System

Abstract

An object of a system according to an embodiment is to efficiently detect a motion of an elderly person and quickly share the motion with a family.SOLUTION: A system includes an installation unit, a collection unit, an aggregation unit, and a sharing unit. The placement unit places a device including a sensor for detecting a motion of the elderly person. The collection unit collects information from the device installed by the installation unit. The aggregation unit aggregates the information collected by the collection unit on a cloud. The sharer shares the information aggregated by the aggregator with a family who has made a contract in advance.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies are not sufficient in efficiently detecting the movements of elderly people and quickly sharing the information with their families, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently detect the movements of elderly people and quickly share the information with their families. [Means for solving the problem]

[0006] The system according to the embodiment includes an installation unit, a collection unit, an aggregation unit, and a sharing unit. The installation unit installs devices equipped with sensors for detecting the movements of elderly people. The collection unit collects information from the devices installed by the installation unit. The aggregation unit aggregates the information collected by the collection unit on the cloud. The sharing unit shares the information aggregated by the aggregation unit with family members who have signed a contract in advance. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently detect the movements of elderly people and quickly share the information with their families. [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 system according to an embodiment of the present invention is an IoT-based system for supporting the safety of elderly people. This system installs devices equipped with sensors to detect elderly people's movements, aggregates the detected information on the cloud, and shares it with family members who have signed up in advance. For example, the system installs devices at entrances and bathrooms to detect elderly people's movements. The detected information is then aggregated on the cloud to centrally manage the elderly people's behavioral patterns. The aggregated information is then shared with family members who have signed up in advance, and notifications are sent if an abnormality is detected. This allows for understanding elderly people's behavioral patterns and enabling prompt response if an abnormality is detected. Furthermore, aggregated behavioral pattern data can be used to develop nursing care services and equipment, and to improve living environments in an aging society. This system can provide support for elderly people to live safely and securely. The system detects elderly people's movements, aggregates the information on the cloud, and shares it with family members to support the safety of elderly people. For example, even if an elderly person lives alone, family members can remotely monitor the elderly person's situation, allowing them to live with peace of mind. Furthermore, prompt response to abnormalities can be performed to ensure the safety of the elderly person.

[0029] An elderly safety support system according to an embodiment includes an installation unit, a collection unit, an aggregation unit, and a sharing unit. The installation unit installs a device equipped with a sensor for detecting the elderly's movements. For example, the installation unit installs the device in a location such as an entrance or a toilet. For example, the device installed in the entrance detects the elderly's leaving and returning home. The installation unit can also detect the frequency and time of the elderly's use of the toilet by the device installed in the toilet. The collection unit collects information from the installed device by the installation unit. For example, the collection unit collects information regarding the elderly's movements from the sensor-equipped device. The collection unit can detect the elderly's movements using, for example, an infrared sensor or a motion sensor. The aggregation unit aggregates the information collected by the collection unit on the cloud. For example, the aggregation unit aggregates the information using a cloud service (e.g., AWS (registered trademark) or Google (registered trademark) Cloud) and centrally manages the elderly's behavioral patterns. The aggregation unit can store and analyze the information using, for example, a database. The sharing unit shares the information aggregated by the aggregation unit with family members who have signed a contract in advance. For example, the sharing unit sends a notification to family members if an abnormality occurs. The sharing unit can send the notification by email or SMS, for example. As a result, the elderly safety support system according to the embodiment can support the safety of the elderly by detecting the movements of the elderly, aggregating the information on the cloud, and sharing it with family members.

[0030] The installation unit can install the device in a location such as an entrance or a toilet. For example, the installation unit installs the device in an entrance to detect when an elderly person leaves or returns home. The installation unit can also install the device in a toilet to detect how often and for how long the elderly person uses the toilet. This allows the device to be installed in an appropriate location to efficiently detect the movements of the elderly person.

[0031] The collection unit can collect information from a device equipped with a sensor for detecting the elderly person's movements. The collection unit detects the elderly person's movements using, for example, an infrared sensor. The collection unit can also detect the elderly person's movements using a motion sensor. Furthermore, the collection unit can detect the elderly person's movements using a pressure sensor. This makes it possible to accurately detect the elderly person's movements and collect information.

[0032] The aggregation unit aggregates information on the cloud and can centrally manage the behavioral patterns of elderly people. The aggregation unit aggregates information using AWS, for example. The aggregation unit can also aggregate information using Google Cloud. Furthermore, the aggregation unit can store and analyze information using a database. This allows centralized management of the behavioral patterns of elderly people and early detection of abnormalities.

[0033] The shared unit can share information with family members who have signed up in advance and send notifications if an abnormality is detected. The shared unit can share information with family members, for example, via email. The shared unit can also share information with family members via SMS. Furthermore, the shared unit can also share information with family members using an app. This allows family members to be quickly notified of any abnormalities in the elderly person and urge them to take action.

[0034] The installation unit can select the optimal installation timing based on the elderly person's lifestyle rhythm at the time of installation. For example, if the elderly person is more active during the day, the installation unit can install the device during daytime hours. Also, if the elderly person is more active at night, the installation unit can install the device during nighttime hours. Also, if the elderly person often goes out on a specific day of the week, the installation unit can install the device on a day other than that day. This allows the device to be installed in accordance with the elderly person's lifestyle rhythm, reducing any sense of discomfort.

[0035] The installation unit can adjust the height and angle of the device based on the physical characteristics of the elderly person when installing the device. For example, the installation unit can adjust the height of the device to match the height of the elderly person to improve visibility. The installation unit can also adjust the angle of the device to match the range of motion of the elderly person to make it easier to operate. The installation unit can also adjust the installation position of the device based on the physical characteristics of the elderly person to improve usability. In this way, the device can be installed to match the physical characteristics of the elderly person, improving usability.

[0036] At the time of installation, the installation unit can analyze the elderly person's past behavioral patterns and select the optimal installation location. For example, the installation unit installs the device in a place where the elderly person frequently passes through, thereby efficiently detecting movement. The installation unit can also install the device in a place where the elderly person stays for a long time, thereby enabling early detection of abnormalities. The installation unit can also analyze the elderly person's behavioral patterns and select the optimal installation location. This makes it possible to install the device based on the elderly person's behavioral patterns and efficiently detect movement.

[0037] The installation unit can select an installation location during installation, taking into consideration the elderly person's living environment. The installation unit selects an installation location for the device based on, for example, the layout of the room, and efficiently detects movement. The installation unit can also select an installation location for the device, taking into consideration the arrangement of furniture, to improve visibility. The installation unit can also select an installation location for the device based on the elderly person's living environment, to improve usability. This allows the device to be installed in accordance with the elderly person's living environment, and improve usability.

[0038] The installation unit can customize the installation location by reflecting the opinions of the elderly person's family and caregivers when installing the device. For example, the installation unit selects the installation location of the device by reflecting the opinions of the elderly person's family. The installation unit can also customize the installation location of the device by reflecting the opinions of the caregivers. The installation unit can also select the optimal installation location based on the opinions of the elderly person's family and caregivers. This allows the device to be installed by reflecting the opinions of the elderly person's family and caregivers, thereby improving ease of use.

[0039] The installation unit can adjust the installation location based on the elderly person's lifestyle habits at the time of installation. The installation unit selects the installation location of the device based on, for example, the elderly person's wake-up time. The installation unit can also adjust the installation location of the device based on the elderly person's meal times. The installation unit can also select the installation location of the device based on the elderly person's lifestyle habits. This makes it possible to install the device in accordance with the elderly person's lifestyle habits and improve ease of use.

[0040] The collection unit can adjust the sensitivity of the sensor based on the elderly person's movement speed and movement range during collection. The collection unit adjusts the sensitivity of the sensor based on the elderly person's movement speed, for example, to collect accurate data. The collection unit can also adjust the sensitivity of the sensor based on the elderly person's movement range to collect data efficiently. The collection unit can also adjust the sensitivity of the sensor based on the elderly person's movement characteristics to collect optimal data. This allows the sensor sensitivity to be adjusted to match the elderly person's movement characteristics, making it possible to collect accurate data.

[0041] The collection unit can optimize the collection algorithm by referring to the elderly person's past behavioral data when collecting data. For example, the collection unit optimizes the collection algorithm by referring to the elderly person's past behavioral data. The collection unit can also analyze the elderly person's behavioral patterns and optimize the collection algorithm. The collection unit can also optimize the collection algorithm based on the elderly person's past behavioral data. This allows the collection algorithm to be optimized based on the elderly person's past behavioral data, making it possible to collect data efficiently.

[0042] The collection unit can adjust the collection timing based on the elderly person's lifestyle rhythm during collection. The collection unit adjusts the collection timing based on the elderly person's lifestyle rhythm, for example, to efficiently collect data. The collection unit can also adjust the collection timing based on the elderly person's activity time to collect accurate data. The collection unit can also adjust the collection timing taking the elderly person's lifestyle rhythm into consideration to collect optimal data. This allows the collection timing to be adjusted to match the elderly person's lifestyle rhythm and efficiently collect data.

[0043] The collection unit can correct the collected data during collection, taking into account the elderly person's living environment. For example, the collection unit corrects the collected data taking into account the temperature of the elderly person's living environment. The collection unit can also correct the collected data taking into account the humidity of the elderly person's living environment. The collection unit can also correct the collected data taking into account changes in the elderly person's living environment. This allows the collected data to be corrected based on the elderly person's living environment, making it possible to provide accurate data.

[0044] The collection unit can monitor the health condition of the elderly person during collection, and increase the collection frequency if an abnormality is detected. For example, the collection unit can monitor the heart rate of the elderly person, and increase the collection frequency if an abnormality is detected. The collection unit can also monitor the blood pressure of the elderly person, and increase the collection frequency if an abnormality is detected. The collection unit can also monitor the health condition of the elderly person, and increase the collection frequency if an abnormality is detected. This makes it possible to monitor the health condition of the elderly person, and to respond quickly if an abnormality is detected.

[0045] The collection unit can customize the collection method by reflecting feedback from the elderly person's family and caregivers when collecting data. For example, the collection unit customizes the collection method by reflecting feedback from the elderly person's family. The collection unit can also customize the collection method by reflecting feedback from caregivers. The collection unit can also customize the collection method based on the opinions of the elderly person's family and caregivers. This allows the collection method to be customized by reflecting the opinions of the elderly person's family and caregivers, thereby improving ease of use.

[0046] During aggregation, the aggregating unit can apply an algorithm that refers to past data and detects anomalies in the current data. For example, the aggregating unit refers to past data and applies an algorithm that detects anomalies in the current data. The aggregating unit can also analyze the behavioral patterns of elderly people and apply an algorithm that detects anomalies in the current data. The aggregating unit can also apply an algorithm that detects anomalies in the current data based on the past data. This makes it possible to detect anomalies in the current data based on the past data and respond quickly.

[0047] The aggregating unit can determine the priority of aggregation based on the importance of the data when aggregating. For example, the aggregating unit prioritizes aggregating important data and efficiently manages the data. The aggregating unit can also prioritize aggregating data related to the health status of elderly people. The aggregating unit can also determine the priority of data aggregation based on the behavioral patterns of elderly people. This allows important data to be prioritized and managed efficiently.

[0048] The aggregation unit can add a function to analyze the behavioral patterns of the elderly person during aggregation and issue an alert if an abnormality is detected. For example, the aggregation unit analyzes the behavioral patterns of the elderly person and issues an alert if an abnormality is detected. The aggregation unit can also issue an alert if an abnormality is detected based on the elderly person's daily rhythm. The aggregation unit can also issue an alert if an abnormality is detected based on the elderly person's behavioral data. This makes it possible to analyze the behavioral patterns of the elderly person and respond quickly if an abnormality is detected.

[0049] The aggregating unit can correct the data taking into account the elderly person's living environment when aggregating. For example, the aggregating unit corrects the data taking into account the temperature of the elderly person's living environment. The aggregating unit can also correct the data taking into account the humidity of the elderly person's living environment. The aggregating unit can also correct the data taking into account changes in the elderly person's living environment. This allows the data to be corrected based on the elderly person's living environment, and accurate information to be provided.

[0050] The aggregating unit can monitor the health condition of the elderly person during aggregation and highlight the data if an abnormality is found. For example, the aggregating unit can monitor the heart rate of the elderly person and highlight the data if an abnormality is found. The aggregating unit can also monitor the blood pressure of the elderly person and highlight the data if an abnormality is found. The aggregating unit can also monitor the health condition of the elderly person and highlight the data if an abnormality is found. This makes it possible to monitor the health condition of the elderly person and respond quickly if an abnormality is found.

[0051] The aggregating unit can customize the data display method by reflecting feedback from the elderly person's family or caregiver when aggregating data. The aggregating unit customizes the data display method by reflecting feedback from the elderly person's family, for example. The aggregating unit can also customize the data display method by reflecting feedback from the caregiver. The aggregating unit can also customize the data display method based on the opinions of the elderly person's family or caregiver. This allows the data display method to be customized by reflecting the opinions of the elderly person's family or caregiver, thereby improving usability.

[0052] When sharing, the sharing unit can add a function to issue an alert if an abnormality is detected based on the elderly person's behavioral patterns. For example, the sharing unit analyzes the elderly person's behavioral patterns and issues an alert if an abnormality is detected. The sharing unit can also issue an alert if an abnormality is detected based on the elderly person's daily rhythm. The sharing unit can also issue an alert if an abnormality is detected based on the elderly person's behavioral data. This makes it possible to detect abnormalities based on the elderly person's behavioral patterns and respond quickly.

[0053] The sharing unit can customize the way information is displayed based on the needs of the elderly person's family or caregiver when sharing. For example, the sharing unit customizes the way information is displayed based on the needs of the elderly person's family. The sharing unit can also customize the way information is displayed based on the needs of the caregiver. The sharing unit can also customize the way information is displayed based on the opinions of the elderly person's family or caregiver. This makes it possible to customize the way information is displayed according to the needs of the elderly person's family or caregiver, thereby improving usability.

[0054] When sharing, the sharing unit can apply an algorithm that detects abnormalities by referring to the elderly person's past behavioral data. For example, the sharing unit can apply an algorithm that detects abnormalities by referring to the elderly person's past behavioral data. The sharing unit can also analyze the elderly person's behavioral patterns and apply an algorithm that detects abnormalities. The sharing unit can also apply an algorithm that detects abnormalities based on the elderly person's past behavioral data. This makes it possible to detect abnormalities based on the elderly person's past behavioral data and respond quickly.

[0055] The shared unit can correct the information taking into account the elderly person's living environment when sharing. For example, the shared unit corrects the information taking into account the temperature of the elderly person's living environment. The shared unit can also correct the information taking into account the humidity of the elderly person's living environment. The shared unit can also correct the information taking into account changes in the elderly person's living environment. This allows the information to be corrected based on the elderly person's living environment, and accurate information to be provided.

[0056] During sharing, the sharing unit can monitor the health condition of the elderly person and highlight information if an abnormality is detected. For example, the sharing unit can monitor the elderly person's heart rate and highlight information if an abnormality is detected. The sharing unit can also monitor the elderly person's blood pressure and highlight information if an abnormality is detected. The sharing unit can also monitor the elderly person's health condition and highlight information if an abnormality is detected. This makes it possible to monitor the elderly person's health condition and respond quickly if an abnormality is detected.

[0057] The sharing unit can customize the way information is displayed by reflecting feedback from the elderly person's family or caregiver when sharing. For example, the sharing unit customizes the way information is displayed by reflecting feedback from the elderly person's family. The sharing unit can also customize the way information is displayed by reflecting feedback from the caregiver. The sharing unit can also customize the way information is displayed based on the opinions of the elderly person's family or caregiver. This allows the way information is displayed to be customized by reflecting the opinions of the elderly person's family or caregiver, thereby improving usability.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] When selecting a location for the device, the installation unit can analyze surrounding environmental sounds and determine the optimal installation location. For example, the installation unit can reduce false detections by installing the device in a quiet location. The installation unit can also use a device with a noise-canceling function when installing the device in a noisy location. Furthermore, the installation unit can monitor fluctuations in environmental sounds in real time and readjust the device installation location as needed. This allows the device installation location to be optimized according to the environmental sounds, enabling accurate motion detection.

[0060] The collection unit analyzes data collected from the device in real time and can immediately issue an alert if an abnormality is detected. For example, the collection unit can issue an alert if the elderly person's movements are different from normal. The collection unit can also issue an alert if the device's battery level is low. Furthermore, the collection unit can also issue an alert if the device's communication status is unstable. This allows abnormalities to be detected in real time and a prompt response to be taken.

[0061] When aggregating data, the aggregator can evaluate the reliability of the data and exclude unreliable data. For example, the aggregator can exclude data when a device sensor malfunctions. The aggregator can also exclude data when a communication error occurs. Furthermore, the aggregator can check the consistency of the data and exclude abnormal data. This allows only reliable data to be aggregated, enabling accurate analysis of behavioral patterns.

[0062] When sharing information, the sharing unit can visualize data, allowing family members to intuitively understand it. For example, the sharing unit can display the elderly person's behavioral patterns in graphs and charts. The sharing unit can also highlight abnormalities when they occur. Furthermore, the sharing unit can display the current situation by comparing it with past data. This makes it easier for family members to intuitively understand the elderly person's situation.

[0063] The installation unit can determine the optimal installation location for the device by taking into account the ambient temperature and humidity. For example, if the device is to be installed in a hot and humid location, the installation unit can use a device with high heat resistance and waterproofing. Alternatively, if the device is to be installed in a cold and dry location, the installation unit can use a device with high cold resistance and moisture resistance. Furthermore, the installation unit can monitor fluctuations in temperature and humidity in real time and readjust the device's installation location as needed. This allows the device's installation location to be optimized according to the ambient temperature and humidity, enabling accurate motion detection.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The installation unit installs devices equipped with sensors to detect the movements of elderly people. For example, the installation unit installs devices in places such as the entrance and toilet, and the device installed at the entrance detects when the elderly person goes out and comes home, while the device installed in the toilet detects how often and for how long the elderly person uses the toilet. Step 2: The collection unit collects information from the devices installed by the installation unit. For example, the collection unit collects information about the movements of the elderly person from devices equipped with sensors, and detects the movements of the elderly person using an infrared sensor or a motion sensor. Step 3: The aggregation unit aggregates the information collected by the collection unit on the cloud. For example, the aggregation unit aggregates the information using a cloud service (e.g., AWS or Google Cloud) and centrally manages the behavioral patterns of elderly people. The aggregation unit can store and analyze the information using a database. Step 4: The sharing unit shares the information aggregated by the aggregation unit with family members who have signed up in advance. For example, the sharing unit sends a notification to family members if there is an abnormality. The sharing unit can send notifications via email or SMS.

[0066] (Example 2) A system according to an embodiment of the present invention is an IoT-based system for supporting the safety of elderly people. This system installs devices equipped with sensors to detect elderly people's movements, aggregates the detected information on the cloud, and shares it with family members who have signed up in advance. For example, the system installs devices at entrances and bathrooms to detect elderly people's movements. The detected information is then aggregated on the cloud to centrally manage the elderly people's behavioral patterns. The aggregated information is then shared with family members who have signed up in advance, and notifications are sent if an abnormality is detected. This allows for understanding elderly people's behavioral patterns and enabling prompt response if an abnormality is detected. Furthermore, aggregated behavioral pattern data can be used to develop nursing care services and equipment, and to improve living environments in an aging society. This system can provide support for elderly people to live safely and securely. The system detects elderly people's movements, aggregates the information on the cloud, and shares it with family members to support the safety of elderly people. For example, even if an elderly person lives alone, family members can remotely monitor the elderly person's situation, allowing them to live with peace of mind. Furthermore, prompt response to abnormalities can be performed to ensure the safety of the elderly person.

[0067] An elderly safety support system according to an embodiment includes an installation unit, a collection unit, an aggregation unit, and a sharing unit. The installation unit installs a device equipped with a sensor for detecting the elderly's movements. For example, the installation unit installs the device in a location such as an entrance or a toilet. For example, the device installed in the entrance detects the elderly's leaving and returning home. The installation unit can also detect the frequency and time of the elderly's use of the toilet by the device installed in the toilet. The collection unit collects information from the installed device by the installation unit. For example, the collection unit collects information regarding the elderly's movements from the sensor-equipped device. The collection unit can detect the elderly's movements using, for example, an infrared sensor or a motion sensor. The aggregation unit aggregates the information collected by the collection unit on the cloud. For example, the aggregation unit aggregates the information using a cloud service (e.g., AWS or Google Cloud) and centrally manages the elderly's behavioral patterns. The aggregation unit can store and analyze the information using, for example, a database. The sharing unit shares the information aggregated by the aggregation unit with family members who have signed a contract in advance. For example, the sharing unit sends a notification to the family members if an abnormality is detected. The sharing unit can send notifications by email or SMS, for example. As a result, the elderly safety support system according to the embodiment can support the safety of the elderly by detecting the movements of the elderly, aggregating the information on the cloud, and sharing it with family members.

[0068] The installation unit can install the device in a location such as an entrance or a toilet. For example, the installation unit installs the device in an entrance to detect when an elderly person leaves or returns home. The installation unit can also install the device in a toilet to detect how often and for how long the elderly person uses the toilet. This allows the device to be installed in an appropriate location to efficiently detect the movements of the elderly person.

[0069] The collection unit can collect information from a device equipped with a sensor for detecting the elderly person's movements. The collection unit detects the elderly person's movements using, for example, an infrared sensor. The collection unit can also detect the elderly person's movements using a motion sensor. Furthermore, the collection unit can detect the elderly person's movements using a pressure sensor. This makes it possible to accurately detect the elderly person's movements and collect information.

[0070] The aggregation unit aggregates information on the cloud and can centrally manage the behavioral patterns of elderly people. The aggregation unit aggregates information using AWS, for example. The aggregation unit can also aggregate information using Google Cloud. Furthermore, the aggregation unit can store and analyze information using a database. This allows centralized management of the behavioral patterns of elderly people and early detection of abnormalities.

[0071] The shared unit can share information with family members who have signed up in advance and send notifications if an abnormality is detected. The shared unit can share information with family members, for example, via email. The shared unit can also share information with family members via SMS. Furthermore, the shared unit can also share information with family members using an app. This allows family members to be quickly notified of any abnormalities in the elderly person and urge them to take action.

[0072] The installation unit can estimate the elderly person's emotions and adjust the installation location of the device based on the estimated elderly person's emotions. For example, if the elderly person is feeling anxious, the installation unit installs the device in an inconspicuous location to provide a sense of security. Furthermore, if the elderly person is relaxed, the installation unit can install the device in a location that is easily visible, thereby providing a sense of security. Furthermore, if the elderly person is feeling stressed, the installation unit can install the device in a location away from the elderly person's daily routine, thereby reducing stress. In this way, the installation location of the device can be adjusted according to the elderly person's emotions and provide a sense of security. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0073] The installation unit can select the optimal installation timing based on the elderly person's lifestyle rhythm at the time of installation. For example, if the elderly person is more active during the day, the installation unit can install the device during daytime hours. Also, if the elderly person is more active at night, the installation unit can install the device during nighttime hours. Also, if the elderly person often goes out on a specific day of the week, the installation unit can install the device on a day other than that day. This allows the device to be installed in accordance with the elderly person's lifestyle rhythm, reducing any sense of discomfort.

[0074] The installation unit can adjust the height and angle of the device based on the physical characteristics of the elderly person when installing the device. For example, the installation unit can adjust the height of the device to match the height of the elderly person to improve visibility. The installation unit can also adjust the angle of the device to match the range of motion of the elderly person to make it easier to operate. The installation unit can also adjust the installation position of the device based on the physical characteristics of the elderly person to improve usability. In this way, the device can be installed to match the physical characteristics of the elderly person, improving usability.

[0075] At the time of installation, the installation unit can analyze the elderly person's past behavioral patterns and select the optimal installation location. For example, the installation unit installs the device in a place where the elderly person frequently passes through, thereby efficiently detecting movement. The installation unit can also install the device in a place where the elderly person stays for a long time, thereby enabling early detection of abnormalities. The installation unit can also analyze the elderly person's behavioral patterns and select the optimal installation location. This makes it possible to install the device based on the elderly person's behavioral patterns and efficiently detect movement.

[0076] The installation unit can estimate the elderly person's emotions and determine the installation order of the devices based on the estimated elderly person's emotions. For example, if the elderly person is feeling anxious, the installation unit can first install the devices at the entrance to provide a sense of security. If the elderly person is feeling relaxed, the installation unit can also install the devices in a private place such as a bathroom. If the elderly person is feeling stressed, the installation unit can also install the devices in a place away from the elderly person's daily routine. This allows the installation order of the devices to be determined according to the elderly person's emotions and provide a sense of security. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] The installation unit can select an installation location during installation, taking into consideration the elderly person's living environment. The installation unit selects an installation location for the device based on, for example, the layout of the room, and efficiently detects movement. The installation unit can also select an installation location for the device, taking into consideration the arrangement of furniture, to improve visibility. The installation unit can also select an installation location for the device based on the elderly person's living environment, to improve usability. This allows the device to be installed in accordance with the elderly person's living environment, and improve usability.

[0078] The installation unit can customize the installation location by reflecting the opinions of the elderly person's family and caregivers when installing the device. For example, the installation unit selects the installation location of the device by reflecting the opinions of the elderly person's family. The installation unit can also customize the installation location of the device by reflecting the opinions of the caregivers. The installation unit can also select the optimal installation location based on the opinions of the elderly person's family and caregivers. This allows the device to be installed by reflecting the opinions of the elderly person's family and caregivers, thereby improving ease of use.

[0079] The installation unit can adjust the installation location based on the elderly person's lifestyle habits at the time of installation. The installation unit selects the installation location of the device based on, for example, the elderly person's wake-up time. The installation unit can also adjust the installation location of the device based on the elderly person's meal times. The installation unit can also select the installation location of the device based on the elderly person's lifestyle habits. This makes it possible to install the device in accordance with the elderly person's lifestyle habits and improve ease of use.

[0080] The collection unit can estimate the elderly person's emotions and adjust the frequency of data collection based on the estimated elderly person's emotions. For example, the collection unit can increase the frequency of data collection when the elderly person is feeling anxious. The collection unit can also decrease the frequency of data collection when the elderly person is feeling relaxed. The collection unit can also adjust the frequency of data collection when the elderly person is feeling stressed. This allows the frequency of data collection to be adjusted according to the elderly person's emotions, providing a sense of security. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0081] The collection unit can adjust the sensitivity of the sensor based on the elderly person's movement speed and movement range during collection. The collection unit adjusts the sensitivity of the sensor based on the elderly person's movement speed, for example, to collect accurate data. The collection unit can also adjust the sensitivity of the sensor based on the elderly person's movement range to collect data efficiently. The collection unit can also adjust the sensitivity of the sensor based on the elderly person's movement characteristics to collect optimal data. This allows the sensor sensitivity to be adjusted to match the elderly person's movement characteristics, making it possible to collect accurate data.

[0082] The collection unit can optimize the collection algorithm by referring to the elderly person's past behavioral data when collecting data. For example, the collection unit optimizes the collection algorithm by referring to the elderly person's past behavioral data. The collection unit can also analyze the elderly person's behavioral patterns and optimize the collection algorithm. The collection unit can also optimize the collection algorithm based on the elderly person's past behavioral data. This allows the collection algorithm to be optimized based on the elderly person's past behavioral data, making it possible to collect data efficiently.

[0083] The collection unit can adjust the collection timing based on the elderly person's lifestyle rhythm during collection. The collection unit adjusts the collection timing based on the elderly person's lifestyle rhythm, for example, to efficiently collect data. The collection unit can also adjust the collection timing based on the elderly person's activity time to collect accurate data. The collection unit can also adjust the collection timing taking the elderly person's lifestyle rhythm into consideration to collect optimal data. This allows the collection timing to be adjusted to match the elderly person's lifestyle rhythm and efficiently collect data.

[0084] The collection unit can estimate the emotions of the elderly person and determine the priority of data to be collected based on the estimated emotions of the elderly person. For example, if the elderly person is feeling anxious, the collection unit can prioritize collecting important data. Furthermore, if the elderly person is relaxed, the collection unit can also prioritize collecting detailed data. Furthermore, if the elderly person is feeling stressed, the collection unit can also prioritize collecting necessary data. In this way, the priority of data to be collected can be determined according to the emotions of the elderly person, and important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0085] The collection unit can correct the collected data during collection, taking into account the elderly person's living environment. For example, the collection unit corrects the collected data taking into account the temperature of the elderly person's living environment. The collection unit can also correct the collected data taking into account the humidity of the elderly person's living environment. The collection unit can also correct the collected data taking into account changes in the elderly person's living environment. This allows the collected data to be corrected based on the elderly person's living environment, making it possible to provide accurate data.

[0086] The collection unit can monitor the health condition of the elderly person during collection, and increase the collection frequency if an abnormality is detected. For example, the collection unit can monitor the heart rate of the elderly person, and increase the collection frequency if an abnormality is detected. The collection unit can also monitor the blood pressure of the elderly person, and increase the collection frequency if an abnormality is detected. The collection unit can also monitor the health condition of the elderly person, and increase the collection frequency if an abnormality is detected. This makes it possible to monitor the health condition of the elderly person, and to respond quickly if an abnormality is detected.

[0087] The collection unit can customize the collection method by reflecting feedback from the elderly person's family and caregivers when collecting data. For example, the collection unit customizes the collection method by reflecting feedback from the elderly person's family. The collection unit can also customize the collection method by reflecting feedback from caregivers. The collection unit can also customize the collection method based on the opinions of the elderly person's family and caregivers. This allows the collection method to be customized by reflecting the opinions of the elderly person's family and caregivers, thereby improving ease of use.

[0088] The aggregation unit can estimate the elderly person's emotions and adjust the data aggregation method based on the estimated elderly person's emotions. For example, if the elderly person feels anxious, the aggregation unit can increase the data aggregation frequency. Also, if the elderly person feels relaxed, the aggregation unit can decrease the data aggregation frequency. Also, if the elderly person feels stressed, the aggregation unit can adjust the data aggregation method. This allows the data aggregation method to be adjusted according to the elderly person's emotions, providing a sense of security. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] During aggregation, the aggregating unit can apply an algorithm that refers to past data and detects anomalies in the current data. For example, the aggregating unit refers to past data and applies an algorithm that detects anomalies in the current data. The aggregating unit can also analyze the behavioral patterns of elderly people and apply an algorithm that detects anomalies in the current data. The aggregating unit can also apply an algorithm that detects anomalies in the current data based on the past data. This makes it possible to detect anomalies in the current data based on the past data and respond quickly.

[0090] The aggregating unit can determine the priority of aggregation based on the importance of the data when aggregating. For example, the aggregating unit prioritizes aggregating important data and efficiently manages the data. The aggregating unit can also prioritize aggregating data related to the health status of elderly people. The aggregating unit can also determine the priority of data aggregation based on the behavioral patterns of elderly people. This allows important data to be prioritized and managed efficiently.

[0091] The aggregation unit can add a function to analyze the behavioral patterns of the elderly person during aggregation and issue an alert if an abnormality is detected. For example, the aggregation unit analyzes the behavioral patterns of the elderly person and issues an alert if an abnormality is detected. The aggregation unit can also issue an alert if an abnormality is detected based on the elderly person's daily rhythm. The aggregation unit can also issue an alert if an abnormality is detected based on the elderly person's behavioral data. This makes it possible to analyze the behavioral patterns of the elderly person and respond quickly if an abnormality is detected.

[0092] The aggregation unit can estimate the elderly person's emotions and adjust the data display method based on the estimated elderly person's emotions. For example, if the elderly person is feeling anxious, the aggregation unit provides a simple, highly visible display method. Furthermore, if the elderly person is relaxed, the aggregation unit can also provide a display method including detailed information. Furthermore, if the elderly person is feeling stressed, the aggregation unit can also display only the necessary information. This makes it possible to adjust the data display method according to the elderly person's emotions and provide a sense of security. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0093] The aggregating unit can correct the data taking into account the elderly person's living environment when aggregating. For example, the aggregating unit corrects the data taking into account the temperature of the elderly person's living environment. The aggregating unit can also correct the data taking into account the humidity of the elderly person's living environment. The aggregating unit can also correct the data taking into account changes in the elderly person's living environment. This allows the data to be corrected based on the elderly person's living environment, and accurate information to be provided.

[0094] The aggregating unit can monitor the health condition of the elderly person during aggregation and highlight the data if an abnormality is found. For example, the aggregating unit can monitor the heart rate of the elderly person and highlight the data if an abnormality is found. The aggregating unit can also monitor the blood pressure of the elderly person and highlight the data if an abnormality is found. The aggregating unit can also monitor the health condition of the elderly person and highlight the data if an abnormality is found. This makes it possible to monitor the health condition of the elderly person and respond quickly if an abnormality is found.

[0095] The aggregating unit can customize the data display method by reflecting feedback from the elderly person's family or caregiver when aggregating data. The aggregating unit customizes the data display method by reflecting feedback from the elderly person's family, for example. The aggregating unit can also customize the data display method by reflecting feedback from the caregiver. The aggregating unit can also customize the data display method based on the opinions of the elderly person's family or caregiver. This allows the data display method to be customized by reflecting the opinions of the elderly person's family or caregiver, thereby improving usability.

[0096] The sharing unit can estimate the elderly person's emotions and adjust the frequency of information sharing based on the estimated elderly person's emotions. For example, the sharing unit can increase the frequency of information sharing when the elderly person is feeling anxious. The sharing unit can also decrease the frequency of information sharing when the elderly person is relaxed. The sharing unit can also adjust the frequency of information sharing when the elderly person is feeling stressed. This allows the frequency of information sharing to be adjusted according to the elderly person's emotions, providing a sense of security. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0097] When sharing, the sharing unit can add a function to issue an alert if an abnormality is detected based on the elderly person's behavioral patterns. For example, the sharing unit analyzes the elderly person's behavioral patterns and issues an alert if an abnormality is detected. The sharing unit can also issue an alert if an abnormality is detected based on the elderly person's daily rhythm. The sharing unit can also issue an alert if an abnormality is detected based on the elderly person's behavioral data. This makes it possible to detect abnormalities based on the elderly person's behavioral patterns and respond quickly.

[0098] The sharing unit can customize the way information is displayed based on the needs of the elderly person's family or caregiver when sharing. For example, the sharing unit customizes the way information is displayed based on the needs of the elderly person's family. The sharing unit can also customize the way information is displayed based on the needs of the caregiver. The sharing unit can also customize the way information is displayed based on the opinions of the elderly person's family or caregiver. This makes it possible to customize the way information is displayed according to the needs of the elderly person's family or caregiver, thereby improving usability.

[0099] When sharing, the sharing unit can apply an algorithm that detects abnormalities by referring to the elderly person's past behavioral data. For example, the sharing unit can apply an algorithm that detects abnormalities by referring to the elderly person's past behavioral data. The sharing unit can also analyze the elderly person's behavioral patterns and apply an algorithm that detects abnormalities. The sharing unit can also apply an algorithm that detects abnormalities based on the elderly person's past behavioral data. This makes it possible to detect abnormalities based on the elderly person's past behavioral data and respond quickly.

[0100] The sharing unit can estimate the emotions of the elderly person and determine the priority of information based on the estimated emotions of the elderly person. For example, if the elderly person is feeling anxious, the sharing unit prioritizes sharing important information. Furthermore, if the elderly person is relaxed, the sharing unit can also prioritize sharing detailed information. Furthermore, if the elderly person is feeling stressed, the sharing unit can also prioritize sharing necessary information. In this way, the priority of information can be determined according to the emotions of the elderly person, and important information can be shared preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0101] The shared unit can correct the information taking into account the elderly person's living environment when sharing. For example, the shared unit corrects the information taking into account the temperature of the elderly person's living environment. The shared unit can also correct the information taking into account the humidity of the elderly person's living environment. The shared unit can also correct the information taking into account changes in the elderly person's living environment. This allows the information to be corrected based on the elderly person's living environment, and accurate information to be provided.

[0102] During sharing, the sharing unit can monitor the health condition of the elderly person and highlight information if an abnormality is detected. For example, the sharing unit can monitor the elderly person's heart rate and highlight information if an abnormality is detected. The sharing unit can also monitor the elderly person's blood pressure and highlight information if an abnormality is detected. The sharing unit can also monitor the elderly person's health condition and highlight information if an abnormality is detected. This makes it possible to monitor the elderly person's health condition and respond quickly if an abnormality is detected.

[0103] The sharing unit can customize the way information is displayed by reflecting feedback from the elderly person's family or caregiver when sharing. For example, the sharing unit customizes the way information is displayed by reflecting feedback from the elderly person's family. The sharing unit can also customize the way information is displayed by reflecting feedback from the caregiver. The sharing unit can also customize the way information is displayed based on the opinions of the elderly person's family or caregiver. This allows the way information is displayed to be customized by reflecting the opinions of the elderly person's family or caregiver, thereby improving usability. === Hard Collateral 1-1 === Each of the multiple elements, including the installation unit, collection unit, aggregation unit, and sharing 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 installation unit is realized by the control unit 46A of the smart device 14, and the device is installed in the entranceway, bathroom, etc. The collection unit detects the elderly person's movements using, for example, the camera 42 or a sensor of the smart device 14, and collects information using the control unit 46A. The aggregation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and aggregates information using a cloud service to manage the elderly person's behavioral patterns. The sharing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and shares the aggregated information with family members and sends a notification if an abnormality occurs. === Hard Collateral 1-2 === Each of the multiple elements, including the installation unit, collection unit, aggregation unit, and sharing 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 installation unit is realized by the control unit 46A of the smart glasses 214, and a device is installed at the entrance, bathroom, or the like. The collection unit detects the elderly person's movements using, for example, the camera 42 or a sensor of the smart glasses 214, and collects information using the control unit 46A. The aggregation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and aggregates information using a cloud service to manage the elderly person's behavioral patterns. The sharing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and shares the aggregated information with family members and sends a notification if an abnormality occurs. === Hard Collateral 1-3 === Each of the multiple elements including the installation unit, collection unit, aggregation unit, and sharing 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 installation unit is realized by the control unit 46A of the headset-type terminal 314, and a device is installed in the entranceway, bathroom, etc. The collection unit detects the elderly person's movements using, for example, the camera 42 or a sensor of the headset-type terminal 314, and collects information using the control unit 46A. The aggregation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and aggregates information using a cloud service to manage the elderly person's behavioral patterns. The sharing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and shares the aggregated information with family members and sends a notification if an abnormality occurs. === Hard Collateral 1-4 === Each of the multiple elements including the installation unit, collection unit, aggregation unit, and sharing unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the installation unit is realized by the control unit 46A of the robot 414 and installs a device in the entranceway, toilet, etc. The collection unit detects the elderly person's movements using, for example, the camera 42 or a sensor of the robot 414 and collects information using the control unit 46A. The aggregation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and aggregates information using a cloud service to manage the elderly person's behavioral patterns. The sharing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and shares the aggregated information with family members and sends a notification if an abnormality occurs.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] When selecting a location for the device, the installation unit can analyze surrounding environmental sounds and determine the optimal installation location. For example, the installation unit can reduce false detections by installing the device in a quiet location. The installation unit can also use a device with a noise-canceling function when installing the device in a noisy location. Furthermore, the installation unit can monitor fluctuations in environmental sounds in real time and readjust the device installation location as needed. This allows the device installation location to be optimized according to the environmental sounds, enabling accurate motion detection.

[0106] The collection unit analyzes data collected from the device in real time and can immediately issue an alert if an abnormality is detected. For example, the collection unit can issue an alert if the elderly person's movements are different from normal. The collection unit can also issue an alert if the device's battery level is low. Furthermore, the collection unit can also issue an alert if the device's communication status is unstable. This allows abnormalities to be detected in real time and a prompt response to be taken.

[0107] When aggregating data, the aggregator can evaluate the reliability of the data and exclude unreliable data. For example, the aggregator can exclude data when a device sensor malfunctions. The aggregator can also exclude data when a communication error occurs. Furthermore, the aggregator can check the consistency of the data and exclude abnormal data. This allows only reliable data to be aggregated, enabling accurate analysis of behavioral patterns.

[0108] When sharing information, the sharing unit can visualize data, allowing family members to intuitively understand it. For example, the sharing unit can display the elderly person's behavioral patterns in graphs and charts. The sharing unit can also highlight abnormalities when they occur. Furthermore, the sharing unit can display the current situation by comparing it with past data. This makes it easier for family members to intuitively understand the elderly person's situation.

[0109] When selecting a location to install the device, the installation unit can estimate the elderly person's emotions and adjust the installation location based on the estimated emotions. For example, if the elderly person is feeling anxious, the installation unit can install the device in an inconspicuous location to provide a sense of security. If the elderly person is relaxed, the installation unit can install the device in a location that is easily visible, providing a sense of security. Furthermore, if the elderly person is feeling stressed, the installation unit can install the device in a location away from the elderly person's daily routine, reducing stress. In this way, the installation location of the device can be adjusted according to the elderly person's emotions to provide a sense of security.

[0110] The collection unit can estimate the emotions of the elderly person and adjust the frequency of data collection based on the estimated emotions. For example, if the elderly person feels anxious, the frequency of data collection can be increased. Also, if the elderly person feels relaxed, the frequency of data collection can be decreased. Furthermore, if the elderly person feels stressed, the frequency of data collection can be adjusted. In this way, the frequency of data collection can be adjusted according to the emotions of the elderly person, providing a sense of security.

[0111] The aggregation unit can estimate the emotions of the elderly person and adjust the data aggregation method based on the estimated emotions. For example, if the elderly person feels anxious, the data aggregation frequency can be increased. Also, if the elderly person feels relaxed, the data aggregation frequency can be decreased. Furthermore, if the elderly person feels stressed, the data aggregation method can be adjusted. In this way, the data aggregation method can be adjusted according to the emotions of the elderly person, providing a sense of security.

[0112] The sharing unit can estimate the elderly person's emotions and adjust the frequency of information sharing based on the estimated emotions. For example, if the elderly person feels anxious, the frequency of information sharing can be increased. Also, if the elderly person feels relaxed, the frequency of information sharing can be decreased. Furthermore, if the elderly person feels stressed, the frequency of information sharing can be adjusted. In this way, the frequency of information sharing can be adjusted according to the elderly person's emotions, providing a sense of security.

[0113] The sharing unit can estimate the emotions of the elderly person and determine the priority of information based on the estimated emotions. For example, if the elderly person is feeling anxious, important information can be shared with priority. Also, if the elderly person is relaxed, detailed information can be shared with priority. Furthermore, if the elderly person is feeling stressed, necessary information can be shared with priority. In this way, the priority of information can be determined according to the emotions of the elderly person, and important information can be shared with priority.

[0114] The installation unit can determine the optimal installation location for the device by taking into account the ambient temperature and humidity. For example, if the device is to be installed in a hot and humid location, the installation unit can use a device with high heat resistance and waterproofing. Alternatively, if the device is to be installed in a cold and dry location, the installation unit can use a device with high cold resistance and moisture resistance. Furthermore, the installation unit can monitor fluctuations in temperature and humidity in real time and readjust the device's installation location as needed. This allows the device's installation location to be optimized according to the ambient temperature and humidity, enabling accurate motion detection.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The installation unit installs devices equipped with sensors to detect the movements of elderly people. For example, the installation unit installs devices in places such as the entrance and toilet, and the device installed at the entrance detects when the elderly person goes out and comes home, while the device installed in the toilet detects how often and for how long the elderly person uses the toilet. Step 2: The collection unit collects information from the devices installed by the installation unit. For example, the collection unit collects information about the movements of the elderly person from devices equipped with sensors, and detects the movements of the elderly person using an infrared sensor or a motion sensor. Step 3: The aggregation unit aggregates the information collected by the collection unit on the cloud. For example, the aggregation unit aggregates the information using a cloud service (e.g., AWS or Google Cloud) and centrally manages the behavioral patterns of elderly people. The aggregation unit can store and analyze the information using a database. Step 4: The sharing unit shares the information aggregated by the aggregation unit with family members who have signed up in advance. For example, the sharing unit sends a notification to family members if there is an abnormality. The sharing unit can send notifications via email or SMS.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 AI 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] 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.

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

[0138] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 AI 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 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.

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

[0154] 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.

[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 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.

[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 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).

[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] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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 AI 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an installation unit that installs a device equipped with a sensor for detecting the movement of the elderly person; a collection unit that collects information from the device installed by the installation unit; an aggregation unit that aggregates the information collected by the collection unit on a cloud; a sharing unit that shares the information aggregated by the aggregation unit with family members who have signed a contract in advance; Equipped with A system characterized by:

2. The installation unit includes: Place the device in an area such as a hallway or bathroom 2. The system of claim 1.

3. The collecting unit Collect information from devices equipped with sensors to detect the movements of elderly people 2. The system of claim 1.

4. The collecting unit is Collecting information on the cloud and managing the behavioral patterns of elderly people in a unified manner 2. The system of claim 1.

5. The common part is Share information with family members who have signed up in advance and send notifications if any abnormalities are detected 2. The system of claim 1.

6. The installation unit includes: Estimate the emotions of the elderly and adjust the location of the device based on the estimated emotions of the elderly.

2. The system of claim 1.

7. The installation unit includes: When installing, select the appropriate installation timing based on the elderly person's daily rhythm.

2. The system of claim 1.

8. The installation unit includes: During installation, adjust the height and angle of the device based on the physical characteristics of the elderly person.

2. The system of claim 1.

9. The installation unit includes: When installing the device, the past behavioral patterns of the elderly person are analyzed to select the optimal installation location.

2. The system of claim 1.

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