System

Abstract

An object of a system according to an embodiment is to analyze behavior history data, detect an abnormal behavior, and notify the abnormal behavior.SOLUTION: A system includes a collection unit, an analysis unit, and a notification unit. The collection unit collects action history data. The analysis unit analyzes the data collected by the collection unit and compares the data with a normal behavior pattern to detect an abnormal behavior. The notification unit notifies the abnormal behavior detected by the analysis 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 technologies do not adequately detect abnormal behavior based on behavioral history data and provide prompt notification, so there is room for improvement.

[0005] The system according to the embodiment aims to analyze behavior history data, detect abnormal behavior, and notify the user of the abnormal behavior. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a notification unit. The collection unit collects behavior history data. The analysis unit analyzes the data collected by the collection unit and compares it with normal behavior patterns to detect abnormal behavior. The notification unit notifies the user of the abnormal behavior detected by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze behavior history data, detect abnormal behavior, and notify the user of the abnormal behavior. [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) An abnormal behavior detection system according to an embodiment of the present invention analyzes behavioral history data from a mobile GPS, detects abnormal behavior, and notifies the user. The abnormal behavior detection system collects behavioral history data acquired from the mobile GPS, analyzes it using AI, and compares it with normal behavior patterns to detect abnormal behavior. If abnormal behavior is detected, the system notifies the user. For example, the abnormal behavior detection system collects detailed data, such as the locations the user visited and the routes they took. Next, the abnormal behavior detection system analyzes the collected data using AI and determines that a 20% deviation from the normal range of behavior or a 30% deviation from the normal moving speed is an abnormality. If abnormal behavior is detected, the abnormal behavior detection system notifies the user via both a smartphone push notification and email. Furthermore, when abnormal behavior is detected, a notification is also sent to emergency contacts. This ensures the user's safety and enables a prompt response when abnormal behavior occurs. This ensures the user's safety and enables a prompt response when abnormal behavior occurs. For example, if a user significantly deviates from the normal range of behavior, a notification is sent immediately, enabling a prompt response. Furthermore, by learning patterns of abnormal behavior, it becomes possible to detect abnormal behavior with greater accuracy.

[0029] An abnormal behavior detection system according to an embodiment includes a collection unit, an analysis unit, and a notification unit. The collection unit collects behavior history data from a mobile GPS. The behavior history data includes, but is not limited to, location information, activity logs, and app usage history. The collection unit collects detailed data, such as the locations the user visited and the routes the user took. The collection unit can also learn the user's behavior patterns and collect data necessary for detecting abnormal behavior. The analysis unit analyzes the data collected by the collection unit and compares it with normal behavior patterns to detect abnormal behavior. The analysis unit can learn abnormal behavior patterns using, for example, machine learning, and consider a 20% deviation from the normal behavior range or a 30% deviation from the normal movement speed to be abnormal. The analysis unit can also analyze the behavior history data using AI to detect abnormal behavior. For example, the analysis unit detects abnormal behavior using an AI model that receives behavior history data and outputs abnormal behavior. The notification unit notifies the user of the abnormal behavior detected by the analysis unit. The notification unit, for example, notifies the user by both a push notification on the smartphone and an email. The notification unit can also send a notification to an emergency contact when abnormal behavior is detected. For example, when abnormal behavior is detected, the notification unit sends a push notification to the smartphone and simultaneously sends an email to the emergency contact. This allows the abnormal behavior detection system according to the embodiment to ensure the safety of the user and respond quickly when abnormal behavior occurs.

[0030] The analysis unit can determine that deviation from the normal range of activity or deviation from the normal moving speed is abnormal. For example, the analysis unit can determine that deviation from the normal range of activity is 20% abnormal. The analysis unit can also determine that deviation from the normal moving speed is 30% abnormal. For example, the analysis unit can geographically analyze the user's range of activity and determine that deviation from the normal range of activity is 20% abnormal. The analysis unit can also analyze the user's moving speed and determine that deviation from the normal moving speed is 30% abnormal. This clarifies the criteria for abnormal behavior, thereby improving the accuracy of detecting abnormal behavior. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without AI. For example, the analysis unit can detect abnormal behavior using an AI model that inputs behavior history data and outputs abnormal behavior.

[0031] The analysis unit can learn abnormal behavior patterns using machine learning. The analysis unit can learn abnormal behavior patterns using, for example, deep learning. The analysis unit can also learn abnormal behavior patterns using a support vector machine. For example, the analysis unit can learn a large amount of behavioral history data using deep learning to extract abnormal behavior patterns. The analysis unit can also classify abnormal behavior patterns using a support vector machine. This improves the accuracy of abnormal behavior detection by using machine learning. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can detect abnormal behavior using an AI model that inputs behavioral history data and outputs abnormal behavior.

[0032] The notification unit can provide notification via smartphone push notification or email. The notification unit, for example, notifies the user of abnormal behavior using smartphone push notification. The notification unit can also provide notification of abnormal behavior using email. For example, when abnormal behavior is detected, the notification unit sends a push notification to the smartphone. When abnormal behavior is detected, the notification unit can also send a notification to the user's email address. In this way, by using multiple notification means, the user can be reliably notified of abnormal behavior. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can provide notification using an AI model that receives the abnormal behavior detection result as input and outputs a notification.

[0033] The notification unit can also send a notification to a specified contact when abnormal behavior is detected. For example, when abnormal behavior is detected, the notification unit sends an email to an emergency contact. Furthermore, when abnormal behavior is detected, the notification unit can also send an SMS to the emergency contact. For example, when abnormal behavior is detected, the notification unit sends an email to the user's emergency contact to notify them of the abnormal behavior. Furthermore, when abnormal behavior is detected, the notification unit can also send an SMS to the user's emergency contact to notify them of the abnormal behavior. In this way, sending a notification to the emergency contact also further ensures the safety of the user. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can perform the notification using an AI model that receives the abnormal behavior detection result as input and outputs a notification.

[0034] The collection unit can analyze the user's past behavioral history and select an appropriate collection method. For example, the collection unit prioritizes collecting data from places the user has frequently visited in the past. The collection unit can also analyze the user's past behavioral patterns and select an efficient collection method. For example, the collection unit sets an optimal collection interval taking into account the user's past movement speed. This enables efficient data collection by analyzing the past behavioral history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can select a collection method using an AI model that inputs the user's past behavioral history data and outputs the optimal collection method.

[0035] When collecting behavioral history data, the collection unit can filter the data based on the user's current activity status and areas of interest. For example, the collection unit filters the data based on the category of place the user is currently visiting (e.g., restaurant, shopping mall, etc.). The collection unit can also filter the data based on the user's current activity (e.g., walking, running, etc.). For example, the collection unit preferentially collects related data based on the user's areas of interest (e.g., sports, music, etc.). This allows highly relevant data to be collected by filtering the data based on the user's current status and interests. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can filter the data using an AI model that inputs data on the user's current activity status and areas of interest and outputs filtered data.

[0036] When collecting behavioral history data, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user uses voice input, the collection unit prioritizes collecting voice data. Also, if the user uses text input, the collection unit can prioritize collecting text data. For example, if the user uses image input, the collection unit prioritizes collecting image data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can select the collection means using an AI model that inputs data on the user's input method and outputs the optimal collection means.

[0037] When collecting behavioral history data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, if the user is traveling, the collection unit can also prioritize collecting data related to the user's travel route. For example, if the user stays in a specific location for a long time, the collection unit prioritizes collecting data related to that location. This allows for efficient collection of highly relevant data by taking the user's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model that inputs the user's geographical location information and outputs highly relevant data.

[0038] When collecting behavioral history data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects data on locations where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related data. For example, the collection unit collects related data by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model that inputs data on the user's social media activities and outputs related data.

[0039] When collecting behavioral history data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, adjusts the collection method based on feedback provided by the user in the past. The collection unit can also preferentially collect specific data from the user's past feedback. For example, the collection unit customizes the collection timing and means by reflecting the user's feedback. This makes it possible to provide the user with an optimal collection method by reflecting past feedback. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can adjust the collection method by using an AI model that uses the user's past feedback data as input and customizes the collection method.

[0040] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of the behavioral history data. The analysis unit, for example, performs the analysis by taking into account the temporal correlation of the behavioral history data. The analysis unit can also perform the analysis by taking into account the geographic correlation of the behavioral history data. For example, the analysis unit performs the analysis by taking into account the correlation of the activity content of the behavioral history data. This improves the accuracy of the analysis by taking into account the interrelationships of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can improve the accuracy of the analysis by using an AI model that inputs the interrelationships of the behavioral history data and outputs analysis results.

[0041] During analysis, the analysis unit can perform the analysis taking into account attribute information of the person who submitted the behavioral history data. The analysis unit, for example, performs the analysis taking into account the age of the person who submitted the behavioral history data. The analysis unit can also perform the analysis taking into account the gender of the person who submitted the behavioral history data. For example, the analysis unit performs the analysis taking into account the occupation of the person who submitted the behavioral history data. This allows for more accurate analysis by taking into account the attribute information of the submitter. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that inputs attribute information of the person who submitted the behavioral history data and outputs analysis results.

[0042] During analysis, the analysis unit can weight the analysis based on the submission frequency of the behavioral history data. For example, the analysis unit can weight data that is submitted frequently more heavily. The analysis unit can also weight data that is submitted less frequently. For example, the analysis unit adjusts the weighting taking into account fluctuations in the submission frequency. By weighting based on the submission frequency, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can weight the analysis using an AI model that inputs the submission frequency of the behavioral history data and outputs weighted analysis results.

[0043] The analysis unit can perform the analysis taking into account the geographical distribution of the behavioral history data. For example, the analysis unit performs the analysis by mapping the geographical distribution of the behavioral history data. The analysis unit can also perform the analysis by identifying geographical clusters. For example, the analysis unit performs the analysis by detecting geographical anomalies. By taking the geographical distribution into consideration, more accurate analysis is possible. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that inputs the geographical distribution of the behavioral history data and outputs analysis results.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the behavioral history data. The analysis unit, for example, performs the analysis by referring to data from the related literature. The analysis unit can also perform the analysis by applying an algorithm from the related literature. For example, the analysis unit performs the analysis by comparing the results of the related literature. By referring to the related literature, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that inputs data from the related literature and outputs analysis results.

[0045] The analysis unit can perform the analysis taking into account the market value of the behavioral history data. For example, the analysis unit performs the analysis by evaluating the market value of the behavioral history data. The analysis unit can also assign a higher weight to data with a higher market value. For example, the analysis unit performs the analysis taking into account fluctuations in market value. By taking market value into account, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that inputs the market value of the behavioral history data and outputs the analysis results.

[0046] The notification unit can adjust the level of detail of the notification based on the importance of the abnormal behavior when making a notification. For example, the notification unit provides a detailed notification for abnormal behavior with a high importance. The notification unit can also provide a concise notification for abnormal behavior with a low importance. For example, the notification unit adjusts the level of detail of the notification taking into account fluctuations in importance. In this way, by adjusting the level of detail of the notification according to the importance of the abnormal behavior, it is possible to prioritize the notification of information that is important to the user. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can adjust the level of detail of the notification using an AI model that inputs importance data of the abnormal behavior and outputs the level of detail of the notification.

[0047] The notification unit can apply different notification algorithms depending on the category of abnormal behavior when making a notification. For example, the notification unit changes the notification algorithm depending on the category of abnormal behavior (such as abnormal movement speed or deviation from activity range). The notification unit can also set notification priorities depending on the category of abnormal behavior. For example, the notification unit customizes the content of the notification depending on the category of abnormal behavior. This enables more appropriate notification by changing the notification algorithm depending on the category of abnormal behavior. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can apply a notification algorithm using an AI model that receives abnormal behavior category data as input and outputs a notification algorithm.

[0048] The notification unit can improve the accuracy of notifications by referring to the user's past notification results when making notifications. For example, the notification unit analyzes the user's past notification results to improve the accuracy of notifications. The notification unit can also select the optimal notification method based on the user's past notification results. For example, the notification unit adjusts the timing of notifications based on the user's past notification results. This improves the accuracy of notifications by referring to the past notification results. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can improve the accuracy of notifications by inputting the user's past notification result data and using an AI model that improves the accuracy of notifications.

[0049] The notification unit can determine the priority of notifications based on the time of occurrence of abnormal behavior when making notifications. For example, if abnormal behavior occurs late at night, the notification unit prioritizes notification. The notification unit can also quickly notify if abnormal behavior occurs during rush hour. For example, if abnormal behavior occurs on a holiday, the notification unit provides more detailed notification than usual. This enables a rapid response by determining the priority of notifications based on the time of occurrence of abnormal behavior. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input data on the time of occurrence of abnormal behavior and determine the priority of notifications using an AI model that determines the priority of notifications.

[0050] The notification unit can adjust the order of notifications based on the relevance of abnormal behavior when making a notification. For example, if the relevance of abnormal behavior is high, the notification unit prioritizes notification. Furthermore, if the relevance of abnormal behavior is low, the notification unit can also postpone notification. For example, the notification unit analyzes the relevance of abnormal behavior and makes notifications in an optimal order. In this way, by adjusting the order of notifications based on the relevance of abnormal behavior, important information can be prioritized. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can adjust the order of notifications using an AI model that inputs abnormal behavior relevance data and adjusts the order of notifications.

[0051] The notification unit can adjust the use of technical terms in the notification according to the user's level of expertise when providing a notification. For example, if the user has technical expertise, the notification unit can use technical terms to provide the notification. Furthermore, if the user does not have technical expertise, the notification unit can also provide the notification in simple language. For example, the notification unit can analyze the user's level of expertise and provide the notification in optimal language. This allows the notification to be more easily understood by adjusting the technical terms in the notification according to the user's level of expertise. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can adjust the technical terms in the notification using an AI model that uses the user's level of expertise as input and adjusts the technical terms in the notification.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The analysis unit can also estimate the user's health condition based on the user's behavioral history data. For example, the analysis unit can analyze the user's walking speed and travel distance to estimate the user's physical fitness level. The analysis unit can also estimate the user's sleep time from the user's behavioral patterns and evaluate the quality of sleep. Furthermore, the analysis unit can analyze data on the user's eating locations to understand the user's eating habits. This allows for a comprehensive evaluation of the user's health condition and can be useful for health management.

[0054] The notification unit can estimate the user's interests and concerns based on the user's behavioral history data and provide related information. For example, the notification unit can analyze the categories of places the user frequently visits (e.g., restaurants, movie theaters, etc.) and notify the user of related event and promotion information. The notification unit can also estimate the user's hobbies and preferences from the user's behavioral patterns and provide related news and articles. Furthermore, the notification unit can provide traffic information and weather forecasts based on the user's travel route. This allows the user to be provided with information tailored to their interests and concerns, improving user convenience.

[0055] The analysis unit can estimate the user's social connections based on the user's behavioral history data. For example, the analysis unit can analyze data on the places the user frequently visits and the people the user behaves with to estimate the user's friendships and family relationships. The analysis unit can also understand the user's social networks, such as at work or school, from the user's behavioral patterns. Furthermore, the analysis unit can analyze the user's social media activity data to evaluate the user's online connections. This makes it possible to comprehensively evaluate the user's social connections and provide social support.

[0056] The analysis unit can evaluate the user's impact on the environment based on the user's behavioral history data. For example, the analysis unit can analyze the user's means of transportation (car, bicycle, walking, etc.) and estimate carbon dioxide emissions. The analysis unit can also analyze the user's consumption behavior (shopping, eating, etc.) and evaluate the environmental impact. Furthermore, the analysis unit can analyze the user's energy consumption (electricity, gas, etc.) and evaluate energy efficiency. This allows for a comprehensive evaluation of the impact of the user's behavior on the environment, which can be useful for environmental protection.

[0057] The analysis unit can estimate the user's economic activity based on the user's behavioral history data. For example, the analysis unit can analyze the user's shopping history to understand consumption trends. The analysis unit can also estimate the frequency of commuting and business trips from the user's movement patterns and evaluate the scale of economic activity. Furthermore, the analysis unit can analyze the user's expenditure data and evaluate the balance between income and expenditure. This makes it possible to comprehensively evaluate the user's economic activity and provide financial advice.

[0058] The analysis unit can estimate a user's learning pattern based on the user's behavioral history data. For example, the analysis unit can analyze the user's study time and study location to evaluate the efficiency of their learning. The analysis unit can also analyze the user's study content (subjects, topics, etc.) to grasp their learning progress. Furthermore, the analysis unit can analyze the user's study method (online, offline, etc.) and suggest the optimal study method. This allows for a comprehensive evaluation of the user's learning pattern and improves learning efficiency.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects behavioral history data from the mobile GPS. The behavioral history data includes location information, activity logs, and app usage history. The collection unit collects detailed data such as which places the user visited and which routes they took, and learns the user's behavioral patterns. Step 2: The analysis unit analyzes the data collected by the collection unit and compares it with normal behavior patterns to detect abnormal behavior. The analysis unit uses machine learning to learn abnormal behavior patterns and considers a 20% deviation from the normal range of behavior or a 30% difference from the normal movement speed to be abnormal. It also uses AI to analyze behavior history data and detect abnormal behavior. Step 3: The notification unit notifies the user of any abnormal behavior detected by the analysis unit. The notification unit notifies the user by both push notification on the smartphone and email, and can also send a notification to emergency contacts when abnormal behavior is detected.

[0061] (Example 2) An abnormal behavior detection system according to an embodiment of the present invention analyzes behavioral history data from a mobile GPS, detects abnormal behavior, and notifies the user. The abnormal behavior detection system collects behavioral history data acquired from the mobile GPS, analyzes it using AI, and compares it with normal behavior patterns to detect abnormal behavior. If abnormal behavior is detected, the system notifies the user. For example, the abnormal behavior detection system collects detailed data, such as the locations the user visited and the routes they took. Next, the abnormal behavior detection system analyzes the collected data using AI and determines that a 20% deviation from the normal range of behavior or a 30% deviation from the normal moving speed is an abnormality. If abnormal behavior is detected, the abnormal behavior detection system notifies the user via both a smartphone push notification and email. Furthermore, when abnormal behavior is detected, a notification is also sent to emergency contacts. This ensures the user's safety and enables a prompt response when abnormal behavior occurs. This ensures the user's safety and enables a prompt response when abnormal behavior occurs. For example, if a user significantly deviates from the normal range of behavior, a notification is sent immediately, enabling a prompt response. Furthermore, by learning patterns of abnormal behavior, it becomes possible to detect abnormal behavior with greater accuracy.

[0062] An abnormal behavior detection system according to an embodiment includes a collection unit, an analysis unit, and a notification unit. The collection unit collects behavior history data from a mobile GPS. The behavior history data includes, but is not limited to, location information, activity logs, and app usage history. The collection unit collects detailed data, such as the locations the user visited and the routes the user took. The collection unit can also learn the user's behavior patterns and collect data necessary for detecting abnormal behavior. The analysis unit analyzes the data collected by the collection unit and compares it with normal behavior patterns to detect abnormal behavior. The analysis unit can learn abnormal behavior patterns using, for example, machine learning, and consider a 20% deviation from the normal behavior range or a 30% deviation from the normal movement speed to be abnormal. The analysis unit can also analyze the behavior history data using AI to detect abnormal behavior. For example, the analysis unit detects abnormal behavior using an AI model that receives behavior history data and outputs abnormal behavior. The notification unit notifies the user of the abnormal behavior detected by the analysis unit. The notification unit, for example, notifies the user by both a push notification on the smartphone and an email. The notification unit can also send a notification to an emergency contact when abnormal behavior is detected. For example, when abnormal behavior is detected, the notification unit sends a push notification to the smartphone and simultaneously sends an email to the emergency contact. This allows the abnormal behavior detection system according to the embodiment to ensure the safety of the user and respond quickly when abnormal behavior occurs.

[0063] The analysis unit can determine that deviation from the normal range of activity or deviation from the normal moving speed is abnormal. For example, the analysis unit can determine that deviation from the normal range of activity is 20% abnormal. The analysis unit can also determine that deviation from the normal moving speed is 30% abnormal. For example, the analysis unit can geographically analyze the user's range of activity and determine that deviation from the normal range of activity is 20% abnormal. The analysis unit can also analyze the user's moving speed and determine that deviation from the normal moving speed is 30% abnormal. This clarifies the criteria for abnormal behavior, thereby improving the accuracy of detecting abnormal behavior. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without AI. For example, the analysis unit can detect abnormal behavior using an AI model that inputs behavior history data and outputs abnormal behavior.

[0064] The analysis unit can learn abnormal behavior patterns using machine learning. The analysis unit can learn abnormal behavior patterns using, for example, deep learning. The analysis unit can also learn abnormal behavior patterns using a support vector machine. For example, the analysis unit can learn a large amount of behavioral history data using deep learning to extract abnormal behavior patterns. The analysis unit can also classify abnormal behavior patterns using a support vector machine. This improves the accuracy of abnormal behavior detection by using machine learning. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can detect abnormal behavior using an AI model that inputs behavioral history data and outputs abnormal behavior.

[0065] The notification unit can provide notification via smartphone push notification or email. The notification unit, for example, notifies the user of abnormal behavior using smartphone push notification. The notification unit can also provide notification of abnormal behavior using email. For example, when abnormal behavior is detected, the notification unit sends a push notification to the smartphone. When abnormal behavior is detected, the notification unit can also send a notification to the user's email address. In this way, by using multiple notification means, the user can be reliably notified of abnormal behavior. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can provide notification using an AI model that receives the abnormal behavior detection result as input and outputs a notification.

[0066] The notification unit can also send a notification to a specified contact when abnormal behavior is detected. For example, when abnormal behavior is detected, the notification unit sends an email to an emergency contact. Furthermore, when abnormal behavior is detected, the notification unit can also send an SMS to the emergency contact. For example, when abnormal behavior is detected, the notification unit sends an email to the user's emergency contact to notify them of the abnormal behavior. Furthermore, when abnormal behavior is detected, the notification unit can also send an SMS to the user's emergency contact to notify them of the abnormal behavior. In this way, sending a notification to the emergency contact also further ensures the safety of the user. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can perform the notification using an AI model that receives the abnormal behavior detection result as input and outputs a notification.

[0067] The collection unit can estimate the user's emotions and adjust the timing of collecting behavioral history data based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the collection timing to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can increase the collection timing to collect detailed behavioral history data. For example, if the user is in a hurry, the collection unit can optimize the collection timing and collect only important data. This reduces the user's burden by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can adjust the collection timing using an AI model that inputs the user's emotion data and outputs the collection timing.

[0068] The collection unit can analyze the user's past behavioral history and select an appropriate collection method. For example, the collection unit prioritizes collecting data from places the user has frequently visited in the past. The collection unit can also analyze the user's past behavioral patterns and select an efficient collection method. For example, the collection unit sets an optimal collection interval taking into account the user's past movement speed. This enables efficient data collection by analyzing the past behavioral history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can select a collection method using an AI model that inputs the user's past behavioral history data and outputs the optimal collection method.

[0069] When collecting behavioral history data, the collection unit can filter the data based on the user's current activity status and areas of interest. For example, the collection unit filters the data based on the category of place the user is currently visiting (e.g., restaurant, shopping mall, etc.). The collection unit can also filter the data based on the user's current activity (e.g., walking, running, etc.). For example, the collection unit preferentially collects related data based on the user's areas of interest (e.g., sports, music, etc.). This allows highly relevant data to be collected by filtering the data based on the user's current status and interests. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can filter the data using an AI model that inputs data on the user's current activity status and areas of interest and outputs filtered data.

[0070] When collecting behavioral history data, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user uses voice input, the collection unit prioritizes collecting voice data. Also, if the user uses text input, the collection unit can prioritize collecting text data. For example, if the user uses image input, the collection unit prioritizes collecting image data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can select the collection means using an AI model that inputs data on the user's input method and outputs the optimal collection means.

[0071] The collection unit can estimate the user's emotions and determine the priority of behavioral history data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting only important data. Furthermore, when the user is relaxed, the collection unit can also prioritize collecting detailed data. For example, when the user is in a hurry, the collection unit prioritizes collecting data that can be collected quickly. This allows important data to be collected preferentially by determining the priority of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can determine the priority of data using an AI model that inputs the user's emotion data and outputs the priority of data to be collected.

[0072] When collecting behavioral history data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, if the user is traveling, the collection unit can also prioritize collecting data related to the user's travel route. For example, if the user stays in a specific location for a long time, the collection unit prioritizes collecting data related to that location. This allows for efficient collection of highly relevant data by taking the user's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model that inputs the user's geographical location information and outputs highly relevant data.

[0073] When collecting behavioral history data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects data on locations where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related data. For example, the collection unit collects related data by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model that inputs data on the user's social media activities and outputs related data.

[0074] When collecting behavioral history data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, adjusts the collection method based on feedback provided by the user in the past. The collection unit can also preferentially collect specific data from the user's past feedback. For example, the collection unit customizes the collection timing and means by reflecting the user's feedback. This makes it possible to provide the user with an optimal collection method by reflecting past feedback. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can adjust the collection method by using an AI model that uses the user's past feedback data as input and customizes the collection method.

[0075] The analysis unit can estimate the user's emotions and adjust the analysis criteria for abnormal behavior based on the estimated user emotions. For example, the analysis unit relaxes the criteria for abnormal behavior when the user is stressed. The analysis unit can also tighten the criteria for abnormal behavior when the user is relaxed. For example, the analysis unit optimizes the criteria for abnormal behavior when the user is in a hurry. This enables more appropriate detection of abnormal behavior by adjusting the analysis criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can adjust the analysis criteria using an AI model that inputs the user's emotion data and adjusts the analysis criteria.

[0076] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of the behavioral history data. The analysis unit, for example, performs the analysis by taking into account the temporal correlation of the behavioral history data. The analysis unit can also perform the analysis by taking into account the geographic correlation of the behavioral history data. For example, the analysis unit performs the analysis by taking into account the correlation of the activity content of the behavioral history data. This improves the accuracy of the analysis by taking into account the interrelationships of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can improve the accuracy of the analysis by using an AI model that inputs the interrelationships of the behavioral history data and outputs analysis results.

[0077] During analysis, the analysis unit can perform the analysis taking into account attribute information of the person who submitted the behavioral history data. The analysis unit, for example, performs the analysis taking into account the age of the person who submitted the behavioral history data. The analysis unit can also perform the analysis taking into account the gender of the person who submitted the behavioral history data. For example, the analysis unit performs the analysis taking into account the occupation of the person who submitted the behavioral history data. This allows for more accurate analysis by taking into account the attribute information of the submitter. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that inputs attribute information of the person who submitted the behavioral history data and outputs analysis results.

[0078] During analysis, the analysis unit can weight the analysis based on the submission frequency of the behavioral history data. For example, the analysis unit can weight data that is submitted frequently more heavily. The analysis unit can also weight data that is submitted less frequently. For example, the analysis unit adjusts the weighting taking into account fluctuations in the submission frequency. By weighting based on the submission frequency, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can weight the analysis using an AI model that inputs the submission frequency of the behavioral history data and outputs weighted analysis results.

[0079] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple display method. Furthermore, if the user is relaxed, the analysis unit can also provide a detailed display method. For example, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the display method to be adjusted according to the user's emotions, making it easier for the user to understand. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can adjust the display method using an AI model that inputs the user's emotion data and adjusts the display method.

[0080] The analysis unit can perform the analysis taking into account the geographical distribution of the behavioral history data. For example, the analysis unit performs the analysis by mapping the geographical distribution of the behavioral history data. The analysis unit can also perform the analysis by identifying geographical clusters. For example, the analysis unit performs the analysis by detecting geographical anomalies. By taking the geographical distribution into consideration, more accurate analysis is possible. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that inputs the geographical distribution of the behavioral history data and outputs analysis results.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the behavioral history data. The analysis unit, for example, performs the analysis by referring to data from the related literature. The analysis unit can also perform the analysis by applying an algorithm from the related literature. For example, the analysis unit performs the analysis by comparing the results of the related literature. By referring to the related literature, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that inputs data from the related literature and outputs analysis results.

[0082] The analysis unit can perform the analysis taking into account the market value of the behavioral history data. For example, the analysis unit performs the analysis by evaluating the market value of the behavioral history data. The analysis unit can also assign a higher weight to data with a higher market value. For example, the analysis unit performs the analysis taking into account fluctuations in market value. By taking market value into account, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that inputs the market value of the behavioral history data and outputs the analysis results.

[0083] The notification unit can estimate the user's emotions and adjust the notification presentation style based on the estimated user emotions. For example, if the user is feeling stressed, the notification unit can provide a gentle notification. Furthermore, if the user is relaxed, the notification unit can also provide a notification with detailed information. For example, if the user is in a hurry, the notification unit can provide a concise and quick notification. This allows for more appropriate notifications by adjusting the notification presentation style according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can adjust the notification presentation style using an AI model that inputs the user's emotion data and adjusts the notification presentation style.

[0084] The notification unit can adjust the level of detail of the notification based on the importance of the abnormal behavior when making a notification. For example, the notification unit provides a detailed notification for abnormal behavior with a high importance. The notification unit can also provide a concise notification for abnormal behavior with a low importance. For example, the notification unit adjusts the level of detail of the notification taking into account fluctuations in importance. In this way, by adjusting the level of detail of the notification according to the importance of the abnormal behavior, it is possible to prioritize the notification of information that is important to the user. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can adjust the level of detail of the notification using an AI model that inputs importance data of the abnormal behavior and outputs the level of detail of the notification.

[0085] The notification unit can apply different notification algorithms depending on the category of abnormal behavior when making a notification. For example, the notification unit changes the notification algorithm depending on the category of abnormal behavior (such as abnormal movement speed or deviation from activity range). The notification unit can also set notification priorities depending on the category of abnormal behavior. For example, the notification unit customizes the content of the notification depending on the category of abnormal behavior. This enables more appropriate notification by changing the notification algorithm depending on the category of abnormal behavior. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can apply a notification algorithm using an AI model that receives abnormal behavior category data as input and outputs a notification algorithm.

[0086] The notification unit can improve the accuracy of notifications by referring to the user's past notification results when making notifications. For example, the notification unit analyzes the user's past notification results to improve the accuracy of notifications. The notification unit can also select the optimal notification method based on the user's past notification results. For example, the notification unit adjusts the timing of notifications based on the user's past notification results. This improves the accuracy of notifications by referring to the past notification results. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can improve the accuracy of notifications by inputting the user's past notification result data and using an AI model that improves the accuracy of notifications.

[0087] The notification unit can estimate the user's emotions and adjust the length of the notification based on the estimated user emotions. For example, the notification unit can provide a short notification when the user is stressed. The notification unit can also provide a detailed notification when the user is relaxed. For example, the notification unit can provide a concise and quick notification when the user is in a hurry. This allows for more appropriate notifications by adjusting the length of the notification according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can adjust the length of the notification using an AI model that inputs user emotion data and adjusts the length of the notification.

[0088] The notification unit can determine the priority of notifications based on the time of occurrence of abnormal behavior when making notifications. For example, if abnormal behavior occurs late at night, the notification unit prioritizes notification. The notification unit can also quickly notify if abnormal behavior occurs during rush hour. For example, if abnormal behavior occurs on a holiday, the notification unit provides more detailed notification than usual. This enables a rapid response by determining the priority of notifications based on the time of occurrence of abnormal behavior. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input data on the time of occurrence of abnormal behavior and determine the priority of notifications using an AI model that determines the priority of notifications.

[0089] The notification unit can adjust the order of notifications based on the relevance of abnormal behavior when making a notification. For example, if the relevance of abnormal behavior is high, the notification unit prioritizes notification. Furthermore, if the relevance of abnormal behavior is low, the notification unit can also postpone notification. For example, the notification unit analyzes the relevance of abnormal behavior and makes notifications in an optimal order. In this way, by adjusting the order of notifications based on the relevance of abnormal behavior, important information can be prioritized. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can adjust the order of notifications using an AI model that inputs abnormal behavior relevance data and adjusts the order of notifications.

[0090] The notification unit can adjust the use of technical terms in the notification according to the user's level of expertise when providing a notification. For example, if the user has technical expertise, the notification unit can use technical terms to provide the notification. Furthermore, if the user does not have technical expertise, the notification unit can also provide the notification in simple language. For example, the notification unit can analyze the user's level of expertise and provide the notification in optimal language. This allows the notification to be more easily understood by adjusting the technical terms in the notification according to the user's level of expertise. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can adjust the technical terms in the notification using an AI model that uses the user's level of expertise as input and adjusts the technical terms in the notification. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and notification unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects behavior history data using the camera 42 or GPS function of the smart device 14 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to detect abnormal behavior. The notification unit is realized, for example, by the control unit 46A of the smart device 14, and sends a smartphone push notification or email when abnormal behavior is detected. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and notification unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects behavior history data using the camera 42 or GPS function of the smart glasses 214 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to detect abnormal behavior. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214, and sends a push notification to the smartphone or an email when abnormal behavior is detected. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and notification unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects behavior history data using the camera 42 or GPS function of the headset type terminal 314 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to detect abnormal behavior. The notification unit is realized, for example, by the control unit 46A of the headset type terminal 314, and sends a smartphone push notification or email when abnormal behavior is detected. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and notification unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects behavior history data using the camera 42 or GPS function of the robot 414 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to detect abnormal behavior. The notification unit is realized, for example, by the control unit 46A of the robot 414, and sends a push notification on a smartphone or an email when abnormal behavior is detected.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The analysis unit can also estimate the user's health condition based on the user's behavioral history data. For example, the analysis unit can analyze the user's walking speed and travel distance to estimate the user's physical fitness level. The analysis unit can also estimate the user's sleep time from the user's behavioral patterns and evaluate the quality of sleep. Furthermore, the analysis unit can analyze data on the user's eating locations to understand the user's eating habits. This allows for a comprehensive evaluation of the user's health condition and can be useful for health management.

[0093] The notification unit can estimate the user's interests and concerns based on the user's behavioral history data and provide related information. For example, the notification unit can analyze the categories of places the user frequently visits (e.g., restaurants, movie theaters, etc.) and notify the user of related event and promotion information. The notification unit can also estimate the user's hobbies and preferences from the user's behavioral patterns and provide related news and articles. Furthermore, the notification unit can provide traffic information and weather forecasts based on the user's travel route. This allows the user to be provided with information tailored to their interests and concerns, improving user convenience.

[0094] The collection unit can estimate the user's emotions and adjust the behavioral history data collection method based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the collection frequency to reduce the user's burden. Also, if the user is relaxed, the collection unit can increase the collection frequency to collect more detailed data. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting only important data. In this way, by adjusting the collection method according to the user's emotions, the user's burden can be reduced and efficient data collection becomes possible.

[0095] The analysis unit can estimate the user's social connections based on the user's behavioral history data. For example, the analysis unit can analyze data on the places the user frequently visits and the people the user behaves with to estimate the user's friendships and family relationships. The analysis unit can also understand the user's social networks, such as at work or school, from the user's behavioral patterns. Furthermore, the analysis unit can analyze the user's social media activity data to evaluate the user's online connections. This makes it possible to comprehensively evaluate the user's social connections and provide social support.

[0096] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated user emotions. For example, if the user is feeling stressed, the notification unit can reduce notifications to reduce the burden on the user. Also, if the user is relaxed, the notification unit can actively notify and provide necessary information. Furthermore, if the user is in a hurry, the notification unit can prioritize only important notifications. In this way, by adjusting the timing of notifications according to the user's emotions, it is possible to reduce the burden on the user and provide appropriate information.

[0097] The analysis unit can evaluate the user's impact on the environment based on the user's behavioral history data. For example, the analysis unit can analyze the user's means of transportation (car, bicycle, walking, etc.) and estimate carbon dioxide emissions. The analysis unit can also analyze the user's consumption behavior (shopping, eating, etc.) and evaluate the environmental impact. Furthermore, the analysis unit can analyze the user's energy consumption (electricity, gas, etc.) and evaluate energy efficiency. This allows for a comprehensive evaluation of the impact of the user's behavior on the environment, which can be useful for environmental protection.

[0098] The collection unit can estimate the user's emotions and adjust the type of data to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting data related to relaxation (music, natural environment, etc.). Also, if the user is relaxed, the collection unit can prioritize collecting active data (sports, travel, etc.). Furthermore, if the user is in a hurry, the collection unit can prioritize collecting efficient data (traffic information, time management, etc.). In this way, by adjusting the type of data to be collected according to the user's emotions, data collection according to the user's needs can be achieved.

[0099] The analysis unit can estimate the user's economic activity based on the user's behavioral history data. For example, the analysis unit can analyze the user's shopping history to understand consumption trends. The analysis unit can also estimate the frequency of commuting and business trips from the user's movement patterns and evaluate the scale of economic activity. Furthermore, the analysis unit can analyze the user's expenditure data and evaluate the balance between income and expenditure. This makes it possible to comprehensively evaluate the user's economic activity and provide financial advice.

[0100] The notification unit can estimate the user's emotions and customize the content of the notification based on the estimated user's emotions. For example, if the user is feeling stressed, the notification unit can provide encouragement or information about relaxation. If the user is feeling relaxed, the notification unit can also provide information about active events or activities. Furthermore, if the user is in a hurry, the notification unit can also provide efficient information (traffic information, schedule management, etc.). In this way, by customizing the content of the notification according to the user's emotions, it is possible to provide useful information to the user.

[0101] The analysis unit can estimate a user's learning pattern based on the user's behavioral history data. For example, the analysis unit can analyze the user's study time and study location to evaluate the efficiency of their learning. The analysis unit can also analyze the user's study content (subjects, topics, etc.) to grasp their learning progress. Furthermore, the analysis unit can analyze the user's study method (online, offline, etc.) and suggest the optimal study method. This allows for a comprehensive evaluation of the user's learning pattern and improves learning efficiency.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection unit collects behavioral history data from the mobile GPS. The behavioral history data includes location information, activity logs, and app usage history. The collection unit collects detailed data such as which places the user visited and which routes they took, and learns the user's behavioral patterns. Step 2: The analysis unit analyzes the data collected by the collection unit and compares it with normal behavior patterns to detect abnormal behavior. The analysis unit uses machine learning to learn abnormal behavior patterns and considers a 20% deviation from the normal range of behavior or a 30% difference from the normal movement speed to be abnormal. It also uses AI to analyze behavior history data and detect abnormal behavior. Step 3: The notification unit notifies the user of any abnormal behavior detected by the analysis unit. The notification unit notifies the user by both push notification on the smartphone and email, and can also send a notification to emergency contacts when abnormal behavior is detected.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0132] 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.

[0133] 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.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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, in order to avoid confusion and to 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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 collection unit that collects behavioral history data; an analysis unit that analyzes the data collected by the collection unit and compares it with a normal behavior pattern to detect abnormal behavior; a notification unit that notifies abnormal behavior detected by the analysis unit; Equipped with A system characterized by:

2. The analysis unit Any deviation from the normal range of movement or movement speed is considered abnormal.

2. The system of claim 1.

3. The analysis unit Using machine learning to learn patterns of abnormal behavior 2. The system of claim 1.

4. The notification unit Notification via smartphone push notification or email 2. The system of claim 1.

5. The notification unit Send notifications to designated contacts when abnormal behavior is detected 2. The system of claim 1.

6. The collecting unit The user's emotions are estimated, and the timing of collecting behavioral history data is adjusted based on the estimated user's emotions.

2. The system of claim 1.

7. The collecting unit Analyze the user's past behavior history and select the appropriate collection method 2. The system of claim 1.

8. The collecting unit When collecting behavioral history data, filtering is performed based on the user's current activities and areas of interest.

2. The system of claim 1.

Images