system

Abstract

The system according to the embodiment aims to ensure the safety of elderly people and reduce the burden on their families and caregivers. [Solution] A system according to an embodiment includes an installation unit, a collection unit, an analysis unit, a notification unit, and a control unit. The installation unit installs a monitoring device. The collection unit collects data from the monitoring device installed by the installation unit. The analysis unit analyzes the data collected by the collection unit and detects abnormalities. The notification unit sends a notification based on the abnormality detected by the analysis unit. The control unit controls the operation device based on the notification sent by the notification unit.

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] With conventional technology, smart IoT devices to ensure the safety of the elderly have not been widely adopted, and there has been an issue of placing a heavy burden on families and caregivers.

[0005] The system according to the embodiment aims to ensure the safety of elderly people and reduce the burden on their families and caregivers. [Means for solving the problem]

[0006] A system according to an embodiment includes an installation unit, a collection unit, an analysis unit, a notification unit, and a control unit. The installation unit installs a monitoring device. The collection unit collects data from the monitoring device installed by the installation unit. The analysis unit analyzes the data collected by the collection unit and detects anomalies. The notification unit sends a notification based on the anomaly detected by the analysis unit. The control unit controls the operation device based on the notification sent by the notification unit. [Effects of the Invention]

[0007] The system according to the embodiment can ensure the safety of elderly people and reduce the burden on their families and caregivers. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. 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) An elderly support system according to an embodiment of the present invention ensures the safety of elderly people living alone and reduces the burden on their families and caregivers. This system packages and sells necessary tools, such as monitoring devices and control devices, with optional installation support. Furthermore, a centralized application is created that is easy to set up and use. This application allows for centralized management of smart IoT devices and easy operation. By linking smart IoT devices with a generating AI, the generating AI automatically analyzes data from the devices and issues necessary instructions. For example, if the monitoring device detects an abnormality in an elderly person, the generating AI automatically sends a notification to family members or caregivers. It is also possible to operate home appliances through the controlling device. This quickly ensures the safety of elderly people and reduces the burden on family members or caregivers. This system provides an environment where elderly people living alone can live safely and alleviates the anxiety of family members or caregivers. It also increases the adoption rate of smart IoT devices, making them easily installable in rental properties. For example, if a monitoring device detects a fall in an elderly person, the generating AI automatically sends a notification to family members or caregivers, enabling a prompt response. Furthermore, by operating home appliances through the operating device, elderly people can avoid having to operate the appliances themselves, which allows the elderly support system to ensure the safety of the elderly and reduce the burden on their families and caregivers.

[0029] An elderly support system according to an embodiment includes an installation unit, a collection unit, an analysis unit, a notification unit, and a control unit. The installation unit installs a monitoring device. Examples of the monitoring device include, but are not limited to, a camera, a sensor, a microphone, etc. The installation unit installs the monitoring device in the elderly's living space, for example. The collection unit collects data from the monitoring device. Examples of the data include, but are not limited to, video data, audio data, and sensor data. For example, the collection unit collects video data from a camera. The collection unit can also collect temperature data from a sensor. The collection unit can also collect audio data from a microphone. The analysis unit analyzes the collected data using a generation AI to detect abnormalities. Examples of abnormalities include, but are not limited to, the presence or absence of movement, the volume of a sound, and a change in temperature. For example, the analysis unit analyzes the video data using the generation AI to detect a fall by the elderly. The analysis unit can also analyze the audio data using the generation AI to detect abnormal sounds. Furthermore, the analysis unit can detect a sudden change in temperature by using the generation AI to analyze the sensor data. The notification unit sends a notification to a family member or caregiver when an abnormality is detected. Examples of notifications include, but are not limited to, email, SMS, app notification, etc. For example, the notification unit sends a notification by email when an abnormality is detected. The notification unit can also send a notification by SMS. The notification unit can also send a notification by app notification. The control unit controls an operation device based on the notification. Examples of operation devices include, but are not limited to, home appliances, smart devices, etc. For example, the control unit controls an air conditioner based on the notification. The control unit can also control lighting. The control unit can also control a television. As a result, the elderly support system according to the embodiment can ensure the safety of the elderly and reduce the burden on family members and caregivers.

[0030] The installation unit can install a monitoring device. For example, the installation unit installs the monitoring device in the elderly person's living space. Examples of monitoring devices include, but are not limited to, cameras, sensors, microphones, etc. For example, the installation unit installs a camera in the living room. The installation unit can also install a sensor in the bedroom. Furthermore, the installation unit can also install a microphone in the kitchen. This makes it possible to install the monitoring device. Some or all of the above-mentioned processing in the installation unit may be performed using, for example, AI, or may be performed without using AI. For example, the installation unit can input the installation location of the monitoring device into the generation AI and have the generation AI select the optimal installation location.

[0031] The collection unit can collect data from the monitoring device. The collection unit, for example, collects video data from a camera. The data includes, for example, video data, audio data, sensor data, etc., but is not limited to these examples. The collection unit, for example, collects video data from a camera. The collection unit can also collect temperature data from a sensor. Furthermore, the collection unit can also collect audio data from a microphone. This enables data collection from the monitoring device. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input data from the monitoring device to a generation AI and cause the generation AI to collect data.

[0032] The analysis unit can analyze the collected data and detect abnormalities using the generation AI. For example, the analysis unit can have the generation AI analyze video data and detect an elderly person falling. Abnormalities include, but are not limited to, the presence or absence of movement, the volume of sound, and temperature changes. For example, the analysis unit can have the generation AI analyze video data and detect an elderly person falling. The analysis unit can also have the generation AI analyze audio data and detect abnormal sounds. Furthermore, the analysis unit can have the generation AI analyze sensor data and detect sudden changes in temperature. This enables data analysis and abnormality detection. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input collected data to the generation AI and have the generation AI detect abnormalities.

[0033] The notification unit can send a notification to a family member or caregiver when an abnormality is detected. The notification unit, for example, sends a notification by email when an abnormality is detected. Notifications include, but are not limited to, email, SMS, app notification, etc. For example, the notification unit can send a notification by email when an abnormality is detected. The notification unit can also send a notification by SMS. Furthermore, the notification unit can also send a notification by app notification. In this way, a notification is sent to a family member or caregiver when an abnormality occurs. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can instruct the generation AI to send a notification when an abnormality is detected, and the generation AI can send the notification.

[0034] The control unit can control the operation device based on the notification. The control unit, for example, controls an air conditioner based on the notification. The operation device includes, for example, home appliances, smart devices, etc., but is not limited to these examples. The control unit, for example, controls an air conditioner based on the notification. The control unit can also control lighting. Furthermore, the control unit can also control a television. This makes it possible to control the operation device based on the notification. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can instruct the generation AI to control the operation device based on the notification, and the generation AI can control the operation device.

[0035] The installation unit can provide installation support. The installation unit provides installation support for, for example, a monitoring device. Installation support includes, for example, guiding the installation procedure and dispatching specialized staff, but is not limited to these examples. The installation unit, for example, guides the installation procedure for the monitoring device. The installation unit can also dispatch specialized staff to support the installation of the monitoring device. In this way, installation support is provided. Some or all of the above-described processing in the installation unit may be performed using, for example, AI, or may be performed without using AI. For example, the installation unit can input the content of the installation support to a generation AI, and the generation AI can provide the installation support.

[0036] The installation unit can analyze the elderly person's lifestyle pattern and determine the optimal installation timing. For example, if the elderly person wakes up at a specific time every morning, the installation unit installs the monitoring device according to that time period. Lifestyle patterns include, but are not limited to, daily behavioral history, activity time periods, etc. For example, if the elderly person frequently goes to the toilet at night, the installation unit installs the monitoring device according to that time period. Furthermore, if the elderly person often goes out during the day, the installation unit can also install the monitoring device according to that time period. This allows the optimal installation timing to be determined based on the elderly person's lifestyle pattern. Some or all of the above-described processing in the installation unit may be performed using, for example, AI, or may be performed without using AI. For example, the installation unit can input the elderly person's lifestyle pattern data into a generation AI, which can then determine the optimal installation timing.

[0037] The installation unit can automatically check the operation of the device during installation and notify the user that the installation is complete. For example, after installing a monitoring device, the installation unit can have the generation AI automatically check the operation of the device to confirm that it is operating normally. Operation checks include, but are not limited to, checking the normal operation of the device and performing communication tests. For example, once the installation unit has completed the operation check of the device, the generation AI can send a notification to the user that the installation is complete. Furthermore, if there is an abnormality in the device's operation, the installation unit can have the generation AI automatically attempt to correct it and send a notification once the correction is complete. In this way, the device's operation is checked and the notification that the installation is complete is automatically performed. Some or all of the above-mentioned processing in the installation unit may be performed using, or without, AI. For example, the installation unit can input device operation check data to the generation AI, which can then check the operation and send a notification that the installation is complete.

[0038] The installation unit can adjust the installation location by reflecting the opinions of family members and caregivers during installation. For example, if a family member wishes to install the monitoring device in the living room, the installation unit adjusts the installation location by reflecting the opinions of the generation AI. Opinions of family members and caregivers include, but are not limited to, preferences regarding installation locations such as the living room, bedroom, and kitchen. For example, if a caregiver wishes to install the monitoring device in the bedroom, the installation unit adjusts the installation location by reflecting the opinions of the family member and caregiver. The installation unit can also comprehensively determine the opinions of the family member and caregiver and determine the optimal installation location. As a result, the installation location is adjusted by reflecting the opinions of the family member and caregiver. Some or all of the above-described processing in the installation unit may be performed using AI, for example, or may be performed without using AI. For example, the installation unit can input opinion data of the family member and caregiver into the generation AI, which can then determine the optimal installation location.

[0039] The installation unit can simplify the device installation method during installation, allowing the user to install the device themselves. For example, the installation unit can use a generation AI to simplify the device installation procedure, allowing the user to install the device themselves. Examples of installation methods include, but are not limited to, using tools and simplifying the procedure. For example, the installation unit can use a generation AI to explain the installation procedure using a video, allowing the user to understand it visually. The installation unit can also provide audio guidance for the installation procedure, allowing the user to understand it audibly. This simplifies the device installation method, allowing the user to install it themselves. Some or all of the above-described processing in the installation unit can be performed using, or without, AI. For example, the installation unit can input installation procedure data into the generation AI, which can then simplify the installation procedure and guide the user through it.

[0040] The collection unit can evaluate the reliability of data and filter unreliable data when collecting the data. For example, the collection unit can detect abnormal sensor values ​​when the generation AI collects data and filter out unreliable data. Data reliability includes, but is not limited to, data consistency, accuracy, and completeness. For example, the collection unit can detect data affected by the external environment when the generation AI collects data and filter out unreliable data. The collection unit can also detect device failures when the generation AI collects data and filter out unreliable data. This filters out unreliable data. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input data reliability evaluation data of the data to the generation AI, and the generation AI can filter out unreliable data.

[0041] The collection unit can apply different collection algorithms depending on the type of data during collection. For example, when the generation AI collects temperature data, the collection unit applies an algorithm dedicated to temperature sensors. Types of data include, but are not limited to, video data, audio data, and sensor data. For example, when the generation AI collects motion data, the collection unit applies an algorithm dedicated to motion sensors. Furthermore, when the generation AI collects audio data, the collection unit can also apply an algorithm dedicated to audio sensors. This allows a collection algorithm to be applied depending on the type of data. Some or all of the above-mentioned processing in the collection unit may be performed using, or without using, AI. For example, the collection unit can input a collection algorithm depending on the type of data to the generation AI, which then applies the algorithm.

[0042] The collection unit can evaluate the relevance of data taking into account device location information at the time of collection. For example, the collection unit evaluates the relevance of data collected by the generation AI based on device location information. Device location information includes, but is not limited to, GPS data, fixed location information, etc. For example, the collection unit identifies the location where an abnormality occurred based on the device location information. The collection unit can also evaluate the reliability of data based on the device location information. This allows the relevance of data to be evaluated based on the device location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input device location information data to the generation AI, and the generation AI can evaluate the relevance of the data.

[0043] The collection unit can optimize the data storage method when collecting data and efficiently manage the data. For example, the collection unit can optimize the data storage method when the generation AI collects data and efficiently manage the data. Data storage methods include, but are not limited to, database design and storage selection. For example, the collection unit can apply a data compression method when the generation AI collects data to save storage capacity. The collection unit can also apply a data backup method when the generation AI collects data to ensure data security. This optimizes the data storage method and efficiently manages the data. Some or all of the above-mentioned processing in the collection unit can be performed using, or without using, AI. For example, the collection unit can input data on the data storage method to the generation AI, and the generation AI can optimize the storage method and efficiently manage the data.

[0044] During analysis, the analysis unit can improve the accuracy of anomaly prediction by referring to past data. In the analysis unit, for example, the generation AI refers to past data and analyzes the pattern of anomaly occurrence. Past data includes, but is not limited to, history data, log data, etc. In the analysis unit, for example, the generation AI improves the accuracy of anomaly prediction based on past data. In addition, the analysis unit can also cause the generation AI to refer to past data and calculate the probability of anomaly occurrence. In this way, the accuracy of anomaly prediction is improved by referring to past data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without using, the generation AI. For example, the analysis unit can input past data into the generation AI, which can improve the accuracy of anomaly prediction.

[0045] The analysis unit can apply different analysis algorithms depending on the type of data during analysis. For example, when the generation AI analyzes temperature data, the analysis unit applies an algorithm dedicated to temperature sensors. Data types include, but are not limited to, video data, audio data, and sensor data. For example, when the generation AI analyzes motion data, the analysis unit applies an algorithm dedicated to motion sensors. Furthermore, when the generation AI analyzes audio data, the analysis unit can also apply an algorithm dedicated to audio sensors. This allows an analysis algorithm to be applied depending on the type of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without using, the generation AI. For example, the analysis unit can input an analysis algorithm depending on the type of data into the generation AI, and the generation AI can apply it.

[0046] The analysis unit can improve the accuracy of the analysis by taking into account the operating status of the device during analysis. For example, the generation AI improves the accuracy of the analysis based on the operating status of the device. The operating status of the device includes, but is not limited to, for example, operating time and error logs. For example, the analysis unit can identify the cause of an abnormality based on the operating status of the device. The analysis unit can also evaluate the reliability of the analysis results based on the operating status of the device. This improves the accuracy of the analysis by taking the operating status of the device into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without using, the generation AI. For example, the analysis unit can input device operating status data to the generation AI, which can improve the accuracy of the analysis.

[0047] During analysis, the analysis unit can improve the accuracy of the analysis by referring to related external data. For example, the generation AI can refer to external weather data to improve the accuracy of the analysis. Examples of external data include, but are not limited to, public databases and data acquired from APIs. For example, the analysis unit can improve the accuracy of the analysis by referring to external traffic data. Furthermore, the analysis unit can also improve the accuracy of the analysis by referring to external health data. This improves the accuracy of the analysis by referring to related external data. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input external data into the generation AI, which can improve the accuracy of the analysis.

[0048] The notification unit can select different notification methods depending on the importance of the notification when notifying. For example, if the importance of the notification is high, the generation AI can notify using a phone call or an emergency alert. The importance of the notification can include, but is not limited to, the urgency and the scope of impact. For example, if the importance of the notification is medium, the generation AI can notify using an SMS or email. Furthermore, if the importance of the notification is low, the generation AI can also notify using an in-app notification or a push notification. This allows the optimal notification method to be selected depending on the importance of the notification. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the notification unit can input notification importance data into the generation AI, which can then select the notification method.

[0049] The notification unit can determine the optimal notification timing by referring to past notification history when sending a notification. For example, the generation AI of the notification unit refers to past notification history and sends a notification at a time when the user is most likely to respond. The notification history includes, but is not limited to, past notification data, log data, etc. For example, the generation AI of the notification unit adjusts the frequency of notifications based on the past notification history. Furthermore, the notification unit can also send notifications in a manner that is most convenient for the user by referring to the past notification history. This allows the optimal notification timing to be determined by referring to the past notification history. Some or all of the above-described processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input past notification history data into the generation AI, which can then determine the optimal notification timing.

[0050] The notification unit can adjust the timing of notifications taking into account the schedules of family members and caregivers. For example, the generation AI of the notification unit references the schedules of family members and caregivers and sends notifications at optimal timing. Examples of family and caregiver schedules include, but are not limited to, linking with a calendar app or manual input. For example, the generation AI of the notification unit adjusts the frequency of notifications based on the schedules of family members and caregivers. Furthermore, the notification unit can also select a notification method by reference to the schedules of family members and caregivers. This allows the timing of notifications to be adjusted taking into account the schedules of family members and caregivers. Some or all of the above-described processing in the notification unit may be performed using, or without, AI. For example, the notification unit can input schedule data of family members and caregivers into the generation AI, which can then adjust the timing of notifications.

[0051] The notification unit can diversify notification methods and send notifications by multiple means, such as email and SMS. For example, the generation AI of the notification unit sends notifications by both email and SMS. Examples of notification methods include, but are not limited to, email, SMS, and app notifications. For example, the generation AI of the notification unit can send notifications by both in-app notifications and push notifications. Furthermore, the generation AI of the notification unit can also send notifications by both phone calls and emergency alerts. This diversifies notification methods and sends notifications by multiple means. Some or all of the above-described processing in the notification unit may be performed using, or without, AI. For example, the notification unit can input notification method data into the generation AI, which can then send notifications by multiple means.

[0052] During control, the control unit can monitor the operating status of the device in real time and perform optimal control. For example, the control unit can have a generation AI monitor the operating status of the device in real time and automatically correct any abnormalities that may occur. Examples of device operating status include, but are not limited to, operating time and error logs. For example, the control unit can have the generation AI select an optimal control method based on the operating status of the device. Furthermore, the control unit can also have the generation AI monitor the operating status of the device in real time and adjust the control method as necessary. This allows the operating status of the device to be monitored in real time and optimal control to be performed. Some or all of the above-described processing in the control unit can be performed using, for example, AI, or without AI. For example, the control unit can input device operating status data to the generation AI, which can then perform optimal control.

[0053] During control, the control unit can apply an optimal control algorithm by referring to past control history. In the control unit, for example, the generation AI refers to the past control history and selects an optimal control algorithm. The control history includes, for example, past control data, log data, etc., but is not limited to these examples. In the control unit, for example, the generation AI improves the accuracy of control based on the past control history. In addition, the control unit can also have the generation AI refer to the past control history and learn how to deal with abnormalities when they occur. In this way, the optimal control algorithm is applied by referring to the past control history. Some or all of the above-mentioned processing in the control unit may be performed using, or without using, AI. For example, the control unit can input past control history data to the generation AI, and the generation AI can apply the optimal control algorithm.

[0054] When controlling an appliance, the control unit can apply different control methods depending on the type of appliance. For example, when the generating AI controls an air conditioner, the control unit applies an algorithm dedicated to temperature adjustment. Types of appliances include, but are not limited to, refrigerators, air conditioners, and lighting. For example, when the generating AI controls lighting, the control unit applies an algorithm dedicated to brightness adjustment. Furthermore, when the generating AI controls a television, the control unit can also apply an algorithm dedicated to channel switching. This allows the optimal control method to be applied depending on the type of appliance. Some or all of the above-mentioned processing in the control unit may be performed using, or without using, AI. For example, the control unit can input data on the type of appliance to the generating AI, which then applies the optimal control method.

[0055] The control unit can adjust the control method by reflecting the opinions of family members and caregivers during control. For example, when a family member requests a temperature setting for an air conditioner, the control unit adjusts the control method by having the generation AI reflect that opinion. Opinions from family members and caregivers include, but are not limited to, air conditioner temperature settings and lighting brightness settings. For example, when a caregiver requests a lighting brightness setting, the control unit adjusts the control method by having the generation AI reflect that opinion. The control unit can also comprehensively evaluate the opinions of family members and caregivers to determine the optimal control method. In this way, the opinions of family members and caregivers are reflected and the control method is adjusted. Some or all of the above-described processing in the control unit may be performed using AI, for example, or may be performed without using AI. For example, the control unit can input opinion data from family members and caregivers into the generation AI, which then determines the optimal control method.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The elderly support system can further include a health monitoring unit. The health monitoring unit periodically measures the elderly person's vital signs (heart rate, blood pressure, body temperature, etc.) and transmits the data to the collection unit. For example, if the heart rate is abnormally high, the analysis unit can analyze the data and detect the abnormality. If the blood pressure rises suddenly, the notification unit can send an emergency notification to family members or caregivers. Furthermore, if the body temperature is abnormally low, the control unit can automatically turn on the heating. This allows the elderly person's health condition to be monitored in real time and enables prompt response.

[0058] The elderly support system can further include a reminder unit. The reminder unit notifies the elderly so that they do not forget to take their medicine or make appointments with medical institutions. For example, when it is nearly time to take their medicine, the reminder unit can notify the elderly by voice or app notification. Also, if there is an appointment with a medical institution, the reminder unit can send a notification the day before. Furthermore, the reminder unit can send similar notifications to family members and caregivers. This makes it easier for the elderly to manage their health without forgetting important appointments.

[0059] The elderly support system may further include an entertainment unit. The entertainment unit provides content that the elderly can enjoy. For example, it can play streaming music and radio programs. It can also recommend television programs and movies. Furthermore, the entertainment unit can provide simple games and puzzles to help maintain cognitive function. This can improve the quality of life of the elderly and reduce feelings of loneliness.

[0060] The elderly support system can further include a communication unit. The communication unit provides a function that allows elderly people to easily contact their family and friends. For example, video calls and voice calls can be easily made. Messages can also be sent and received. Furthermore, the communication unit can automatically display the latest photos and videos of family and friends. This allows elderly people to maintain social connections without becoming isolated.

[0061] The elderly support system can further include an environmental monitoring unit. The environmental monitoring unit monitors the elderly's living environment and maintains a comfortable environment. For example, it can measure the indoor temperature and humidity and keep them within an appropriate range. It can also use an air quality sensor to detect harmful substances in the air and encourage ventilation as needed. It can also automatically adjust the brightness of lighting to provide an environment that is gentle on the eyes. This creates an environment where the elderly can live comfortably.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The installation unit installs a monitoring device. The monitoring device may include, but is not limited to, a camera, a sensor, a microphone, etc. The installation unit installs the monitoring device in the living space of the elderly person, for example. Step 2: The collection unit collects data from the monitoring device. The data includes, but is not limited to, video data, audio data, and sensor data. For example, the collection unit collects video data from a camera. The collection unit can also collect temperature data from a sensor. The collection unit can also collect audio data from a microphone. Step 3: The analysis unit uses the generation AI to analyze the collected data and detect abnormalities. Examples of abnormalities include, but are not limited to, the presence or absence of movement, the volume of sound, and temperature changes. For example, the analysis unit uses the generation AI to analyze video data and detect falls by an elderly person. The analysis unit can also use the generation AI to analyze audio data and detect abnormal sounds. Furthermore, the analysis unit can also use the generation AI to analyze sensor data and detect sudden changes in temperature. Step 4: The notification unit sends a notification to a family member or caregiver when an abnormality is detected. Notifications include, but are not limited to, email, SMS, app notification, etc. For example, when an abnormality is detected, the notification unit sends a notification by email. The notification unit can also send a notification by SMS. The notification unit can also send a notification by app notification. Step 5: The control unit controls the operation device based on the notification. Examples of the operation device include, but are not limited to, home appliances and smart devices. For example, the control unit controls an air conditioner based on the notification. The control unit can also control lighting. Furthermore, the control unit can also control a television.

[0064] (Example 2) An elderly support system according to an embodiment of the present invention ensures the safety of elderly people living alone and reduces the burden on their families and caregivers. This system packages and sells necessary tools, such as monitoring devices and control devices, with optional installation support. Furthermore, a centralized application is created that is easy to set up and use. This application allows for centralized management of smart IoT devices and easy operation. By linking smart IoT devices with a generating AI, the generating AI automatically analyzes data from the devices and issues necessary instructions. For example, if the monitoring device detects an abnormality in an elderly person, the generating AI automatically sends a notification to family members or caregivers. It is also possible to operate home appliances through the controlling device. This quickly ensures the safety of elderly people and reduces the burden on family members or caregivers. This system provides an environment where elderly people living alone can live safely and alleviates the anxiety of family members or caregivers. It also increases the adoption rate of smart IoT devices, making them easily installable in rental properties. For example, if a monitoring device detects a fall in an elderly person, the generating AI automatically sends a notification to family members or caregivers, enabling a prompt response. Furthermore, by operating home appliances through the operating device, elderly people can avoid having to operate the appliances themselves, which allows the elderly support system to ensure the safety of the elderly and reduce the burden on their families and caregivers.

[0065] An elderly support system according to an embodiment includes an installation unit, a collection unit, an analysis unit, a notification unit, and a control unit. The installation unit installs a monitoring device. Examples of the monitoring device include, but are not limited to, a camera, a sensor, a microphone, etc. The installation unit installs the monitoring device in the elderly's living space, for example. The collection unit collects data from the monitoring device. Examples of the data include, but are not limited to, video data, audio data, and sensor data. For example, the collection unit collects video data from a camera. The collection unit can also collect temperature data from a sensor. The collection unit can also collect audio data from a microphone. The analysis unit analyzes the collected data using a generation AI to detect abnormalities. Examples of abnormalities include, but are not limited to, the presence or absence of movement, the volume of a sound, and a change in temperature. For example, the analysis unit analyzes the video data using the generation AI to detect a fall by the elderly. The analysis unit can also analyze the audio data using the generation AI to detect abnormal sounds. Furthermore, the analysis unit can detect a sudden change in temperature by using the generation AI to analyze the sensor data. The notification unit sends a notification to a family member or caregiver when an abnormality is detected. Examples of notifications include, but are not limited to, email, SMS, app notification, etc. For example, the notification unit sends a notification by email when an abnormality is detected. The notification unit can also send a notification by SMS. The notification unit can also send a notification by app notification. The control unit controls an operation device based on the notification. Examples of operation devices include, but are not limited to, home appliances, smart devices, etc. For example, the control unit controls an air conditioner based on the notification. The control unit can also control lighting. The control unit can also control a television. As a result, the elderly support system according to the embodiment can ensure the safety of the elderly and reduce the burden on family members and caregivers.

[0066] The installation unit can install a monitoring device. For example, the installation unit installs the monitoring device in the elderly person's living space. Examples of monitoring devices include, but are not limited to, cameras, sensors, microphones, etc. For example, the installation unit installs a camera in the living room. The installation unit can also install a sensor in the bedroom. Furthermore, the installation unit can also install a microphone in the kitchen. This makes it possible to install the monitoring device. Some or all of the above-mentioned processing in the installation unit may be performed using, for example, AI, or may be performed without using AI. For example, the installation unit can input the installation location of the monitoring device into the generation AI and have the generation AI select the optimal installation location.

[0067] The collection unit can collect data from the monitoring device. The collection unit, for example, collects video data from a camera. The data includes, for example, video data, audio data, sensor data, etc., but is not limited to these examples. The collection unit, for example, collects video data from a camera. The collection unit can also collect temperature data from a sensor. Furthermore, the collection unit can also collect audio data from a microphone. This enables data collection from the monitoring device. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input data from the monitoring device to a generation AI and cause the generation AI to collect data.

[0068] The analysis unit can analyze the collected data and detect abnormalities using the generation AI. For example, the analysis unit can have the generation AI analyze video data and detect an elderly person falling. Abnormalities include, but are not limited to, the presence or absence of movement, the volume of sound, and temperature changes. For example, the analysis unit can have the generation AI analyze video data and detect an elderly person falling. The analysis unit can also have the generation AI analyze audio data and detect abnormal sounds. Furthermore, the analysis unit can have the generation AI analyze sensor data and detect sudden changes in temperature. This enables data analysis and abnormality detection. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input collected data to the generation AI and have the generation AI detect abnormalities.

[0069] The notification unit can send a notification to a family member or caregiver when an abnormality is detected. The notification unit, for example, sends a notification by email when an abnormality is detected. Notifications include, but are not limited to, email, SMS, app notification, etc. For example, the notification unit can send a notification by email when an abnormality is detected. The notification unit can also send a notification by SMS. Furthermore, the notification unit can also send a notification by app notification. In this way, a notification is sent to a family member or caregiver when an abnormality occurs. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can instruct the generation AI to send a notification when an abnormality is detected, and the generation AI can send the notification.

[0070] The control unit can control the operation device based on the notification. The control unit, for example, controls an air conditioner based on the notification. The operation device includes, for example, home appliances, smart devices, etc., but is not limited to these examples. The control unit, for example, controls an air conditioner based on the notification. The control unit can also control lighting. Furthermore, the control unit can also control a television. This makes it possible to control the operation device based on the notification. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can instruct the generation AI to control the operation device based on the notification, and the generation AI can control the operation device.

[0071] The installation unit can provide installation support. The installation unit provides installation support for, for example, a monitoring device. Installation support includes, for example, guiding the installation procedure and dispatching specialized staff, but is not limited to these examples. The installation unit, for example, guides the installation procedure for the monitoring device. The installation unit can also dispatch specialized staff to support the installation of the monitoring device. In this way, installation support is provided. Some or all of the above-described processing in the installation unit may be performed using, for example, AI, or may be performed without using AI. For example, the installation unit can input the content of the installation support to a generation AI, and the generation AI can provide the installation support.

[0072] The elderly support system further includes an installation unit that estimates the user's emotions and optimizes the installation location of the monitoring device based on the estimated user emotions. For example, when the user feels anxious, the installation unit causes the generation AI to install the monitoring device in a location where the user spends the most time, such as the living room or bedroom. Examples of user emotions include, but are not limited to, anxiety, relaxation, and stress. For example, when the user feels relaxed, the installation unit causes the generation AI to install the monitoring device in a location to prevent intrusion from outside, such as the entrance or hallway. Furthermore, when the user feels stressed, the installation unit can also install the monitoring device in a location where accidents are likely to occur, such as the bathroom or kitchen. In this way, the installation location of the monitoring device is optimized based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the installation unit may be performed using, for example, AI or without AI. For example, the installation unit can input the user's emotional data into the generation AI, which can then select the optimal installation location.

[0073] The installation unit can analyze the elderly person's lifestyle pattern and determine the optimal installation timing. For example, if the elderly person wakes up at a specific time every morning, the installation unit installs the monitoring device according to that time period. Lifestyle patterns include, but are not limited to, daily behavioral history, activity time periods, etc. For example, if the elderly person frequently goes to the toilet at night, the installation unit installs the monitoring device according to that time period. Furthermore, if the elderly person often goes out during the day, the installation unit can also install the monitoring device according to that time period. This allows the optimal installation timing to be determined based on the elderly person's lifestyle pattern. Some or all of the above-described processing in the installation unit may be performed using, for example, AI, or may be performed without using AI. For example, the installation unit can input the elderly person's lifestyle pattern data into a generation AI, which can then determine the optimal installation timing.

[0074] The installation unit can automatically check the operation of the device during installation and notify the user that the installation is complete. For example, after installing a monitoring device, the installation unit can have the generation AI automatically check the operation of the device to confirm that it is operating normally. Operation checks include, but are not limited to, checking the normal operation of the device and performing communication tests. For example, once the installation unit has completed the operation check of the device, the generation AI can send a notification to the user that the installation is complete. Furthermore, if there is an abnormality in the device's operation, the installation unit can have the generation AI automatically attempt to correct it and send a notification once the correction is complete. In this way, the device's operation is checked and the notification that the installation is complete is automatically performed. Some or all of the above-mentioned processing in the installation unit may be performed using, or without, AI. For example, the installation unit can input device operation check data to the generation AI, which can then check the operation and send a notification that the installation is complete.

[0075] The installation unit can estimate the user's emotions and select the type of device to install based on the estimated user's emotions. For example, if the user is feeling anxious, the installation unit's generation AI selects a device with enhanced monitoring capabilities, such as a camera or a sensor. Examples of user emotions include, but are not limited to, anxiety, relaxation, and stress. For example, if the user is feeling relaxed, the installation unit's generation AI selects a device that is easy to operate, such as a voice assistant or a remote control. Furthermore, if the user is feeling stressed, the installation unit's generation AI can also select a device with an emergency call function. In this way, the type of device to install is selected based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the installation unit may be performed using, for example, AI, or without AI. For example, the installation unit can input the user's emotion data into the generation AI, which can select the optimal type of device.

[0076] The installation unit can adjust the installation location by reflecting the opinions of family members and caregivers during installation. For example, if a family member wishes to install the monitoring device in the living room, the installation unit adjusts the installation location by reflecting the opinions of the generation AI. Opinions of family members and caregivers include, but are not limited to, preferences regarding installation locations such as the living room, bedroom, and kitchen. For example, if a caregiver wishes to install the monitoring device in the bedroom, the installation unit adjusts the installation location by reflecting the opinions of the family member and caregiver. The installation unit can also comprehensively determine the opinions of the family member and caregiver and determine the optimal installation location. As a result, the installation location is adjusted by reflecting the opinions of the family member and caregiver. Some or all of the above-described processing in the installation unit may be performed using AI, for example, or may be performed without using AI. For example, the installation unit can input opinion data of the family member and caregiver into the generation AI, which can then determine the optimal installation location.

[0077] The installation unit can simplify the device installation method during installation, allowing the user to install the device themselves. For example, the installation unit can use a generation AI to simplify the device installation procedure, allowing the user to install the device themselves. Examples of installation methods include, but are not limited to, using tools and simplifying the procedure. For example, the installation unit can use a generation AI to explain the installation procedure using a video, allowing the user to understand it visually. The installation unit can also provide audio guidance for the installation procedure, allowing the user to understand it audibly. This simplifies the device installation method, allowing the user to install it themselves. Some or all of the above-described processing in the installation unit can be performed using, or without, AI. For example, the installation unit can input installation procedure data into the generation AI, which can then simplify the installation procedure and guide the user through it.

[0078] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user emotions. For example, if the user feels anxious, the collection unit causes the generation AI to increase the frequency of data collection and monitor in real time. Examples of user emotions include, but are not limited to, anxiety, relaxation, stress, etc. For example, if the user feels relaxed, the collection unit causes the generation AI to reduce the frequency of data collection and collect data only when necessary. Furthermore, if the user feels stressed, the collection unit can also cause the generation AI to appropriately adjust the frequency of data collection to avoid excessive monitoring. This adjusts the frequency of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input user emotion data into the generation AI, which can then adjust the frequency of data collection.

[0079] The collection unit can evaluate the reliability of data and filter unreliable data when collecting the data. For example, the collection unit can detect abnormal sensor values ​​when the generation AI collects data and filter out unreliable data. Data reliability includes, but is not limited to, data consistency, accuracy, and completeness. For example, the collection unit can detect data affected by the external environment when the generation AI collects data and filter out unreliable data. The collection unit can also detect device failures when the generation AI collects data and filter out unreliable data. This filters out unreliable data. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input data reliability evaluation data of the data to the generation AI, and the generation AI can filter out unreliable data.

[0080] The collection unit can apply different collection algorithms depending on the type of data during collection. For example, when the generation AI collects temperature data, the collection unit applies an algorithm dedicated to temperature sensors. Types of data include, but are not limited to, video data, audio data, and sensor data. For example, when the generation AI collects motion data, the collection unit applies an algorithm dedicated to motion sensors. Furthermore, when the generation AI collects audio data, the collection unit can also apply an algorithm dedicated to audio sensors. This allows a collection algorithm to be applied depending on the type of data. Some or all of the above-mentioned processing in the collection unit may be performed using, or without using, AI. For example, the collection unit can input a collection algorithm depending on the type of data to the generation AI, which then applies the algorithm.

[0081] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user feels anxious, the collection unit causes the generation AI to prioritize collection of health data. Examples of user emotions include, but are not limited to, anxiety, relaxation, stress, etc. For example, if the user feels relaxed, the collection unit causes the generation AI to prioritize collection of environmental data. Furthermore, if the user feels stressed, the collection unit can also cause the generation AI to prioritize collection of motion data. This determines the priority of data to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and determine the priority of data to be collected by the generation AI.

[0082] The collection unit can evaluate the relevance of data taking into account device location information at the time of collection. For example, the collection unit evaluates the relevance of data collected by the generation AI based on device location information. Device location information includes, but is not limited to, GPS data, fixed location information, etc. For example, the collection unit identifies the location where an abnormality occurred based on the device location information. The collection unit can also evaluate the reliability of data based on the device location information. This allows the relevance of data to be evaluated based on the device location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input device location information data to the generation AI, and the generation AI can evaluate the relevance of the data.

[0083] The collection unit can optimize the data storage method when collecting data and efficiently manage the data. For example, the collection unit can optimize the data storage method when the generation AI collects data and efficiently manage the data. Data storage methods include, but are not limited to, database design and storage selection. For example, the collection unit can apply a data compression method when the generation AI collects data to save storage capacity. The collection unit can also apply a data backup method when the generation AI collects data to ensure data security. This optimizes the data storage method and efficiently manages the data. Some or all of the above-mentioned processing in the collection unit can be performed using, or without using, AI. For example, the collection unit can input data on the data storage method to the generation AI, and the generation AI can optimize the storage method and efficiently manage the data.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the generation AI can provide a simple, highly visible display method. Examples of user emotions include, but are not limited to, nervousness, relaxation, and hurry. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This adjusts the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI, and the generation AI can adjust the display method of the analysis results.

[0085] During analysis, the analysis unit can improve the accuracy of anomaly prediction by referring to past data. In the analysis unit, for example, the generation AI refers to past data and analyzes the pattern of anomaly occurrence. Past data includes, but is not limited to, history data, log data, etc. In the analysis unit, for example, the generation AI improves the accuracy of anomaly prediction based on past data. In addition, the analysis unit can also cause the generation AI to refer to past data and calculate the probability of anomaly occurrence. In this way, the accuracy of anomaly prediction is improved by referring to past data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without using, the generation AI. For example, the analysis unit can input past data into the generation AI, which can improve the accuracy of anomaly prediction.

[0086] The analysis unit can apply different analysis algorithms depending on the type of data during analysis. For example, when the generation AI analyzes temperature data, the analysis unit applies an algorithm dedicated to temperature sensors. Data types include, but are not limited to, video data, audio data, and sensor data. For example, when the generation AI analyzes motion data, the analysis unit applies an algorithm dedicated to motion sensors. Furthermore, when the generation AI analyzes audio data, the analysis unit can also apply an algorithm dedicated to audio sensors. This allows an analysis algorithm to be applied depending on the type of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without using, the generation AI. For example, the analysis unit can input an analysis algorithm depending on the type of data into the generation AI, and the generation AI can apply it.

[0087] The analysis unit can estimate the user's emotions and determine the priority of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit causes the generation AI to prioritize the analysis results of the health data. Examples of user emotions include, but are not limited to, anxiety, relaxation, stress, etc. For example, if the user is feeling relaxed, the analysis unit causes the generation AI to prioritize the analysis results of the environmental data. Furthermore, if the user is feeling stressed, the analysis unit can also cause the generation AI to prioritize the analysis results of the motion data. In this way, the priority of the analysis results is determined based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI, and the generation AI can determine the priority of the analysis results.

[0088] The analysis unit can improve the accuracy of the analysis by taking into account the operating status of the device during analysis. For example, the generation AI improves the accuracy of the analysis based on the operating status of the device. The operating status of the device includes, but is not limited to, for example, operating time and error logs. For example, the analysis unit can identify the cause of an abnormality based on the operating status of the device. The analysis unit can also evaluate the reliability of the analysis results based on the operating status of the device. This improves the accuracy of the analysis by taking the operating status of the device into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without using, the generation AI. For example, the analysis unit can input device operating status data to the generation AI, which can improve the accuracy of the analysis.

[0089] During analysis, the analysis unit can improve the accuracy of the analysis by referring to related external data. For example, the generation AI can refer to external weather data to improve the accuracy of the analysis. Examples of external data include, but are not limited to, public databases and data acquired from APIs. For example, the analysis unit can improve the accuracy of the analysis by referring to external traffic data. Furthermore, the analysis unit can also improve the accuracy of the analysis by referring to external health data. This improves the accuracy of the analysis by referring to related external data. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input external data into the generation AI, which can improve the accuracy of the analysis.

[0090] The notification unit can estimate the user's emotions and customize the content of the notification based on the estimated user's emotions. For example, if the user is feeling anxious, the generation AI sends a notification including a detailed explanation. Examples of user emotions include, but are not limited to, anxiety, relaxation, stress, etc. For example, if the user is feeling relaxed, the generation AI sends a concise notification. Furthermore, if the user is feeling stressed, the generation AI can also send a notification with content that gives a sense of security. This customizes the content of the notification based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or without AI. For example, the notification unit can input the user's emotion data into the generation AI, which can then customize the content of the notification.

[0091] The notification unit can select different notification methods depending on the importance of the notification when notifying. For example, if the importance of the notification is high, the generation AI can notify using a phone call or an emergency alert. The importance of the notification can include, but is not limited to, the urgency and the scope of impact. For example, if the importance of the notification is medium, the generation AI can notify using an SMS or email. Furthermore, if the importance of the notification is low, the generation AI can also notify using an in-app notification or a push notification. This allows the optimal notification method to be selected depending on the importance of the notification. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the notification unit can input notification importance data into the generation AI, which can then select the notification method.

[0092] The notification unit can determine the optimal notification timing by referring to past notification history when sending a notification. For example, the generation AI of the notification unit refers to past notification history and sends a notification at a time when the user is most likely to respond. The notification history includes, but is not limited to, past notification data, log data, etc. For example, the generation AI of the notification unit adjusts the frequency of notifications based on the past notification history. Furthermore, the notification unit can also send notifications in a manner that is most convenient for the user by referring to the past notification history. This allows the optimal notification timing to be determined by referring to the past notification history. Some or all of the above-described processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input past notification history data into the generation AI, which can then determine the optimal notification timing.

[0093] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user's emotions. For example, if the user is feeling anxious, the generation AI prioritizes health-related notifications. Examples of user emotions include, but are not limited to, anxiety, relaxation, and stress. For example, if the user is feeling relaxed, the generation AI prioritizes environment-related notifications. Furthermore, if the user is feeling stressed, the generation AI can prioritize action-related notifications. This determines the priority of notifications based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input the user's emotion data into the generation AI, which can then determine the priority of notifications.

[0094] The notification unit can adjust the timing of notifications taking into account the schedules of family members and caregivers. For example, the generation AI of the notification unit references the schedules of family members and caregivers and sends notifications at optimal timing. Examples of family and caregiver schedules include, but are not limited to, linking with a calendar app or manual input. For example, the generation AI of the notification unit adjusts the frequency of notifications based on the schedules of family members and caregivers. Furthermore, the notification unit can also select a notification method by reference to the schedules of family members and caregivers. This allows the timing of notifications to be adjusted taking into account the schedules of family members and caregivers. Some or all of the above-described processing in the notification unit may be performed using, or without, AI. For example, the notification unit can input schedule data of family members and caregivers into the generation AI, which can then adjust the timing of notifications.

[0095] The notification unit can diversify notification methods and send notifications by multiple means, such as email and SMS. For example, the generation AI of the notification unit sends notifications by both email and SMS. Examples of notification methods include, but are not limited to, email, SMS, and app notifications. For example, the generation AI of the notification unit can send notifications by both in-app notifications and push notifications. Furthermore, the generation AI of the notification unit can also send notifications by both phone calls and emergency alerts. This diversifies notification methods and sends notifications by multiple means. Some or all of the above-described processing in the notification unit may be performed using, or without, AI. For example, the notification unit can input notification method data into the generation AI, which can then send notifications by multiple means.

[0096] The control unit can estimate the user's emotions and adjust the control method of the operating device based on the estimated user's emotions. For example, when the user feels anxious, the control unit causes the generation AI to increase the response speed of the operating device. Examples of user emotions include, but are not limited to, anxiety, relaxation, stress, etc. For example, when the user feels relaxed, the control unit causes the generation AI to slow down the response speed of the operating device. Furthermore, when the user feels stressed, the control unit can also cause the generation AI to simplify the operating procedure of the operating device. This adjusts the control method of the operating device based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the control unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the control unit can input user's emotion data into the generation AI, and the generation AI can adjust the control method of the operating device.

[0097] During control, the control unit can monitor the operating status of the device in real time and perform optimal control. For example, the control unit can have a generation AI monitor the operating status of the device in real time and automatically correct any abnormalities that may occur. Examples of device operating status include, but are not limited to, operating time and error logs. For example, the control unit can have the generation AI select an optimal control method based on the operating status of the device. Furthermore, the control unit can also have the generation AI monitor the operating status of the device in real time and adjust the control method as necessary. This allows the operating status of the device to be monitored in real time and optimal control to be performed. Some or all of the above-described processing in the control unit can be performed using, for example, AI, or without AI. For example, the control unit can input device operating status data to the generation AI, which can then perform optimal control.

[0098] During control, the control unit can apply an optimal control algorithm by referring to past control history. In the control unit, for example, the generation AI refers to the past control history and selects an optimal control algorithm. The control history includes, for example, past control data, log data, etc., but is not limited to these examples. In the control unit, for example, the generation AI improves the accuracy of control based on the past control history. In addition, the control unit can also have the generation AI refer to the past control history and learn how to deal with abnormalities when they occur. In this way, the optimal control algorithm is applied by referring to the past control history. Some or all of the above-mentioned processing in the control unit may be performed using, or without using, AI. For example, the control unit can input past control history data to the generation AI, and the generation AI can apply the optimal control algorithm.

[0099] The control unit can estimate the user's emotions and determine control priorities based on the estimated user emotions. For example, if the user is feeling anxious, the control unit causes the generation AI to prioritize health-related control. Examples of user emotions include, but are not limited to, anxiety, relaxation, stress, etc. For example, if the user is feeling relaxed, the control unit causes the generation AI to prioritize environment-related control. Furthermore, if the user is feeling stressed, the control unit can also cause the generation AI to prioritize movement-related control. In this way, control priorities are determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the control unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the control unit can input user emotion data into the generation AI, which then determines the control priorities.

[0100] When controlling an appliance, the control unit can apply different control methods depending on the type of appliance. For example, when the generating AI controls an air conditioner, the control unit applies an algorithm dedicated to temperature adjustment. Types of appliances include, but are not limited to, refrigerators, air conditioners, and lighting. For example, when the generating AI controls lighting, the control unit applies an algorithm dedicated to brightness adjustment. Furthermore, when the generating AI controls a television, the control unit can also apply an algorithm dedicated to channel switching. This allows the optimal control method to be applied depending on the type of appliance. Some or all of the above-mentioned processing in the control unit may be performed using, or without using, AI. For example, the control unit can input data on the type of appliance to the generating AI, which then applies the optimal control method.

[0101] The control unit can adjust the control method by reflecting the opinions of family members and caregivers during control. For example, when a family member requests a temperature setting for an air conditioner, the control unit adjusts the control method by having the generation AI reflect that opinion. Opinions from family members and caregivers include, but are not limited to, air conditioner temperature settings and lighting brightness settings. For example, when a caregiver requests a lighting brightness setting, the control unit adjusts the control method by having the generation AI reflect that opinion. The control unit can also comprehensively evaluate the opinions of family members and caregivers to determine the optimal control method. In this way, the opinions of family members and caregivers are reflected and the control method is adjusted. Some or all of the above-described processing in the control unit may be performed using AI, for example, or may be performed without using AI. For example, the control unit can input opinion data from family members and caregivers into the generation AI, which then determines the optimal control method. === Hard Collateral 1-1 === Each of the multiple elements, including the installation unit, collection unit, analysis unit, notification unit, and control unit, described above, is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the installation unit installs the monitoring device in the elderly person's living space using the control unit 46A of the smart device 14. The collection unit collects data using the camera 42 and microphone 38B of the smart device 14. The analysis unit analyzes the data using the generation AI by the specific processing unit 290 of the data processing device 12 and detects abnormalities. The notification unit sends a notification to a family member or caregiver when the specific processing unit 290 of the data processing device 12 detects an abnormality. The control unit controls the operation device based on the notification by the control unit 46A of the smart device 14. Furthermore, the installation unit estimates the user's emotions, and the generation AI selects the optimal installation location. === Hard Collateral 1-2 === Each of the multiple elements, including the installation unit, collection unit, analysis unit, notification unit, and control unit, described above, is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the installation unit installs the monitoring device in the elderly person's living space via the control unit 46A of the smart glasses 214. The collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit analyzes the data using the generation AI via the specific processing unit 290 of the data processing device 12 and detects abnormalities. The notification unit sends a notification to a family member or caregiver when the specific processing unit 290 of the data processing device 12 detects an abnormality. The control unit controls the operation device based on the notification via the control unit 46A of the smart glasses 214. Furthermore, the installation unit estimates the user's emotions, and the generation AI selects the optimal installation location. === Hard Collateral 1-3 === Each of the multiple elements including the installation unit, collection unit, analysis unit, notification unit, and control unit described above is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the installation unit installs the monitoring device in the elderly person's living space using the control unit 46A of the headset type terminal 314. The collection unit collects data using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit analyzes the data using the generation AI by the specific processing unit 290 of the data processing device 12 and detects abnormalities. The notification unit sends a notification to family members or caregivers when the specific processing unit 290 of the data processing device 12 detects an abnormality. The control unit controls the operation device based on the notification by the control unit 46A of the headset type terminal 314. Furthermore, the installation unit estimates the user's emotions, and the generation AI selects the optimal installation location. === Hard Collateral 1-4 === Each of the multiple elements including the installation unit, collection unit, analysis unit, notification unit, and control unit described above is realized, for example, in at least one of the robot 414 and the data processing device 12. For example, the installation unit installs a monitoring device in the elderly person's living space using the control unit 46A of the robot 414. The collection unit collects data using the camera 42 and microphone 238 of the robot 414. The analysis unit analyzes the data using the generation AI by the specific processing unit 290 of the data processing device 12 and detects abnormalities. The notification unit sends a notification to a family member or caregiver when the specific processing unit 290 of the data processing device 12 detects an abnormality. The control unit controls the operation device based on the notification by the control unit 46A of the robot 414. Furthermore, the installation unit estimates the user's emotions, and the generation AI selects the optimal installation location.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The elderly support system can further include a health monitoring unit. The health monitoring unit periodically measures the elderly person's vital signs (heart rate, blood pressure, body temperature, etc.) and transmits the data to the collection unit. For example, if the heart rate is abnormally high, the analysis unit can analyze the data and detect the abnormality. If the blood pressure rises suddenly, the notification unit can send an emergency notification to family members or caregivers. Furthermore, if the body temperature is abnormally low, the control unit can automatically turn on the heating. This allows the elderly person's health condition to be monitored in real time and enables prompt response.

[0104] The elderly support system can further include a reminder unit. The reminder unit notifies the elderly so that they do not forget to take their medicine or make appointments with medical institutions. For example, when it is nearly time to take their medicine, the reminder unit can notify the elderly by voice or app notification. Also, if there is an appointment with a medical institution, the reminder unit can send a notification the day before. Furthermore, the reminder unit can send similar notifications to family members and caregivers. This makes it easier for the elderly to manage their health without forgetting important appointments.

[0105] The elderly support system may further include an entertainment unit. The entertainment unit provides content that the elderly can enjoy. For example, it can play streaming music and radio programs. It can also recommend television programs and movies. Furthermore, the entertainment unit can provide simple games and puzzles to help maintain cognitive function. This can improve the quality of life of the elderly and reduce feelings of loneliness.

[0106] The elderly support system can further include a communication unit. The communication unit provides a function that allows elderly people to easily contact their family and friends. For example, video calls and voice calls can be easily made. Messages can also be sent and received. Furthermore, the communication unit can automatically display the latest photos and videos of family and friends. This allows elderly people to maintain social connections without becoming isolated.

[0107] The elderly support system can further include an environmental monitoring unit. The environmental monitoring unit monitors the elderly's living environment and maintains a comfortable environment. For example, it can measure the indoor temperature and humidity and keep them within an appropriate range. It can also use an air quality sensor to detect harmful substances in the air and encourage ventilation as needed. It can also automatically adjust the brightness of lighting to provide an environment that is gentle on the eyes. This creates an environment where the elderly can live comfortably.

[0108] The elderly support system can further estimate the emotions of the elderly using an emotion estimation function and adjust the content of the reminder based on the estimated emotions. For example, if the elderly is feeling stressed, the reminder unit can send a notification in kind words. If the elderly is feeling relaxed, the reminder unit can send a concise notification. Furthermore, if the elderly is feeling anxious, the reminder unit can send a notification with a detailed explanation. This provides reminders that take the emotions of the elderly into consideration.

[0109] The elderly support system can further estimate the emotions of the elderly using an emotion estimation function and adjust the entertainment content based on the estimated emotions. For example, if the elderly is sad, the entertainment unit can recommend happy music or comedy movies. If the elderly is relaxed, the entertainment unit can provide relaxing music or nature videos. If the elderly is excited, the entertainment unit can provide calming content. In this way, entertainment is provided according to the emotions of the elderly.

[0110] The elderly support system can further estimate the emotions of the elderly using an emotion estimation function and adjust the communication method based on the estimated emotions. For example, if the elderly feels lonely, the communication unit can send a notification urging family and friends to contact them. If the elderly feels relaxed, the communication unit can suggest light conversation. Furthermore, if the elderly feels stressed, the communication unit can send an encouraging message. In this way, communication is provided according to the emotions of the elderly.

[0111] The elderly support system can further estimate the emotions of the elderly using an emotion estimation function and adjust the environmental monitoring settings based on the estimated emotions. For example, if the elderly feels anxious, the environmental monitoring unit can brighten the lights in the room. If the elderly feels relaxed, the environmental monitoring unit can keep the room temperature within a comfortable range. Furthermore, if the elderly feels stressed, the environmental monitoring unit can encourage ventilation to improve air quality. This provides a comfortable environment according to the elderly's emotions.

[0112] The elderly support system can further estimate the emotions of the elderly using an emotion estimation function and adjust the frequency of health monitoring based on the estimated emotions. For example, if the elderly feels anxious, the health monitoring unit can increase the frequency of vital sign measurements. If the elderly feels relaxed, the health monitoring unit can decrease the frequency of measurements. Furthermore, if the elderly feels stressed, the health monitoring unit can perform measurements at an appropriate frequency to avoid excessive monitoring. This provides health monitoring that is tailored to the emotions of the elderly.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The installation unit installs a monitoring device. The monitoring device may include, but is not limited to, a camera, a sensor, a microphone, etc. The installation unit installs the monitoring device in the living space of the elderly person, for example. Step 2: The collection unit collects data from the monitoring device. The data includes, but is not limited to, video data, audio data, and sensor data. For example, the collection unit collects video data from a camera. The collection unit can also collect temperature data from a sensor. The collection unit can also collect audio data from a microphone. Step 3: The analysis unit uses the generation AI to analyze the collected data and detect abnormalities. Examples of abnormalities include, but are not limited to, the presence or absence of movement, the volume of sound, and temperature changes. For example, the analysis unit uses the generation AI to analyze video data and detect falls by an elderly person. The analysis unit can also use the generation AI to analyze audio data and detect abnormal sounds. Furthermore, the analysis unit can also use the generation AI to analyze sensor data and detect sudden changes in temperature. Step 4: The notification unit sends a notification to a family member or caregiver when an abnormality is detected. Notifications include, but are not limited to, email, SMS, app notification, etc. For example, when an abnormality is detected, the notification unit sends a notification by email. The notification unit can also send a notification by SMS. The notification unit can also send a notification by app notification. Step 5: The control unit controls the operation device based on the notification. Examples of the operation device include, but are not limited to, home appliances and smart devices. For example, the control unit controls an air conditioner based on the notification. The control unit can also control lighting. Furthermore, the control unit can also control a television.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The 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.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 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.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0162] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0163] 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.

[0164] 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.

[0165] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an installation unit that installs the monitoring device; a collection unit that collects data from the monitoring devices installed by the installation unit; an analysis unit that analyzes the data collected by the collection unit and detects abnormalities; a notification unit that sends a notification based on the abnormality detected by the analysis unit; a control unit that controls an operation device based on the notification sent by the notification unit. A system characterized by:

2. The installation unit includes: Installing monitoring devices 2. The system of claim 1.

3. The collecting unit Collecting data from monitoring devices 2. The system of claim 1.

4. The analysis unit Generative AI analyzes collected data and detects anomalies 2. The system of claim 1.

5. The notification unit Sends notifications to family members or caregivers if an abnormality is detected 2. The system of claim 1.

6. The control unit Controlling a steering device based on notifications 2. The system of claim 1.

7. The installation unit includes: Provide installation support 2. The system of claim 1.

8. The installation unit includes: Estimating user emotions and optimizing the installation location of monitoring devices based on the estimated user emotions 2. The system of claim 1.

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