system

Abstract

The system according to this embodiment aims to analyze images of stool and support continuous health management. [Solution] The system according to the embodiment comprises a posting unit, an analysis unit, a provision unit, an awarding unit, and a collaboration unit. The posting unit takes and posts images of stool. The analysis unit analyzes the stool images posted by the posting unit and visualizes the user's health status. The provision unit provides the user with health reports and advice based on the health status analyzed by the analysis unit. The awarding unit awards points for continued posting of stool images by the posting unit. The collaboration unit collaborates with medical institutions to share the stool data analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to grasp the daily health condition from the image of feces, and continuous health management is difficult.

[0005] The system according to the embodiment aims to analyze the image of feces and support continuous health management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a posting unit, an analysis unit, a provision unit, an awarding unit, and a collaboration unit. The posting unit takes and posts images of stool. The analysis unit analyzes the stool images posted by the posting unit and visualizes the user's health status. The provision unit provides the user with health reports and advice based on the health status analyzed by the analysis unit. The awarding unit awards points for continued posting of stool images by the posting unit. The collaboration unit collaborates with medical institutions to share the stool data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze images of stool and support continuous health management. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The health management system according to an embodiment of the present invention is a system that supports health management simply by taking and posting photos of daily stool. This health management system analyzes images of stool and visualizes the user's health status. For example, it analyzes the shape, color, and texture of the stool to determine whether it is healthy or unhealthy. Furthermore, points are awarded for continuously posting images of stool, encouraging users to make posting a habit. The health management system analyzes the stool data posted daily and provides easy-to-understand health reports and advice. For example, it provides advice on improving diet and lifestyle habits based on the condition of the stool. In addition, the stool data is linked with medical institutions, eliminating the need for stool tests during health checkups. This makes it easier to understand daily physical condition and proposes a new style of health management that prevents lifestyle-related diseases and digestive system problems. For example, when a user takes a photo of their stool and posts it to the health management system, the system analyzes the shape, color, and texture of the stool and determines the user's health status. For example, if the stool is hard and small, it may indicate constipation, and the health management system will advise increasing fiber intake. If the stool is soft and watery, it may indicate diarrhea, and the health management system will advise staying hydrated and eating easily digestible foods. Furthermore, users are awarded points for continuously posting images of their stool. For example, posting an image of their stool every day earns them 10 points per day, accumulating 70 points in a week. This encourages users to develop the habit of posting images of their stool, making it easier to maintain consistent health management. The health management system also links stool data with medical institutions. For example, when a user visits an internal medicine or gastroenterology clinic, the health management system provides the doctor with the stool data it has analyzed. This makes it easier for doctors to understand the user's stool condition, eliminating the need for stool tests during health checkups. This reduces the psychological burden on users during health checkups. In this way, the health management system supports daily health management by analyzing stool images using generation AI and visualizing health status. Furthermore, by awarding points and linking data with medical institutions, it promotes the habit of posting images of stool and proposes a new health management style that prevents lifestyle-related diseases and digestive system problems. In this way, the health management system can understand the daily state of stool and support health management.

[0029] The health management system according to this embodiment comprises a posting unit, an analysis unit, a provision unit, an assignment unit, and a linking unit. The posting unit takes and posts images of stool. The posting unit can, for example, take images of stool using a smartphone camera and post them through a dedicated app. The posting unit also has a function to adjust the shooting angle, resolution, and lighting conditions when taking images of stool. For example, the posting unit automatically sets an appropriate shooting angle and resolution when taking images of stool. The posting unit also has a function to adjust the lighting conditions when taking images of stool. For example, the posting unit automatically sets appropriate lighting conditions when taking images of stool. The analysis unit analyzes the images of stool posted by the posting unit and visualizes the health status. The analysis unit, for example, uses a generation AI to analyze the shape, color, and texture of the stool and makes a determination of whether it is healthy stool or unhealthy stool. For example, the analysis unit analyzes the shape of the stool and makes a determination of whether it is hard stool, soft stool, diarrheal stool, etc. The analysis unit also analyzes the color of the stool and makes a determination of whether it is a normal color or an unhealthy color. Furthermore, the analysis unit analyzes the texture of the stool and determines whether it has a normal or unhealthy texture. The provision unit provides users with health reports and advice based on the health status analyzed by the analysis unit. For example, the provision unit may advise on areas for improvement in diet and lifestyle based on the condition of the stool. For example, if the stool is hard and small, the provision unit may advise increasing fiber intake. Also, if the stool is soft and watery, the provision unit may advise staying hydrated and eating easily digestible foods. In addition, the provision unit can also advise on areas for improvement in exercise and sleep based on the condition of the stool. For example, the provision unit may provide advice on appropriate exercise and sleep based on the condition of the stool. The awarding unit awards points for continuous posting of stool images submitted by the posting unit. For example, the awarding unit may award 10 points per day for posting a stool image every day. The awarding unit may also award bonus points if certain conditions are met. For example, the awarding unit may award bonus points for posting a stool image for one week consecutively. Furthermore, the points system can also award points for participating in specific events or campaigns. For example, the points system awards points for participating in specific events or campaigns.The collaboration unit connects the stool data analyzed by the analysis unit with medical institutions. The collaboration unit automatically transmits the stool data to medical institutions, for example. For example, the collaboration unit automatically sets the timing and method of transmitting the stool data to medical institutions. The collaboration unit also provides an interface that allows doctors to view the stool data. For example, the collaboration unit provides a dedicated app or web interface for doctors to view the stool data. As a result, the health management system according to this embodiment can support daily health management by analyzing stool images and visualizing health status.

[0030] The posting section allows users to take and submit images of their stool. For example, users can take images using their smartphone camera and submit them through a dedicated app. Specifically, the app automatically sets the optimal shooting angle and resolution when the user takes an image. For instance, the app recognizes the shape and location of the stool and suggests the best shooting angle. The resolution is also automatically adjusted to ensure that details of the stool are clearly visible. Furthermore, the app senses ambient light and uses the flash as needed to provide appropriate lighting conditions. This allows users to easily take and submit high-quality images of their stool. Submitted images are securely stored on a dedicated cloud server and accessible to the analysis section. The posting section also prioritizes user privacy when taking images; image data is encrypted before transmission. It also includes a function to review submitted images and retake them if necessary. This allows users to submit images of their stool with peace of mind. Additionally, the posting section provides voice guidance and tutorials to simplify the process of submitting images. This ensures that even first-time users can operate the system without difficulty.

[0031] The analysis unit analyzes images of stool submitted by the posting unit to visualize the user's health status. The analysis uses a generation AI to analyze the shape, color, and texture of the stool, determining whether it is healthy or unhealthy. Specifically, to analyze the shape of the stool, the generation AI uses image recognition technology to extract the stool's outline and classify its shape. For example, it identifies shapes such as hard stool, soft stool, and diarrhea. Furthermore, to analyze the color of the stool, it analyzes the hue, saturation, and brightness of the image to determine whether the color is normal or abnormal. For example, healthy stool is brown, but abnormal stool may contain black, red, or green. Additionally, to analyze the texture of the stool, it performs texture analysis on the image to evaluate the roughness and smoothness of the stool's surface. This allows for the determination of normal or abnormal texture. The analysis unit integrates these analysis results to comprehensively evaluate the user's health status. For example, if the stool is hard, black in color, and has a rough texture, it may indicate constipation or indigestion. The analysis results are provided to the user in a visually easy-to-understand format, allowing them to grasp changes in their health status at a glance. Furthermore, the analysis unit analyzes health trends by comparing them with past stool data, supporting long-term health management.

[0032] The service provider provides users with health reports and advice based on their health status analyzed by the analysis unit. Specifically, it advises improvements to diet and lifestyle habits based on the condition of the stool. For example, if the stool is hard and small, it advises increasing fiber intake. If the stool is soft and watery, it advises staying hydrated and eating easily digestible foods. The service provider generates a personalized health report based on the user's stool condition. This report includes detailed analysis results of the stool's shape, color, and texture, and provides specific advice based on these findings. For example, if the stool is hard, it will provide a list of foods high in fiber and exercise methods effective for relieving constipation. If the stool color is abnormal, it will provide information on possible dietary and lifestyle habits that may be causing it. Furthermore, the service provider proposes a specific action plan to improve the user's health. For example, it encourages improvement by recording daily meals and exercise levels. The service provider also has a function to send regular reminders to help users continue managing their health. This allows users to efficiently manage their daily health.

[0033] The points department awards points for continuous posting of stool images submitted by the posting department. Specifically, 10 points are awarded for posting a stool image every day. Bonus points can also be awarded if certain conditions are met. For example, bonus points may be awarded for posting stool images for one week consecutively. The points department has introduced a point system to increase users' motivation to continue managing their health. Users can use the points they earn to exchange for health-related products and services. For example, they can use points to purchase health foods, supplements, or fitness gym memberships. They can also earn additional points by participating in specific events and campaigns. For example, they can earn bonus points by participating in health management quizzes or posting stool images during a specific period. Furthermore, the points department manages the history of points earned by users and makes it available for users to check at any time. This allows users to track their progress in health management and maintain continuous motivation.

[0034] The Collaboration Unit connects stool data analyzed by the Analysis Unit with medical institutions. Specifically, it automatically transmits stool data to medical institutions. For example, it automatically sets the timing and method of transmitting stool data to medical institutions. The Collaboration Unit transmits stool data to medical institutions only after obtaining the user's consent. This allows doctors to check the user's stool data in real time and make necessary diagnoses and treatments. The Collaboration Unit provides a dedicated app and web interface for doctors to view the stool data. This allows doctors to easily check the stool data and understand the user's health status. In addition, the Collaboration Unit standardizes the data transmission method and format to facilitate smooth collaboration with medical institutions. This makes it easier to share data between different medical institutions, making user health management more efficient. Furthermore, the Collaboration Unit also has a function to provide users with feedback from medical institutions. For example, it notifies users of advice and diagnosis results from doctors and prompts them to take necessary actions. This allows users to manage their health more appropriately through collaboration with medical institutions.

[0035] The analysis unit can analyze the shape, color, and texture of stool and determine whether it is healthy or unhealthy. For example, the analysis unit can analyze the shape of stool and determine whether it is hard, soft, or diarrheal. For example, to analyze the shape of stool, the analysis unit uses a generation AI to classify the shape of stool. The analysis unit can also analyze the color of stool and determine whether it is a normal or unhealthy color. For example, to analyze the color of stool, the analysis unit uses a generation AI to classify the color of stool. Furthermore, the analysis unit can analyze the texture of stool and determine whether it is a normal or unhealthy texture. For example, to analyze the texture of stool, the analysis unit uses a generation AI to classify the texture of stool. In this way, by analyzing the shape, color, and texture of stool, it becomes possible to determine the state of health. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the analysis unit can input data on the shape, color, and texture of stool into a generation AI, and the generation AI can analyze the shape, color, and texture of stool.

[0036] The service provider can provide advice on improving diet and lifestyle habits based on the condition of the stool. For example, if the stool is hard and small, the service provider may advise increasing fiber intake. For example, if the stool is hard and small, the service provider may advise consuming foods rich in dietary fiber. Also, if the stool is soft and watery, the service provider may advise staying hydrated and eating easily digestible foods. For example, if the stool is soft and watery, the service provider may advise frequent hydration. Furthermore, the service provider can also provide advice on improving exercise and sleep based on the condition of the stool. For example, the service provider may provide appropriate exercise and sleep advice based on the condition of the stool. In this way, providing advice based on the condition of the stool promotes improvements in lifestyle habits. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input stool condition data into AI, and the AI ​​can provide advice on improving diet and lifestyle habits.

[0037] The point-granting unit can award points for posting images of daily bowel movements. For example, the point-granting unit can award 10 points per day for posting images of daily bowel movements. The point-granting unit can also award bonus points when certain conditions are met. For example, the point-granting unit can award bonus points for posting images of bowel movements for one week in a row. Furthermore, the point-granting unit can also award points for participating in specific events or campaigns. For example, the point-granting unit can award points for participating in specific events or campaigns. This way, posting images of daily bowel movements will earn points and encourage the habit of posting. Some or all of the above processes in the point-granting unit may be performed using AI or not. For example, the point-granting unit can input the data of posted images of bowel movements into an AI, which can then award points.

[0038] The collaboration unit can automatically transmit stool data to medical institutions. For example, the collaboration unit automatically transmits stool data to medical institutions. For example, the collaboration unit automatically sets the timing and method of transmitting stool data to medical institutions. The collaboration unit also provides an interface that allows doctors to view the stool data. For example, the collaboration unit provides a dedicated app or web interface for doctors to view the stool data. This eliminates the need for stool tests during health checkups by automatically transmitting stool data to medical institutions. Some or all of the above processing in the collaboration unit may be performed using AI or not. For example, the collaboration unit can input stool data into AI, and the AI ​​can transmit the stool data to medical institutions.

[0039] The collaboration unit can provide an interface that allows physicians to view stool data. For example, the collaboration unit can provide a dedicated app or web interface for physicians to view stool data. For example, the collaboration unit can provide a dedicated app for physicians to view stool data, making it easy for physicians to access the data. Alternatively, the collaboration unit can also provide a web interface for physicians to view stool data. For example, the collaboration unit can provide a web interface for physicians to view stool data, making it easy for physicians to access the data. By providing an interface that allows physicians to view stool data, collaboration with medical institutions can be streamlined. Some or all of the above processing in the collaboration unit may be performed using AI or not. For example, the collaboration unit can input stool data into AI, and the AI ​​can generate an interface that provides the stool data to physicians.

[0040] The posting function can analyze a user's past posting history and select the optimal posting method. For example, the posting function can analyze the time slots when a user frequently posted in the past and send notifications prompting them to post during those times. The posting function can also prioritize suggesting posting methods (photos, videos, etc.) that the user has used in the past. The posting function can also send notifications prompting users to post on specific days or times based on their past posting history. This allows the posting function to select the optimal posting method by analyzing the user's past posting history. Some or all of the above processing in the posting function may be performed using AI or not. For example, the posting function can input the user's past posting history data into AI, which can then select the optimal posting method.

[0041] The posting function can filter stool image submissions based on the user's current health and lifestyle. For example, if a user is unwell, the posting function can send a notification suggesting they refrain from submitting stool image submissions. The posting function can also send a notification encouraging users to submit stool image submissions if they are in good health. The posting function can also adjust the timing of stool image submissions based on the user's lifestyle (e.g., traveling, working). This allows for appropriate submissions by filtering stool image submissions based on the user's health and lifestyle. Some or all of the above processing in the posting function may be performed using AI or not. For example, the posting function can input data on the user's health and lifestyle into an AI, which can then filter stool image submissions.

[0042] The posting function can prioritize posting images of stool that are highly relevant, taking into account the user's geographical location when the user posts images of stool. For example, if the user is traveling, the posting function can suggest prioritizing the posting of images of stool related to the food and lifestyle of their travel destination. For example, if the user is traveling, the posting function can suggest prioritizing the posting of images of stool related to the food and lifestyle of their travel destination. For example, if the user is at home, the posting function can suggest prioritizing the posting of images of stool related to their usual food and lifestyle. For example, if the user is in a specific region, the posting function can suggest prioritizing the posting of images of stool related to the food and lifestyle of that region. For example, if the user is in a specific region, the posting function can suggest prioritizing the posting of images of stool related to the food and lifestyle of that region. In this way, by taking the user's geographical location into consideration, images of stool that are highly relevant are prioritized. Some or all of the above processing in the posting function may be performed using AI or not. For example, the posting function can input the user's geographical location data into the AI, which can then prioritize posting images of highly relevant flights.

[0043] The posting function can analyze a user's social media activity when they post an image of stool and make relevant posts. For example, if a user posts about health on social media, the posting function can send a notification prompting them to post an image of stool. The posting function can also suggest that a user post an image of stool related to a meal if they post about food on social media. The posting function can also suggest that a user post an image of stool related to exercise if they post about exercise on social media. In this way, relevant images of stool are posted by analyzing the user's social media activity. Some or all of the above processing in the posting function may be performed using AI or not. For example, the posting function can input the user's social media activity data into AI, and the AI ​​can post relevant images of stool.

[0044] The analysis unit can improve the accuracy of its analysis of stool shape, color, and texture by referring to past data. For example, the analysis unit can compare and analyze the current stool shape, color, and texture based on past stool data. The analysis unit can also analyze changes in health status based on past stool data. The analysis unit can also detect abnormal stool conditions early based on past stool data. This improves the accuracy of stool analysis by referring to past data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input past stool data into AI, which can improve the accuracy of stool analysis.

[0045] The analysis unit can perform stool analysis while considering the user's dietary history and lifestyle. For example, the analysis unit can analyze the stool condition based on the user's dietary history. The analysis unit can also analyze the stool condition based on the user's lifestyle (exercise, sleep, etc.). The analysis unit can also analyze the stool condition by comprehensively considering the user's dietary history and lifestyle. This improves the accuracy of stool analysis by considering the user's dietary history and lifestyle. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's dietary history and lifestyle data into the AI, which can then perform the stool analysis.

[0046] The analysis unit can perform stool analysis while considering the user's geographical background. For example, if the user lives in a specific region, the analysis unit can perform stool analysis while considering the diet and lifestyle of that region. For example, if the user lives in a specific region, the analysis unit can perform stool analysis while considering the diet and lifestyle of that region. For example, if the user is traveling, the analysis unit can perform stool analysis while considering the diet and lifestyle of that destination. For example, if the user lives in a different region, the analysis unit can perform stool analysis while considering the diet and lifestyle of that region. For example, if the user lives in a different region, the analysis unit can perform stool analysis while considering the diet and lifestyle of that region. This improves the accuracy of stool analysis by considering the user's geographical background. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input the user's geographical background data into AI, and the AI ​​can perform stool analysis.

[0047] The analysis unit can improve the accuracy of its analysis by referring to relevant medical literature when analyzing stool samples. For example, the analysis unit can improve the accuracy of its analysis by referring to the latest medical literature when analyzing stool samples. The analysis unit can also improve the accuracy of its analysis by referring to past medical literature when analyzing stool samples. The analysis unit can also improve the accuracy of its analysis by comprehensively referring to relevant medical literature when analyzing stool samples. As a result, the accuracy of the stool analysis is improved by referring to relevant medical literature. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input relevant medical literature data into the AI, and the AI ​​can improve the accuracy of the stool analysis.

[0048] The service provider can provide optimal advice by referring to the user's past health data when providing health reports and advice. For example, the service provider can advise on optimal diet and lifestyle improvements based on the user's past health data. The service provider can also provide advice on specific health problems based on the user's past health data. The service provider can also provide optimal advice by comprehensively referring to the user's past health data. This allows the service provider to provide optimal advice by referring to the user's past health data. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past health data into AI, and the AI ​​can provide optimal advice.

[0049] The service provider can customize health reports and advice based on the user's current living situation. For example, if the user is traveling, the service provider can provide advice based on the diet and lifestyle of the travel destination. Similarly, if the user is at work, the service provider can provide advice based on work-related stress and lifestyle. Furthermore, if the user is at home, the service provider can provide advice based on their usual diet and lifestyle. This allows for more appropriate advice to be provided by customizing it based on the user's current living situation. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's current living situation data into the AI, which can then customize the advice.

[0050] The service provider can provide optimal advice by considering the user's geographical location when providing health reports and advice. For example, if the user lives in a specific region, the service provider can provide advice based on the diet and lifestyle of that region. For example, if the user lives in a specific region, the service provider can provide advice based on the diet and lifestyle of that region. For example, if the user is traveling, the service provider can provide advice based on the diet and lifestyle of that destination. For example, if the user lives in a different region, the service provider can provide advice based on the diet and lifestyle of that region. This allows the service provider to provide optimal advice by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location data into AI, and the AI ​​can provide optimal advice.

[0051] The service provider can analyze a user's social media activity and provide relevant advice when providing health reports and advice. For example, if a user posts health-related content on social media, the service provider can provide advice related to that content. For example, if a user posts diet-related content on social media, the service provider can provide advice related to that content. For example, if a user posts exercise-related content on social media, the service provider can provide advice related to that content. In this way, by analyzing a user's social media activity, relevant advice can be provided. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into AI, and the AI ​​can provide relevant advice.

[0052] The point awarding unit can select the optimal point awarding method by referring to the user's past posting history when awarding points. For example, the point awarding unit can analyze the time slots when the user frequently posted in the past and award points during those time slots. The point awarding unit can also prioritize awarding points for posting methods (photos, videos, etc.) that the user has used in the past. The point awarding unit can also award points on specific days of the week or time slots based on the user's past posting history. This allows the system to select the optimal point awarding method by referring to the user's past posting history. Some or all of the above processing in the point awarding unit may be performed using AI or not. For example, the point awarding unit can input the user's past posting history data into AI, which can then select the optimal point awarding method.

[0053] The point awarding unit can filter points based on the user's current health and lifestyle when awarding points. For example, the awarding unit may suggest refraining from awarding points if the user is unwell. The awarding unit can also award points if the user is in good health. The awarding unit can also adjust the timing of point awarding based on the user's lifestyle (e.g., traveling, working). This allows for appropriate point awarding by filtering points based on the user's health and lifestyle. Some or all of the above processing in the awarding unit may be performed using AI or not. For example, the awarding unit can input data on the user's health and lifestyle into the AI, which can then filter point awarding.

[0054] The point awarding unit can prioritize awarding points that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is traveling, the awarding unit can prioritize awarding points related to meals and lifestyle habits at the travel destination. For example, if the user is traveling, the awarding unit can prioritize awarding points related to meals and lifestyle habits at the travel destination. For example, if the user is at home, the awarding unit can prioritize awarding points related to meals and lifestyle habits at the user's home. For example, if the user is in a specific region, the awarding unit can prioritize awarding points related to meals and lifestyle habits in that region. For example, if the user is in a specific region, the awarding unit can prioritize awarding points related to meals and lifestyle habits in that region. In this way, highly relevant points are preferentially awarded by taking into account the user's geographical location. Some or all of the above processing in the awarding unit may be performed using AI or not. For example, the awarding unit can input the user's geographical location data into AI, and the AI ​​can preferentially award highly relevant points.

[0055] The point awarding unit can analyze a user's social media activity and award relevant points when awarding points. For example, if a user posts health-related content on social media, the awarding unit can award points related to that content. Similarly, if a user posts food-related content on social media, the awarding unit can award points related to that content. Furthermore, if a user posts exercise-related content on social media, the awarding unit can award points related to that exercise content. In this way, relevant points are awarded by analyzing the user's social media activity. Some or all of the above processing in the awarding unit may be performed using AI or not. For example, the awarding unit can input the user's social media activity data into AI, which can then award relevant points.

[0056] The collaboration unit can select the optimal collaboration method by referring to past collaboration history when collaborating with medical institutions. For example, the collaboration unit can select the optimal data collaboration method based on past collaboration history. The collaboration unit can also improve the collaboration method with medical institutions based on past collaboration history. For example, the collaboration unit can improve the collaboration method with medical institutions based on past collaboration history. The collaboration unit can also select the optimal data collaboration method by comprehensively referring to past collaboration history. For example, the collaboration unit can select the optimal data collaboration method by comprehensively referring to past collaboration history. This allows the optimal data collaboration method to be selected by referring to past collaboration history. Some or all of the above processes in the collaboration unit may be performed using AI or not. For example, the collaboration unit can input past collaboration history data into AI, and the AI ​​can select the optimal data collaboration method.

[0057] The integration unit can filter data when integrating with medical institutions based on the user's current health status and lifestyle. For example, if the user is unwell, the integration unit may suggest refraining from data integration. The integration unit can also proceed with data integration if the user is in good health. The integration unit can also adjust the timing of data integration based on the user's lifestyle (e.g., traveling, working). This allows for appropriate data integration by filtering data based on the user's health status and lifestyle. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input data on the user's health status and lifestyle into an AI, which can then filter the data integration.

[0058] The integration unit can prioritize the integration of highly relevant data by considering the user's geographical location when integrating data with medical institutions. For example, if the user lives in a specific region, the integration unit will prioritize data integration with medical institutions in that region. Furthermore, if the user is traveling, the integration unit can prioritize data integration with medical institutions in their travel destination. Also, if the user lives in a different region, the integration unit can prioritize data integration with medical institutions in that region. This ensures that highly relevant data is prioritized by considering the user's geographical location. Some or all of the above processing in the integration unit may be performed using AI, or not. For example, the integration unit can input the user's geographical location data into AI, which can then prioritize the integration of highly relevant data.

[0059] The collaboration unit can analyze a user's social media activity and link relevant data when linking data with medical institutions. For example, if a user posts health-related content on social media, the collaboration unit can link data related to that post with the medical institution. For example, if a user posts health-related content on social media, the collaboration unit can link data related to that post with the medical institution. For example, if a user posts food-related content on social media, the collaboration unit can link data related to that food-related content with the medical institution. For example, if a user posts exercise-related content on social media, the collaboration unit can link data related to that exercise-related content with the medical institution. For example, if a user posts exercise-related content on social media, the collaboration unit can link data related to that exercise-related content with the medical institution. In this way, relevant data is linked with medical institutions by analyzing the user's social media activity. Some or all of the above processing in the collaboration unit may be performed using AI or not. For example, the collaboration unit can input the user's social media activity data into AI, and the AI ​​can link relevant data with medical institutions.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The analysis unit can improve the accuracy of its analysis of stool shape, color, and texture by referring to past data. For example, it can compare and analyze the current stool shape, color, and texture based on past stool data. It can also analyze changes in health status based on past stool data. Furthermore, it can detect abnormal stool conditions early based on past stool data. In this way, the accuracy of stool analysis is improved by referring to past data.

[0062] The posting function can analyze a user's past posting history and select the most suitable posting method. For example, it can analyze the times when a user frequently posted in the past and send notifications prompting them to post during those times. It can also prioritize suggesting posting methods (photos, videos, etc.) that the user has used in the past. Furthermore, it can send notifications prompting users to post on specific days of the week or at specific times based on their past posting history. In this way, the system can select the most suitable posting method by analyzing a user's past posting history.

[0063] The analysis unit can perform stool analysis while considering the user's dietary history and lifestyle. For example, it can analyze the stool condition based on the user's dietary history. It can also analyze the stool condition based on the user's lifestyle (exercise, sleep, etc.). Furthermore, it can analyze the stool condition by comprehensively considering the user's dietary history and lifestyle. This improves the accuracy of stool analysis by taking the user's dietary history and lifestyle into account.

[0064] The service provider can provide optimal advice by referring to the user's past health data when delivering health reports and advice. For example, it can advise on optimal diet and lifestyle improvements based on the user's past health data. It can also provide advice on specific health problems based on the user's past health data. Furthermore, it can provide optimal advice by comprehensively referring to the user's past health data. In this way, it can provide optimal advice by referring to the user's past health data.

[0065] The point awarding unit can filter points based on the user's current health and lifestyle. For example, if a user is unwell, it can suggest withholding points. Conversely, if the user is in good health, points can be awarded. Furthermore, the timing of point awarding can be adjusted based on the user's lifestyle (e.g., traveling, working). This allows for appropriate point awarding by filtering points based on the user's health and lifestyle.

[0066] The collaboration department can select the optimal collaboration method when collaborating with medical institutions by referring to past collaboration history. For example, it can select the optimal data collaboration method based on past collaboration history. It can also improve the collaboration method with medical institutions based on past collaboration history. Furthermore, it can select the optimal data collaboration method by comprehensively referring to past collaboration history. In this way, the optimal data collaboration method can be selected by referring to past collaboration history.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The posting function takes a picture of the stool and posts it. For example, you can take a picture of the stool using your smartphone camera and post it through the dedicated app. The posting function also has a function to adjust the shooting angle, resolution, and lighting conditions when taking pictures of the stool. For example, the posting function automatically sets the appropriate shooting angle and resolution when taking pictures of the stool. The posting function also has a function to adjust the lighting conditions when taking pictures of the stool. For example, the posting function automatically sets the appropriate lighting conditions when taking pictures of the stool. Step 2: The analysis unit analyzes the stool images submitted by the posting unit to visualize the health status. For example, it uses a generation AI to analyze the shape, color, and texture of the stool to determine whether it is healthy or unhealthy. For example, it analyzes the shape of the stool to determine whether it is hard, soft, or diarrheal. It also analyzes the color of the stool to determine whether it is a normal or unhealthy color. Furthermore, it analyzes the texture of the stool to determine whether it has a normal or unhealthy texture. Step 3: The service provider provides users with health reports and advice based on their health status analyzed by the analysis unit. For example, it may advise on improvements to diet and lifestyle based on stool consistency. For instance, if stool is hard and small, it may advise increasing fiber intake. If stool is soft and watery, it may advise staying hydrated and eating easily digestible foods. Furthermore, it can also advise on improvements to exercise and sleep based on stool consistency. For example, it may provide advice on appropriate exercise and sleep based on stool consistency. Step 4: The points awarding department awards points for continuous posting of images of stool submitted by the posting department. For example, 10 points will be awarded for posting an image of stool every day. Bonus points can also be awarded for meeting certain conditions. For example, bonus points may be awarded for posting an image of stool for one week in a row. Furthermore, points can also be awarded for participating in specific events or campaigns. For example, points may be awarded for participating in a specific event or campaign. Step 5: The collaboration unit connects the stool data analyzed by the analysis unit with medical institutions. For example, it automatically sends the stool data to medical institutions. For example, it automatically sets the timing and method of sending the stool data to medical institutions. It also provides an interface that allows doctors to view the stool data. For example, it provides a dedicated app or web interface for doctors to view the stool data.

[0069] (Example of form 2) The health management system according to an embodiment of the present invention is a system that supports health management simply by taking and posting photos of daily stool. This health management system analyzes images of stool and visualizes the user's health status. For example, it analyzes the shape, color, and texture of the stool to determine whether it is healthy or unhealthy. Furthermore, points are awarded for continuously posting images of stool, encouraging users to make posting a habit. The health management system analyzes the stool data posted daily and provides easy-to-understand health reports and advice. For example, it provides advice on improving diet and lifestyle habits based on the condition of the stool. In addition, the stool data is linked with medical institutions, eliminating the need for stool tests during health checkups. This makes it easier to understand daily physical condition and proposes a new style of health management that prevents lifestyle-related diseases and digestive system problems. For example, when a user takes a photo of their stool and posts it to the health management system, the system analyzes the shape, color, and texture of the stool and determines the user's health status. For example, if the stool is hard and small, it may indicate constipation, and the health management system will advise increasing fiber intake. If the stool is soft and watery, it may indicate diarrhea, and the health management system will advise staying hydrated and eating easily digestible foods. Furthermore, users are awarded points for continuously posting images of their stool. For example, posting an image of their stool every day earns them 10 points per day, accumulating 70 points in a week. This encourages users to develop the habit of posting images of their stool, making it easier to maintain consistent health management. The health management system also links stool data with medical institutions. For example, when a user visits an internal medicine or gastroenterology clinic, the health management system provides the doctor with the stool data it has analyzed. This makes it easier for doctors to understand the user's stool condition, eliminating the need for stool tests during health checkups. This reduces the psychological burden on users during health checkups. In this way, the health management system supports daily health management by analyzing stool images using generation AI and visualizing health status. Furthermore, by awarding points and linking data with medical institutions, it promotes the habit of posting images of stool and proposes a new health management style that prevents lifestyle-related diseases and digestive system problems. In this way, the health management system can understand the daily state of stool and support health management.

[0070] The health management system according to this embodiment comprises a posting unit, an analysis unit, a provision unit, an assignment unit, and a linking unit. The posting unit takes and posts images of stool. The posting unit can, for example, take images of stool using a smartphone camera and post them through a dedicated app. The posting unit also has a function to adjust the shooting angle, resolution, and lighting conditions when taking images of stool. For example, the posting unit automatically sets an appropriate shooting angle and resolution when taking images of stool. The posting unit also has a function to adjust the lighting conditions when taking images of stool. For example, the posting unit automatically sets appropriate lighting conditions when taking images of stool. The analysis unit analyzes the images of stool posted by the posting unit and visualizes the health status. The analysis unit, for example, uses a generation AI to analyze the shape, color, and texture of the stool and makes a determination of whether it is healthy stool or unhealthy stool. For example, the analysis unit analyzes the shape of the stool and makes a determination of whether it is hard stool, soft stool, diarrheal stool, etc. The analysis unit also analyzes the color of the stool and makes a determination of whether it is a normal color or an unhealthy color. Furthermore, the analysis unit analyzes the texture of the stool and determines whether it has a normal or unhealthy texture. The provision unit provides users with health reports and advice based on the health status analyzed by the analysis unit. For example, the provision unit may advise on areas for improvement in diet and lifestyle based on the condition of the stool. For example, if the stool is hard and small, the provision unit may advise increasing fiber intake. Also, if the stool is soft and watery, the provision unit may advise staying hydrated and eating easily digestible foods. In addition, the provision unit can also advise on areas for improvement in exercise and sleep based on the condition of the stool. For example, the provision unit may provide advice on appropriate exercise and sleep based on the condition of the stool. The awarding unit awards points for continuous posting of stool images submitted by the posting unit. For example, the awarding unit may award 10 points per day for posting a stool image every day. The awarding unit may also award bonus points if certain conditions are met. For example, the awarding unit may award bonus points for posting a stool image for one week consecutively. Furthermore, the points system can also award points for participating in specific events or campaigns. For example, the points system awards points for participating in specific events or campaigns.The collaboration unit connects the stool data analyzed by the analysis unit with medical institutions. The collaboration unit automatically transmits the stool data to medical institutions, for example. For example, the collaboration unit automatically sets the timing and method of transmitting the stool data to medical institutions. The collaboration unit also provides an interface that allows doctors to view the stool data. For example, the collaboration unit provides a dedicated app or web interface for doctors to view the stool data. As a result, the health management system according to this embodiment can support daily health management by analyzing stool images and visualizing health status.

[0071] The posting section allows users to take and submit images of their stool. For example, users can take images using their smartphone camera and submit them through a dedicated app. Specifically, the app automatically sets the optimal shooting angle and resolution when the user takes an image. For instance, the app recognizes the shape and location of the stool and suggests the best shooting angle. The resolution is also automatically adjusted to ensure that details of the stool are clearly visible. Furthermore, the app senses ambient light and uses the flash as needed to provide appropriate lighting conditions. This allows users to easily take and submit high-quality images of their stool. Submitted images are securely stored on a dedicated cloud server and accessible to the analysis section. The posting section also prioritizes user privacy when taking images; image data is encrypted before transmission. It also includes a function to review submitted images and retake them if necessary. This allows users to submit images of their stool with peace of mind. Additionally, the posting section provides voice guidance and tutorials to simplify the process of submitting images. This ensures that even first-time users can operate the system without difficulty.

[0072] The analysis unit analyzes images of stool submitted by the posting unit to visualize the user's health status. The analysis uses a generation AI to analyze the shape, color, and texture of the stool, determining whether it is healthy or unhealthy. Specifically, to analyze the shape of the stool, the generation AI uses image recognition technology to extract the stool's outline and classify its shape. For example, it identifies shapes such as hard stool, soft stool, and diarrhea. Furthermore, to analyze the color of the stool, it analyzes the hue, saturation, and brightness of the image to determine whether the color is normal or abnormal. For example, healthy stool is brown, but abnormal stool may contain black, red, or green. Additionally, to analyze the texture of the stool, it performs texture analysis on the image to evaluate the roughness and smoothness of the stool's surface. This allows for the determination of normal or abnormal texture. The analysis unit integrates these analysis results to comprehensively evaluate the user's health status. For example, if the stool is hard, black in color, and has a rough texture, it may indicate constipation or indigestion. The analysis results are provided to the user in a visually easy-to-understand format, allowing them to grasp changes in their health status at a glance. Furthermore, the analysis unit analyzes health trends by comparing them with past stool data, supporting long-term health management.

[0073] The service provider provides users with health reports and advice based on their health status analyzed by the analysis unit. Specifically, it advises improvements to diet and lifestyle habits based on the condition of the stool. For example, if the stool is hard and small, it advises increasing fiber intake. If the stool is soft and watery, it advises staying hydrated and eating easily digestible foods. The service provider generates a personalized health report based on the user's stool condition. This report includes detailed analysis results of the stool's shape, color, and texture, and provides specific advice based on these findings. For example, if the stool is hard, it will provide a list of foods high in fiber and exercise methods effective for relieving constipation. If the stool color is abnormal, it will provide information on possible dietary and lifestyle habits that may be causing it. Furthermore, the service provider proposes a specific action plan to improve the user's health. For example, it encourages improvement by recording daily meals and exercise levels. The service provider also has a function to send regular reminders to help users continue managing their health. This allows users to efficiently manage their daily health.

[0074] The points department awards points for continuous posting of stool images submitted by the posting department. Specifically, 10 points are awarded for posting a stool image every day. Bonus points can also be awarded if certain conditions are met. For example, bonus points may be awarded for posting stool images for one week consecutively. The points department has introduced a point system to increase users' motivation to continue managing their health. Users can use the points they earn to exchange for health-related products and services. For example, they can use points to purchase health foods, supplements, or fitness gym memberships. They can also earn additional points by participating in specific events and campaigns. For example, they can earn bonus points by participating in health management quizzes or posting stool images during a specific period. Furthermore, the points department manages the history of points earned by users and makes it available for users to check at any time. This allows users to track their progress in health management and maintain continuous motivation.

[0075] The Collaboration Unit connects stool data analyzed by the Analysis Unit with medical institutions. Specifically, it automatically transmits stool data to medical institutions. For example, it automatically sets the timing and method of transmitting stool data to medical institutions. The Collaboration Unit transmits stool data to medical institutions only after obtaining the user's consent. This allows doctors to check the user's stool data in real time and make necessary diagnoses and treatments. The Collaboration Unit provides a dedicated app and web interface for doctors to view the stool data. This allows doctors to easily check the stool data and understand the user's health status. In addition, the Collaboration Unit standardizes the data transmission method and format to facilitate smooth collaboration with medical institutions. This makes it easier to share data between different medical institutions, making user health management more efficient. Furthermore, the Collaboration Unit also has a function to provide users with feedback from medical institutions. For example, it notifies users of advice and diagnosis results from doctors and prompts them to take necessary actions. This allows users to manage their health more appropriately through collaboration with medical institutions.

[0076] The analysis unit can analyze the shape, color, and texture of stool and determine whether it is healthy or unhealthy. For example, the analysis unit can analyze the shape of stool and determine whether it is hard, soft, or diarrheal. For example, to analyze the shape of stool, the analysis unit uses a generation AI to classify the shape of stool. The analysis unit can also analyze the color of stool and determine whether it is a normal or unhealthy color. For example, to analyze the color of stool, the analysis unit uses a generation AI to classify the color of stool. Furthermore, the analysis unit can analyze the texture of stool and determine whether it is a normal or unhealthy texture. For example, to analyze the texture of stool, the analysis unit uses a generation AI to classify the texture of stool. In this way, by analyzing the shape, color, and texture of stool, it becomes possible to determine the state of health. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the analysis unit can input data on the shape, color, and texture of stool into a generation AI, and the generation AI can analyze the shape, color, and texture of stool.

[0077] The service provider can provide advice on improving diet and lifestyle habits based on the condition of the stool. For example, if the stool is hard and small, the service provider may advise increasing fiber intake. For example, if the stool is hard and small, the service provider may advise consuming foods rich in dietary fiber. Also, if the stool is soft and watery, the service provider may advise staying hydrated and eating easily digestible foods. For example, if the stool is soft and watery, the service provider may advise frequent hydration. Furthermore, the service provider can also provide advice on improving exercise and sleep based on the condition of the stool. For example, the service provider may provide appropriate exercise and sleep advice based on the condition of the stool. In this way, providing advice based on the condition of the stool promotes improvements in lifestyle habits. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input stool condition data into AI, and the AI ​​can provide advice on improving diet and lifestyle habits.

[0078] The point-granting unit can award points for posting images of daily bowel movements. For example, the point-granting unit can award 10 points per day for posting images of daily bowel movements. The point-granting unit can also award bonus points when certain conditions are met. For example, the point-granting unit can award bonus points for posting images of bowel movements for one week in a row. Furthermore, the point-granting unit can also award points for participating in specific events or campaigns. For example, the point-granting unit can award points for participating in specific events or campaigns. This way, posting images of daily bowel movements will earn points and encourage the habit of posting. Some or all of the above processes in the point-granting unit may be performed using AI or not. For example, the point-granting unit can input the data of posted images of bowel movements into an AI, which can then award points.

[0079] The collaboration unit can automatically transmit stool data to medical institutions. For example, the collaboration unit automatically transmits stool data to medical institutions. For example, the collaboration unit automatically sets the timing and method of transmitting stool data to medical institutions. The collaboration unit also provides an interface that allows doctors to view the stool data. For example, the collaboration unit provides a dedicated app or web interface for doctors to view the stool data. This eliminates the need for stool tests during health checkups by automatically transmitting stool data to medical institutions. Some or all of the above processing in the collaboration unit may be performed using AI or not. For example, the collaboration unit can input stool data into AI, and the AI ​​can transmit the stool data to medical institutions.

[0080] The collaboration unit can provide an interface that allows physicians to view stool data. For example, the collaboration unit can provide a dedicated app or web interface for physicians to view stool data. For example, the collaboration unit can provide a dedicated app for physicians to view stool data, making it easy for physicians to access the data. Alternatively, the collaboration unit can also provide a web interface for physicians to view stool data. For example, the collaboration unit can provide a web interface for physicians to view stool data, making it easy for physicians to access the data. By providing an interface that allows physicians to view stool data, collaboration with medical institutions can be streamlined. Some or all of the above processing in the collaboration unit may be performed using AI or not. For example, the collaboration unit can input stool data into AI, and the AI ​​can generate an interface that provides the stool data to physicians.

[0081] The posting function can estimate the user's emotions and adjust the timing of their stool image posts based on those emotions. For example, if the user is feeling stressed, the posting function can send a notification prompting them to post a stool image during a time when they can relax. The posting function can also send a notification prompting the user to post a stool image immediately if they are relaxed. The posting function can also send a notification suggesting that the user postpone posting a stool image if they are busy. This adjusts the timing of stool image posts based on the user's emotions, thereby promoting the habit of posting. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the posting section may be performed using AI or not. For example, the posting section can input user sentiment data into the AI, which can then adjust the timing of the posting of images of feces.

[0082] The posting function can analyze a user's past posting history and select the optimal posting method. For example, the posting function can analyze the time slots when a user frequently posted in the past and send notifications prompting them to post during those times. The posting function can also prioritize suggesting posting methods (photos, videos, etc.) that the user has used in the past. The posting function can also send notifications prompting users to post on specific days or times based on their past posting history. This allows the posting function to select the optimal posting method by analyzing the user's past posting history. Some or all of the above processing in the posting function may be performed using AI or not. For example, the posting function can input the user's past posting history data into AI, which can then select the optimal posting method.

[0083] The posting function can filter stool image submissions based on the user's current health and lifestyle. For example, if a user is unwell, the posting function can send a notification suggesting they refrain from submitting stool image submissions. The posting function can also send a notification encouraging users to submit stool image submissions if they are in good health. The posting function can also adjust the timing of stool image submissions based on the user's lifestyle (e.g., traveling, working). This allows for appropriate submissions by filtering stool image submissions based on the user's health and lifestyle. Some or all of the above processing in the posting function may be performed using AI or not. For example, the posting function can input data on the user's health and lifestyle into an AI, which can then filter stool image submissions.

[0084] The posting function can estimate the user's emotions and determine the priority of the stool images to post based on the estimated emotions. For example, if the user is stressed, the posting function may suggest prioritizing the posting of high-importance stool images. For example, if the user is relaxed, the posting function may suggest posting all stool images equally. For example, if the user is relaxed, the posting function may suggest posting all stool images equally. For example, if the user is busy, the posting function may suggest postponing less important stool images. For example, if the user is busy, the posting function may suggest postponing less important stool images. In this way, by prioritizing stool images based on the user's emotions, important stool images are posted preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the posting section may be performed using AI or not. For example, the posting section can input user sentiment data into the AI, which can then determine the priority of images related to feces.

[0085] The posting function can prioritize posting images of stool that are highly relevant, taking into account the user's geographical location when the user posts images of stool. For example, if the user is traveling, the posting function can suggest prioritizing the posting of images of stool related to the food and lifestyle of their travel destination. For example, if the user is traveling, the posting function can suggest prioritizing the posting of images of stool related to the food and lifestyle of their travel destination. For example, if the user is at home, the posting function can suggest prioritizing the posting of images of stool related to their usual food and lifestyle. For example, if the user is in a specific region, the posting function can suggest prioritizing the posting of images of stool related to the food and lifestyle of that region. For example, if the user is in a specific region, the posting function can suggest prioritizing the posting of images of stool related to the food and lifestyle of that region. In this way, by taking the user's geographical location into consideration, images of stool that are highly relevant are prioritized. Some or all of the above processing in the posting function may be performed using AI or not. For example, the posting function can input the user's geographical location data into the AI, which can then prioritize posting images of highly relevant flights.

[0086] The posting function can analyze a user's social media activity when they post an image of stool and make relevant posts. For example, if a user posts about health on social media, the posting function can send a notification prompting them to post an image of stool. The posting function can also suggest that a user post an image of stool related to a meal if they post about food on social media. The posting function can also suggest that a user post an image of stool related to exercise if they post about exercise on social media. In this way, relevant images of stool are posted by analyzing the user's social media activity. Some or all of the above processing in the posting function may be performed using AI or not. For example, the posting function can input the user's social media activity data into AI, and the AI ​​can post relevant images of stool.

[0087] The analysis unit can estimate the user's emotions and adjust the stool analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simplified analysis result. For example, if the user is stressed, the analysis unit can provide a simplified analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. The analysis unit can also provide a rapid analysis result if the user is in a hurry. For example, if the user is in a hurry, the analysis unit can provide a rapid analysis result. In this way, appropriate analysis results are provided by adjusting the stool analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is 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 processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input the user's emotion data into the AI, and the AI ​​can adjust the stool analysis method.

[0088] The analysis unit can improve the accuracy of its analysis of stool shape, color, and texture by referring to past data. For example, the analysis unit can compare and analyze the current stool shape, color, and texture based on past stool data. The analysis unit can also analyze changes in health status based on past stool data. The analysis unit can also detect abnormal stool conditions early based on past stool data. This improves the accuracy of stool analysis by referring to past data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input past stool data into AI, which can improve the accuracy of stool analysis.

[0089] The analysis unit can perform stool analysis while considering the user's dietary history and lifestyle. For example, the analysis unit can analyze the stool condition based on the user's dietary history. The analysis unit can also analyze the stool condition based on the user's lifestyle (exercise, sleep, etc.). The analysis unit can also analyze the stool condition by comprehensively considering the user's dietary history and lifestyle. This improves the accuracy of stool analysis by considering the user's dietary history and lifestyle. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's dietary history and lifestyle data into the AI, which can then perform the stool analysis.

[0090] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. Also, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Also, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, appropriate display is provided by adjusting the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is 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 processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into the AI, which can then adjust how the analysis results are displayed.

[0091] The analysis unit can perform stool analysis while considering the user's geographical background. For example, if the user lives in a specific region, the analysis unit can perform stool analysis while considering the diet and lifestyle of that region. For example, if the user lives in a specific region, the analysis unit can perform stool analysis while considering the diet and lifestyle of that region. For example, if the user is traveling, the analysis unit can perform stool analysis while considering the diet and lifestyle of that destination. For example, if the user lives in a different region, the analysis unit can perform stool analysis while considering the diet and lifestyle of that region. For example, if the user lives in a different region, the analysis unit can perform stool analysis while considering the diet and lifestyle of that region. This improves the accuracy of stool analysis by considering the user's geographical background. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input the user's geographical background data into AI, and the AI ​​can perform stool analysis.

[0092] The analysis unit can improve the accuracy of its analysis by referring to relevant medical literature when analyzing stool samples. For example, the analysis unit can improve the accuracy of its analysis by referring to the latest medical literature when analyzing stool samples. The analysis unit can also improve the accuracy of its analysis by referring to past medical literature when analyzing stool samples. The analysis unit can also improve the accuracy of its analysis by comprehensively referring to relevant medical literature when analyzing stool samples. As a result, the accuracy of the stool analysis is improved by referring to relevant medical literature. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input relevant medical literature data into the AI, and the AI ​​can improve the accuracy of the stool analysis.

[0093] The service provider can estimate the user's emotions and adjust the way health reports and advice are presented based on the estimated emotions. For example, if the user is stressed, the service provider can provide concise and easy-to-understand language. For example, if the user is stressed, the service provider can provide concise and easy-to-understand language. For example, if the user is relaxed, the service provider can provide detailed language. For example, if the user is relaxed, the service provider can provide detailed language. For example, if the user is in a hurry, the service provider can provide concise language. For example, if the user is in a hurry, the service provider can provide concise language. By adjusting the way health reports and advice are presented based on the user's emotions, appropriate information is provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotional data into the AI, which can then adjust how health reports and advice are presented.

[0094] The service provider can provide optimal advice by referring to the user's past health data when providing health reports and advice. For example, the service provider can advise on optimal diet and lifestyle improvements based on the user's past health data. The service provider can also provide advice on specific health problems based on the user's past health data. The service provider can also provide optimal advice by comprehensively referring to the user's past health data. This allows the service provider to provide optimal advice by referring to the user's past health data. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past health data into AI, and the AI ​​can provide optimal advice.

[0095] The service provider can customize health reports and advice based on the user's current living situation. For example, if the user is traveling, the service provider can provide advice based on the diet and lifestyle of the travel destination. Similarly, if the user is at work, the service provider can provide advice based on work-related stress and lifestyle. Furthermore, if the user is at home, the service provider can provide advice based on their usual diet and lifestyle. This allows for more appropriate advice to be provided by customizing it based on the user's current living situation. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's current living situation data into the AI, which can then customize the advice.

[0096] The service provider can estimate the user's emotions and prioritize health reports and advice based on those emotions. For example, if the user is stressed, the service provider will prioritize providing high-priority advice. If the user is relaxed, the service provider can distribute all advice equally. If the user is in a hurry, the service provider can postpone less important advice. This ensures that important information is prioritized by prioritizing health reports and advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotional data into the AI, which can then determine the priority of health reports and advice.

[0097] The service provider can provide optimal advice by considering the user's geographical location when providing health reports and advice. For example, if the user lives in a specific region, the service provider can provide advice based on the diet and lifestyle of that region. For example, if the user lives in a specific region, the service provider can provide advice based on the diet and lifestyle of that region. For example, if the user is traveling, the service provider can provide advice based on the diet and lifestyle of that destination. For example, if the user lives in a different region, the service provider can provide advice based on the diet and lifestyle of that region. This allows the service provider to provide optimal advice by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location data into AI, and the AI ​​can provide optimal advice.

[0098] The service provider can analyze a user's social media activity and provide relevant advice when providing health reports and advice. For example, if a user posts health-related content on social media, the service provider can provide advice related to that content. For example, if a user posts diet-related content on social media, the service provider can provide advice related to that content. For example, if a user posts exercise-related content on social media, the service provider can provide advice related to that content. In this way, by analyzing a user's social media activity, relevant advice can be provided. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into AI, and the AI ​​can provide relevant advice.

[0099] The point awarding unit can estimate the user's emotions and adjust the timing of point awarding based on the estimated emotions. For example, if the user is feeling stressed, the awarding unit will award points during a time when the user can relax. For example, if the user is feeling stressed, the awarding unit will award points during a time when the user can relax. The awarding unit can also award points immediately if the user is relaxed. For example, if the user is relaxed, the awarding unit will award points immediately. The awarding unit can also postpone awarding points if the user is busy. For example, if the awarding unit postpones awarding points if the user is busy. In this way, the effectiveness of point awarding is maximized by adjusting the timing of point awarding based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the awarding unit may be performed using AI or not. For example, the point awarding unit can input user emotion data into the AI, which can then adjust the timing of point awarding.

[0100] The point awarding unit can select the optimal point awarding method by referring to the user's past posting history when awarding points. For example, the point awarding unit can analyze the time slots when the user frequently posted in the past and award points during those time slots. The point awarding unit can also prioritize awarding points for posting methods (photos, videos, etc.) that the user has used in the past. The point awarding unit can also award points on specific days of the week or time slots based on the user's past posting history. This allows the system to select the optimal point awarding method by referring to the user's past posting history. Some or all of the above processing in the point awarding unit may be performed using AI or not. For example, the point awarding unit can input the user's past posting history data into AI, which can then select the optimal point awarding method.

[0101] The point awarding unit can filter points based on the user's current health and lifestyle when awarding points. For example, the awarding unit may suggest refraining from awarding points if the user is unwell. The awarding unit can also award points if the user is in good health. The awarding unit can also adjust the timing of point awarding based on the user's lifestyle (e.g., traveling, working). This allows for appropriate point awarding by filtering points based on the user's health and lifestyle. Some or all of the above processing in the awarding unit may be performed using AI or not. For example, the awarding unit can input data on the user's health and lifestyle into the AI, which can then filter point awarding.

[0102] The point awarding unit can estimate the user's emotions and determine the priority of point awarding based on the estimated user emotions. For example, if the user is stressed, the awarding unit will prioritize awarding points of high importance. For example, if the user is stressed, the awarding unit will prioritize awarding points of high importance. Also, if the user is relaxed, the awarding unit will award all points equally. For example, if the user is relaxed, the awarding unit will award all points equally. Also, if the user is busy, the awarding unit will postpone awarding points of low importance. For example, if the user is busy, the awarding unit will postpone awarding points of low importance. In this way, by determining the priority of point awarding based on the user's emotions, important points are given priority. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is 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 processing in the awarding unit may be performed using AI or not using AI. For example, the point awarding unit can input user emotion data into the AI, which can then determine the priority for awarding points.

[0103] The point awarding unit can prioritize awarding points that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is traveling, the awarding unit can prioritize awarding points related to meals and lifestyle habits at the travel destination. For example, if the user is traveling, the awarding unit can prioritize awarding points related to meals and lifestyle habits at the travel destination. For example, if the user is at home, the awarding unit can prioritize awarding points related to meals and lifestyle habits at the user's home. For example, if the user is in a specific region, the awarding unit can prioritize awarding points related to meals and lifestyle habits in that region. For example, if the user is in a specific region, the awarding unit can prioritize awarding points related to meals and lifestyle habits in that region. In this way, highly relevant points are preferentially awarded by taking into account the user's geographical location. Some or all of the above processing in the awarding unit may be performed using AI or not. For example, the awarding unit can input the user's geographical location data into AI, and the AI ​​can preferentially award highly relevant points.

[0104] The point awarding unit can analyze a user's social media activity and award relevant points when awarding points. For example, if a user posts health-related content on social media, the awarding unit can award points related to that content. Similarly, if a user posts food-related content on social media, the awarding unit can award points related to that content. Furthermore, if a user posts exercise-related content on social media, the awarding unit can award points related to that exercise content. In this way, relevant points are awarded by analyzing the user's social media activity. Some or all of the above processing in the awarding unit may be performed using AI or not. For example, the awarding unit can input the user's social media activity data into AI, which can then award relevant points.

[0105] The integration unit can estimate the user's emotions and adjust the data integration method with medical institutions based on the estimated user emotions. For example, if the user is feeling stressed, the integration unit can provide a simplified data integration method. For example, if the user is feeling stressed, the integration unit can provide a simplified data integration method. For example, if the user is relaxed, the integration unit can provide a detailed data integration method. For example, if the user is relaxed, the integration unit can provide a detailed data integration method. For example, if the user is in a hurry, the integration unit can provide a rapid data integration method. For example, if the user is in a hurry, the integration unit can provide a rapid data integration method. By adjusting the data integration method with medical institutions based on the user's emotions, appropriate data integration can be achieved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the collaboration unit can input user emotion data into the AI, which can then adjust how data is shared with medical institutions.

[0106] The collaboration unit can select the optimal collaboration method by referring to past collaboration history when collaborating with medical institutions. For example, the collaboration unit can select the optimal data collaboration method based on past collaboration history. The collaboration unit can also improve the collaboration method with medical institutions based on past collaboration history. For example, the collaboration unit can improve the collaboration method with medical institutions based on past collaboration history. The collaboration unit can also select the optimal data collaboration method by comprehensively referring to past collaboration history. For example, the collaboration unit can select the optimal data collaboration method by comprehensively referring to past collaboration history. This allows the optimal data collaboration method to be selected by referring to past collaboration history. Some or all of the above processes in the collaboration unit may be performed using AI or not. For example, the collaboration unit can input past collaboration history data into AI, and the AI ​​can select the optimal data collaboration method.

[0107] The integration unit can filter data when integrating with medical institutions based on the user's current health status and lifestyle. For example, if the user is unwell, the integration unit may suggest refraining from data integration. The integration unit can also proceed with data integration if the user is in good health. The integration unit can also adjust the timing of data integration based on the user's lifestyle (e.g., traveling, working). This allows for appropriate data integration by filtering data based on the user's health status and lifestyle. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input data on the user's health status and lifestyle into an AI, which can then filter the data integration.

[0108] The integration unit can estimate the user's emotions and determine the priority of data integration with medical institutions based on the estimated emotions. For example, if the user is stressed, the integration unit will prioritize data integration of high importance. For example, if the user is stressed, the integration unit will prioritize data integration of high importance. For example, if the user is relaxed, the integration unit will prioritize data integration of all data. For example, if the user is relaxed, the integration unit will prioritize data integration of all data. For example, if the user is busy, the integration unit will postpone data integration of low importance. For example, if the user is busy, the integration unit will postpone data integration of low importance. In this way, by determining the priority of data integration with medical institutions based on the user's emotions, important data is prioritized for integration. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the collaboration unit can input user emotion data into the AI, which can then determine the priority of data sharing with medical institutions.

[0109] The integration unit can prioritize the integration of highly relevant data by considering the user's geographical location when integrating data with medical institutions. For example, if the user lives in a specific region, the integration unit will prioritize data integration with medical institutions in that region. Furthermore, if the user is traveling, the integration unit can prioritize data integration with medical institutions in their travel destination. Also, if the user lives in a different region, the integration unit can prioritize data integration with medical institutions in that region. This ensures that highly relevant data is prioritized by considering the user's geographical location. Some or all of the above processing in the integration unit may be performed using AI, or not. For example, the integration unit can input the user's geographical location data into AI, which can then prioritize the integration of highly relevant data.

[0110] The collaboration unit can analyze a user's social media activity and link relevant data when linking data with medical institutions. For example, if a user posts health-related content on social media, the collaboration unit can link data related to that post with the medical institution. For example, if a user posts health-related content on social media, the collaboration unit can link data related to that post with the medical institution. For example, if a user posts food-related content on social media, the collaboration unit can link data related to that food-related content with the medical institution. For example, if a user posts exercise-related content on social media, the collaboration unit can link data related to that exercise-related content with the medical institution. For example, if a user posts exercise-related content on social media, the collaboration unit can link data related to that exercise-related content with the medical institution. In this way, relevant data is linked with medical institutions by analyzing the user's social media activity. Some or all of the above processing in the collaboration unit may be performed using AI or not. For example, the collaboration unit can input the user's social media activity data into AI, and the AI ​​can link relevant data with medical institutions.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The posting function can estimate the user's emotions and adjust the timing of posts based on those emotions. For example, if a user is feeling stressed, it can send a notification encouraging them to post during a time when they can relax. If the user is relaxed, it can send a notification encouraging them to post immediately. Furthermore, if the user is busy, it can send a notification suggesting they postpone posting. By adjusting the timing of posts based on the user's emotions, this helps to encourage the habit of posting.

[0113] The analysis unit can improve the accuracy of its analysis of stool shape, color, and texture by referring to past data. For example, it can compare and analyze the current stool shape, color, and texture based on past stool data. It can also analyze changes in health status based on past stool data. Furthermore, it can detect abnormal stool conditions early based on past stool data. In this way, the accuracy of stool analysis is improved by referring to past data.

[0114] The service provider can estimate the user's emotions and adjust the way health reports and advice are presented based on those estimates. For example, if the user is stressed, a concise and easy-to-understand presentation can be provided. If the user is relaxed, a more detailed presentation can be offered. Furthermore, if the user is in a hurry, a concise and to-the-point presentation can be provided. By adjusting the presentation of health reports and advice based on the user's emotions, appropriate information is provided.

[0115] The point awarding unit can estimate the user's emotions and adjust the timing of point awarding based on those emotions. For example, if the user is feeling stressed, points can be awarded during a time when they can relax. Conversely, if the user is relaxed, points can be awarded immediately. Furthermore, if the user is busy, point awarding can be postponed. In this way, the effectiveness of point awarding is maximized by adjusting the timing of point awarding based on the user's emotions.

[0116] The integration unit can estimate the user's emotions and adjust the data integration method with medical institutions based on the estimated emotions. For example, if the user is stressed, it can provide a simplified data integration method. If the user is relaxed, it can provide a more detailed data integration method. Furthermore, if the user is in a hurry, it can provide a rapid data integration method. By adjusting the data integration method with medical institutions based on the user's emotions, appropriate data integration can be achieved.

[0117] The posting function can analyze a user's past posting history and select the most suitable posting method. For example, it can analyze the times when a user frequently posted in the past and send notifications prompting them to post during those times. It can also prioritize suggesting posting methods (photos, videos, etc.) that the user has used in the past. Furthermore, it can send notifications prompting users to post on specific days of the week or at specific times based on their past posting history. In this way, the system can select the most suitable posting method by analyzing a user's past posting history.

[0118] The analysis unit can perform stool analysis while considering the user's dietary history and lifestyle. For example, it can analyze the stool condition based on the user's dietary history. It can also analyze the stool condition based on the user's lifestyle (exercise, sleep, etc.). Furthermore, it can analyze the stool condition by comprehensively considering the user's dietary history and lifestyle. This improves the accuracy of stool analysis by taking the user's dietary history and lifestyle into account.

[0119] The service provider can provide optimal advice by referring to the user's past health data when delivering health reports and advice. For example, it can advise on optimal diet and lifestyle improvements based on the user's past health data. It can also provide advice on specific health problems based on the user's past health data. Furthermore, it can provide optimal advice by comprehensively referring to the user's past health data. In this way, it can provide optimal advice by referring to the user's past health data.

[0120] The point awarding unit can filter points based on the user's current health and lifestyle. For example, if a user is unwell, it can suggest withholding points. Conversely, if the user is in good health, points can be awarded. Furthermore, the timing of point awarding can be adjusted based on the user's lifestyle (e.g., traveling, working). This allows for appropriate point awarding by filtering points based on the user's health and lifestyle.

[0121] The collaboration department can select the optimal collaboration method when collaborating with medical institutions by referring to past collaboration history. For example, it can select the optimal data collaboration method based on past collaboration history. It can also improve the collaboration method with medical institutions based on past collaboration history. Furthermore, it can select the optimal data collaboration method by comprehensively referring to past collaboration history. In this way, the optimal data collaboration method can be selected by referring to past collaboration history.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The posting function takes a picture of the stool and posts it. For example, you can take a picture of the stool using your smartphone camera and post it through the dedicated app. The posting function also has a function to adjust the shooting angle, resolution, and lighting conditions when taking pictures of the stool. For example, the posting function automatically sets the appropriate shooting angle and resolution when taking pictures of the stool. The posting function also has a function to adjust the lighting conditions when taking pictures of the stool. For example, the posting function automatically sets the appropriate lighting conditions when taking pictures of the stool. Step 2: The analysis unit analyzes the stool images submitted by the posting unit to visualize the health status. For example, it uses a generation AI to analyze the shape, color, and texture of the stool to determine whether it is healthy or unhealthy. For example, it analyzes the shape of the stool to determine whether it is hard, soft, or diarrheal. It also analyzes the color of the stool to determine whether it is a normal or unhealthy color. Furthermore, it analyzes the texture of the stool to determine whether it has a normal or unhealthy texture. Step 3: The service provider provides users with health reports and advice based on their health status analyzed by the analysis unit. For example, it may advise on improvements to diet and lifestyle based on stool consistency. For instance, if stool is hard and small, it may advise increasing fiber intake. If stool is soft and watery, it may advise staying hydrated and eating easily digestible foods. Furthermore, it can also advise on improvements to exercise and sleep based on stool consistency. For example, it may provide advice on appropriate exercise and sleep based on stool consistency. Step 4: The points awarding department awards points for continuous posting of images of stool submitted by the posting department. For example, 10 points will be awarded for posting an image of stool every day. Bonus points can also be awarded for meeting certain conditions. For example, bonus points may be awarded for posting an image of stool for one week in a row. Furthermore, points can also be awarded for participating in specific events or campaigns. For example, points may be awarded for participating in a specific event or campaign. Step 5: The collaboration unit connects the stool data analyzed by the analysis unit with medical institutions. For example, it automatically sends the stool data to medical institutions. For example, it automatically sets the timing and method of sending the stool data to medical institutions. It also provides an interface that allows doctors to view the stool data. For example, it provides a dedicated app or web interface for doctors to view the stool data.

[0124] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0127] Each of the multiple elements described above, including the posting unit, analysis unit, provision unit, awarding unit, and collaboration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the posting unit takes an image of stool using the camera 42 of the smart device 14 and posts it through a dedicated app. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the shape, color, and texture of the stool using generating AI. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides health reports and advice based on the condition of the stool. The awarding unit is implemented in the specific processing unit 46A of the smart device 14 and awards points for continuous posting of stool images. The collaboration unit is implemented in the specific processing unit 290 of the data processing unit 12 and automatically transmits stool data to a medical institution. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 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.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the posting unit, analysis unit, provision unit, awarding unit, and collaboration unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the posting unit takes an image of stool using the camera 42 of the smart glasses 214 and posts it through a dedicated app. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the shape, color, and texture of the stool using generating AI. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides health reports and advice based on the condition of the stool. The awarding unit is implemented in the specific processing unit 46A of the smart glasses 214 and awards points for continuous posting of stool images. The collaboration unit is implemented in the specific processing unit 290 of the data processing unit 12 and automatically transmits stool data to a medical institution. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the posting unit, analysis unit, provision unit, awarding unit, and collaboration unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the posting unit takes an image of stool using the camera 42 of the headset terminal 314 and posts it through a dedicated app. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the shape, color, and texture of the stool using generating AI. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides health reports and advice based on the condition of the stool. The awarding unit is implemented by, for example, the control unit 46A of the headset terminal 314 and awards points for continuous posting of stool images. The collaboration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically transmits stool data to a medical institution. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 7, the 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.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the posting unit, analysis unit, provision unit, awarding unit, and collaboration unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the posting unit takes an image of the stool using the camera 42 of the robot 414 and posts it through a dedicated app. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the shape, color, and texture of the stool using generating AI. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides health reports and advice based on the condition of the stool. The awarding unit is implemented by, for example, the control unit 46A of the robot 414 and awards points for continuous posting of stool images. The collaboration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically transmits the stool data to a medical institution. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0177] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] 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.

[0187] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) A posting section where people take pictures of their stool and submit them, An analysis unit analyzes the images of stool submitted by the aforementioned submission unit and visualizes the health status, A provision unit provides users with health reports and advice based on the health status analyzed by the aforementioned analysis unit. A point awarding unit that awards points for continued posting of images of stool submitted by the aforementioned posting unit, The system includes a collaboration unit that communicates the stool data analyzed by the aforementioned analysis unit with a medical institution. A system characterized by the following features. (Note 2) The aforementioned analysis unit, The shape, color, and texture of the stool are analyzed to determine whether it is healthy or unhealthy. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Based on the condition of your stool, we will advise you on areas where you can improve your diet and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned attachment unit, Points are awarded for posting a picture of your daily bowel movement. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned linkage unit is, Automatically send stool data to medical institutions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned linkage unit is, Provides an interface that allows doctors to view the data. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned submission section, The system estimates the user's emotions and adjusts the timing of poop image posts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned submission section, Analyze the user's past posting history and select the optimal posting method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned submission section, When users upload images of their stool, the system filters them based on their current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned submission section, It estimates the user's emotions and prioritizes the images of the user's bowel movements to be posted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned submission section, When users submit images of their stool, the system prioritizes posts that are highly relevant, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned submission section, When a user posts an image of their stool, the system analyzes their social media activity and makes relevant posts. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the stool analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing the shape, color, and texture of stool, past data is referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing stool samples, the analysis takes into account the user's dietary history and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing stool samples, the analysis takes into account the user's geographical background. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When analyzing stool samples, we refer to relevant medical literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way health reports and advice are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing health reports and advice, we refer to the user's past health data to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing health reports and advice, customize them based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and prioritizes health reports and advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing health reports and advice, we take the user's geographical location into consideration to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing health reports and advice, we analyze users' social media activity to offer relevant advice. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned attachment unit, The system estimates the user's emotions and adjusts the timing of point awarding based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned attachment unit, When awarding points, the system will refer to the user's past posting history to select the most suitable point awarding method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned attachment unit, When awarding points, filtering is performed based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned attachment unit, The system estimates the user's emotions and determines the priority for awarding points based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned attachment unit, When awarding points, the system prioritizes awarding points that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned attachment unit, When awarding points, the system analyzes the user's social media activity and awards points based on relevant activity. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned linkage unit is, The system estimates the user's emotions and adjusts the data sharing method with healthcare institutions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned linkage unit is, When sharing data with medical institutions, the optimal sharing method is selected by referring to past sharing history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned linkage unit is, When sharing data with medical institutions, filtering is performed based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned linkage unit is, The system estimates user emotions and prioritizes data sharing with healthcare institutions based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned linkage unit is, When sharing data with medical institutions, the system prioritizes sharing highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned linkage unit is, When sharing data with medical institutions, analyze users' social media activity and link relevant data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A posting section where people take pictures of their stool and submit them, An analysis unit analyzes the images of stool submitted by the aforementioned submission unit and visualizes the health status, A provision unit provides users with health reports and advice based on the health status analyzed by the aforementioned analysis unit. A point awarding unit that awards points for continued posting of images of stool submitted by the aforementioned posting unit, The system includes a collaboration unit that communicates the stool data analyzed by the aforementioned analysis unit with a medical institution. A system characterized by the following features.

2. The aforementioned analysis unit, The shape, color, and texture of the stool are analyzed to determine whether it is healthy or unhealthy. The system according to feature 1.

3. The aforementioned supply unit is, Based on the condition of your stool, we will advise you on areas where you can improve your diet and lifestyle. The system according to feature 1.

4. The aforementioned attachment unit, Points are awarded for posting a picture of your daily bowel movement. The system according to feature 1.

5. The aforementioned linkage unit is, Automatically send stool data to medical institutions. The system according to feature 1.

6. The aforementioned linkage unit is, Provides an interface that allows doctors to view the data. The system according to feature 1.

7. The aforementioned submission section, The system estimates the user's emotions and adjusts the timing of poop image posts based on those estimated emotions. The system according to feature 1.

8. The aforementioned submission section, Analyze the user's past posting history and select the optimal posting method. The system according to feature 1.

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