System

Abstract

An object of a system according to an embodiment is to determine a risk on the basis of an action history, a habit, and a state of a user and provide an appropriate countermeasure.SOLUTION: A system includes a collection unit, an analysis unit, a provision unit, and an output unit. The collection unit collects data on a behavior history, a habit, and a state of a user. The analysis unit analyzes the data collected by the collection unit, and determines and quantifies the risk. The provision unit provides an alert or a countermeasure based on the risk determined by the analysis unit. The output unit notifies the user of the risk based on the alert or the countermeasure provided by the providing 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 assess risks or provide countermeasures based on a user's behavioral history, habits, and status, and there is room for improvement.

[0005] The system according to the embodiment aims to determine risks based on the user's behavioral history, habits, and status, and to provide appropriate countermeasures. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and an output unit. The collection unit collects data related to a user's behavioral history, habits, and status. The analysis unit analyzes the data collected by the collection unit and determines and quantifies risks. The provision unit provides alerts and countermeasures based on the risks determined by the analysis unit. The output unit notifies the user of risks based on the alerts and countermeasures provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can determine risks based on the user's behavioral history, habits, and status, and provide appropriate countermeasures. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A risk management system according to an embodiment of the present invention uses AI to analyze a user's behavioral history, habits, and status, and to assess and quantify risks. The risk management system collects data on the user's behavioral history, habits, and status, analyzes it using AI, and assesses and quantifies risks associated with alcohol consumption, sleep, incomplete tasks, lack of exercise, unbalanced diet, stress level, and the like. Furthermore, the system provides alerts and countermeasures for high-risk behaviors and statuses. For example, the risk management system collects data such as the user's amount of alcohol consumption, amount of sleep, incomplete tasks, lack of exercise, unbalanced diet, and high stress level. The risk management system then analyzes the collected data using AI to assess and quantify risks. For example, if a user consumes alcohol excessively, the system assesses the risk as high and quantifies it. The risk management system also provides alerts and countermeasures for high-risk behaviors and statuses. For example, if a user consumes alcohol excessively, the system issues an alert and suggests appropriate countermeasures. This allows the user to minimize risks and live a safe and healthy life. This allows the risk management system to clarify the risks associated with the user's behavior and condition and provide appropriate countermeasures. For example, by providing alerts to avoid excessive drinking and countermeasures to alleviate lack of sleep, the user can lead a healthy life. Furthermore, by providing alerts and countermeasures for incomplete tasks, the user can complete tasks efficiently. In this way, the risk management system allows the user to achieve a safe and healthy life while minimizing risks.

[0029] A risk management system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and an output unit. The collection unit collects data related to a user's behavioral history, habits, and condition. The user's behavioral history includes, but is not limited to, location information and app usage history. For example, the collection unit collects the user's location information using a GPS. The collection unit can also collect app usage history as log data. The collection unit also collects data related to the user's habits. For example, the collection unit records the user's eating patterns and exercise frequency. The collection unit also collects data related to the user's condition. For example, the collection unit measures the user's health condition and stress level using a sensor. The analysis unit analyzes the data collected by the collection unit to determine and quantify risks. Risks include, but are not limited to, health risks and security risks. The analysis unit analyzes the collected data using, for example, AI to determine risks. The analysis unit can also use a scoring system to quantify risks. For example, the analysis unit represents the health risk with a score from 0 to 100. The provision unit provides an alert and countermeasures based on the risk determined by the analysis unit. The alert may include, but is not limited to, a notification method or a type of alert. For example, the provision unit sends a push notification to the user. The provision unit also provides countermeasures for high-risk behaviors or conditions. For example, the provision unit suggests an exercise plan to the user to overcome lack of exercise. The output unit notifies the user of the risk based on the alert and countermeasures provided by the provision unit. For example, the output unit notifies the user of the risk. For example, the output unit displays a notification on the user's smartphone. The output unit also urges the user to take appropriate countermeasures. For example, the output unit notifies the user to execute the exercise plan. As a result, the risk management system according to the embodiment can determine risks based on the user's behavioral history, habits, and condition, and provide appropriate alerts and countermeasures, thereby enabling a safe and healthy life.

[0030] The analysis unit can analyze risk factors such as lack of exercise, unbalanced diet, and stress level, and determine and quantify the risk. The analysis unit, for example, analyzes risk factors for lack of exercise. For example, the analysis unit collects data on the user's exercise frequency and amount and determines the risk of lack of exercise. The analysis unit can also analyze risk factors for unbalanced diet. For example, the analysis unit collects data on the user's diet type and nutritional balance and determines the risk of unbalanced diet. The analysis unit can also analyze risk factors for stress level. For example, the analysis unit collects data on the user's stress test results and self-reports and determines the risk of stress level. This enables more detailed risk determination by analyzing risk factors such as lack of exercise, unbalanced diet, and stress level. Some or all of the above-mentioned processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on exercise frequency and nutritional balance into a generation AI and have the generation AI perform a risk determination.

[0031] The providing unit can provide alerts and countermeasures for high-risk behaviors and conditions. The providing unit, for example, provides alerts for high-risk behaviors. For example, if the user consumes excessive alcohol, the providing unit issues an alert and suggests appropriate countermeasures. The providing unit can also provide countermeasures for high-risk conditions. For example, if the user is sleep-deprived, the providing unit issues an alert and suggests countermeasures to improve sleep. The providing unit can also provide alerts and countermeasures for lack of exercise and unbalanced diet. For example, the providing unit suggests an exercise plan or a meal plan to the user. This allows the user's risk to be minimized by providing appropriate alerts and countermeasures for high-risk behaviors and conditions. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the results of the risk assessment to the generating AI and cause the generating AI to issue alerts and suggest countermeasures.

[0032] The output unit can notify the user of the risk and urge them to take measures. The output unit, for example, notifies the user of the risk. For example, the output unit displays a notification on the user's smartphone to notify them of the risk. The output unit can also urge the user to take appropriate measures. For example, the output unit can notify the user to follow an exercise plan. The output unit can also notify the user to follow a meal plan. In this way, by notifying the user of the risk and urging them to take appropriate measures, the user's safety and health can be maintained. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the content of the risk notification to a generation AI and cause the generation AI to generate the notification.

[0033] The collection unit can analyze the user's past behavioral history and select the optimal data collection method. The collection unit can optimize the timing of data collection based on, for example, the user's frequently performed behavior in the past. For example, the collection unit can retrieve the user's behavioral history from a database and perform frequency analysis. The collection unit can also concentrate data collection during a specific time period based on the user's past behavioral history. For example, the collection unit can analyze the user's behavioral patterns as time-series data and determine the optimal data collection timing. The collection unit can also analyze the user's behavioral patterns and select the most efficient data collection method. For example, the collection unit can classify the user's behavioral patterns using a clustering algorithm and select the optimal data collection method for each cluster. This allows the analysis of the user's past behavioral history to select the optimal data collection method and enable efficient data collection. 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 input the user's behavioral history data into a generation AI and have the generation AI select the optimal data collection method.

[0034] When collecting data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, the collection unit collects only data related to the task the user is currently working on. For example, the collection unit references the user's calendar information and prioritizes collecting data related to the user's current task. The collection unit can also prioritize collecting highly relevant data based on the user's areas of interest. For example, the collection unit analyzes the user's social media activity and collects data related to the user's areas of interest. The collection unit can also adjust the scope of data collection depending on the user's living situation (e.g., at work, on vacation, etc.). For example, the collection unit determines the user's current living situation based on the user's location information and adjusts the scope of data collection. This allows highly relevant data to be collected by filtering the data based on the user's current living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's living situation data to a generation AI and have the generation AI perform data filtering.

[0035] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit prioritizes collecting voice data. For example, the collection unit records the user's voice with a microphone and converts it into text data using voice recognition technology. The collection unit can also prioritize collecting text data if the user uses text input. For example, the collection unit collects text entered by the user in real time and stores it in a database. The collection unit can also prioritize collecting image data if the user uses image input. For example, the collection unit analyzes images taken by the user and extracts related information. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. 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 input the user's input data into a generation AI and have the generation AI select the optimal collection means.

[0036] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. For example, the collection unit acquires the user's location information using GPS and collects data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the user's travel destination. For example, the collection unit collects information about the user's travel destination and prioritizes collecting related data. Furthermore, when the user is at home, the collection unit can prioritize collecting data around the user's home. For example, the collection unit collects surrounding data based on the user's home location information. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's location information data to the generation AI and cause the generation AI to collect highly relevant data.

[0037] The collection unit may analyze the user's social media activities and collect related data when collecting data. The collection unit may collect related data based on, for example, information shared by the user on social media. For example, the collection unit may analyze the content of the user's social media posts and collect related data. The collection unit may also analyze the user's social media activities and collect related data. For example, the collection unit may analyze the number of likes and comments on the user's social media posts and collect related data. The collection unit may also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit may collect related data based on information shared by the user's friends. This allows for efficient collection of related data by analyzing the user's 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 may input the user's social media data into a generation AI and cause the generation AI to collect related data.

[0038] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit, for example, adjusts the data collection method based on feedback provided by the user in the past. For example, the collection unit retrieves the user's past feedback from a database and customizes the collection method. The collection unit can also select the type of data to collect based on the user's past feedback. For example, the collection unit analyzes the user's feedback and determines the type of data to collect. The collection unit can also adjust the frequency and timing of data collection by reflecting the user's feedback. For example, the collection unit adjusts the frequency of data collection based on the user's feedback. This allows the collection method to be customized by reflecting the user's past feedback, enabling efficient data collection. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's feedback data to a generation AI and have the generation AI customize the collection method.

[0039] During analysis, the analysis unit can adjust the level of detail of the risk assessment based on the importance of the behavioral history. For example, the analysis unit performs a detailed risk assessment on behaviors with high importance in the user's behavioral history. For example, the analysis unit retrieves the user's behavioral history from a database and performs an importance analysis. The analysis unit can also perform a simplified risk assessment on behaviors with low importance in the user's behavioral history. For example, the analysis unit analyzes the user's behavioral history as time-series data and adjusts the level of detail of the risk assessment based on the importance. The analysis unit can also dynamically adjust the level of detail of the risk assessment according to the importance of the user's behavioral history. For example, the analysis unit classifies the user's behavioral history using a clustering algorithm and adjusts the level of detail of the risk assessment for each cluster. This enables efficient risk assessment by adjusting the level of detail of the risk assessment based on the importance of the behavioral history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's behavioral history data to a generation AI and cause the generation AI to adjust the level of detail of the risk assessment.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the risk factor. For example, the analysis unit applies a specific analysis algorithm to a risk factor related to alcohol consumption. For example, the analysis unit collects data on the user's alcohol consumption and applies an algorithm to determine the drinking risk. The analysis unit can also apply a different analysis algorithm to a risk factor related to sleep deprivation. For example, the analysis unit collects data on the user's sleep time and applies an algorithm to determine the sleep risk. The analysis unit can also apply a different analysis algorithm to a risk factor related to lack of exercise. For example, the analysis unit collects data on the user's exercise amount and applies an algorithm to determine the exercise risk. This allows for more accurate risk determination by applying different analysis algorithms depending on the category of the risk factor. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input risk factor data to a generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0041] During analysis, the analysis unit can improve the accuracy of risk assessment by referring to the user's past analysis results. The analysis unit, for example, adjusts the risk assessment algorithm based on the user's past analysis results. For example, the analysis unit retrieves the user's past risk assessment results from a database and adjusts the parameters of the algorithm. The analysis unit can also set parameters for improving the accuracy of risk assessment based on the user's past analysis results. For example, the analysis unit improves the risk assessment model based on past data. The analysis unit can also improve the risk assessment model by referring to the user's past analysis results. For example, the analysis unit establishes a feedback loop for improving the accuracy of the model based on the past risk assessment results. This allows the accuracy of risk assessment to be improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and cause the generation AI to improve the accuracy of risk assessment.

[0042] During analysis, the analysis unit can weight risk assessment based on the time of data collection. For example, the analysis unit assigns a higher weight to recently collected data. For example, the analysis unit obtains the time of data collection from a database and assigns weights. The analysis unit can also assign a lower weight to older data. For example, the analysis unit dynamically adjusts the weight of risk assessment based on the time of data collection. The analysis unit can also dynamically adjust the weight of risk assessment according to the time of data collection. For example, the analysis unit analyzes the time of data collection as time-series data and assigns weights. This enables accurate risk assessment based on the latest data by weighting the risk assessment based on the time of data collection. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the time of data collection to a generation AI and have the generation AI perform the weighting of risk assessment.

[0043] During analysis, the analysis unit can adjust the order of risk assessment based on the relevance of the data. For example, the analysis unit prioritizes risk assessment for highly relevant data. For example, the analysis unit obtains data relevance from a database and adjusts the order. The analysis unit can also postpone risk assessment for less relevant data. For example, the analysis unit dynamically adjusts the order of risk assessment based on the relevance of the data. The analysis unit can also dynamically adjust the order of risk assessment according to the relevance of the data. For example, the analysis unit evaluates data relevance using correlation analysis and adjusts the order. This enables efficient risk assessment by adjusting the order of risk assessment based on the relevance 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 input data on the relevance of the data to the generation AI and cause the generation AI to adjust the order of risk assessment.

[0044] During analysis, the analysis unit can adjust the presentation method of the analysis results according to the user's level of expertise. For example, if the user has expertise, the analysis unit provides detailed analysis results. For example, the analysis unit retrieves the user's level of expertise from a database and adjusts the presentation method. Furthermore, if the user does not have expertise, the analysis unit can provide concise and easy-to-understand analysis results. For example, the analysis unit dynamically adjusts the presentation method of the analysis results based on the user's level of expertise. Furthermore, the analysis unit can dynamically adjust the presentation method of the analysis results according to the user's level of expertise. For example, the analysis unit collects the user's level of expertise as questionnaire data and adjusts the presentation method. By adjusting the presentation method of the analysis results according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into a generation AI and cause the generation AI to adjust the presentation method of the analysis results.

[0045] The providing unit can adjust the level of detail of alerts and countermeasures based on the severity of the risk when providing the alerts and countermeasures. For example, the providing unit provides detailed alerts and countermeasures for high-severity risks. For example, the providing unit retrieves the severity of the risk from a database and adjusts the level of detail. The providing unit can also provide simplified alerts and countermeasures for low-severity risks. For example, the providing unit dynamically adjusts the level of detail of alerts and countermeasures based on the severity of the risk. The providing unit can also dynamically adjust the level of detail of alerts and countermeasures according to the severity of the risk. For example, the providing unit evaluates the severity of the risk using impact analysis and adjusts the level of detail. In this way, by adjusting the level of detail of alerts and countermeasures based on the severity of the risk, detailed countermeasures can be provided for important risks. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input risk severity data to a generation AI and cause the generation AI to adjust the level of detail of alerts and countermeasures.

[0046] The providing unit can apply different countermeasure algorithms depending on the category of the risk factor when providing the data. For example, the providing unit applies a specific countermeasure algorithm to a risk factor related to alcohol consumption. For example, the providing unit collects the user's alcohol consumption as data and applies a countermeasure algorithm for reducing alcohol-related risks. The providing unit can also apply a different countermeasure algorithm to a risk factor related to lack of sleep. For example, the providing unit collects the user's sleep time as data and applies a countermeasure algorithm for reducing sleep risks. The providing unit can also apply a different countermeasure algorithm to a risk factor related to lack of exercise. For example, the providing unit collects the user's exercise amount as data and applies a countermeasure algorithm for reducing exercise risks. In this way, by applying different countermeasure algorithms depending on the category of the risk factor, more effective countermeasures can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the risk factors to the generating AI and cause the generating AI to apply an appropriate countermeasure algorithm.

[0047] The providing unit can improve the accuracy of alerts and countermeasures by referring to the user's past countermeasure results when providing the alerts and countermeasures. The providing unit, for example, adjusts the algorithm for alerts and countermeasures based on the user's past countermeasure results. For example, the providing unit retrieves the user's past countermeasure results from a database and adjusts algorithm parameters. The providing unit can also set parameters for providing optimal alerts and countermeasures based on the user's past countermeasure results. For example, the providing unit improves the alert and countermeasure models based on past data. The providing unit can also improve the alert and countermeasure models by referring to the user's past countermeasure results. For example, the providing unit builds a feedback loop to improve the accuracy of the model based on the past countermeasure results. This allows the accuracy of alerts and countermeasures to be improved by referring to the user's past countermeasure results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past countermeasure result data into the generation AI and cause the generation AI to improve the accuracy of alerts and countermeasures.

[0048] The providing unit can weight alerts and countermeasures based on the time of risk occurrence when providing the data. For example, the providing unit sets a higher weight for alerts and countermeasures for risks that have occurred recently. For example, the providing unit obtains the time of risk occurrence from a database and performs weighting. The providing unit can also set a lower weight for alerts and countermeasures for older risks. For example, the providing unit dynamically adjusts the weighting of alerts and countermeasures based on the time of risk occurrence. The providing unit can also dynamically adjust the weighting of alerts and countermeasures according to the time of risk occurrence. For example, the providing unit analyzes the time of risk occurrence as time-series data and performs weighting. This allows for weighting of alerts and countermeasures based on the time of risk occurrence, thereby enabling a prompt response to the latest risks. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the time of risk occurrence into a generation AI and have the generation AI perform weighting of alerts and countermeasures.

[0049] The providing unit can adjust the order of alerts and countermeasures based on risk relevance when providing the alerts and countermeasures. For example, the providing unit prioritizes providing alerts and countermeasures for highly relevant risks. For example, the providing unit obtains risk relevance from a database and adjusts the order. The providing unit can also postpone alerts and countermeasures for less relevant risks. For example, the providing unit dynamically adjusts the order of alerts and countermeasures based on risk relevance. The providing unit can also dynamically adjust the order of alerts and countermeasures according to risk relevance. For example, the providing unit evaluates risk relevance using correlation analysis and adjusts the order. This enables efficient risk management by adjusting the order of alerts and countermeasures based on risk relevance. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input risk relevance data to a generation AI and cause the generation AI to adjust the order of alerts and countermeasures.

[0050] The providing unit can adjust the presentation method of alerts and countermeasures according to the user's level of expertise when providing them. For example, if the user has expertise, the providing unit provides detailed alerts and countermeasures. For example, the providing unit retrieves the user's level of expertise from a database and adjusts the presentation method. Furthermore, if the user does not have expertise, the providing unit can provide concise and easy-to-understand alerts and countermeasures. For example, the providing unit dynamically adjusts the presentation method of alerts and countermeasures based on the user's level of expertise. Furthermore, the providing unit can dynamically adjust the presentation method of alerts and countermeasures according to the user's level of expertise. For example, the providing unit collects the user's level of expertise as questionnaire data and adjusts the presentation method. In this way, by adjusting the presentation method of alerts and countermeasures according to the user's level of expertise, it is possible to provide alerts and countermeasures that are easy for the user to understand. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's level of expertise data into a generation AI and cause the generation AI to adjust the presentation method of alerts and countermeasures.

[0051] The output unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the output unit prioritizes the selection of a notification method that the user has previously preferred. For example, the output unit retrieves the user's past notification history from a database and selects the optimal notification method. The output unit can also select the optimal notification timing based on the user's past notification history. For example, the output unit analyzes the user's past notification history and determines the optimal notification timing. The output unit can also customize the notification content based on the user's past notification history. For example, the output unit adjusts the notification content based on the user's past notification history. This allows the optimal notification method to be selected by referring to the user's past notification history, enabling efficient notification. Some or all of the above-described processing in the output unit may be performed using, or without, AI. For example, the output unit can input the user's notification history data to a generation AI and have the generation AI select the optimal notification method.

[0052] The output unit can customize the notification content according to the user's current task when sending a notification. For example, if the user is at work, the output unit provides work-related notification content. For example, the output unit references the user's calendar information and provides notification content related to the user's current task. Furthermore, if the user is on vacation, the output unit can also provide relaxing notification content. For example, the output unit determines that the user is on vacation based on the user's location information and provides relaxing notification content. Furthermore, if the user is exercising, the output unit can also provide exercise-related notification content. For example, the output unit provides exercise-related notification content based on the user's exercise data. This allows for customizing the notification content according to the user's current task, thereby providing highly relevant notifications. Some or all of the above-described processing in the output unit may be performed using, or without, AI. For example, the output unit can input the user's task data into a generation AI and have the generation AI customize the notification content.

[0053] The output unit can select the optimal notification method by taking into account the user's device information when providing a notification. For example, if the user is using a smartphone, the output unit prioritizes push notification. For example, the output unit obtains the user's device information from a database and selects the optimal notification method. Furthermore, if the user is using a tablet, the output unit can provide a notification method optimized for a large screen. For example, the output unit adjusts the notification method based on the user's device information. Furthermore, if the user is using a smartwatch, the output unit can provide a simple and highly visible notification method. For example, the output unit selects the notification method based on the user's device information. This allows the optimal notification method to be selected by taking the user's device information into consideration, enabling efficient notification. Some or all of the above-described processing in the output unit may be performed using, or without, AI. For example, the output unit can input the user's device information data into a generation AI and have the generation AI select the optimal notification method.

[0054] The output unit can make the notification content multilingual based on the user's language setting when providing the notification. The output unit, for example, automatically sets the notification language based on the language setting of the user's device. For example, the output unit retrieves the user's language setting from a database and makes the notification content multilingual. The output unit can also provide a language switching function when the user uses multiple languages. For example, the output unit provides a language switching function based on the user's language setting. The output unit can also provide notifications in a specific language selected by the user. For example, the output unit can make the notification content multilingual based on the user's language setting. This makes it possible to provide notifications that are easy for the user to understand by making the notification content multilingual based on the user's language setting. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without AI. For example, the output unit can input the user's language setting data to a generation AI and cause the generation AI to perform multilingualization of the notification content.

[0055] The output unit can select the optimal notification timing by referring to the user's calendar information when sending a notification. The output unit, for example, references the schedule registered in the user's calendar and selects the optimal notification timing. For example, the output unit retrieves the user's calendar information from a database and selects the optimal notification timing. The output unit can also provide notifications related to specific events based on the user's calendar information. For example, the output unit provides notifications related to specific events based on the user's calendar information. The output unit can also suggest the optimal notification timing based on the schedule based on the user's calendar information. For example, the output unit suggests the optimal notification timing based on the user's calendar information. This allows the optimal notification timing to be selected by referring to the user's calendar information, enabling efficient notification. Some or all of the above-described processing in the output unit may be performed using, or without, AI. For example, the output unit can input the user's calendar information data to a generation AI and cause the generation AI to select the optimal notification timing.

[0056] Upon notification, the output unit can analyze the user's social media activity and provide related information. The output unit, for example, provides information about locations where the user checked in on social media. For example, the output unit can analyze the content of the user's social media posts and provide related information. The output unit can also analyze the user's social media activity and provide related information. For example, the output unit can analyze the number of likes and comments on the user's social media posts and provide related information. The output unit can also provide related information by referring to the activities of the user's friends on social media. For example, the output unit can provide related information based on information shared by the user's friends. In this way, by analyzing the user's social media activity, related information can be efficiently provided. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the user's social media data to a generation AI and cause the generation AI to provide related information.

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

[0058] The analysis unit can predict risks based on the user's behavioral history. For example, the analysis unit can analyze the user's past behavioral patterns and predict future risks. The analysis unit can also analyze the user's behavioral history as time-series data and calculate the probability of risk occurrence. Furthermore, the analysis unit can classify the user's behavioral history using a clustering algorithm and predict risks for each cluster. In this way, by predicting risks based on the user's behavioral history, measures can be taken to avoid risks in advance.

[0059] The collection unit can adjust the frequency of data collection based on the remaining battery level of the user's device. For example, the collection unit can reduce the frequency of data collection when the remaining battery level is low. The collection unit can also increase the frequency of data collection when the remaining battery level is sufficient. Furthermore, the collection unit can moderately adjust the frequency of data collection when the remaining battery level is medium. This allows the battery life of the device to be extended by adjusting the frequency of data collection according to the remaining battery level of the user's device.

[0060] The providing unit can predict the occurrence of a risk based on the user's past behavioral history and provide an alert in advance for the predicted risk. For example, the providing unit can analyze the user's past behavioral patterns and calculate the probability of the risk occurring. The providing unit can also analyze the user's behavioral history as time-series data and predict the timing of the risk occurrence. Furthermore, the providing unit can classify the user's behavioral history using a clustering algorithm and predict the occurrence of a risk for each cluster. In this way, the occurrence of a risk can be predicted based on the user's past behavioral history and an alert can be provided in advance, thereby preventing the risk from occurring.

[0061] The analysis unit can identify risk occurrence patterns based on the user's behavioral history and propose risk prevention measures. For example, the analysis unit can analyze the user's past behavioral patterns to identify risk occurrence patterns. The analysis unit can also analyze the user's behavioral history as time-series data to visualize risk occurrence patterns. Furthermore, the analysis unit can classify the user's behavioral history using a clustering algorithm and propose risk prevention measures for each cluster. In this way, risk occurrence patterns can be identified based on the user's behavioral history and proposed risk prevention measures, thereby preventing risks before they occur.

[0062] The collection unit can adjust the timing of data collection based on the user's device usage. For example, the collection unit can reduce the frequency of data collection when the user uses the device frequently. Alternatively, the collection unit can increase the frequency of data collection when the user does not use the device often. Furthermore, the collection unit can adjust the timing of data collection according to the time of day when the user uses the device. This makes it possible to optimize device performance by adjusting the timing of data collection according to the user's device usage.

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

[0064] Step 1: The collection unit collects data on the user's behavioral history, habits, and status. For example, the collection unit collects the user's location information using GPS and app usage history as log data. The collection unit also uses sensors to measure the user's eating patterns, exercise frequency, health condition, and stress level. Step 2: The analysis unit analyzes the data collected by the collection unit and determines and quantifies risks. For example, the analysis unit uses AI to analyze the data and determine health and security risks. The analysis unit also quantifies risks using a scoring system, expressing health risks as scores from 0 to 100, for example. Step 3: The provider provides alerts and countermeasures based on the risks determined by the analyzer. For example, the provider sends a push notification to the user and suggests countermeasures such as an exercise plan for high-risk behaviors or conditions. Step 4: The output unit notifies the user of the risk based on the alerts and countermeasures provided by the provision unit. For example, the output unit displays a notification on the user's smartphone and urges the user to take appropriate countermeasures.

[0065] (Example 2) A risk management system according to an embodiment of the present invention uses AI to analyze a user's behavioral history, habits, and status, and to assess and quantify risks. The risk management system collects data on the user's behavioral history, habits, and status, analyzes it using AI, and assesses and quantifies risks associated with alcohol consumption, sleep, incomplete tasks, lack of exercise, unbalanced diet, stress level, and the like. Furthermore, the system provides alerts and countermeasures for high-risk behaviors and statuses. For example, the risk management system collects data such as the user's amount of alcohol consumption, amount of sleep, incomplete tasks, lack of exercise, unbalanced diet, and high stress level. The risk management system then analyzes the collected data using AI to assess and quantify risks. For example, if a user consumes alcohol excessively, the system assesses the risk as high and quantifies it. The risk management system also provides alerts and countermeasures for high-risk behaviors and statuses. For example, if a user consumes alcohol excessively, the system issues an alert and suggests appropriate countermeasures. This allows the user to minimize risks and live a safe and healthy life. This allows the risk management system to clarify the risks associated with the user's behavior and condition and provide appropriate countermeasures. For example, by providing alerts to avoid excessive drinking and countermeasures to alleviate lack of sleep, the user can lead a healthy life. Furthermore, by providing alerts and countermeasures for incomplete tasks, the user can complete tasks efficiently. In this way, the risk management system allows the user to achieve a safe and healthy life while minimizing risks.

[0066] A risk management system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and an output unit. The collection unit collects data related to a user's behavioral history, habits, and condition. The user's behavioral history includes, but is not limited to, location information and app usage history. For example, the collection unit collects the user's location information using a GPS. The collection unit can also collect app usage history as log data. The collection unit also collects data related to the user's habits. For example, the collection unit records the user's eating patterns and exercise frequency. The collection unit also collects data related to the user's condition. For example, the collection unit measures the user's health condition and stress level using a sensor. The analysis unit analyzes the data collected by the collection unit to determine and quantify risks. Risks include, but are not limited to, health risks and security risks. The analysis unit analyzes the collected data using, for example, AI to determine risks. The analysis unit can also use a scoring system to quantify risks. For example, the analysis unit represents the health risk with a score from 0 to 100. The provision unit provides an alert and countermeasures based on the risk determined by the analysis unit. The alert may include, but is not limited to, a notification method or a type of alert. For example, the provision unit sends a push notification to the user. The provision unit also provides countermeasures for high-risk behaviors or conditions. For example, the provision unit suggests an exercise plan to the user to overcome lack of exercise. The output unit notifies the user of the risk based on the alert and countermeasures provided by the provision unit. For example, the output unit notifies the user of the risk. For example, the output unit displays a notification on the user's smartphone. The output unit also urges the user to take appropriate countermeasures. For example, the output unit notifies the user to execute the exercise plan. As a result, the risk management system according to the embodiment can determine risks based on the user's behavioral history, habits, and condition, and provide appropriate alerts and countermeasures, thereby enabling a safe and healthy life.

[0067] The analysis unit can analyze risk factors such as lack of exercise, unbalanced diet, and stress level, and determine and quantify the risk. The analysis unit, for example, analyzes risk factors for lack of exercise. For example, the analysis unit collects data on the user's exercise frequency and amount and determines the risk of lack of exercise. The analysis unit can also analyze risk factors for unbalanced diet. For example, the analysis unit collects data on the user's diet type and nutritional balance and determines the risk of unbalanced diet. The analysis unit can also analyze risk factors for stress level. For example, the analysis unit collects data on the user's stress test results and self-reports and determines the risk of stress level. This enables more detailed risk determination by analyzing risk factors such as lack of exercise, unbalanced diet, and stress level. Some or all of the above-mentioned processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on exercise frequency and nutritional balance into a generation AI and have the generation AI perform a risk determination.

[0068] The providing unit can provide alerts and countermeasures for high-risk behaviors and conditions. The providing unit, for example, provides alerts for high-risk behaviors. For example, if the user consumes excessive alcohol, the providing unit issues an alert and suggests appropriate countermeasures. The providing unit can also provide countermeasures for high-risk conditions. For example, if the user is sleep-deprived, the providing unit issues an alert and suggests countermeasures to improve sleep. The providing unit can also provide alerts and countermeasures for lack of exercise and unbalanced diet. For example, the providing unit suggests an exercise plan or a meal plan to the user. This allows the user's risk to be minimized by providing appropriate alerts and countermeasures for high-risk behaviors and conditions. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the results of the risk assessment to the generating AI and cause the generating AI to issue alerts and suggest countermeasures.

[0069] The output unit can notify the user of the risk and urge them to take measures. The output unit, for example, notifies the user of the risk. For example, the output unit displays a notification on the user's smartphone to notify them of the risk. The output unit can also urge the user to take appropriate measures. For example, the output unit can notify the user to follow an exercise plan. The output unit can also notify the user to follow a meal plan. In this way, by notifying the user of the risk and urging them to take appropriate measures, the user's safety and health can be maintained. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the content of the risk notification to a generation AI and cause the generation AI to generate the notification.

[0070] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the user's burden. For example, the collection unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the collection unit can increase the collection frequency to collect more detailed data. For example, the collection unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the collection unit can collect only the minimum amount of data necessary and process it quickly. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the timing of data collection to be adjusted according to the user's emotions, reducing the user's burden and collecting more accurate data. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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 may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data to the generation AI and cause the generation AI to adjust the timing of data collection.

[0071] The collection unit can analyze the user's past behavioral history and select the optimal data collection method. The collection unit can optimize the timing of data collection based on, for example, the user's frequently performed behavior in the past. For example, the collection unit can retrieve the user's behavioral history from a database and perform frequency analysis. The collection unit can also concentrate data collection during a specific time period based on the user's past behavioral history. For example, the collection unit can analyze the user's behavioral patterns as time-series data and determine the optimal data collection timing. The collection unit can also analyze the user's behavioral patterns and select the most efficient data collection method. For example, the collection unit can classify the user's behavioral patterns using a clustering algorithm and select the optimal data collection method for each cluster. This allows the analysis of the user's past behavioral history to select the optimal data collection method and enable efficient data collection. 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 input the user's behavioral history data into a generation AI and have the generation AI select the optimal data collection method.

[0072] When collecting data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, the collection unit collects only data related to the task the user is currently working on. For example, the collection unit references the user's calendar information and prioritizes collecting data related to the user's current task. The collection unit can also prioritize collecting highly relevant data based on the user's areas of interest. For example, the collection unit analyzes the user's social media activity and collects data related to the user's areas of interest. The collection unit can also adjust the scope of data collection depending on the user's living situation (e.g., at work, on vacation, etc.). For example, the collection unit determines the user's current living situation based on the user's location information and adjusts the scope of data collection. This allows highly relevant data to be collected by filtering the data based on the user's current living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's living situation data to a generation AI and have the generation AI perform data filtering.

[0073] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit prioritizes collecting voice data. For example, the collection unit records the user's voice with a microphone and converts it into text data using voice recognition technology. The collection unit can also prioritize collecting text data if the user uses text input. For example, the collection unit collects text entered by the user in real time and stores it in a database. The collection unit can also prioritize collecting image data if the user uses image input. For example, the collection unit analyzes images taken by the user and extracts related information. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. 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 input the user's input data into a generation AI and have the generation AI select the optimal collection means.

[0074] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting stress-related data. For example, the collection unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the collection unit can prioritize collecting data related to the user's health. For example, the collection unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting data of high importance. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. Thus, by determining the priority of data to be collected according to the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative 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 may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data into the generation AI and have the generation AI determine the priority of the data.

[0075] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. For example, the collection unit acquires the user's location information using GPS and collects data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the user's travel destination. For example, the collection unit collects information about the user's travel destination and prioritizes collecting related data. Furthermore, when the user is at home, the collection unit can prioritize collecting data around the user's home. For example, the collection unit collects surrounding data based on the user's home location information. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's location information data to the generation AI and cause the generation AI to collect highly relevant data.

[0076] The collection unit may analyze the user's social media activities and collect related data when collecting data. The collection unit may collect related data based on, for example, information shared by the user on social media. For example, the collection unit may analyze the content of the user's social media posts and collect related data. The collection unit may also analyze the user's social media activities and collect related data. For example, the collection unit may analyze the number of likes and comments on the user's social media posts and collect related data. The collection unit may also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit may collect related data based on information shared by the user's friends. This allows for efficient collection of related data by analyzing the user's 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 may input the user's social media data into a generation AI and cause the generation AI to collect related data.

[0077] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit, for example, adjusts the data collection method based on feedback provided by the user in the past. For example, the collection unit retrieves the user's past feedback from a database and customizes the collection method. The collection unit can also select the type of data to collect based on the user's past feedback. For example, the collection unit analyzes the user's feedback and determines the type of data to collect. The collection unit can also adjust the frequency and timing of data collection by reflecting the user's feedback. For example, the collection unit adjusts the frequency of data collection based on the user's feedback. This allows the collection method to be customized by reflecting the user's past feedback, enabling efficient data collection. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's feedback data to a generation AI and have the generation AI customize the collection method.

[0078] The analysis unit can estimate the user's emotions and adjust the risk assessment criteria based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can tighten the stress-related risk assessment criteria. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. The analysis unit can also relax the risk assessment criteria if the user is relaxed. For example, the analysis unit can record the user's voice and estimate the user's emotions using voice analysis technology. The analysis unit can also set criteria for quickly assessing risk if the user is in a hurry. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows for more accurate risk assessment by adjusting the risk assessment criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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 analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and cause the generation AI to adjust the risk assessment criteria.

[0079] During analysis, the analysis unit can adjust the level of detail of the risk assessment based on the importance of the behavioral history. For example, the analysis unit performs a detailed risk assessment on behaviors with high importance in the user's behavioral history. For example, the analysis unit retrieves the user's behavioral history from a database and performs an importance analysis. The analysis unit can also perform a simplified risk assessment on behaviors with low importance in the user's behavioral history. For example, the analysis unit analyzes the user's behavioral history as time-series data and adjusts the level of detail of the risk assessment based on the importance. The analysis unit can also dynamically adjust the level of detail of the risk assessment according to the importance of the user's behavioral history. For example, the analysis unit classifies the user's behavioral history using a clustering algorithm and adjusts the level of detail of the risk assessment for each cluster. This enables efficient risk assessment by adjusting the level of detail of the risk assessment based on the importance of the behavioral history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's behavioral history data to a generation AI and cause the generation AI to adjust the level of detail of the risk assessment.

[0080] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the risk factor. For example, the analysis unit applies a specific analysis algorithm to a risk factor related to alcohol consumption. For example, the analysis unit collects data on the user's alcohol consumption and applies an algorithm to determine the drinking risk. The analysis unit can also apply a different analysis algorithm to a risk factor related to sleep deprivation. For example, the analysis unit collects data on the user's sleep time and applies an algorithm to determine the sleep risk. The analysis unit can also apply a different analysis algorithm to a risk factor related to lack of exercise. For example, the analysis unit collects data on the user's exercise amount and applies an algorithm to determine the exercise risk. This allows for more accurate risk determination by applying different analysis algorithms depending on the category of the risk factor. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input risk factor data to a generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0081] During analysis, the analysis unit can improve the accuracy of risk assessment by referring to the user's past analysis results. The analysis unit, for example, adjusts the risk assessment algorithm based on the user's past analysis results. For example, the analysis unit retrieves the user's past risk assessment results from a database and adjusts the parameters of the algorithm. The analysis unit can also set parameters for improving the accuracy of risk assessment based on the user's past analysis results. For example, the analysis unit improves the risk assessment model based on past data. The analysis unit can also improve the risk assessment model by referring to the user's past analysis results. For example, the analysis unit establishes a feedback loop for improving the accuracy of the model based on the past risk assessment results. This allows the accuracy of risk assessment to be improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and cause the generation AI to improve the accuracy of risk assessment.

[0082] The analysis unit can estimate the user's emotions and prioritize risk assessments based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit prioritizes stress-related risk assessments. For example, the analysis unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the analysis unit can prioritize health-related risk assessments. For example, the analysis unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the analysis unit can prioritize risk assessments of higher importance. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the system to prioritize risk assessments based on the user's emotions, thereby prioritizing important risks. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. 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 may input user emotion data to the generation AI and have the generation AI determine the priority of risk assessment.

[0083] During analysis, the analysis unit can weight risk assessment based on the time of data collection. For example, the analysis unit assigns a higher weight to recently collected data. For example, the analysis unit obtains the time of data collection from a database and assigns weights. The analysis unit can also assign a lower weight to older data. For example, the analysis unit dynamically adjusts the weight of risk assessment based on the time of data collection. The analysis unit can also dynamically adjust the weight of risk assessment according to the time of data collection. For example, the analysis unit analyzes the time of data collection as time-series data and assigns weights. This enables accurate risk assessment based on the latest data by weighting the risk assessment based on the time of data collection. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the time of data collection to a generation AI and have the generation AI perform the weighting of risk assessment.

[0084] During analysis, the analysis unit can adjust the order of risk assessment based on the relevance of the data. For example, the analysis unit prioritizes risk assessment for highly relevant data. For example, the analysis unit obtains data relevance from a database and adjusts the order. The analysis unit can also postpone risk assessment for less relevant data. For example, the analysis unit dynamically adjusts the order of risk assessment based on the relevance of the data. The analysis unit can also dynamically adjust the order of risk assessment according to the relevance of the data. For example, the analysis unit evaluates data relevance using correlation analysis and adjusts the order. This enables efficient risk assessment by adjusting the order of risk assessment based on the relevance 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 input data on the relevance of the data to the generation AI and cause the generation AI to adjust the order of risk assessment.

[0085] During analysis, the analysis unit can adjust the presentation method of the analysis results according to the user's level of expertise. For example, if the user has expertise, the analysis unit provides detailed analysis results. For example, the analysis unit retrieves the user's level of expertise from a database and adjusts the presentation method. Furthermore, if the user does not have expertise, the analysis unit can provide concise and easy-to-understand analysis results. For example, the analysis unit dynamically adjusts the presentation method of the analysis results based on the user's level of expertise. Furthermore, the analysis unit can dynamically adjust the presentation method of the analysis results according to the user's level of expertise. For example, the analysis unit collects the user's level of expertise as questionnaire data and adjusts the presentation method. By adjusting the presentation method of the analysis results according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into a generation AI and cause the generation AI to adjust the presentation method of the analysis results.

[0086] The providing unit can estimate the user's emotions and adjust the way alerts and countermeasures are presented based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit provides alerts and countermeasures in a calmer manner. For example, the providing unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the providing unit can provide detailed alerts and countermeasures. For example, the providing unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the providing unit can provide concise and prompt alerts and countermeasures. For example, the providing unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the alerts and countermeasures to be presented in a way that is more acceptable to the user by adjusting the way alerts and countermeasures are presented based on 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 providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data to the generating AI and cause the generating AI to adjust the way alerts and countermeasures are expressed.

[0087] The providing unit can adjust the level of detail of alerts and countermeasures based on the severity of the risk when providing the alerts and countermeasures. For example, the providing unit provides detailed alerts and countermeasures for high-severity risks. For example, the providing unit retrieves the severity of the risk from a database and adjusts the level of detail. The providing unit can also provide simplified alerts and countermeasures for low-severity risks. For example, the providing unit dynamically adjusts the level of detail of alerts and countermeasures based on the severity of the risk. The providing unit can also dynamically adjust the level of detail of alerts and countermeasures according to the severity of the risk. For example, the providing unit evaluates the severity of the risk using impact analysis and adjusts the level of detail. In this way, by adjusting the level of detail of alerts and countermeasures based on the severity of the risk, detailed countermeasures can be provided for important risks. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input risk severity data to a generation AI and cause the generation AI to adjust the level of detail of alerts and countermeasures.

[0088] The providing unit can apply different countermeasure algorithms depending on the category of the risk factor when providing the data. For example, the providing unit applies a specific countermeasure algorithm to a risk factor related to alcohol consumption. For example, the providing unit collects the user's alcohol consumption as data and applies a countermeasure algorithm for reducing alcohol-related risks. The providing unit can also apply a different countermeasure algorithm to a risk factor related to lack of sleep. For example, the providing unit collects the user's sleep time as data and applies a countermeasure algorithm for reducing sleep risks. The providing unit can also apply a different countermeasure algorithm to a risk factor related to lack of exercise. For example, the providing unit collects the user's exercise amount as data and applies a countermeasure algorithm for reducing exercise risks. In this way, by applying different countermeasure algorithms depending on the category of the risk factor, more effective countermeasures can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the risk factors to the generating AI and cause the generating AI to apply an appropriate countermeasure algorithm.

[0089] The providing unit can improve the accuracy of alerts and countermeasures by referring to the user's past countermeasure results when providing the alerts and countermeasures. The providing unit, for example, adjusts the algorithm for alerts and countermeasures based on the user's past countermeasure results. For example, the providing unit retrieves the user's past countermeasure results from a database and adjusts algorithm parameters. The providing unit can also set parameters for providing optimal alerts and countermeasures based on the user's past countermeasure results. For example, the providing unit improves the alert and countermeasure models based on past data. The providing unit can also improve the alert and countermeasure models by referring to the user's past countermeasure results. For example, the providing unit builds a feedback loop to improve the accuracy of the model based on the past countermeasure results. This allows the accuracy of alerts and countermeasures to be improved by referring to the user's past countermeasure results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past countermeasure result data into the generation AI and cause the generation AI to improve the accuracy of alerts and countermeasures.

[0090] The providing unit can estimate the user's emotions and prioritize alerts and countermeasures based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit prioritizes stress-related alerts and countermeasures. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the providing unit can prioritize health-related alerts and countermeasures. For example, the providing unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is in a hurry, the providing unit can prioritize alerts and countermeasures with high importance. For example, the providing unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the system to prioritize alerts and countermeasures based on the user's emotions, thereby providing important alerts and countermeasures. Emotion estimation is achieved using an emotion estimation function, such as 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 providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data to the generating AI and cause the generating AI to determine the priority of alerts and countermeasures.

[0091] The providing unit can weight alerts and countermeasures based on the time of risk occurrence when providing the data. For example, the providing unit sets a higher weight for alerts and countermeasures for risks that have occurred recently. For example, the providing unit obtains the time of risk occurrence from a database and performs weighting. The providing unit can also set a lower weight for alerts and countermeasures for older risks. For example, the providing unit dynamically adjusts the weighting of alerts and countermeasures based on the time of risk occurrence. The providing unit can also dynamically adjust the weighting of alerts and countermeasures according to the time of risk occurrence. For example, the providing unit analyzes the time of risk occurrence as time-series data and performs weighting. This allows for weighting of alerts and countermeasures based on the time of risk occurrence, thereby enabling a prompt response to the latest risks. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the time of risk occurrence into a generation AI and have the generation AI perform weighting of alerts and countermeasures.

[0092] The providing unit can adjust the order of alerts and countermeasures based on risk relevance when providing the alerts and countermeasures. For example, the providing unit prioritizes providing alerts and countermeasures for highly relevant risks. For example, the providing unit obtains risk relevance from a database and adjusts the order. The providing unit can also postpone alerts and countermeasures for less relevant risks. For example, the providing unit dynamically adjusts the order of alerts and countermeasures based on risk relevance. The providing unit can also dynamically adjust the order of alerts and countermeasures according to risk relevance. For example, the providing unit evaluates risk relevance using correlation analysis and adjusts the order. This enables efficient risk management by adjusting the order of alerts and countermeasures based on risk relevance. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input risk relevance data to a generation AI and cause the generation AI to adjust the order of alerts and countermeasures.

[0093] The providing unit can adjust the presentation method of alerts and countermeasures according to the user's level of expertise when providing them. For example, if the user has expertise, the providing unit provides detailed alerts and countermeasures. For example, the providing unit retrieves the user's level of expertise from a database and adjusts the presentation method. Furthermore, if the user does not have expertise, the providing unit can provide concise and easy-to-understand alerts and countermeasures. For example, the providing unit dynamically adjusts the presentation method of alerts and countermeasures based on the user's level of expertise. Furthermore, the providing unit can dynamically adjust the presentation method of alerts and countermeasures according to the user's level of expertise. For example, the providing unit collects the user's level of expertise as questionnaire data and adjusts the presentation method. In this way, by adjusting the presentation method of alerts and countermeasures according to the user's level of expertise, it is possible to provide alerts and countermeasures that are easy for the user to understand. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's level of expertise data into a generation AI and cause the generation AI to adjust the presentation method of alerts and countermeasures.

[0094] The output unit can estimate the user's emotions and adjust the notification expression method based on the estimated user emotions. For example, if the user is feeling stressed, the output unit can provide a notification using a calm expression. For example, the output unit can capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. The output unit can also provide a detailed notification if the user is relaxed. For example, the output unit can record the user's voice and estimate the user's emotions using voice analysis technology. The output unit can also provide a concise and quick notification if the user is in a hurry. For example, the output unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the notification expression method to be adjusted according to the user's emotions, thereby providing a notification that is easy for the user to accept. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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 output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit may input user emotion data to the generation AI and cause the generation AI to adjust the notification expression method.

[0095] The output unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the output unit prioritizes the selection of a notification method that the user has previously preferred. For example, the output unit retrieves the user's past notification history from a database and selects the optimal notification method. The output unit can also select the optimal notification timing based on the user's past notification history. For example, the output unit analyzes the user's past notification history and determines the optimal notification timing. The output unit can also customize the notification content based on the user's past notification history. For example, the output unit adjusts the notification content based on the user's past notification history. This allows the optimal notification method to be selected by referring to the user's past notification history, enabling efficient notification. Some or all of the above-described processing in the output unit may be performed using, or without, AI. For example, the output unit can input the user's notification history data to a generation AI and have the generation AI select the optimal notification method.

[0096] The output unit can customize the notification content according to the user's current task when sending a notification. For example, if the user is at work, the output unit provides work-related notification content. For example, the output unit references the user's calendar information and provides notification content related to the user's current task. Furthermore, if the user is on vacation, the output unit can also provide relaxing notification content. For example, the output unit determines that the user is on vacation based on the user's location information and provides relaxing notification content. Furthermore, if the user is exercising, the output unit can also provide exercise-related notification content. For example, the output unit provides exercise-related notification content based on the user's exercise data. This allows for customizing the notification content according to the user's current task, thereby providing highly relevant notifications. Some or all of the above-described processing in the output unit may be performed using, or without, AI. For example, the output unit can input the user's task data into a generation AI and have the generation AI customize the notification content.

[0097] The output unit can select the optimal notification method by taking into account the user's device information when providing a notification. For example, if the user is using a smartphone, the output unit prioritizes push notification. For example, the output unit obtains the user's device information from a database and selects the optimal notification method. Furthermore, if the user is using a tablet, the output unit can provide a notification method optimized for a large screen. For example, the output unit adjusts the notification method based on the user's device information. Furthermore, if the user is using a smartwatch, the output unit can provide a simple and highly visible notification method. For example, the output unit selects the notification method based on the user's device information. This allows the optimal notification method to be selected by taking the user's device information into consideration, enabling efficient notification. Some or all of the above-described processing in the output unit may be performed using, or without, AI. For example, the output unit can input the user's device information data into a generation AI and have the generation AI select the optimal notification method.

[0098] The output unit can estimate the user's emotions and prioritize notifications based on the estimated user emotions. For example, if the user is feeling stressed, the output unit prioritizes stress-related notifications. For example, the output unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the output unit can prioritize health-related notifications. For example, the output unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the output unit can prioritize notifications of high importance. For example, the output unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the device to prioritize notifications based on the user's emotions, thereby providing important notifications first. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative 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 output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit may input user emotion data to the generation AI and have the generation AI determine the priority of notifications.

[0099] The output unit can make the notification content multilingual based on the user's language setting when providing the notification. The output unit, for example, automatically sets the notification language based on the language setting of the user's device. For example, the output unit retrieves the user's language setting from a database and makes the notification content multilingual. The output unit can also provide a language switching function when the user uses multiple languages. For example, the output unit provides a language switching function based on the user's language setting. The output unit can also provide notifications in a specific language selected by the user. For example, the output unit can make the notification content multilingual based on the user's language setting. This makes it possible to provide notifications that are easy for the user to understand by making the notification content multilingual based on the user's language setting. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without AI. For example, the output unit can input the user's language setting data to a generation AI and cause the generation AI to perform multilingualization of the notification content.

[0100] The output unit can select the optimal notification timing by referring to the user's calendar information when sending a notification. The output unit, for example, references the schedule registered in the user's calendar and selects the optimal notification timing. For example, the output unit retrieves the user's calendar information from a database and selects the optimal notification timing. The output unit can also provide notifications related to specific events based on the user's calendar information. For example, the output unit provides notifications related to specific events based on the user's calendar information. The output unit can also suggest the optimal notification timing based on the schedule based on the user's calendar information. For example, the output unit suggests the optimal notification timing based on the user's calendar information. This allows the optimal notification timing to be selected by referring to the user's calendar information, enabling efficient notification. Some or all of the above-described processing in the output unit may be performed using, or without, AI. For example, the output unit can input the user's calendar information data to a generation AI and cause the generation AI to select the optimal notification timing.

[0101] Upon notification, the output unit can analyze the user's social media activity and provide related information. The output unit, for example, provides information about locations where the user checked in on social media. For example, the output unit can analyze the content of the user's social media posts and provide related information. The output unit can also analyze the user's social media activity and provide related information. For example, the output unit can analyze the number of likes and comments on the user's social media posts and provide related information. The output unit can also provide related information by referring to the activities of the user's friends on social media. For example, the output unit can provide related information based on information shared by the user's friends. In this way, by analyzing the user's social media activity, related information can be efficiently provided. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the user's social media data to a generation AI and cause the generation AI to provide related information. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, and output 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 can collect data on the user's behavioral history, habits, and condition using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and AI analyzes the collected data to determine and quantify risks. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides alerts and countermeasures for high-risk behaviors and conditions. The output unit is realized by the control unit 46A of the smart device 14, and notifies the user of risks and prompts them to take appropriate countermeasures. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, and output 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 can collect data on the user's behavioral history, habits, and condition using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and AI analyzes the collected data to determine and quantify risks. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides alerts and countermeasures for high-risk behaviors and conditions. The output unit is realized by the control unit 46A of the smart glasses 214, and notifies the user of risks and prompts them to take appropriate countermeasures. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and output 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 can collect data on the user's behavioral history, habits, and condition using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and AI analyzes the collected data to determine and quantify risks. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides alerts and countermeasures for high-risk behaviors and conditions. The output unit is realized by the control unit 46A of the headset-type terminal 314, and notifies the user of risks and urges them to take appropriate countermeasures. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and output 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 can collect data on the user's behavioral history, habits, and condition using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and AI analyzes the collected data to determine and quantify risks. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides alerts and countermeasures for high-risk behaviors and conditions. The output unit is realized by the control unit 46A of the robot 414, and notifies the user of risks and urges them to take appropriate countermeasures.

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

[0103] The analysis unit can predict risks based on the user's behavioral history. For example, the analysis unit can analyze the user's past behavioral patterns and predict future risks. The analysis unit can also analyze the user's behavioral history as time-series data and calculate the probability of risk occurrence. Furthermore, the analysis unit can classify the user's behavioral history using a clustering algorithm and predict risks for each cluster. In this way, by predicting risks based on the user's behavioral history, measures can be taken to avoid risks in advance.

[0104] The providing unit can estimate the user's emotions and adjust the timing of the alert based on the estimated user's emotions. For example, the providing unit can delay the timing of the alert if the user is feeling stressed. The providing unit can also issue an alert immediately if the user is relaxed. Furthermore, the providing unit can reduce the frequency of alerts if the user is in a hurry. In this way, by adjusting the timing of the alert according to the user's emotions, it is possible to provide alerts at the optimal timing for the user.

[0105] The collection unit can adjust the frequency of data collection based on the remaining battery level of the user's device. For example, the collection unit can reduce the frequency of data collection when the remaining battery level is low. The collection unit can also increase the frequency of data collection when the remaining battery level is sufficient. Furthermore, the collection unit can moderately adjust the frequency of data collection when the remaining battery level is medium. This allows the battery life of the device to be extended by adjusting the frequency of data collection according to the remaining battery level of the user's device.

[0106] The analysis unit can estimate the user's emotions and classify the type of risk based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize classifying stress-related risks. Also, if the user is relaxed, the analysis unit can prioritize classifying health-related risks. Furthermore, if the user is in a hurry, the analysis unit can prioritize classifying high-priority risks. In this way, more effective risk management is possible by classifying the type of risk according to the user's emotions.

[0107] The providing unit can predict the occurrence of a risk based on the user's past behavioral history and provide an alert in advance for the predicted risk. For example, the providing unit can analyze the user's past behavioral patterns and calculate the probability of the risk occurring. The providing unit can also analyze the user's behavioral history as time-series data and predict the timing of the risk occurrence. Furthermore, the providing unit can classify the user's behavioral history using a clustering algorithm and predict the occurrence of a risk for each cluster. In this way, the occurrence of a risk can be predicted based on the user's past behavioral history and an alert can be provided in advance, thereby preventing the risk from occurring.

[0108] The collection unit can estimate the user's emotions and adjust the data collection method based on the estimated user's emotions. For example, the collection unit can select a non-invasive data collection method when the user is feeling stressed. Alternatively, the collection unit can select an invasive data collection method to collect detailed data when the user is relaxed. Furthermore, the collection unit can select a method for quickly collecting data when the user is in a hurry. In this way, adjusting the data collection method according to the user's emotions can reduce the burden on the user and collect more accurate data.

[0109] The analysis unit can identify risk occurrence patterns based on the user's behavioral history and propose risk prevention measures. For example, the analysis unit can analyze the user's past behavioral patterns to identify risk occurrence patterns. The analysis unit can also analyze the user's behavioral history as time-series data to visualize risk occurrence patterns. Furthermore, the analysis unit can classify the user's behavioral history using a clustering algorithm and propose risk prevention measures for each cluster. In this way, risk occurrence patterns can be identified based on the user's behavioral history and proposed risk prevention measures, thereby preventing risks before they occur.

[0110] The providing unit can estimate the user's emotions and customize the content of the measures based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can suggest measures to help the user relax. Also, if the user is relaxed, the providing unit can suggest measures to maintain health. Furthermore, if the user is in a hurry, the providing unit can suggest measures that can be implemented quickly. In this way, by customizing the content of the measures according to the user's emotions, it is possible to provide the optimal measures for the user.

[0111] The collection unit can adjust the timing of data collection based on the user's device usage. For example, the collection unit can reduce the frequency of data collection when the user uses the device frequently. Alternatively, the collection unit can increase the frequency of data collection when the user does not use the device often. Furthermore, the collection unit can adjust the timing of data collection according to the time of day when the user uses the device. This makes it possible to optimize device performance by adjusting the timing of data collection according to the user's device usage.

[0112] The analysis unit can estimate the user's emotions and determine the priority of risks based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize stress-related risks. If the user is relaxed, the analysis unit can also prioritize health-related risks. Furthermore, if the user is in a hurry, the analysis unit can also prioritize high-priority risks. In this way, by determining the priority of risks according to the user's emotions, it is possible to prioritize and manage important risks.

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

[0114] Step 1: The collection unit collects data on the user's behavioral history, habits, and status. For example, the collection unit collects the user's location information using GPS and app usage history as log data. The collection unit also uses sensors to measure the user's eating patterns, exercise frequency, health condition, and stress level. Step 2: The analysis unit analyzes the data collected by the collection unit and determines and quantifies risks. For example, the analysis unit uses AI to analyze the data and determine health and security risks. The analysis unit also quantifies risks using a scoring system, expressing health risks as scores from 0 to 100, for example. Step 3: The provider provides alerts and countermeasures based on the risks determined by the analyzer. For example, the provider sends a push notification to the user and suggests countermeasures such as an exercise plan for high-risk behaviors or conditions. Step 4: The output unit notifies the user of the risk based on the alerts and countermeasures provided by the provision unit. For example, the output unit displays a notification on the user's smartphone and urges the user to take appropriate countermeasures.

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

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0184] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, 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.

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

[0186] [Explanation of symbols]

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

Claims

1. A system comprising: a collection unit that collects data on a user's behavioral history, habits, and condition; an analysis unit that analyzes the data collected by the collection unit and determines and quantifies risks; a provision unit that provides alerts and countermeasures based on the risks determined by the analysis unit; and an output unit that notifies the user of risks based on the alerts and countermeasures provided by the provision unit.

2. 2. The system according to claim 1, wherein the analysis unit analyzes risk factors such as lack of exercise, unbalanced diet, and stress level, and determines and quantifies the risk.

3. The system according to claim 1 , wherein the providing unit provides alerts and countermeasures for high-risk behaviors and situations.

4. The system according to claim 1 , wherein the output unit notifies a user of a risk and urges the user to take measures.

5. The system according to claim 1 , wherein the collection unit estimates a user's emotion and adjusts the timing of data collection based on the estimated user's emotion.

6. The collecting unit Analyze users' past behavioral history and select the optimal data collection method 2. The system of claim 1.

7. The collecting unit Filtering data collection based on the user's current life situation and interests 2. The system of claim 1.

8. The collecting unit When collecting data, select the optimal collection method depending on the user's input method 2. The system of claim 1.

Images