system

Abstract

The system according to the embodiment aims to monitor the range of movement of a senior, detect abnormal movements, and notify family members. [Solution] A system according to an embodiment includes an acquisition unit, an analysis unit, a detection unit, and a transmission unit. The acquisition unit acquires location information. The analysis unit analyzes the information acquired by the acquisition unit. The detection unit detects an abnormality based on the information analyzed by the analysis unit. The transmission unit notifies family members of the abnormality detected by the detection unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately monitor the range of activities of seniors, detect abnormal movements, and notify family members, so there is room for improvement.

[0005] The system according to the embodiment aims to monitor the range of movement of a senior, detect abnormal movements, and notify family members. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a detection unit, and a transmission unit. The acquisition unit acquires location information. The analysis unit analyzes the information acquired by the acquisition unit. The detection unit detects an abnormality based on the information analyzed by the analysis unit. The transmission unit notifies a family member of the abnormality detected by the detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can monitor the range of movement of a senior, detect abnormal movements, and notify family members. [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 records the range of a senior's activities and sends an alert to family members when abnormal behavior is detected. This system records the senior's usual range of activities, compares the recorded range of activities with the senior's current behavior to detect abnormalities, and sends an alert to family members when an abnormality is detected. For example, the system acquires the senior's location information and records the senior's daily visits and travel routes. The system then uses AI to compare the recorded range of activities with the senior's current behavior to detect abnormal behavior. For example, the system detects when the senior spends a long time in a place they do not usually visit or when the senior is significantly deviating from their usual travel route. When an abnormality is detected, the system sends an alert to the family members via smartphone notification, email, phone call, or other means. For example, if the senior deviates from their usual range of activities, an alert such as "The senior is behaving abnormally" is sent to the family members. This allows the system to ensure the senior's safety and provide peace of mind to the family members. This allows the system to record the senior's range of activities and send an alert to family members when abnormal behavior is detected. For example, if the senior gets lost or has an accident, abnormal behavior can be detected early and a prompt response can be taken. In addition, by recording the range of activities of seniors, it is possible to understand their daily health conditions and lifestyle patterns.

[0029] The system according to the embodiment includes an acquisition unit, an analysis unit, a detection unit, and a transmission unit. The acquisition unit acquires location information of the senior. For example, the acquisition unit can acquire location information using a GPS device or a smartphone. The acquisition unit can also acquire location information using Wi-Fi location information or cell tower data. The acquisition unit can periodically acquire location information to record the senior's daily activity range. The analysis unit analyzes the information acquired by the acquisition unit. For example, the analysis unit can analyze the senior's behavioral patterns using a machine learning algorithm. The analysis unit can also analyze the senior's current location information and compare it with the senior's normal activity range. The analysis unit can learn the senior's behavioral patterns to detect abnormal movements and improve the accuracy of anomaly detection. The detection unit detects anomalies based on the information analyzed by the analysis unit. For example, the detection unit can detect when the senior stays in a place they normally do not visit for a long time or when they deviate significantly from their normal travel route. The detection unit can also compare the senior's behavioral data with past anomaly cases to improve the accuracy of anomaly detection. Furthermore, the detection unit can estimate the senior's emotions and adjust the criteria for detecting anomalies based on the estimated emotions. The transmission unit notifies family members of anomalies detected by the detection unit. For example, the transmission unit can send an alert via a smartphone notification, email, or phone call. The transmission unit can also adjust the content of the alert based on the senior's emotions. Furthermore, the transmission unit can integrate the senior's behavioral data and environmental data to improve the accuracy of the alert. As a result, the system according to the embodiment can store the senior's range of activity and send an alert to family members when abnormal movement is detected.

[0030] The acquisition unit can use a GPS device or a smartphone. The acquisition unit can acquire the senior's location information using, for example, a portable GPS device. The acquisition unit can also acquire the location information using a smartwatch. Furthermore, the acquisition unit can install a dedicated app on a smartphone and acquire location information using a location information service. This makes it possible to accurately acquire the senior's location information. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input location information acquired from a GPS device or smartphone into the generation AI and cause the generation AI to analyze the location information.

[0031] The analysis unit can use a machine learning algorithm. The analysis unit can analyze the behavioral patterns of seniors using, for example, a decision tree. The analysis unit can also analyze the behavioral patterns using a random forest. Furthermore, the analysis unit can analyze the behavioral patterns using a neural network. This allows for accurate analysis of the behavioral patterns of seniors. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the behavioral data of seniors to a generation AI and cause the generation AI to analyze the behavioral patterns.

[0032] The transmitting unit can send an alert via smartphone notification, email, or phone call. The transmitting unit can send the alert, for example, using a smartphone push notification. The transmitting unit can also send the alert using an SMS notification. The transmitting unit can also send an emergency email. For example, if a senior moves outside of their normal range of movement, the transmitting unit can send an alert to family members stating, "The senior is behaving abnormally." This allows the alert to be sent to family members quickly. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the content of the alert into a generating AI and have the generating AI generate the alert.

[0033] The acquisition unit can learn the senior's past behavioral patterns and select the acquisition timing. For example, if the senior takes a walk at the same time every day, the acquisition unit can acquire location information during that time period. Also, if the senior visits a specific place on a specific day of the week, the acquisition unit can acquire location information on that day. Furthermore, if the senior's behavioral pattern changes, the acquisition unit can adjust the acquisition timing based on the new pattern. This makes it possible to optimize the timing of acquiring location information based on the senior's behavioral pattern. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the senior's past behavioral data into the generation AI and have the generation AI select the acquisition timing.

[0034] The acquisition unit can monitor the senior's current health condition and acquire location information when an abnormality is detected. For example, the acquisition unit can acquire location information when the senior's heart rate is abnormally high. The acquisition unit can also acquire location information when the senior's blood pressure is abnormally low. Furthermore, the acquisition unit can acquire location information when the senior falls. In this way, by acquiring location information based on the senior's health condition, abnormalities can be detected early. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the senior's health data into the generation AI and cause the generation AI to acquire location information based on the detected abnormality.

[0035] The acquisition unit simultaneously acquires environmental information about the senior's surroundings, enabling more accurate detection of abnormalities in the range of movement. For example, the acquisition unit can acquire location information when the senior stays in a high-temperature environment for a long time. The acquisition unit can also acquire location information when the senior is in a noisy place. Furthermore, the acquisition unit can also acquire location information when the senior is in a humid place. This allows for more accurate detection of abnormalities in the range of movement by taking environmental information into consideration. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input environmental data about the senior to the generation AI and cause the generation AI to perform abnormality detection based on the environmental information.

[0036] The acquisition unit can prioritize acquiring location information in a specific area based on the senior's geographical location information. For example, when the senior is near his or her home, the acquisition unit can prioritize acquiring location information in that area. Furthermore, when the senior is in a park that the senior often visits, the acquisition unit can prioritize acquiring location information in that area. Furthermore, when the senior is in a commercial facility, the acquisition unit can prioritize acquiring location information in that area. Thus, by prioritizing acquisition of location information in a specific area, the senior's range of activity can be more accurately understood. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the senior's geographical location information to the generation AI and cause the generation AI to prioritize acquisition of location information in a specific area.

[0037] The acquisition unit can analyze the social media activity of the senior and acquire related location information. For example, the acquisition unit can acquire location information of places where the senior has checked in on social media. The acquisition unit can also acquire location information of photos posted by the senior on social media. Furthermore, the acquisition unit can acquire location information of places shared by the senior with friends on social media. In this way, related location information can be acquired by analyzing the social media activity of the senior. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the social media data of the senior to the generation AI and cause the generation AI to acquire related location information.

[0038] The acquisition unit can customize the acquisition method by reflecting the senior's past feedback. For example, if the senior has previously requested that the frequency of location information acquisition be increased, the acquisition unit can reflect that request. Furthermore, if the senior has previously requested that location information be acquired at a specific location, the acquisition unit can customize the acquisition method at that location. Furthermore, if the senior has previously requested that the accuracy of location information be increased, the acquisition unit can also reflect that request. In this way, the acquisition method can be customized by reflecting the senior's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the senior's past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0039] The analysis unit can improve the accuracy of anomaly detection based on the senior's past behavioral data. The analysis unit can improve the accuracy of anomaly detection based on, for example, data on places the senior has visited in the past. The analysis unit can also improve the accuracy of anomaly detection by analyzing the senior's past movement patterns. Furthermore, the analysis unit can also improve the accuracy of anomaly detection by referring to the senior's past behavioral data. In this way, by referring to the senior's past behavioral data, the accuracy of anomaly detection is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the senior's past behavioral data into the generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0040] The analysis unit can learn the behavioral patterns of seniors in real time and optimize the analysis algorithm. The analysis unit can, for example, collect current behavioral data of seniors in real time and optimize the analysis algorithm. The analysis unit can also learn in real time and adjust the analysis algorithm when the behavioral patterns of seniors change. Furthermore, the analysis unit can analyze the behavioral data of seniors in real time and improve the accuracy of anomaly detection. In this way, the analysis algorithm can be optimized by learning the behavioral patterns of seniors in real time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the real-time behavioral data of seniors to a generation AI and cause the generation AI to optimize the analysis algorithm.

[0041] The analysis unit can integrate the senior's behavioral data and environmental data to improve the accuracy of anomaly detection. The analysis unit can, for example, integrate the senior's behavioral data and temperature data to improve the accuracy of anomaly detection. The analysis unit can also integrate the senior's behavioral data and humidity data to improve the accuracy of anomaly detection. Furthermore, the analysis unit can integrate the senior's behavioral data and noise data to improve the accuracy of anomaly detection. In this way, by integrating the senior's behavioral data and environmental data, the accuracy of anomaly detection is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the senior's behavioral data and environmental data into the generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0042] The analysis unit can geographically cluster the behavioral data of seniors to improve the accuracy of anomaly detection. For example, the analysis unit can geographically cluster the behavioral data of seniors to improve the accuracy of anomaly detection. The analysis unit can also geographically cluster the behavioral data of seniors to improve the anomaly detection algorithm. Furthermore, the analysis unit can geographically cluster the behavioral data of seniors to adjust anomaly detection parameters. In this way, the accuracy of anomaly detection is improved by geographically clustering the behavioral data of seniors. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the behavioral data of seniors to a generation AI and cause the generation AI to perform geographic clustering.

[0043] The analysis unit can improve the analysis algorithm by referring to the behavioral data of seniors and related literature. The analysis unit can, for example, improve the analysis algorithm by referring to the behavioral data of seniors and related literature. The analysis unit can also improve the accuracy of anomaly detection by referring to the behavioral data of seniors and related literature. Furthermore, the analysis unit can adjust the parameters of the analysis algorithm by referring to the behavioral data of seniors and related literature. In this way, the analysis algorithm can be improved by referring to the behavioral data of seniors and related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the behavioral data of seniors and related literature into the generation AI and cause the generation AI to improve the analysis algorithm.

[0044] The analysis unit can integrate senior behavioral data and market data to improve the accuracy of anomaly detection. For example, the analysis unit can integrate senior behavioral data and market data to improve the accuracy of anomaly detection. The analysis unit can also integrate senior behavioral data and market data to improve the anomaly detection algorithm. Furthermore, the analysis unit can integrate senior behavioral data and market data to adjust anomaly detection parameters. In this way, the accuracy of anomaly detection is improved by integrating senior behavioral data and market data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input senior behavioral data and market data into a generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0045] The detection unit can compare the senior's behavioral data with past abnormality cases to improve the accuracy of anomaly detection. For example, the detection unit can compare the senior's behavioral data with past abnormality cases to improve the accuracy of anomaly detection. The detection unit can also compare the senior's behavioral data with past abnormality cases to improve the anomaly detection algorithm. Furthermore, the detection unit can compare the senior's behavioral data with past abnormality cases to adjust the anomaly detection parameters. In this way, by comparing the senior's behavioral data with past abnormality cases, the accuracy of anomaly detection is improved. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the senior's behavioral data and past abnormality cases into a generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0046] The detection unit can monitor the behavioral data of seniors in real time and optimize the timing of anomaly detection. For example, the detection unit can monitor the behavioral data of seniors in real time and optimize the timing of anomaly detection. The detection unit can also monitor the behavioral data of seniors in real time and improve the anomaly detection algorithm. Furthermore, the detection unit can monitor the behavioral data of seniors in real time and adjust anomaly detection parameters. In this way, by monitoring the behavioral data of seniors in real time, the timing of anomaly detection can be optimized. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input the real-time behavioral data of seniors to a generation AI and cause the generation AI to optimize the timing of anomaly detection.

[0047] The detection unit can integrate the senior's behavioral data with environmental data to improve the accuracy of anomaly detection. The detection unit can, for example, integrate the senior's behavioral data with temperature data to improve the accuracy of anomaly detection. The detection unit can also integrate the senior's behavioral data with humidity data to improve the accuracy of anomaly detection. Furthermore, the detection unit can also integrate the senior's behavioral data with noise data to improve the accuracy of anomaly detection. In this way, by integrating the senior's behavioral data with environmental data, the accuracy of anomaly detection is improved. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the senior's behavioral data and environmental data into a generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0048] The detection unit can geographically cluster the behavioral data of seniors to improve the accuracy of anomaly detection. For example, the detection unit can geographically cluster the behavioral data of seniors to improve the accuracy of anomaly detection. The detection unit can also geographically cluster the behavioral data of seniors to improve the anomaly detection algorithm. Furthermore, the detection unit can geographically cluster the behavioral data of seniors to adjust anomaly detection parameters. In this way, by geographically clustering the behavioral data of seniors, the accuracy of anomaly detection is improved. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the behavioral data of seniors to a generation AI and cause the generation AI to perform geographic clustering.

[0049] The detection unit can improve the anomaly detection algorithm by referring to the behavioral data of seniors and related literature. The detection unit can, for example, improve the anomaly detection algorithm by referring to the behavioral data of seniors and related literature. The detection unit can also improve the accuracy of anomaly detection by referring to the behavioral data of seniors and related literature. Furthermore, the detection unit can adjust the anomaly detection parameters by referring to the behavioral data of seniors and related literature. In this way, the anomaly detection algorithm can be improved by referring to the behavioral data of seniors and related literature. Some or all of the above-described processing in the detection unit can be performed using AI, for example, or without AI. For example, the detection unit can input the behavioral data of seniors and related literature into the generation AI and cause the generation AI to improve the anomaly detection algorithm.

[0050] The detection unit can integrate senior behavioral data and market data to improve the accuracy of anomaly detection. For example, the detection unit can integrate senior behavioral data and market data to improve the accuracy of anomaly detection. The detection unit can also integrate senior behavioral data and market data to improve the anomaly detection algorithm. Furthermore, the detection unit can integrate senior behavioral data and market data to adjust anomaly detection parameters. In this way, by integrating senior behavioral data and market data, the accuracy of anomaly detection is improved. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input senior behavioral data and market data into a generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0051] The transmission unit can optimize the timing of sending an alert by referring to the senior's past behavioral data. For example, the transmission unit can transmit an alert during a time period when the senior has behaved abnormally in the past. The transmission unit can also set the optimal timing of sending an alert based on the senior's past behavioral data. Furthermore, if the senior's behavioral pattern changes, the transmission unit can adjust the timing of sending an alert based on the new pattern. In this way, the timing of sending an alert can be optimized by referring to the senior's past behavioral data. Some or all of the above-described processing in the transmission unit may be performed using AI, for example, or may be performed without using AI. For example, the transmission unit can input the senior's past behavioral data into the generation AI and cause the generation AI to optimize the timing of sending an alert.

[0052] The transmission unit can monitor the senior's behavioral data in real time and adjust the frequency of alert transmission. For example, the transmission unit can monitor the senior's behavioral data in real time and increase the frequency of alert transmission when abnormalities occur frequently. The transmission unit can also monitor the senior's behavioral data in real time and decrease the frequency of alert transmission when abnormalities are rare. Furthermore, the transmission unit can monitor the senior's behavioral data in real time and set the frequency of alert transmission to medium when abnormalities are moderate. In this way, the frequency of alert transmission can be adjusted by monitoring the senior's behavioral data in real time. Some or all of the above-described processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can input the senior's real-time behavioral data to a generation AI and cause the generation AI to adjust the frequency of alert transmission.

[0053] The transmitting unit can integrate the senior's behavioral data and environmental data to improve the accuracy of the alert. The transmitting unit can, for example, integrate the senior's behavioral data and temperature data to improve the accuracy of the alert. The transmitting unit can also integrate the senior's behavioral data and humidity data to improve the accuracy of the alert. Furthermore, the transmitting unit can also integrate the senior's behavioral data and noise data to improve the accuracy of the alert. In this way, by integrating the senior's behavioral data and environmental data, the accuracy of the alert is improved. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the senior's behavioral data and environmental data into the generating AI and cause the generating AI to improve the accuracy of the alert.

[0054] The transmitting unit can geographically cluster the behavioral data of seniors to improve the accuracy of alerts. For example, the transmitting unit can geographically cluster the behavioral data of seniors to improve the accuracy of alerts. The transmitting unit can also geographically cluster the behavioral data of seniors to improve the alert algorithm. Furthermore, the transmitting unit can geographically cluster the behavioral data of seniors to adjust the parameters of the alert. As a result, the accuracy of the alert is improved by geographically clustering the behavioral data of seniors. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or may be performed without using AI. For example, the transmitting unit can input the behavioral data of seniors to a generation AI and cause the generation AI to perform geographic clustering.

[0055] The transmission unit can improve the content of the alert by referring to the behavioral data of the senior and related literature. The transmission unit can improve the content of the alert by referring to, for example, the behavioral data of the senior and related literature. The transmission unit can also improve the accuracy of the alert by referring to the behavioral data of the senior and related literature. Furthermore, the transmission unit can adjust the parameters of the alert by referring to the behavioral data of the senior and related literature. In this way, the content of the alert can be improved by referring to the behavioral data of the senior and related literature. Some or all of the above-mentioned processing in the transmission unit can be performed using, for example, AI, or can be performed without using AI. For example, the transmission unit can input the behavioral data of the senior and related literature into the generation AI and cause the generation AI to improve the content of the alert.

[0056] The transmission unit can integrate the behavioral data of seniors with market data to improve the accuracy of alerts. For example, the transmission unit can integrate the behavioral data of seniors with market data to improve the accuracy of alerts. The transmission unit can also integrate the behavioral data of seniors with market data to improve the alert algorithm. Furthermore, the transmission unit can integrate the behavioral data of seniors with market data to adjust the parameters of the alert. In this way, the accuracy of the alert is improved by integrating the behavioral data of seniors with market data. Some or all of the above-described processing in the transmission unit may be performed using AI, for example, or may be performed without using AI. For example, the transmission unit can input the behavioral data of seniors and market data into a generation AI and cause the generation AI to improve the accuracy of the alert.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The acquisition unit can not only acquire the senior's location information but also simultaneously acquire the senior's health data. For example, the acquisition unit can acquire vital data such as the senior's heart rate, blood pressure, and body temperature. The acquisition unit can also acquire data such as the senior's sleep patterns and amount of exercise. Furthermore, the acquisition unit can also acquire data such as the senior's diet and water intake. This allows for a comprehensive understanding of the senior's health condition, and if an abnormality is detected, a prompt response can be made.

[0059] The analysis unit can not only analyze the behavioral patterns of seniors, but also analyze their health data. For example, the analysis unit can analyze vital data such as the senior's heart rate, blood pressure, and body temperature to detect abnormalities. The analysis unit can also analyze data such as the senior's sleep patterns and amount of exercise to evaluate their health condition. Furthermore, the analysis unit can analyze data such as the senior's diet and water intake to evaluate their nutritional status. This allows for a comprehensive evaluation of the senior's health condition, and if an abnormality is detected, a prompt response can be taken.

[0060] The detection unit can not only analyze the behavioral data of seniors, but also analyze their health data. For example, the detection unit can analyze vital data such as the senior's heart rate, blood pressure, and body temperature to detect abnormalities. The detection unit can also analyze data such as the senior's sleep patterns and amount of exercise to evaluate their health condition. Furthermore, the detection unit can analyze data such as the senior's diet and water intake to evaluate their nutritional status. This allows for a comprehensive evaluation of the senior's health condition, and if an abnormality is detected, a prompt response can be taken.

[0061] The transmitting unit can not only notify the senior's family of the senior's behavioral data and health data, but also the senior's medical institution. For example, the transmitting unit can transmit vital data such as the senior's heart rate, blood pressure, and body temperature to a medical institution. The transmitting unit can also transmit data such as the senior's sleep patterns and amount of exercise to a medical institution. Furthermore, the transmitting unit can also transmit data such as the senior's diet and water intake to a medical institution. This allows the senior's health condition to be shared with a medical institution, and if an abnormality is detected, the medical institution can respond quickly.

[0062] The acquisition unit can not only acquire the senior's location information, but also simultaneously acquire environmental information about the senior's surroundings. For example, the acquisition unit can acquire location information when the senior stays in a hot environment for a long time. The acquisition unit can also acquire location information when the senior is in a noisy place. Furthermore, the acquisition unit can also acquire location information when the senior is in a humid place. In this way, by taking environmental information into consideration, abnormalities in the range of movement can be detected more accurately.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The acquisition unit acquires location information of the senior. For example, the acquisition unit can acquire location information using a GPS device or a smartphone. The acquisition unit can also acquire location information using Wi-Fi location information or cell tower data. Furthermore, the acquisition unit can periodically acquire location information to record the senior's daily range of movement. Step 2: The analysis unit analyzes the information acquired by the acquisition unit. For example, the analysis unit can analyze the behavioral patterns of the senior using a machine learning algorithm. It can also analyze the senior's current location information and compare it with the senior's normal range of movement. Furthermore, the analysis unit can learn the behavioral patterns of the senior to detect abnormal movements and improve the accuracy of anomaly detection. Step 3: The detection unit detects anomalies based on the information analyzed by the analysis unit. For example, it can detect when a senior stays in a place they normally don't visit for a long time, or when they deviate significantly from their normal route. It can also compare the senior's behavioral data with past anomaly cases to improve the accuracy of anomaly detection. It can also estimate the senior's emotions and adjust the anomaly detection criteria based on the estimated senior's emotions. Step 4: The transmitter notifies the family of any abnormalities detected by the detector. For example, alerts can be sent via smartphone notifications, email, or phone calls. The content of the alert can also be adjusted based on the senior's emotions. Furthermore, the accuracy of the alerts can be improved by integrating the senior's behavioral data with environmental data.

[0065] (Example 2) A system according to an embodiment of the present invention records the range of a senior's activities and sends an alert to family members when abnormal behavior is detected. This system records the senior's usual range of activities, compares the recorded range of activities with the senior's current behavior to detect abnormalities, and sends an alert to family members when an abnormality is detected. For example, the system acquires the senior's location information and records the senior's daily visits and travel routes. The system then uses AI to compare the recorded range of activities with the senior's current behavior to detect abnormal behavior. For example, the system detects when the senior spends a long time in a place they do not usually visit or when the senior is significantly deviating from their usual travel route. When an abnormality is detected, the system sends an alert to the family members via smartphone notification, email, phone call, or other means. For example, if the senior deviates from their usual range of activities, an alert such as "The senior is behaving abnormally" is sent to the family members. This allows the system to ensure the senior's safety and provide peace of mind to the family members. This allows the system to record the senior's range of activities and send an alert to family members when abnormal behavior is detected. For example, if the senior gets lost or has an accident, abnormal behavior can be detected early and a prompt response can be taken. In addition, by recording the range of activities of seniors, it is possible to understand their daily health conditions and lifestyle patterns.

[0066] The system according to the embodiment includes an acquisition unit, an analysis unit, a detection unit, and a transmission unit. The acquisition unit acquires location information of the senior. For example, the acquisition unit can acquire location information using a GPS device or a smartphone. The acquisition unit can also acquire location information using Wi-Fi location information or cell tower data. The acquisition unit can periodically acquire location information to record the senior's daily activity range. The analysis unit analyzes the information acquired by the acquisition unit. For example, the analysis unit can analyze the senior's behavioral patterns using a machine learning algorithm. The analysis unit can also analyze the senior's current location information and compare it with the senior's normal activity range. The analysis unit can learn the senior's behavioral patterns to detect abnormal movements and improve the accuracy of anomaly detection. The detection unit detects anomalies based on the information analyzed by the analysis unit. For example, the detection unit can detect when the senior stays in a place they normally do not visit for a long time or when they deviate significantly from their normal travel route. The detection unit can also compare the senior's behavioral data with past anomaly cases to improve the accuracy of anomaly detection. Furthermore, the detection unit can estimate the senior's emotions and adjust the criteria for detecting anomalies based on the estimated emotions. The transmission unit notifies family members of anomalies detected by the detection unit. For example, the transmission unit can send an alert via a smartphone notification, email, or phone call. The transmission unit can also adjust the content of the alert based on the senior's emotions. Furthermore, the transmission unit can integrate the senior's behavioral data and environmental data to improve the accuracy of the alert. As a result, the system according to the embodiment can store the senior's range of activity and send an alert to family members when abnormal movement is detected.

[0067] The acquisition unit can use a GPS device or a smartphone. The acquisition unit can acquire the senior's location information using, for example, a portable GPS device. The acquisition unit can also acquire the location information using a smartwatch. Furthermore, the acquisition unit can install a dedicated app on a smartphone and acquire location information using a location information service. This makes it possible to accurately acquire the senior's location information. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input location information acquired from a GPS device or smartphone into the generation AI and cause the generation AI to analyze the location information.

[0068] The analysis unit can use a machine learning algorithm. The analysis unit can analyze the behavioral patterns of seniors using, for example, a decision tree. The analysis unit can also analyze the behavioral patterns using a random forest. Furthermore, the analysis unit can analyze the behavioral patterns using a neural network. This allows for accurate analysis of the behavioral patterns of seniors. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the behavioral data of seniors to a generation AI and cause the generation AI to analyze the behavioral patterns.

[0069] The transmitting unit can send an alert via smartphone notification, email, or phone call. The transmitting unit can send the alert, for example, using a smartphone push notification. The transmitting unit can also send the alert using an SMS notification. The transmitting unit can also send an emergency email. For example, if a senior moves outside of their normal range of movement, the transmitting unit can send an alert to family members stating, "The senior is behaving abnormally." This allows the alert to be sent to family members quickly. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the content of the alert into a generating AI and have the generating AI generate the alert.

[0070] The acquisition unit can estimate the senior's emotions and adjust the frequency of location information acquisition based on the estimated senior's emotions. For example, if the senior is feeling anxious, the acquisition unit can increase the frequency of location information acquisition. If the senior is relaxed, the acquisition unit can also decrease the frequency of location information acquisition. If the senior is excited, the acquisition unit can also set the frequency of location information acquisition to a medium level. This enables more appropriate monitoring by adjusting the frequency of location information acquisition according to the senior's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the senior's emotion data into the generation AI and cause the generation AI to adjust the frequency of location information acquisition based on the emotion.

[0071] The acquisition unit can learn the senior's past behavioral patterns and select the acquisition timing. For example, if the senior takes a walk at the same time every day, the acquisition unit can acquire location information during that time period. Also, if the senior visits a specific place on a specific day of the week, the acquisition unit can acquire location information on that day. Furthermore, if the senior's behavioral pattern changes, the acquisition unit can adjust the acquisition timing based on the new pattern. This makes it possible to optimize the timing of acquiring location information based on the senior's behavioral pattern. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the senior's past behavioral data into the generation AI and have the generation AI select the acquisition timing.

[0072] The acquisition unit can monitor the senior's current health condition and acquire location information when an abnormality is detected. For example, the acquisition unit can acquire location information when the senior's heart rate is abnormally high. The acquisition unit can also acquire location information when the senior's blood pressure is abnormally low. Furthermore, the acquisition unit can acquire location information when the senior falls. In this way, by acquiring location information based on the senior's health condition, abnormalities can be detected early. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the senior's health data into the generation AI and cause the generation AI to acquire location information based on the detected abnormality.

[0073] The acquisition unit simultaneously acquires environmental information about the senior's surroundings, enabling more accurate detection of abnormalities in the range of movement. For example, the acquisition unit can acquire location information when the senior stays in a high-temperature environment for a long time. The acquisition unit can also acquire location information when the senior is in a noisy place. Furthermore, the acquisition unit can also acquire location information when the senior is in a humid place. This allows for more accurate detection of abnormalities in the range of movement by taking environmental information into consideration. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input environmental data about the senior to the generation AI and cause the generation AI to perform abnormality detection based on the environmental information.

[0074] The acquisition unit can estimate the senior's emotions and adjust the accuracy of the acquired location information based on the estimated senior's emotions. For example, the acquisition unit can increase the accuracy of the location information when the senior is feeling anxious. The acquisition unit can also decrease the accuracy of the location information when the senior is relaxed. Furthermore, the acquisition unit can set the accuracy of the location information to a medium level when the senior is excited. This enables more appropriate monitoring by adjusting the accuracy of the location information according to the senior's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the acquisition unit can input the senior's emotion data into the generation AI and cause the generation AI to adjust the accuracy of the location information based on the emotion.

[0075] The acquisition unit can prioritize acquiring location information in a specific area based on the senior's geographical location information. For example, when the senior is near his or her home, the acquisition unit can prioritize acquiring location information in that area. Furthermore, when the senior is in a park that the senior often visits, the acquisition unit can prioritize acquiring location information in that area. Furthermore, when the senior is in a commercial facility, the acquisition unit can prioritize acquiring location information in that area. Thus, by prioritizing acquisition of location information in a specific area, the senior's range of activity can be more accurately understood. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the senior's geographical location information to the generation AI and cause the generation AI to prioritize acquisition of location information in a specific area.

[0076] The acquisition unit can analyze the social media activity of the senior and acquire related location information. For example, the acquisition unit can acquire location information of places where the senior has checked in on social media. The acquisition unit can also acquire location information of photos posted by the senior on social media. Furthermore, the acquisition unit can acquire location information of places shared by the senior with friends on social media. In this way, related location information can be acquired by analyzing the social media activity of the senior. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the social media data of the senior to the generation AI and cause the generation AI to acquire related location information.

[0077] The acquisition unit can customize the acquisition method by reflecting the senior's past feedback. For example, if the senior has previously requested that the frequency of location information acquisition be increased, the acquisition unit can reflect that request. Furthermore, if the senior has previously requested that location information be acquired at a specific location, the acquisition unit can customize the acquisition method at that location. Furthermore, if the senior has previously requested that the accuracy of location information be increased, the acquisition unit can also reflect that request. In this way, the acquisition method can be customized by reflecting the senior's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the senior's past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0078] The analysis unit can estimate the senior's emotions and adjust the parameters of the analysis algorithm based on the estimated senior's emotions. For example, if the senior is feeling anxious, the analysis unit can increase the sensitivity of the analysis algorithm. Furthermore, if the senior is relaxed, the analysis unit can also decrease the sensitivity of the analysis algorithm. Furthermore, if the senior is excited, the analysis unit can set the sensitivity of the analysis algorithm to a medium level. This improves analysis accuracy by adjusting the parameters of the analysis algorithm according to the senior's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the senior's emotion data into the generation AI and cause the generation AI to adjust the parameters of the analysis algorithm based on the emotion.

[0079] The analysis unit can improve the accuracy of anomaly detection based on the senior's past behavioral data. The analysis unit can improve the accuracy of anomaly detection based on, for example, data on places the senior has visited in the past. The analysis unit can also improve the accuracy of anomaly detection by analyzing the senior's past movement patterns. Furthermore, the analysis unit can also improve the accuracy of anomaly detection by referring to the senior's past behavioral data. In this way, by referring to the senior's past behavioral data, the accuracy of anomaly detection is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the senior's past behavioral data into the generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0080] The analysis unit can learn the behavioral patterns of seniors in real time and optimize the analysis algorithm. The analysis unit can, for example, collect current behavioral data of seniors in real time and optimize the analysis algorithm. The analysis unit can also learn in real time and adjust the analysis algorithm when the behavioral patterns of seniors change. Furthermore, the analysis unit can analyze the behavioral data of seniors in real time and improve the accuracy of anomaly detection. In this way, the analysis algorithm can be optimized by learning the behavioral patterns of seniors in real time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the real-time behavioral data of seniors to a generation AI and cause the generation AI to optimize the analysis algorithm.

[0081] The analysis unit can integrate the senior's behavioral data and environmental data to improve the accuracy of anomaly detection. The analysis unit can, for example, integrate the senior's behavioral data and temperature data to improve the accuracy of anomaly detection. The analysis unit can also integrate the senior's behavioral data and humidity data to improve the accuracy of anomaly detection. Furthermore, the analysis unit can integrate the senior's behavioral data and noise data to improve the accuracy of anomaly detection. In this way, by integrating the senior's behavioral data and environmental data, the accuracy of anomaly detection is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the senior's behavioral data and environmental data into the generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0082] The analysis unit can estimate the senior's emotions and adjust the display method of the analysis results based on the estimated senior's emotions. For example, if the senior is feeling anxious, the analysis unit can provide a simple, highly visible display method. Furthermore, if the senior is relaxed, the analysis unit can provide a display method including detailed information. Furthermore, if the senior is excited, the analysis unit can provide a visually stimulating display method. This enables more appropriate information to be provided by adjusting the display method of the analysis results according to the senior's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the senior's emotion data into the generation AI and cause the generation AI to adjust the display method of the analysis results based on the emotion.

[0083] The analysis unit can geographically cluster the behavioral data of seniors to improve the accuracy of anomaly detection. For example, the analysis unit can geographically cluster the behavioral data of seniors to improve the accuracy of anomaly detection. The analysis unit can also geographically cluster the behavioral data of seniors to improve the anomaly detection algorithm. Furthermore, the analysis unit can geographically cluster the behavioral data of seniors to adjust anomaly detection parameters. In this way, the accuracy of anomaly detection is improved by geographically clustering the behavioral data of seniors. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the behavioral data of seniors to a generation AI and cause the generation AI to perform geographic clustering.

[0084] The analysis unit can improve the analysis algorithm by referring to the behavioral data of seniors and related literature. The analysis unit can, for example, improve the analysis algorithm by referring to the behavioral data of seniors and related literature. The analysis unit can also improve the accuracy of anomaly detection by referring to the behavioral data of seniors and related literature. Furthermore, the analysis unit can adjust the parameters of the analysis algorithm by referring to the behavioral data of seniors and related literature. In this way, the analysis algorithm can be improved by referring to the behavioral data of seniors and related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the behavioral data of seniors and related literature into the generation AI and cause the generation AI to improve the analysis algorithm.

[0085] The analysis unit can integrate senior behavioral data and market data to improve the accuracy of anomaly detection. For example, the analysis unit can integrate senior behavioral data and market data to improve the accuracy of anomaly detection. The analysis unit can also integrate senior behavioral data and market data to improve the anomaly detection algorithm. Furthermore, the analysis unit can integrate senior behavioral data and market data to adjust anomaly detection parameters. In this way, the accuracy of anomaly detection is improved by integrating senior behavioral data and market data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input senior behavioral data and market data into a generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0086] The detection unit can estimate the senior's emotions and adjust the anomaly detection criteria based on the estimated senior's emotions. For example, the detection unit can tighten the anomaly detection criteria when the senior is feeling anxious. The detection unit can also loosen the anomaly detection criteria when the senior is relaxed. Furthermore, the detection unit can set the anomaly detection criteria to a moderate level when the senior is excited. This enables more appropriate anomaly detection by adjusting the anomaly detection criteria according to the senior's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the detection unit can be performed using, for example, an AI, or without an AI. For example, the detection unit can input the senior's emotion data into the generation AI and cause the generation AI to adjust the anomaly detection criteria based on the emotion.

[0087] The detection unit can compare the senior's behavioral data with past abnormality cases to improve the accuracy of anomaly detection. For example, the detection unit can compare the senior's behavioral data with past abnormality cases to improve the accuracy of anomaly detection. The detection unit can also compare the senior's behavioral data with past abnormality cases to improve the anomaly detection algorithm. Furthermore, the detection unit can compare the senior's behavioral data with past abnormality cases to adjust the anomaly detection parameters. In this way, by comparing the senior's behavioral data with past abnormality cases, the accuracy of anomaly detection is improved. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the senior's behavioral data and past abnormality cases into a generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0088] The detection unit can monitor the behavioral data of seniors in real time and optimize the timing of anomaly detection. For example, the detection unit can monitor the behavioral data of seniors in real time and optimize the timing of anomaly detection. The detection unit can also monitor the behavioral data of seniors in real time and improve the anomaly detection algorithm. Furthermore, the detection unit can monitor the behavioral data of seniors in real time and adjust anomaly detection parameters. In this way, by monitoring the behavioral data of seniors in real time, the timing of anomaly detection can be optimized. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input the real-time behavioral data of seniors to a generation AI and cause the generation AI to optimize the timing of anomaly detection.

[0089] The detection unit can integrate the senior's behavioral data with environmental data to improve the accuracy of anomaly detection. The detection unit can, for example, integrate the senior's behavioral data with temperature data to improve the accuracy of anomaly detection. The detection unit can also integrate the senior's behavioral data with humidity data to improve the accuracy of anomaly detection. Furthermore, the detection unit can also integrate the senior's behavioral data with noise data to improve the accuracy of anomaly detection. In this way, by integrating the senior's behavioral data with environmental data, the accuracy of anomaly detection is improved. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the senior's behavioral data and environmental data into a generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0090] The detection unit can estimate the senior's emotions and determine the priority of anomaly detection based on the estimated senior's emotions. For example, if the senior is feeling anxious, the detection unit can set a high priority for anomaly detection. Furthermore, if the senior is relaxed, the detection unit can also set a low priority for anomaly detection. Furthermore, if the senior is excited, the detection unit can set a medium priority for anomaly detection. This enables a more appropriate response by determining the priority of anomaly detection according to the senior's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input the senior's emotion data into the generation AI and cause the generation AI to determine the priority of anomaly detection based on the emotion.

[0091] The detection unit can geographically cluster the behavioral data of seniors to improve the accuracy of anomaly detection. For example, the detection unit can geographically cluster the behavioral data of seniors to improve the accuracy of anomaly detection. The detection unit can also geographically cluster the behavioral data of seniors to improve the anomaly detection algorithm. Furthermore, the detection unit can geographically cluster the behavioral data of seniors to adjust anomaly detection parameters. In this way, by geographically clustering the behavioral data of seniors, the accuracy of anomaly detection is improved. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the behavioral data of seniors to a generation AI and cause the generation AI to perform geographic clustering.

[0092] The detection unit can improve the anomaly detection algorithm by referring to the behavioral data of seniors and related literature. The detection unit can, for example, improve the anomaly detection algorithm by referring to the behavioral data of seniors and related literature. The detection unit can also improve the accuracy of anomaly detection by referring to the behavioral data of seniors and related literature. Furthermore, the detection unit can adjust the anomaly detection parameters by referring to the behavioral data of seniors and related literature. In this way, the anomaly detection algorithm can be improved by referring to the behavioral data of seniors and related literature. Some or all of the above-described processing in the detection unit can be performed using AI, for example, or without AI. For example, the detection unit can input the behavioral data of seniors and related literature into the generation AI and cause the generation AI to improve the anomaly detection algorithm.

[0093] The detection unit can integrate senior behavioral data and market data to improve the accuracy of anomaly detection. For example, the detection unit can integrate senior behavioral data and market data to improve the accuracy of anomaly detection. The detection unit can also integrate senior behavioral data and market data to improve the anomaly detection algorithm. Furthermore, the detection unit can integrate senior behavioral data and market data to adjust anomaly detection parameters. In this way, by integrating senior behavioral data and market data, the accuracy of anomaly detection is improved. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input senior behavioral data and market data into a generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0094] The transmission unit can estimate the senior's emotions and adjust the content of the alert based on the estimated senior's emotions. For example, if the senior is feeling anxious, the transmission unit can provide detailed alert content. Furthermore, if the senior is relaxed, the transmission unit can also simplify the alert content. Furthermore, if the senior is excited, the transmission unit can set the alert content to a medium level. This allows for adjusting the alert content according to the senior's emotions, thereby enabling more appropriate information to be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can input the senior's emotion data into the generation AI and cause the generation AI to adjust the alert content based on the emotion.

[0095] The transmission unit can optimize the timing of sending an alert by referring to the senior's past behavioral data. For example, the transmission unit can transmit an alert during a time period when the senior has behaved abnormally in the past. The transmission unit can also set the optimal timing of sending an alert based on the senior's past behavioral data. Furthermore, if the senior's behavioral pattern changes, the transmission unit can adjust the timing of sending an alert based on the new pattern. In this way, the timing of sending an alert can be optimized by referring to the senior's past behavioral data. Some or all of the above-described processing in the transmission unit may be performed using AI, for example, or may be performed without using AI. For example, the transmission unit can input the senior's past behavioral data into the generation AI and cause the generation AI to optimize the timing of sending an alert.

[0096] The transmission unit can monitor the senior's behavioral data in real time and adjust the frequency of alert transmission. For example, the transmission unit can monitor the senior's behavioral data in real time and increase the frequency of alert transmission when abnormalities occur frequently. The transmission unit can also monitor the senior's behavioral data in real time and decrease the frequency of alert transmission when abnormalities are rare. Furthermore, the transmission unit can monitor the senior's behavioral data in real time and set the frequency of alert transmission to medium when abnormalities are moderate. In this way, the frequency of alert transmission can be adjusted by monitoring the senior's behavioral data in real time. Some or all of the above-described processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can input the senior's real-time behavioral data to a generation AI and cause the generation AI to adjust the frequency of alert transmission.

[0097] The transmitting unit can integrate the senior's behavioral data and environmental data to improve the accuracy of the alert. The transmitting unit can, for example, integrate the senior's behavioral data and temperature data to improve the accuracy of the alert. The transmitting unit can also integrate the senior's behavioral data and humidity data to improve the accuracy of the alert. Furthermore, the transmitting unit can also integrate the senior's behavioral data and noise data to improve the accuracy of the alert. In this way, by integrating the senior's behavioral data and environmental data, the accuracy of the alert is improved. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the senior's behavioral data and environmental data into the generating AI and cause the generating AI to improve the accuracy of the alert.

[0098] The transmission unit can estimate the senior's emotions and adjust the alert transmission method based on the estimated senior's emotions. For example, if the senior is feeling anxious, the transmission unit can transmit the alert by voice. If the senior is relaxed, the transmission unit can also transmit the alert by text. If the senior is excited, the transmission unit can also transmit the alert by video. This enables more appropriate information to be provided by adjusting the alert transmission method according to the senior's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can input the senior's emotion data into the generation AI and cause the generation AI to adjust the alert transmission method based on the emotion.

[0099] The transmitting unit can geographically cluster the behavioral data of seniors to improve the accuracy of alerts. For example, the transmitting unit can geographically cluster the behavioral data of seniors to improve the accuracy of alerts. The transmitting unit can also geographically cluster the behavioral data of seniors to improve the alert algorithm. Furthermore, the transmitting unit can geographically cluster the behavioral data of seniors to adjust the parameters of the alert. As a result, the accuracy of the alert is improved by geographically clustering the behavioral data of seniors. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or may be performed without using AI. For example, the transmitting unit can input the behavioral data of seniors to a generation AI and cause the generation AI to perform geographic clustering.

[0100] The transmission unit can improve the content of the alert by referring to the behavioral data of the senior and related literature. The transmission unit can improve the content of the alert by referring to, for example, the behavioral data of the senior and related literature. The transmission unit can also improve the accuracy of the alert by referring to the behavioral data of the senior and related literature. Furthermore, the transmission unit can adjust the parameters of the alert by referring to the behavioral data of the senior and related literature. In this way, the content of the alert can be improved by referring to the behavioral data of the senior and related literature. Some or all of the above-mentioned processing in the transmission unit can be performed using, for example, AI, or can be performed without using AI. For example, the transmission unit can input the behavioral data of the senior and related literature into the generation AI and cause the generation AI to improve the content of the alert.

[0101] The transmission unit can integrate the behavioral data of seniors with market data to improve the accuracy of alerts. For example, the transmission unit can integrate the behavioral data of seniors with market data to improve the accuracy of alerts. The transmission unit can also integrate the behavioral data of seniors with market data to improve the alert algorithm. Furthermore, the transmission unit can integrate the behavioral data of seniors with market data to adjust the parameters of the alert. In this way, the accuracy of the alert is improved by integrating the behavioral data of seniors with market data. Some or all of the above-described processing in the transmission unit may be performed using AI, for example, or may be performed without using AI. For example, the transmission unit can input the behavioral data of seniors and market data into a generation AI and cause the generation AI to improve the accuracy of the alert. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition unit, analysis unit, detection unit, and transmission 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 acquisition unit acquires the senior's location information using a GPS device or Wi-Fi location information of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the senior's behavioral patterns using a machine learning algorithm. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects abnormal movements of the senior. The transmission unit is realized, for example, by the control unit 46A of the smart device 14 and sends an alert to family members when an abnormality is detected. === Hard Collateral 1-2 === Each of the multiple elements including the acquisition unit, analysis unit, detection unit, and transmission 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 acquisition unit acquires the senior's location information using a GPS device or Wi-Fi location information of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the senior's behavioral patterns using a machine learning algorithm. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects abnormal movements of the senior. The transmission unit is realized, for example, by the control unit 46A of the smart glasses 214 and sends an alert to family members when an abnormality is detected. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, detection unit, and transmission unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit acquires location information of the senior using a GPS device or Wi-Fi location information of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the senior's behavioral patterns using a machine learning algorithm. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects abnormal movements of the senior. The transmission unit is realized, for example, by the control unit 46A of the headset type terminal 314 and sends an alert to family members when an abnormality is detected. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, analysis unit, detection unit, and transmission unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires location information of the senior using a GPS device or Wi-Fi location information of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the senior's behavioral patterns using a machine learning algorithm. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects abnormal movements of the senior. The transmission unit is realized, for example, by the control unit 46A of the robot 414 and sends an alert to family members when an abnormality is detected.

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

[0103] The acquisition unit can not only acquire the senior's location information but also simultaneously acquire the senior's health data. For example, the acquisition unit can acquire vital data such as the senior's heart rate, blood pressure, and body temperature. The acquisition unit can also acquire data such as the senior's sleep patterns and amount of exercise. Furthermore, the acquisition unit can also acquire data such as the senior's diet and water intake. This allows for a comprehensive understanding of the senior's health condition, and if an abnormality is detected, a prompt response can be made.

[0104] The analysis unit can not only analyze the behavioral patterns of seniors, but also analyze their health data. For example, the analysis unit can analyze vital data such as the senior's heart rate, blood pressure, and body temperature to detect abnormalities. The analysis unit can also analyze data such as the senior's sleep patterns and amount of exercise to evaluate their health condition. Furthermore, the analysis unit can analyze data such as the senior's diet and water intake to evaluate their nutritional status. This allows for a comprehensive evaluation of the senior's health condition, and if an abnormality is detected, a prompt response can be taken.

[0105] The detection unit can not only analyze the behavioral data of seniors, but also analyze their health data. For example, the detection unit can analyze vital data such as the senior's heart rate, blood pressure, and body temperature to detect abnormalities. The detection unit can also analyze data such as the senior's sleep patterns and amount of exercise to evaluate their health condition. Furthermore, the detection unit can analyze data such as the senior's diet and water intake to evaluate their nutritional status. This allows for a comprehensive evaluation of the senior's health condition, and if an abnormality is detected, a prompt response can be taken.

[0106] The transmitting unit can not only notify the senior's family of the senior's behavioral data and health data, but also the senior's medical institution. For example, the transmitting unit can transmit vital data such as the senior's heart rate, blood pressure, and body temperature to a medical institution. The transmitting unit can also transmit data such as the senior's sleep patterns and amount of exercise to a medical institution. Furthermore, the transmitting unit can also transmit data such as the senior's diet and water intake to a medical institution. This allows the senior's health condition to be shared with a medical institution, and if an abnormality is detected, the medical institution can respond quickly.

[0107] The acquisition unit can estimate the senior's emotions and adjust the frequency of acquiring location information based on the senior's estimated emotions. For example, if the senior is feeling anxious, the frequency of acquiring location information can be increased. Also, if the senior is relaxed, the frequency of acquiring location information can be reduced. Furthermore, if the senior is excited, the frequency of acquiring location information can be set to a medium level. This allows for more appropriate monitoring by adjusting the frequency of acquiring location information according to the senior's emotions.

[0108] The analysis unit can estimate the senior's emotions and adjust the parameters of the analysis algorithm based on the estimated senior's emotions. For example, if the senior is feeling anxious, the sensitivity of the analysis algorithm can be increased. Also, if the senior is relaxed, the sensitivity of the analysis algorithm can be decreased. Furthermore, if the senior is excited, the sensitivity of the analysis algorithm can be set to a medium level. In this way, by adjusting the parameters of the analysis algorithm according to the senior's emotions, the analysis accuracy can be improved.

[0109] The detection unit can estimate the senior's emotions and adjust the anomaly detection criteria based on the estimated senior's emotions. For example, if the senior is feeling anxious, the anomaly detection criteria can be set to be stricter. Also, if the senior is relaxed, the anomaly detection criteria can be set to be looser. Furthermore, if the senior is excited, the anomaly detection criteria can be set to be medium. In this way, by adjusting the anomaly detection criteria according to the senior's emotions, more appropriate anomaly detection becomes possible.

[0110] The transmission unit can estimate the senior's emotions and adjust the content of the alert based on the estimated senior's emotions. For example, if the senior is feeling anxious, the content of the alert can be detailed. If the senior is relaxed, the content of the alert can be simplified. Furthermore, if the senior is excited, the content of the alert can be set to a medium level. In this way, by adjusting the content of the alert according to the senior's emotions, more appropriate information can be provided.

[0111] The transmission unit can estimate the senior's emotions and adjust the alert transmission method based on the senior's estimated emotions. For example, if the senior is feeling anxious, the alert can be sent by voice. If the senior is relaxed, the alert can be sent by text. Furthermore, if the senior is excited, the alert can be sent by video. In this way, by adjusting the alert transmission method according to the senior's emotions, more appropriate information can be provided.

[0112] The acquisition unit can not only acquire the senior's location information, but also simultaneously acquire environmental information about the senior's surroundings. For example, the acquisition unit can acquire location information when the senior stays in a hot environment for a long time. The acquisition unit can also acquire location information when the senior is in a noisy place. Furthermore, the acquisition unit can also acquire location information when the senior is in a humid place. In this way, by taking environmental information into consideration, abnormalities in the range of movement can be detected more accurately.

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

[0114] Step 1: The acquisition unit acquires location information of the senior. For example, the acquisition unit can acquire location information using a GPS device or a smartphone. The acquisition unit can also acquire location information using Wi-Fi location information or cell tower data. Furthermore, the acquisition unit can periodically acquire location information to record the senior's daily range of movement. Step 2: The analysis unit analyzes the information acquired by the acquisition unit. For example, the analysis unit can analyze the behavioral patterns of the senior using a machine learning algorithm. It can also analyze the senior's current location information and compare it with the senior's normal range of movement. Furthermore, the analysis unit can learn the behavioral patterns of the senior to detect abnormal movements and improve the accuracy of anomaly detection. Step 3: The detection unit detects anomalies based on the information analyzed by the analysis unit. For example, it can detect when a senior stays in a place they normally don't visit for a long time, or when they deviate significantly from their normal route. It can also compare the senior's behavioral data with past anomaly cases to improve the accuracy of anomaly detection. It can also estimate the senior's emotions and adjust the anomaly detection criteria based on the estimated senior's emotions. Step 4: The transmitter notifies the family of any abnormalities detected by the detector. For example, alerts can be sent via smartphone notifications, email, or phone calls. The content of the alert can also be adjusted based on the senior's emotions. Furthermore, the accuracy of the alerts can be improved by integrating the senior's behavioral data with environmental data.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0117] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

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

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

[0126] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0129] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0145] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0152] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0158] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0159] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0160] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0168] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0170] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0171] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0172] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0176] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0177] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0178] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0179] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0180] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0181] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0183] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0184] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0185] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0186] [Explanation of symbols]

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

Claims

1. an acquisition unit that acquires location information; an analysis unit that analyzes the information acquired by the acquisition unit; a detection unit that detects an abnormality based on the information analyzed by the analysis unit; a transmitting unit that notifies family members of an abnormality detected by the detecting unit; Equipped with A system characterized by:

2. The acquisition unit Using a GPS device or smartphone 2. The system of claim 1.

3. The analysis unit Using machine learning algorithms 2. The system of claim 1.

4. The transmission unit Send alerts via smartphone notification, email, or phone call 2. The system of claim 1.

5. The acquisition unit Estimate the senior's emotions and adjust the frequency of location information acquisition based on the estimated senior's emotions.

2. The system of claim 1.

6. The acquisition unit Learn the past behavioral patterns of seniors and determine the best time to acquire them 2. The system of claim 1.

7. The acquisition unit Monitor the current health status of seniors and obtain location information if an abnormality is detected 2. The system of claim 1.

8. The acquisition unit Simultaneously acquires information about the senior's surrounding environment to more accurately detect abnormalities in their range of movement.

2. The system of claim 1.

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