System

Abstract

An object of a system according to an embodiment is to visualize an intestinal environment of an individual and suggest an appropriate care product based on the intestinal environment.SOLUTION: A system according to an embodiment includes a lifestyle habit data collecting unit, a stool recording unit, an intestinal environment visualizing unit, and a match score predicting unit. The lifestyle habit data collection unit collects lifestyle habit data. The feces recording unit records feces. The intestinal environment visualizing unit visualizes the intestinal environment based on the lifestyle data and the stool record. The match score prediction unit predicts a match score based on the intestinal environment and a database of public bowel activity or constipation care products and makes a personalized recommendation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to accurately grasp an individual's intestinal environment and recommend appropriate care products based on that information.

[0005] The system according to the embodiment aims to visualize an individual's intestinal environment and recommend appropriate care products based on the visualization. [Means for solving the problem]

[0006] The system according to the embodiment includes a lifestyle data collection unit, a stool recording unit, an intestinal environment visualization unit, and a match score prediction unit. The lifestyle data collection unit collects lifestyle data. The stool recording unit records stool. The intestinal environment visualization unit visualizes the intestinal environment based on the lifestyle data and stool records. The match score prediction unit predicts a match score based on the intestinal environment and a database of intestinal activity or constipation care products available on the market, and makes individual suggestions. [Effects of the Invention]

[0007] The system according to the embodiment can visualize an individual's intestinal environment and recommend appropriate care products based on the visualization. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The AI ​​service according to an embodiment of the present invention is a system for improving the intestinal environment and achieving a healthy lifestyle. This system collects an individual's lifestyle data (number of steps, sleep, diet) and stool records, and visualizes the intestinal environment in images based on this data. It also predicts a match score from a database of intestinal health and constipation care products available on the market, and makes individual recommendations. This enables the AI ​​service to effectively manage the user's intestinal environment and achieve a healthy lifestyle.

[0029] The AI ​​service according to the embodiment includes a lifestyle data collection unit, a stool recording unit, an intestinal environment visualization unit, and a match score prediction unit. The lifestyle data collection unit collects lifestyle data from a user. For example, it records the user's steps, sleep time, and dietary details through a smartwatch or smartphone app. The lifestyle data collection unit can also collect the user's exercise data. For example, it collects exercise data using a pedometer or fitness tracker. The stool recording unit records the condition of the user's stool. For example, it inputs the color, shape, and frequency of stool into an app. The stool recording unit can also take and record photos of the stool. For example, it takes photos of the stool and analyzes its condition using AI. The intestinal environment visualization unit visualizes the intestinal environment based on the lifestyle data and the stool records. For example, it generates graphs and diagrams showing the balance of bacteria in the intestines and the condition of the intestinal wall. The intestinal environment visualization unit can also display changes in the intestinal environment over time. For example, it displays changes in the intestinal environment in a graph compared with past data. The match score prediction unit predicts a match score based on the intestinal environment and a database of intestinal activity and constipation care products available on the market, and makes individual recommendations. For example, it predicts how effective a specific probiotic or dietary fiber supplement will be for the user's intestinal environment and calculates a match score. The match score prediction unit can also recommend products that are optimal for the user's intestinal environment. For example, it prioritizes recommendations for products with a high match score. This enables the AI ​​service according to the embodiment to effectively manage the user's intestinal environment and achieve a healthy lifestyle. For example, improving lifestyle habits and using appropriate products can be expected to prevent or improve constipation. Furthermore, visualization of the intestinal environment allows users to intuitively understand their health condition and take appropriate measures.

[0030] The lifestyle data collection unit uses a generation AI to estimate stress levels from lifestyle data and evaluate the impact on the intestinal environment. The lifestyle data collection unit collects lifestyle data such as the user's number of steps, sleep time, and dietary content, and uses the generation AI to estimate the stress level. For example, a short sleep time or a low number of steps may indicate a high stress level. The lifestyle data collection unit also quantifies the stress level based on the collected lifestyle data using the generation AI, and evaluates the numerical value as the impact on the intestinal environment. For example, a high stress level may disrupt the balance of intestinal bacteria. The lifestyle data collection unit also makes suggestions to the user to improve their lifestyle habits to reduce stress based on the estimated stress level. For example, it may suggest exercise or diet to help them relax. This allows the stress level to be evaluated and the impact on the intestinal environment to be understood.

[0031] The lifestyle data collection unit can analyze the impact of seasonal or weather changes on the intestinal environment based on the lifestyle data. For example, the lifestyle data collection unit collects the user's lifestyle data and seasonal and weather data, and analyzes the correlation between them using a generation AI. For example, it evaluates the possibility that a decrease in the number of steps taken in winter will worsen the intestinal environment. The lifestyle data collection unit also analyzes the impact of seasonal and weather changes on the user's lifestyle and reflects the results in the evaluation of the intestinal environment. For example, it evaluates the possibility that a decrease in sleep time during the rainy season will worsen the intestinal environment. The lifestyle data collection unit also makes suggestions for improving lifestyle habits in accordance with seasonal and weather changes. For example, it suggests indoor exercises in winter and relaxing meals during the rainy season. This makes it possible to understand the impact of seasonal and weather changes on the intestinal environment.

[0032] The lifestyle data collection unit collects genetic information in addition to lifestyle data, and can analyze the impact of genetic factors on the intestinal environment. For example, the lifestyle data collection unit collects a user's lifestyle data and genetic information and analyzes the correlation using generative AI. For example, it evaluates the impact of specific genes on the intestinal environment. The lifestyle data collection unit also analyzes the characteristics of the user's intestinal environment based on the genetic information and reflects the results in lifestyle improvement suggestions. For example, it suggests specific diets to users with specific genes. The lifestyle data collection unit also integrates genetic information and lifestyle data to make optimal intestinal environment improvement suggestions to users. For example, it suggests specific exercises and diets to users who are genetically prone to poor intestinal environments. This makes it possible to understand the impact of genetic factors on the intestinal environment.

[0033] The lifestyle data collection unit can suggest a new exercise program that has a positive effect on the intestinal environment based on the lifestyle data. For example, the lifestyle data collection unit analyzes the user's lifestyle data and suggests an exercise program that has a positive effect on the intestinal environment. For example, it suggests exercises such as walking or yoga. Furthermore, the lifestyle data collection unit uses a generation AI to automatically generate an optimal exercise program for the user based on the collected lifestyle data. For example, it adjusts the exercise program according to the user's number of steps and sleep time. Furthermore, the lifestyle data collection unit expects that the user's intestinal environment will improve by implementing the suggested exercise program. For example, it monitors changes in the intestinal environment after implementing the exercise program. This makes it possible to suggest an exercise program that has a positive effect on the intestinal environment.

[0034] The stool recording unit uses a generation AI to estimate the type and balance of intestinal bacteria from stool records, allowing for a detailed assessment of the intestinal environment. The stool recording unit collects data such as the color, shape, and frequency of stool recorded by the user, and uses a generation AI to estimate the type and balance of intestinal bacteria. For example, if the color of the stool changes, it evaluates the increase or decrease of specific bacteria. The stool recording unit also evaluates the detailed state of the intestinal environment based on the stool record data. For example, if the shape of the stool changes, it evaluates the possibility of an imbalance in the intestinal bacteria. The stool recording unit also makes optimal suggestions for improving the intestinal environment based on the estimated type and balance of intestinal bacteria. For example, it suggests specific probiotics or dietary fiber supplements. This allows for the evaluation of the type and balance of intestinal bacteria and a detailed understanding of the state of the intestinal environment.

[0035] The stool recording unit can analyze the correlation between dietary content and stool condition based on the stool records and propose an optimal meal plan. For example, the stool recording unit collects stool data and dietary content recorded by the user and analyzes the correlation using a generation AI. For example, it evaluates the impact of specific ingredients on stool condition. The stool recording unit also uses the generation AI to propose an optimal meal plan to the user based on the correlation between dietary content and stool condition. For example, it suggests ingredients and recipes that will improve constipation. The stool recording unit also hopes that implementing the proposed meal plan will improve the user's stool condition. For example, it monitors changes in stool condition after implementing the meal plan. This allows it to analyze the correlation between dietary content and stool condition and propose an optimal meal plan.

[0036] The stool recording unit collects stool records as well as urine records, and can analyze the relationship between the intestinal environment and the internal environment. For example, the stool recording unit collects stool data and urine data recorded by the user and uses generation AI to analyze the relationship. For example, it evaluates changes in the color and frequency of stool and urine. The stool recording unit also uses generation AI to evaluate the relationship between the intestinal environment and the internal environment based on the stool and urine records. For example, it evaluates cases where constipation and urinary abnormalities occur simultaneously. The stool recording unit also integrates the stool and urine record data and makes optimal suggestions for improving the internal environment to the user. For example, it suggests specific foods and supplements. This makes it possible to analyze the relationship between the intestinal environment and the internal environment.

[0037] The stool recording unit can suggest new ingredients and recipes that have a positive effect on the intestinal environment based on the stool records. For example, the stool recording unit analyzes stool data recorded by the user and suggests ingredients and recipes that have a positive effect on the intestinal environment. For example, it suggests ingredients and recipes that will improve constipation. Furthermore, the stool recording unit uses a generation AI to automatically generate ingredients and recipes that are optimal for the user based on the stool record data. For example, it adjusts ingredients and recipes according to the condition of the stool. Furthermore, the stool recording unit hopes that by implementing the suggested ingredients and recipes, the condition of the user's stool will improve. For example, it monitors changes in the condition of the stool after implementing the ingredients or recipes. This makes it possible to suggest ingredients and recipes that have a positive effect on the intestinal environment.

[0038] The intestinal environment visualization unit uses a generation AI to display dynamic changes in intestinal bacteria in real time from visualized data of the intestinal environment. For example, the generation AI in the intestinal environment visualization unit displays dynamic changes in intestinal bacteria in real time based on the user's lifestyle data and stool records. For example, it displays changes in the balance of intestinal bacteria in graphs and animations. The intestinal environment visualization unit also analyzes changes in the type and balance of intestinal bacteria in real time based on collected data and visualizes the results. For example, it shows the increase or decrease of specific bacteria using colors and shapes. The intestinal environment visualization unit also displays dynamic changes in intestinal bacteria in real time when the user changes their lifestyle. For example, it visualizes changes in intestinal bacteria after changing their diet. This makes it possible to display dynamic changes in intestinal bacteria in real time.

[0039] The intestinal environment visualization unit can predict the future of the intestinal environment based on the visualized data of the intestinal environment and suggest preventive measures. In the intestinal environment visualization unit, for example, the generation AI predicts the future of the intestinal environment based on the collected lifestyle data and stool records. For example, it predicts changes in the intestinal environment if the current lifestyle is continued. The intestinal environment visualization unit also suggests preventive measures to the user based on the results of the future prediction. For example, if there is a possibility that the intestinal environment will deteriorate, it suggests improvements to the lifestyle. The intestinal environment visualization unit also visualizes the future prediction data so that the user can intuitively understand it. For example, it displays the future prediction of the intestinal environment in a graph or chart. This makes it possible to predict the future of the intestinal environment and suggest preventive measures.

[0040] The intestinal environment visualization unit can display the intestinal environment visualization data in three dimensions using a 3D model or VR technology, allowing for a more intuitive understanding. For example, the intestinal environment visualization unit uses a generation AI to generate a 3D model of the intestinal environment based on collected lifestyle data and stool records, and displays it three-dimensionally to the user. For example, it displays the balance of intestinal bacteria in a 3D graph. The intestinal environment visualization unit also uses VR technology to allow the user to experience the intestinal environment in a virtual space. For example, the state of the intestinal environment can be visually confirmed using VR goggles. The intestinal environment visualization unit also uses 3D models or VR technology to display changes in the intestinal environment in real time. For example, changes in the intestinal environment due to changes in lifestyle can be shown in a 3D model. This allows the intestinal environment to be displayed three-dimensionally, allowing for an intuitive understanding.

[0041] The intestinal environment visualization unit can integrate the intestinal environment visualization data with other health data (e.g., blood test results) to visualize the overall health status. For example, the intestinal environment visualization unit integrates the collected intestinal environment data with other health data (e.g., blood test results), and the generation AI visualizes the overall health status. For example, the intestinal environment and blood test results are displayed simultaneously. The intestinal environment visualization unit also generates graphs and charts that show the user's overall health status based on the integrated health data. For example, it displays a graph showing the correlation between the intestinal environment and blood test results. The intestinal environment visualization unit also visualizes the overall health status, allowing the user to intuitively understand their health status. For example, it provides a dashboard that allows the user to check changes in health status at a glance. This makes it possible to visualize the overall health status.

[0042] The match score prediction unit can use the generative AI to simulate the effects of a product based on the intestinal environment data and product database, and recommend the optimal product. For example, the match score prediction unit collects the user's intestinal environment data and product database, and the generative AI simulates the effects of the product. For example, it evaluates the impact of a specific probiotic on the intestinal environment. The match score prediction unit also recommends the optimal product for the user based on the simulation results. For example, it prioritizes the recommendation of products that are likely to improve the intestinal environment. The match score prediction unit also makes it easier for the user to select the product that suits them by simulating the effects of the product. For example, it displays the simulation results in a graph or chart. This makes it possible to simulate the effects of the product and recommend the optimal product.

[0043] The match score prediction unit takes into account the user's past product use history when predicting the match score, allowing for more accurate suggestions. For example, the match score prediction unit collects the user's past product use history, and the generation AI predicts the match score based on that data. For example, it evaluates the effectiveness of products used in the past and suggests products to use next. The match score prediction unit also suggests the most suitable product for the user based on the past product use history. For example, it suggests products that contain the same ingredients as products that have been effective in the past. The match score prediction unit also takes into account the past product use history when predicting the match score, making it easier for the user to select a product that suits them. For example, it automatically generates the most suitable product based on past data. This allows for more accurate suggestions to be made by taking into account the past product use history.

[0044] The match score prediction unit takes the user's living environment into account when predicting the match score, and can suggest products specific to the region. The match score prediction unit, for example, collects the user's living environment data (e.g., the climate and food culture of the region where they live), and the generation AI predicts the match score based on that data. For example, it suggests products that are suitable for the local climate. The match score prediction unit also suggests products specific to the region to the user based on the living environment data. For example, it suggests probiotics and supplements that suit the local food culture. The match score prediction unit also takes the living environment data into account when predicting the match score, making it easier for the user to select products that suit them. For example, it automatically generates products that take into account the characteristics of the region. This makes it possible to suggest products specific to the region.

[0045] The match score prediction unit can share the match score prediction results with the user's family and friends and suggest products for joint use. The match score prediction unit, for example, provides a function for sharing the match score prediction results with the user's family and friends. For example, it can suggest products that can be used by the whole family. The match score prediction unit also suggests products for joint use based on the shared match score data. For example, it can suggest probiotics that are suitable for the intestinal environment of the whole family. The match score prediction unit also makes it easier for the user to select products together with family and friends by sharing the match score prediction results. For example, it can select products for joint use through a sharing function. This makes it possible to suggest products that can be shared and used by family and friends.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The lifestyle data collection unit collects the user's lifestyle data. For example, it records the user's steps, sleep time, and dietary habits through a smartwatch or smartphone app. The lifestyle data collection unit can also collect the user's exercise data. For example, it collects exercise data using a pedometer or fitness tracker. The stool recording unit records the user's stool condition. For example, the color, shape, and frequency of stool are entered into the app. The stool recording unit can also take and record photos of the stool. For example, the stool is photographed and analyzed by AI. The intestinal environment visualization unit visualizes the intestinal environment based on the lifestyle data and stool records. For example, it generates graphs and diagrams showing the balance of intestinal bacteria and the condition of the intestinal wall. The intestinal environment visualization unit can also display changes in the intestinal environment over time. For example, it can graph changes in the intestinal environment compared with past data. The match score prediction unit predicts the match score based on the intestinal environment and a database of intestinal health and constipation care products available on the market, and makes individual suggestions. For example, the system predicts how effective a specific probiotic or dietary fiber supplement will be for the user's intestinal environment and calculates a match score. The match score prediction unit can also suggest products that are optimal for the user's intestinal environment. For example, products with a high match score are prioritized. This allows the AI ​​service according to the embodiment to effectively manage the user's intestinal environment and achieve a healthy lifestyle. For example, improving lifestyle habits and using appropriate products can be expected to prevent or improve constipation. Furthermore, visualization of the intestinal environment allows users to intuitively understand their health condition and take appropriate measures.

[0048] The lifestyle data collection unit can analyze the impact of seasonal or weather changes on the intestinal environment based on the lifestyle data. For example, it collects the user's lifestyle data and seasonal and weather data, and uses generation AI to analyze the correlation between them. For example, it evaluates the possibility that a decrease in the number of steps taken in winter will worsen the intestinal environment. The lifestyle data collection unit also analyzes the impact of seasonal and weather changes on the user's lifestyle and reflects the results in the evaluation of the intestinal environment. For example, it evaluates the possibility that a decrease in sleep time during the rainy season will worsen the intestinal environment. The lifestyle data collection unit also makes suggestions for improving lifestyle habits in response to seasonal and weather changes. For example, it suggests indoor exercises in winter and relaxing meals during the rainy season. This makes it possible to understand the impact of seasonal and weather changes on the intestinal environment.

[0049] The lifestyle data collection unit collects genetic information in addition to lifestyle data, and can analyze the impact of genetic factors on the intestinal environment. For example, it collects a user's lifestyle data and genetic information and uses generation AI to analyze the correlation between them. For example, it evaluates the impact of specific genes on the intestinal environment. The lifestyle data collection unit also analyzes the characteristics of the user's intestinal environment based on the genetic information and reflects the results in lifestyle improvement suggestions. For example, it suggests specific diets to users with specific genes. The lifestyle data collection unit also integrates genetic information and lifestyle data to make optimal intestinal environment improvement suggestions to users. For example, it suggests specific exercises and diets to users who are genetically prone to poor intestinal environments. This makes it possible to understand the impact of genetic factors on the intestinal environment.

[0050] The lifestyle data collection unit can suggest new exercise programs that have a positive effect on the intestinal environment based on the lifestyle data. For example, it analyzes the user's lifestyle data and suggests exercise programs that have a positive effect on the intestinal environment. For example, it suggests exercises such as walking or yoga. Furthermore, the lifestyle data collection unit uses a generation AI to automatically generate an optimal exercise program for the user based on the collected lifestyle data. For example, it adjusts the exercise program according to the user's number of steps and sleep time. Furthermore, the lifestyle data collection unit expects that the user's intestinal environment will improve by implementing the suggested exercise program. For example, it monitors changes in the intestinal environment after implementing the exercise program. This makes it possible to suggest exercise programs that have a positive effect on the intestinal environment.

[0051] The stool recording unit uses a generation AI to estimate the type and balance of intestinal bacteria from stool records, allowing for a detailed assessment of the intestinal environment. For example, it collects data such as the color, shape, and frequency of stool recorded by the user and uses the generation AI to estimate the type and balance of intestinal bacteria. For example, if the color of the stool changes, it evaluates the increase or decrease of specific bacteria. The stool recording unit also evaluates the detailed state of the intestinal environment based on the stool record data. For example, if the shape of the stool changes, it evaluates the possibility of an imbalance in the intestinal bacteria. Based on the estimated type and balance of intestinal bacteria, the stool recording unit also makes optimal suggestions for improving the intestinal environment to the user. For example, it suggests specific probiotics or dietary fiber supplements. This allows for the evaluation of the type and balance of intestinal bacteria and a detailed understanding of the state of the intestinal environment.

[0052] The stool recording unit can analyze the correlation between dietary content and stool condition based on stool records and propose an optimal meal plan. For example, it collects stool data and dietary content recorded by the user and uses a generation AI to analyze the correlation. For example, it evaluates the impact of specific ingredients on stool condition. The stool recording unit then uses the generation AI to propose an optimal meal plan to the user based on the correlation between dietary content and stool condition. For example, it suggests ingredients and recipes that will improve constipation. The stool recording unit also hopes that implementing the proposed meal plan will improve the user's stool condition. For example, it monitors changes in stool condition after implementing the meal plan. This allows it to analyze the correlation between dietary content and stool condition and propose an optimal meal plan.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The lifestyle data collection unit collects the user's lifestyle data. For example, the number of steps taken, sleep time, and dietary details of the user are recorded through a smartwatch or smartphone app. The lifestyle data collection unit can also collect the user's exercise data. For example, the exercise data is collected using a pedometer or fitness tracker. Step 2: The stool recording unit records the condition of the user's stool. For example, the color, shape, frequency, etc. of the stool are entered into the app. The stool recording unit can also take and record photos of the stool. For example, a photo of the stool can be taken and the AI ​​can analyze its condition. Step 3: The intestinal environment visualization unit visualizes the intestinal environment based on the lifestyle data and stool records. For example, it generates graphs and diagrams showing the balance of bacteria in the intestines and the condition of the intestinal wall. The intestinal environment visualization unit can also display changes in the intestinal environment over time. For example, it displays changes in the intestinal environment in a graph, comparing them with past data. Step 4: The match score prediction unit predicts a match score based on the intestinal environment and a database of intestinal activity and constipation care products available on the market, and makes personalized recommendations. For example, it predicts how effective a specific probiotic or dietary fiber supplement will be for the user's intestinal environment and calculates a match score. The match score prediction unit can also suggest products that are optimal for the user's intestinal environment. For example, it prioritizes recommendations for products with a high match score.

[0055] (Example 2) The AI ​​service according to an embodiment of the present invention is a system for improving the intestinal environment and achieving a healthy lifestyle. This system collects an individual's lifestyle data (number of steps, sleep, diet) and stool records, and visualizes the intestinal environment in images based on this data. It also predicts a match score from a database of intestinal health and constipation care products available on the market, and makes individual recommendations. This enables the AI ​​service to effectively manage the user's intestinal environment and achieve a healthy lifestyle.

[0056] The AI ​​service according to the embodiment includes a lifestyle data collection unit, a stool recording unit, an intestinal environment visualization unit, and a match score prediction unit. The lifestyle data collection unit collects lifestyle data from a user. For example, it records the user's steps, sleep time, and dietary details through a smartwatch or smartphone app. The lifestyle data collection unit can also collect the user's exercise data. For example, it collects exercise data using a pedometer or fitness tracker. The stool recording unit records the condition of the user's stool. For example, it inputs the color, shape, and frequency of stool into an app. The stool recording unit can also take and record photos of the stool. For example, it takes photos of the stool and analyzes its condition using AI. The intestinal environment visualization unit visualizes the intestinal environment based on the lifestyle data and the stool records. For example, it generates graphs and diagrams showing the balance of bacteria in the intestines and the condition of the intestinal wall. The intestinal environment visualization unit can also display changes in the intestinal environment over time. For example, it displays changes in the intestinal environment in a graph compared with past data. The match score prediction unit predicts a match score based on the intestinal environment and a database of intestinal activity and constipation care products available on the market, and makes individual recommendations. For example, it predicts how effective a specific probiotic or dietary fiber supplement will be for the user's intestinal environment and calculates a match score. The match score prediction unit can also recommend products that are optimal for the user's intestinal environment. For example, it prioritizes recommendations for products with a high match score. This enables the AI ​​service according to the embodiment to effectively manage the user's intestinal environment and achieve a healthy lifestyle. For example, improving lifestyle habits and using appropriate products can be expected to prevent or improve constipation. Furthermore, visualization of the intestinal environment allows users to intuitively understand their health condition and take appropriate measures.

[0057] The lifestyle data collection unit uses a generation AI to estimate stress levels from lifestyle data and evaluate the impact on the intestinal environment. The lifestyle data collection unit collects lifestyle data such as the user's number of steps, sleep time, and dietary content, and uses the generation AI to estimate the stress level. For example, a short sleep time or a low number of steps may indicate a high stress level. The lifestyle data collection unit also quantifies the stress level based on the collected lifestyle data using the generation AI, and evaluates the numerical value as the impact on the intestinal environment. For example, a high stress level may disrupt the balance of intestinal bacteria. The lifestyle data collection unit also makes suggestions to the user to improve their lifestyle habits to reduce stress based on the estimated stress level. For example, it may suggest exercise or diet to help them relax. This allows the stress level to be evaluated and the impact on the intestinal environment to be understood.

[0058] The lifestyle data collection unit can analyze the impact of seasonal or weather changes on the intestinal environment based on the lifestyle data. For example, the lifestyle data collection unit collects the user's lifestyle data and seasonal and weather data, and analyzes the correlation between them using a generation AI. For example, it evaluates the possibility that a decrease in the number of steps taken in winter will worsen the intestinal environment. The lifestyle data collection unit also analyzes the impact of seasonal and weather changes on the user's lifestyle and reflects the results in the evaluation of the intestinal environment. For example, it evaluates the possibility that a decrease in sleep time during the rainy season will worsen the intestinal environment. The lifestyle data collection unit also makes suggestions for improving lifestyle habits in accordance with seasonal and weather changes. For example, it suggests indoor exercises in winter and relaxing meals during the rainy season. This makes it possible to understand the impact of seasonal and weather changes on the intestinal environment.

[0059] The lifestyle data collection unit uses the emotion estimation function to analyze the emotional state together with the lifestyle data, and can evaluate the impact of emotional fluctuations on the intestinal environment. For example, the lifestyle data collection unit simultaneously collects the user's lifestyle data and emotional state and analyzes the correlation using a generation AI. For example, it evaluates the possibility that a negative emotional state will worsen the intestinal environment. The lifestyle data collection unit also uses the emotion estimation function to quantify the user's emotional state and evaluates the numerical value as the impact on the intestinal environment. For example, it evaluates the possibility that high stress or anxiety will disrupt the balance of intestinal bacteria. Furthermore, the lifestyle data collection unit makes suggestions to the user to improve their lifestyle habits to improve their emotions based on the estimated emotional state. For example, it suggests exercise or diet to help them relax. This makes it possible to understand the impact of emotional fluctuations on the intestinal environment.

[0060] The lifestyle data collection unit collects genetic information in addition to lifestyle data, and can analyze the impact of genetic factors on the intestinal environment. For example, the lifestyle data collection unit collects a user's lifestyle data and genetic information and analyzes the correlation using generative AI. For example, it evaluates the impact of specific genes on the intestinal environment. The lifestyle data collection unit also analyzes the characteristics of the user's intestinal environment based on the genetic information and reflects the results in lifestyle improvement suggestions. For example, it suggests specific diets to users with specific genes. The lifestyle data collection unit also integrates genetic information and lifestyle data to make optimal intestinal environment improvement suggestions to users. For example, it suggests specific exercises and diets to users who are genetically prone to poor intestinal environments. This makes it possible to understand the impact of genetic factors on the intestinal environment.

[0061] The lifestyle data collection unit can suggest a new exercise program that has a positive effect on the intestinal environment based on the lifestyle data. For example, the lifestyle data collection unit analyzes the user's lifestyle data and suggests an exercise program that has a positive effect on the intestinal environment. For example, it suggests exercises such as walking or yoga. Furthermore, the lifestyle data collection unit uses a generation AI to automatically generate an optimal exercise program for the user based on the collected lifestyle data. For example, it adjusts the exercise program according to the user's number of steps and sleep time. Furthermore, the lifestyle data collection unit expects that the user's intestinal environment will improve by implementing the suggested exercise program. For example, it monitors changes in the intestinal environment after implementing the exercise program. This makes it possible to suggest an exercise program that has a positive effect on the intestinal environment.

[0062] The lifestyle data collection unit uses the emotion estimation function to monitor emotions in real time when a user performs a specific lifestyle habit, and can suggest lifestyle habits that elicit positive emotions. The lifestyle data collection unit uses the emotion estimation function to monitor emotions in real time when a user performs a specific lifestyle habit. For example, it analyzes the emotional state during exercise or eating. The lifestyle data collection unit also suggests lifestyle habits that elicit positive emotions from the user based on the emotion estimation results. For example, it suggests relaxing exercises or enjoyable meals. The lifestyle data collection unit also monitors the user's emotional state in real time, and suggests lifestyle adjustments if emotions become negative. For example, it suggests lifestyle habits for relaxation if stress is high. This makes it possible to suggest lifestyle habits that elicit positive emotions.

[0063] The stool recording unit uses a generation AI to estimate the type and balance of intestinal bacteria from stool records, allowing for a detailed assessment of the intestinal environment. The stool recording unit collects data such as the color, shape, and frequency of stool recorded by the user, and uses a generation AI to estimate the type and balance of intestinal bacteria. For example, if the color of the stool changes, it evaluates the increase or decrease of specific bacteria. The stool recording unit also evaluates the detailed state of the intestinal environment based on the stool record data. For example, if the shape of the stool changes, it evaluates the possibility of an imbalance in the intestinal bacteria. The stool recording unit also makes optimal suggestions for improving the intestinal environment based on the estimated type and balance of intestinal bacteria. For example, it suggests specific probiotics or dietary fiber supplements. This allows for the evaluation of the type and balance of intestinal bacteria and a detailed understanding of the state of the intestinal environment.

[0064] The stool recording unit can analyze the correlation between dietary content and stool condition based on the stool records and propose an optimal meal plan. For example, the stool recording unit collects stool data and dietary content recorded by the user and analyzes the correlation using a generation AI. For example, it evaluates the impact of specific ingredients on stool condition. The stool recording unit also uses the generation AI to propose an optimal meal plan to the user based on the correlation between dietary content and stool condition. For example, it suggests ingredients and recipes that will improve constipation. The stool recording unit also hopes that implementing the proposed meal plan will improve the user's stool condition. For example, it monitors changes in stool condition after implementing the meal plan. This allows it to analyze the correlation between dietary content and stool condition and propose an optimal meal plan.

[0065] The stool recording unit uses the emotion estimation function to analyze the emotion at the time of stool recording and can evaluate the impact of emotional fluctuations on the state of the stool. For example, when a user records stool, the stool recording unit uses the emotion estimation function to analyze the emotion at that time. For example, it evaluates whether stress or anxiety is high when constipated. Furthermore, the stool recording unit evaluates the impact of emotional fluctuations on the state of the stool based on the emotion estimation result. For example, it evaluates the possibility that negative emotions worsen constipation. Furthermore, the stool recording unit makes suggestions to the user on lifestyle improvements to improve their emotions based on the estimation result of the emotional state. For example, it suggests exercise or diet to help them relax. This makes it possible to evaluate the impact of emotional fluctuations on the state of the stool.

[0066] The stool recording unit collects stool records as well as urine records, and can analyze the relationship between the intestinal environment and the internal environment. For example, the stool recording unit collects stool data and urine data recorded by the user and uses generation AI to analyze the relationship. For example, it evaluates changes in the color and frequency of stool and urine. The stool recording unit also uses generation AI to evaluate the relationship between the intestinal environment and the internal environment based on the stool and urine records. For example, it evaluates cases where constipation and urinary abnormalities occur simultaneously. The stool recording unit also integrates the stool and urine record data and makes optimal suggestions for improving the internal environment to the user. For example, it suggests specific foods and supplements. This makes it possible to analyze the relationship between the intestinal environment and the internal environment.

[0067] The stool recording unit can suggest new ingredients and recipes that have a positive effect on the intestinal environment based on the stool records. For example, the stool recording unit analyzes stool data recorded by the user and suggests ingredients and recipes that have a positive effect on the intestinal environment. For example, it suggests ingredients and recipes that will improve constipation. Furthermore, the stool recording unit uses a generation AI to automatically generate ingredients and recipes that are optimal for the user based on the stool record data. For example, it adjusts ingredients and recipes according to the condition of the stool. Furthermore, the stool recording unit hopes that by implementing the suggested ingredients and recipes, the condition of the user's stool will improve. For example, it monitors changes in the condition of the stool after implementing the ingredients or recipes. This makes it possible to suggest ingredients and recipes that have a positive effect on the intestinal environment.

[0068] The stool recording unit uses the emotion estimation function to monitor emotions in real time when a user records stool, and can suggest constipation care methods that elicit positive emotions. The stool recording unit, for example, uses the emotion estimation function to monitor emotions in real time when a user records stool. For example, it evaluates whether stress or anxiety is high during constipation. The stool recording unit also suggests constipation care methods that elicit positive emotions in the user based on the emotion estimation results. For example, it suggests relaxing exercises or enjoyable meals. The stool recording unit also monitors the user's emotional state in real time, and suggests adjusting the constipation care method if emotions become negative. For example, if stress is high, it suggests a constipation care method that helps relaxation. In this way, it is possible to suggest constipation care methods that elicit positive emotions.

[0069] The intestinal environment visualization unit uses a generation AI to display dynamic changes in intestinal bacteria in real time from visualized data of the intestinal environment. For example, the generation AI in the intestinal environment visualization unit displays dynamic changes in intestinal bacteria in real time based on the user's lifestyle data and stool records. For example, it displays changes in the balance of intestinal bacteria in graphs and animations. The intestinal environment visualization unit also analyzes changes in the type and balance of intestinal bacteria in real time based on collected data and visualizes the results. For example, it shows the increase or decrease of specific bacteria using colors and shapes. The intestinal environment visualization unit also displays dynamic changes in intestinal bacteria in real time when the user changes their lifestyle. For example, it visualizes changes in intestinal bacteria after changing their diet. This makes it possible to display dynamic changes in intestinal bacteria in real time.

[0070] The intestinal environment visualization unit can predict the future of the intestinal environment based on the visualized data of the intestinal environment and suggest preventive measures. In the intestinal environment visualization unit, for example, the generation AI predicts the future of the intestinal environment based on the collected lifestyle data and stool records. For example, it predicts changes in the intestinal environment if the current lifestyle is continued. The intestinal environment visualization unit also suggests preventive measures to the user based on the results of the future prediction. For example, if there is a possibility that the intestinal environment will deteriorate, it suggests improvements to the lifestyle. The intestinal environment visualization unit also visualizes the future prediction data so that the user can intuitively understand it. For example, it displays the future prediction of the intestinal environment in a graph or chart. This makes it possible to predict the future of the intestinal environment and suggest preventive measures.

[0071] The intestinal environment visualization unit can use the emotion estimation function to analyze the emotional response to the intestinal environment visualization data and propose a visualization method that elicits positive emotions. The intestinal environment visualization unit, for example, uses the emotion estimation function to analyze the user's emotional response to the intestinal environment visualization data. For example, it analyzes the user's facial expression and voice when viewing the visualization data. The intestinal environment visualization unit also proposes a visualization method that elicits positive emotions in the user based on the emotion estimation results. For example, it elicits positive emotions by changing colors or designs. The intestinal environment visualization unit also monitors the user's emotional response in real time and dynamically adjusts the visualization method. For example, if negative emotions are detected, it changes the display method of the visualization data. This makes it possible to propose a visualization method that elicits positive emotions.

[0072] The intestinal environment visualization unit can display the intestinal environment visualization data in three dimensions using a 3D model or VR technology, allowing for a more intuitive understanding. For example, the intestinal environment visualization unit uses a generation AI to generate a 3D model of the intestinal environment based on collected lifestyle data and stool records, and displays it three-dimensionally to the user. For example, it displays the balance of intestinal bacteria in a 3D graph. The intestinal environment visualization unit also uses VR technology to allow the user to experience the intestinal environment in a virtual space. For example, the state of the intestinal environment can be visually confirmed using VR goggles. The intestinal environment visualization unit also uses 3D models or VR technology to display changes in the intestinal environment in real time. For example, changes in the intestinal environment due to changes in lifestyle can be shown in a 3D model. This allows the intestinal environment to be displayed three-dimensionally, allowing for an intuitive understanding.

[0073] The intestinal environment visualization unit can integrate the intestinal environment visualization data with other health data (e.g., blood test results) to visualize the overall health status. For example, the intestinal environment visualization unit integrates the collected intestinal environment data with other health data (e.g., blood test results), and the generation AI visualizes the overall health status. For example, the intestinal environment and blood test results are displayed simultaneously. The intestinal environment visualization unit also generates graphs and charts that show the user's overall health status based on the integrated health data. For example, it displays a graph showing the correlation between the intestinal environment and blood test results. The intestinal environment visualization unit also visualizes the overall health status, allowing the user to intuitively understand their health status. For example, it provides a dashboard that allows the user to check changes in health status at a glance. This makes it possible to visualize the overall health status.

[0074] The intestinal environment visualization unit uses the emotion estimation function to monitor the emotional response to the intestinal environment visualization data in real time and can propose a visualization method that elicits positive emotions. The intestinal environment visualization unit, for example, monitors the user's emotional response to the intestinal environment visualization data in real time. For example, it analyzes the facial expressions and voice of the user when viewing the visualization data. The intestinal environment visualization unit also proposes a visualization method that elicits positive emotions in the user based on the emotion estimation results. For example, it elicits positive emotions by changing colors or designs. The intestinal environment visualization unit also monitors the user's emotional response in real time and dynamically adjusts the visualization method. For example, if negative emotions are detected, it changes the display method of the visualization data. This makes it possible to propose a visualization method that elicits positive emotions.

[0075] The match score prediction unit can use the generative AI to simulate the effects of a product based on the intestinal environment data and product database, and recommend the optimal product. For example, the match score prediction unit collects the user's intestinal environment data and product database, and the generative AI simulates the effects of the product. For example, it evaluates the impact of a specific probiotic on the intestinal environment. The match score prediction unit also recommends the optimal product for the user based on the simulation results. For example, it prioritizes the recommendation of products that are likely to improve the intestinal environment. The match score prediction unit also makes it easier for the user to select the product that suits them by simulating the effects of the product. For example, it displays the simulation results in a graph or chart. This makes it possible to simulate the effects of the product and recommend the optimal product.

[0076] The match score prediction unit takes into account the user's past product use history when predicting the match score, allowing for more accurate suggestions. For example, the match score prediction unit collects the user's past product use history, and the generation AI predicts the match score based on that data. For example, it evaluates the effectiveness of products used in the past and suggests products to use next. The match score prediction unit also suggests the most suitable product for the user based on the past product use history. For example, it suggests products that contain the same ingredients as products that have been effective in the past. The match score prediction unit also takes into account the past product use history when predicting the match score, making it easier for the user to select a product that suits them. For example, it automatically generates the most suitable product based on past data. This allows for more accurate suggestions to be made by taking into account the past product use history.

[0077] The match score prediction unit uses the emotion estimation function to analyze the emotional response to the proposed product and can preferentially suggest products that elicit positive emotions. The match score prediction unit, for example, uses the emotion estimation function to analyze the user's emotional response to the proposed product. For example, it analyzes the user's facial expression and voice when looking at the product. The match score prediction unit also preferentially suggests products that elicit positive emotions from the user based on the emotion estimation results. For example, it preferentially displays products with high emotion scores. The match score prediction unit also monitors the user's emotional response in real time and dynamically adjusts the products to be suggested. For example, if negative emotions are detected, it suggests a different product. This makes it possible to preferentially suggest products that elicit positive emotions.

[0078] The match score prediction unit takes the user's living environment into account when predicting the match score, and can suggest products specific to the region. The match score prediction unit, for example, collects the user's living environment data (e.g., the climate and food culture of the region where they live), and the generation AI predicts the match score based on that data. For example, it suggests products that are suitable for the local climate. The match score prediction unit also suggests products specific to the region to the user based on the living environment data. For example, it suggests probiotics and supplements that suit the local food culture. The match score prediction unit also takes the living environment data into account when predicting the match score, making it easier for the user to select products that suit them. For example, it automatically generates products that take into account the characteristics of the region. This makes it possible to suggest products specific to the region.

[0079] The match score prediction unit can share the match score prediction results with the user's family and friends and suggest products for joint use. The match score prediction unit, for example, provides a function for sharing the match score prediction results with the user's family and friends. For example, it can suggest products that can be used by the whole family. The match score prediction unit also suggests products for joint use based on the shared match score data. For example, it can suggest probiotics that are suitable for the intestinal environment of the whole family. The match score prediction unit also makes it easier for the user to select products together with family and friends by sharing the match score prediction results. For example, it can select products for joint use through a sharing function. This makes it possible to suggest products that can be shared and used by family and friends.

[0080] The match score prediction unit uses the emotion estimation function to monitor the emotional response to proposed products in real time and can continuously suggest products that elicit positive emotions. The match score prediction unit, for example, monitors the user's emotional response to proposed products in real time. For example, it analyzes the user's facial expressions and voice when looking at the product. The match score prediction unit also continuously suggests products that elicit positive emotions in the user based on the emotion estimation results. For example, it preferentially displays products with high emotion scores. The match score prediction unit also monitors the user's emotional response in real time and dynamically adjusts the products to be suggested. For example, if negative emotions are detected, it suggests a different product. This makes it possible to continuously suggest products that elicit positive emotions.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The lifestyle data collection unit collects the user's lifestyle data. For example, it records the user's steps, sleep time, and dietary habits through a smartwatch or smartphone app. The lifestyle data collection unit can also collect the user's exercise data. For example, it collects exercise data using a pedometer or fitness tracker. The stool recording unit records the user's stool condition. For example, the color, shape, and frequency of stool are entered into the app. The stool recording unit can also take and record photos of the stool. For example, the stool is photographed and analyzed by AI. The intestinal environment visualization unit visualizes the intestinal environment based on the lifestyle data and stool records. For example, it generates graphs and diagrams showing the balance of intestinal bacteria and the condition of the intestinal wall. The intestinal environment visualization unit can also display changes in the intestinal environment over time. For example, it can graph changes in the intestinal environment compared with past data. The match score prediction unit predicts the match score based on the intestinal environment and a database of intestinal health and constipation care products available on the market, and makes individual suggestions. For example, the system predicts how effective a specific probiotic or dietary fiber supplement will be for the user's intestinal environment and calculates a match score. The match score prediction unit can also suggest products that are optimal for the user's intestinal environment. For example, products with a high match score are prioritized. This allows the AI ​​service according to the embodiment to effectively manage the user's intestinal environment and achieve a healthy lifestyle. For example, improving lifestyle habits and using appropriate products can be expected to prevent or improve constipation. Furthermore, visualization of the intestinal environment allows users to intuitively understand their health condition and take appropriate measures.

[0083] The lifestyle data collection unit uses the generation AI to estimate stress levels from lifestyle data and evaluate the impact on the intestinal environment. For example, it collects lifestyle data such as the user's number of steps, sleep time, and dietary content, and uses the generation AI to estimate stress levels. For example, it determines that a user's stress level is high if they sleep less or take fewer steps. The lifestyle data collection unit also quantifies stress levels based on the collected lifestyle data using the generation AI, and evaluates the numerical value as the impact on the intestinal environment. For example, a high stress level may disrupt the balance of intestinal bacteria. The lifestyle data collection unit also makes suggestions to the user about lifestyle improvements to reduce stress based on the estimated stress level. For example, it suggests exercise or diet to help them relax. This makes it possible to evaluate stress levels and understand the impact on the intestinal environment.

[0084] The lifestyle data collection unit can analyze the impact of seasonal or weather changes on the intestinal environment based on the lifestyle data. For example, it collects the user's lifestyle data and seasonal and weather data, and uses generation AI to analyze the correlation between them. For example, it evaluates the possibility that a decrease in the number of steps taken in winter will worsen the intestinal environment. The lifestyle data collection unit also analyzes the impact of seasonal and weather changes on the user's lifestyle and reflects the results in the evaluation of the intestinal environment. For example, it evaluates the possibility that a decrease in sleep time during the rainy season will worsen the intestinal environment. The lifestyle data collection unit also makes suggestions for improving lifestyle habits in response to seasonal and weather changes. For example, it suggests indoor exercises in winter and relaxing meals during the rainy season. This makes it possible to understand the impact of seasonal and weather changes on the intestinal environment.

[0085] The lifestyle data collection unit uses the emotion estimation function to analyze the emotional state together with the lifestyle data, and can evaluate the impact of emotional fluctuations on the intestinal environment. For example, it can simultaneously collect the user's lifestyle data and emotional state and use a generation AI to analyze the correlation between them. For example, it can evaluate the possibility that a negative emotional state will worsen the intestinal environment. The lifestyle data collection unit also uses the emotion estimation function to quantify the user's emotional state and evaluate the numerical value as the impact on the intestinal environment. For example, it can evaluate the possibility that high stress or anxiety will disrupt the balance of intestinal bacteria. Furthermore, based on the estimated emotional state, the lifestyle data collection unit can suggest lifestyle improvements to the user to improve their emotions. For example, it can suggest exercise or diet to help them relax. This makes it possible to understand the impact of emotional fluctuations on the intestinal environment.

[0086] The lifestyle data collection unit collects genetic information in addition to lifestyle data, and can analyze the impact of genetic factors on the intestinal environment. For example, it collects a user's lifestyle data and genetic information and uses generation AI to analyze the correlation between them. For example, it evaluates the impact of specific genes on the intestinal environment. The lifestyle data collection unit also analyzes the characteristics of the user's intestinal environment based on the genetic information and reflects the results in lifestyle improvement suggestions. For example, it suggests specific diets to users with specific genes. The lifestyle data collection unit also integrates genetic information and lifestyle data to make optimal intestinal environment improvement suggestions to users. For example, it suggests specific exercises and diets to users who are genetically prone to poor intestinal environments. This makes it possible to understand the impact of genetic factors on the intestinal environment.

[0087] The lifestyle data collection unit can suggest new exercise programs that have a positive effect on the intestinal environment based on the lifestyle data. For example, it analyzes the user's lifestyle data and suggests exercise programs that have a positive effect on the intestinal environment. For example, it suggests exercises such as walking or yoga. Furthermore, the lifestyle data collection unit uses a generation AI to automatically generate an optimal exercise program for the user based on the collected lifestyle data. For example, it adjusts the exercise program according to the user's number of steps and sleep time. Furthermore, the lifestyle data collection unit expects that the user's intestinal environment will improve by implementing the suggested exercise program. For example, it monitors changes in the intestinal environment after implementing the exercise program. This makes it possible to suggest exercise programs that have a positive effect on the intestinal environment.

[0088] The lifestyle habit data collection unit can use the emotion estimation function to monitor emotions in real time when a user performs a specific lifestyle habit and suggest lifestyle habits that elicit positive emotions. For example, the emotion estimation function can be used to monitor emotions in real time when a user performs a specific lifestyle habit. For example, the emotion estimation function can be used to analyze the user's emotional state during exercise or eating. The lifestyle habit data collection unit can also suggest lifestyle habits that elicit positive emotions for the user based on the emotion estimation results. For example, it can suggest relaxing exercises or enjoyable meals. The lifestyle habit data collection unit can also monitor the user's emotional state in real time and suggest lifestyle adjustments if emotions become negative. For example, it can suggest lifestyle habits that help the user relax if stress levels are high. This makes it possible to suggest lifestyle habits that elicit positive emotions.

[0089] The stool recording unit uses a generation AI to estimate the type and balance of intestinal bacteria from stool records, allowing for a detailed assessment of the intestinal environment. For example, it collects data such as the color, shape, and frequency of stool recorded by the user and uses the generation AI to estimate the type and balance of intestinal bacteria. For example, if the color of the stool changes, it evaluates the increase or decrease of specific bacteria. The stool recording unit also evaluates the detailed state of the intestinal environment based on the stool record data. For example, if the shape of the stool changes, it evaluates the possibility of an imbalance in the intestinal bacteria. Based on the estimated type and balance of intestinal bacteria, the stool recording unit also makes optimal suggestions for improving the intestinal environment to the user. For example, it suggests specific probiotics or dietary fiber supplements. This allows for the evaluation of the type and balance of intestinal bacteria and a detailed understanding of the state of the intestinal environment.

[0090] The stool recording unit can analyze the correlation between dietary content and stool condition based on stool records and propose an optimal meal plan. For example, it collects stool data and dietary content recorded by the user and uses a generation AI to analyze the correlation. For example, it evaluates the impact of specific ingredients on stool condition. The stool recording unit then uses the generation AI to propose an optimal meal plan to the user based on the correlation between dietary content and stool condition. For example, it suggests ingredients and recipes that will improve constipation. The stool recording unit also hopes that implementing the proposed meal plan will improve the user's stool condition. For example, it monitors changes in stool condition after implementing the meal plan. This allows it to analyze the correlation between dietary content and stool condition and propose an optimal meal plan.

[0091] The stool recording unit can use the emotion estimation function to analyze emotions at the time of stool recording and evaluate the impact of emotional fluctuations on the state of the stool. For example, when a user records stool, the emotion estimation function is used to analyze the emotions at that time. For example, it evaluates whether stress or anxiety is high when constipated. Furthermore, the stool recording unit evaluates the impact of emotional fluctuations on the state of the stool based on the emotion estimation results. For example, it evaluates the possibility that negative emotions worsen constipation. Furthermore, the stool recording unit makes suggestions to the user on lifestyle improvements to improve emotions based on the estimation results of the emotional state. For example, it suggests exercise or diet to help relax. This makes it possible to evaluate the impact of emotional fluctuations on the state of the stool.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The lifestyle data collection unit collects the user's lifestyle data. For example, the number of steps taken, sleep time, and dietary details of the user are recorded through a smartwatch or smartphone app. The lifestyle data collection unit can also collect the user's exercise data. For example, the exercise data is collected using a pedometer or fitness tracker. Step 2: The stool recording unit records the condition of the user's stool. For example, the color, shape, frequency, etc. of the stool are entered into the app. The stool recording unit can also take and record photos of the stool. For example, a photo of the stool can be taken and the AI ​​can analyze its condition. Step 3: The intestinal environment visualization unit visualizes the intestinal environment based on the lifestyle data and stool records. For example, it generates graphs and diagrams showing the balance of bacteria in the intestines and the condition of the intestinal wall. The intestinal environment visualization unit can also display changes in the intestinal environment over time. For example, it displays changes in the intestinal environment in a graph, comparing them with past data. Step 4: The match score prediction unit predicts a match score based on the intestinal environment and a database of intestinal activity and constipation care products available on the market, and makes personalized recommendations. For example, it predicts how effective a specific probiotic or dietary fiber supplement will be for the user's intestinal environment and calculates a match score. The match score prediction unit can also suggest products that are optimal for the user's intestinal environment. For example, it prioritizes recommendations for products with a high match score.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] 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.

[0153] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a lifestyle data collection unit that collects lifestyle data; a stool recording unit that records stool; an intestinal environment visualization unit that visualizes the intestinal environment based on the lifestyle habit data and the stool record; A match score prediction unit that predicts a match score based on the intestinal environment and a database of intestinal activity or constipation care products available in the market, and makes an individual suggestion. A system characterized by:

2. The lifestyle habit data collection unit Using a generative AI to estimate stress levels from the lifestyle data and evaluate the impact on the intestinal environment.

2. The system of claim 1.

3. The mail recording unit Using generative AI to estimate the types and balance of intestinal bacteria from the stool records, and evaluate the detailed state of the intestinal environment.

2. The system of claim 1.

4. The intestinal environment visualization unit Using generative AI to display dynamic changes in intestinal bacteria in real time from the visualized data of the intestinal environment.

2. The system of claim 1.

5. The match score prediction unit Using generative AI to simulate product effects based on intestinal environment data and product databases, and recommend optimal products 2. The system of claim 1.

6. The lifestyle habit data collection unit Analyzing the emotional state together with the lifestyle data and evaluating the influence of emotional fluctuations on the intestinal environment.

2. The system of claim 1.

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