System

Abstract

An object of a system according to an embodiment is to easily perform health management in daily life and make it easy for an individual to grasp his or her own health condition.SOLUTION: A system includes a collection unit, an analysis unit, and an advice providing unit. The collection unit collects health data. The analysis unit analyzes the data collected by the collection unit and evaluates the health condition of the user. The advice providing unit provides health advice customized based on the evaluation result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have made it difficult for individuals to easily manage their health in their daily lives, making it difficult for them to grasp their own health status.

[0005] The system according to the embodiment aims to enable individuals to easily manage their health in their daily lives and to easily understand their own health condition. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and an advice providing unit. The collection unit collects health data. The analysis unit analyzes the data collected by the collection unit and evaluates the user's health condition. The advice providing unit provides customized health advice based on the evaluation results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows individuals to easily manage their health in their daily lives and easily understand their own health condition. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A health management system according to an embodiment of the present invention collects a user's health data, analyzes it using a generation AI, and provides customized health advice. When a user sits on the toilet, the health management system collects the health data, which the generation AI analyzes to evaluate the user's health status. For example, the health management system collects data such as weight, blood pressure, heart rate, and urine components. The generation AI then analyzes the collected data to evaluate the user's health status. For example, the generation AI may detect insufficient fluid intake from urine components or evaluate stress levels from heart rate fluctuations. The health management system then provides customized health advice based on the evaluation results obtained by the generation AI. For example, the health management system may recommend increasing fluid intake or suggest breathing techniques to help relax. This allows the user to use their toilet time to receive advice on their health status and areas for improvement. This allows the health management system to effectively support the user's health management and make it easier for individuals to pay attention to their own health. For example, it enables easy health management in daily life and helps increase health awareness.

[0029] A health management system according to an embodiment includes a collection unit, an analysis unit, and an advice provision unit. The collection unit collects health data of a user. The health data includes, but is not limited to, weight, blood pressure, heart rate, and urine components. For example, the collection unit measures weight using a scale. The collection unit can also measure blood pressure using a sphygmomanometer. The collection unit can also measure heart rate using a heart rate sensor. The collection unit can also analyze urine components using a urine analysis device. For example, the collection unit measures weight using a scale and collects the data. The collection unit can also measure blood pressure using a sphygmomanometer and collect the data. The collection unit can also measure heart rate using a heart rate sensor and collect the data. The analysis unit uses a generative AI to analyze the data collected by the collection unit and evaluate the user's health condition. The analysis is performed using, for example, statistical analysis of data or a machine learning algorithm, but is not limited to, examples. For example, the analysis unit detects insufficient water intake from urine components. The analysis unit can also evaluate stress levels from fluctuations in heart rate. The analysis unit can also evaluate the health condition from blood pressure fluctuations. For example, the analysis unit can analyze urine components to detect insufficient fluid intake. The analysis unit can also analyze heart rate fluctuations to evaluate stress levels. The analysis unit can also analyze blood pressure fluctuations to evaluate the health condition. The advice providing unit uses a generative AI to provide customized health advice based on the evaluation results obtained by the analysis unit. The advice is provided, for example, based on specific advice and a provision method based on the user's health condition, but is not limited to such examples. For example, the advice providing unit can recommend increasing fluid intake. The advice providing unit can also suggest breathing techniques for relaxation. The advice providing unit can also suggest improvements to the diet. For example, the advice providing unit can recommend increasing fluid intake. The advice providing unit can also suggest breathing techniques for relaxation. The advice providing unit can also suggest improvements to the diet.As a result, the health management system according to the embodiment can collect and analyze the user's health data and provide customized health advice. Some or all of the above-described processing in the advice providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the advice providing unit can provide advice using a generation AI model that inputs the evaluation results obtained by the analysis unit and outputs customized health advice.

[0030] The collection unit can collect health data including weight, blood pressure, heart rate, and urine components. Health data includes, but is not limited to, weight, blood pressure, heart rate, and urine components. For example, the collection unit measures weight using a scale. The collection unit can also measure blood pressure using a sphygmomanometer. The collection unit can also measure heart rate using a heart rate sensor. The collection unit can also analyze urine components using a urinalysis device. For example, the collection unit can measure weight using a scale and collect the data. The collection unit can also measure blood pressure using a sphygmomanometer and collect the data. The collection unit can also measure heart rate using a heart rate sensor and collect the data. This allows for the collection of various health data, enabling a more detailed evaluation of health status. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input weight data measured using a scale into the generation AI and have the generation AI analyze the weight data.

[0031] The analysis unit can analyze the collected data and detect insufficient fluid intake from urine components. The analysis unit, for example, analyzes urine components to detect insufficient fluid intake. The analysis can be performed using, for example, statistical analysis of data or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit can analyze urine components and detect insufficient fluid intake if a certain threshold is exceeded. The analysis unit can also analyze fluctuations in urine components to detect insufficient fluid intake. The analysis unit can also analyze patterns of urine components to detect insufficient fluid intake. For example, the analysis unit can analyze urine components and detect insufficient fluid intake if a certain threshold is exceeded. The analysis unit can also analyze fluctuations in urine components to detect insufficient fluid intake. The analysis unit can also analyze patterns of urine components to detect insufficient fluid intake. By analyzing urine components, it is possible to detect insufficient fluid intake and provide appropriate advice. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI or without a generation AI. For example, the analysis unit can input urine component data into the generation AI and cause the generation AI to detect insufficient fluid intake.

[0032] The analysis unit can evaluate the stress level from the heart rate fluctuation. The analysis unit, for example, analyzes the heart rate fluctuation and evaluates the stress level. The analysis is performed using, for example, statistical analysis of data or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit analyzes the standard deviation of the heart rate and evaluates the stress level. The analysis unit can also analyze the heart rate fluctuation pattern and evaluate the stress level. The analysis unit can also analyze the heart rate fluctuation range and evaluate the stress level. For example, the analysis unit analyzes the standard deviation of the heart rate and evaluates the stress level. The analysis unit can also analyze the heart rate fluctuation pattern and evaluate the stress level. The analysis unit can also analyze the heart rate fluctuation range and evaluate the stress level. In this way, by analyzing the heart rate fluctuation, the stress level can be evaluated and appropriate advice can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input heart rate data to the generation AI and cause the generation AI to evaluate the stress level.

[0033] The advice providing unit can recommend increasing the amount of water intake. The advice providing unit, for example, recommends increasing the amount of water intake. The advice can be, for example, specific advice based on the user's health condition or based on a provision method, but is not limited to such examples. For example, the advice providing unit presents a guideline for daily water intake. The advice providing unit can also suggest specific types of drinks. The advice providing unit can also suggest the timing of water intake. For example, the advice providing unit presents a guideline for daily water intake. The advice providing unit can also suggest specific types of drinks. The advice providing unit can also suggest the timing of water intake. In this way, by recommending increasing the amount of water intake, the user's health condition is improved. Some or all of the above-described processing in the advice providing unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the advice providing unit can provide advice using a generation AI model that inputs the evaluation result obtained by the analysis unit and outputs advice to increase water intake.

[0034] The advice providing unit can suggest a breathing technique for relaxation. The advice providing unit can suggest, for example, a breathing technique for relaxation. The advice can be, for example, specific advice based on the user's health condition or a provision method, but is not limited to such examples. For example, the advice providing unit can suggest a deep breathing technique. The advice providing unit can also suggest abdominal breathing. The advice providing unit can also suggest a breathing rhythm. For example, the advice providing unit can suggest a deep breathing technique. The advice providing unit can also suggest abdominal breathing. The advice providing unit can also suggest a breathing rhythm. By suggesting a breathing technique for relaxation, the user's stress is reduced. Some or all of the above-described processing in the advice providing unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the advice providing unit can provide advice using a generation AI model that inputs the evaluation result obtained by the analysis unit and outputs a breathing technique for relaxation.

[0035] The collection unit can analyze the user's past health data and select the optimal collection method. For example, the collection unit can collect data from past data during times when the user is most relaxed. For example, the collection unit can also select a method (audio, text, image, etc.) that will provide the user with the most accurate data from past data. For example, the collection unit can also collect data from past data during times when the user's health condition is most stable. This allows the optimal collection method to be selected by analyzing past data, enabling efficient data collection. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the collection unit can input past health data into a generation AI and have the generation AI select the optimal collection method.

[0036] When collecting health data, the collection unit can filter the data based on the user's current lifestyle or dietary content. For example, the collection unit can collect data immediately after the user finishes a meal and filter the data based on the dietary content. For example, the collection unit can also collect data immediately after the user finishes exercising and filter the data based on the exercise content. For example, the collection unit can collect data when the user is feeling stressed and filter the data based on the stress level. This allows for more accurate health data to be collected by filtering the data based on the lifestyle and dietary content. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's dietary content data into the generation AI and have the generation AI filter the data.

[0037] When collecting health data, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit collects the health data by voice. For example, if the user prefers text input, the collection unit can also collect the health data by text. For example, if the user prefers image input, the collection unit can also collect the health data by image. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's input data into the generation AI and have the generation AI select the optimal collection means.

[0038] When collecting health data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is at high altitude, the collection unit can prioritize collecting oxygen concentration and heart rate data. For example, when the user is in an urban area, the collection unit can also prioritize collecting air quality and allergy-related data. For example, when the user is exercising, the collection unit can also prioritize collecting exercise intensity and calorie consumption data. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's geographical location data into the generation AI and cause the generation AI to select highly relevant data.

[0039] When collecting health data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit analyzes the content posted by the user on social media and collects related health data. For example, the collection unit can also collect related health data based on the user's social media check-in information. For example, the collection unit can also collect related health data by referring to the activities of the user's friends on social media. In this way, related health data can be collected by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's social media data into the generation AI and cause the generation AI to collect related health data.

[0040] When collecting health data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit selects the optimal collection method based on feedback provided by the user in the past. For example, the collection unit can also preferentially use a collection method that the user previously preferred. For example, the collection unit can analyze the user's past feedback and improve the collection method. This allows the collection method to be customized by reflecting past feedback, enabling efficient data collection. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the health data. For example, the analysis unit performs a detailed analysis on important data (heart rate, blood pressure, etc.). For example, the analysis unit can also perform a simplified analysis on less important data (weight, urine components, etc.). For example, the analysis unit can also prioritize analysis of more important data depending on the user's health condition. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the health data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input important data into the generation AI and have the generation AI perform a detailed analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of health data. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. For example, the analysis unit can also apply a chemical component analysis algorithm to urine component data. For example, the analysis unit can also apply a blood pressure variability analysis algorithm to blood pressure data. This allows for more accurate analysis by applying different analysis algorithms depending on the category of health data. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input heart rate data to the generation AI and have the generation AI perform heart rate variability analysis.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the past analysis results. The analysis unit can also build a feedback loop for improving the accuracy of the analysis based on the past analysis results. This makes it possible to improve the accuracy of the analysis by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input past analysis results into the generation AI and have the generation AI correct the current analysis result.

[0044] During analysis, the analysis unit can determine the priority of analysis based on when the health data was collected. For example, the analysis unit prioritizes analysis of recently collected data. For example, the analysis unit can postpone analysis of older collected data. For example, the analysis unit can dynamically adjust the priority of analysis based on when the health data was collected. This enables efficient analysis by determining the priority of analysis based on when the health data was collected. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input recently collected data into the generation AI and have the generation AI perform the analysis preferentially.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the health data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit can postpone analysis of less relevant data. For example, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the health data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input highly relevant data to the generation AI and have the generation AI perform the analysis preferentially.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit can explain the analysis results in simple terms to a user with little technical expertise. For example, the analysis unit can also explain the analysis results using detailed technical terminology to a user with technical expertise. For example, the analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide analysis results that are easier to understand by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's level of expertise into the generation AI and have the generation AI execute the use of technical terminology.

[0047] When providing advice, the advice providing unit can adjust the level of detail of the advice based on the importance of the health condition. For example, the advice providing unit provides detailed advice for important health conditions (high blood pressure, abnormal heart rate, etc.). For example, the advice providing unit can also provide simplified advice for less important health conditions (mild weight gain, etc.). For example, the advice providing unit can also prioritize providing advice with higher importance depending on the user's health condition. This enables efficient advice by adjusting the level of detail of the advice based on the importance of the health condition. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, a generation AI. For example, the advice providing unit can input important health condition data into the generation AI and have the generation AI execute detailed advice.

[0048] When providing advice, the advice providing unit can apply different advice algorithms depending on the health condition category. For example, the advice providing unit applies a heart rate management advice algorithm to an abnormal heart rate. For example, the advice providing unit can also apply a urine management advice algorithm to an abnormal urine component. For example, the advice providing unit can also apply a blood pressure management advice algorithm to an abnormal blood pressure. This enables more accurate advice to be provided by applying different advice algorithms depending on the health condition category. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, the generation AI, for example. For example, the advice providing unit can input heart rate data to the generation AI and cause the generation AI to execute advice on heart rate management.

[0049] When providing advice, the advice providing unit can improve the accuracy of the advice by referring to past advice results for the user. The advice providing unit, for example, corrects current advice based on past advice results. The advice providing unit can also adjust the advice algorithm by referring to past advice results. The advice providing unit can also build a feedback loop for improving the accuracy of advice based on past advice results, for example. This makes it possible to improve the accuracy of advice by referring to past advice results. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, a generation AI, for example. For example, the advice providing unit can input past advice results into the generation AI and cause the generation AI to correct the current advice.

[0050] When providing advice, the advice providing unit can determine the priority of advice based on the collection date of the health data. For example, the advice providing unit can prioritize advice based on recently collected data. For example, the advice providing unit can postpone advice based on data collected earlier. For example, the advice providing unit can dynamically adjust the priority of advice based on the collection date. This enables efficient advice by determining the priority of advice based on the collection date of the health data. Some or all of the above-mentioned processing in the advice providing unit can be performed using, or without, the generation AI. For example, the advice providing unit can input recently collected data into the generation AI and have the generation AI execute advice preferentially.

[0051] When providing advice, the advice providing unit can adjust the order of advice based on the relevance of health data. For example, the advice providing unit can prioritize providing advice based on highly relevant data. For example, the advice providing unit can also postpone advice based on less relevant data. For example, the advice providing unit can dynamically adjust the order of advice based on the relevance of data. This enables efficient advice to be provided by adjusting the order of advice based on the relevance of health data. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, the generation AI. For example, the advice providing unit can input highly relevant data to the generation AI and have the generation AI execute advice preferentially.

[0052] When providing advice, the advice providing unit can adjust the use of technical terminology in the advice according to the user's level of expertise. For example, the advice providing unit provides advice in simple language to a user with little technical expertise. For example, the advice providing unit can also provide advice using detailed technical terminology to a user with technical expertise. For example, the advice providing unit can also adjust the way the advice is expressed according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the advice according to the user's level of expertise, it is possible to provide advice that is easier to understand. Some or all of the above-mentioned processing in the advice providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the advice providing unit can input the user's level of expertise to the generation AI and cause the generation AI to use technical terminology.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The collection unit collects the user's sleep data, and the analysis unit analyzes the data to evaluate the quality of sleep. For example, the collection unit monitors the user's heart rate and breathing patterns while sleeping and records the depth of sleep and the frequency of interruptions. The collection unit can also detect the number of times the user turns over in their sleep and fluctuations in body temperature. Based on this data, the analysis unit can evaluate the user's sleep quality and identify areas for improvement. This allows the user to understand their own sleep patterns and receive advice on how to get better sleep.

[0055] The analysis unit can analyze the user's dietary data and evaluate the nutritional balance. For example, the analysis unit can analyze the types and amounts of ingredients consumed by the user and evaluate the intake of vitamins and minerals. The analysis unit can also analyze the user's eating patterns and identify excess intake or nutrient deficiencies. Furthermore, the analysis unit can propose a healthy meal plan based on the user's dietary data. This allows the user to review their eating habits and receive advice on achieving a balanced diet.

[0056] The advice providing unit can propose an individual exercise plan based on the user's exercise data. For example, the advice providing unit can analyze the user's exercise frequency and intensity and propose an appropriate amount of exercise. The advice providing unit can also create an effective training menu based on the user's exercise history. Furthermore, the advice providing unit can also suggest the type and timing of exercise depending on the user's goals. This allows the user to receive specific advice to improve their exercise habits and maintain a healthy lifestyle.

[0057] The collection unit collects the user's activity data, and the analysis unit analyzes the data to evaluate the user's activity level. For example, the collection unit monitors the user's number of steps and distance traveled and records the user's daily activity level. The collection unit can also detect the user's exercise intensity and calories burned. Based on this data, the analysis unit can evaluate the user's activity level and set appropriate exercise goals. This allows the user to understand their own activity level and receive specific advice for maintaining a healthy lifestyle.

[0058] The analysis unit can analyze the user's health data and provide health advice according to the season and weather. For example, the analysis unit can recommend taking vitamin D in the winter. The analysis unit can also emphasize the importance of staying hydrated in the summer. Furthermore, the analysis unit can suggest allergy countermeasures during the pollen season. This allows the user to manage their health according to the season and weather and receive advice for more effective health maintenance.

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

[0060] Step 1: The collection unit collects the user's health data. The health data includes, for example, weight, blood pressure, heart rate, and urine components. The collection unit measures and collects this data using a scale, blood pressure monitor, heart rate sensor, urine test device, etc. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit and evaluate the user's health condition. The analysis is performed using statistical analysis of the data and machine learning algorithms. For example, it can detect insufficient fluid intake from urine components, evaluate stress levels from heart rate fluctuations, and evaluate health condition from blood pressure fluctuations. Step 3: The advice provider uses the generation AI to provide customized health advice based on the evaluation results obtained by the analysis component. The advice is based on specific advice and delivery methods based on the user's health condition. For example, the advice provider may recommend increasing water intake, suggest breathing techniques to relax, or suggest improvements to the user's diet.

[0061] (Example 2) A health management system according to an embodiment of the present invention collects a user's health data, analyzes it using a generation AI, and provides customized health advice. When a user sits on the toilet, the health management system collects the health data, which the generation AI analyzes to evaluate the user's health status. For example, the health management system collects data such as weight, blood pressure, heart rate, and urine components. The generation AI then analyzes the collected data to evaluate the user's health status. For example, the generation AI may detect insufficient fluid intake from urine components or evaluate stress levels from heart rate fluctuations. The health management system then provides customized health advice based on the evaluation results obtained by the generation AI. For example, the health management system may recommend increasing fluid intake or suggest breathing techniques to help relax. This allows the user to use their toilet time to receive advice on their health status and areas for improvement. This allows the health management system to effectively support the user's health management and make it easier for individuals to pay attention to their own health. For example, it enables easy health management in daily life and helps increase health awareness.

[0062] A health management system according to an embodiment includes a collection unit, an analysis unit, and an advice provision unit. The collection unit collects health data of a user. The health data includes, but is not limited to, weight, blood pressure, heart rate, and urine components. For example, the collection unit measures weight using a scale. The collection unit can also measure blood pressure using a sphygmomanometer. The collection unit can also measure heart rate using a heart rate sensor. The collection unit can also analyze urine components using a urine analysis device. For example, the collection unit measures weight using a scale and collects the data. The collection unit can also measure blood pressure using a sphygmomanometer and collect the data. The collection unit can also measure heart rate using a heart rate sensor and collect the data. The analysis unit uses a generative AI to analyze the data collected by the collection unit and evaluate the user's health condition. The analysis is performed using, for example, statistical analysis of data or a machine learning algorithm, but is not limited to, examples. For example, the analysis unit detects insufficient water intake from urine components. The analysis unit can also evaluate stress levels from fluctuations in heart rate. The analysis unit can also evaluate the health condition from blood pressure fluctuations. For example, the analysis unit can analyze urine components to detect insufficient fluid intake. The analysis unit can also analyze heart rate fluctuations to evaluate stress levels. The analysis unit can also analyze blood pressure fluctuations to evaluate the health condition. The advice providing unit uses a generative AI to provide customized health advice based on the evaluation results obtained by the analysis unit. The advice is provided, for example, based on specific advice and a provision method based on the user's health condition, but is not limited to such examples. For example, the advice providing unit can recommend increasing fluid intake. The advice providing unit can also suggest breathing techniques for relaxation. The advice providing unit can also suggest improvements to the diet. For example, the advice providing unit can recommend increasing fluid intake. The advice providing unit can also suggest breathing techniques for relaxation. The advice providing unit can also suggest improvements to the diet.As a result, the health management system according to the embodiment can collect and analyze the user's health data and provide customized health advice. Some or all of the above-described processing in the advice providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the advice providing unit can provide advice using a generation AI model that inputs the evaluation results obtained by the analysis unit and outputs customized health advice.

[0063] The collection unit can collect health data including weight, blood pressure, heart rate, and urine components. Health data includes, but is not limited to, weight, blood pressure, heart rate, and urine components. For example, the collection unit measures weight using a scale. The collection unit can also measure blood pressure using a sphygmomanometer. The collection unit can also measure heart rate using a heart rate sensor. The collection unit can also analyze urine components using a urinalysis device. For example, the collection unit can measure weight using a scale and collect the data. The collection unit can also measure blood pressure using a sphygmomanometer and collect the data. The collection unit can also measure heart rate using a heart rate sensor and collect the data. This allows for the collection of various health data, enabling a more detailed evaluation of health status. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input weight data measured using a scale into the generation AI and have the generation AI analyze the weight data.

[0064] The analysis unit can analyze the collected data and detect insufficient fluid intake from urine components. The analysis unit, for example, analyzes urine components to detect insufficient fluid intake. The analysis can be performed using, for example, statistical analysis of data or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit can analyze urine components and detect insufficient fluid intake if a certain threshold is exceeded. The analysis unit can also analyze fluctuations in urine components to detect insufficient fluid intake. The analysis unit can also analyze patterns of urine components to detect insufficient fluid intake. For example, the analysis unit can analyze urine components and detect insufficient fluid intake if a certain threshold is exceeded. The analysis unit can also analyze fluctuations in urine components to detect insufficient fluid intake. The analysis unit can also analyze patterns of urine components to detect insufficient fluid intake. By analyzing urine components, it is possible to detect insufficient fluid intake and provide appropriate advice. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI or without a generation AI. For example, the analysis unit can input urine component data into the generation AI and cause the generation AI to detect insufficient fluid intake.

[0065] The analysis unit can evaluate the stress level from the heart rate fluctuation. The analysis unit, for example, analyzes the heart rate fluctuation and evaluates the stress level. The analysis is performed using, for example, statistical analysis of data or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit analyzes the standard deviation of the heart rate and evaluates the stress level. The analysis unit can also analyze the heart rate fluctuation pattern and evaluate the stress level. The analysis unit can also analyze the heart rate fluctuation range and evaluate the stress level. For example, the analysis unit analyzes the standard deviation of the heart rate and evaluates the stress level. The analysis unit can also analyze the heart rate fluctuation pattern and evaluate the stress level. The analysis unit can also analyze the heart rate fluctuation range and evaluate the stress level. In this way, by analyzing the heart rate fluctuation, the stress level can be evaluated and appropriate advice can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input heart rate data to the generation AI and cause the generation AI to evaluate the stress level.

[0066] The advice providing unit can recommend increasing the amount of water intake. The advice providing unit, for example, recommends increasing the amount of water intake. The advice can be, for example, specific advice based on the user's health condition or based on a provision method, but is not limited to such examples. For example, the advice providing unit presents a guideline for daily water intake. The advice providing unit can also suggest specific types of drinks. The advice providing unit can also suggest the timing of water intake. For example, the advice providing unit presents a guideline for daily water intake. The advice providing unit can also suggest specific types of drinks. The advice providing unit can also suggest the timing of water intake. In this way, by recommending increasing the amount of water intake, the user's health condition is improved. Some or all of the above-described processing in the advice providing unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the advice providing unit can provide advice using a generation AI model that inputs the evaluation result obtained by the analysis unit and outputs advice to increase water intake.

[0067] The advice providing unit can suggest a breathing technique for relaxation. The advice providing unit can suggest, for example, a breathing technique for relaxation. The advice can be, for example, specific advice based on the user's health condition or a provision method, but is not limited to such examples. For example, the advice providing unit can suggest a deep breathing technique. The advice providing unit can also suggest abdominal breathing. The advice providing unit can also suggest a breathing rhythm. For example, the advice providing unit can suggest a deep breathing technique. The advice providing unit can also suggest abdominal breathing. The advice providing unit can also suggest a breathing rhythm. By suggesting a breathing technique for relaxation, the user's stress is reduced. Some or all of the above-described processing in the advice providing unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the advice providing unit can provide advice using a generation AI model that inputs the evaluation result obtained by the analysis unit and outputs a breathing technique for relaxation.

[0068] The collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit adjusts the collection of health data to occur when the user is relaxed. For example, if the user is relaxed, the collection unit can collect data frequently to obtain detailed health data. For example, if the user is in a hurry, the collection unit can minimize data collection and quickly collect only necessary data. This allows for more accurate health data to be collected by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, the generation AI, or without the generation AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0069] The collection unit can analyze the user's past health data and select the optimal collection method. For example, the collection unit can collect data from past data during times when the user is most relaxed. For example, the collection unit can also select a method (audio, text, image, etc.) that will provide the user with the most accurate data from past data. For example, the collection unit can also collect data from past data during times when the user's health condition is most stable. This allows the optimal collection method to be selected by analyzing past data, enabling efficient data collection. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the collection unit can input past health data into a generation AI and have the generation AI select the optimal collection method.

[0070] When collecting health data, the collection unit can filter the data based on the user's current lifestyle or dietary content. For example, the collection unit can collect data immediately after the user finishes a meal and filter the data based on the dietary content. For example, the collection unit can also collect data immediately after the user finishes exercising and filter the data based on the exercise content. For example, the collection unit can collect data when the user is feeling stressed and filter the data based on the stress level. This allows for more accurate health data to be collected by filtering the data based on the lifestyle and dietary content. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's dietary content data into the generation AI and have the generation AI filter the data.

[0071] When collecting health data, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit collects the health data by voice. For example, if the user prefers text input, the collection unit can also collect the health data by text. For example, if the user prefers image input, the collection unit can also collect the health data by image. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's input data into the generation AI and have the generation AI select the optimal collection means.

[0072] The collection unit can estimate the user's emotions and determine the priority of health data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting heart rate and blood pressure data. For example, if the user is relaxed, the collection unit can also prioritize collecting urine composition and weight data. For example, if the user is in a hurry, the collection unit can prioritize collecting only the most important data. This allows important data to be collected preferentially by determining the priority of data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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 can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0073] When collecting health data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is at high altitude, the collection unit can prioritize collecting oxygen concentration and heart rate data. For example, when the user is in an urban area, the collection unit can also prioritize collecting air quality and allergy-related data. For example, when the user is exercising, the collection unit can also prioritize collecting exercise intensity and calorie consumption data. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's geographical location data into the generation AI and cause the generation AI to select highly relevant data.

[0074] When collecting health data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit analyzes the content posted by the user on social media and collects related health data. For example, the collection unit can also collect related health data based on the user's social media check-in information. For example, the collection unit can also collect related health data by referring to the activities of the user's friends on social media. In this way, related health data can be collected by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's social media data into the generation AI and cause the generation AI to collect related health data.

[0075] When collecting health data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit selects the optimal collection method based on feedback provided by the user in the past. For example, the collection unit can also preferentially use a collection method that the user previously preferred. For example, the collection unit can analyze the user's past feedback and improve the collection method. This allows the collection method to be customized by reflecting past feedback, enabling efficient data collection. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI customize the collection method.

[0076] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide simple, highly visible analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. This allows for adjusting the way the analysis is presented according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0077] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the health data. For example, the analysis unit performs a detailed analysis on important data (heart rate, blood pressure, etc.). For example, the analysis unit can also perform a simplified analysis on less important data (weight, urine components, etc.). For example, the analysis unit can also prioritize analysis of more important data depending on the user's health condition. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the health data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input important data into the generation AI and have the generation AI perform a detailed analysis.

[0078] During analysis, the analysis unit can apply different analysis algorithms depending on the category of health data. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. For example, the analysis unit can also apply a chemical component analysis algorithm to urine component data. For example, the analysis unit can also apply a blood pressure variability analysis algorithm to blood pressure data. This allows for more accurate analysis by applying different analysis algorithms depending on the category of health data. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input heart rate data to the generation AI and have the generation AI perform heart rate variability analysis.

[0079] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the past analysis results. The analysis unit can also build a feedback loop for improving the accuracy of the analysis based on the past analysis results. This makes it possible to improve the accuracy of the analysis by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input past analysis results into the generation AI and have the generation AI correct the current analysis result.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a short and concise analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can also provide a concise analysis result. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0081] During analysis, the analysis unit can determine the priority of analysis based on when the health data was collected. For example, the analysis unit prioritizes analysis of recently collected data. For example, the analysis unit can postpone analysis of older collected data. For example, the analysis unit can dynamically adjust the priority of analysis based on when the health data was collected. This enables efficient analysis by determining the priority of analysis based on when the health data was collected. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input recently collected data into the generation AI and have the generation AI perform the analysis preferentially.

[0082] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the health data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit can postpone analysis of less relevant data. For example, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the health data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input highly relevant data to the generation AI and have the generation AI perform the analysis preferentially.

[0083] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit can explain the analysis results in simple terms to a user with little technical expertise. For example, the analysis unit can also explain the analysis results using detailed technical terminology to a user with technical expertise. For example, the analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide analysis results that are easier to understand by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's level of expertise into the generation AI and have the generation AI execute the use of technical terminology.

[0084] The advice providing unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the advice providing unit can provide simple, highly visible advice. For example, if the user is relaxed, the advice providing unit can also provide detailed advice. For example, if the user is in a hurry, the advice providing unit can also provide advice that focuses on the main points. This allows for adjusting the way the advice is expressed depending on the user's emotions, thereby providing more appropriate advice. Emotion estimation is achieved using an emotion estimation function, for example, using 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 advice providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the advice providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0085] When providing advice, the advice providing unit can adjust the level of detail of the advice based on the importance of the health condition. For example, the advice providing unit provides detailed advice for important health conditions (high blood pressure, abnormal heart rate, etc.). For example, the advice providing unit can also provide simplified advice for less important health conditions (mild weight gain, etc.). For example, the advice providing unit can also prioritize providing advice with higher importance depending on the user's health condition. This enables efficient advice by adjusting the level of detail of the advice based on the importance of the health condition. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, a generation AI. For example, the advice providing unit can input important health condition data into the generation AI and have the generation AI execute detailed advice.

[0086] When providing advice, the advice providing unit can apply different advice algorithms depending on the health condition category. For example, the advice providing unit applies a heart rate management advice algorithm to an abnormal heart rate. For example, the advice providing unit can also apply a urine management advice algorithm to an abnormal urine component. For example, the advice providing unit can also apply a blood pressure management advice algorithm to an abnormal blood pressure. This enables more accurate advice to be provided by applying different advice algorithms depending on the health condition category. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, the generation AI, for example. For example, the advice providing unit can input heart rate data to the generation AI and cause the generation AI to execute advice on heart rate management.

[0087] When providing advice, the advice providing unit can improve the accuracy of the advice by referring to past advice results for the user. The advice providing unit, for example, corrects current advice based on past advice results. The advice providing unit can also adjust the advice algorithm by referring to past advice results. The advice providing unit can also build a feedback loop for improving the accuracy of advice based on past advice results, for example. This makes it possible to improve the accuracy of advice by referring to past advice results. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, a generation AI, for example. For example, the advice providing unit can input past advice results into the generation AI and cause the generation AI to correct the current advice.

[0088] The advice providing unit can estimate the user's emotions and adjust the length of the advice based on the estimated user's emotions. For example, if the user is feeling stressed, the advice providing unit can provide short, to-the-point advice. For example, if the user is relaxed, the advice providing unit can also provide detailed advice. For example, if the user is in a hurry, the advice providing unit can also provide concise advice. This allows for adjusting the length of the advice according to the user's emotions, thereby providing more appropriate advice. The emotion estimation is realized using an emotion estimation function, for example, using 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 advice providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the advice providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0089] When providing advice, the advice providing unit can determine the priority of advice based on the collection date of the health data. For example, the advice providing unit can prioritize advice based on recently collected data. For example, the advice providing unit can postpone advice based on data collected earlier. For example, the advice providing unit can dynamically adjust the priority of advice based on the collection date. This enables efficient advice by determining the priority of advice based on the collection date of the health data. Some or all of the above-mentioned processing in the advice providing unit can be performed using, or without, the generation AI. For example, the advice providing unit can input recently collected data into the generation AI and have the generation AI execute advice preferentially.

[0090] When providing advice, the advice providing unit can adjust the order of advice based on the relevance of health data. For example, the advice providing unit can prioritize providing advice based on highly relevant data. For example, the advice providing unit can also postpone advice based on less relevant data. For example, the advice providing unit can dynamically adjust the order of advice based on the relevance of data. This enables efficient advice to be provided by adjusting the order of advice based on the relevance of health data. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, the generation AI. For example, the advice providing unit can input highly relevant data to the generation AI and have the generation AI execute advice preferentially.

[0091] When providing advice, the advice providing unit can adjust the use of technical terminology in the advice according to the user's level of expertise. For example, the advice providing unit provides advice in simple language to a user with little technical expertise. For example, the advice providing unit can also provide advice using detailed technical terminology to a user with technical expertise. For example, the advice providing unit can also adjust the way the advice is expressed according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the advice according to the user's level of expertise, it is possible to provide advice that is easier to understand. Some or all of the above-mentioned processing in the advice providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the advice providing unit can input the user's level of expertise to the generation AI and cause the generation AI to use technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and advice providing unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect health data of the user using the camera 42 or microphone 38B of the smart device 14. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and evaluates the user's health condition. For example, the advice providing unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and provides customized health advice. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and advice providing unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect health data of the user using the camera 42 or the microphone 238 of the smart glasses 214. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and evaluates the user's health status. For example, the advice providing unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and provides customized health advice. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and advice provision 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 can collect health data of the user using the camera 42 or the microphone 238 of the headset type terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and evaluates the user's health condition. For example, the advice provision unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and provides customized health advice. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and advice providing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect health data of the user using the camera 42 or the microphone 238 of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and evaluates the health condition of the user. For example, the advice providing unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and provides customized health advice.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The collection unit collects the user's sleep data, and the analysis unit analyzes the data to evaluate the quality of sleep. For example, the collection unit monitors the user's heart rate and breathing patterns while sleeping and records the depth of sleep and the frequency of interruptions. The collection unit can also detect the number of times the user turns over in their sleep and fluctuations in body temperature. Based on this data, the analysis unit can evaluate the user's sleep quality and identify areas for improvement. This allows the user to understand their own sleep patterns and receive advice on how to get better sleep.

[0094] The analysis unit can analyze the user's dietary data and evaluate the nutritional balance. For example, the analysis unit can analyze the types and amounts of ingredients consumed by the user and evaluate the intake of vitamins and minerals. The analysis unit can also analyze the user's eating patterns and identify excess intake or nutrient deficiencies. Furthermore, the analysis unit can propose a healthy meal plan based on the user's dietary data. This allows the user to review their eating habits and receive advice on achieving a balanced diet.

[0095] The advice providing unit can propose an individual exercise plan based on the user's exercise data. For example, the advice providing unit can analyze the user's exercise frequency and intensity and propose an appropriate amount of exercise. The advice providing unit can also create an effective training menu based on the user's exercise history. Furthermore, the advice providing unit can also suggest the type and timing of exercise depending on the user's goals. This allows the user to receive specific advice to improve their exercise habits and maintain a healthy lifestyle.

[0096] The collection unit can monitor the user's stress level in real time, and the analysis unit can analyze the data to provide stress management advice. For example, the collection unit can monitor the user's heart rate and electrodermal activity to detect signs of stress. The collection unit can also detect the user's breathing patterns and muscle tension. Based on this data, the analysis unit can evaluate the user's stress level and suggest relaxation methods and activities to reduce stress. This allows the user to receive advice on how to effectively manage their stress and maintain their physical and mental health.

[0097] The analysis unit can estimate the user's emotions and suggest appropriate relaxation methods based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can suggest deep breathing or meditation. If the user is tired, the analysis unit can also suggest light stretching or a short break. Furthermore, if the user is relaxed, the analysis unit can also suggest music or aromatherapy to help maintain relaxation. This allows the user to practice relaxation methods that suit their emotional state and receive advice on maintaining mental and physical balance.

[0098] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the user. Also, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the collection unit can quickly collect only the minimum amount of data necessary. In this way, by adjusting the frequency of data collection according to the user's emotional state, more accurate and useful health data can be collected.

[0099] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible notification. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can also provide a concise notification that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the notification method of the analysis results according to the user's emotional state.

[0100] The collection unit collects the user's activity data, and the analysis unit analyzes the data to evaluate the user's activity level. For example, the collection unit monitors the user's number of steps and distance traveled and records the user's daily activity level. The collection unit can also detect the user's exercise intensity and calories burned. Based on this data, the analysis unit can evaluate the user's activity level and set appropriate exercise goals. This allows the user to understand their own activity level and receive specific advice for maintaining a healthy lifestyle.

[0101] The analysis unit can analyze the user's health data and provide health advice according to the season and weather. For example, the analysis unit can recommend taking vitamin D in the winter. The analysis unit can also emphasize the importance of staying hydrated in the summer. Furthermore, the analysis unit can suggest allergy countermeasures during the pollen season. This allows the user to manage their health according to the season and weather and receive advice for more effective health maintenance.

[0102] The advice providing unit can propose an individual mental health care plan based on the user's health data. For example, the advice providing unit can analyze the user's stress level and sleep data and propose appropriate mental health care methods. The advice providing unit can also suggest relaxation techniques or the need for counseling based on the user's emotional data. Furthermore, the advice providing unit can also provide a specific action plan for mental health care based on the user's lifestyle habit data. This allows the user to receive advice on effectively managing their own mental health and maintaining a balance between their mind and body.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection unit collects the user's health data. The health data includes, for example, weight, blood pressure, heart rate, and urine components. The collection unit measures and collects this data using a scale, blood pressure monitor, heart rate sensor, urine test device, etc. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit and evaluate the user's health condition. The analysis is performed using statistical analysis of the data and machine learning algorithms. For example, it can detect insufficient fluid intake from urine components, evaluate stress levels from heart rate fluctuations, and evaluate health condition from blood pressure fluctuations. Step 3: The advice provider uses the generation AI to provide customized health advice based on the evaluation results obtained by the analysis component. The advice is based on specific advice and delivery methods based on the user's health condition. For example, the advice provider may recommend increasing water intake, suggest breathing techniques to relax, or suggest improvements to the user's diet.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] 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.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] 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.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] 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.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] 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.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0149] 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.

[0150] 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.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] 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.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0168] 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.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0176] [Explanation of symbols]

[0177] 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 health data; an analysis unit that analyzes the data collected by the collection unit and evaluates the health condition of the user; an advice providing unit that provides customized health advice based on the evaluation results obtained by the analysis unit; A system characterized by:

2. The collecting unit Collect health data including weight, blood pressure, heart rate, and urine composition 2. The system of claim 1.

3. The analysis unit Analyzing the collected data and detecting insufficient fluid intake from urine components 2. The system of claim 1.

4. The analysis unit Assessing stress levels from heart rate variability 2. The system of claim 1.

5. The advice providing unit Encourage increased fluid intake 2. The system of claim 1.

6. The advice providing unit Suggest breathing exercises to help you relax 2. The system of claim 1.

7. The collecting unit The system estimates the user's emotions and adjusts the timing of health data collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze users' past health data and select the appropriate collection method 2. The system of claim 1.

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