system

Abstract

The system according to the embodiment aims to efficiently provide a monitoring service for elderly people. [Solution] A system according to an embodiment includes a providing unit, an analysis unit, and another providing unit. The providing unit provides a monitoring device using a communication infrastructure. The analysis unit analyzes information obtained from the monitoring device provided by the providing unit. The providing unit provides a report or alert using a generation AI based on the information analyzed by the analysis 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] Conventional technologies do not adequately provide elderly care services, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently provide a monitoring service for elderly people. [Means for solving the problem]

[0006] A system according to an embodiment includes a providing unit, an analysis unit, and another providing unit. The providing unit provides a monitoring device using a communication infrastructure. The analysis unit analyzes information obtained from the monitoring device provided by the providing unit. The providing unit provides a report or alert using a generation AI based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently provide elderly care services. [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) A monitoring system according to an embodiment of the present invention provides a monitoring device using a communication infrastructure and uses a generation AI to provide reports and alerts. The monitoring system monitors the living conditions of elderly people living alone in real time and provides reports and alerts based on information analyzed using a generation AI. For example, the monitoring system provides conventional smart cameras and various sensor devices. This allows for real-time monitoring of the living conditions of elderly people living alone. For example, the smart camera captures images of the room, and the sensor device detects temperature, humidity, and movement. Next, the monitoring system provides a pet-like robot. This robot has smart camera and sensor functions and communicates using a generation AI. For example, the robot can converse with the elderly and issue alerts as needed. This not only serves as a monitor, but also as a companion. Furthermore, the monitoring system uses a generation AI to analyze information obtained from the monitoring device and provide reports and alerts. For example, if abnormal movement or temperature changes are detected, the generation AI analyzes the information and issues appropriate alerts. It can also periodically generate reports on the living conditions and provide them to family members and caregivers. This allows elderly people living alone to continue living safely at home. It also makes it easier for family members and caregivers to understand the elderly person's situation and provide appropriate support. This allows the monitoring system to monitor the living conditions of elderly people living alone in real time and provide reports and alerts based on information analyzed using the generation AI. For example, if abnormal movement or temperature changes are detected, the generation AI will analyze the information and send out appropriate alerts. It can also generate periodic reports on living conditions and provide them to family members and caregivers. This allows the monitoring system to allow elderly people living alone to continue living at home with peace of mind. It also makes it easier for family members and caregivers to understand the elderly person's situation and provide appropriate support.

[0029] A monitoring system according to an embodiment includes a providing unit, an analyzing unit, and a providing unit. The providing unit provides a monitoring device using a communication infrastructure. Examples of the monitoring device include, but are not limited to, smart cameras and various sensor devices. The providing unit captures images of a room using, for example, a smart camera. The providing unit can also provide a sensor device that detects temperature, humidity, and movement. For example, the providing unit measures the room temperature using a temperature sensor and the room humidity using a humidity sensor. The providing unit can also provide a motion sensor that detects movement. The analyzing unit analyzes information obtained from the monitoring device provided by the providing unit. The analysis is performed using, for example, data mining or a machine learning algorithm, but is not limited to, examples. For example, the analyzing unit analyzes the information obtained from the monitoring device using data mining technology. The analyzing unit can also analyze the information obtained from the monitoring device using a machine learning algorithm. The providing unit provides reports and alerts using a generation AI based on the information analyzed by the analyzing unit. The reports and alerts are provided in the form of, for example, a text report, a voice alert, an email notification, or the like, but are not limited to, examples. For example, the providing unit uses the generation AI to detect abnormal movements or temperature changes and issue an alert. The providing unit can also use the generation AI to periodically generate reports on the living conditions and provide them to family members and caregivers. This allows the monitoring system according to the embodiment to monitor the living conditions of elderly people living alone in real time and provide reports and alerts based on information analyzed using the generation AI. For example, the providing unit uses the generation AI to detect abnormal movements or temperature changes and issue an alert. The providing unit can also use the generation AI to periodically generate reports on the living conditions and provide them to family members and caregivers. This allows the monitoring system to enable elderly people living alone to continue living safely at home. This also makes it easier for family members and caregivers to understand the elderly person's situation and provide appropriate support.

[0030] The monitoring system includes a pet-type robot unit that uses a generative AI to communicate with the elderly and issue alerts as needed. The pet-type robot unit communicates with the elderly using the generative AI. For example, the pet-type robot unit can engage in voice dialogue with the elderly. The pet-type robot unit can also communicate with the elderly using gesture and facial expression recognition. For example, the pet-type robot unit can recognize the elderly's facial expressions and respond according to those expressions. The pet-type robot unit can also analyze the tone and speed of the elderly's voice and respond according to their emotions. The pet-type robot unit also issues alerts as needed. For example, the pet-type robot unit can issue an alert if the elderly person falls or if abnormal movement is detected. The pet-type robot unit can also issue an alert if the elderly person's health condition deteriorates. This allows the pet-type robot unit to not only monitor the elderly but also serve as a partner. Some or all of the above-mentioned processing in the pet-type robot unit may be performed, for example, using a generative AI, or may be performed without using a generative AI. For example, the pet robot can input the tone and speed of an elderly person's voice into the generation AI, which can then estimate their emotions. This allows the pet robot to communicate with the elderly person and issue alerts as necessary.

[0031] The monitoring system includes a sensor unit that detects temperature, humidity, and movement. The sensor unit detects temperature, humidity, and movement. For example, the sensor unit can measure the temperature of a room using a temperature sensor. The sensor unit can also measure the humidity of a room using a humidity sensor. The sensor unit also includes a motion sensor that detects movement. For example, the sensor unit can detect movement in a room and provide that information. This allows the sensor unit to monitor the living conditions of the elderly person in real time. Some or all of the above-mentioned processing in the sensor unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the sensor unit can input data obtained from a temperature sensor or humidity sensor into the generation AI and have the generation AI perform an analysis of the data. This allows the sensor unit to detect temperature, humidity, and movement and monitor the living conditions of the elderly person in real time.

[0032] The monitoring system includes a camera unit that captures images of the room. The camera unit captures images of the room. For example, the camera unit can capture images of the entire room using a 360-degree camera. The camera unit can also capture high-resolution images using a 4K resolution camera. The camera unit also includes an infrared camera that can capture images at night. For example, the camera unit can capture images of the room at night using an infrared camera. This allows the camera unit to visually monitor the living conditions of the elderly person. Some or all of the above-described processing in the camera unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the camera unit can input captured image data into a generation AI and have the generation AI perform analysis of the data. This allows the camera unit to capture images of the room and visually monitor the living conditions of the elderly person.

[0033] The providing unit can utilize the generation AI to detect abnormal movement or temperature changes and issue an alert. The providing unit utilizes the generation AI to detect abnormal movement or temperature changes. For example, the providing unit can use the generation AI to compare abnormal movement with normal movement and detect abnormal movement. The providing unit can also use the generation AI to detect sudden changes in temperature. Furthermore, the providing unit issues an alert when an abnormality is detected. For example, the providing unit can issue an audio alert when abnormal movement is detected. The providing unit can also send an email notification when a sudden change in temperature is detected. This allows the providing unit to quickly detect and respond to abnormalities. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input data on abnormal movement or temperature changes into the generation AI and have the generation AI perform an analysis of the data. This allows the providing unit to utilize the generation AI to detect abnormal movement or temperature changes and issue an alert.

[0034] The providing unit can periodically generate a living situation report and provide it to family members and caregivers. The providing unit periodically generates a living situation report. For example, the providing unit can generate a living situation report daily, weekly, monthly, or the like. The providing unit also provides the generated report to family members and caregivers. For example, the providing unit can send the generated report by email. The providing unit can also provide the generated report through a web application or a mobile application. This allows the providing unit to allow family members and caregivers to understand the elderly person's situation. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input data on the living situation into the generation AI and have the generation AI perform an analysis of the data. This allows the providing unit to periodically generate a living situation report and provide it to family members and caregivers.

[0035] The providing unit can analyze the user's past usage history and select the optimal provision method. The providing unit analyzes the user's past usage history and selects the optimal provision method. For example, the providing unit can suggest the optimal device based on the type of monitoring device the user has used in the past. The providing unit can also analyze the user's past usage frequency and determine the appropriate provision frequency. Furthermore, the providing unit can customize the provision method by referring to the user's past feedback. In this way, the providing unit can analyze the user's past usage history and select the optimal provision method. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past usage history data into the generation AI and cause the generation AI to select the optimal provision method. In this way, the providing unit can analyze the user's past usage history and select the optimal provision method.

[0036] The providing unit can perform filtering based on the user's current living situation and areas of interest when providing the monitoring device. The providing unit can perform filtering based on the user's current living situation and areas of interest when providing the monitoring device. For example, if the user is interested in health, the providing unit can provide a monitoring device specialized for health management. Furthermore, if the user has a pet, the providing unit can also provide a device that can monitor the pet's movements. Furthermore, if the user spends time on a hobby, the providing unit can also provide information related to the hobby. This allows the providing unit to perform filtering based on the user's living situation and areas of interest. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the providing unit can input data on the user's living situation and areas of interest into the generation AI and have the generation AI perform filtering. This allows the providing unit to perform filtering based on the user's current living situation and areas of interest.

[0037] The providing unit can select the optimal provision means according to the user's input method when providing the monitoring device. The providing unit can select the optimal provision means according to the user's input method when providing the monitoring device. For example, if the user prefers voice input, the providing unit can provide a monitoring device that can be operated by voice. Furthermore, if the user prefers text input, the providing unit can also provide a monitoring device that can be operated by text. Furthermore, if the user prefers image input, the providing unit can also provide a monitoring device with an image recognition function. This allows the providing unit to select the optimal provision means according to the user's input method. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input data of the user's input method into the generation AI and cause the generation AI to select the optimal provision means. This allows the providing unit to select the optimal provision means according to the user's input method.

[0038] When providing a monitoring device, the providing unit can prioritize providing a highly relevant device by taking into consideration the user's geographical location information. When providing a monitoring device, the providing unit can prioritize providing a highly relevant device by taking into consideration the user's geographical location information. For example, if the user lives in an urban area, the providing unit can provide a monitoring device suitable for an urban environment. Furthermore, if the user lives in a suburban area, the providing unit can provide a monitoring device that can cover a wide area. Furthermore, if the user lives in a cold region, the providing unit can provide a monitoring device that is suitable for cold regions. In this way, the providing unit can prioritize providing a highly relevant device by taking into consideration the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input data on the user's geographical location information into the generation AI and cause the generation AI to select a highly relevant device. In this way, the providing unit can prioritize providing a highly relevant device by taking into consideration the user's geographical location information.

[0039] The providing unit can analyze the user's social media activity and provide the related device when providing the monitoring device. The providing unit can analyze the user's social media activity and provide the related device when providing the monitoring device. For example, if the user posts about their health on social media, the providing unit can provide a monitoring device specialized for health management. Furthermore, if the user posts about their pet, the providing unit can provide a device that can monitor the pet's movements. Furthermore, if the user posts about their travel, the providing unit can provide a device that ensures the user's safety during their travel. In this way, the providing unit can analyze the user's social media activity and provide the related device. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the providing unit can input data on the user's social media activity into the generation AI and cause the generation AI to select the related device. In this way, the providing unit can analyze the user's social media activity and provide the related device.

[0040] The providing unit can customize the provision method by reflecting the user's past feedback when providing a monitoring device. The providing unit customizes the provision method by reflecting the user's past feedback when providing a monitoring device. For example, if the user was dissatisfied with a monitoring device provided in the past, the providing unit can provide a device that resolves that dissatisfaction. Furthermore, if the user gave a high rating to a monitoring device provided in the past, the providing unit can provide a similar device. Furthermore, the providing unit can improve the provision method based on the user's past feedback and provide a service with higher satisfaction. This allows the providing unit to customize the provision method by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input data of the user's past feedback into the generation AI and cause the generation AI to customize the provision method. This allows the providing unit to customize the provision method by reflecting the user's past feedback.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the information during analysis. For example, the analysis unit performs a detailed analysis on information of high importance. The analysis unit can also perform a brief analysis on information of low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on information of medium importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the information.

[0042] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply an analysis algorithm specialized for health management to health information. The analysis unit can also apply an analysis algorithm specialized for environmental monitoring to environmental information. The analysis unit can also apply an analysis algorithm specialized for motion analysis to motion information. This allows the analysis unit to apply different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input information category data to the generation AI and cause the generation AI to apply different analysis algorithms. This allows the analysis unit to apply different analysis algorithms depending on the category of information.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the analysis accuracy. Furthermore, the analysis unit can minimize analysis errors by referring to the user's past analysis results. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results.

[0044] The analysis unit can determine the analysis priority based on the time when the information was acquired during analysis. The analysis unit can determine the analysis priority based on the time when the information was acquired during analysis. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also lower the priority of analysis of older information. Furthermore, the analysis unit can also analyze information of moderate newness with a moderate priority. This allows the analysis unit to determine the analysis priority based on the time when the information was acquired. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input information acquisition time data into the generation AI and have the generation AI determine the analysis priority. This allows the analysis unit to determine the analysis priority based on the time when the information was acquired.

[0045] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit adjusts the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of information with high relevance. The analysis unit can also lower the priority of analysis of information with low relevance. Furthermore, the analysis unit can also analyze information with moderate relevance with moderate priority. This allows the analysis unit to adjust the order of analysis based on the relevance of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input relevance data of information to the generation AI and have the generation AI adjust the order of analysis. This allows the analysis unit to adjust the order of analysis based on the relevance of information.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit performs an analysis that uses a lot of technical terms. Alternatively, if the user does not have technical expertise, the analysis unit can perform a concise and easy-to-understand analysis. Furthermore, the analysis unit can perform an analysis that uses appropriate technical terms according to the user's level of expertise. This allows the analysis unit to adjust the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI execute the use of technical terms. This allows the analysis unit to adjust the use of technical terms in the analysis according to the user's level of expertise.

[0047] The pet-type robot unit can provide optimal conversation content by referring to the user's past conversation history during communication. The pet-type robot unit can provide optimal conversation content by referring to the user's past conversation history during communication. For example, the pet-type robot unit can provide related topics based on topics the user has previously discussed. The pet-type robot unit can also provide topics that the user is likely to be interested in based on the user's past conversation history. Furthermore, the pet-type robot unit can smooth the flow of conversation by referring to the user's past conversation history. In this way, the pet-type robot unit can provide optimal conversation content by referring to the user's past conversation history. Some or all of the above-described processing in the pet-type robot unit may be performed using, or without, a generation AI. For example, the pet-type robot unit can input the user's past conversation history data into the generation AI and cause the generation AI to provide optimal conversation content. In this way, the pet-type robot unit can provide optimal conversation content by referring to the user's past conversation history.

[0048] The pet-type robot unit can customize the conversation content based on the user's current living situation during communication. The pet-type robot unit customizes the conversation content based on the user's current living situation during communication. For example, if the user is interested in health, the pet-type robot unit can provide topics related to health. Furthermore, if the user spends time on a hobby, the pet-type robot unit can provide topics related to that hobby. Furthermore, if the user is interested in traveling, the pet-type robot unit can provide topics related to travel. This allows the pet-type robot unit to customize the conversation content based on the user's current living situation. Some or all of the above-mentioned processing in the pet-type robot unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the pet-type robot unit can input data on the user's current living situation into the generation AI and have the generation AI customize the conversation content. This allows the pet-type robot unit to customize the conversation content based on the user's current living situation.

[0049] The pet-type robot unit can improve the conversation content by reflecting user feedback during communication. The pet-type robot unit improves the conversation content by reflecting user feedback during communication. For example, if the user is dissatisfied with conversation content provided in the past, the pet-type robot unit can provide conversation content that resolves that dissatisfaction. Furthermore, if the user has given a high rating to conversation content provided in the past, the pet-type robot unit can provide similar conversation content. Furthermore, the pet-type robot unit can improve the conversation content based on the user's past feedback and provide more satisfying communication. In this way, the pet-type robot unit can improve the conversation content by reflecting user feedback. Some or all of the above-described processing in the pet-type robot unit may be performed using, or without, a generation AI. For example, the pet-type robot unit can input user feedback data into the generation AI and cause the generation AI to improve the conversation content. In this way, the pet-type robot unit can improve the conversation content by reflecting user feedback.

[0050] The pet-type robot unit can provide optimal conversation content during communication by taking into account the user's geographical location information. The pet-type robot unit can provide optimal conversation content during communication by taking into account the user's geographical location information. For example, if the user lives in an urban area, the pet-type robot unit can provide topics related to the city. Furthermore, if the user lives in the suburbs, the pet-type robot unit can provide topics related to nature and the outdoors. Furthermore, if the user is traveling, the pet-type robot unit can provide topics related to the travel destination. In this way, the pet-type robot unit can provide optimal conversation content by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the pet-type robot unit may be performed using, or without, a generation AI. For example, the pet-type robot unit can input the user's geographical location information data into the generation AI and cause the generation AI to provide optimal conversation content. In this way, the pet-type robot unit can provide optimal conversation content by taking into account the user's geographical location information.

[0051] The pet-type robot unit can analyze the user's social media activity and suggest conversation content during communication. The pet-type robot unit can analyze the user's social media activity and suggest conversation content during communication. For example, if the user posts about health on social media, the pet-type robot unit can suggest health-related topics. Furthermore, if the user posts about pets, the pet-type robot unit can suggest pet-related topics. Furthermore, if the user posts about travel, the pet-type robot unit can suggest travel-related topics. In this way, the pet-type robot unit can analyze the user's social media activity and suggest conversation content. Some or all of the above-mentioned processing in the pet-type robot unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the pet-type robot unit can input the user's social media activity data into the generation AI and have the generation AI suggest conversation content. In this way, the pet-type robot unit can analyze the user's social media activity and suggest conversation content.

[0052] The pet-type robot unit can customize the conversation content by reflecting the user's past feedback during communication. The pet-type robot unit customizes the conversation content by reflecting the user's past feedback during communication. For example, if the user is dissatisfied with conversation content provided in the past, the pet-type robot unit can provide conversation content that resolves that dissatisfaction. Furthermore, if the user has given a high rating to conversation content provided in the past, the pet-type robot unit can provide similar conversation content. Furthermore, the pet-type robot unit can improve the conversation content based on the user's past feedback and provide more satisfying communication. This allows the pet-type robot unit to customize the conversation content by reflecting the user's past feedback. Some or all of the above-described processing in the pet-type robot unit may be performed using, or without, a generation AI. For example, the pet-type robot unit can input the user's past feedback data into the generation AI and have the generation AI customize the conversation content. This allows the pet-type robot unit to customize the conversation content by reflecting the user's past feedback.

[0053] The sensor unit can optimize the detection algorithm by referring to past data when the sensor is performing detection. The sensor unit can optimize the detection algorithm by referring to past data when the sensor is performing detection. For example, the sensor unit adjusts the sensor's detection algorithm based on past data to improve accuracy. The sensor unit can also extract specific patterns from past data and reflect them in the detection algorithm. Furthermore, the sensor unit can apply an algorithm that minimizes false detections by referring to past data. In this way, the sensor unit can optimize the detection algorithm by referring to past data. Some or all of the above-described processing in the sensor unit may be performed using, or without, a generation AI. For example, the sensor unit can input past data into the generation AI and have the generation AI optimize the detection algorithm. In this way, the sensor unit can optimize the detection algorithm by referring to past data.

[0054] The sensor unit can analyze the user's lifestyle rhythm during sensor detection and set the optimal detection timing. The sensor unit can analyze the user's lifestyle rhythm during sensor detection and set the optimal detection timing. For example, the sensor unit adjusts the sensor's detection timing based on the user's lifestyle rhythm. The sensor unit can also increase the sensor's detection frequency to match the user's active time period. The sensor unit can also set the sensor's detection frequency to match the user's resting time period. This allows the sensor unit to set the detection timing based on the user's lifestyle rhythm. Some or all of the above-described processing in the sensor unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the sensor unit can input the user's lifestyle rhythm data into the generation AI and have the generation AI set the detection timing. This allows the sensor unit to analyze the user's lifestyle rhythm and set the optimal detection timing.

[0055] The sensor unit can improve the detection method by reflecting user feedback during sensor detection. The sensor unit can improve the detection method by reflecting user feedback during sensor detection. For example, if a user is dissatisfied with a detection method previously provided, the sensor unit can provide a detection method that resolves that dissatisfaction. Furthermore, if a user has given a high rating to a detection method previously provided, the sensor unit can provide a similar detection method. Furthermore, the sensor unit can improve the detection method based on the user's past feedback and provide a service with higher satisfaction. In this way, the sensor unit can improve the detection method by reflecting user feedback. Some or all of the above-described processing in the sensor unit may be performed using, or without, a generation AI. For example, the sensor unit can input user feedback data into the generation AI and cause the generation AI to improve the detection method. In this way, the sensor unit can improve the detection method by reflecting user feedback.

[0056] The sensor unit can select the optimal detection method by taking into account the user's geographical location information when performing sensor detection. The sensor unit can select the optimal detection method by taking into account the user's geographical location information when performing sensor detection. For example, if the user lives in an urban area, the sensor unit can provide a detection method suitable for urban environments. Furthermore, if the user lives in a suburban area, the sensor unit can provide a detection method that can cover a wide area. Furthermore, if the user lives in a cold region, the sensor unit can provide a detection method suitable for cold regions. This allows the sensor unit to select the optimal detection method by taking into account the user's geographical location information. Some or all of the above-described processing in the sensor unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the sensor unit can input the user's geographical location information data into the generation AI and have the generation AI select the optimal detection method. This allows the sensor unit to select the optimal detection method by taking into account the user's geographical location information.

[0057] The sensor unit can analyze the user's social media activity and suggest a detection method when the sensor detects something. The sensor unit can analyze the user's social media activity and suggest a detection method when the sensor detects something. For example, if the user posts about health on social media, the sensor unit can provide a detection method specialized for health management. Furthermore, if the user posts about pets, the sensor unit can provide a detection method that can monitor the pet's movements. Furthermore, if the user posts about travel, the sensor unit can provide a detection method that ensures safety during travel. In this way, the sensor unit can analyze the user's social media activity and suggest a detection method. Some or all of the above-mentioned processing in the sensor unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the sensor unit can input the user's social media activity data into the generation AI and have the generation AI execute the detection method suggestion. In this way, the sensor unit can analyze the user's social media activity and suggest a detection method.

[0058] The sensor unit can customize the detection method by reflecting the user's past feedback when performing sensor detection. The sensor unit customizes the detection method by reflecting the user's past feedback when performing sensor detection. For example, if the user is dissatisfied with a detection method previously provided, the sensor unit can provide a detection method that resolves the dissatisfaction. Furthermore, if the user has given a high rating to a detection method previously provided, the sensor unit can provide a similar detection method. Furthermore, the sensor unit can improve the detection method based on the user's past feedback and provide a service with higher satisfaction. In this way, the sensor unit can customize the detection method by reflecting the user's past feedback. Some or all of the above-described processing in the sensor unit may be performed using, or without, a generation AI. For example, the sensor unit can input the user's past feedback data into the generation AI and have the generation AI customize the detection method. In this way, the sensor unit can customize the detection method by reflecting the user's past feedback.

[0059] The camera unit can optimize the shooting algorithm by referring to past shooting data when shooting with the camera. The camera unit optimizes the shooting algorithm by referring to past shooting data when shooting with the camera. For example, the camera unit adjusts the shooting algorithm based on the past shooting data to improve accuracy. The camera unit can also extract specific patterns from the past shooting data and reflect them in the shooting algorithm. Furthermore, the camera unit can apply an algorithm that minimizes false detections by referring to the past shooting data. In this way, the camera unit can optimize the shooting algorithm by referring to the past shooting data. Some or all of the above-mentioned processing in the camera unit may be performed using, or without, a generation AI. For example, the camera unit can input past shooting data into the generation AI and have the generation AI optimize the shooting algorithm. In this way, the camera unit can optimize the shooting algorithm by referring to the past shooting data.

[0060] The camera unit can analyze the user's lifestyle and set the optimal timing for taking a photo with the camera. The camera unit can analyze the user's lifestyle and set the optimal timing for taking a photo with the camera. For example, the camera unit adjusts the timing for taking a photo with the camera based on the user's lifestyle. The camera unit can also increase the frequency of taking photos with the camera to match the time periods when the user is active. The camera unit can also set the frequency of taking photos with the camera to be lower to match the time periods when the user is resting. This allows the camera unit to set the timing for taking a photo based on the user's lifestyle. Some or all of the above-described processing in the camera unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the camera unit can input the user's lifestyle data into the generation AI and have the generation AI set the timing for taking a photo. This allows the camera unit to analyze the user's lifestyle and set the optimal timing for taking a photo.

[0061] The camera unit can improve the shooting method by reflecting user feedback when taking a photo with the camera. The camera unit improves the shooting method by reflecting user feedback when taking a photo with the camera. For example, if a user is dissatisfied with a shooting method previously provided, the camera unit provides a shooting method that resolves the dissatisfaction. Furthermore, if a user has given a high rating to a shooting method previously provided, the camera unit can provide a similar shooting method. Furthermore, the camera unit can improve the shooting method based on the user's past feedback and provide a service with higher satisfaction. In this way, the camera unit can improve the shooting method by reflecting user feedback. Some or all of the above-described processing in the camera unit may be performed using, or without, a generation AI. For example, the camera unit can input user feedback data into the generation AI and cause the generation AI to improve the shooting method. In this way, the camera unit can improve the shooting method by reflecting user feedback.

[0062] The camera unit can select the optimal shooting method by taking into account the user's geographical location information when taking a picture with the camera. The camera unit selects the optimal shooting method by taking into account the user's geographical location information when taking a picture with the camera. For example, if the user lives in an urban area, the camera unit can provide a shooting method suitable for urban environments. Furthermore, if the user lives in a suburban area, the camera unit can provide a shooting method that can cover a wide area. Furthermore, if the user lives in a cold region, the camera unit can provide a shooting method suitable for cold regions. This allows the camera unit to select the optimal shooting method by taking into account the user's geographical location information. Some or all of the above-described processing in the camera unit may be performed using, or without, a generation AI. For example, the camera unit can input the user's geographical location information data into the generation AI and have the generation AI select the optimal shooting method. This allows the camera unit to select the optimal shooting method by taking into account the user's geographical location information.

[0063] When taking a picture with the camera, the camera unit can analyze the user's social media activity and suggest a shooting method. When taking a picture with the camera, the camera unit can analyze the user's social media activity and suggest a shooting method. For example, if a user posts about health on social media, the camera unit can provide a shooting method specialized for health management. Furthermore, if a user posts about pets, the camera unit can provide a shooting method that allows the user to monitor the pet's movements. Furthermore, if a user posts about travel, the camera unit can provide a shooting method that ensures the user's safety during the trip. In this way, the camera unit can analyze the user's social media activity and suggest a shooting method. Some or all of the above-mentioned processing in the camera unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the camera unit can input the user's social media activity data into the generation AI and have the generation AI execute a shooting method suggestion. In this way, the camera unit can analyze the user's social media activity and suggest a shooting method.

[0064] The camera unit can customize the shooting method by reflecting the user's past feedback when taking a photo with the camera. The camera unit customizes the shooting method by reflecting the user's past feedback when taking a photo with the camera. For example, if the user is dissatisfied with a shooting method previously provided, the camera unit can provide a shooting method that resolves the dissatisfaction. Furthermore, if the user has given a high rating to a shooting method previously provided, the camera unit can provide a similar shooting method. Furthermore, the camera unit can improve the shooting method based on the user's past feedback and provide a more satisfying service. In this way, the camera unit can customize the shooting method by reflecting the user's past feedback. Some or all of the above-described processing in the camera unit may be performed using, or without, a generation AI. For example, the camera unit can input the user's past feedback data into the generation AI and have the generation AI customize the shooting method. In this way, the camera unit can customize the shooting method by reflecting the user's past feedback.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The providing unit can monitor the user's health condition and notify a medical institution if an abnormality is detected. For example, if the providing unit detects an abnormality in the heart rate or blood pressure, it immediately notifies a medical institution. The providing unit can also automatically call an ambulance if the user falls. Furthermore, the providing unit can send reminders for regular health checks based on the user's health condition. This allows the providing unit to monitor the user's health condition in real time and respond quickly.

[0067] The sensor unit can monitor the user's activity level and recommend appropriate exercise. For example, if the user has been sitting for a long time, the sensor unit can notify the user to stand up and stretch. The sensor unit can also count the user's steps and encourage exercise if the user has not reached the target number of steps. Furthermore, the sensor unit can monitor the user's heart rate and recommend appropriate exercise intensity. In this way, the sensor unit can support the user in maintaining their health.

[0068] The providing unit can manage the user's schedule and remind the user of important appointments. For example, the providing unit can link with the user's calendar and send reminders for meetings or medical appointments. The providing unit can also suggest appropriate break times based on the user's schedule. Furthermore, the providing unit can also suggest optimal routes based on the user's schedule. In this way, the providing unit can support the user's schedule management.

[0069] The analysis unit can analyze the user's past behavioral data and predict behavioral patterns. For example, if the user has a habit of jogging every morning, the analysis unit can set an appropriate alert for that time period. Also, if the user has a habit of shopping on a specific day of the week, the analysis unit can send a reminder for that day. Furthermore, if the user has a habit of relaxing during a specific time period, the analysis unit can make suggestions for relaxing during that time period. In this way, the analysis unit can predict the user's behavioral patterns and provide appropriate support.

[0070] The providing unit can suggest related events and activities based on the user's hobbies and interests. For example, if the user is interested in music, the providing unit can provide information about nearby concerts. If the user is interested in sports, the providing unit can also provide information about local sporting events. Furthermore, if the user is interested in art, the providing unit can also provide information about exhibitions at museums and galleries. This allows the providing unit to suggest related events and activities based on the user's hobbies and interests.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The provider uses the communication infrastructure to provide a monitoring device. The monitoring device may include a smart camera or various sensor devices. For example, the provider may use a smart camera to capture images of a room and provide a temperature sensor, humidity sensor, and motion sensor. Step 2: The analysis unit analyzes the information obtained from the monitoring device provided by the provision unit. The analysis is performed using data mining and machine learning algorithms. Step 3: The provider uses AI generation to provide reports and alerts based on the information analyzed by the analyzer. Reports and alerts are provided in the form of text reports, voice alerts, email notifications, etc. For example, an alert can be sent upon detecting abnormal movements or temperature changes. Periodic reports on the user's living conditions can also be generated and provided to family members or caregivers.

[0073] (Example 2) A monitoring system according to an embodiment of the present invention provides a monitoring device using a communication infrastructure and uses a generation AI to provide reports and alerts. The monitoring system monitors the living conditions of elderly people living alone in real time and provides reports and alerts based on information analyzed using a generation AI. For example, the monitoring system provides conventional smart cameras and various sensor devices. This allows for real-time monitoring of the living conditions of elderly people living alone. For example, the smart camera captures images of the room, and the sensor device detects temperature, humidity, and movement. Next, the monitoring system provides a pet-like robot. This robot has smart camera and sensor functions and communicates using a generation AI. For example, the robot can converse with the elderly and issue alerts as needed. This not only serves as a monitor, but also as a companion. Furthermore, the monitoring system uses a generation AI to analyze information obtained from the monitoring device and provide reports and alerts. For example, if abnormal movement or temperature changes are detected, the generation AI analyzes the information and issues appropriate alerts. It can also periodically generate reports on the living conditions and provide them to family members and caregivers. This allows elderly people living alone to continue living safely at home. It also makes it easier for family members and caregivers to understand the elderly person's situation and provide appropriate support. This allows the monitoring system to monitor the living conditions of elderly people living alone in real time and provide reports and alerts based on information analyzed using the generation AI. For example, if abnormal movement or temperature changes are detected, the generation AI will analyze the information and send out appropriate alerts. It can also generate periodic reports on living conditions and provide them to family members and caregivers. This allows the monitoring system to allow elderly people living alone to continue living at home with peace of mind. It also makes it easier for family members and caregivers to understand the elderly person's situation and provide appropriate support.

[0074] A monitoring system according to an embodiment includes a providing unit, an analyzing unit, and a providing unit. The providing unit provides a monitoring device using a communication infrastructure. Examples of the monitoring device include, but are not limited to, smart cameras and various sensor devices. The providing unit captures images of a room using, for example, a smart camera. The providing unit can also provide a sensor device that detects temperature, humidity, and movement. For example, the providing unit measures the room temperature using a temperature sensor and the room humidity using a humidity sensor. The providing unit can also provide a motion sensor that detects movement. The analyzing unit analyzes information obtained from the monitoring device provided by the providing unit. The analysis is performed using, for example, data mining or a machine learning algorithm, but is not limited to, examples. For example, the analyzing unit analyzes the information obtained from the monitoring device using data mining technology. The analyzing unit can also analyze the information obtained from the monitoring device using a machine learning algorithm. The providing unit provides reports and alerts using a generation AI based on the information analyzed by the analyzing unit. The reports and alerts are provided in the form of, for example, a text report, a voice alert, an email notification, or the like, but are not limited to, examples. For example, the providing unit uses the generation AI to detect abnormal movements or temperature changes and issue an alert. The providing unit can also use the generation AI to periodically generate reports on the living conditions and provide them to family members and caregivers. This allows the monitoring system according to the embodiment to monitor the living conditions of elderly people living alone in real time and provide reports and alerts based on information analyzed using the generation AI. For example, the providing unit uses the generation AI to detect abnormal movements or temperature changes and issue an alert. The providing unit can also use the generation AI to periodically generate reports on the living conditions and provide them to family members and caregivers. This allows the monitoring system to enable elderly people living alone to continue living safely at home. This also makes it easier for family members and caregivers to understand the elderly person's situation and provide appropriate support.

[0075] The monitoring system includes a pet-type robot unit that uses a generative AI to communicate with the elderly and issue alerts as needed. The pet-type robot unit communicates with the elderly using the generative AI. For example, the pet-type robot unit can engage in voice dialogue with the elderly. The pet-type robot unit can also communicate with the elderly using gesture and facial expression recognition. For example, the pet-type robot unit can recognize the elderly's facial expressions and respond according to those expressions. The pet-type robot unit can also analyze the tone and speed of the elderly's voice and respond according to their emotions. The pet-type robot unit also issues alerts as needed. For example, the pet-type robot unit can issue an alert if the elderly person falls or if abnormal movement is detected. The pet-type robot unit can also issue an alert if the elderly person's health condition deteriorates. This allows the pet-type robot unit to not only monitor the elderly but also serve as a partner. Some or all of the above-mentioned processing in the pet-type robot unit may be performed, for example, using a generative AI, or may be performed without using a generative AI. For example, the pet robot can input the tone and speed of an elderly person's voice into the generation AI, which can then estimate their emotions. This allows the pet robot to communicate with the elderly person and issue alerts as necessary.

[0076] The monitoring system includes a sensor unit that detects temperature, humidity, and movement. The sensor unit detects temperature, humidity, and movement. For example, the sensor unit can measure the temperature of a room using a temperature sensor. The sensor unit can also measure the humidity of a room using a humidity sensor. The sensor unit also includes a motion sensor that detects movement. For example, the sensor unit can detect movement in a room and provide that information. This allows the sensor unit to monitor the living conditions of the elderly person in real time. Some or all of the above-mentioned processing in the sensor unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the sensor unit can input data obtained from a temperature sensor or humidity sensor into the generation AI and have the generation AI perform an analysis of the data. This allows the sensor unit to detect temperature, humidity, and movement and monitor the living conditions of the elderly person in real time.

[0077] The monitoring system includes a camera unit that captures images of the room. The camera unit captures images of the room. For example, the camera unit can capture images of the entire room using a 360-degree camera. The camera unit can also capture high-resolution images using a 4K resolution camera. The camera unit also includes an infrared camera that can capture images at night. For example, the camera unit can capture images of the room at night using an infrared camera. This allows the camera unit to visually monitor the living conditions of the elderly person. Some or all of the above-described processing in the camera unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the camera unit can input captured image data into a generation AI and have the generation AI perform analysis of the data. This allows the camera unit to capture images of the room and visually monitor the living conditions of the elderly person.

[0078] The providing unit can utilize the generation AI to detect abnormal movement or temperature changes and issue an alert. The providing unit utilizes the generation AI to detect abnormal movement or temperature changes. For example, the providing unit can use the generation AI to compare abnormal movement with normal movement and detect abnormal movement. The providing unit can also use the generation AI to detect sudden changes in temperature. Furthermore, the providing unit issues an alert when an abnormality is detected. For example, the providing unit can issue an audio alert when abnormal movement is detected. The providing unit can also send an email notification when a sudden change in temperature is detected. This allows the providing unit to quickly detect and respond to abnormalities. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input data on abnormal movement or temperature changes into the generation AI and have the generation AI perform an analysis of the data. This allows the providing unit to utilize the generation AI to detect abnormal movement or temperature changes and issue an alert.

[0079] The providing unit can periodically generate a living situation report and provide it to family members and caregivers. The providing unit periodically generates a living situation report. For example, the providing unit can generate a living situation report daily, weekly, monthly, or the like. The providing unit also provides the generated report to family members and caregivers. For example, the providing unit can send the generated report by email. The providing unit can also provide the generated report through a web application or a mobile application. This allows the providing unit to allow family members and caregivers to understand the elderly person's situation. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input data on the living situation into the generation AI and have the generation AI perform an analysis of the data. This allows the providing unit to periodically generate a living situation report and provide it to family members and caregivers.

[0080] The providing unit can estimate the user's emotions and adjust the timing of providing the monitoring device based on the estimated user emotions. The providing unit can estimate the user's emotions and adjust the timing of providing the monitoring device based on the estimated user emotions. For example, if the user is feeling anxious, the providing unit can immediately provide the monitoring device to give a sense of security. Furthermore, if the user is relaxed, the providing unit can delay the timing of providing the monitoring device and introduce the monitoring device in a natural manner. Furthermore, if the user is busy, the providing unit can adjust the timing of providing the monitoring device to match the user's schedule. In this way, the providing unit can adjust the timing of providing the monitoring device according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's facial expression data or voice data into the generation AI and cause the generation AI to estimate the emotion. This allows the providing unit to adjust the timing of providing the monitoring device based on the user's emotions.

[0081] The providing unit can analyze the user's past usage history and select the optimal provision method. The providing unit analyzes the user's past usage history and selects the optimal provision method. For example, the providing unit can suggest the optimal device based on the type of monitoring device the user has used in the past. The providing unit can also analyze the user's past usage frequency and determine the appropriate provision frequency. Furthermore, the providing unit can customize the provision method by referring to the user's past feedback. In this way, the providing unit can analyze the user's past usage history and select the optimal provision method. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past usage history data into the generation AI and cause the generation AI to select the optimal provision method. In this way, the providing unit can analyze the user's past usage history and select the optimal provision method.

[0082] The providing unit can perform filtering based on the user's current living situation and areas of interest when providing the monitoring device. The providing unit can perform filtering based on the user's current living situation and areas of interest when providing the monitoring device. For example, if the user is interested in health, the providing unit can provide a monitoring device specialized for health management. Furthermore, if the user has a pet, the providing unit can also provide a device that can monitor the pet's movements. Furthermore, if the user spends time on a hobby, the providing unit can also provide information related to the hobby. This allows the providing unit to perform filtering based on the user's living situation and areas of interest. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the providing unit can input data on the user's living situation and areas of interest into the generation AI and have the generation AI perform filtering. This allows the providing unit to perform filtering based on the user's current living situation and areas of interest.

[0083] The providing unit can select the optimal provision means according to the user's input method when providing the monitoring device. The providing unit can select the optimal provision means according to the user's input method when providing the monitoring device. For example, if the user prefers voice input, the providing unit can provide a monitoring device that can be operated by voice. Furthermore, if the user prefers text input, the providing unit can also provide a monitoring device that can be operated by text. Furthermore, if the user prefers image input, the providing unit can also provide a monitoring device with an image recognition function. This allows the providing unit to select the optimal provision means according to the user's input method. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input data of the user's input method into the generation AI and cause the generation AI to select the optimal provision means. This allows the providing unit to select the optimal provision means according to the user's input method.

[0084] The providing unit can estimate the user's emotions and determine the priority of the monitoring devices to be provided based on the estimated user emotions. The providing unit can estimate the user's emotions and determine the priority of the monitoring devices to be provided based on the estimated user emotions. For example, if the user is feeling anxious, the providing unit can prioritize providing the monitoring device that provides the most reassurance. Furthermore, if the user is relaxed, the providing unit can also provide the minimum necessary monitoring device. Furthermore, if the user is busy, the providing unit can prioritize providing the monitoring device that is easy to install. This allows the providing unit to determine the priority of the monitoring devices according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's facial expression data or voice data into the generation AI and cause the generation AI to estimate the emotion. This allows the providing unit to determine the priority of the monitoring devices to be provided based on the user's emotions.

[0085] When providing a monitoring device, the providing unit can prioritize providing a highly relevant device by taking into consideration the user's geographical location information. When providing a monitoring device, the providing unit can prioritize providing a highly relevant device by taking into consideration the user's geographical location information. For example, if the user lives in an urban area, the providing unit can provide a monitoring device suitable for an urban environment. Furthermore, if the user lives in a suburban area, the providing unit can provide a monitoring device that can cover a wide area. Furthermore, if the user lives in a cold region, the providing unit can provide a monitoring device that is suitable for cold regions. In this way, the providing unit can prioritize providing a highly relevant device by taking into consideration the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input data on the user's geographical location information into the generation AI and cause the generation AI to select a highly relevant device. In this way, the providing unit can prioritize providing a highly relevant device by taking into consideration the user's geographical location information.

[0086] The providing unit can analyze the user's social media activity and provide the related device when providing the monitoring device. The providing unit can analyze the user's social media activity and provide the related device when providing the monitoring device. For example, if the user posts about their health on social media, the providing unit can provide a monitoring device specialized for health management. Furthermore, if the user posts about their pet, the providing unit can provide a device that can monitor the pet's movements. Furthermore, if the user posts about their travel, the providing unit can provide a device that ensures the user's safety during their travel. In this way, the providing unit can analyze the user's social media activity and provide the related device. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the providing unit can input data on the user's social media activity into the generation AI and cause the generation AI to select the related device. In this way, the providing unit can analyze the user's social media activity and provide the related device.

[0087] The providing unit can customize the provision method by reflecting the user's past feedback when providing a monitoring device. The providing unit customizes the provision method by reflecting the user's past feedback when providing a monitoring device. For example, if the user was dissatisfied with a monitoring device provided in the past, the providing unit can provide a device that resolves that dissatisfaction. Furthermore, if the user gave a high rating to a monitoring device provided in the past, the providing unit can provide a similar device. Furthermore, the providing unit can improve the provision method based on the user's past feedback and provide a service with higher satisfaction. This allows the providing unit to customize the provision method by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input data of the user's past feedback into the generation AI and cause the generation AI to customize the provision method. This allows the providing unit to customize the provision method by reflecting the user's past feedback.

[0088] The analysis unit can estimate the user's emotions and adjust the analysis presentation method based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the analysis presentation method based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit uses a concise presentation method that conveys a sense of security. If the user is relaxed, the analysis unit can use a presentation method that includes detailed information. If the user is in a hurry, the analysis unit can use a presentation method that focuses on the main points. This allows the analysis unit to adjust the analysis presentation method according to 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's facial expression data and voice data into the generation AI and have the generation AI perform emotion estimation. This allows the analysis unit to adjust the way the analysis is presented based on the user's emotions.

[0089] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the information during analysis. For example, the analysis unit performs a detailed analysis on information of high importance. The analysis unit can also perform a brief analysis on information of low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on information of medium importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the information.

[0090] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply an analysis algorithm specialized for health management to health information. The analysis unit can also apply an analysis algorithm specialized for environmental monitoring to environmental information. The analysis unit can also apply an analysis algorithm specialized for motion analysis to motion information. This allows the analysis unit to apply different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input information category data to the generation AI and cause the generation AI to apply different analysis algorithms. This allows the analysis unit to apply different analysis algorithms depending on the category of information.

[0091] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the analysis accuracy. Furthermore, the analysis unit can minimize analysis errors by referring to the user's past analysis results. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results.

[0092] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can perform a short and concise analysis. Furthermore, if the user is relaxed, the analysis unit can also perform a detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can also perform a quick analysis. This allows the analysis unit to adjust the length of the analysis 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, 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 analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's facial expression data or voice data into the generation AI and have the generation AI perform emotion estimation. This allows the analysis unit to adjust the length of the analysis based on the user's emotions.

[0093] The analysis unit can determine the analysis priority based on the time when the information was acquired during analysis. The analysis unit can determine the analysis priority based on the time when the information was acquired during analysis. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also lower the priority of analysis of older information. Furthermore, the analysis unit can also analyze information of moderate newness with a moderate priority. This allows the analysis unit to determine the analysis priority based on the time when the information was acquired. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input information acquisition time data into the generation AI and have the generation AI determine the analysis priority. This allows the analysis unit to determine the analysis priority based on the time when the information was acquired.

[0094] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit adjusts the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of information with high relevance. The analysis unit can also lower the priority of analysis of information with low relevance. Furthermore, the analysis unit can also analyze information with moderate relevance with moderate priority. This allows the analysis unit to adjust the order of analysis based on the relevance of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input relevance data of information to the generation AI and have the generation AI adjust the order of analysis. This allows the analysis unit to adjust the order of analysis based on the relevance of information.

[0095] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit performs an analysis that uses a lot of technical terms. Alternatively, if the user does not have technical expertise, the analysis unit can perform a concise and easy-to-understand analysis. Furthermore, the analysis unit can perform an analysis that uses appropriate technical terms according to the user's level of expertise. This allows the analysis unit to adjust the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI execute the use of technical terms. This allows the analysis unit to adjust the use of technical terms in the analysis according to the user's level of expertise.

[0096] The pet robot unit can estimate the user's emotions and adjust the communication method based on the estimated user's emotions. The pet robot unit can estimate the user's emotions and adjust the communication method based on the estimated user's emotions. For example, if the user is feeling anxious, the pet robot unit can speak to the user in a gentle voice. If the user is relaxed, the pet robot unit can also provide pleasant topics of conversation. If the user is in a hurry, the pet robot unit can also communicate briefly. This allows the pet robot unit to adjust the communication method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the pet robot unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the pet robot unit can input the user's facial expression data and voice data into the generation AI and have the generation AI perform emotion estimation. This allows the pet robot section to adjust the method of communication based on the user's emotions.

[0097] The pet-type robot unit can provide optimal conversation content by referring to the user's past conversation history during communication. The pet-type robot unit can provide optimal conversation content by referring to the user's past conversation history during communication. For example, the pet-type robot unit can provide related topics based on topics the user has previously discussed. The pet-type robot unit can also provide topics that the user is likely to be interested in based on the user's past conversation history. Furthermore, the pet-type robot unit can smooth the flow of conversation by referring to the user's past conversation history. In this way, the pet-type robot unit can provide optimal conversation content by referring to the user's past conversation history. Some or all of the above-described processing in the pet-type robot unit may be performed using, or without, a generation AI. For example, the pet-type robot unit can input the user's past conversation history data into the generation AI and cause the generation AI to provide optimal conversation content. In this way, the pet-type robot unit can provide optimal conversation content by referring to the user's past conversation history.

[0098] The pet-type robot unit can customize the conversation content based on the user's current living situation during communication. The pet-type robot unit customizes the conversation content based on the user's current living situation during communication. For example, if the user is interested in health, the pet-type robot unit can provide topics related to health. Furthermore, if the user spends time on a hobby, the pet-type robot unit can provide topics related to that hobby. Furthermore, if the user is interested in traveling, the pet-type robot unit can provide topics related to travel. This allows the pet-type robot unit to customize the conversation content based on the user's current living situation. Some or all of the above-mentioned processing in the pet-type robot unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the pet-type robot unit can input data on the user's current living situation into the generation AI and have the generation AI customize the conversation content. This allows the pet-type robot unit to customize the conversation content based on the user's current living situation.

[0099] The pet-type robot unit can improve the conversation content by reflecting user feedback during communication. The pet-type robot unit improves the conversation content by reflecting user feedback during communication. For example, if the user is dissatisfied with conversation content provided in the past, the pet-type robot unit can provide conversation content that resolves that dissatisfaction. Furthermore, if the user has given a high rating to conversation content provided in the past, the pet-type robot unit can provide similar conversation content. Furthermore, the pet-type robot unit can improve the conversation content based on the user's past feedback and provide more satisfying communication. In this way, the pet-type robot unit can improve the conversation content by reflecting user feedback. Some or all of the above-described processing in the pet-type robot unit may be performed using, or without, a generation AI. For example, the pet-type robot unit can input user feedback data into the generation AI and cause the generation AI to improve the conversation content. In this way, the pet-type robot unit can improve the conversation content by reflecting user feedback.

[0100] The pet robot unit can estimate the user's emotions and determine communication priorities based on the estimated user emotions. The pet robot unit can estimate the user's emotions and determine communication priorities based on the estimated user emotions. For example, the pet robot unit prioritizes communication when the user is feeling anxious. The pet robot unit can also provide the minimum amount of communication necessary when the user is relaxed. Furthermore, the pet robot unit can provide brief communication when the user is busy. This allows the pet robot unit to determine communication priorities 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the pet robot unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the pet robot unit can input the user's facial expression data and voice data into the generation AI and have the generation AI perform emotion estimation. This allows the pet robot section to determine the priority of communication based on the user's emotions.

[0101] The pet-type robot unit can provide optimal conversation content during communication by taking into account the user's geographical location information. The pet-type robot unit can provide optimal conversation content during communication by taking into account the user's geographical location information. For example, if the user lives in an urban area, the pet-type robot unit can provide topics related to the city. Furthermore, if the user lives in the suburbs, the pet-type robot unit can provide topics related to nature and the outdoors. Furthermore, if the user is traveling, the pet-type robot unit can provide topics related to the travel destination. In this way, the pet-type robot unit can provide optimal conversation content by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the pet-type robot unit may be performed using, or without, a generation AI. For example, the pet-type robot unit can input the user's geographical location information data into the generation AI and cause the generation AI to provide optimal conversation content. In this way, the pet-type robot unit can provide optimal conversation content by taking into account the user's geographical location information.

[0102] The pet-type robot unit can analyze the user's social media activity and suggest conversation content during communication. The pet-type robot unit can analyze the user's social media activity and suggest conversation content during communication. For example, if the user posts about health on social media, the pet-type robot unit can suggest health-related topics. Furthermore, if the user posts about pets, the pet-type robot unit can suggest pet-related topics. Furthermore, if the user posts about travel, the pet-type robot unit can suggest travel-related topics. In this way, the pet-type robot unit can analyze the user's social media activity and suggest conversation content. Some or all of the above-mentioned processing in the pet-type robot unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the pet-type robot unit can input the user's social media activity data into the generation AI and have the generation AI suggest conversation content. In this way, the pet-type robot unit can analyze the user's social media activity and suggest conversation content.

[0103] The pet-type robot unit can customize the conversation content by reflecting the user's past feedback during communication. The pet-type robot unit customizes the conversation content by reflecting the user's past feedback during communication. For example, if the user is dissatisfied with conversation content provided in the past, the pet-type robot unit can provide conversation content that resolves that dissatisfaction. Furthermore, if the user has given a high rating to conversation content provided in the past, the pet-type robot unit can provide similar conversation content. Furthermore, the pet-type robot unit can improve the conversation content based on the user's past feedback and provide more satisfying communication. This allows the pet-type robot unit to customize the conversation content by reflecting the user's past feedback. Some or all of the above-described processing in the pet-type robot unit may be performed using, or without, a generation AI. For example, the pet-type robot unit can input the user's past feedback data into the generation AI and have the generation AI customize the conversation content. This allows the pet-type robot unit to customize the conversation content by reflecting the user's past feedback.

[0104] The sensor unit can estimate the user's emotion and adjust the sensitivity of the sensor based on the estimated emotion. The sensor unit can estimate the user's emotion and adjust the sensitivity of the sensor based on the estimated emotion. For example, if the user feels anxious, the sensor unit increases the sensitivity of the sensor to detect abnormalities early. The sensor unit can also set the sensitivity of the sensor to normal when the user is relaxed. Furthermore, if the user is in a hurry, the sensor unit can temporarily set the sensitivity of the sensor to a low level to prevent false detection. This allows the sensor unit to adjust the sensitivity of the sensor according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the sensor unit can be performed using, for example, the generation AI. For example, the sensor unit can input the user's facial expression data or voice data into the generation AI and have the generation AI perform emotion estimation. This allows the sensor unit to adjust the sensitivity of the sensor based on the user's emotions.

[0105] The sensor unit can optimize the detection algorithm by referring to past data when the sensor is performing detection. The sensor unit can optimize the detection algorithm by referring to past data when the sensor is performing detection. For example, the sensor unit adjusts the sensor's detection algorithm based on past data to improve accuracy. The sensor unit can also extract specific patterns from past data and reflect them in the detection algorithm. Furthermore, the sensor unit can apply an algorithm that minimizes false detections by referring to past data. In this way, the sensor unit can optimize the detection algorithm by referring to past data. Some or all of the above-described processing in the sensor unit may be performed using, or without, a generation AI. For example, the sensor unit can input past data into the generation AI and have the generation AI optimize the detection algorithm. In this way, the sensor unit can optimize the detection algorithm by referring to past data.

[0106] The sensor unit can analyze the user's lifestyle rhythm during sensor detection and set the optimal detection timing. The sensor unit can analyze the user's lifestyle rhythm during sensor detection and set the optimal detection timing. For example, the sensor unit adjusts the sensor's detection timing based on the user's lifestyle rhythm. The sensor unit can also increase the sensor's detection frequency to match the user's active time period. The sensor unit can also set the sensor's detection frequency to match the user's resting time period. This allows the sensor unit to set the detection timing based on the user's lifestyle rhythm. Some or all of the above-described processing in the sensor unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the sensor unit can input the user's lifestyle rhythm data into the generation AI and have the generation AI set the detection timing. This allows the sensor unit to analyze the user's lifestyle rhythm and set the optimal detection timing.

[0107] The sensor unit can improve the detection method by reflecting user feedback during sensor detection. The sensor unit can improve the detection method by reflecting user feedback during sensor detection. For example, if a user is dissatisfied with a detection method previously provided, the sensor unit can provide a detection method that resolves that dissatisfaction. Furthermore, if a user has given a high rating to a detection method previously provided, the sensor unit can provide a similar detection method. Furthermore, the sensor unit can improve the detection method based on the user's past feedback and provide a service with higher satisfaction. In this way, the sensor unit can improve the detection method by reflecting user feedback. Some or all of the above-described processing in the sensor unit may be performed using, or without, a generation AI. For example, the sensor unit can input user feedback data into the generation AI and cause the generation AI to improve the detection method. In this way, the sensor unit can improve the detection method by reflecting user feedback.

[0108] The sensor unit can estimate the user's emotions and prioritize sensors based on the estimated user emotions. The sensor unit can estimate the user's emotions and prioritize sensors based on the estimated user emotions. For example, if the user is feeling anxious, the sensor unit can prioritize sensors that provide the most reassurance. Furthermore, if the user is relaxed, the sensor unit can also prioritize sensors that are easy to install. This allows the sensor unit to prioritize sensors 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, 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 sensor unit can be performed using, for example, the generation AI, or can be performed without the generation AI. For example, the sensor unit can input the user's facial expression data or voice data into the generation AI and have the generation AI perform emotion estimation. This allows the sensor unit to prioritize sensors based on the user's emotions.

[0109] The sensor unit can select the optimal detection method by taking into account the user's geographical location information when performing sensor detection. The sensor unit can select the optimal detection method by taking into account the user's geographical location information when performing sensor detection. For example, if the user lives in an urban area, the sensor unit can provide a detection method suitable for urban environments. Furthermore, if the user lives in a suburban area, the sensor unit can provide a detection method that can cover a wide area. Furthermore, if the user lives in a cold region, the sensor unit can provide a detection method suitable for cold regions. This allows the sensor unit to select the optimal detection method by taking into account the user's geographical location information. Some or all of the above-described processing in the sensor unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the sensor unit can input the user's geographical location information data into the generation AI and have the generation AI select the optimal detection method. This allows the sensor unit to select the optimal detection method by taking into account the user's geographical location information.

[0110] The sensor unit can analyze the user's social media activity and suggest a detection method when the sensor detects something. The sensor unit can analyze the user's social media activity and suggest a detection method when the sensor detects something. For example, if the user posts about health on social media, the sensor unit can provide a detection method specialized for health management. Furthermore, if the user posts about pets, the sensor unit can provide a detection method that can monitor the pet's movements. Furthermore, if the user posts about travel, the sensor unit can provide a detection method that ensures safety during travel. In this way, the sensor unit can analyze the user's social media activity and suggest a detection method. Some or all of the above-mentioned processing in the sensor unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the sensor unit can input the user's social media activity data into the generation AI and have the generation AI execute the detection method suggestion. In this way, the sensor unit can analyze the user's social media activity and suggest a detection method.

[0111] The sensor unit can customize the detection method by reflecting the user's past feedback when performing sensor detection. The sensor unit customizes the detection method by reflecting the user's past feedback when performing sensor detection. For example, if the user is dissatisfied with a detection method previously provided, the sensor unit can provide a detection method that resolves the dissatisfaction. Furthermore, if the user has given a high rating to a detection method previously provided, the sensor unit can provide a similar detection method. Furthermore, the sensor unit can improve the detection method based on the user's past feedback and provide a service with higher satisfaction. In this way, the sensor unit can customize the detection method by reflecting the user's past feedback. Some or all of the above-described processing in the sensor unit may be performed using, or without, a generation AI. For example, the sensor unit can input the user's past feedback data into the generation AI and have the generation AI customize the detection method. In this way, the sensor unit can customize the detection method by reflecting the user's past feedback.

[0112] The camera unit can estimate the user's emotions and adjust the camera's shooting method based on the estimated user's emotions. The camera unit can estimate the user's emotions and adjust the camera's shooting method based on the estimated user's emotions. For example, if the user is feeling anxious, the camera unit can provide a shooting method that gives a sense of security. Furthermore, if the user is relaxed, the camera unit can provide a natural shooting method. Furthermore, if the user is in a hurry, the camera unit can quickly shoot. This allows the camera unit to adjust the camera's shooting method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the camera unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the camera unit can input the user's facial expression data and voice data into the generation AI and have the generation AI perform emotion estimation. This allows the camera unit to adjust the camera's shooting method based on the user's emotions.

[0113] The camera unit can optimize the shooting algorithm by referring to past shooting data when shooting with the camera. The camera unit optimizes the shooting algorithm by referring to past shooting data when shooting with the camera. For example, the camera unit adjusts the shooting algorithm based on the past shooting data to improve accuracy. The camera unit can also extract specific patterns from the past shooting data and reflect them in the shooting algorithm. Furthermore, the camera unit can apply an algorithm that minimizes false detections by referring to the past shooting data. In this way, the camera unit can optimize the shooting algorithm by referring to the past shooting data. Some or all of the above-mentioned processing in the camera unit may be performed using, or without, a generation AI. For example, the camera unit can input past shooting data into the generation AI and have the generation AI optimize the shooting algorithm. In this way, the camera unit can optimize the shooting algorithm by referring to the past shooting data.

[0114] The camera unit can analyze the user's lifestyle and set the optimal timing for taking a photo with the camera. The camera unit can analyze the user's lifestyle and set the optimal timing for taking a photo with the camera. For example, the camera unit adjusts the timing for taking a photo with the camera based on the user's lifestyle. The camera unit can also increase the frequency of taking photos with the camera to match the time periods when the user is active. The camera unit can also set the frequency of taking photos with the camera to be lower to match the time periods when the user is resting. This allows the camera unit to set the timing for taking a photo based on the user's lifestyle. Some or all of the above-described processing in the camera unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the camera unit can input the user's lifestyle data into the generation AI and have the generation AI set the timing for taking a photo. This allows the camera unit to analyze the user's lifestyle and set the optimal timing for taking a photo.

[0115] The camera unit can improve the shooting method by reflecting user feedback when taking a photo with the camera. The camera unit improves the shooting method by reflecting user feedback when taking a photo with the camera. For example, if a user is dissatisfied with a shooting method previously provided, the camera unit provides a shooting method that resolves the dissatisfaction. Furthermore, if a user has given a high rating to a shooting method previously provided, the camera unit can provide a similar shooting method. Furthermore, the camera unit can improve the shooting method based on the user's past feedback and provide a service with higher satisfaction. In this way, the camera unit can improve the shooting method by reflecting user feedback. Some or all of the above-described processing in the camera unit may be performed using, or without, a generation AI. For example, the camera unit can input user feedback data into the generation AI and cause the generation AI to improve the shooting method. In this way, the camera unit can improve the shooting method by reflecting user feedback.

[0116] The camera unit can estimate the user's emotions and determine the priority of cameras based on the estimated user emotions. The camera unit can estimate the user's emotions and determine the priority of cameras based on the estimated user emotions. For example, if the user is feeling anxious, the camera unit can prioritize cameras that provide the most reassurance. Furthermore, if the user is relaxed, the camera unit can also prioritize cameras that are the minimum necessary. Furthermore, if the user is busy, the camera unit can prioritize cameras that are easy to install. This allows the camera unit to determine the priority of cameras 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, 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 camera unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the camera unit can input the user's facial expression data and voice data into the generation AI and have the generation AI perform emotion estimation. This allows the camera unit to determine the priority of cameras based on the user's emotions.

[0117] The camera unit can select the optimal shooting method by taking into account the user's geographical location information when taking a picture with the camera. The camera unit selects the optimal shooting method by taking into account the user's geographical location information when taking a picture with the camera. For example, if the user lives in an urban area, the camera unit can provide a shooting method suitable for urban environments. Furthermore, if the user lives in a suburban area, the camera unit can provide a shooting method that can cover a wide area. Furthermore, if the user lives in a cold region, the camera unit can provide a shooting method suitable for cold regions. This allows the camera unit to select the optimal shooting method by taking into account the user's geographical location information. Some or all of the above-described processing in the camera unit may be performed using, or without, a generation AI. For example, the camera unit can input the user's geographical location information data into the generation AI and have the generation AI select the optimal shooting method. This allows the camera unit to select the optimal shooting method by taking into account the user's geographical location information.

[0118] When taking a picture with the camera, the camera unit can analyze the user's social media activity and suggest a shooting method. When taking a picture with the camera, the camera unit can analyze the user's social media activity and suggest a shooting method. For example, if a user posts about health on social media, the camera unit can provide a shooting method specialized for health management. Furthermore, if a user posts about pets, the camera unit can provide a shooting method that allows the user to monitor the pet's movements. Furthermore, if a user posts about travel, the camera unit can provide a shooting method that ensures the user's safety during the trip. In this way, the camera unit can analyze the user's social media activity and suggest a shooting method. Some or all of the above-mentioned processing in the camera unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the camera unit can input the user's social media activity data into the generation AI and have the generation AI execute a shooting method suggestion. In this way, the camera unit can analyze the user's social media activity and suggest a shooting method.

[0119] The camera unit can customize the shooting method by reflecting the user's past feedback when taking a photo with the camera. The camera unit customizes the shooting method by reflecting the user's past feedback when taking a photo with the camera. For example, if the user is dissatisfied with a shooting method previously provided, the camera unit can provide a shooting method that resolves the dissatisfaction. Furthermore, if the user has given a high rating to a shooting method previously provided, the camera unit can provide a similar shooting method. Furthermore, the camera unit can improve the shooting method based on the user's past feedback and provide a more satisfying service. In this way, the camera unit can customize the shooting method by reflecting the user's past feedback. Some or all of the above-described processing in the camera unit may be performed using, or without, a generation AI. For example, the camera unit can input the user's past feedback data into the generation AI and have the generation AI customize the shooting method. In this way, the camera unit can customize the shooting method by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the providing unit, analyzing unit, pet-type robot unit, sensor unit, and camera unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the providing unit is realized by the control unit 46A of the smart device 14 and provides a smart camera or sensor device. The analyzing unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes information obtained from the monitoring device. The pet-type robot unit is realized by the control unit 46A of the smart device 14 and communicates with the elderly person and issues alerts as necessary. The sensor unit is realized by the control unit 46A of the smart device 14 and detects temperature, humidity, and movement. The camera unit is realized by the control unit 46A of the smart device 14 and captures images of the room. === Hard Collateral 1-2 === Each of the multiple elements, including the providing unit, analyzing unit, pet-type robot unit, sensor unit, and camera unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the providing unit is realized by the control unit 46A of the smart glasses 214 and provides a smart camera and a sensor device. The analyzing unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes information obtained from the monitoring device. The pet-type robot unit is realized by the control unit 46A of the smart glasses 214 and communicates with the elderly person and issues an alert as necessary. The sensor unit is realized by the control unit 46A of the smart glasses 214 and detects temperature, humidity, and movement. The camera unit is realized by the control unit 46A of the smart glasses 214 and captures images of the room. === Hard Collateral 1-3 === Each of the multiple elements including the providing unit, analyzing unit, pet-type robot unit, sensor unit, and camera unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the providing unit is realized by the control unit 46A of the headset-type terminal 314 and provides a smart camera or sensor device. The analyzing unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes information obtained from the monitoring device. The pet-type robot unit is realized by the control unit 46A of the headset-type terminal 314 and communicates with the elderly person and issues alerts as necessary. The sensor unit is realized by the control unit 46A of the headset-type terminal 314 and detects temperature, humidity, and movement. The camera unit is realized by the control unit 46A of the headset-type terminal 314 and takes pictures of the room. === Hard Collateral 1-4 === Each of the multiple elements including the providing unit, analyzing unit, pet-type robot unit, sensor unit, and camera unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the providing unit is realized by the control unit 46A of the robot 414 and provides a smart camera or sensor device. The analyzing unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes information obtained from the monitoring device. The pet-type robot unit is realized by the control unit 46A of the robot 414 and communicates with the elderly person and issues alerts as necessary. The sensor unit is realized by the control unit 46A of the robot 414 and detects temperature, humidity, and movement. The camera unit is realized by the control unit 46A of the robot 414 and takes pictures of the room.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The providing unit can monitor the user's health condition and notify a medical institution if an abnormality is detected. For example, if the providing unit detects an abnormality in the heart rate or blood pressure, it immediately notifies a medical institution. The providing unit can also automatically call an ambulance if the user falls. Furthermore, the providing unit can send reminders for regular health checks based on the user's health condition. This allows the providing unit to monitor the user's health condition in real time and respond quickly.

[0122] The pet robot section can estimate the user's emotions and play music based on the estimated user's emotions. For example, if the user is relaxed, the pet robot section can play relaxing music. If the user is sad, the pet robot section can play music that brightens the mood. Furthermore, if the user wants to concentrate, the pet robot section can play music that helps the user concentrate. In this way, the pet robot section can provide optimal music according to the user's emotions.

[0123] The sensor unit can monitor the user's activity level and recommend appropriate exercise. For example, if the user has been sitting for a long time, the sensor unit can notify the user to stand up and stretch. The sensor unit can also count the user's steps and encourage exercise if the user has not reached the target number of steps. Furthermore, the sensor unit can monitor the user's heart rate and recommend appropriate exercise intensity. In this way, the sensor unit can support the user in maintaining their health.

[0124] The camera unit can analyze the user's facial expression and display a message according to the user's emotion. For example, if the user is smiling, the camera unit can display a positive message. If the user is sad, the camera unit can also display an encouraging message. Furthermore, if the user is surprised, the camera unit can also display a message that alleviates the user's surprise. In this way, the camera unit can provide an appropriate message according to the user's emotion.

[0125] The providing unit can manage the user's schedule and remind the user of important appointments. For example, the providing unit can link with the user's calendar and send reminders for meetings or medical appointments. The providing unit can also suggest appropriate break times based on the user's schedule. Furthermore, the providing unit can also suggest optimal routes based on the user's schedule. In this way, the providing unit can support the user's schedule management.

[0126] The providing unit can estimate the user's emotions and suggest meals based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can suggest a meal that will help the user relax. Also, if the user is tired, the providing unit can suggest a meal that will replenish energy. Furthermore, if the user is feeling energetic, the providing unit can suggest a balanced meal. In this way, the providing unit can suggest the optimal meal according to the user's emotions.

[0127] The analysis unit can analyze the user's past behavioral data and predict behavioral patterns. For example, if the user has a habit of jogging every morning, the analysis unit can set an appropriate alert for that time period. Also, if the user has a habit of shopping on a specific day of the week, the analysis unit can send a reminder for that day. Furthermore, if the user has a habit of relaxing during a specific time period, the analysis unit can make suggestions for relaxing during that time period. In this way, the analysis unit can predict the user's behavioral patterns and provide appropriate support.

[0128] The providing unit can estimate the user's emotions and suggest a relaxation method based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can suggest deep breathing or meditation. If the user is tired, the providing unit can also suggest light stretching or a massage. Furthermore, if the user is relaxed, the providing unit can also suggest music or aromas to help the user maintain relaxation. In this way, the providing unit can suggest the optimal relaxation method according to the user's emotions.

[0129] The providing unit can suggest related events and activities based on the user's hobbies and interests. For example, if the user is interested in music, the providing unit can provide information about nearby concerts. If the user is interested in sports, the providing unit can also provide information about local sporting events. Furthermore, if the user is interested in art, the providing unit can also provide information about exhibitions at museums and galleries. This allows the providing unit to suggest related events and activities based on the user's hobbies and interests.

[0130] The providing unit can estimate the user's emotions and adjust the communication method based on the estimated user's emotions. For example, if the user feels anxious, the providing unit speaks to the user in gentle words. If the user feels relaxed, the providing unit can also provide pleasant topics. Furthermore, if the user is in a hurry, the providing unit can also provide concise communication. In this way, the providing unit can provide the optimal communication method according to the user's emotions.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The provider uses the communication infrastructure to provide a monitoring device. The monitoring device may include a smart camera or various sensor devices. For example, the provider may use a smart camera to capture images of a room and provide a temperature sensor, humidity sensor, and motion sensor. Step 2: The analysis unit analyzes the information obtained from the monitoring device provided by the provision unit. The analysis is performed using data mining and machine learning algorithms. Step 3: The provider uses AI generation to provide reports and alerts based on the information analyzed by the analyzer. Reports and alerts are provided in the form of text reports, voice alerts, email notifications, etc. For example, an alert can be sent upon detecting abnormal movements or temperature changes. Periodic reports on the user's living conditions can also be generated and provided to family members or caregivers.

[0133] 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.

[0134] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a providing unit that provides a monitoring device using a communication infrastructure; an analysis unit that analyzes information obtained from the monitoring device and provided by the providing unit; a providing unit that provides a report or an alert by utilizing a generating AI based on the information analyzed by the analyzing unit; Equipped with A system characterized by:

2. It will have a pet-like robot section that will utilize generative AI to communicate with the elderly and send out alerts as needed.

2. The system of claim 1.

3. Equipped with a sensor that detects temperature, humidity, and movement 2. The system of claim 1.

4. Equipped with a camera unit that captures the state of the room 2. The system of claim 1.

5. The providing unit Uses generative AI to detect abnormal movement or temperature changes and send alerts 2. The system of claim 1.

6. The providing unit Generate regular reports of living conditions and provide them to family members or caregivers 2. The system of claim 1.

7. The providing unit The user's emotion is estimated, and the timing of providing the monitoring device is adjusted based on the estimated user's emotion.

2. The system of claim 1.

8. The providing unit Analyze users' past usage history and select the optimal delivery method 2. The system of claim 1.

9. The providing unit When providing monitoring devices, filtering is performed based on the user's current living situation and areas of interest.

2. The system of claim 1.

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