System

Abstract

To provide a system according to an embodiment that analyzes lifelog data on a user, and properly suggests an action to take.SOLUTION: A system according to an embodiment comprises: a collection unit; an analysis unit; and a recommendation unit. The collection unit is configured to collect lifelog data on a user. The analysis unit is configured to analyze the lifelog data collected by the collection unit. The recommendation unit is configured to suggest an action to take as a recommendation on the basis of the data analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately utilize a user's life log data to appropriately suggest the next action to be taken, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze a user's life log data and appropriately suggest the next action to be taken. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects life log data of a user. The analysis unit analyzes the life log data collected by the collection unit. The recommendation unit presents a recommendation on a next action to be taken based on the data analyzed by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can analyze the user's life log data and appropriately suggest the next action to be taken. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A health management system according to an embodiment of the present invention is a system in which an AI presents a recommendation of the next action to be taken on an event basis based on life log data such as the user's wake-up / sleep time, activities such as walking and eating, blood pressure and heart rate, without requiring inquiries from the user. This health management system is a mechanism in which the user's life log data is collected, analyzed by the AI, and the next action to be taken is presented as a recommendation. For example, data such as the user's wake-up / sleep time, activities such as walking and eating, blood pressure and heart rate are collected. For this purpose, these data are collected in real time using a wearable device. Next, the collected life log data is analyzed by the AI. The AI ​​uses machine learning and deep learning to analyze the user's behavioral patterns and health status, and detects abnormal patterns and events that require attention. For example, events such as staying awake for a long time, eating too much, and high blood pressure are detected. Based on the detected events, the AI ​​presents the next action to be taken as a recommendation. For example, if a user has been awake for a long time, the AI ​​will recommend actions such as "take a break", if the user has eaten too much, "eat less", and if the user has high blood pressure, "relax". This mechanism allows the user to take appropriate actions based on their health condition and behavioral patterns. The AI ​​automatically presents recommendations without the need for user inquiries, which saves the user time. In addition, by collecting and analyzing data in real time, it is possible to respond quickly. For example, if a user has been awake for a long time, the AI ​​will recommend "take a break". This allows the user to take a break at the appropriate time and maintain their health. In addition, if the user has eaten too much, the AI ​​will recommend "eat less". This allows the user to control the amount of food they eat and maintain a healthy diet. In addition, if the user's blood pressure is high, the AI ​​will recommend "relax". This allows the user to reduce stress and keep their blood pressure normal.In this way, AI can analyze a user's life log data and recommend the next action to be taken, thereby supporting the user's health management. This allows the health management system to monitor the user's health condition in real time and recommend appropriate actions.

[0029] The health management system according to the embodiment includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects life log data of a user. The user's life log data includes, for example, wake-up / sleep time, walking and eating activities, blood pressure, heart rate, etc., but is not limited to such examples. The collection unit collects these data in real time, for example, using a wearable device. Wearable devices include a smart watch and a fitness tracker. For example, the collection unit can collect the user's heart rate and number of steps in real time using a smart watch. The collection unit can also collect the user's exercise amount and calories burned using a fitness tracker. Furthermore, the collection unit can periodically measure the user's blood pressure using a blood pressure monitor and collect data. The analysis unit analyzes the life log data collected by the collection unit. The analysis unit analyzes the collected data using machine learning and deep learning, and analyzes the user's behavioral patterns and health condition. For example, the analysis unit can analyze the user's behavioral patterns using a machine learning algorithm. The analysis unit can also analyze the user's health condition using a deep learning algorithm. For example, the analysis unit can use a neural network to analyze the user's heart rate and blood pressure fluctuations and detect abnormal patterns. Furthermore, the analysis unit can use a convolutional neural network to analyze the user's walking pattern and detect abnormal walking patterns. The recommendation unit presents the next action to be taken as a recommendation based on the data analyzed by the analysis unit. The recommendation unit recommends actions such as "take a break" if the user has been awake for a long time, "avoid eating" if the user has eaten too much, and "relax" if the user has high blood pressure. For example, the recommendation unit can recommend taking a break if the user has been awake for a long time. The recommendation unit can also recommend abstaining from eating if the user has eaten too much.Furthermore, the recommendation unit can also recommend relaxing when the user's blood pressure is high. This allows the health management system according to the embodiment to monitor the user's health condition in real time and recommend appropriate actions. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can make recommendations using an AI model that receives data analyzed by the analysis unit as input and outputs the next action to be taken.

[0030] The collection unit collects the user's life log data. The user's life log data includes, but is not limited to, wake-up / sleep times, walking and eating activities, blood pressure, and heart rate. The collection unit collects these data in real time, for example, using a wearable device. The wearable device includes a smart watch and a fitness tracker. For example, the collection unit can collect the user's heart rate and number of steps in real time using a smart watch. The collection unit can also collect the user's amount of exercise and calories burned using a fitness tracker. Furthermore, the collection unit can periodically measure the user's blood pressure using a blood pressure monitor and collect data. The collection unit centrally manages the data obtained from these devices and transmits it to a cloud server so that the analysis unit and recommendation unit can access it. For example, the smart watch measures the user's heart rate every minute and transmits the data to a smartphone via Bluetooth (registered trademark) or Wi-Fi. The smartphone uploads these data to a cloud server and collects the data in real time. The fitness tracker measures the number of steps and the amount of exercise of the user, and similarly transmits the data to a cloud server. The blood pressure monitor records fluctuations in blood pressure by having the user measure it periodically, and collects data. This allows the collection unit to monitor the user's health condition in real time and provide detailed data. Furthermore, the collection unit can use an algorithm to integrate data from multiple devices and remove outliers and noise to improve the accuracy of the data. For example, by comparing data from a smartwatch and a fitness tracker and filtering out data that does not match, more accurate data can be collected. This allows the collection unit to accurately grasp the user's health condition and provide high-quality data to the analysis unit and recommendation unit.

[0031] The analysis unit analyzes the life log data collected by the collection unit. The analysis unit uses machine learning and deep learning to analyze the collected data and analyze the user's behavioral patterns and health condition. For example, the analysis unit can use a machine learning algorithm to analyze the user's behavioral patterns. The analysis unit can also use a deep learning algorithm to analyze the user's health condition. For example, the analysis unit can use a neural network to analyze the user's heart rate and blood pressure fluctuations and detect abnormal patterns. Furthermore, the analysis unit can use a convolutional neural network to analyze the user's walking pattern and detect abnormal walking patterns. The analysis unit can use these algorithms to monitor the user's health condition in real time, and if an abnormality is detected, can immediately issue an alert. For example, if the user's heart rate is abnormally high or blood pressure rises rapidly, the analysis unit can issue an alert and alert the user. The analysis unit can also analyze trends in the user's health condition based on past data and support long-term health management. For example, it can analyze data from the past few months to grasp changes in the user's health condition and predict future risks. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings, allowing the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The recommendation unit presents the next action to be taken as a recommendation based on the data analyzed by the analysis unit. The recommendation unit recommends actions such as "take a break" when the user has been awake for a long time, "avoid eating" when the user has eaten too much, and "relax" when the user has high blood pressure. For example, the recommendation unit can recommend taking a break when the user has been awake for a long time. The recommendation unit can also recommend abstaining from eating when the user has eaten too much. Furthermore, the recommendation unit can also recommend relaxing when the user's blood pressure is high. This allows the health management system according to the embodiment to monitor the user's health condition in real time and recommend an appropriate action. Part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can make a recommendation using an AI model that inputs data analyzed by the analysis unit and outputs an action to be taken next. Specifically, the recommendation unit uses a machine learning model to suggest optimal actions based on the user's behavioral patterns and health condition. For example, the recommendation unit can predict what actions the user should take and suggest appropriate actions using a model that has learned the user's past data. Furthermore, the recommendation unit can collect user feedback and continuously improve the accuracy of the suggestions. For example, the results of the user performing the suggested action can be collected as feedback and used as learning data for the model, allowing for more accurate suggestions. This allows the recommendation unit to support the user's health management and contribute to improving the health condition.

[0033] The collection unit can collect data on the user's wake-up and sleep times, walking or eating activities, blood pressure, and heart rate using a wearable device. The collection unit collects the user's wake-up and sleep times using, for example, a smart watch. For example, the collection unit can monitor the user's sleep patterns and record the wake-up and sleep times using a sensor of the smart watch. The collection unit can also collect the user's walking activity using a fitness tracker. For example, the collection unit can count the user's steps and record the walking activity using a pedometer of the fitness tracker. Furthermore, the collection unit can monitor the user's heart rate in real time and collect heart rate data using a smart watch or a fitness tracker. For example, the collection unit measures the user's heart rate using a heart rate sensor of the smart watch and collects data. This allows the collection unit to collect the user's life log data in real time using a wearable device. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data obtained from a wearable device into the generation AI and have the generation AI analyze and filter the data.

[0034] The analysis unit can analyze the collected data using machine learning or deep learning to analyze the behavioral patterns and health condition of the user. The analysis unit can analyze the collected data using, for example, a machine learning algorithm. For example, the analysis unit can analyze the behavioral patterns of the user using a decision tree algorithm. The analysis unit can also analyze the health condition of the user using a support vector machine. Furthermore, the analysis unit can analyze the collected data using a deep learning algorithm. For example, the analysis unit can analyze the fluctuations in the user's heart rate and blood pressure using a neural network to detect abnormal patterns. The analysis unit can also analyze the user's walking pattern using a convolutional neural network to detect abnormal walking patterns. Thereby, the analysis unit can improve the accuracy of the data analysis using machine learning or deep learning. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to the generation AI and cause the generation AI to execute the analysis of the data.

[0035] The recommendation unit can recommend the action of "taking a break" if the user has been awake for a long time, "avoiding eating" if the user has eaten too much, and "relaxing" if the user has high blood pressure based on the analysis result. For example, the recommendation unit can recommend "taking a break" if the user has been awake for a long time based on the analysis result. For example, the recommendation unit can recommend taking a break if the user has been awake for more than 8 hours. The recommendation unit can also recommend "avoiding eating" if the user has eaten too much. For example, the recommendation unit can recommend abstaining from eating if the user has exceeded the daily calorie intake. Furthermore, the recommendation unit can also recommend "relaxing" if the user has high blood pressure. For example, the recommendation unit can recommend relaxing if the user's systolic blood pressure exceeds 140 mmHg. This allows the recommendation unit to support the user's health management by recommending appropriate actions based on the analysis results. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit may input the analysis results to a generation AI and cause the generation AI to execute the next action to be taken.

[0036] The collection unit can analyze the user's past life log data and select an appropriate collection method. The collection unit customizes the collection method based on, for example, activities that the user frequently performed in the past. For example, the collection unit can analyze the user's frequent exercise and meal patterns in the past and adjust the collection method based on the patterns. The collection unit can also adjust the collection method based on the user's past health condition. For example, the collection unit can analyze the user's past blood pressure and heart rate data and select a collection method according to the health condition. Furthermore, the collection unit can analyze the user's past data collection pattern and suggest an optimal collection method. For example, the collection unit can analyze what device the user used to collect data in the past and suggest an optimal collection method based on the analysis. This allows the collection unit to select an optimal collection method by analyzing past data. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past life log data to the generation AI and cause the generation AI to select an optimal collection method.

[0037] The collection unit can filter the life log data based on the user's current living situation and field of interest when collecting the life log data. The collection unit filters the data to be collected based on, for example, the activity currently being performed by the user. For example, when the user is exercising, the collection unit can preferentially collect data related to exercise. The collection unit can also select the data to be collected based on the user's field of interest. For example, when the user is interested in health, the collection unit can preferentially collect data related to health. Furthermore, the collection unit can adjust the type of data to be collected according to the user's living situation. For example, when the user is at work, the collection unit can preferentially collect data related to work. As a result, the collection unit filters the data based on the current living situation and field of interest, thereby improving the accuracy of the data to be collected. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's current living situation and field of interest to the generation AI and cause the generation AI to filter the data.

[0038] The collection unit can preferentially collect highly relevant data based on the geographical location information of the user when collecting life log data. For example, when the user is in a specific location, the collection unit preferentially collects data related to the location. For example, when the user is in a gym, the collection unit can preferentially collect data related to exercise. In addition, when the user is moving, the collection unit can also preferentially collect data related to movement. For example, when the user is driving a car, the collection unit can preferentially collect data related to driving. Furthermore, when the user is at home, the collection unit can also preferentially collect data related to activities at home. For example, when the user is relaxing at home, the collection unit can preferentially collect data related to relaxation. In this way, the collection unit can preferentially collect highly relevant data by taking the geographical location information into consideration. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to the generation AI and cause the generation AI to perform preferential collection of highly relevant data.

[0039] The collection unit can analyze the user's social media activities and collect related data when collecting life log data. The collection unit collects related data based on, for example, activities shared by the user on social media. For example, when the user shares a record of exercise on social media, the collection unit can collect data related to exercise. The collection unit can also analyze the content of the user's social media posts and collect related data. For example, when the user posts a photo of a meal on social media, the collection unit can collect data related to meals. Furthermore, the collection unit can also collect related data taking into account the user's social media friendships. For example, the collection unit can collect related data based on activities shared by the user's friends on social media. In this way, the collection unit can collect related data by analyzing social media activities. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activities to the generation AI and cause the generation AI to execute collection of related data.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. The analysis unit performs a detailed analysis on important data, for example. For example, the analysis unit can perform a detailed analysis when the data of the user's heart rate or blood pressure shows an abnormal value. The analysis unit can also perform a simplified analysis on data of low importance. For example, the analysis unit can perform a simplified analysis when the user's step count data is within a normal range. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. For example, the analysis unit can perform a detailed analysis when the user's health condition changes suddenly, and perform a simplified analysis when the health condition is stable. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data, thereby enabling efficient data analysis. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a health-related analysis algorithm to health data. For example, the analysis unit can apply an algorithm that evaluates health risks to data on the user's heart rate and blood pressure. The analysis unit can also apply an activity-related analysis algorithm to activity data. For example, the analysis unit can apply an algorithm that analyzes movement patterns to data on the user's number of steps and amount of exercise. Furthermore, the analysis unit can apply a sleep-related analysis algorithm to sleep data. For example, the analysis unit can apply an algorithm that evaluates the quality of sleep to data on the user's sleep pattern. Thereby, the analysis unit applies an appropriate analysis algorithm depending on the category of data, thereby improving the analysis accuracy. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0042] The analysis unit can determine the priority of the analysis based on the collection time of the data during the analysis. The analysis unit, for example, prioritizes the analysis of the latest data. For example, the analysis unit can prioritize the analysis of the latest heart rate and blood pressure data of the user. The analysis unit can also analyze the latest data while referring to past data. For example, the analysis unit can analyze the latest data while referring to data on the user's past health condition. Furthermore, the analysis unit can dynamically adjust the priority of the analysis according to the collection time of the data. For example, the analysis unit can analyze the latest data with priority when the user's health condition changes suddenly, and can analyze the latest data while referring to past data when the health condition is stable. Thereby, the analysis unit can prioritize the analysis of the latest data by determining the priority of the analysis based on the collection time of the data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collection time of the data to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0043] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, when the analysis unit determines that the data of the user's heart rate or blood pressure is highly relevant, the analysis unit can prioritize analysis of these data. The analysis unit can also postpone analysis of less relevant data. For example, when the analysis unit determines that the data of the user's step count is less relevant, the analysis unit can postpone analysis of these data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of data. For example, when the user's health condition changes suddenly, the analysis unit can prioritize analysis of highly relevant data, and when the health condition is stable, the analysis unit can postpone analysis of less relevant data. This allows the analysis unit to adjust the order of analysis based on the relevance of data, thereby enabling efficient data analysis. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of data to the generation AI and cause the generation AI to adjust the order of analysis.

[0044] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the event at the time of recommendation. For example, the recommendation unit provides a detailed recommendation for an important event. For example, the recommendation unit can provide a detailed recommendation for an event that has a significant impact on the health of the user. The recommendation unit can also provide a simple recommendation for an event with a low level of importance. For example, the recommendation unit can provide a simple recommendation for the daily activity of the user. Furthermore, the recommendation unit can dynamically adjust the level of detail of the recommendation according to the importance of the event. For example, the recommendation unit can provide a detailed recommendation when the health condition of the user changes suddenly, and provide a simple recommendation when the health condition is stable. As a result, the recommendation unit can adjust the level of detail of the recommendation based on the importance of the event, thereby enabling efficient recommendation. Some or all of the above-mentioned processes in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit may input the importance of an event to the generation AI and cause the generation AI to adjust the level of detail of the recommendation.

[0045] The recommendation unit can apply different recommendation algorithms depending on the category of the event when making a recommendation. For example, the recommendation unit applies a health-related recommendation algorithm to a health-related event. For example, the recommendation unit can apply an algorithm that evaluates a health risk based on data on the user's heart rate and blood pressure. The recommendation unit can also apply an activity-related recommendation algorithm to an activity-related event. For example, the recommendation unit can apply an algorithm that analyzes an exercise pattern based on data on the user's number of steps and amount of exercise. Furthermore, the recommendation unit can also apply a sleep-related recommendation algorithm to a sleep-related event. For example, the recommendation unit can apply an algorithm that evaluates the quality of sleep based on data on the user's sleep pattern. As a result, the recommendation unit applies an appropriate recommendation algorithm depending on the category of the event, thereby improving the accuracy of the recommendation. Some or all of the above-mentioned processes in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit may input the category of the event to the generation AI and cause the generation AI to apply an appropriate recommendation algorithm.

[0046] The recommendation unit can determine the priority of the recommendation based on the occurrence time of the event at the time of recommendation. The recommendation unit, for example, preferentially recommends the latest event. For example, the recommendation unit can preferentially recommend the latest event based on the latest health data of the user. The recommendation unit can also recommend the latest event while referring to past events. For example, the recommendation unit can recommend the latest event while referring to data on the user's past health condition. Furthermore, the recommendation unit can dynamically adjust the priority of the recommendation according to the occurrence time of the event. For example, the recommendation unit can preferentially recommend the latest event when the user's health condition changes suddenly, and can make a recommendation while referring to past events when the health condition is stable. As a result, the recommendation unit can preferentially recommend the latest event by determining the priority of the recommendation based on the occurrence time of the event. Some or all of the above-mentioned processes in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit may input the occurrence time of an event to the generation AI and cause the generation AI to determine the priority order of recommendations.

[0047] The recommendation unit can adjust the order of recommendations based on the relevance of events at the time of recommendation. For example, the recommendation unit preferentially recommends events with high relevance. For example, the recommendation unit can preferentially recommend events determined to be highly relevant to the user's health state. The recommendation unit can also postpone events with low relevance. For example, the recommendation unit can postpone and recommend events determined to be less relevant to the user's daily activities. Furthermore, the recommendation unit can dynamically adjust the order of recommendations according to the relevance of events. For example, when the user's health state changes suddenly, the recommendation unit can preferentially recommend events with high relevance, and when the health state is stable, can postpone and recommend events with low relevance. As a result, the recommendation unit can adjust the order of recommendations based on the relevance of events, thereby enabling efficient recommendation. Some or all of the above-mentioned processes in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input event relevance to the generation AI and cause the generation AI to adjust the order of recommendations.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The health management system may further include an environmental sensor unit that collects environmental data of the user. The environmental sensor unit collects data such as temperature, humidity, illuminance, and noise level. This allows the impact of the user's living environment on health to be analyzed. For example, if the temperature is too high, it may recommend "moving to a cooler place." Also, if the humidity is too low, it may recommend "using a humidifier." Furthermore, if the noise level is high, it may recommend "moving to a quieter place." This allows appropriate actions to be taken based on the user's living environment.

[0050] The collection unit can automatically recognize the contents of a user's meal using image analysis technology and evaluate the balance of nutrients. For example, when a user takes a photo of a meal, the collection unit can analyze the image and identify the ingredients and type of dish. This allows the system to grasp the details of the user's meal and make recommendations such as "eat more vegetables" if the nutritional balance is unbalanced. It can also automatically calculate the calories of a meal and provide advice to prevent overeating. Furthermore, if the meal contains ingredients that contain a specific allergen, it can recommend "avoiding the allergen."

[0051] The analysis unit can predict future health risks based on the user's past health data. For example, by analyzing past blood pressure and heart rate data, it can predict the possibility of an increased risk of high blood pressure and heart disease in the future. It can also predict future health risks due to lack of exercise based on past exercise data. Furthermore, it can predict health risks due to nutritional imbalance based on past dietary data. This allows the user to understand future health risks in advance and take preventive action.

[0052] The collection unit can dynamically adjust the frequency of data collection based on the user's lifestyle. For example, if the user exercises regularly, the frequency of data collection during exercise can be increased. Also, if the user leads an irregular lifestyle, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user maintains a healthy lifestyle, the frequency of data collection can be reduced to collect the minimum amount of data necessary. This allows the collection unit to flexibly collect data according to the user's lifestyle.

[0053] The analysis unit can compare the user's health data with the data of other users to evaluate the relative health condition. For example, it can evaluate how normal the user's heart rate and blood pressure are compared with other users of the same age. It can also evaluate whether the user's amount of exercise is appropriate compared with other users with the same exercise habits. It can also evaluate whether the user's nutritional balance is appropriate compared with other users with the same eating habits. This allows the user to understand their own health condition in comparison with other users.

[0054] The recommendation unit can recommend actions for region-specific health risks based on the user's geographical location information. For example, if the user lives in a hot and humid region, it can recommend actions for preventing heatstroke. Also, if the user lives in an area with high air pollution, it can recommend refraining from going outside and using an air purifier. Furthermore, if the user lives in a cold region, it can recommend actions for measures against the cold. This allows the user to take appropriate actions for region-specific health risks.

[0055] The recommendation unit can analyze the user's social media activity and customize recommendations based on the user's interests. For example, if the user frequently posts about fitness on social media, actions related to fitness can be recommended. Also, if the user posts about healthy eating, actions related to healthy eating can be recommended. Furthermore, if the user posts about stress management, actions related to stress management can be recommended. This allows for personalized recommendations based on the user's interests.

[0056] The process flow of the first embodiment will be briefly described below.

[0057] Step 1: The collection unit collects the user's life log data. The user's life log data includes wake-up / sleep times, walking and eating activities, blood pressure, heart rate, etc. The collection unit collects these data in real time using a wearable device (such as a smart watch or a fitness tracker). For example, the collection unit collects the user's heart rate and number of steps in real time using a smart watch, collects the amount of exercise and calories burned using a fitness tracker, and periodically measures blood pressure using a blood pressure monitor. Step 2: The analysis unit analyzes the life log data collected by the collection unit. The analysis unit uses machine learning and deep learning to analyze the collected data and analyze the user's behavioral patterns and health condition. For example, a machine learning algorithm is used to analyze behavioral patterns, and a deep learning algorithm is used to analyze health conditions. Specifically, a neural network is used to analyze fluctuations in heart rate and blood pressure to detect abnormal patterns, and a convolutional neural network is used to analyze walking patterns to detect abnormal walking patterns. Step 3: The recommendation unit presents a recommendation for the next action to be taken based on the data analyzed by the analysis unit. For example, it may recommend actions such as "take a break" if the person has been awake for a long time, "eat less" if the person has eaten too much, or "relax" if the person has high blood pressure. The recommendation unit can also make recommendations using an AI model that takes the data analyzed by the analysis unit as input and outputs the next action to be taken.

[0058] (Example 2) A health management system according to an embodiment of the present invention is a system in which an AI presents a recommendation of the next action to be taken on an event basis based on life log data such as the user's wake-up / sleep time, activities such as walking and eating, blood pressure and heart rate, without requiring inquiries from the user. This health management system is a mechanism in which the user's life log data is collected, analyzed by the AI, and the next action to be taken is presented as a recommendation. For example, data such as the user's wake-up / sleep time, activities such as walking and eating, blood pressure and heart rate are collected. For this purpose, these data are collected in real time using a wearable device. Next, the collected life log data is analyzed by the AI. The AI ​​uses machine learning and deep learning to analyze the user's behavioral patterns and health status, and detects abnormal patterns and events that require attention. For example, events such as staying awake for a long time, eating too much, and high blood pressure are detected. Based on the detected events, the AI ​​presents the next action to be taken as a recommendation. For example, if a user has been awake for a long time, the AI ​​will recommend actions such as "take a break", if the user has eaten too much, "eat less", and if the user has high blood pressure, "relax". This mechanism allows the user to take appropriate actions based on their health condition and behavioral patterns. The AI ​​automatically presents recommendations without the need for user inquiries, which saves the user time. In addition, by collecting and analyzing data in real time, it is possible to respond quickly. For example, if a user has been awake for a long time, the AI ​​will recommend "take a break". This allows the user to take a break at the appropriate time and maintain their health. In addition, if the user has eaten too much, the AI ​​will recommend "eat less". This allows the user to control the amount of food they eat and maintain a healthy diet. In addition, if the user's blood pressure is high, the AI ​​will recommend "relax". This allows the user to reduce stress and keep their blood pressure normal.In this way, AI can analyze a user's life log data and recommend the next action to be taken, thereby supporting the user's health management. This allows the health management system to monitor the user's health condition in real time and recommend appropriate actions.

[0059] The health management system according to the embodiment includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects life log data of a user. The user's life log data includes, for example, wake-up / sleep time, walking and eating activities, blood pressure, heart rate, etc., but is not limited to such examples. The collection unit collects these data in real time, for example, using a wearable device. Wearable devices include a smart watch and a fitness tracker. For example, the collection unit can collect the user's heart rate and number of steps in real time using a smart watch. The collection unit can also collect the user's exercise amount and calories burned using a fitness tracker. Furthermore, the collection unit can periodically measure the user's blood pressure using a blood pressure monitor and collect data. The analysis unit analyzes the life log data collected by the collection unit. The analysis unit analyzes the collected data using machine learning and deep learning, and analyzes the user's behavioral patterns and health condition. For example, the analysis unit can analyze the user's behavioral patterns using a machine learning algorithm. The analysis unit can also analyze the user's health condition using a deep learning algorithm. For example, the analysis unit can use a neural network to analyze the user's heart rate and blood pressure fluctuations and detect abnormal patterns. Furthermore, the analysis unit can use a convolutional neural network to analyze the user's walking pattern and detect abnormal walking patterns. The recommendation unit presents the next action to be taken as a recommendation based on the data analyzed by the analysis unit. The recommendation unit recommends actions such as "take a break" if the user has been awake for a long time, "avoid eating" if the user has eaten too much, and "relax" if the user has high blood pressure. For example, the recommendation unit can recommend taking a break if the user has been awake for a long time. The recommendation unit can also recommend abstaining from eating if the user has eaten too much.Furthermore, the recommendation unit can also recommend relaxing when the user's blood pressure is high. This allows the health management system according to the embodiment to monitor the user's health condition in real time and recommend appropriate actions. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can make recommendations using an AI model that receives data analyzed by the analysis unit as input and outputs the next action to be taken.

[0060] The collection unit collects the user's life log data. The user's life log data includes, but is not limited to, for example, wake-up / sleep times, walking and eating activities, blood pressure, and heart rate. The collection unit collects these data in real time, for example, using a wearable device. The wearable device includes a smart watch and a fitness tracker. For example, the collection unit can collect the user's heart rate and number of steps in real time using a smart watch. The collection unit can also collect the user's amount of exercise and calories burned using a fitness tracker. Furthermore, the collection unit can periodically measure the user's blood pressure using a blood pressure monitor and collect data. The collection unit centrally manages the data obtained from these devices and transmits it to a cloud server so that the analysis unit and recommendation unit can access it. For example, the smart watch measures the user's heart rate every minute and transmits the data to a smartphone via Bluetooth or Wi-Fi. The smartphone uploads these data to a cloud server and collects the data in real time. The fitness tracker measures the number of steps and the amount of exercise of the user, and similarly transmits the data to a cloud server. The blood pressure monitor records fluctuations in blood pressure by having the user measure it periodically, and collects data. This allows the collection unit to monitor the user's health condition in real time and provide detailed data. Furthermore, the collection unit can use an algorithm to integrate data from multiple devices and remove outliers and noise to improve the accuracy of the data. For example, by comparing data from a smartwatch and a fitness tracker and filtering out data that does not match, more accurate data can be collected. This allows the collection unit to accurately grasp the user's health condition and provide high-quality data to the analysis unit and recommendation unit.

[0061] The analysis unit analyzes the life log data collected by the collection unit. The analysis unit uses machine learning and deep learning to analyze the collected data and analyze the user's behavioral patterns and health condition. For example, the analysis unit can use a machine learning algorithm to analyze the user's behavioral patterns. The analysis unit can also use a deep learning algorithm to analyze the user's health condition. For example, the analysis unit can use a neural network to analyze the user's heart rate and blood pressure fluctuations and detect abnormal patterns. Furthermore, the analysis unit can use a convolutional neural network to analyze the user's walking pattern and detect abnormal walking patterns. The analysis unit can use these algorithms to monitor the user's health condition in real time, and if an abnormality is detected, can immediately issue an alert. For example, if the user's heart rate is abnormally high or blood pressure rises rapidly, the analysis unit can issue an alert and alert the user. The analysis unit can also analyze trends in the user's health condition based on past data and support long-term health management. For example, it can analyze data from the past few months to grasp changes in the user's health condition and predict future risks. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings, allowing the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0062] The recommendation unit presents the next action to be taken as a recommendation based on the data analyzed by the analysis unit. The recommendation unit recommends actions such as "take a break" when the user has been awake for a long time, "avoid eating" when the user has eaten too much, and "relax" when the user has high blood pressure. For example, the recommendation unit can recommend taking a break when the user has been awake for a long time. The recommendation unit can also recommend abstaining from eating when the user has eaten too much. Furthermore, the recommendation unit can also recommend relaxing when the user's blood pressure is high. This allows the health management system according to the embodiment to monitor the user's health condition in real time and recommend an appropriate action. Part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can make a recommendation using an AI model that inputs data analyzed by the analysis unit and outputs an action to be taken next. Specifically, the recommendation unit uses a machine learning model to suggest optimal actions based on the user's behavioral patterns and health condition. For example, the recommendation unit can predict what actions the user should take and suggest appropriate actions using a model that has learned the user's past data. Furthermore, the recommendation unit can collect user feedback and continuously improve the accuracy of the suggestions. For example, the results of the user performing the suggested action can be collected as feedback and used as learning data for the model, allowing for more accurate suggestions. This allows the recommendation unit to support the user's health management and contribute to improving the health condition.

[0063] The collection unit can collect data on the user's wake-up and sleep times, walking or eating activities, blood pressure, and heart rate using a wearable device. The collection unit collects the user's wake-up and sleep times using, for example, a smart watch. For example, the collection unit can monitor the user's sleep patterns and record the wake-up and sleep times using a sensor of the smart watch. The collection unit can also collect the user's walking activity using a fitness tracker. For example, the collection unit can count the user's steps and record the walking activity using a pedometer of the fitness tracker. Furthermore, the collection unit can monitor the user's heart rate in real time and collect heart rate data using a smart watch or a fitness tracker. For example, the collection unit measures the user's heart rate using a heart rate sensor of the smart watch and collects data. This allows the collection unit to collect the user's life log data in real time using a wearable device. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data obtained from a wearable device into the generation AI and have the generation AI analyze and filter the data.

[0064] The analysis unit can analyze the collected data using machine learning or deep learning to analyze the behavioral patterns and health condition of the user. The analysis unit can analyze the collected data using, for example, a machine learning algorithm. For example, the analysis unit can analyze the behavioral patterns of the user using a decision tree algorithm. The analysis unit can also analyze the health condition of the user using a support vector machine. Furthermore, the analysis unit can analyze the collected data using a deep learning algorithm. For example, the analysis unit can analyze the fluctuations in the user's heart rate and blood pressure using a neural network to detect abnormal patterns. The analysis unit can also analyze the user's walking pattern using a convolutional neural network to detect abnormal walking patterns. Thereby, the analysis unit can improve the accuracy of the data analysis using machine learning or deep learning. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to the generation AI and cause the generation AI to execute the analysis of the data.

[0065] The recommendation unit can recommend the action of "taking a break" if the user has been awake for a long time, "avoiding eating" if the user has eaten too much, and "relaxing" if the user has high blood pressure based on the analysis result. For example, the recommendation unit can recommend "taking a break" if the user has been awake for a long time based on the analysis result. For example, the recommendation unit can recommend taking a break if the user has been awake for more than 8 hours. The recommendation unit can also recommend "avoiding eating" if the user has eaten too much. For example, the recommendation unit can recommend abstaining from eating if the user has exceeded the daily calorie intake. Furthermore, the recommendation unit can also recommend "relaxing" if the user has high blood pressure. For example, the recommendation unit can recommend relaxing if the user's systolic blood pressure exceeds 140 mmHg. This allows the recommendation unit to support the user's health management by recommending appropriate actions based on the analysis results. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit may input the analysis results to a generation AI and cause the generation AI to execute the next action to be taken.

[0066] The collection unit can estimate the user's emotions and adjust the collection timing of the life log data based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit increases the collection timing to collect detailed data. For example, the collection unit can monitor the fluctuations in the user's heart rate and blood pressure in real time, and increase the collection frequency when it is determined that the stress level is high. In addition, when the user is relaxed, the collection unit can relax the collection timing to collect the minimum amount of data required. For example, the collection unit can reduce the collection frequency when the user's heart rate and blood pressure are stable. Furthermore, when the user is in a hurry, the collection unit can shorten the collection timing to quickly collect data. For example, the collection unit can monitor the user's movement speed and activity level, and increase the collection frequency when it is determined that the user is in a hurry. This allows the collection unit to adjust the collection timing according to the user's emotions, thereby enabling more appropriate data collection. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the collection timing.

[0067] The collection unit can analyze the user's past life log data and select an appropriate collection method. The collection unit customizes the collection method based on, for example, activities that the user frequently performed in the past. For example, the collection unit can analyze the user's frequent exercise and meal patterns in the past and adjust the collection method based on the patterns. The collection unit can also adjust the collection method based on the user's past health condition. For example, the collection unit can analyze the user's past blood pressure and heart rate data and select a collection method according to the health condition. Furthermore, the collection unit can analyze the user's past data collection pattern and suggest an optimal collection method. For example, the collection unit can analyze what device the user used to collect data in the past and suggest an optimal collection method based on the analysis. This allows the collection unit to select an optimal collection method by analyzing past data. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past life log data to the generation AI and cause the generation AI to select an optimal collection method.

[0068] The collection unit can filter the life log data based on the user's current living situation and field of interest when collecting the life log data. The collection unit filters the data to be collected based on, for example, the activity currently being performed by the user. For example, when the user is exercising, the collection unit can preferentially collect data related to exercise. The collection unit can also select the data to be collected based on the user's field of interest. For example, when the user is interested in health, the collection unit can preferentially collect data related to health. Furthermore, the collection unit can adjust the type of data to be collected according to the user's living situation. For example, when the user is at work, the collection unit can preferentially collect data related to work. As a result, the collection unit filters the data based on the current living situation and field of interest, thereby improving the accuracy of the data to be collected. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's current living situation and field of interest to the generation AI and cause the generation AI to filter the data.

[0069] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit preferentially collects stress-related data. For example, the collection unit can monitor the fluctuations in the user's heart rate and blood pressure, and preferentially collect stress-related data when it is determined that the stress level is high. In addition, the collection unit can also preferentially collect relaxation-related data when the user is relaxed. For example, the collection unit can preferentially collect relaxation-related data when the user's heart rate and blood pressure are stable. Furthermore, the collection unit can also preferentially collect data related to urgent activities when the user is in a hurry. For example, the collection unit can monitor the user's moving speed and activity level, and preferentially collect data related to urgent activities when it is determined that the user is in a hurry. In this way, the collection unit can preferentially collect important data by determining the priority of data based on emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit may input the user's emotion data to the generation AI and cause the generation AI to determine the priority order of the data.

[0070] The collection unit can preferentially collect highly relevant data based on the geographical location information of the user when collecting life log data. For example, when the user is in a specific location, the collection unit preferentially collects data related to the location. For example, when the user is in a gym, the collection unit can preferentially collect data related to exercise. In addition, when the user is moving, the collection unit can also preferentially collect data related to movement. For example, when the user is driving a car, the collection unit can preferentially collect data related to driving. Furthermore, when the user is at home, the collection unit can also preferentially collect data related to activities at home. For example, when the user is relaxing at home, the collection unit can preferentially collect data related to relaxation. In this way, the collection unit can preferentially collect highly relevant data by taking the geographical location information into consideration. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to the generation AI and cause the generation AI to perform preferential collection of highly relevant data.

[0071] The collection unit can analyze the user's social media activities and collect related data when collecting life log data. The collection unit collects related data based on, for example, activities shared by the user on social media. For example, when the user shares a record of exercise on social media, the collection unit can collect data related to exercise. The collection unit can also analyze the content of the user's social media posts and collect related data. For example, when the user posts a photo of a meal on social media, the collection unit can collect data related to meals. Furthermore, the collection unit can also collect related data taking into account the user's social media friendships. For example, the collection unit can collect related data based on activities shared by the user's friends on social media. In this way, the collection unit can collect related data by analyzing social media activities. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activities to the generation AI and cause the generation AI to execute collection of related data.

[0072] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is stressed, the analysis unit provides a simple and highly visible analysis result. For example, when it is determined that the user is stressed, the analysis unit can provide the analysis result in a format that is visually easy to understand using graphs and charts. In addition, when the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, when it is determined that the user is relaxed, the analysis unit can provide a detailed text report. In addition, when the user is in a hurry, the analysis unit can also provide an analysis result that covers the main points. For example, when it is determined that the user is in a hurry, the analysis unit can provide an analysis result that briefly summarizes the main points. Thereby, the analysis unit can provide an analysis result that is easy for the user to understand by adjusting the expression method of the analysis based on the emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part 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 may input the user's emotion data to the generation AI and cause the generation AI to adjust the method of expressing the analysis.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. The analysis unit performs a detailed analysis on important data, for example. For example, the analysis unit can perform a detailed analysis when the data of the user's heart rate or blood pressure shows an abnormal value. The analysis unit can also perform a simplified analysis on data of low importance. For example, the analysis unit can perform a simplified analysis when the user's step count data is within a normal range. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. For example, the analysis unit can perform a detailed analysis when the user's health condition changes suddenly, and perform a simplified analysis when the health condition is stable. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data, thereby enabling efficient data analysis. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a health-related analysis algorithm to health data. For example, the analysis unit can apply an algorithm that evaluates health risks to data on the user's heart rate and blood pressure. The analysis unit can also apply an activity-related analysis algorithm to activity data. For example, the analysis unit can apply an algorithm that analyzes movement patterns to data on the user's number of steps and amount of exercise. Furthermore, the analysis unit can apply a sleep-related analysis algorithm to sleep data. For example, the analysis unit can apply an algorithm that evaluates the quality of sleep to data on the user's sleep pattern. Thereby, the analysis unit applies an appropriate analysis algorithm depending on the category of data, thereby improving the analysis accuracy. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0075] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is stressed, the analysis unit performs a short and to the point analysis. For example, when it is determined that the user is stressed, the analysis unit can provide an analysis result that briefly summarizes the main points. In addition, the analysis unit can perform a detailed analysis when the user is relaxed. For example, when it is determined that the user is relaxed, the analysis unit can provide a detailed text report. In addition, the analysis unit can perform a quick analysis when the user is in a hurry. For example, when it is determined that the user is in a hurry, the analysis unit can perform an analysis in a short time and provide an analysis result that summarizes the main points. Thereby, the analysis unit can provide an analysis result of an appropriate length for the user by adjusting the length of the analysis based on the emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part 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 may input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the analysis.

[0076] The analysis unit can determine the priority of the analysis based on the collection time of the data during the analysis. The analysis unit, for example, prioritizes the analysis of the latest data. For example, the analysis unit can prioritize the analysis of the latest heart rate and blood pressure data of the user. The analysis unit can also analyze the latest data while referring to past data. For example, the analysis unit can analyze the latest data while referring to data on the user's past health condition. Furthermore, the analysis unit can dynamically adjust the priority of the analysis according to the collection time of the data. For example, the analysis unit can analyze the latest data with priority when the user's health condition changes suddenly, and can analyze the latest data while referring to past data when the health condition is stable. Thereby, the analysis unit can prioritize the analysis of the latest data by determining the priority of the analysis based on the collection time of the data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collection time of the data to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0077] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, when the analysis unit determines that the data of the user's heart rate or blood pressure is highly relevant, the analysis unit can prioritize analysis of these data. The analysis unit can also postpone analysis of less relevant data. For example, when the analysis unit determines that the data of the user's step count is less relevant, the analysis unit can postpone analysis of these data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of data. For example, when the user's health condition changes suddenly, the analysis unit can prioritize analysis of highly relevant data, and when the health condition is stable, the analysis unit can postpone analysis of less relevant data. This allows the analysis unit to adjust the order of analysis based on the relevance of data, thereby enabling efficient data analysis. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of data to the generation AI and cause the generation AI to adjust the order of analysis.

[0078] The recommendation unit can estimate the user's emotions and adjust the way the recommendation is expressed based on the estimated user's emotions. For example, when the user is feeling stressed, the recommendation unit provides a simple and highly visible recommendation. For example, when it is determined that the user is feeling stressed, the recommendation unit can provide a recommendation in a visually easy-to-understand format using graphs and charts. In addition, the recommendation unit can provide a detailed recommendation when the user is relaxed. For example, when it is determined that the user is relaxed, the recommendation unit can provide a detailed text report. Furthermore, when the user is in a hurry, the recommendation unit can provide a recommendation that covers the main points. For example, when it is determined that the user is in a hurry, the recommendation unit can provide a recommendation that briefly summarizes the main points. As a result, the recommendation unit can provide a recommendation that is easy for the user to understand by adjusting the way the recommendation is expressed based on emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the recommendation unit may be performed, for example, using an AI or may be performed without using an AI. For example, the recommendation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the way the recommendation is expressed.

[0079] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the event at the time of recommendation. For example, the recommendation unit provides a detailed recommendation for an important event. For example, the recommendation unit can provide a detailed recommendation for an event that has a significant impact on the health of the user. The recommendation unit can also provide a simple recommendation for an event with a low level of importance. For example, the recommendation unit can provide a simple recommendation for the daily activity of the user. Furthermore, the recommendation unit can dynamically adjust the level of detail of the recommendation according to the importance of the event. For example, the recommendation unit can provide a detailed recommendation when the health condition of the user changes suddenly, and provide a simple recommendation when the health condition is stable. As a result, the recommendation unit can adjust the level of detail of the recommendation based on the importance of the event, thereby enabling efficient recommendation. Some or all of the above-mentioned processes in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit may input the importance of an event to the generation AI and cause the generation AI to adjust the level of detail of the recommendation.

[0080] The recommendation unit can apply different recommendation algorithms depending on the category of the event when making a recommendation. For example, the recommendation unit applies a health-related recommendation algorithm to a health-related event. For example, the recommendation unit can apply an algorithm that evaluates a health risk based on data on the user's heart rate and blood pressure. The recommendation unit can also apply an activity-related recommendation algorithm to an activity-related event. For example, the recommendation unit can apply an algorithm that analyzes an exercise pattern based on data on the user's number of steps and amount of exercise. Furthermore, the recommendation unit can also apply a sleep-related recommendation algorithm to a sleep-related event. For example, the recommendation unit can apply an algorithm that evaluates the quality of sleep based on data on the user's sleep pattern. As a result, the recommendation unit applies an appropriate recommendation algorithm depending on the category of the event, thereby improving the accuracy of the recommendation. Some or all of the above-mentioned processes in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit may input the category of the event to the generation AI and cause the generation AI to apply an appropriate recommendation algorithm.

[0081] The recommendation unit can estimate the user's emotion and adjust the length of the recommendation based on the estimated user's emotion. For example, when the user is stressed, the recommendation unit provides a short and to the point recommendation. For example, when it is determined that the user is stressed, the recommendation unit can provide a recommendation that briefly summarizes the main points. In addition, the recommendation unit can provide a detailed recommendation when the user is relaxed. For example, when it is determined that the user is relaxed, the recommendation unit can provide a detailed text report. Furthermore, the recommendation unit can provide a recommendation quickly when the user is in a hurry. For example, when it is determined that the user is in a hurry, the recommendation unit can provide a recommendation in a short time and present content that covers the main points. In this way, the recommendation unit can provide a recommendation of an appropriate length for the user by adjusting the length of the recommendation based on the emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the recommendation unit may be performed, for example, using an AI or may be performed without using an AI. For example, the recommendation unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the recommendation.

[0082] The recommendation unit can determine the priority of the recommendation based on the occurrence time of the event at the time of recommendation. The recommendation unit, for example, preferentially recommends the latest event. For example, the recommendation unit can preferentially recommend the latest event based on the latest health data of the user. The recommendation unit can also recommend the latest event while referring to past events. For example, the recommendation unit can recommend the latest event while referring to data on the user's past health condition. Furthermore, the recommendation unit can dynamically adjust the priority of the recommendation according to the occurrence time of the event. For example, the recommendation unit can preferentially recommend the latest event when the user's health condition changes suddenly, and can make a recommendation while referring to past events when the health condition is stable. As a result, the recommendation unit can preferentially recommend the latest event by determining the priority of the recommendation based on the occurrence time of the event. Some or all of the above-mentioned processes in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit may input the occurrence time of an event to the generation AI and cause the generation AI to determine the priority order of recommendations.

[0083] The recommendation unit can adjust the order of recommendations based on the relevance of events at the time of recommendation. For example, the recommendation unit preferentially recommends events with high relevance. For example, the recommendation unit can preferentially recommend events determined to be highly relevant to the user's health state. The recommendation unit can also postpone events with low relevance. For example, the recommendation unit can postpone and recommend events determined to be less relevant to the user's daily activities. Furthermore, the recommendation unit can dynamically adjust the order of recommendations according to the relevance of events. For example, when the user's health state changes suddenly, the recommendation unit can preferentially recommend events with high relevance, and when the health state is stable, can postpone and recommend events with low relevance. As a result, the recommendation unit can adjust the order of recommendations based on the relevance of events, thereby enabling efficient recommendation. Some or all of the above-mentioned processes in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input event relevance to the generation AI and cause the generation AI to adjust the order of recommendations.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The health management system may further include an environmental sensor unit that collects environmental data of the user. The environmental sensor unit collects data such as temperature, humidity, illuminance, and noise level. This allows the impact of the user's living environment on health to be analyzed. For example, if the temperature is too high, it may recommend "moving to a cooler place." Also, if the humidity is too low, it may recommend "using a humidifier." Furthermore, if the noise level is high, it may recommend "moving to a quieter place." This allows appropriate actions to be taken based on the user's living environment.

[0086] The collection unit can automatically recognize the contents of a user's meal using image analysis technology and evaluate the balance of nutrients. For example, when a user takes a photo of a meal, the collection unit can analyze the image and identify the ingredients and type of dish. This allows the system to grasp the details of the user's meal and make recommendations such as "eat more vegetables" if the nutritional balance is unbalanced. It can also automatically calculate the calories of a meal and provide advice to prevent overeating. Furthermore, if the meal contains ingredients that contain a specific allergen, it can recommend "avoiding the allergen."

[0087] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated user's emotions. For example, if the user is feeling stressed, relaxing music or images can be provided at the same time as the analysis results are notified. Also, if the user is relaxed, additional related information that may be of interest to the user can be provided when notifying the user of detailed analysis results. Furthermore, if the user is in a hurry, a notification that briefly summarizes the analysis results can be sent and a link that allows the user to check the details later can be provided. This makes it possible to realize a flexible notification method according to the user's emotions.

[0088] The recommendation unit can estimate the user's emotions and adjust the timing of recommendations based on the estimated user's emotions. For example, if the user is feeling stressed, recommendations can be provided at a timing that allows the user to relax. Also, if the user is relaxed, detailed recommendations can be provided. Furthermore, if the user is in a hurry, concise and to the point recommendations can be provided quickly. This makes it possible to provide recommendations at appropriate timing according to the user's emotions.

[0089] The collection unit can estimate the user's emotion and adjust the type of data to be collected based on the estimated user's emotion. For example, if the user is feeling stressed, stress-related data can be preferentially collected. Also, if the user is relaxed, relaxation-related data can be preferentially collected. Furthermore, if the user is in a hurry, data related to a rushed activity can be preferentially collected. In this way, the collection unit can preferentially collect important data by adjusting the type of data based on the emotion.

[0090] The analysis unit can predict future health risks based on the user's past health data. For example, by analyzing past blood pressure and heart rate data, it can predict the possibility of an increased risk of high blood pressure and heart disease in the future. It can also predict future health risks due to lack of exercise based on past exercise data. Furthermore, it can predict health risks due to nutritional imbalance based on past dietary data. This allows the user to understand future health risks in advance and take preventive action.

[0091] The collection unit can dynamically adjust the frequency of data collection based on the user's lifestyle. For example, if the user exercises regularly, the frequency of data collection during exercise can be increased. Also, if the user leads an irregular lifestyle, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user maintains a healthy lifestyle, the frequency of data collection can be reduced to collect the minimum amount of data necessary. This allows the collection unit to flexibly collect data according to the user's lifestyle.

[0092] The analysis unit can compare the user's health data with the data of other users to evaluate the relative health condition. For example, it can evaluate how normal the user's heart rate and blood pressure are compared with other users of the same age. It can also evaluate whether the user's amount of exercise is appropriate compared with other users with the same exercise habits. It can also evaluate whether the user's nutritional balance is appropriate compared with other users with the same eating habits. This allows the user to understand their own health condition in comparison with other users.

[0093] The recommendation unit can recommend actions for region-specific health risks based on the user's geographical location information. For example, if the user lives in a hot and humid region, it can recommend actions for preventing heatstroke. Also, if the user lives in an area with high air pollution, it can recommend refraining from going outside and using an air purifier. Furthermore, if the user lives in a cold region, it can recommend actions for measures against the cold. This allows the user to take appropriate actions for region-specific health risks.

[0094] The recommendation unit can analyze the user's social media activity and customize recommendations based on the user's interests. For example, if the user frequently posts about fitness on social media, actions related to fitness can be recommended. Also, if the user posts about healthy eating, actions related to healthy eating can be recommended. Furthermore, if the user posts about stress management, actions related to stress management can be recommended. This allows for personalized recommendations based on the user's interests.

[0095] The process flow of the second embodiment will be briefly described below.

[0096] Step 1: The collection unit collects the user's life log data. The user's life log data includes wake-up / sleep times, walking and eating activities, blood pressure, heart rate, etc. The collection unit collects these data in real time using a wearable device (such as a smart watch or a fitness tracker). For example, the collection unit collects the user's heart rate and number of steps in real time using a smart watch, collects the amount of exercise and calories burned using a fitness tracker, and periodically measures blood pressure using a blood pressure monitor. Step 2: The analysis unit analyzes the life log data collected by the collection unit. The analysis unit uses machine learning and deep learning to analyze the collected data and analyze the user's behavioral patterns and health condition. For example, a machine learning algorithm is used to analyze behavioral patterns, and a deep learning algorithm is used to analyze health conditions. Specifically, a neural network is used to analyze fluctuations in heart rate and blood pressure to detect abnormal patterns, and a convolutional neural network is used to analyze walking patterns to detect abnormal walking patterns. Step 3: The recommendation unit presents a recommendation for the next action to be taken based on the data analyzed by the analysis unit. For example, it may recommend actions such as "take a break" if the person has been awake for a long time, "eat less" if the person has eaten too much, or "relax" if the person has high blood pressure. The recommendation unit can also make recommendations using an AI model that takes the data analyzed by the analysis unit as input and outputs the next action to be taken.

[0097] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0098] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0099] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0100] Each of the multiple elements including the collection unit, analysis unit, and recommendation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the user's life log data in real time using a sensor or a camera of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and presents the next action to be taken based on the analysis result. The collection unit is also realized, for example, by the control unit 46A of the smart device 14, and can adjust the collection timing according to the user's emotions. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] 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, the RAM 30, and the 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.

[0104] 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.

[0105] 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 the voice according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0107] 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 performed in a secure state.

[0108] Fig. 4 shows an example of 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0113] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0114] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0115] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and recommendation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's life log data in real time using a sensor or a camera of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and presents the next action to be taken based on the analysis result. The collection unit is also realized, for example, by the control unit 46A of the smart glasses 214, and can adjust the collection timing according to the user's emotions. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0119] 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, the RAM 30, and the 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.

[0120] 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.

[0121] 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 the voice according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0123] 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 performed in a secure state.

[0124] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0127] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0128] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0129] 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 voice indicating a user input for 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.

[0130] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0131] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and recommendation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects the user's life log data in real time using a sensor or a camera of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and presents the next action to be taken based on the analysis result. The collection unit is also realized, for example, by the control unit 46A of the headset type terminal 314, and can adjust the collection timing according to the user's emotions. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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, the RAM 30, and the 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.

[0136] 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.

[0137] 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 the voice according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0139] 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 performed in a secure state.

[0140] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0141] 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.

[0142] 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.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0144] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0145] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0146] 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 a voice indicating a user input for 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.

[0147] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0148] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0149] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and recommendation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's life log data in real time using a sensor or a camera of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and presents the next action to be taken based on the analysis result. The collection unit is also realized, for example, by the control unit 46A of the robot 414, and can adjust the collection timing according to the user's emotions. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0150] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0151] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0152] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0153] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0154] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0155] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0157] 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.

[0158] 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 Universal Serial Bus (USB) 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.

[0159] In addition, 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 upon request from the data processing device 12.

[0160] 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.

[0161] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0162] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0163] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0164] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0165] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0166] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0167] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0168] (Appendix 1) A collection unit that collects life log data of a user; an analysis unit that analyzes the life log data collected by the collection unit; A recommendation unit that presents a next action to be taken as a recommendation based on the data analyzed by the analysis unit. A system characterized by: (Appendix 2) The collecting unit includes: Wearable devices are used to collect data on users' wake and sleep times, walking or eating activities, blood pressure and heart rate. 2. The system of claim 1, (Appendix 3) The analysis unit is Using machine learning or deep learning to analyze the collected data and analyze the user's behavioral patterns and health status 2. The system of claim 1, (Appendix 4) The recommendation unit includes: Based on the analysis results, the system recommends actions such as "take a break" if the person has been awake for a long time, "eating less" if the person has eaten too much, and "relax" if the person has high blood pressure. 2. The system of claim 1, (Appendix 5) The collecting unit includes: To estimate a user's emotion and adjust the timing of collecting life log data based on the estimated user's emotion. 2. The system of claim 1, (Appendix 6) The collecting unit includes: Analyze users' past life log data and select the appropriate collection method 2. The system of claim 1, (Appendix 7) The collecting unit includes: When collecting life log data, it is filtered based on the user's current life situation and areas of interest. 2. The system of claim 1, (Appendix 8) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting life log data, prioritize collection of relevant data based on the user's geographic location. 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting life log data, analyze users' social media activities and collect related data. 2. The system of claim 1, (Appendix 11) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 12) The analysis unit is During analysis, adjust the level of detail based on the importance of the data 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, apply different analysis algorithms depending on the category of data 2. The system of claim 1, (Appendix 14) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, prioritize analysis based on when the data was collected 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1, (Appendix 17) The recommendation unit includes: Infer user sentiment and adjust recommendation presentation based on the inferred user sentiment 2. The system of claim 1, (Appendix 18) The recommendation unit includes: At recommendation time, adjust recommendation granularity based on event importance 2. The system of claim 1, (Appendix 19) The recommendation unit includes: During recommendations, apply different recommendation algorithms depending on the category of the event 2. The system of claim 1, (Appendix 20) The recommendation unit includes: Infer user sentiment and adjust the length of recommendations based on the inferred user sentiment 2. The system of claim 1, (Appendix 21) The recommendation unit includes: During recommendations, prioritize recommendations based on when an event occurred 2. The system of claim 1, (Appendix 22) The recommendation unit includes: During recommendations, adjust the order of recommendations based on event relevance 2. The system of claim 1, [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects life log data of a user; an analysis unit that analyzes the life log data collected by the collection unit; A recommendation unit that presents a next action to be taken as a recommendation based on the data analyzed by the analysis unit. A system characterized by:

2. The collecting unit includes: Wearable devices are used to collect data on users' wake and sleep times, walking or eating activities, blood pressure and heart rate 2. The system of claim 1 .

3. The analysis unit is Using machine learning or deep learning, the collected data is analysed to analyse the user's behavioural patterns and health conditions.

2. The system of claim 1 .

4. The collecting unit includes: To estimate a user's emotion and adjust the timing of collecting life log data based on the estimated user's emotion.

2. The system of claim 1 .

5. The collecting unit includes: Analyze users' past life log data and select the appropriate collection method 2. The system of claim 1 .

6. The collecting unit includes: When collecting life log data, it is filtered based on the user's current life situation and areas of interest.

2. The system of claim 1 .

7. The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1 .

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