System

Abstract

An object of a system according to an embodiment is to detect an abnormal behavior of a senior at an early stage and take an appropriate action.SOLUTION: A system includes an activity range storage unit, an abnormality detection unit, an alert transmission unit, and a police notification unit. The activity range storage unit stores a routine activity range of the senior. The abnormality detection unit detects an abnormal motion by comparing the activity range stored by the activity range storage unit with an actual activity. The alert transmitter transmits the abnormal motion detected by the abnormality detector to the family as an alert. The police notification unit transmits a notification to a nearby police when the family recognizes the abnormality.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem of making it difficult to detect abnormal behavior in seniors early and take appropriate action.

[0005] The system according to the embodiment aims to detect abnormal behavior of seniors at an early stage and take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a movement range storage unit, an abnormality detection unit, an alert transmission unit, and a police notification unit. The movement range storage unit stores the daily movement range of a senior. The abnormality detection unit detects abnormal behavior by comparing the movement range stored by the movement range storage unit with actual behavior. The alert transmission unit transmits an alert of the abnormal behavior detected by the abnormality detection unit to family members. The police notification unit transmits a notification to nearby police if the family members determine that there is an abnormality. [Effects of the Invention]

[0007] The system according to the embodiment can detect abnormal behavior of seniors at an early stage and take appropriate measures. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A senior safety management system according to an embodiment of the present invention is a system that stores the range of a senior's activities, sends an alert to family members when it detects abnormal behavior, and sends a notification to the nearby police if the family members recognize the behavior as abnormal. As a result, the senior safety management system can ensure the safety of seniors by quickly detecting abnormal behavior by seniors and notifying family members and the police.

[0029] A senior safety management system according to an embodiment includes an activity range storage unit, an abnormality detection unit, an alert transmission unit, and a police notification unit. The activity range storage unit stores the daily activity range of a senior. For example, the generation AI learns what places a senior usually visits and what activities they do at what time of day. The generation AI stores the activity range based on the senior's behavioral data. The anomaly detection unit compares the stored activity range with the senior's actual behavior to detect abnormal behavior. For example, the generation AI determines an abnormality when a senior stays in a place they normally do not visit for a long time or when their behavior deviates significantly from their normal behavioral pattern. The alert transmission unit sends an alert to the family regarding the abnormal behavior detected by the anomaly detection unit. For example, the generation AI sends an alert to the family including the senior's current location information and details of the behavior determined to be abnormal. The police notification unit sends a notification to the nearby police station if the family determines an abnormality. For example, the generation AI sends a notification to the police including the senior's current location information and details of the behavior determined to be abnormal. This allows the senior safety management system to quickly detect any abnormal movements of seniors and notify their families and the police, thereby ensuring the safety of seniors.

[0030] The activity range memory unit simultaneously learns the senior's health condition and mood fluctuations, making it possible to identify factors that influence behavioral patterns. For example, when the generation AI records the senior's activity range, the activity range memory unit monitors the senior's health condition (e.g., heart rate and blood pressure) in real time and reflects this data in the behavioral pattern. This identifies the impact of health condition on behavior. The generation AI also analyzes the senior's diary and voice memos to record the senior's mood fluctuations and reflects the mood fluctuations in the behavioral pattern. For example, it identifies behavioral patterns when the senior is feeling stressed. When recording the senior's activity range, the generation AI also collects the senior's dietary and sleep data and analyzes the impact these have on behavioral patterns. For example, it identifies the impact of lack of sleep on behavior. This makes it possible to identify behavioral patterns that take into account the senior's health condition and mood fluctuations.

[0031] The activity range memory unit can compare a senior's past activity history with their current activity to detect changes in their long-term activity patterns. For example, the generation AI stores the senior's past activity history in a database and compares it with their current activity to detect changes in their long-term activity patterns. For example, it analyzes changes based on activity data from the past year. The generation AI also analyzes the senior's activity history on a monthly basis to identify changes in seasonal activity patterns. For example, it detects a trend toward fewer outings in the winter. The generation AI also analyzes the senior's activity history on a weekly basis to identify changes in their weekly activity patterns. For example, it detects a trend toward an increased frequency of visiting certain places on weekends. This makes it possible to detect changes in the senior's long-term activity patterns.

[0032] The activity range memory unit simultaneously learns the activity ranges of the senior's friends and family, and can memorize behavioral patterns that take social connections into account. For example, the generation AI simultaneously records the activity ranges of the senior's friends and family, and reflects social connections in the senior's behavioral patterns. For example, it records the time the senior spends with friends. In addition, when recording the senior's activity range, it identifies joint activities with family and reflects that data in the behavioral patterns. For example, it records how often the senior goes shopping with family. In addition, the generation AI analyzes the behavioral data of the senior's friends and family, and makes adjustments to the senior's behavioral patterns that take social connections into account. For example, it records regular gatherings with friends. This makes it possible to memorize behavioral patterns that take the senior's social connections into account.

[0033] The activity range memory unit can also integrate data from devices used by seniors and store more detailed activity patterns. In the activity range memory unit, for example, the generation AI collects location data from the senior's smartphone and records the activity range. For example, it analyzes the senior's movement route based on GPS data. The generation AI also collects health data (e.g., number of steps and heart rate) from wearable devices and reflects this in the activity pattern. For example, it records changes in exercise volume. The generation AI also integrates data from devices used by seniors and stores more detailed activity patterns. For example, it analyzes daily activity levels based on data from a smartwatch. This makes it possible to store detailed activity patterns that integrate data from devices used by seniors.

[0034] The anomaly detection unit can determine abnormalities by taking into account not only changes in a senior's behavioral patterns, but also changes in the environment. For example, when the generation AI analyzes a senior's behavioral patterns, the anomaly detection unit integrates weather data and detects abnormal movements. For example, going out in bad weather is determined to be abnormal. The generation AI also collects traffic condition data and reflects it in the senior's behavioral patterns. For example, behavior that significantly exceeds the normal travel time during traffic jams is determined to be abnormal. The generation AI also corrects the senior's behavioral patterns by taking into account changes in the environment (for example, the change of seasons). For example, it detects abnormalities based on changes in behavioral patterns depending on the season. This makes it possible to detect abnormalities that take environmental changes into account.

[0035] The anomaly detection unit can analyze senior behavioral data in real time, enabling early detection of abnormalities. For example, the anomaly detection unit builds a system in which a generation AI collects senior behavioral data in real time and immediately detects abnormal movements. For example, it analyzes GPS data in real time. It also analyzes senior behavioral data in real time and develops an algorithm for early detection of abnormalities. For example, it immediately detects deviations from normal behavioral patterns. It also builds a system in which a generation AI monitors senior behavioral data in real time and early detection of abnormal movements. For example, it immediately identifies abnormal movement routes. This enables early detection of abnormalities in real time.

[0036] The anomaly detection unit can integrate the senior's behavioral data with other datasets to improve the accuracy of detecting abnormalities. For example, the generation AI integrates the senior's health data (e.g., heart rate and blood pressure) to improve the accuracy of detecting abnormal behavior. For example, it determines abnormalities based on changes in health status. The generation AI also collects the senior's social activity data (e.g., events attended and volunteer activities) and uses this as reference when detecting abnormal behavior. For example, it determines abnormalities based on the frequency of social activities. The generation AI also integrates the senior's behavioral data with other datasets (e.g., dietary data and sleep data) to improve the accuracy of detecting abnormalities. For example, it determines abnormalities based on dietary and sleep patterns. In this way, by integrating with other datasets, the accuracy of detecting abnormalities is improved.

[0037] The alert sending unit can notify family members of not only the senior's current location information, but also past behavioral history and factors that may be causing the abnormality. For example, when the generation AI sends an alert to family members, the alert sending unit includes the senior's current location information and past behavioral history. For example, it sends an alert with the content, "Dad has been staying in a place he doesn't usually go for a long time. Please check his behavioral history for the past week." It also notifies family members of factors that may be causing the abnormality (for example, changes in health status or environment). For example, it sends an alert with the content, "Dad's heart rate is rising sharply. He is exhibiting abnormal behavior." It also notifies family members of the senior's current location information and factors that may be causing the abnormality. For example, it sends an alert with the content, "Dad has been staying in a place he doesn't usually go for a long time. Please check on him as the weather is getting worse." This allows family members to receive detailed information and take appropriate action.

[0038] The alert sending unit can provide the content of the alert in a format that is easy for family members to understand (for example, a visual map or graph). For example, when the generation AI sends an alert to family members, the alert sending unit displays the senior's current location information on a visual map. For example, it displays the senior's current location and details of abnormal behavior on a map. It also provides the content of the alert in graph format to enable family members to intuitively understand abnormal behavior. For example, it displays the frequency and time period of abnormal behavior in a graph. It also displays the senior's behavior pattern on a visual map or graph when the generation AI sends an alert to family members, to make abnormal behavior easier to understand. For example, it provides a graph that compares past behavior history with current abnormal behavior. This allows family members to intuitively understand the content of the alert.

[0039] The alert sending unit can send notifications not only to family members but also to the senior's friends and neighbors, promoting support throughout the community. For example, the alert sending unit can build a system in which, when the generation AI sends an alert to family members, it simultaneously sends notifications to the senior's friends and neighbors. For example, it can notify the senior's friends and neighbors of details of abnormal behavior. It can also send alerts not only to family members but also to the senior's friends and neighbors, promoting support throughout the community. For example, if a senior stays in a place they don't usually go to for a long time, it can ask neighbors to check on them. It can also build a system in which, when the generation AI sends an alert to family members, it can also send notifications to the senior's friends and neighbors, promoting support throughout the community. For example, if a senior gets lost, it can ask neighbors to search for them. This makes it possible to promote support throughout the community.

[0040] The alert sending unit can provide the content of the alert in multiple languages, making it possible to accommodate family members who speak different languages. For example, the alert sending unit builds a system in which, when the generation AI sends an alert to a family member, the content of the alert is provided in multiple languages. For example, alerts are sent in multiple languages, such as English, Spanish, and Chinese. In addition, a function for automatically translating the content of the alert is added to accommodate family members who speak different languages. For example, the alert is translated according to the family member's language setting. In addition, a system is built in which, when the generation AI sends an alert to a family member, the content of the alert is provided in multiple languages, making it possible to accommodate family members who speak different languages. For example, the alert is sent in a language selected by the family member. This makes it possible to accommodate family members who speak different languages.

[0041] The police notification unit can notify the police not only of the senior's current location information, but also of his / her past behavioral history and factors that may be causing the abnormality. For example, when the generation AI sends a notification to the police, the police notification unit includes the senior's current location information and past behavioral history. For example, it sends a notification with content such as, "Your father has been staying in a place he / she doesn't usually go for a long time. Please check his / her behavioral history over the past week." It also notifies the police with factors that may be causing the abnormality (for example, changes in health condition or environment). For example, it sends a notification with content such as, "Your father's heart rate is rising sharply. He / she is exhibiting abnormal behavior." It also notifies the police with the senior's current location information and factors that may be causing the abnormality. For example, it sends a notification with content such as, "Your father has been staying in a place he / she doesn't usually go for a long time. Please check on him / her as the weather is getting worse." This allows the police to receive detailed information, enabling a prompt response.

[0042] The police notification unit can adapt the content of the notification to the police's work flow, enabling a rapid response. For example, when the generation AI sends a notification to the police, the police notification unit builds a system that adapts the content of the notification to the police's work flow. For example, it sends the notification directly to the police's system. In addition, the notification format is standardized to adapt the content of the notification to the police's work flow and enable a rapid response. For example, it uses a notification format that includes the information required by the police. In addition, when the generation AI sends a notification to the police, the unit builds a system that adapts the content of the notification to the police's work flow, enabling a rapid response. For example, it works in conjunction with the police's system and automatically sends the notification. This adapts the content to the police's work flow, enabling a rapid response.

[0043] The police notification unit can send notifications not only to the police but also to local medical institutions and welfare facilities, allowing for comprehensive support. For example, the police notification unit will build a system in which, when the generation AI sends a notification to the police, it simultaneously sends notifications to local medical institutions and welfare facilities. For example, it will notify medical institutions and welfare facilities of abnormal behavior by a senior. It will also send notifications not only to the police but also to local medical institutions and welfare facilities, providing comprehensive support. For example, if a senior has a health problem, it will request a medical institution to respond quickly. It will also build a system in which, when the generation AI sends a notification to the police, it will send notifications to local medical institutions and welfare facilities, providing comprehensive support. For example, if a senior gets lost, it will request a search from a medical institution or welfare facility. This will make it possible to provide comprehensive support in cooperation with local medical institutions and welfare facilities.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The senior safety management system can further include an environmental sensor unit. The environmental sensor unit can collect environmental data (e.g., temperature, humidity, and illuminance) around the senior in real time and detect abnormal environmental changes. For example, if the temperature in the room rises suddenly, the environmental sensor unit detects the abnormality and sends an alert to the family. The environmental sensor unit can also detect abnormalities if the humidity becomes abnormally high or if the illuminance changes suddenly. This enables early detection of abnormalities based on changes in the environment around the senior.

[0046] The senior safety management system can further include a behavior prediction unit. The behavior prediction unit predicts future behavior based on the senior's past behavior data and can detect abnormalities by comparing the predicted behavior with actual behavior. For example, if a senior has the habit of visiting a specific place on a specific day of the week every week, that behavior is predicted, but if the senior does not actually visit the place, it is determined to be abnormal. The behavior prediction unit can also analyze changes in the senior's behavior patterns over the long term and detect discrepancies between predicted behavior and actual behavior. This enables early detection of abnormalities based on the senior's future behavior.

[0047] The senior safety management system can further include a health monitoring unit. The health monitoring unit can collect the senior's health data (e.g., heart rate, blood pressure, body temperature) in real time and detect abnormal health conditions. For example, if the heart rate suddenly rises, the health monitoring unit detects the abnormality and sends an alert to family members. The health monitoring unit can also detect abnormalities if the blood pressure becomes abnormally high or if the body temperature changes suddenly. This enables early detection of abnormalities based on the senior's health condition.

[0048] The senior safety management system can further include a behavior prediction unit. The behavior prediction unit predicts future behavior based on the senior's past behavior data and can detect abnormalities by comparing the predicted behavior with actual behavior. For example, if a senior has the habit of visiting a specific place on a specific day of the week every week, that behavior is predicted, but if the senior does not actually visit the place, it is determined to be abnormal. The behavior prediction unit can also analyze changes in the senior's behavior patterns over the long term and detect discrepancies between predicted behavior and actual behavior. This enables early detection of abnormalities based on the senior's future behavior.

[0049] The senior safety management system can further include a behavior prediction unit. The behavior prediction unit predicts future behavior based on the senior's past behavior data and can detect abnormalities by comparing the predicted behavior with actual behavior. For example, if a senior has the habit of visiting a specific place on a specific day of the week every week, that behavior is predicted, but if the senior does not actually visit the place, it is determined to be abnormal. The behavior prediction unit can also analyze changes in the senior's behavior patterns over the long term and detect discrepancies between predicted behavior and actual behavior. This enables early detection of abnormalities based on the senior's future behavior.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The activity range memory stores the senior's daily activity range. For example, the generation AI learns what places the senior usually visits and what activities they do at what times, and stores the activity range based on the senior's behavioral data. Step 2: The anomaly detection unit compares the memorized range of movement with actual behavior to detect abnormal movements. For example, the generation AI will determine that an abnormality exists if a senior stays for a long time in a place they don't usually go to, or if their movements deviate significantly from their normal behavioral patterns. Step 3: The alert sending unit sends an alert to the family about the abnormal behavior detected by the anomaly detection unit. For example, the generation AI sends an alert to the family including the senior's current location information and details of the behavior that was determined to be abnormal. Step 4: The police notification unit sends a notification to the local police if the family member detects something unusual. For example, the generation AI sends a notification to the police that includes the senior's current location and details of the behavior that was deemed abnormal.

[0052] (Example 2) A senior safety management system according to an embodiment of the present invention is a system that stores the range of a senior's activities, sends an alert to family members when it detects abnormal behavior, and sends a notification to the nearby police if the family members recognize the behavior as abnormal. As a result, the senior safety management system can ensure the safety of seniors by quickly detecting abnormal behavior by seniors and notifying family members and the police.

[0053] A senior safety management system according to an embodiment includes an activity range storage unit, an abnormality detection unit, an alert transmission unit, and a police notification unit. The activity range storage unit stores the daily activity range of a senior. For example, the generation AI learns what places a senior usually visits and what activities they do at what time of day. The generation AI stores the activity range based on the senior's behavioral data. The anomaly detection unit compares the stored activity range with the senior's actual behavior to detect abnormal behavior. For example, the generation AI determines an abnormality when a senior stays in a place they normally do not visit for a long time or when their behavior deviates significantly from their normal behavioral pattern. The alert transmission unit sends an alert to the family regarding the abnormal behavior detected by the anomaly detection unit. For example, the generation AI sends an alert to the family including the senior's current location information and details of the behavior determined to be abnormal. The police notification unit sends a notification to the nearby police station if the family determines an abnormality. For example, the generation AI sends a notification to the police including the senior's current location information and details of the behavior determined to be abnormal. This allows the senior safety management system to quickly detect any abnormal movements of seniors and notify their families and the police, thereby ensuring the safety of seniors.

[0054] The activity range memory unit simultaneously learns the senior's health condition and mood fluctuations, making it possible to identify factors that influence behavioral patterns. For example, when the generation AI records the senior's activity range, the activity range memory unit monitors the senior's health condition (e.g., heart rate and blood pressure) in real time and reflects this data in the behavioral pattern. This identifies the impact of health condition on behavior. The generation AI also analyzes the senior's diary and voice memos to record the senior's mood fluctuations and reflects the mood fluctuations in the behavioral pattern. For example, it identifies behavioral patterns when the senior is feeling stressed. When recording the senior's activity range, the generation AI also collects the senior's dietary and sleep data and analyzes the impact these have on behavioral patterns. For example, it identifies the impact of lack of sleep on behavior. This makes it possible to identify behavioral patterns that take into account the senior's health condition and mood fluctuations.

[0055] The activity range memory unit can compare a senior's past activity history with their current activity to detect changes in their long-term activity patterns. For example, the generation AI stores the senior's past activity history in a database and compares it with their current activity to detect changes in their long-term activity patterns. For example, it analyzes changes based on activity data from the past year. The generation AI also analyzes the senior's activity history on a monthly basis to identify changes in seasonal activity patterns. For example, it detects a trend toward fewer outings in the winter. The generation AI also analyzes the senior's activity history on a weekly basis to identify changes in their weekly activity patterns. For example, it detects a trend toward an increased frequency of visiting certain places on weekends. This makes it possible to detect changes in the senior's long-term activity patterns.

[0056] When recording a senior's range of activities using the emotion estimation function, the activity range memory unit can correct the behavioral patterns based on the senior's emotional state, allowing for more accurate storage of the range of activities. For example, the generation AI in the activity range memory unit analyzes the senior's emotional state in real time and corrects the behavioral patterns based on that data. For example, it identifies and records the behavioral patterns of when the senior is feeling anxious. In addition, when recording a senior's range of activities using the emotion estimation function, it identifies behavioral changes that correspond to the emotional state. For example, it records the behavioral patterns of when the senior is feeling happy. In addition, an algorithm is developed that corrects the behavioral patterns based on the senior's emotional state, allowing the generation AI to store a more accurate range of activities. For example, it adjusts the behavioral patterns based on the emotion score. This makes it possible to store an accurate range of activities that takes the senior's emotional state into account.

[0057] The activity range memory unit simultaneously learns the activity ranges of the senior's friends and family, and can memorize behavioral patterns that take social connections into account. For example, the generation AI simultaneously records the activity ranges of the senior's friends and family, and reflects social connections in the senior's behavioral patterns. For example, it records the time the senior spends with friends. In addition, when recording the senior's activity range, it identifies joint activities with family and reflects that data in the behavioral patterns. For example, it records how often the senior goes shopping with family. In addition, the generation AI analyzes the behavioral data of the senior's friends and family, and makes adjustments to the senior's behavioral patterns that take social connections into account. For example, it records regular gatherings with friends. This makes it possible to memorize behavioral patterns that take the senior's social connections into account.

[0058] The activity range memory unit can also integrate data from devices used by seniors and store more detailed activity patterns. In the activity range memory unit, for example, the generation AI collects location data from the senior's smartphone and records the activity range. For example, it analyzes the senior's movement route based on GPS data. The generation AI also collects health data (e.g., number of steps and heart rate) from wearable devices and reflects this in the activity pattern. For example, it records changes in exercise volume. The generation AI also integrates data from devices used by seniors and stores more detailed activity patterns. For example, it analyzes daily activity levels based on data from a smartwatch. This makes it possible to store detailed activity patterns that integrate data from devices used by seniors.

[0059] The anomaly detection unit can determine abnormalities by taking into account not only changes in a senior's behavioral patterns, but also changes in the environment. For example, when the generation AI analyzes a senior's behavioral patterns, the anomaly detection unit integrates weather data and detects abnormal movements. For example, going out in bad weather is determined to be abnormal. The generation AI also collects traffic condition data and reflects it in the senior's behavioral patterns. For example, behavior that significantly exceeds the normal travel time during traffic jams is determined to be abnormal. The generation AI also corrects the senior's behavioral patterns by taking into account changes in the environment (for example, the change of seasons). For example, it detects abnormalities based on changes in behavioral patterns depending on the season. This makes it possible to detect abnormalities that take environmental changes into account.

[0060] The anomaly detection unit can analyze senior behavioral data in real time, enabling early detection of abnormalities. For example, the anomaly detection unit builds a system in which a generation AI collects senior behavioral data in real time and immediately detects abnormal movements. For example, it analyzes GPS data in real time. It also analyzes senior behavioral data in real time and develops an algorithm for early detection of abnormalities. For example, it immediately detects deviations from normal behavioral patterns. It also builds a system in which a generation AI monitors senior behavioral data in real time and early detection of abnormal movements. For example, it immediately identifies abnormal movement routes. This enables early detection of abnormalities in real time.

[0061] The anomaly detection unit uses the emotion estimation function to detect abnormal behavior based on the emotional state of seniors, enabling early detection of emotionally unstable states. For example, the anomaly detection unit uses a generative AI to analyze the emotional state of seniors in real time and build a system that detects emotionally unstable states early. For example, it judges behavior when the emotion score is low as abnormal. In addition, it uses the emotion estimation function to develop an algorithm that detects abnormal behavior based on the emotional state of seniors. For example, it judges sudden changes in emotion as abnormal. In addition, it builds a system that monitors the emotional state of seniors in real time and detects emotionally unstable states early. For example, it detects abnormalities based on fluctuations in emotion scores. This makes it possible to detect emotionally unstable states early.

[0062] The anomaly detection unit can integrate the senior's behavioral data with other datasets to improve the accuracy of detecting abnormalities. For example, the generation AI integrates the senior's health data (e.g., heart rate and blood pressure) to improve the accuracy of detecting abnormal behavior. For example, it determines abnormalities based on changes in health status. The generation AI also collects the senior's social activity data (e.g., events attended and volunteer activities) and uses this as reference when detecting abnormal behavior. For example, it determines abnormalities based on the frequency of social activities. The generation AI also integrates the senior's behavioral data with other datasets (e.g., dietary data and sleep data) to improve the accuracy of detecting abnormalities. For example, it determines abnormalities based on dietary and sleep patterns. In this way, by integrating with other datasets, the accuracy of detecting abnormalities is improved.

[0063] The alert sending unit can notify family members of not only the senior's current location information, but also past behavioral history and factors that may be causing the abnormality. For example, when the generation AI sends an alert to family members, the alert sending unit includes the senior's current location information and past behavioral history. For example, it sends an alert with the content, "Dad has been staying in a place he doesn't usually go for a long time. Please check his behavioral history for the past week." It also notifies family members of factors that may be causing the abnormality (for example, changes in health status or environment). For example, it sends an alert with the content, "Dad's heart rate is rising sharply. He is exhibiting abnormal behavior." It also notifies family members of the senior's current location information and factors that may be causing the abnormality. For example, it sends an alert with the content, "Dad has been staying in a place he doesn't usually go for a long time. Please check on him as the weather is getting worse." This allows family members to receive detailed information and take appropriate action.

[0064] The alert sending unit can provide the content of the alert in a format that is easy for family members to understand (for example, a visual map or graph). For example, when the generation AI sends an alert to family members, the alert sending unit displays the senior's current location information on a visual map. For example, it displays the senior's current location and details of abnormal behavior on a map. It also provides the content of the alert in graph format to enable family members to intuitively understand abnormal behavior. For example, it displays the frequency and time period of abnormal behavior in a graph. It also displays the senior's behavior pattern on a visual map or graph when the generation AI sends an alert to family members, to make abnormal behavior easier to understand. For example, it provides a graph that compares past behavior history with current abnormal behavior. This allows family members to intuitively understand the content of the alert.

[0065] The alert sending unit uses the emotion estimation function to determine the urgency of the alert based on the senior's emotional state and can urge family members to take an appropriate action. For example, the alert sending unit uses a generation AI to analyze the senior's emotional state in real time and determine the urgency of the alert based on the emotion score. For example, if the emotion score is low, it sends a high-urgency alert. The emotion estimation function also determines the urgency of the alert based on the senior's emotional state and urges family members to take an appropriate action. For example, if a senior is feeling anxious, it sends an alert urging family members to take a prompt action. A system is also constructed that monitors the senior's emotional state in real time and determines the urgency of the alert based on the emotion score. For example, if the emotion score drops suddenly, it sends a high-urgency alert. This makes it possible to determine the urgency of the alert based on the senior's emotional state and urge family members to take an appropriate action.

[0066] The alert sending unit can send notifications not only to family members but also to the senior's friends and neighbors, promoting support throughout the community. For example, the alert sending unit can build a system in which, when the generation AI sends an alert to family members, it simultaneously sends notifications to the senior's friends and neighbors. For example, it can notify the senior's friends and neighbors of details of abnormal behavior. It can also send alerts not only to family members but also to the senior's friends and neighbors, promoting support throughout the community. For example, if a senior stays in a place they don't usually go to for a long time, it can ask neighbors to check on them. It can also build a system in which, when the generation AI sends an alert to family members, it can also send notifications to the senior's friends and neighbors, promoting support throughout the community. For example, if a senior gets lost, it can ask neighbors to search for them. This makes it possible to promote support throughout the community.

[0067] The alert sending unit can provide the content of the alert in multiple languages, making it possible to accommodate family members who speak different languages. For example, the alert sending unit builds a system in which, when the generation AI sends an alert to a family member, the content of the alert is provided in multiple languages. For example, alerts are sent in multiple languages, such as English, Spanish, and Chinese. In addition, a function for automatically translating the content of the alert is added to accommodate family members who speak different languages. For example, the alert is translated according to the family member's language setting. In addition, a system is built in which, when the generation AI sends an alert to a family member, the content of the alert is provided in multiple languages, making it possible to accommodate family members who speak different languages. For example, the alert is sent in a language selected by the family member. This makes it possible to accommodate family members who speak different languages.

[0068] The police notification unit can notify the police not only of the senior's current location information, but also of his / her past behavioral history and factors that may be causing the abnormality. For example, when the generation AI sends a notification to the police, the police notification unit includes the senior's current location information and past behavioral history. For example, it sends a notification with content such as, "Your father has been staying in a place he / she doesn't usually go for a long time. Please check his / her behavioral history over the past week." It also notifies the police with factors that may be causing the abnormality (for example, changes in health condition or environment). For example, it sends a notification with content such as, "Your father's heart rate is rising sharply. He / she is exhibiting abnormal behavior." It also notifies the police with the senior's current location information and factors that may be causing the abnormality. For example, it sends a notification with content such as, "Your father has been staying in a place he / she doesn't usually go for a long time. Please check on him / her as the weather is getting worse." This allows the police to receive detailed information, enabling a prompt response.

[0069] The police notification unit can adapt the content of the notification to the police's work flow, enabling a rapid response. For example, when the generation AI sends a notification to the police, the police notification unit builds a system that adapts the content of the notification to the police's work flow. For example, it sends the notification directly to the police's system. In addition, the notification format is standardized to adapt the content of the notification to the police's work flow and enable a rapid response. For example, it uses a notification format that includes the information required by the police. In addition, when the generation AI sends a notification to the police, the unit builds a system that adapts the content of the notification to the police's work flow, enabling a rapid response. For example, it works in conjunction with the police's system and automatically sends the notification. This adapts the content to the police's work flow, enabling a rapid response.

[0070] The police notification unit uses the emotion estimation function to determine the urgency of the notification based on the senior's emotional state and urges the police to take an appropriate action. For example, the police notification unit uses a generation AI to analyze the senior's emotional state in real time and determine the urgency of the notification based on the emotion score. For example, if the emotion score is low, a high-urgency notification is sent to the police. The emotion estimation function also determines the urgency of the notification based on the senior's emotional state and urges the police to take an appropriate action. For example, if a senior is feeling anxious, a notification is sent to the police urging them to take a prompt action. A system is also constructed that monitors the senior's emotional state in real time and determines the urgency of the notification based on the emotion score. For example, if the emotion score drops suddenly, a high-urgency notification is sent to the police. This makes it possible to determine the urgency of the notification based on the senior's emotional state and urge the police to take an appropriate action.

[0071] The police notification unit can send notifications not only to the police but also to local medical institutions and welfare facilities, allowing for comprehensive support. For example, the police notification unit will build a system in which, when the generation AI sends a notification to the police, it simultaneously sends notifications to local medical institutions and welfare facilities. For example, it will notify medical institutions and welfare facilities of abnormal behavior by a senior. It will also send notifications not only to the police but also to local medical institutions and welfare facilities, providing comprehensive support. For example, if a senior has a health problem, it will request a medical institution to respond quickly. It will also build a system in which, when the generation AI sends a notification to the police, it will send notifications to local medical institutions and welfare facilities, providing comprehensive support. For example, if a senior gets lost, it will request a search from a medical institution or welfare facility. This will make it possible to provide comprehensive support in cooperation with local medical institutions and welfare facilities.

[0072] The police notification unit uses the emotion estimation function to determine the urgency of the notification based on the senior's emotional state and urges the police to take an appropriate action. For example, the police notification unit uses a generation AI to analyze the senior's emotional state in real time and determine the urgency of the notification based on the emotion score. For example, if the emotion score is low, a high-urgency notification is sent to the police. The emotion estimation function also determines the urgency of the notification based on the senior's emotional state and urges the police to take an appropriate action. For example, if a senior is feeling anxious, a notification is sent to the police urging them to take a prompt action. A system is also constructed that monitors the senior's emotional state in real time and determines the urgency of the notification based on the emotion score. For example, if the emotion score drops suddenly, a high-urgency notification is sent to the police. This makes it possible to determine the urgency of the notification based on the senior's emotional state and urge the police to take an appropriate action.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The senior safety management system can further include a voice recognition unit. The voice recognition unit can analyze the senior's voice in real time and detect abnormal statements or calls for help. For example, if a senior calls out "help," the voice recognition unit detects the statement and sends an alert to family members or the police. The voice recognition unit can also detect an abnormality if the senior speaks in a tone or content that differs from normal conversation. For example, if the senior is confused or angry, the system can analyze the emotion from the voice and determine that an abnormality has occurred. This enables early detection of abnormalities based on the senior's voice.

[0075] The senior safety management system can further include an environmental sensor unit. The environmental sensor unit can collect environmental data (e.g., temperature, humidity, and illuminance) around the senior in real time and detect abnormal environmental changes. For example, if the temperature in the room rises suddenly, the environmental sensor unit detects the abnormality and sends an alert to the family. The environmental sensor unit can also detect abnormalities if the humidity becomes abnormally high or if the illuminance changes suddenly. This enables early detection of abnormalities based on changes in the environment around the senior.

[0076] The senior safety management system can further include a behavior prediction unit. The behavior prediction unit predicts future behavior based on the senior's past behavior data and can detect abnormalities by comparing the predicted behavior with actual behavior. For example, if a senior has the habit of visiting a specific place on a specific day of the week every week, that behavior is predicted, but if the senior does not actually visit the place, it is determined to be abnormal. The behavior prediction unit can also analyze changes in the senior's behavior patterns over the long term and detect discrepancies between predicted behavior and actual behavior. This enables early detection of abnormalities based on the senior's future behavior.

[0077] The Senior Safety Management System also uses an emotion estimation function to correct behavioral patterns based on the emotional state of the senior, allowing it to memorize a more accurate range of behavior. For example, it identifies and records behavioral patterns when a senior is feeling anxious. In addition, the emotion estimation function is used to identify changes in behavior according to the senior's emotional state when recording the senior's range of behavior. For example, it records behavioral patterns when a senior is feeling happy. In addition, an algorithm has been developed to correct behavioral patterns based on the senior's emotional state, allowing the generation AI to memorize a more accurate range of behavior. For example, it adjusts behavioral patterns based on emotion scores. This makes it possible to memorize an accurate range of behavior that takes the senior's emotional state into account.

[0078] The senior safety management system can further include a health monitoring unit. The health monitoring unit can collect the senior's health data (e.g., heart rate, blood pressure, body temperature) in real time and detect abnormal health conditions. For example, if the heart rate suddenly rises, the health monitoring unit detects the abnormality and sends an alert to family members. The health monitoring unit can also detect abnormalities if the blood pressure becomes abnormally high or if the body temperature changes suddenly. This enables early detection of abnormalities based on the senior's health condition.

[0079] The senior safety management system can also use an emotion estimation function to detect abnormal behavior based on the emotional state of seniors, enabling early detection of emotionally unstable states. For example, a system can be built in which the generative AI analyzes the emotional state of seniors in real time and detects emotionally unstable states early. For example, behavior when the emotion score is low can be judged as abnormal. In addition, an algorithm can be developed using the emotion estimation function to detect abnormal behavior based on the emotional state of seniors. For example, sudden changes in emotion can be judged as abnormal. In addition, a system can be built in which the emotional state of seniors can be monitored in real time and emotionally unstable states can be detected early. For example, abnormalities can be detected based on fluctuations in the emotion score. This makes it possible to detect emotionally unstable states early.

[0080] The senior safety management system can further include a behavior prediction unit. The behavior prediction unit predicts future behavior based on the senior's past behavior data and can detect abnormalities by comparing the predicted behavior with actual behavior. For example, if a senior has the habit of visiting a specific place on a specific day of the week every week, that behavior is predicted, but if the senior does not actually visit the place, it is determined to be abnormal. The behavior prediction unit can also analyze changes in the senior's behavior patterns over the long term and detect discrepancies between predicted behavior and actual behavior. This enables early detection of abnormalities based on the senior's future behavior.

[0081] The senior safety management system also uses an emotion estimation function to determine the urgency of the alert based on the senior's emotional state, and can prompt family members to take an appropriate action. For example, the generation AI analyzes the senior's emotional state in real time and determines the urgency of the alert based on the emotion score. For example, if the emotion score is low, an alert with a high urgency is sent. The emotion estimation function can also be used to determine the urgency of the alert based on the senior's emotional state, and prompt family members to take an appropriate action. For example, if a senior is feeling anxious, an alert is sent urging family members to take a prompt action. A system can also be built that monitors the senior's emotional state in real time and determines the urgency of the alert based on the emotion score. For example, an alert with a high urgency is sent if the emotion score drops suddenly. This makes it possible to determine the urgency of the alert based on the senior's emotional state, and prompt family members to take an appropriate action.

[0082] The senior safety management system can further include a behavior prediction unit. The behavior prediction unit predicts future behavior based on the senior's past behavior data and can detect abnormalities by comparing the predicted behavior with actual behavior. For example, if a senior has the habit of visiting a specific place on a specific day of the week every week, that behavior is predicted, but if the senior does not actually visit the place, it is determined to be abnormal. The behavior prediction unit can also analyze changes in the senior's behavior patterns over the long term and detect discrepancies between predicted behavior and actual behavior. This enables early detection of abnormalities based on the senior's future behavior.

[0083] The Senior Safety Management System also uses an emotion estimation function to determine the urgency of the notification based on the emotional state of the senior, and can prompt the police to take an appropriate action. For example, the generation AI analyzes the emotional state of the senior in real time and determines the urgency of the notification based on the emotion score. For example, if the emotion score is low, a high-urgency notification is sent to the police. The emotion estimation function can also be used to determine the urgency of the notification based on the senior's emotional state, and prompt the police to take an appropriate action. For example, if a senior is feeling anxious, a notification is sent to the police urging them to take a prompt action. A system can also be built that monitors the emotional state of seniors in real time and determines the urgency of the notification based on the emotion score. For example, if the emotion score drops suddenly, a high-urgency notification is sent to the police. This makes it possible to determine the urgency of the notification based on the senior's emotional state, and prompt the police to take an appropriate action.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The activity range memory stores the senior's daily activity range. For example, the generation AI learns what places the senior usually visits and what activities they do at what times, and stores the activity range based on the senior's behavioral data. Step 2: The anomaly detection unit compares the memorized range of movement with actual behavior to detect abnormal movements. For example, the generation AI will determine that an abnormality exists if a senior stays for a long time in a place they don't usually go to, or if their movements deviate significantly from their normal behavioral patterns. Step 3: The alert sending unit sends an alert to the family about the abnormal behavior detected by the anomaly detection unit. For example, the generation AI sends an alert to the family including the senior's current location information and details of the behavior that was determined to be abnormal. Step 4: The police notification unit sends a notification to the local police if the family member detects something unusual. For example, the generation AI sends a notification to the police that includes the senior's current location and details of the behavior that was deemed abnormal.

[0086] 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.

[0087] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0088] 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0099] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 7, the 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.

[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 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.

[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 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).

[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] 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.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0153] 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 activity range storage unit that stores the daily activity range of the senior; an abnormality detection unit that detects abnormal movements by comparing the movement range stored in the movement range storage unit with actual movements; an alert sending unit that sends an alert to a family member about the abnormal movement detected by the abnormality detecting unit; and a police notification unit that notifies the nearby police if the family member determines that something is wrong. A system characterized by:

2. The movement range storage unit When recording the range of activities of the senior, the behavioral pattern is corrected based on the emotional state of the senior, and a more accurate range of activities is stored.

2. The system of claim 1.

3. The abnormality detection unit Determine abnormalities by taking into account not only changes in the behavioral patterns of the senior but also changes in the environment.

2. The system of claim 1.

4. The alert sending unit The system notifies the family of the senior's current location information, as well as past behavioral history and factors that may be causing the abnormality.

2. The system of claim 1.

5. The police notification department: The police will be notified of not only the senior's current location but also his / her past behavioral history and possible causes of the abnormality.

2. The system of claim 1.

6. The movement range storage unit It also integrates data from the senior's devices to memorize more detailed behavioral patterns.

2. The system of claim 1.

7. The abnormality detection unit Detect abnormal movements based on the senior's emotional state and discover emotional instability at an early stage.

2. The system of claim 1.

8. The alert sending unit The urgency of the alert is determined based on the emotional state of the senior, and the family is prompted to take appropriate action.

2. The system of claim 1.

Images