system

Abstract

An object of a system according to an embodiment is to automatically learn a behavior pattern of a target person, detect an abnormality, and issue an alert.SOLUTION: A system according to an embodiment includes a GPS device, a AI learning unit, an abnormality detection unit, and an alert generation unit. The GPS device keeps track of the current location of the subject. The AI learning unit automatically learns a daily behavior pattern based on the position information of the target person acquired by the GPS device. The abnormality detection unit detects an abnormality by comparing the behavior pattern learned by the AI learning unit with the current position information. The alert issuing unit issues an alert based on the abnormality detected by the abnormality detection unit.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 does not adequately provide systems that can automatically learn a subject's behavioral patterns, detect abnormalities, and issue alerts, so there is room for improvement.

[0005] The system according to the embodiment aims to automatically learn the behavioral patterns of a subject, detect abnormalities, and issue an alert. [Means for solving the problem]

[0006] The system according to the embodiment includes a GPS device, an AI learning unit, an anomaly detection unit, and an alert issuance unit. The GPS device determines the current location of the subject. The AI ​​learning unit automatically learns the subject's daily behavioral patterns based on the subject's location information acquired by the GPS device. The anomaly detection unit detects anomalies by comparing the behavioral patterns learned by the AI ​​learning unit with the current location information. The alert issuance unit issues an alert based on the anomaly detected by the anomaly detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically learn the behavioral patterns of a subject, detect abnormalities, and issue an alert. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The behavior pattern learning system according to an embodiment of the present invention uses GPS to track the current location of a target person, and AI automatically learns their daily behavior patterns, issuing an alert if an abnormality is detected. This makes it possible for the behavior pattern learning system to prevent childcare accidents and elderly disappearances, thereby realizing a society in which people can live in peace of mind.

[0029] A behavioral pattern learning system according to an embodiment includes a GPS device, an AI learning unit, an anomaly detection unit, and an alert issuing unit. The GPS device tracks the current location of a subject. For example, by having the subject carry a GPS device, the current location can be tracked in real time. The GPS device can also periodically transmit location information and record it in the system. For example, the GPS device can record the route a child takes home from school or places that an elderly person regularly visits. The AI ​​learning unit automatically learns the subject's daily behavioral patterns based on the location information acquired by the GPS device. For example, if a child goes to school at the same time every day and returns home by the same route, the AI ​​can learn this pattern. Similarly, if an elderly person goes for a walk at a specific time every day, the AI ​​can also learn this behavior. The AI ​​inputs the subject's location information and the time of day, and the AI ​​analyzes and learns the behavioral patterns based on this. The anomaly detection unit detects anomalies by comparing the behavioral patterns learned by the AI ​​learning unit with the current location information. For example, an anomaly may occur when a child deviates from their usual route home or when an elderly person does not return home even after a significant amount of time has passed since their usual walk. The anomaly detection unit detects an abnormality and sends an alert to the guardian or caregiver. The alert issuance unit issues an alert based on the abnormality detected by the anomaly detection unit. For example, the content of the alert may include the location and time when the abnormality occurred, and the current location information of the subject. As a result, the behavior pattern learning system according to the embodiment can detect an abnormality based on the current location and behavior pattern of the subject and quickly issue an alert, thereby preventing child care accidents and elderly disappearances.

[0030] A GPS device can be combined with an acceleration sensor to record a subject's movement patterns and integrate and analyze location information and movement data. For example, a GPS device can combine an acceleration sensor to record a subject's movement patterns in detail. For example, it can detect changes in walking speed and direction in real time and integrate and analyze the data with location information. An acceleration sensor can also be used to detect abnormalities if the subject falls or makes sudden movements. For example, it can analyze the acceleration data at the time of a fall and immediately issue an alert. Data from a GPS device and an acceleration sensor can also be integrated to analyze a subject's movement patterns in detail. For example, it can learn changes in daily movement routes and speed and respond quickly when an abnormality occurs. This allows for detailed recording of a subject's movement patterns and improves the accuracy of abnormality detection.

[0031] A GPS device can add a temperature sensor to collect environmental information around the subject and detect abnormalities based on the environmental information. For example, a GPS device can add a temperature sensor to record the environmental temperature around the subject in real time. For example, an alert can be issued if an abnormal temperature change is detected. The temperature sensor can also be used to detect abnormalities when the subject is in an extreme temperature environment. For example, an alert can be issued if there is a high risk of heatstroke. Data from the GPS device and the temperature sensor can be integrated to perform a detailed analysis of the subject's environmental information. For example, the device can learn daily temperature changes and respond quickly when an abnormality occurs. This allows the device to collect environmental information around the subject and improve the accuracy of anomaly detection.

[0032] GPS devices can be installed on drones and can track the location information of targets from the air in real time. For example, GPS devices can be installed on drones to create a system that tracks the location information of targets from the air in real time. For example, this can be used to efficiently monitor wide areas. Drones can also be used to track the location information of targets from the air and respond quickly if an abnormality occurs. For example, they can be used for monitoring mountainous areas and large parks. Furthermore, by linking the drone's camera with a GPS device, the location information and video data of targets can be recorded simultaneously. For example, if an abnormality occurs, a quick response can be made based on the video data. This makes it possible to track the location information of targets from the air in real time and efficiently monitor wide areas.

[0033] GPS devices are applied to pets and vehicles, and can track the location information of pets and vehicles in real time and issue alerts if an abnormality occurs. For example, a GPS device can be attached to a pet to build a system that tracks the pet's location information in real time. For example, if a pet gets lost, the location can be quickly identified. Also, a GPS device can be installed in a vehicle to build a system that tracks the vehicle's location information in real time. For example, it can be used to prevent vehicle theft and confirm the vehicle's location. Also, a system can be built that monitors the location information of pets and vehicles in real time and issues alerts if an abnormality occurs. For example, an alert can be issued if a pet leaves a designated area or if the vehicle behaves suspiciously. This can be used to prevent pets from getting lost and vehicles from being theft.

[0034] The AI ​​learning unit integrates the subject's past health data and lifestyle data, allowing it to learn more accurate behavioral patterns. For example, when learning behavioral patterns, the AI ​​learning unit integrates the subject's past health data. For example, it analyzes behavioral patterns taking into account medical history and medication history. Lifestyle data is also input into the AI ​​and used to learn behavioral patterns. For example, it analyzes behavioral patterns based on eating and exercise habits. Health data and lifestyle data are also integrated, allowing the AI ​​to learn more accurate behavioral patterns. For example, it analyzes behavioral patterns in response to changes in health status. This allows the subject's health data and lifestyle data to be integrated, allowing it to learn more accurate behavioral patterns.

[0035] The AI ​​learning unit can provide a dashboard that visualizes the learned behavioral patterns and allows the subject and their family to intuitively understand them. The AI ​​learning unit, for example, provides a dashboard that visualizes the learned behavioral patterns. For example, daily behavioral routes and time periods are displayed in graphs or maps. The behavioral patterns are also visualized so that the subject and their family can intuitively understand them. For example, behavioral patterns when an abnormality occurs are highlighted. The dashboard also displays the behavioral patterns learned by the AI ​​in real time. For example, the current location information and behavioral history can be confirmed at a glance. This visualizes the behavioral patterns and allows the subject and their family to intuitively understand them.

[0036] The AI ​​learning unit can gain new insights by comparing the learned behavioral patterns with other subjects and analyzing common and different patterns. For example, the AI ​​learning unit compares the learned behavioral patterns with other subjects and analyzes common patterns. For example, it compares the behavioral patterns of subjects of the same age group or gender. The AI ​​can also analyze the behavioral patterns of different subjects and identify similarities and differences. For example, it can analyze whether a particular behavioral pattern is a sign of abnormality. New insights can also be gained through comparative analysis of behavioral patterns. For example, it can analyze the impact that a particular behavioral pattern has on health status. This allows the behavioral patterns to be compared with other subjects and new insights to be gained.

[0037] The AI ​​learning unit can adapt the learning of behavioral patterns to specific events and seasonal fluctuations, and analyze seasonal behavioral patterns. For example, when learning behavioral patterns, the AI ​​learning unit takes specific events and seasonal fluctuations into consideration. For example, it analyzes seasonal behavioral patterns and detects abnormalities. The AI ​​also learns seasonal behavioral patterns and responds quickly when an abnormality occurs. For example, it identifies summer and winter behavioral patterns. The AI ​​also analyzes behavioral patterns that correspond to specific events and seasonal fluctuations. For example, it learns behavioral patterns on public holidays and days off and detects abnormalities. This makes it possible to analyze behavioral patterns that correspond to specific events and seasonal fluctuations.

[0038] When detecting an anomaly, the anomaly detection unit can develop an algorithm that references the subject's past abnormal data and detects similar abnormal patterns early. For example, when detecting an anomaly, the anomaly detection unit develops an algorithm that references the subject's past abnormal data. For example, it detects abnormalities early based on past abnormal patterns. Furthermore, in order to detect similar abnormal patterns early, the AI ​​learns from past abnormal data. For example, it identifies signs of anomalies early. Furthermore, it develops an algorithm that improves the accuracy of anomaly detection based on past abnormal data. For example, it analyzes the frequency and patterns of abnormalities and responds quickly. This makes it possible to refer to past abnormal data and detect similar abnormal patterns early.

[0039] The anomaly detection unit can evaluate the urgency of an alert when an abnormality is detected, taking into account environmental information around the subject. For example, the anomaly detection unit develops an algorithm that takes into account environmental information around the subject when an abnormality is detected. For example, the anomaly detection unit evaluates the urgency of an alert based on weather and traffic conditions. Weather data is also collected in real time, and the urgency of the alert is adjusted when an abnormality occurs. For example, the urgency is set high in bad weather. The urgency of an alert when an abnormality occurs is also evaluated based on traffic condition data. For example, if there is traffic congestion, it is determined that a prompt response is required. This makes it possible to evaluate the urgency of an alert taking into account environmental information around the subject.

[0040] The anomaly detection and alert issuance system can also be applied to employee safety management in companies and anomaly detection in factory machinery. The anomaly detection and alert issuance system is applied, for example, to employee safety management in companies. For example, it monitors employee location information and behavioral patterns, and issues an alert when an abnormality occurs. The anomaly detection and alert issuance system is also applied to anomaly detection in factory machinery. For example, it monitors machine operation data in real time, and issues an alert when an abnormality occurs. The anomaly detection and alert issuance system is also applied to overall safety management in companies. For example, it monitors employee health and work environment, and responds quickly when an abnormality occurs. This allows for applications to employee safety management in companies and anomaly detection in factory machinery.

[0041] The anomaly detection unit can also send an alert to the subject's family and friends when an anomaly is detected, allowing multiple people to respond quickly. The anomaly detection unit, for example, builds a system that sends an alert to the subject's family and friends when an anomaly is detected. For example, alerts are sent to multiple contacts simultaneously. The alert also contains detailed information so that family and friends can respond quickly. For example, the alert may notify the location and time of the anomaly and the subject's current situation. Furthermore, when an anomaly is detected, the family and friends can check the situation in real time. For example, the alert is received via a dedicated app or website. This allows alerts to be sent to family and friends when an anomaly is detected, allowing multiple people to respond quickly.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The behavioral pattern learning system can add a heart rate sensor to monitor the subject's health condition. For example, an alert can be issued if the heart rate is abnormally high or low. The system can also analyze the subject's stress level and exercise volume based on the heart rate data, and respond quickly if an abnormality occurs. For example, an alert can be issued if insufficient or excessive exercise is detected. Heart rate data can also be integrated with other health data to detect abnormalities with greater accuracy. This allows the subject's health condition to be monitored in real time, and a quick response can be made if an abnormality occurs.

[0044] The behavioral pattern learning system can collect a subject's dietary data and use it to monitor their health. For example, it can record the contents of meals and calorie intake and analyze their health. It can also detect nutritional imbalances based on the dietary data and issue an alert if an abnormality occurs. For example, it can issue an alert if a specific nutrient is lacking. It can also integrate dietary data with other health data to enable more accurate health monitoring. This allows the collection of a subject's dietary data to be used to monitor their health.

[0045] The behavioral pattern learning system can collect a subject's sleep data and use it to monitor their health. For example, it can record sleep duration and sleep quality and analyze their health. It can also detect insufficient or excessive sleep based on the sleep data and issue an alert if an abnormality occurs. For example, it can issue an alert if the sleep duration is extremely short or long. It can also integrate the sleep data with other health data to monitor health with greater accuracy. This allows the collection of a subject's sleep data to be used for health monitoring.

[0046] The behavioral pattern learning system can collect a subject's social interaction data and assess their risk of isolation. For example, it can record how often the subject interacts with others and analyze their risk of isolation. It can also detect signs of isolation based on the social interaction data and issue an alert if an abnormality occurs. For example, it can issue an alert if the subject has not interacted with others for a long period of time. It can also integrate the social interaction data with other health data to provide a more accurate assessment of the risk of isolation. This makes it possible to collect a subject's social interaction data and assess their risk of isolation.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The GPS device determines the subject's current location. For example, by having the subject carry a GPS device, their current location can be determined in real time. The GPS device can also periodically transmit location information and record it in the system. For example, the route a child takes home from school or the places an elderly person regularly visits can be recorded. Step 2: The AI ​​learning unit automatically learns the subject's daily behavioral patterns based on the subject's location information obtained by the GPS device. For example, if a child goes to school at the same time every day and returns home by the same route, the AI ​​will learn that pattern. Similarly, if an elderly person goes for a walk at a specific time every day, the AI ​​will also learn that behavior. The input to the AI ​​is the subject's location information and the time of day, and the AI ​​will analyze and learn behavioral patterns based on this. Step 3: The anomaly detection unit compares the behavioral patterns learned by the AI ​​learning unit with the current location information to detect anomalies. For example, if a child deviates from their usual route home, or if an elderly person does not return home after significantly exceeding their usual walking time, this would be an anomaly detection unit. The anomaly detection unit detects an anomaly and sends an alert to the parent or caregiver. Step 4: The alert issuing unit issues an alert based on the abnormality detected by the abnormality detection unit. For example, the content of the alert may include the location and time when the abnormality occurred, and the current location information of the target person. As a result, the behavior pattern learning system according to the embodiment can detect an abnormality based on the target person's current location and behavior pattern, and quickly issue an alert, thereby preventing child care accidents and elderly people from disappearing.

[0049] (Example 2) The behavior pattern learning system according to an embodiment of the present invention uses GPS to track the current location of a target person, and AI automatically learns their daily behavior patterns, issuing an alert if an abnormality is detected. This makes it possible for the behavior pattern learning system to prevent childcare accidents and elderly disappearances, thereby realizing a society in which people can live in peace of mind.

[0050] A behavioral pattern learning system according to an embodiment includes a GPS device, an AI learning unit, an anomaly detection unit, and an alert issuing unit. The GPS device tracks the current location of a subject. For example, by having the subject carry a GPS device, the current location can be tracked in real time. The GPS device can also periodically transmit location information and record it in the system. For example, the GPS device can record the route a child takes home from school or places that an elderly person regularly visits. The AI ​​learning unit automatically learns the subject's daily behavioral patterns based on the location information acquired by the GPS device. For example, if a child goes to school at the same time every day and returns home by the same route, the AI ​​can learn this pattern. Similarly, if an elderly person goes for a walk at a specific time every day, the AI ​​can also learn this behavior. The AI ​​inputs the subject's location information and the time of day, and the AI ​​analyzes and learns the behavioral patterns based on this. The anomaly detection unit detects anomalies by comparing the behavioral patterns learned by the AI ​​learning unit with the current location information. For example, an anomaly may occur when a child deviates from their usual route home or when an elderly person does not return home even after a significant amount of time has passed since their usual walk. The anomaly detection unit detects an abnormality and sends an alert to the guardian or caregiver. The alert issuance unit issues an alert based on the abnormality detected by the anomaly detection unit. For example, the content of the alert may include the location and time when the abnormality occurred, and the current location information of the subject. As a result, the behavior pattern learning system according to the embodiment can detect an abnormality based on the current location and behavior pattern of the subject and quickly issue an alert, thereby preventing child care accidents and elderly disappearances.

[0051] A GPS device can be combined with an acceleration sensor to record a subject's movement patterns and integrate and analyze location information and movement data. For example, a GPS device can combine an acceleration sensor to record a subject's movement patterns in detail. For example, it can detect changes in walking speed and direction in real time and integrate and analyze the data with location information. An acceleration sensor can also be used to detect abnormalities if the subject falls or makes sudden movements. For example, it can analyze the acceleration data at the time of a fall and immediately issue an alert. Data from a GPS device and an acceleration sensor can also be integrated to analyze a subject's movement patterns in detail. For example, it can learn changes in daily movement routes and speed and respond quickly when an abnormality occurs. This allows for detailed recording of a subject's movement patterns and improves the accuracy of abnormality detection.

[0052] A GPS device can add a temperature sensor to collect environmental information around the subject and detect abnormalities based on the environmental information. For example, a GPS device can add a temperature sensor to record the environmental temperature around the subject in real time. For example, an alert can be issued if an abnormal temperature change is detected. The temperature sensor can also be used to detect abnormalities when the subject is in an extreme temperature environment. For example, an alert can be issued if there is a high risk of heatstroke. Data from the GPS device and the temperature sensor can be integrated to perform a detailed analysis of the subject's environmental information. For example, the device can learn daily temperature changes and respond quickly when an abnormality occurs. This allows the device to collect environmental information around the subject and improve the accuracy of anomaly detection.

[0053] The GPS device uses a wearable device equipped with an emotion estimation function to monitor the emotional state of a subject in real time, and can issue an alert when an abnormality occurs, taking into account the emotional data. The GPS device, for example, uses a wearable device equipped with an emotion estimation function to monitor the emotional state of a subject in real time. For example, it analyzes stress and anxiety levels and issues an alert when an abnormality occurs. It also records the subject's emotional state in detail based on the emotion estimation data and issues an alert when an abnormality occurs, taking into account the emotional data. For example, it issues an alert when there is a large fluctuation in emotion. It also integrates the GPS device with an emotion estimation function to simultaneously analyze the subject's location information and emotional state. For example, it issues an alert when emotions become unstable in a specific location. This makes it possible to monitor the subject's emotional state and issue an alert when an abnormality occurs, taking into account the emotional data.

[0054] GPS devices can be installed on drones and can track the location information of targets from the air in real time. For example, GPS devices can be installed on drones to create a system that tracks the location information of targets from the air in real time. For example, this can be used to efficiently monitor wide areas. Drones can also be used to track the location information of targets from the air and respond quickly if an abnormality occurs. For example, they can be used for monitoring mountainous areas and large parks. Furthermore, by linking the drone's camera with a GPS device, the location information and video data of targets can be recorded simultaneously. For example, if an abnormality occurs, a quick response can be made based on the video data. This makes it possible to track the location information of targets from the air in real time and efficiently monitor wide areas.

[0055] GPS devices are applied to pets and vehicles, and can track the location information of pets and vehicles in real time and issue alerts if an abnormality occurs. For example, a GPS device can be attached to a pet to build a system that tracks the pet's location information in real time. For example, if a pet gets lost, the location can be quickly identified. Also, a GPS device can be installed in a vehicle to build a system that tracks the vehicle's location information in real time. For example, it can be used to prevent vehicle theft and confirm the vehicle's location. Also, a system can be built that monitors the location information of pets and vehicles in real time and issues alerts if an abnormality occurs. For example, an alert can be issued if a pet leaves a designated area or if the vehicle behaves suspiciously. This can be used to prevent pets from getting lost and vehicles from being theft.

[0056] A GPS device can use its emotion estimation function to record the emotional state of a subject when they are in a specific location and evaluate whether that location is safe for them. For example, a GPS device can use its emotion estimation function to record the emotional state of a subject when they are in a specific location. For example, it can collect emotion data at locations such as schools and parks. It can also evaluate whether a specific location is safe for the subject based on the emotion estimation data. For example, it can determine that a location is safe if the subject's emotions are stable. It can also integrate the GPS device with the emotion estimation function to simultaneously analyze the subject's location information and emotional state. For example, it can issue an alert if the subject's emotions become unstable in a specific location. This makes it possible to record the emotional state of a subject when they are in a specific location and evaluate whether that location is safe.

[0057] The AI ​​learning unit integrates the subject's past health data and lifestyle data, allowing it to learn more accurate behavioral patterns. For example, when learning behavioral patterns, the AI ​​learning unit integrates the subject's past health data. For example, it analyzes behavioral patterns taking into account medical history and medication history. Lifestyle data is also input into the AI ​​and used to learn behavioral patterns. For example, it analyzes behavioral patterns based on eating and exercise habits. Health data and lifestyle data are also integrated, allowing the AI ​​to learn more accurate behavioral patterns. For example, it analyzes behavioral patterns in response to changes in health status. This allows the subject's health data and lifestyle data to be integrated, allowing it to learn more accurate behavioral patterns.

[0058] The AI ​​learning unit can provide a dashboard that visualizes the learned behavioral patterns and allows the subject and their family to intuitively understand them. The AI ​​learning unit, for example, provides a dashboard that visualizes the learned behavioral patterns. For example, daily behavioral routes and time periods are displayed in graphs or maps. The behavioral patterns are also visualized so that the subject and their family can intuitively understand them. For example, behavioral patterns when an abnormality occurs are highlighted. The dashboard also displays the behavioral patterns learned by the AI ​​in real time. For example, the current location information and behavioral history can be confirmed at a glance. This visualizes the behavioral patterns and allows the subject and their family to intuitively understand them.

[0059] The AI ​​learning unit uses the emotion estimation function to add the emotional state of the subject to the learning data, allowing it to analyze behavioral patterns that take emotional fluctuations into account. For example, the AI ​​learning unit uses the emotion estimation function to add the emotional state of the subject to the learning data. For example, it analyzes stress and anxiety levels and reflects this in behavioral patterns. The AI ​​also analyzes behavioral patterns that take emotional fluctuations into account. For example, it identifies behavioral patterns during periods of emotional instability. Furthermore, based on the emotion estimation data, the AI ​​learns more accurate behavioral patterns. For example, it makes it easier to detect abnormalities when there are large emotional fluctuations. This allows it to add the emotional state to the learning data and analyze behavioral patterns that take emotional fluctuations into account.

[0060] The AI ​​learning unit can gain new insights by comparing the learned behavioral patterns with other subjects and analyzing common and different patterns. For example, the AI ​​learning unit compares the learned behavioral patterns with other subjects and analyzes common patterns. For example, it compares the behavioral patterns of subjects of the same age group or gender. The AI ​​can also analyze the behavioral patterns of different subjects and identify similarities and differences. For example, it can analyze whether a particular behavioral pattern is a sign of abnormality. New insights can also be gained through comparative analysis of behavioral patterns. For example, it can analyze the impact that a particular behavioral pattern has on health status. This allows the behavioral patterns to be compared with other subjects and new insights to be gained.

[0061] The AI ​​learning unit can adapt the learning of behavioral patterns to specific events and seasonal fluctuations, and analyze seasonal behavioral patterns. For example, when learning behavioral patterns, the AI ​​learning unit takes specific events and seasonal fluctuations into consideration. For example, it analyzes seasonal behavioral patterns and detects abnormalities. The AI ​​also learns seasonal behavioral patterns and responds quickly when an abnormality occurs. For example, it identifies summer and winter behavioral patterns. The AI ​​also analyzes behavioral patterns that correspond to specific events and seasonal fluctuations. For example, it learns behavioral patterns on public holidays and days off and detects abnormalities. This makes it possible to analyze behavioral patterns that correspond to specific events and seasonal fluctuations.

[0062] The AI ​​learning unit uses the emotion estimation function to analyze the emotional state of the subject when they take a specific action, and can learn changes in behavioral patterns based on emotions. The AI ​​learning unit, for example, uses the emotion estimation function to analyze the emotional state of the subject when they take a specific action. For example, it analyzes stress and anxiety levels and reflects this in behavioral patterns. The AI ​​also learns changes in behavioral patterns based on emotions. For example, it identifies behavioral patterns during periods of emotional instability. The AI ​​also learns more accurate behavioral patterns based on the emotion estimation data. For example, it makes it easier to detect abnormalities when there are large emotional fluctuations. This makes it possible to analyze emotional states and learn changes in behavioral patterns based on emotions.

[0063] When detecting an anomaly, the anomaly detection unit can develop an algorithm that references the subject's past abnormal data and detects similar abnormal patterns early. For example, when detecting an anomaly, the anomaly detection unit develops an algorithm that references the subject's past abnormal data. For example, it detects abnormalities early based on past abnormal patterns. Furthermore, in order to detect similar abnormal patterns early, the AI ​​learns from past abnormal data. For example, it identifies signs of anomalies early. Furthermore, it develops an algorithm that improves the accuracy of anomaly detection based on past abnormal data. For example, it analyzes the frequency and patterns of abnormalities and responds quickly. This makes it possible to refer to past abnormal data and detect similar abnormal patterns early.

[0064] The anomaly detection unit can evaluate the urgency of an alert when an abnormality is detected, taking into account environmental information around the subject. For example, the anomaly detection unit develops an algorithm that takes into account environmental information around the subject when an abnormality is detected. For example, the anomaly detection unit evaluates the urgency of an alert based on weather and traffic conditions. Weather data is also collected in real time, and the urgency of the alert is adjusted when an abnormality occurs. For example, the urgency is set high in bad weather. The urgency of an alert when an abnormality occurs is also evaluated based on traffic condition data. For example, if there is traffic congestion, it is determined that a prompt response is required. This makes it possible to evaluate the urgency of an alert taking into account environmental information around the subject.

[0065] The anomaly detection unit uses the emotion estimation function to monitor the emotional state of a subject in real time when an abnormality occurs, and can issue an alert that includes the emotional data. The anomaly detection unit, for example, uses the emotion estimation function to monitor the emotional state of a subject in real time when an abnormality occurs. For example, it analyzes stress and anxiety levels and reflects this in the alert. It also adjusts the content of the alert when an abnormality occurs based on the emotion data. For example, it sets a high urgency level when emotions are unstable. It also builds a system that issues alerts that include the emotion estimation data. For example, it responds quickly based on the emotion data when an abnormality occurs. This makes it possible to monitor the emotional state when an abnormality occurs, and issue an alert that includes the emotion data.

[0066] The anomaly detection and alert issuance system can also be applied to employee safety management in companies and anomaly detection in factory machinery. The anomaly detection and alert issuance system is applied, for example, to employee safety management in companies. For example, it monitors employee location information and behavioral patterns, and issues an alert when an abnormality occurs. The anomaly detection and alert issuance system is also applied to anomaly detection in factory machinery. For example, it monitors machine operation data in real time, and issues an alert when an abnormality occurs. The anomaly detection and alert issuance system is also applied to overall safety management in companies. For example, it monitors employee health and work environment, and responds quickly when an abnormality occurs. This allows for applications to employee safety management in companies and anomaly detection in factory machinery.

[0067] The anomaly detection unit can also send an alert to the subject's family and friends when an anomaly is detected, allowing multiple people to respond quickly. The anomaly detection unit, for example, builds a system that sends an alert to the subject's family and friends when an anomaly is detected. For example, alerts are sent to multiple contacts simultaneously. The alert also contains detailed information so that family and friends can respond quickly. For example, the alert may notify the location and time of the anomaly and the subject's current situation. Furthermore, when an anomaly is detected, the family and friends can check the situation in real time. For example, the alert is received via a dedicated app or website. This allows alerts to be sent to family and friends when an anomaly is detected, allowing multiple people to respond quickly.

[0068] The anomaly detection unit can use the emotion estimation function to collect the emotional reactions of people in the vicinity when an abnormality occurs, and use this data to grasp the overall situation. The anomaly detection unit, for example, uses the emotion estimation function to collect the emotional reactions of people in the vicinity when an abnormality occurs. For example, the emotional states of family and friends are analyzed to grasp the overall situation. Furthermore, based on the emotional reaction data of people in the vicinity, a response to an abnormality is adjusted. For example, if emotions are unstable, a quick response is determined to be necessary. Furthermore, the emotion estimation data is used to build a system that grasps the overall situation when an abnormality occurs. For example, response priorities are determined based on the emotion data. In this way, the emotional reactions of people in the vicinity when an abnormality occurs can be collected and used as data to grasp the overall situation.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The behavioral pattern learning system can add a heart rate sensor to monitor the subject's health condition. For example, an alert can be issued if the heart rate is abnormally high or low. The system can also analyze the subject's stress level and exercise volume based on the heart rate data, and respond quickly if an abnormality occurs. For example, an alert can be issued if insufficient or excessive exercise is detected. Heart rate data can also be integrated with other health data to detect abnormalities with greater accuracy. This allows the subject's health condition to be monitored in real time, and a quick response can be made if an abnormality occurs.

[0071] The behavioral pattern learning system can collect a subject's dietary data and use it to monitor their health. For example, it can record the contents of meals and calorie intake and analyze their health. It can also detect nutritional imbalances based on the dietary data and issue an alert if an abnormality occurs. For example, it can issue an alert if a specific nutrient is lacking. It can also integrate dietary data with other health data to enable more accurate health monitoring. This allows the collection of a subject's dietary data to be used to monitor their health.

[0072] The behavioral pattern learning system can collect a subject's sleep data and use it to monitor their health. For example, it can record sleep duration and sleep quality and analyze their health. It can also detect insufficient or excessive sleep based on the sleep data and issue an alert if an abnormality occurs. For example, it can issue an alert if the sleep duration is extremely short or long. It can also integrate the sleep data with other health data to monitor health with greater accuracy. This allows the collection of a subject's sleep data to be used for health monitoring.

[0073] The behavioral pattern learning system can collect a subject's social interaction data and assess their risk of isolation. For example, it can record how often the subject interacts with others and analyze their risk of isolation. It can also detect signs of isolation based on the social interaction data and issue an alert if an abnormality occurs. For example, it can issue an alert if the subject has not interacted with others for a long period of time. It can also integrate the social interaction data with other health data to provide a more accurate assessment of the risk of isolation. This makes it possible to collect a subject's social interaction data and assess their risk of isolation.

[0074] The behavioral pattern learning system can estimate the emotional state of a subject and analyze behavioral patterns based on emotional fluctuations. For example, it can analyze the subject's facial expressions and voice data to estimate the emotional state. It can also issue an alert if an abnormality occurs based on the emotional fluctuations. For example, it can issue an alert if the subject's emotions are extremely unstable. It can also integrate emotional data with other behavioral data to detect abnormalities with higher accuracy. This makes it possible to estimate the subject's emotional state and analyze behavioral patterns based on emotional fluctuations.

[0075] The behavioral pattern learning system can estimate the emotional state of a subject and analyze the impact of specific behaviors on emotions. For example, it can record the emotional state of a subject when they exercise and analyze the impact of exercise on emotions. It can also evaluate the impact of specific behaviors on emotions based on the emotional data and issue an alert if an abnormality occurs. For example, it can issue an alert if emotions become extremely unstable after exercise. It can also integrate emotional data with other behavioral data to detect abnormalities with higher accuracy. This makes it possible to estimate the emotional state of a subject and analyze the impact of specific behaviors on emotions.

[0076] A behavioral pattern learning system can estimate the emotional state of a subject and predict behavioral patterns based on emotional fluctuations. For example, it can predict the subject's future emotional state based on past emotional data and analyze behavioral patterns. It can also predict future behavioral patterns based on emotional data and issue an alert before an abnormality occurs. For example, it can issue a preventative alert before emotions become unstable. It can also integrate emotional data with other behavioral data to predict behavioral patterns with higher accuracy. This makes it possible to estimate the subject's emotional state and predict behavioral patterns based on emotional fluctuations.

[0077] The behavioral pattern learning system can estimate the emotional state of a subject and assess the risk of abnormalities based on emotional fluctuations. For example, the system analyzes the risk of abnormalities based on the subject's emotional data. It also issues an alert before an abnormality occurs based on emotional fluctuations. For example, it issues a preventative alert before emotions become unstable. It can also integrate emotional data with other behavioral data to more accurately assess the risk of abnormalities. This makes it possible to estimate the emotional state of a subject and assess the risk of abnormalities based on emotional fluctuations.

[0078] The behavioral pattern learning system can estimate the emotional state of a subject and predict the occurrence of an abnormality based on emotional fluctuations. For example, it can predict the future emotional state based on the subject's past emotional data and analyze the risk of an abnormality occurring. It can also predict future abnormalities based on the emotional data and issue preventative alerts. For example, it can issue alerts before emotions become unstable. It can also integrate emotional data with other behavioral data to make more accurate predictions of abnormalities. This makes it possible to estimate the emotional state of a subject and predict the occurrence of an abnormality based on emotional fluctuations.

[0079] The behavioral pattern learning system can estimate the emotional state of a subject and predict the occurrence of an abnormality based on emotional fluctuations. For example, it can predict the future emotional state based on the subject's past emotional data and analyze the risk of an abnormality occurring. It can also predict future abnormalities based on the emotional data and issue preventative alerts. For example, it can issue alerts before emotions become unstable. It can also integrate emotional data with other behavioral data to make more accurate predictions of abnormalities. This makes it possible to estimate the emotional state of a subject and predict the occurrence of an abnormality based on emotional fluctuations.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The GPS device determines the subject's current location. For example, by having the subject carry a GPS device, their current location can be determined in real time. The GPS device can also periodically transmit location information and record it in the system. For example, the route a child takes home from school or the places an elderly person regularly visits can be recorded. Step 2: The AI ​​learning unit automatically learns the subject's daily behavioral patterns based on the subject's location information obtained by the GPS device. For example, if a child goes to school at the same time every day and returns home by the same route, the AI ​​will learn that pattern. Similarly, if an elderly person goes for a walk at a specific time every day, the AI ​​will also learn that behavior. The input to the AI ​​is the subject's location information and the time of day, and the AI ​​will analyze and learn behavioral patterns based on this. Step 3: The anomaly detection unit compares the behavioral patterns learned by the AI ​​learning unit with the current location information to detect anomalies. For example, if a child deviates from their usual route home, or if an elderly person does not return home after significantly exceeding their usual walking time, this would be an anomaly detection unit. The anomaly detection unit detects an anomaly and sends an alert to the parent or caregiver. Step 4: The alert issuing unit issues an alert based on the abnormality detected by the abnormality detection unit. For example, the content of the alert may include the location and time when the abnormality occurred, and the current location information of the target person. As a result, the behavior pattern learning system according to the embodiment can detect an abnormality based on the target person's current location and behavior pattern, and quickly issue an alert, thereby preventing child care accidents and elderly people from disappearing.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A GPS device that determines the subject's current location, an AI learning unit that automatically learns daily behavior patterns of the subject based on the location information of the subject acquired by the GPS device; an anomaly detection unit that compares the behavioral pattern learned by the AI ​​learning unit with the current location information to detect an anomaly; an alert issuing unit that issues an alert based on the abnormality detected by the abnormality detection unit. A system characterized by:

2. The GPS device An acceleration sensor is combined to record the movement patterns of the subject, and the position information and the movement data are integrated and analyzed.

2. The system of claim 1.

3. The GPS device A temperature sensor is added to collect environmental information around the subject, and an abnormality is detected based on the environmental information.

2. The system of claim 1.

4. The GPS device A wearable device equipped with an emotion estimation function is used to monitor the emotional state of the subject in real time, and an alert is issued when an abnormality occurs, taking into account the emotional data.

2. The system of claim 1.

5. The GPS device It is mounted on a drone and tracks the target's location information from the air in real time.

2. The system of claim 1.

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