. The alert notification unit also uses the generation AI to analyze the behavioral log data and detect abnormalities. Step 5: The camera footage confirmation unit checks the camera footage when necessary. For example, when an alert notification is received, the camera footage can be checked to confirm the actual situation. The camera footage confirmation unit also uses the generation AI to analyze the camera footage and provide the necessary information.

[0054] (Example 2) The behavior analysis system according to an embodiment of the present invention analyzes the behavior of elderly family members, records the logs, provides information in the form of reports, detects abnormal behavior and notifies the family members, and checks camera footage when necessary. This allows the behavior analysis system to effectively monitor the safety and health of elderly family members even from a distance.

[0055] A behavior analysis system according to an embodiment includes a behavior analysis unit, a log recording unit, a report generation unit, an alert notification unit, and a camera video confirmation unit. The behavior analysis unit analyzes the behavior of elderly family members using sensors, audio, and video. For example, it monitors the frequency of walking around the room, meal times, sleep patterns, and the like. The behavior analysis unit generates a behavior log by using a generation AI that analyzes data from the sensors and cameras. The log recording unit records the behavior analyzed by the behavior analysis unit in a log. For example, the data analyzed by the generation AI is saved as time-series data. The log recording unit can also save data in text format or as an event log. The report generation unit provides information in report format based on the log saved by the log recording unit. For example, the generation AI can provide information such as, "Today's walking distance was 5,000 steps, meals were eaten three times, and sleep time was 8 hours." The report generation unit can also provide information visually using PDF format, graphs, and charts. The alert notification unit detects behavior logs that suggest progression of dementia or symptoms of depression and sends an alert to family members. For example, an alert is issued if abnormal behavior such as walking less than usual, eating less, or sleeping for an extremely short time is detected. The alert notification unit uses the generation AI to analyze behavior log data and detect abnormalities. The camera footage confirmation unit checks camera footage when necessary. For example, upon receiving an alert notification, the user can check the camera footage to confirm the actual situation. The camera footage confirmation unit also uses the generation AI to analyze the camera footage and provide necessary information. This allows the behavior analysis system according to the embodiment to effectively monitor the safety and health of elderly family members, even from a distance. For example, understanding daily behavioral patterns allows for quick response when an abnormality occurs. It can also be useful for early detection of dementia and depression.

[0056] The behavior analysis unit can use the emotion estimation function to estimate the emotion behind the behavior and record changes in emotion in the log recording unit. The behavior analysis unit, for example, analyzes daily behavior (e.g., eating, walking, watching TV, etc.) performed by an elderly family member and estimates the emotion at that time. For example, it estimates joy or satisfaction from facial expressions and voice during meals and records the result in a log. The behavior analysis unit also uses the emotion estimation function to estimate the emotion behind the behavior. For example, it uses facial expression recognition technology to analyze changes in facial expressions and estimate emotions. The behavior analysis unit also uses voice analysis technology to analyze the tone and speed of voice and estimate emotions. For example, it estimates emotions based on changes in voice tone and speed. In this way, it is possible to estimate the emotion behind the behavior and record changes in emotions in a log.

[0057] The behavior analysis unit can incorporate an algorithm that compares data from the elderly family member's past behavior and detects abnormal behavior early. The behavior analysis unit, for example, accumulates data from the elderly family member's past behavior and compares it with their current behavior to detect abnormal behavior early. For example, it compares their walking pattern from the past year with their current walking pattern to detect abnormalities. The behavior analysis unit also incorporates an algorithm that uses a generative AI to compare data from the past behavior and detect abnormal behavior earlier. For example, it uses a machine learning algorithm to learn abnormal behavior patterns from past data and compare it with current behavior to detect abnormalities. The behavior analysis unit also uses a rule-based detection method to detect abnormal behavior by comparing it with past data. For example, it determines that a specific behavior pattern is abnormal if it continues for a certain period of time. This allows for early detection of abnormal behavior by comparing it with past behavior data.

[0058] The behavior analysis unit can analyze fluctuations in behavior patterns taking environmental factors into account. The behavior analysis unit, for example, acquires weather data and analyzes the impact on the behavior patterns of elderly family members. For example, it records in a log the differences in behavior patterns on rainy days and sunny days. The behavior analysis unit also analyzes fluctuations in behavior patterns taking seasonal changes into account. For example, it analyzes the differences in behavior patterns between summer and winter and records them in a log. The behavior analysis unit also monitors indoor temperature and analyzes fluctuations in behavior patterns. For example, it analyzes the differences in behavior patterns when the indoor temperature is high and when it is low. This makes it possible to analyze fluctuations in behavior patterns taking environmental factors into account.

[0059] The behavior analysis unit can also be applied to analyzing pet behavior, allowing for monitoring of pet health. For example, the behavior analysis unit monitors pet behavior using sensors and cameras, and the generation AI analyzes that behavior and records it in a log. For example, it records the time and amount of meals, the amount of exercise, etc. The behavior analysis unit also analyzes behavior patterns to monitor pet health. For example, it analyzes the pet's sleep time and activity level to evaluate its health. The behavior analysis unit also analyzes behavior patterns to detect abnormal behavior in pets. For example, it detects abnormal movements or a decrease in appetite. This allows for application to pet behavior analysis, allowing for monitoring of pet health.

[0060] The behavior analysis unit can be applied to analyze employee behavior in the workplace and monitor employee stress and fatigue. For example, the behavior analysis unit monitors employee behavior in the workplace using sensors and cameras, and the generation AI analyzes that behavior and records it in a log. For example, it records work hours, break times, work efficiency, etc. The behavior analysis unit also analyzes behavior patterns to monitor employee stress and fatigue. For example, it analyzes changes in heart rate and work efficiency to evaluate stress and fatigue. The behavior analysis unit also analyzes behavior patterns to detect abnormal employee behavior. For example, it detects long periods of inactivity or abnormal movements. This can be applied to analyzing employee behavior in the workplace and monitoring employee stress and fatigue.

[0061] The behavior analysis unit uses the emotion estimation function to notify the family of the emotional state of the elderly family member based on the results of the behavior analysis, thereby encouraging emotional support. The behavior analysis unit, for example, develops a system that estimates emotions based on the results of the behavior analysis of the elderly family member and notifies the family of the emotional state. For example, the emotional state is reported to the family based on an emotion score. The behavior analysis unit also uses the emotion estimation function to estimate the emotional state of the elderly family member based on the results of the behavior analysis. For example, facial expression recognition technology is used to analyze changes in facial expressions and estimate emotions. The behavior analysis unit also uses voice analysis technology to analyze the tone and speed of voice and estimate emotions. For example, emotions are estimated based on changes in tone and speed of voice. This makes it possible to notify the family of the emotional state of the elderly family member based on the results of the behavior analysis, thereby encouraging emotional support.

[0062] The report generation unit can use the emotion estimation function to include the emotional state of the elderly family member in the report. For example, when checking the log, the report generation unit uses the generation AI to estimate emotions and include the emotional state of the elderly family member in the report. For example, it provides information such as, "Today's emotional state is stable, and there are no particular problems." The report generation unit also uses the emotion estimation function to include the emotional state of the elderly family member in the report. For example, it uses facial expression recognition technology to analyze changes in facial expressions and estimate emotions. The report generation unit also uses voice analysis technology to analyze the tone and speed of voice and estimate emotions. For example, it estimates emotions based on changes in tone and speed of voice. In this way, by including the emotional state of the elderly family member in the report, the family can obtain more detailed information.

[0063] The report generation unit can add a function that compares with past data and highlights abnormal patterns. For example, when checking logs, the report generation unit adds a function that allows the generation AI to compare with past data and highlight abnormal patterns. For example, it can detect abnormal behavior by comparing with data from the past week. The report generation unit also adds a function that allows the generation AI to compare with past data and highlight abnormal patterns. For example, it can determine that an abnormality exists when a specific behavior pattern continues for a certain period of time. The report generation unit also adds a function that allows the generation AI to compare with past data and highlight abnormal patterns. For example, it can highlight abnormalities using color coding or alert displays. This allows family members to quickly identify abnormalities by comparing with past data and highlighting abnormal patterns.

[0064] The report generation unit can add a function that predicts the behavioral patterns of elderly family members and includes future behavior in the report. For example, the report generation unit adds a function in which the generation AI analyzes the behavioral patterns of elderly family members and predicts future behavior. For example, it provides information such as, "Tomorrow's walking distance is predicted to be about 5,000 steps." The report generation unit also predicts behavioral patterns and includes future behavior in the report. For example, it uses a machine learning algorithm to predict future behavior from past data. The report generation unit also adds a function in which the generation AI predicts behavioral patterns and includes future behavior in the report. For example, it predicts future behavior based on trend analysis of past data. In this way, by predicting the behavioral patterns of elderly family members and including future behavior in the report, family members can consider countermeasures in advance.

[0065] The report generation unit can be applied to logging student behavior at school, allowing the development of a system that allows teachers and parents to monitor student behavior. For example, the report generation unit monitors students' behavior at school using sensors and cameras, and the generation AI analyzes and logs that behavior. For example, it records the student's level of concentration during class and their behavior during breaks. The report generation unit also generates reports based on the data analyzed by the generation AI and provides them to teachers and parents. For example, it can provide information such as, "Today's student concentration during class was high, and they actively interacted with their friends during breaks." The report generation unit can also provide information visually using PDF format, graphs, and charts. This can be applied to logging student behavior at school, allowing teachers and parents to monitor student behavior.

[0066] The report generation unit can be applied to athletes' training logs to develop a system that allows coaches to monitor their performance. For example, the report generation unit monitors athletes' training using sensors and cameras, and the generation AI analyzes their behavior and records the log. For example, it records the intensity and duration of training. The report generation unit also generates a report based on the data analyzed by the generation AI and provides it to the coach. For example, it provides information such as "Today's training intensity was high and lasted for one hour." The report generation unit can also provide information visually using PDF format, graphs, and charts. This can be applied to athletes' training logs to enable coaches to monitor their performance.

[0067] The report generation unit can add a function to use the emotion estimation function to visualize changes in emotions in graphs or charts to make it easier for family members to understand the emotional state of their elderly family members when checking logs. For example, the report generation unit visualizes the emotion estimation results in graphs or charts to enable family members to intuitively understand the emotional state of their elderly family members. For example, it displays daily emotion scores in a line graph. The report generation unit also uses the emotion estimation function to visualize changes in emotions when checking logs. For example, it uses a timeline chart to display changes in emotions over time. The report generation unit also adds a function to enable the generation AI to visualize the emotion estimation results in graphs or charts. For example, it uses a heat map to visually display the intensity of emotions. In this way, by visualizing changes in emotions in graphs or charts, family members can intuitively understand the emotional state of their elderly family members.

[0068] The alert notification unit can use the emotion estimation function to introduce an algorithm that detects abnormal behavior based on changes in emotion. For example, the alert notification unit uses the emotion estimation function to analyze changes in the emotions of elderly family members and develop an algorithm that detects abnormal behavior. For example, an abnormality is detected when the emotion score drops suddenly. The alert notification unit also detects abnormal behavior based on changes in emotion. For example, it uses facial expression recognition technology to analyze changes in facial expressions and detects abnormalities. The alert notification unit also uses voice analysis technology to detect abnormalities based on changes in voice tone or speed. For example, it detects abnormalities based on sudden changes in voice tone or speed. In this way, by detecting abnormal behavior based on changes in emotion, symptoms of dementia and depression can be discovered early.

[0069] The alert notification unit can be added with a function to monitor the social activities of elderly family members and detect social isolation. The alert notification unit, for example, monitors the social activities of elderly family members, and the generation AI analyzes the data to detect social isolation. For example, it records the frequency of conversations with friends and the number of times people go out. The alert notification unit also analyzes social activities to detect social isolation. For example, it detects social isolation based on not going out for a certain period of time or a decrease in conversations with friends. The alert notification unit also adds a function to analyze social activities and detect social isolation. For example, it analyzes the frequency of participation in community activities to detect social isolation. This allows the social activities of elderly family members to be monitored and social isolation to be detected early.

[0070] The alert notification unit can add a function to analyze the sleep patterns of elderly family members in detail and detect sleep disorders early. The alert notification unit, for example, monitors the sleep patterns of elderly family members using a sensor, and the generation AI analyzes the data to detect sleep disorders. For example, it records sleep time and sleep quality. The alert notification unit also analyzes sleep patterns in detail and detects sleep disorders. For example, it analyzes the number of awakenings during the night and the depth of sleep to detect sleep disorders. The alert notification unit also adds a function to analyze sleep patterns in detail and detect sleep disorders. For example, it analyzes signs of insomnia and hypersomnia to detect sleep disorders. This allows for detailed analysis of the sleep patterns of elderly family members and early detection of sleep disorders.

[0071] The alert notification unit can be applied to student mental health monitoring, allowing the development of a system that detects signs of stress and depression at school early. The alert notification unit, for example, monitors students' behavior using sensors or cameras, and the generation AI analyzes the data to record their mental health status. For example, it records their concentration level during class and their behavior during breaks. The alert notification unit also analyzes behavioral patterns to detect signs of stress and depression at school early. For example, it analyzes changes in behavior and emotions to detect signs of stress and depression. The alert notification unit can also develop a system in which the generation AI analyzes behavioral patterns to detect signs of stress and depression early. For example, it detects signs of stress and depression based on changes in heart rate and behavioral patterns. This can be applied to student mental health monitoring, allowing the early detection of signs of stress and depression at school.

[0072] The alert notification unit can be applied to employee mental health monitoring in the workplace, allowing the development of a system that detects signs of stress and burnout early. For example, the alert notification unit monitors employee behavior in the workplace using sensors or cameras, and the generation AI analyzes the data to record mental health status. For example, it records work hours, break times, work efficiency, etc. The alert notification unit also analyzes behavioral patterns to detect signs of stress and burnout in the workplace early. For example, it analyzes changes in behavior and emotions to detect signs of stress and burnout. The alert notification unit can also develop a system in which the generation AI analyzes behavioral patterns to detect signs of stress and burnout early. For example, it detects signs of stress and burnout based on changes in heart rate and behavioral patterns. This can be applied to employee mental health monitoring in the workplace, allowing for early detection of signs of stress and burnout.

[0073] The alert notification unit can add a function to visualize changes in emotions in graphs or charts using the emotion estimation function to help family members understand the emotional state of their elderly family members when symptoms of dementia or depression are detected. The alert notification unit, for example, visualizes the emotion estimation results in graphs or charts to allow family members to intuitively understand the emotional state of their elderly family members. For example, it displays daily emotion scores in a line graph. The alert notification unit also uses the emotion estimation function to visualize changes in emotions when symptoms of dementia or depression are detected. For example, it uses a timeline chart to display changes in emotions over time. The alert notification unit also adds a function to visualize the emotion estimation results of the generation AI in graphs or charts. For example, it uses a heat map to visually display the intensity of emotions. This allows family members to intuitively understand the emotional state of their elderly family members by visualizing changes in emotions in graphs or charts.

[0074] The camera video confirmation unit can be added with a function of displaying the emotional state of an elderly family member in the video in real time using an emotion estimation function. The camera video confirmation unit, for example, develops a system that analyzes camera video in real time and displays the emotional state of an elderly family member. For example, it analyzes facial expressions and voice in the video and displays an emotion score. The camera video confirmation unit also uses the emotion estimation function to display the emotional state in the video in real time. For example, it uses facial expression recognition technology to analyze changes in facial expressions and estimate emotions. The camera video confirmation unit also uses voice analysis technology to analyze the tone and speed of voice and estimate emotions. For example, it estimates emotions based on changes in voice tone and speed. In this way, by displaying the emotional state of an elderly family member in the video in real time, family members can intuitively understand the emotions of their elderly family member.

[0075] The camera footage confirmation unit can be equipped with a function that automatically highlights abnormal behavior, allowing family members to quickly identify any abnormalities. For example, the camera footage confirmation unit will develop a system that analyzes camera footage in real time and automatically highlights abnormal behavior. For example, it will detect falls or long periods of inactivity and highlight them in the footage. The camera footage confirmation unit will also automatically highlight abnormal behavior. For example, it will highlight abnormalities using color coding or alert displays. The camera footage confirmation unit will also add a function that uses the generation AI to automatically highlight abnormal behavior. For example, it will detect abnormal movements or stops and mark them in the footage. This will allow family members to quickly identify any abnormalities by automatically highlighting abnormal behavior.

[0076] The camera video confirmation unit can be added with a function that analyzes video and automatically extracts important events. For example, a system can be developed for the camera video confirmation unit that analyzes camera video in real time and automatically extracts important events. For example, it can detect falls or long periods of inactivity and mark them in the video. The camera video confirmation unit can also automatically extract important events. For example, it can highlight important events using color coding or alert displays. The camera video confirmation unit can also be added with a function that uses the generation AI to automatically extract important events. For example, it can detect abnormal movements or stops and mark them in the video. This allows important events to be automatically extracted, allowing family members to respond quickly.

[0077] The camera footage confirmation unit can be applied to a pet monitoring system to develop a system that detects abnormal pet behavior. For example, the camera footage confirmation unit monitors pet behavior with a camera, and a generation AI analyzes the video to develop a system that detects abnormal behavior. For example, it detects abnormal movements or long periods of inactivity. The camera footage confirmation unit also analyzes behavioral patterns to detect abnormal pet behavior. For example, it detects abnormal vocalizations or a decrease in appetite. The camera footage confirmation unit also develops a system that uses a generation AI to analyze pet behavior and detect abnormal behavior. For example, it detects abnormal movements or long periods of inactivity and marks them in the video. This allows the system to be applied to a pet monitoring system to detect abnormal pet behavior.

[0078] The camera footage confirmation unit can be applied to factory monitoring systems to develop systems that detect abnormal machine operation. For example, the camera footage confirmation unit monitors machines in a factory with a camera, and a generation AI analyzes the footage to detect abnormal operation. For example, it detects abnormal vibrations or stoppages. The camera footage confirmation unit also analyzes operation patterns to detect abnormal machine operation. For example, it detects abnormal movements or stoppages. The camera footage confirmation unit also analyzes machines in a factory with a generation AI to develop systems that detect abnormal operation. For example, it detects abnormal vibrations or stoppages and marks them in the footage. This can be applied to factory monitoring systems to detect abnormal machine operation.

[0079] The camera video confirmation unit can add a function to use an emotion estimation function to display emotional changes in real time so that family members can easily understand the emotional state of their elderly family members when viewing camera video. The camera video confirmation unit, for example, develops a system that analyzes camera video in real time and displays the emotional state of elderly family members. For example, it analyzes facial expressions and voice in the video and displays an emotion score. The camera video confirmation unit also uses the emotion estimation function to display emotional changes in real time when viewing camera video. For example, it uses facial expression recognition technology to analyze changes in facial expressions and estimate emotions. The camera video confirmation unit also uses voice analysis technology to analyze the tone and speed of voice and estimate emotions. For example, it estimates emotions based on changes in voice tone and speed. In this way, by displaying emotional changes in real time, family members can intuitively understand the emotional state of their elderly family members.

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

[0081] When analyzing a user's behavior, the behavior analysis unit can infer the motivation behind the behavior and record changes in motivation in the log recording unit. For example, it can analyze the reason why a user repeats a particular behavior and infer the motivation. For example, it can analyze whether changes in meal times or frequency are due to health reasons or simply a change in habit. The behavior analysis unit can also detect abnormal behavior based on changes in motivation. For example, it can detect an abnormality when behavior that differs from normal motivation is observed. This allows for more detailed behavior analysis by analyzing the motivation behind the behavior and recording changes in motivation in a log.

[0082] The behavior analysis unit uses the emotion estimation function to monitor the user's emotional state in real time and instantly record any changes in emotion in the log recording unit. For example, it can estimate emotions from facial expressions and voice while the user is watching television and record the emotions in a log in real time. The behavior analysis unit can also detect abnormal behavior based on changes in emotion. For example, it can detect an abnormality when an emotion different from the normal emotional state is observed. This allows for faster response by monitoring changes in emotion in real time and instantly recording them in a log.

[0083] When analyzing a user's behavior, the behavior analysis unit can predict behavior patterns and predict future behavior. For example, it can predict the next day's behavior pattern based on past behavior data. For example, it can provide information such as, "Tomorrow's walking distance is predicted to be about 5,000 steps." The behavior analysis unit can also detect abnormal behavior based on predicted behavior patterns. For example, it can detect an abnormality when the predicted behavior pattern differs significantly from the actual behavior. This makes it possible to predict behavior patterns and future behavior, allowing countermeasures to be considered in advance.

[0084] The behavior analysis unit can use the emotion estimation function to estimate the motivation for a behavior based on the user's emotional state and record changes in motivation in the log recording unit. For example, the behavior analysis unit can analyze the emotional state when the user performs a specific behavior and estimate the motivation. For example, the behavior analysis unit can estimate emotions from facial expressions and voice while eating and record the motivation. The behavior analysis unit can also detect changes in motivation based on changes in emotions. For example, it can detect a change in motivation when an emotion different from the normal emotional state is observed. This allows for more detailed behavior analysis by estimating the motivation for a behavior based on the emotional state and recording changes in motivation in a log.

[0085] When analyzing user behavior, the behavior analysis unit can cluster behavior patterns and identify different behavior patterns. For example, the behavior data of the user can be analyzed using a clustering algorithm to identify different behavior patterns. For example, it can distinguish between everyday behavior patterns and behavior patterns during specific events. The behavior analysis unit can also detect abnormal behavior based on the clustered behavior patterns. For example, it can detect an abnormality when behavior that differs from normal clusters is observed. In this way, clustering behavior patterns and identifying different behavior patterns enables more detailed behavior analysis.

[0086] The behavior analysis unit can use the emotion estimation function to predict behavior based on the user's emotional state and record the predicted behavior in the log recording unit. For example, it can analyze the user's emotional state and predict the next behavior based on that emotion. For example, it can predict that a normal behavior pattern will continue if the user's emotions are stable, and predict that abnormal behavior may occur if the user's emotions are unstable. The behavior analysis unit can also detect abnormal behavior based on the predicted behavior. For example, it can detect an abnormality when the predicted behavior differs significantly from the actual behavior. In this way, by predicting behavior based on the emotional state and recording the predicted behavior in a log, it is possible to consider countermeasures in advance.

[0087] When analyzing a user's behavior, the behavior analysis unit can evaluate the impact of the behavior and predict the outcome of the behavior. For example, it can evaluate the impact of a specific behavior on health and predict the outcome. For example, it can predict the positive impact that an increase in exercise will have on health. The behavior analysis unit can also detect abnormal behavior based on the impact of the behavior. For example, it can detect an abnormality when behavior that has a negative impact on health is observed. This allows for more detailed behavior analysis by evaluating the impact of the behavior and predicting the outcome of the behavior.

[0088] The behavior analysis unit can use the emotion estimation function to evaluate the impact of behavior based on the user's emotional state and predict the outcome of the behavior. For example, the behavior analysis unit analyzes the user's emotional state and evaluates the impact of behavior based on the emotion. For example, if the emotion is stable, a positive impact is predicted, and if the emotion is unstable, a negative impact is predicted. The behavior analysis unit can also evaluate the impact of behavior based on changes in emotion. For example, it evaluates the impact of changes in emotion on behavior. This enables more detailed behavior analysis by evaluating the impact of behavior based on the emotional state and predicting the outcome of the behavior.

[0089] When analyzing a user's behavior, the behavior analysis unit can evaluate the risk of the behavior and identify high-risk behavior. For example, it can evaluate the risk that a specific behavior poses to health and identify that risk. For example, it can evaluate the risk that lack of exercise poses to health. The behavior analysis unit can also detect abnormal behavior based on high-risk behavior. For example, it can detect an abnormality when high-risk behavior that has a negative impact on health is observed. This allows for more detailed behavior analysis by evaluating the risk of behavior and identifying high-risk behavior.

[0090] The behavior analysis unit can use the emotion estimation function to evaluate the risk of behavior based on the user's emotional state and identify high-risk behavior. For example, it analyzes the user's emotional state and evaluates the risk of behavior based on that emotion. For example, if the user's emotions are unstable, high-risk behavior is predicted. The behavior analysis unit can also evaluate the risk of behavior based on changes in emotions. For example, it evaluates the risk that changes in emotions pose to behavior. This enables more detailed behavior analysis by evaluating the risk of behavior based on the emotional state and identifying high-risk behavior.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The behavior analysis unit analyzes the behavior of elderly family members using sensors, audio, and video. For example, it monitors how often they walk around the room, meal times, and sleep patterns. The behavior analysis unit also uses a generation AI to analyze data from sensors and cameras to generate a behavior log. Step 2: The log recording unit records the behavior analyzed by the behavior analysis unit in a log. For example, the data analyzed by the generation AI is saved as time-series data. The log recording unit can also save the data in text format or as an event log. Step 3: The report generation unit provides information in the form of a report based on the logs recorded by the log recording unit. For example, the generation AI may provide information such as, "Today's walking distance was 5,000 steps, meals were 3 times, and sleep time was 8 hours." The report generation unit can also provide information visually using PDF format, graphs, and charts. Step 4: The alert notification unit detects behavioral logs that may indicate the progression of dementia or symptoms of depression and sends an alert to family members. For example, an alert is issued if abnormal behavior such as walking less than usual, eating less, or sleeping for an extremely short time is detected. The alert notification unit also uses the generation AI to analyze the behavioral log data and detect abnormalities. Step 5: The camera footage confirmation unit checks the camera footage when necessary. For example, when an alert notification is received, the camera footage can be checked to confirm the actual situation. The camera footage confirmation unit also uses the generation AI to analyze the camera footage and provide the necessary information.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] 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.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] 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.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] 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.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] 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.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0152] 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.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 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 behavior analysis unit that analyzes the behavior of elderly family members using sensors, audio, and video; a log recording unit that records the behavior analyzed by the behavior analysis unit in a log; a report generation unit that provides information in a report format based on the log recorded by the log recording unit; An alert notification unit that detects behavioral logs that may indicate the progression of dementia or symptoms of depression and sends an alert to family members; A camera image confirmation unit that checks the camera image when necessary is provided. A system characterized by:

2. The behavior analysis unit The emotion behind the behavior is estimated, and the change in emotion is recorded in the log recorder.

2. The system of claim 1.

3. The behavior analysis unit It can also be used to analyze pet behavior and monitor pet health.

2. The system of claim 1.

4. The alert notification unit Introducing algorithms to detect abnormal behavior based on changes in emotions 2. The system of claim 1.

5. The report generation unit Include the emotional state of elderly family members in your reports 2. The system of claim 1.

6. The camera image confirmation unit Add a function to display the emotional state of elderly family members in real time in the video 2. The system of claim 1.

7. The behavior analysis unit Informs elderly family members of their emotional state based on behavioral analysis results and encourages emotional support 2. The system of claim 1.

8. The alert notification unit Add a function to visualize changes in emotions in graphs and charts to help family members understand the emotional state of their elderly loved ones when symptoms of dementia or depression are detected.

2. The system of claim 1.

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